ethereum blockchain

description: the underlying distributed ledger technology for the Ethereum platform

77 results

pages: 434 words: 77,974

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey
Published 8 Dec 2020

As seen in Figure 3-8, if a user has 10 ETH in Address A, they will generate Address B on the new Ethereum blockchain, and generate Address C on the Ethereum Classic blockchain. On the Ethereum blockchain they will then move 10 ETH to Address B, and on the Ethereum Classic blockchain they’ll move the 10 ETH to Address C. After the funds are distributed to separate addresses, if someone attempts to do a replay attack on their funds, it won’t work because the balance of funds on each blockchain will be different. Figure 3-8. Protecting against replay attacks on Ethereum (ETH) and Ethereum Classic (ETC) Exchanges that had the resources and expertise to protect against replay attacks were not affected.

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The fork meant changing the Ethereum blockchain so that The DAO hack had effectively never happened, violating the principle of immutability. This was a controversial decision that was resisted by some members of the community, who chose to continue with the unaltered version of the blockchain. Ethereum Classic is a smart contract blockchain that still exists today, but its developer community is small and not as robust as Ethereum’s. Other Ethereum forks The DAO hack warmed up the cryptocurrency community to the idea of forks. In addition to creating Ethereum Classic, the Ethereum blockchain has been forked several other times to compensate for vulnerabilities and other changes in code.

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There was no recourse for The DAO’s developers to update the deployed contract code itself, because it was stored immutably on the blockchain. The only way to rectify the situation was to deploy a new contract and move the remaining funds over—a cumbersome and painful process. This event led to the Ethereum Foundation forking the Ethereum blockchain, in order to undo the damage. It created two distinct versions of Ethereum: the original blockchain with the stolen funds still credited to the attacker, known as Ethereum Classic, and a forked version that retracted said funds, which continued to be known as Ethereum. This hard fork moved the stolen funds to a recovery address so their rightful owners could reclaim them.

pages: 960 words: 125,049

Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D.
Published 23 Dec 2018

Bitcoin, Ethereum’s Development Culture balance, world state and, Ethereum State Bamboo, Introduction to Ethereum High-Level Languages Bancor, Real-World Examples: ERC20 and Bancor batching, The JSON-RPC Interface batchTransfer function, Real-World Examples: PoWHC and Batch Transfer Overflow (CVE-2018–10299) big-endian, defined, Quick Glossary BIP-32 standardextended public and private keys, Extended public and private keys HD wallets and, HD Wallets (BIP-32) and Paths (BIP-43/44)-Index numbers for normal and hardened derivation BIP-39 standard, Seeds and Mnemonic Codes (BIP-39), Mnemonic Code Words (BIP-39)-Working with mnemonic codesderiving seed from mnemonic words, From mnemonic to seed generating code words with, Generating mnemonic words libraries, Working with mnemonic codes optional passphrase with, Optional passphrase in BIP-39 working with mnemonic codes, Working with mnemonic codes BIP-43 standard, Navigating the HD wallet tree structure BIP-44 standard, Navigating the HD wallet tree structure BIPs (see Bitcoin improvement proposals) Bitcoinas token, Tokens on Ethereum development culture, Ethereum’s Development Culture Ethereum blockchain compared to Bitcoin blockchain, Ethereum: A General-Purpose Blockchain Ethereum compared to, Compared to Bitcoin Ethereum definition compared to, Ethereum Clients limitations of, The Birth of Ethereum Bitcoin Core, Components of a Blockchain Bitcoin improvement proposals (BIPs), Quick GlossaryHierarchical Deterministic Wallets (BIP-32/BIP-44), Hierarchical Deterministic Wallets (BIP-32/BIP-44) Mnemonic Code Words (BIP-39), Seeds and Mnemonic Codes (BIP-39), Mnemonic Code Words (BIP-39)-Working with mnemonic codes Multipurpose HD Wallet Structure (BIP-43), HD Wallets (BIP-32) and Paths (BIP-43/44)-Navigating the HD wallet tree structure bitcoind client, Components of a Blockchain blind calls, Raw call, delegatecall block gas limit, Block Gas Limit block object, Block context block timestamp manipulation security threat, Block Timestamp Manipulation-Real-World Example: GovernMentalpreventative techniques, Preventative Techniques real-world example: GovernMental, Real-World Example: GovernMental vulnerability, The Vulnerability block, defined, Quick Glossary blockchaincomponents of, Components of a Blockchain, Ethereum’s Components creating contract on, Creating the Contract on the Blockchain-Withdrawing from Our Contract defined, Quick Glossary Ethereum as developer's blockchain, Why Learn Ethereum?

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first synchronization of, The First Synchronization of Ethereum-Based Blockchains-Parity’s Geth compatibility mode on-blockchain testing, On-Blockchain Testing recording transactions on, Recording on the Blockchain warnings and cautions, Ethereum Addresses and Transactions in this Book BlockOne IQ, Oracle Client Interfaces in Solidity brainwallets, mnemonic words vs., Mnemonic Code Words (BIP-39) broadcast (multicast) oracle, Oracle Design Patterns browser wallets, Browser Wallets burn (see ether burn) Buterin, Vitalik, Quick Glossaryand birth of Ethereum, The Birth of Ethereum and Casper, Casper: Ethereum’s Proof-of-Stake Algorithm and Dagger algorithm, Ethash: Ethereum’s Proof-of-Work Algorithm on tokens, Tokens on Ethereum bytecode, Quick Glossary(see also EVM bytecode) bytecode operations, The EVM Instruction Set (Bytecode Operations)-The EVM Instruction Set (Bytecode Operations) Byzantium fork, Quick Glossary, Ethereum’s Four Stages of Development C CALL opcode, DELEGATECALL calls, external, Unchecked CALL Return Values-Real-World Example: Etherpot and King of the Ether Casper, Casper: Ethereum’s Proof-of-Stake Algorithm Casper CBC, Casper: Ethereum’s Proof-of-Stake Algorithm Casper FFG, Casper: Ethereum’s Proof-of-Stake Algorithm chain code, Extended public and private keys chain identifier, Raw Transaction Creation with EIP-155 ChainLink, Decentralized Oracles checks-effects-interactions pattern, Preventative Techniques checksumEIP-55 and, Hex Encoding with Checksum in Capitalization (EIP-55)-Detecting an error in an EIP-55 encoded address in Ethereum address formats, Ethereum Address Formats in mnemonic code word generation, Generating mnemonic words child private keys, Hardened child key derivation Cipher Browser, Mobile (Smartphone) Wallets class inheritance, Class Inheritance clients, Ethereum, Ethereum Clients-Conclusionsand JSON-RPC API, The JSON-RPC Interface-Parity’s Geth compatibility mode Ethereum-based networks and, Ethereum Networks-Local Blockchain Simulation Advantages and Disadvantages first synchronization of Ethereum-based blockchains, The First Synchronization of Ethereum-Based Blockchains-Parity’s Geth compatibility mode full node hardware requirements, Hardware Requirements for a Full Node Geth and, Go-Ethereum (Geth)-Building Geth from source code Parity and, Parity remote, Remote Ethereum Clients-Mist running, Running an Ethereum Client-Building Geth from source code software requirements for building/running, Software Requirements for Building and Running a Client (Node)-Building Geth from source code code examples, obtaining and using, Code Examples cold-storage wallets, Extended public and private keys command-line interface, Software Requirements for Building and Running a Client (Node) comments and questions, How to Contact Us compiler directive, Selecting a Solidity Compiler and Language Version compilingdefined, Quick Glossary Faucet.sol contract, Compiling the Faucet Contract-Compiling the Faucet Contract protecting against overflow errors at the compiler level, Protecting Against Overflow Errors at the Compiler Level Vyper, Compilation concurrency, nonces and, Concurrency, Transaction Origination, and Nonces consensus, Consensus-ConclusionsCasper as Ethereum PoS algorithm, Casper: Ethereum’s Proof-of-Stake Algorithm controversy and competition, Controversy and Competition defined, Quick Glossary Ethash as Ethereum PoW algorithm, Ethash: Ethereum’s Proof-of-Work Algorithm principles of, Principles of Consensus via proof of stake, Consensus via Proof of Stake (PoS) via proof of work, Consensus via Proof of Work consensus rules, Quick Glossary constant (function keyword), Functions Constantinople fork, Quick Glossary, Ethereum’s Four Stages of Development constructor function, Contract Constructor and selfdestructadding to faucet example, Adding a Constructor and selfdestruct to Our Faucet Example contract name modification security threat, Constructors with Care constructor/contract name modification security threatpreventative techniques, Preventative Techniques real-world example: Rubixi, Real-World Example: Rubixi vulnerability, The Vulnerability contact information, How to Contact Us contract accounts, Externally Owned Accounts (EOAs) and Contracts(see also smart contracts) creating, A Simple Contract: A Test Ether Faucet-A Simple Contract: A Test Ether Faucet(see also Faucet.sol contract) defined, Quick Glossary EOAs compared to, Smart Contracts and Solidity contract creation transaction, Quick Glossary, Special Transaction: Contract Creation-Special Transaction: Contract Creation, What Is a Smart Contract?

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ERC (Ethereum Request for Comments), Quick Glossary(see also EIPs (Ethereum Improvement Proposals)) ERC20 token standard, The ERC20 Token Standard-ERC20 implementationsdata structures, ERC20 data structures front-running vulnerability, Real-World Examples: ERC20 and Bancor interface defined in Solidity, The ERC20 interface defined in Solidity issues with ERC20 tokens, Issues with ERC20 Tokens METoken creation/launch example, Launching Our Own ERC20 Token-Demonstrating the “approve & transferFrom” workflow optional functions, ERC20 optional functions required functions and events, ERC20 required functions and events transfer functions, ERC20 workflows: “transfer” and “approve & transferFrom”-ERC20 workflows: “transfer” and “approve & transferFrom” Vyper implementation of, Compilation ERC223 token standard proposal, ERC223: A Proposed Token Contract Interface Standard ERC721 non-fungible token standard, ERC721: Non-fungible Token (Deed) Standard-ERC721: Non-fungible Token (Deed) Standard ERC777 token standard proposal, ERC777: A Proposed Token Contract Interface Standard-ERC777 hooks error handling, Solidity, Error Handling (assert, require, revert) ETC (see Ethereum Classic) ETF (EthereumFog), Other Notable Ethereum Forks eth nodes, Middle Layer: The .eth Nodes Ethash, Quick Glossary, Ethash: Ethereum’s Proof-of-Work Algorithm ETHB (EtherBTC), Other Notable Ethereum Forks Ether (cryptocurrency), Quick Glossary ether (generally)gas and, Implications of Turing Completeness testnet, Getting Some Test Ether unexpected ether security threat, Unexpected Ether-Further Examples ether burn, Transaction Recipient, Special Transaction: Contract Creation EtherBTC (ETHB), Other Notable Ethereum Forks Ethereum (generally)about, What Is Ethereum?-What This Book Will Teach You and EVM, Introducing the World Computer as general-purpose blockchain, Ethereum: A General-Purpose Blockchain basics, Ethereum Basics-Conclusions birth of, The Birth of Ethereum Bitcoin compared to, Compared to Bitcoin blockchain components, Components of a Blockchain, Ethereum’s Components clients (see clients, Ethereum) control and responsibility, Control and Responsibility-Control and Responsibility currency units, Ether Currency Units DApps and, From General-Purpose Blockchains to Decentralized Applications (DApps) development culture, Ethereum’s Development Culture EIPs, Ethereum Improvement Proposals (EIPs) EOAs and contracts, Externally Owned Accounts (EOAs) and Contracts Ethereum Classic compared to, Ethereum and Ethereum Classic fork history, Ethereum Fork History-Other Notable Ethereum Forks four stages of development, Ethereum’s Four Stages of Development MetaMask basics, Getting Started with MetaMask-Exploring the Transaction History of an Address purpose of, Compared to Bitcoin reasons to learn, Why Learn Ethereum?

Mastering Blockchain, Second Edition
by Imran Bashir
Published 28 Mar 2018

Topics such as history, the definition of smart contracts, Ricardian contracts, Oracles, and the theoretical aspects of smart contracts are presented in this chapter. Chapter 10, Ethereum 101, introduces the design and architecture of the Ethereum blockchain in detail. It covers various technical concepts related to the Ethereum blockchain that explains the underlying principles, features, and components of this platform in depth. Chapter 11, Further Ethereum, continues the introduction of Ethereum from pervious chapter and covers topics related to Ethereum Virtual Machine, mining and supporting protocols for Ethereum. Chapter 12, Ethereum Development Environment, covers the topics related to setting up private networks for Ethereum smart contract development and programming.

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Sequence concatenation For all There exists Union ᴧ Contract creation function Logical AND Increment : Such that Floor, lowest element {} Set Ceiling, highest element () Function of tuple No of bytes [] Array indexing Exclusive OR Logical OR (a ,b) Real numbers >= a and < b > Is greater than Empty set, null + Addition - Subtraction ∑ Summation { Describing various cases of if, otherwise Now in the next and upcoming sections, we will introduce Ethereum blockchain and its core elements. Ethereum blockchain Ethereum, just like any other blockchain, can be visualized as a transaction-based state machine. This definition is mentioned in the Ethereum yellow paper written by Dr. Gavin Wood. The core idea is that in Ethereum blockchain, a genesis state is transformed into a final state by executing transactions incrementally. The final transformation is then accepted as the absolute undisputed version of the state. In the following diagram, the Ethereum state transition function is shown, where a transaction execution has resulted in a state transition: Ethereum State transition function In the preceding example, a transfer of two Ether from address 4718bf7a to address 741f7a2 is initiated.

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Network name Network ID / Chain ID Ethereum mainnet 1 Morden 2 Ropsten 3 Rinkeby 4 Kovan 42 Ethereum Classic mainnet 61 More discussion on how to connect to testnet and how to set up private nets will be discussed in Chapter 12, Ethereum Development Environment. Components of the Ethereum ecosystem The Ethereum blockchain stack consists of various components. At the core, there is the Ethereum blockchain running on the peer-to-peer Ethereum network. Secondly, there's an Ethereum client (usually Geth) that runs on the nodes and connects to the peer-to-peer Ethereum network from where blockchain is downloaded and stored locally. It provides various functions, such as mining and account management. The local copy of the blockchain is synchronized regularly with the network. Another component is the web3.js library that allows interaction with the geth client via the Remote Procedure Call (RPC) interface.

pages: 349 words: 102,827

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo
Published 13 Jul 2020

It was this parallel chain’s own cryptocurrency. Ether was only mined on the new chain. Blockchains are supposed to work on economic incentives, but in this case, the old Ethereum chain, which was later called Ethereum Classic, emerged from people who ignored immediate economic incentives and instead spent time and money to make sure an immutable Ethereum chain survived. Maybe the chain’s cryptocurrency would later gain in value and they would be compensated. “We believe in decentralized, censorship-resistant, permissionless blockchains. We believe in the original vision of Ethereum as a world computer you can’t shut down, running irreversible smart contracts,” the Ethereum Classic website said.

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The many steps it took them to get there and the difference between the two are all too technical to get into in much detail here, but at the time of writing, Vitalik’s Casper FFG was the proof-of-stake version to be included in the Ethereum road map first, while Vlad’s would be added at a later stage. Vitalik was also researching how Ethereum would be able to process more transactions per second, or in other words, how Ethereum would be able to scale. There are different mechanisms that blockchain developers have concocted to solve this issue. Sharding, plasma, zero-knowledge proofs, and state channels are so far the best-known ones. Of those, sharding is the only one that’s meant to be built into the Ethereum blockchain itself, or what’s known as a “Layer 1 solution,” while the others are “Layer 2 solutions,” which are built on top of the main chain and don’t require changes to the base-level protocol.

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“Transaction volume more than doubled, number of new accounts created per day passed 100,000, and the number of nodes increased,” said an upbeat fourth-quarter roundup posted by the Ethereum Foundation on January 2, 2018. “We are entering a new phase in the industry’s growth: the phase where we are finally going from experiments and tests to real, live applications.” Vitalik posted news on Ethereum’s scaling efforts on the same day. “With the Ethereum blockchain reaching 1 million transactions per day, and both Ethereum and other blockchain projects frequently reaching their full transaction capacity, the need for scaling progress is becoming more and more clear and urgent,” he wrote.

pages: 506 words: 151,753

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin
Published 22 Feb 2022

Vitalik Buterin, “Ethereum Foundation Internal Update,” Ethereum Foundation Blog, January 7, 2016, https://blog.ethereum.org/2016/01/07/2394. 35. “About the Ethereum Foundation,” Ethereum.org/foundation via Wayback Machine, September 6, 2015, https://web.archive.org/web/20150906200827/https://www.ethereum.org/foundation; “About the Ethereum Foundation,” Ethereum.org/foundation via Wayback Machine, March 4, 2016, https://web.archive.org/web/20160304212822/https://www.ethereum.org/foundation. 36. June (@JUN_SYNQA), “Omise is now official Special advisor (Thomas) for Ethereum. #omise #blockchain #ethereum https://ethereum.org/foundation,” Twitter, March 8, 2016, https://twitter.com/jun_omise/status/707168442449661952. 37.

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Stephan Tual, “A message from Stephan Tual,” Ethereum Foundation Blog, September 3, 2015, https://blog.ethereum.org/2015/09/03/a-message-from-stephan-tual. 10. Vitalik Buterin, “The Evolution of Ethereum,” September 28, 2015, Ethereum Foundation Blog, https://blog.ethereum.org/2015/09/28/the-evolution-of-ethereum. 11. Shanghai’s Wanxiang Blockchain Labs. 12. Jemima Kelly, “Nine of World’s Biggest Banks Join to Form Blockchain Partnership,” Reuters, September 15, 2015, https://www.reuters.com/article/us-banks-blockchain-idUSKCN0RF24M20150915; Jemima Kelley, “Thirteen More Top Banks Join R3 Blockchain Consortium,” Reuters, September 29, 2015, https://www.reuters.com/article/banks-blockchain-idUSL5N11Z2QE20150929. 13.

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He Says It Was Fiction,” BuzzFeed News, September 19, 2018, https://www.buzzfeednews.com/article/ryanmac/ethereum-cofounder-sex-underage-girl-fiction. 26. Ming Chan, “To Infinity and Beyond!,” Ethereum Foundation Blog, January 31, 2018, https://blog.ethereum.org/2018/01/31/to-infinity-and-beyond. The other four Ethereum Foundation Blog posts were “Devcon3 videos available now!,” November 26, 2017, https://blog.ethereum.org/2017/11/26/devcon3-vids-available-now; “Devcon3!!!,” November 16, 2017, https://blog.ethereum.org/2017/11/16/devcon3; “The Devcon2 site is now live!,” July 8, 2016, https://blog.ethereum.org/2016/07/08/devcon2-site-now-live; “Ethereum Foundation and Wanxiang Blockchain Labs announce a blockbuster event combining Devcon2 and the 2nd Global Blockchain Summit in Shanghai, September 19–24, 2016,” April 5, 2016, https://blog.ethereum.org/2016/04/05/devcon2-and-blockchain-summit-shanghai-september2016. 27.

pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond
by Chris Burniske and Jack Tatar
Published 19 Oct 2017

Dmitry Buterin is also very much involved in the cryptoasset world as cofounder of Blockgeeks and other influential startups. 3. http://fortune.com/ethereum-blockchain-vitalik-buterin/. 4. http://www.ioi2012.org/competition/results-2/. 5. https://backchannel.com/the-uncanny-mind-that-built-ethereum-9b448dc9d14f#.4yr8yhfp8. 6. https://blog.ethereum.org/2014/01/23/ethereum-now-going-public/. 7. http://counterparty.io/platform/. 8. https://steemit.com/ethereum/@najoh/beyond-bitcoin-and-crypto-currency-ethereum. 9. https://blog.ethereum.org/2014/01/23/ethereum-now-going-public/. 10. https://github.com/ethereum/wiki/wiki/white-paper. 11. Turing complete refers to a system that is effectively capable of the full functionality of a general purpose computer.

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Figure 13.17 The growth of Ethereum’s unique addresses Data sourced from Etherscan.io Number of Transactions Figures 13.18 and 13.19 show the number of transactions using Bitcoin and Ethereum’s blockchains respectively. The rising numbers are healthy signs for each of the blockchains and their associated cryptoassets. This information for bitcoin can be accessed on Blockchain.info22 and for ether at Etherscan.23 Figure 13.18 Number of transactions per day using Bitcoin’s blockchain Data sourced from Blockchain.info Figure 13.19 Number of transactions per day using Ethereum’s blockchain Data sourced from Etherscan.io Dollar Value of Transactions While the number of transactions is an important metric, it says nothing about the monetary value of those transactions.

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Beyond the financial services industry, others that are exploring the applications of blockchain technology include the music industry, real estate, insurance, healthcare, networking, polling, supply chains, charities, gun tracking, law enforcement, governments, and more.6 Throughout this book, we will focus on public blockchains and their native assets, or what we will define as cryptoassets, because we believe this is where the greatest opportunity awaits the innovative investor. Sometimes, cryptoassets have the exact same name as their parent blockchain but with different capitalization. Other times there’s a slightly different name for the asset. For example, the native asset of Bitcoin’s blockchain is bitcoin, the native asset of Ethereum’s blockchain is ether, the native asset of Litecoin’s blockchain is litecoin, etc. Many public blockchains are markedly different from one another.

pages: 348 words: 97,277

The Truth Machine: The Blockchain and the Future of Everything
by Paul Vigna and Michael J. Casey
Published 27 Feb 2018

Beale, Inga Behlendorf, Brian Belt and Road Blockchain Consortium Benet, Juan Berners-Lee, Tim Bessemer Venture Partners Big Data Birch, David bitcoin, use of the term Bitcoin “civil war” consensus logic and cybersecurity and Cypherpunk movement and decentralization digital assets and financial sector and forks history of and open-source innovation permissionless ideal of price of and privacy Satoshi Nakamoto, (pseudonymous creator) and scalability and security SHA-256 hashing algorithm and trust Bitcoin Cash (BCH) Bitcoin Core Bitfinex BitFury BitLand BitLicense regulation Bitmain BitPesa black-hat hackers blockchain agnostic blockchain and blockchain technologies censorship resistance and Cypherpunk community definition and use of the term distributed trust protocol and double-spending problem and energy sector and financial inclusion and financial industry and governance and citizenship hashes history of and identity information and innovation and art and international agencies and Internet of Things and Internet 3.0 potential of provably signed transactions and record-keeping and registries replicated and security sequentially linked and cryptographically secured transactions software-driving consensus and supply chains talent pool tokens and trust as truth machine See also Bitcoin; distributed ledger technology; Ethereum; permissioned (private) blockchains; permissionless blockchains Blockchain Capital blockchain-distributed ledger. See also double-spending Blockchain Health Blockchain.info blockchain labs block.one Blockstream Bloq Blue Apron Bosch Brave New Brave Software Inc. Basic Attention token Breitman, Arthur Breitman, Kathleen Brexit. See also United Kingdom Brody, Paul Burniske, Chris Buterin, Vitalik BuzzFeed Byrne, Preston capitalism Carlson-Wee, Olaf Casares, Wences Casey, Michael.

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Barry Silbert came up with the SegWit2x compromise: “Bitcoin Scaling Agreement at Consensus 2017,” Digital Currency Group, Medium, May 23, 2017, https://medium.com/@DCGco/bitcoin-scaling-agreement-at-consensus-2017-133521fe9a77. When Buterin released his white paper in December 2013: Vitalik Buterin, “Ethereum White Paper: A Next Generation Smart Contract & Decentralized Application Platform,” http://www.the-blockchain.com/docs/Ethereum_white_paper-a_next_generation_smart_contract_and_decentralized_application_platform-vitalik-buterin.pdf. “You’re just as likely to find a web developer…”: “An Ode to the Ethereum Community,” Steemit, October 2016, https://steemit.com/ethereum/@owaisted/an-ode-to-the-ethereum-community. After launching Ethereum at the North American Bitcoin Conference: Interview with Michael J. Casey, Miami, January 26, 2014.

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For the 2015 estimate, see: “State of the Commons,” https://stateof.creativecommons.org/2015/. An Ethereum-based service called adChain: Robert Hof, “How MetaX Plans to Use Blockchain to Stop Ad Fraud,” Forbes, March 21, 2017, https://www.forbes.com/sites/roberthof/2017/03/21/how-metax-plans-to-use-blockchain-to-stop-ad-fraud/#2e417d0e59da. “This hash is unique to the book”: “Age of Cryptocurrency, Recorded on the Bitcoin Blockchain,” CoinDesk, February 3, 2015, https://www.coindesk.com/age-of-cryptocurrency-bitcoin-blockchain/. “There are millions of artists on the planet”: Heap interviewed by Michael J. Casey on sidelines of the Blockchain Summit 2017, Necker Island, British Virgin Islands, July 28, 2017.

pages: 179 words: 42,081

DeFi and the Future of Finance
by Campbell R. Harvey , Ashwin Ramachandran , Joey Santoro , Vitalik Buterin and Fred Ehrsam
Published 23 Aug 2021

ERC-1155. Ethereum Request for Comments (ERC) related to defining a multitoken model, in which a contract can hold balances of a number of tokens, either fungible or non-fungible. Ethereum (ETH). In existence since 2015, second largest cryptocurrency or blockchain. Its native cryptocurrency is known as ether (ETH). Ethereum’s blockchain has the capability of running computer programs known as smart contracts. It is considered a distributed computational platform and sometimes referred to as the Ethereum Virtual Machine. Ethereum 2.0. A proposed improvement on the Ethereum blockchain that uses horizontal scaling, proof-of-stake consensus and other enhancements.

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These new currencies can adjust their parameters such as inflation and mechanism for consensus via their underlying blockchain to create different value propositions. We will discuss blockchain and cryptocurrency in greater depth later on but for now will focus on a particular cryptocurrency with special relevance to DeFi. ETHEREUM AND DeFi Ethereum (ETH) is currently the second largest cryptocurrency by market cap ($260b). Vitalik Buterin introduced the idea in 2014, and Ethereum mined its first block in 2015. Ethereum is in some sense a logical extension of the applications of Bitcoin because it allows for smart contracts – which are code that lives on a blockchain, can control assets and data, and define interactions between the assets, data, and network participants.

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ORACLES An interesting problem with blockchain protocols is that they are isolated from the world outside of their ledger. That is, the Ethereum blockchain authoritatively knows what is happening only on the Ethereum blockchain and not, for example, the level of the S&P 500 or which team won the Super Bowl. This limitation constrains applications to Ethereum native contracts and tokens, thus reducing the utility of the smart contract platform; it is generally known as the oracle problem. In the context of smart contract platforms, an oracle is any data source for reporting information external to the blockchain. How can we create an oracle that can authoritatively speak about off-chain information in a trust-minimized way?

pages: 309 words: 54,839

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard
Published 23 Jul 2017

In computer science terms, this approach could only have worked by first solving the halting problem: you would need to be able to determine the outcome of any possible Ethereum program without actually running it and observing the result.361) The DAO was shut down soon after, and on 20 July the Ethereum Foundation – several of whose principals were curators of The DAO362 and/or heavily invested in it – changed how the actual code of Ethereum interpreted their blockchain (the “immutable” ledger) so as to wind back the hack and take back their money. The blockchain was “immutable,” so they changed how it was interpreted. The “impossible” bailout had happened. This illustrated the final major problem with smart contracts: CODE IS LAW until the whales are in danger of losing money. Ethereum promptly split into two separate blockchains, each with its own currency – Ethereum (ETH), the wound-back version, supported by the Ethereum Foundation, and Ethereum Classic (ETC), the original code and blockchain – because all this was too greedy even for crypto fans to put up with.

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The developers have always stated that Ethereum is explicitly experimental and unfinished (and never mind the hundreds of millions of dollars in ether swilling around in it), and that the promised fancy functionality will need years of work.302 They occasionally boggle at people treating it as much more of a finished product than they do.303 Ethereum advocates talk up corporate adoption by Microsoft and other companies – it’s a popular choice of platform for business blockchain trials, and its smart contract functionality is reused by a lot of other blockchain software – but this is adoption of the software to run separate in-house blockchains, not adoption of the public Ethereum chain and currency. Buterin’s quantum quest Before Ethereum, Vitalik Buterin put considerable effort in 2013 into trying to convince investors to fund him to build a quantum computer.

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An August 2015 blog post from Vitalik Buterin discusses “public”, “consortium” and “private” blockchains. Bitcoin and Ethereum are “public” blockchains.377 This comment chain on the post concisely summarises the innovations the private blockchain brings: Andrey Zamovskiy: Let’s just admit that blockchain is simply a new type of replication algorithm for a database cluster. That’s it. Vitalik Buterin: Correct. Plus Merkle trees. The Merkle trees are actually important. Andrey Zamovskiy: Merkle trees have not been invented with bitcoin, they’ve just got an adoption. Of course, one use case is that a “private blockchain”378 or “mutualized database structure”379 might sound less suspect to anti-trust authorities than a “cartel”.

pages: 80 words: 21,077

Stake Hodler Capitalism: Blockchain and DeFi
by Amr Hazem Wahba Metwaly
Published 21 Mar 2021

Speed inefficiency Bitcoin is the best example of the potential inefficiencies of blockchain. Bitcoin’s PoW (proof of wor) system takes about ten minutes to include a new blockchain block. At that speed, it’s calculated that the blockchain network can only take care of about seven transactions per second (TPS). Although other cryptocurrencies such as Ethereum function better than Bitcoin, they are still restricted by blockchain. Legacy Visa systems, for context, can process up to 24,000 TPS. Solutions to this problem have been in development for years. Currently, some blockchains are bragging over 30,000 transactions per second. Illegal activities On the blockchain network, users are protected from hacks and preserves privacy.

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It is mainly used to perform smart contracts on various blockchain platforms such as Ethereum. It was developed by Christian Reitwissner, Alex Beregsazi, and several former Ethereum key contributors to enter smart contracts on blockchain platforms. The Solidity programming language is primarily intended for code development and implementation of the Ethereum virtual machine. Furthermore, smart contracts establish the rules and penalties surrounding an agreement in the same way that a conventional contract does, but it also enforces those obligations automatically. ● Vyper Vyper allows you to program on Ethereum, a blockchain-based virtual machine that permits smart contracts' design and execution without the need for centralized or trusted intermediaries.

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The list of its potentials goes on and on. Chapter 4: Ethereum and DeFi Let’s start by specifying that presently, most if not pretty much all of the DeFi projects are built on Ethereum. Ethereum is a decentralized blockchain that allows other decentralized blockchain applications (dApps) to be built on smart contracts, and some of these apps can have their exchangeable tokens. The compound is one of the protocols that primarily deal with decentralized financial services that save and lend cryptocurrency. The main reason for this is the fairly powerful Ethereum smart contract platforms that provide the robustness that allows you to write smart high-level contracts containing all the necessary logic for your DeFi application.

pages: 218 words: 68,648

Confessions of a Crypto Millionaire: My Unlikely Escape From Corporate America
by Dan Conway
Published 8 Sep 2019

While I was struggling to master the corporate world, which had always eluded me, a young genius named Vitalik Buterin was creating Ethereum. More on Vitalik later. Ethereum is a “Turing complete blockchain,” which means it can be programmed to do anything. Whereas Bitcoin has the functionality of a calculator and can transact digital money, Ethereum has the functionality of a computer and is a trustless—thus, supremely trustworthy —platform to transact any type of business. To understand this, you need to think of Ethereum not as a way to buy coffee with digital money but as a computer that can be accessed with the cryptocurrency, ether (ETH). The role of trust is a big deal in blockchain philosophy. Corporations do business by pooling resources and establishing trust with their customers, employees, and partners.

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These reports provide trust to managers to verify that their departments are creating economic value, as promised. Ethereum’s killer app is the smart contract, an unalterable, ironclad agreement between two or more parties that is validated by the blockchain. If something happens at point X, the blockchain enforces the contracted action at point Y. The people whose computers are “mining blocks” (anyone who wants to) are rewarded with a small amount of ETH for validating these transactions on the blockchain. This is the engine that keeps Ethereum running without any central control or funding. As a fully functional third-party ledger, Ethereum has the potential, as it matures, to run corporate alternatives as decentralized entities.

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Unfortunately, something was happening right then on the Ethereum blockchain that would concern everyone who owned ETH. On April 15, the Decentralized Autonomous Organization (DAO) had launched as the first ambitious Ethereum-based decentralized application on the Ethereum blockchain. The DAO was the embodiment of my two great hopes: that a decentralized alternative to the corporation was possible and that successful projects would cause the value of ETH to rise exponentially. The problem was that the DAO was years, maybe decades, too early. Ethereum was still experimental technology. By comparison, the DAO was nearly science fiction.

pages: 226 words: 65,516

Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street
by Jeff John Roberts
Published 15 Dec 2020

Big companies soon sat up and took notice, building their own applications on top of Ethereum. IBM used a version of Ethereum to track customers’ identities while Walmart used the blockchain to track pork shipments from China to the United States. Banks experimented with a private version of blockchain to move money back and forth. Even state governments got into the act as Vermont tested putting land titles on a blockchain. The possibilities were endless. For Vitalik, the flurry of corporate interest was an unintended—and unwelcome—development. For him, the point of Ethereum was not to help big companies make money but rather to disrupt those companies by offering their services on decentralized networks.

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The holdouts argued that code is law, ledger updates are incontrovertible, and no matter the consequences, a human intervention could not be justified. Spurning the hard fork, the splinter group continued to build on the original blockchain, calling it—and the digital currency associated with it—Ethereum Classic. Today, Ethereum and Ethereum Classic operate as separate realms, two versions of what was once one reality. Both are going strong. While the former is forty times more valuable—Ethereum was worth more than $45 billion in mid-2020—both are adding new blocks to their respective chains every fifteen seconds or so. The DAO debacle briefly damaged Ethereum’s credibility, but did little to halt its steady rise as the first serious challenger to bitcoin.

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Meanwhile, thanks in large part to Ethereum, dozens and then hundreds of other cryptocurrencies began to take off. • • • Ethereum, you may recall, was Vitalik’s smart contract machine that had emerged as bitcoin’s main rival in the blockchain world. But it also served as the most popular platform for building other cryptocurrency projects. Suppose someone wanted to offer file storage or sports betting on a blockchain? One option would be to build a blockchain specifically for that purpose. A much easier option, though, would be to use smart contracts to build that service on top of Ethereum. In the emerging crypto industry, Ethereum was like a new type of internet, and these new third-party projects—like file sharing or sports betting—were the websites that ran on top of it.

pages: 271 words: 52,814

Blockchain: Blueprint for a New Economy
by Melanie Swan
Published 22 Jan 2014

Rather than being a blockchain, or a protocol running over a blockchain, or a metaprotocol running over a protocol like other projects, Ethereum is a fundamental underlying infrastructure platform that can run all blockchains and protocols, rather like a unified universal development platform. Each full node in the Ethereum network runs the Ethereum Virtual Machine for seamless distributed program (smart contract) execution. Ethereum is the underlying blockchain-agnostic, protocol-agnostic platform for application development to write smart contracts that can call multiple other blockchains, protocols, and cryptocurrencies. Ethereum has its own distributed ecosystem, which is envisioned to include file serving, messaging, and reputation vouching. The first component is Swarm (“Ethereum-Swarm,” not to be confused with the crowdfunding site Swarm) as a decentralized file-serving method.

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-M2M/IoT Bitcoin Payment Network to Enable the Machine Economy and consensus models, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe extensibility of, Extensibility of Blockchain Technology Concepts for facilitating big data predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation future applications, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe limitations of (see limitations) organizational capabilities, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity tracking capabilities, Fundamental Economic Principles: Discovery, Value Attribution, and Exchange-Fundamental Economic Principles: Discovery, Value Attribution, and Exchange blockchain-recorded marriage, Decentralized Governance Services BlockCypher, Blockchain Development Platforms and APIs BOINC, DAOs and DACs bond deposit postings, Technical Challenges Brin, David, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel BTCjam, Financial Services business model challenges, Business Model Challenges Buttercoin, Financial Services Byrne, Patrick, Financial Services C Campus Cryptocurrency Network, Campuscoin Campuscoin, Campuscoin-Campuscoin censorship, Internet (see decentralized DNS system) Chain, Blockchain Development Platforms and APIs challenges (see see limitations) charity donations, Charity Donations and the Blockchain—Sean’s Outpost China, Relation to Fiat Currency ChromaWallet, Wallet Development Projects Chronobit, Virtual Notary, Bitnotar, and Chronobit Circle Internet Financial, eWallet Services and Personal Cryptosecurity Codius, Financial Services coin drops, Coin Drops as a Strategy for Public Adoption coin mixing, eWallet Services and Personal Cryptosecurity coin, defining, Terminology and Concepts, Currency, Token, Tokenizing Coinapult, Global Public Health: Bitcoin for Contagious Disease Relief Coinapult LOCKS, Relation to Fiat Currency Coinbase, Merchant Acceptance of Bitcoin, Financial Services CoinBeyond, Merchant Acceptance of Bitcoin Coinffeine, Financial Services Coinify, Merchant Acceptance of Bitcoin Coinprism, Wallet Development Projects Coinspace, Crowdfunding CoinSpark, Wallet Development Projects colored coins, Smart Property, Blockchain 2.0 Protocol Projects community supercomputing, Community Supercomputing Communitycoin, Currency, Token, Tokenizing-Communitycoin: Hayek’s Private Currencies Vie for Attention complementary currency systems, Demurrage Currencies: Potentially Incitory and Redistributable concepts, redefining, Terminology and Concepts-Terminology and Concepts consensus models, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe consensus-derived information, Blockchain Consensus Increases the Information Resolution of the Universe contagious disease relief, Global Public Health: Bitcoin for Contagious Disease Relief contracts, Blockchain 2.0: Contracts-The Blockchain as a Path to Artificial Intelligence (see also smart contracts) crowdfunding, Crowdfunding-Crowdfunding financial services, Financial Services-Financial Services marriage, Decentralized Governance Services prediction markets, Bitcoin Prediction Markets smart property, Smart Property-Smart Property wallet development projects, Wallet Development Projects copyright protection, Monegraph: Online Graphics Protection Counterparty, Blockchain 2.0 Protocol Projects, Counterparty Re-creates Ethereum’s Smart Contract Platform Counterparty currency (XCP), Currency, Token, Tokenizing Counterwallet, Wallet Development Projects crowdfunding, Crowdfunding-Crowdfunding cryptocurrencies benefits of, Currency, Token, Tokenizing cryptosecurity, eWallet Services and Personal Cryptosecurity eWallet services, eWallet Services and Personal Cryptosecurity mechanics of, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin merchant acceptance, Merchant Acceptance of Bitcoin cryptosecurity challenges, eWallet Services and Personal Cryptosecurity cryptowallet, Blockchain Neutrality currency, Technology Stack: Blockchain, Protocol, Currency-Regulatory Status, Currency, Token, Tokenizing-Extensibility of Demurrage Concept and Features Campuscoin, Campuscoin-Campuscoin coin drops, Coin Drops as a Strategy for Public Adoption Communitycoin, Communitycoin: Hayek’s Private Currencies Vie for Attention-Communitycoin: Hayek’s Private Currencies Vie for Attention cryptocurrencies, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin decentralizing, Communitycoin: Hayek’s Private Currencies Vie for Attention defining, Currency, Token, Tokenizing-Currency, Token, Tokenizing, Currency: New Meanings demurrage, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems fiat currency, Relation to Fiat Currency-Relation to Fiat Currency monetary and nonmonetary, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies new meanings, Currency: New Meanings technology stack, Technology Stack: Blockchain, Protocol, Currency-Technology Stack: Blockchain, Protocol, Currency currency mulitplicity, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies D DAOs, DAOs and DACs-DAOs and DACs DAOs/DACs, DAOs and DACs-DAOs and DACs, Batched Notary Chains as a Class of Blockchain Infrastructure, Blockchain Government Dapps, Dapps-Dapps, Extensibility of Demurrage Concept and Features Dark Coin, eWallet Services and Personal Cryptosecurity dark pools, Technical Challenges Dark Wallet, eWallet Services and Personal Cryptosecurity DASs, DASs and Self-Bootstrapped Organizations DDP, Crowdfunding decentralization, Smart Contracts, Centralization-Decentralization Tension and Equilibrium decentralized applications (Dapps), Dapps-Dapps decentralized autonomous organization/corporation (DAO) (see DAOs/DACs) decentralized autonomous societies (DASs), DASs and Self-Bootstrapped Organizations decentralized autonomy, eWallet Services and Personal Cryptosecurity decentralized DNS, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity challenges of, Challenges and Other Decentralized DNS Services and digital identity, Decentralized DNS Functionality Beyond Free Speech: Digital Identity-Decentralized DNS Functionality Beyond Free Speech: Digital Identity DotP2P, Challenges and Other Decentralized DNS Services decentralized file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation decentralized secure file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation deeds, Decentralized Governance Services demurrage currencies, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features action-incitory features, Extensibility of Demurrage Concept and Features limitations of, Demurrage Currencies: Potentially Incitory and Redistributable digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also blockchain attestation services) hashing and timestamping, Hashing Plus Timestamping-Limitations online graphics protection, Monegraph: Online Graphics Protection digital cryptography, Ethereum: Turing-Complete Virtual Machine, Public/Private-Key Cryptography 101 digital divide, defining, Digital Divide of Bitcoin digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records dispute resolution, PrecedentCoin: Blockchain Dispute Resolution DIYweathermodeling, Community Supercomputing DNAnexus, Genomecoin, GenomicResearchcoin Dogecoin, Technology Stack: Blockchain, Protocol, Currency, Currency Multiplicity: Monetary and Nonmonetary Currencies, Scandals and Public Perception DotP2P, Challenges and Other Decentralized DNS Services double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems DriveShare, DAOs and DACs dynamic redistribution of currency (see demurrage currency) E education (see learning and literacy) Electronic Freedom Foundation (EFF), Distributed Censorship-Resistant Organizational Models EMR (electronic medical record) system, EMRs on the Blockchain: Personal Health Record Storage Ethereum, Crowdfunding, Blockchain 2.0 Protocol Projects, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine-Counterparty Re-creates Ethereum’s Smart Contract Platform eWallet services, eWallet Services and Personal Cryptosecurity ExperimentalResultscoin, Blockchain Academic Publishing: Journalcoin F Fairlay, Bitcoin Prediction Markets fiat currency, Relation to Fiat Currency-Relation to Fiat Currency file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation financial services, Regulatory Status, Financial Services-Financial Services, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity, Government Regulation Fitbit, Personal Thinking Blockchains, Blockchain Health Research Commons, Extensibility of Demurrage Concept and Features Florincoin, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel Folding@Home, DAOs and DACs, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing franculates, Blockchain Government freedom of speech, Namecoin: Decentralized Domain Name System, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel (see also decentralized DNS system) Freicoin, Demurrage Currencies: Potentially Incitory and Redistributable fundraising (see crowdfunding) futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets G GBIcoin, Demurrage Currencies: Potentially Incitory and Redistributable GBIs (Guaranteed Basic Income initiatives), Demurrage Currencies: Potentially Incitory and Redistributable Gems, Blockchain Development Platforms and APIs, Dapps Genecoin, Blockchain Genomics Genomecoin, Genomecoin, GenomicResearchcoin Genomic Data Commons, Genomecoin, GenomicResearchcoin genomic sequencing, Blockchain Genomics 2.0: Industrialized All-Human-Scale Sequencing Solution-Genomecoin, GenomicResearchcoin GenomicResearchcoin, Genomecoin, GenomicResearchcoin genomics, consumer, Blockchain Genomics-Genomecoin, GenomicResearchcoin Git, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation GitHub, Blockchain Academic Publishing: Journalcoin, Currency Multiplicity: Monetary and Nonmonetary Currencies global public health, Global Public Health: Bitcoin for Contagious Disease Relief GoCoin, Financial Services GoToLunchcoin, Terminology and Concepts governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance decentralized services, Decentralized Governance Services-Decentralized Governance Services dispute resolution, PrecedentCoin: Blockchain Dispute Resolution futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections personalized governance services, Blockchain Government random-sample elections, Random-Sample Elections societal maturity impact of blockchain governance, Societal Maturity Impact of Blockchain Governance government regulation, Regulatory Status, Government Regulation-Government Regulation Gridcoin, Blockchain Science: Gridcoin, Foldingcoin-Blockchain Science: Gridcoin, Foldingcoin H hashing, Hashing Plus Timestamping-Limitations, Batched Notary Chains as a Class of Blockchain Infrastructure, Technical Challenges Hayek, Friedrich, Communitycoin: Hayek’s Private Currencies Vie for Attention, Demurrage Currencies: Potentially Incitory and Redistributable, Conclusion, The Blockchain Is an Information Technology health, Blockchain Health-Virus Bank, Seed Vault Backup as demurrage currency, Extensibility of Demurrage Concept and Features doctor vendor RFP services, Doctor Vendor RFP Services and Assurance Contracts health notary services, Blockchain Health Notary health research commons , Blockchain Health Research Commons health spending, Healthcoin healthcare decision making and advocacy, Liquid Democracy and Random-Sample Elections personal health record storage, EMRs on the Blockchain: Personal Health Record Storage virus bank and seed vault backup, Virus Bank, Seed Vault Backup Healthcoin, Healthcoin, Demurrage Currencies: Potentially Incitory and Redistributable I identity authentication, eWallet Services and Personal Cryptosecurity, Blockchain 2.0: Contracts, Smart Property, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records Indiegogo, Crowdfunding, Dapps industry scandals, Scandals and Public Perception infrastructure needs and issues, Technical Challenges inheritance gifts, Smart Contracts intellectual property, Monegraph: Online Graphics Protection (see also digital art) Internet administration, Distributed Censorship-Resistant Organizational Models Internet Archive, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Personal Thinking Blockchains Internet censorship prevention (see Decentralized DNS system) Intuit Quickbooks, Merchant Acceptance of Bitcoin IP protection, Hashing Plus Timestamping IPFS project, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation J Johnston, David, Blockchain Technology Could Be Used in the Administration of All Quanta Journalcoin, Blockchain Academic Publishing: Journalcoin Judobaby, Crowdfunding justice applications for censorship-resistant organizational models, Distributed Censorship-Resistant Organizational Models-Distributed Censorship-Resistant Organizational Models digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also digital art, blockchain attestation services) digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records freedom of speech/anti-censorship, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance (see also governance) Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection (see also decentralized DNS) K Kickstarter, Crowdfunding, Community Supercomputing Kipochi, Blockchain Neutrality, Global Public Health: Bitcoin for Contagious Disease Relief, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Koinify, Crowdfunding, Dapps Kraken, Financial Services L latency, Blockchain 2.0 Protocol Projects, Technical Challenges, Technical Challenges, Scandals and Public Perception LaZooz, Dapps, Campuscoin, Extensibility of Demurrage Concept and Features Learncoin, Learncoin learning and literacy, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges learning contract exchanges, Learning Contract Exchanges Ledra Capital, Blockchain 2.0: Contracts, Ledra Capital Mega Master Blockchain List legal implications crowdfunding, Crowdfunding smart contracts, Smart Contracts lending, trustless, Smart Property Lighthouse, Crowdfunding limitations, Limitations-Overall: Decentralization Trends Likely to Persist business model challenges, Business Model Challenges government regulation, Government Regulation-Government Regulation personal records privacy challenges, Privacy Challenges for Personal Records scandals and public perception, Scandals and Public Perception-Scandals and Public Perception technical challenges, Technical Challenges-Technical Challenges Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections Litecoin, Technology Stack: Blockchain, Protocol, Currency, Technology Stack: Blockchain, Protocol, Currency, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel, Currency Multiplicity: Monetary and Nonmonetary Currencies, Technical Challenges literacy (see learning and literacy) LTBcoin, Wallet Development Projects, Currency, Token, Tokenizing M M2M/IoT infrastructure, M2M/IoT Bitcoin Payment Network to Enable the Machine Economy, Blockchain Development Platforms and APIs, Blockchain Academic Publishing: Journalcoin-The Blockchain Is Not for Every Situation, The Blockchain Is an Information Technology Maidsafe, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Technical Challenges Manna, Crowdfunding marriage, blockchain recorded, Decentralized Governance Services Mastercoin, Blockchain 2.0 Protocol Projects mechanics of cryptocurrencies, How a Cryptocurrency Works Medici, Financial Services mega master blockchain list, Ledra Capital Mega Master Blockchain List-Ledra Capital Mega Master Blockchain List Melotic, Crowdfunding, Wallet Development Projects merchant acceptance, Merchant Acceptance of Bitcoin merchant payment fees, Summary: Blockchain 1.0 in Practical Use messaging, Ethereum: Turing-Complete Virtual Machine, Dapps, Challenges and Other Decentralized DNS Services, Technical Challenges MetaDisk, DAOs and DACs mindfiles, Personal Thinking Blockchains MIT Bitcoin Project, Campuscoin Monegraph, Monegraph: Online Graphics Protection money (see currency) MOOCs (massive open online courses), Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Moroz, Tatiana, Communitycoin: Hayek’s Private Currencies Vie for Attention multicurrency systems, Demurrage Currencies: Potentially Incitory and Redistributable N Nakamoto, Satoshi, Blockchain 2.0: Contracts, Blockchain 2.0: Contracts Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection Nationcoin, Coin Drops as a Strategy for Public Adoption, Demurrage Currencies: Potentially Incitory and Redistributable notary chains, Batched Notary Chains as a Class of Blockchain Infrastructure notary services, Hashing Plus Timestamping, Blockchain Health Notary NSA surveillance, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel NXT, Technology Stack: Blockchain, Protocol, Currency, Blockchain 2.0 Protocol Projects O offline wallets, Technical Challenges OneName, Digital Identity Verification-Digital Identity Verification OneWallet, Wallet Development Projects online graphics protection, Monegraph: Online Graphics Protection-Monegraph: Online Graphics Protection Open Assets, Blockchain 2.0 Protocol Projects Open Transactions, Blockchain 2.0 Protocol Projects OpenBazaar, Dapps, Government Regulation Ostel, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel P passports, Decentralized Governance Services PayPal, The Double-Spend and Byzantine Generals’ Computing Problems, Financial Services, Distributed Censorship-Resistant Organizational Models peer-to-peer lending, Financial Services Peercoin, Technology Stack: Blockchain, Protocol, Currency personal cryptosecurity, eWallet Services and Personal Cryptosecurity personal data rights, Blockchain Genomics personal mindfile blockchains, Personal Thinking Blockchains personal thinking chains, Personal Thinking Blockchains-Personal Thinking Blockchains physical asset keys, Blockchain 2.0: Contracts, Smart Property plagiarism detection/avoidance, Blockchain Academic Publishing: Journalcoin Precedent, PrecedentCoin: Blockchain Dispute Resolution, Terminology and Concepts prediction markets, Bitcoin Prediction Markets, DASs and Self-Bootstrapped Organizations, Decentralized Governance Services, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Predictious, Bitcoin Prediction Markets predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation privacy challenges, Privacy Challenges for Personal Records private key, eWallet Services and Personal Cryptosecurity Proof of Existence, Proof of Existence-Proof of Existence proof of stake, Blockchain 2.0 Protocol Projects, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges proof of work, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges-Technical Challenges property ownership, Smart Property property registration, Decentralized Governance Services public documents registries, Decentralized Governance Services public health, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Global Public Health: Bitcoin for Contagious Disease Relief public perception, Scandals and Public Perception-Scandals and Public Perception public/private key cryptography, Public/Private-Key Cryptography 101-Public/Private-Key Cryptography 101 publishing, academic, Blockchain Academic Publishing: Journalcoin-Blockchain Academic Publishing: Journalcoin pull technology, eWallet Services and Personal Cryptosecurity push technology, eWallet Services and Personal Cryptosecurity R random-sample elections, Random-Sample Elections Realcoin, Relation to Fiat Currency redistribution of currency (see demurrage currency) regulation, Government Regulation-Government Regulation regulatory status, Regulatory Status reputation vouching, Ethereum: Turing-Complete Virtual Machine Researchcoin, Blockchain Academic Publishing: Journalcoin REST APIs, Technical Challenges Ripple, Technology Stack: Blockchain, Protocol, Currency, Relation to Fiat Currency, Blockchain 2.0 Protocol Projects Ripple Labs, Financial Services Roadcoin, Blockchain Government S Saldo.mx, Blockchain Neutrality scandals, Scandals and Public Perception science, Blockchain Science: Gridcoin, Foldingcoin-Charity Donations and the Blockchain—Sean’s Outpost community supercomputing, Community Supercomputing global public health, Global Public Health: Bitcoin for Contagious Disease Relief Sean's Outpost, Charity Donations and the Blockchain—Sean’s Outpost secret messaging, Ethereum: Turing-Complete Virtual Machine security issues, Technical Challenges self-bootstrapped organizations, DASs and Self-Bootstrapped Organizations self-directing assets, Automatic Markets and Tradenets self-enforced code, Smart Property self-sufficiency, Smart Contracts SETI@home, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing size and bandwidth, Technical Challenges smart contracts, Smart Contracts-Smart Contracts, Smart Contract Advocates on Behalf of Digital Intelligence automatic markets and tradenets, Automatic Markets and Tradenets Counterparty, Counterparty Re-creates Ethereum’s Smart Contract Platform DAOs/DACs, DAOs and DACs-DAOs and DACs Dapps, Dapps-Dapps DASs, DASs and Self-Bootstrapped Organizations Ethereum, Ethereum: Turing-Complete Virtual Machine increasingly autonomous, Dapps, DAOs, DACs, and DASs: Increasingly Autonomous Smart Contracts-Automatic Markets and Tradenets smart literacy contracts, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges smart property, Smart Property-Smart Property, Monegraph: Online Graphics Protection smartwatch, Extensibility of Demurrage Concept and Features Snowden, Edward, Distributed Censorship-Resistant Organizational Models social contracts, Smart Contracts social network currencies, Currency Multiplicity: Monetary and Nonmonetary Currencies Stellar, Blockchain Development Platforms and APIs stock market, Financial Services Storj, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Dapps, Technical Challenges Stripe, Blockchain Development Platforms and APIs supercomputing, Community Supercomputing Svalbard Global Seed Vault, Virus Bank, Seed Vault Backup Swancoin, Smart Property swaps exchange, Financial Services Swarm, Crowdfunding, Dapps Swarm (Ethereum), Ethereum: Turing-Complete Virtual Machine Swarmops, Crowdfunding T Tatianacoin, Communitycoin: Hayek’s Private Currencies Vie for Attention technical challenges, Technical Challenges-Technical Challenges Tendermint, Technical Challenges Tera Exchange, Financial Services terminology, Terminology and Concepts-Terminology and Concepts 37Coins, Global Public Health: Bitcoin for Contagious Disease Relief throughput, Technical Challenges timestamping, Hashing Plus Timestamping-Limitations titling, Decentralized Governance Services tradenets, Automatic Markets and Tradenets transaction fees, Summary: Blockchain 1.0 in Practical Use Tribecoin, Coin Drops as a Strategy for Public Adoption trustless lending, Smart Property Truthcoin, Futarchy: Two-Step Democracy with Voting + Prediction Markets Turing completeness, Ethereum: Turing-Complete Virtual Machine Twister, Dapps Twitter, Monegraph: Online Graphics Protection U Uber, Government Regulation unbanked/underbanked markets, Blockchain Neutrality usability issues, Technical Challenges V value chain composition, How a Cryptocurrency Works versioning issues, Technical Challenges Virtual Notary, Virtual Notary, Bitnotar, and Chronobit voting and prediction, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets W wallet APIs, Blockchain Development Platforms and APIs wallet companies, Wallet Development Projects wallet software, How a Cryptocurrency Works wasted resources, Technical Challenges Wayback Machine, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation Wedbush Securities, Financial Services Whatevercoin, Terminology and Concepts WikiLeaks, Distributed Censorship-Resistant Organizational Models Wikinomics, Community Supercomputing World Citizen project, Decentralized Governance Services X Xapo, eWallet Services and Personal Cryptosecurity Z Zennet Supercomputer, Community Supercomputing Zooko's Triangle, Decentralized DNS Functionality Beyond Free Speech: Digital Identity About the Author Melanie Swan is the Founder of the Institute for Blockchain Studies and a Contemporary Philosophy MA candidate at Kingston University London and Université Paris VIII.

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pages: 135 words: 26,407

How to DeFi
by Coingecko , Darren Lau , Sze Jin Teh , Kristian Kho , Erina Azmi , Tm Lee and Bobby Ong
Published 22 Mar 2020

Like Argent, Metamask is a non-custodial wallet and it acts as both a wallet and an interaction bridge for the Ethereum network. You can store your Ethereum and ERC20 tokens on Metamask. Acting as an interaction bridge, Metamask enables you to use all Decentralized Applications (Dapps) that are hosted on the Ethereum Network. Without the use of an interaction bridge like MetaMask, your browser would not be able to access the Ethereum blockchain unless you were running a full Ethereum node and have the entire Ethereum blockchain of over 400GB downloaded on your computer. On a technical level, MetaMask does this by injecting a javascript library known as web3.js written by the core Ethereum developers into your browser’s page to enable you to easily interact with the Ethereum network.

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It is a useful tool to visualize and track where your assets are across the different DeFi protocols. E Ethereum Ethereum is an open-source, programmable, decentralized platform built on blockchain technology. Compared to Bitcoin, Ethereum allows for scripting languages which has allowed for application development. Ether Ether is the cryptocurrency that powers the Ethereum blockchain. It is the fuel for the apps on the decentralized Ethereum network ERC-20 ERC is an abbreviation for Ethereum Request for Comment and 20 is the proposal identifier. It is an official protocol for proposing improvements to the Ethereum network. ERC-20 refers to the commonly adopted standard used to create tokens on Ethereum.

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(Aaron Hay) https://medium.com/coinmonks/how-decentralized-is-decentralized-finance-89aea3070e8f Mapping Decentralized Finance https://outlierventures.io/wp-content/uploads/2019/06/Mapping-Decentralised-Finance-DeFi-report.pdf Market Report: 2019 DeFi Year in Review https://defirate.com/market-report-2019/ DeFi #3 – 2020: The Borderless State of DeFi https://research.binance.com/analysis/2020-borderless-state-of-defi Decentralized Finance with Tom Schmidt (Software Engineering Daily) https://softwareengineeringdaily.com/2020/02/25/decentralized-finance-with-tom-schmidt/ Part Two: Getting Into DeFi Chapter Three: The Decentralized Layer: Ethereum What is Ethereum? As mentioned in Chapter 1, the majority of the DeFi Dapps are currently being built on the Ethereum blockchain. But what exactly is Ethereum? Ethereum is a global, open-source platform for decentralized applications. You can think of it as a world computer that cannot be shut down. On Ethereum, software developers can write smart contracts that control digital value through a set of criteria and are accessible anywhere in the world.

pages: 161 words: 44,488

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology
by William Mougayar
Published 25 Apr 2016

Bitcoin was that first public blockchain, and it inspired many others. Ethereum was another major public blockchain that has grown rapidly to establish itself as the second largest and significant public, multi-purpose blockchain. One of the primary differences between a public and private blockchain is that public blockchains typically have a generic purpose and are generally cheaper to use, whereas private blockchains have a more specific usage, and they are more expensive to set up because the cost is born by fewer owners. We can also expect special purpose public blockchains to emerge, for example, the Zcash one that promises to deliver total privacy.

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“My Financial Stack as a Millennial,” Sachin Rekhi, http://www.sachinrekhi.com/my-financial-stack-as-a-millennial. 6. Update to the Global Landscape of Blockchain Companies in Financial Services, William Mougayar, http://startupmanagement.org/2015/12/08/update-to-the-global-landscape-of-blockchain-companies-in-financial-services/. 7. “Blockchain 2015: Strategic Analysis in Financial Services,” William Mougayar,http://www.slideshare.net/wmougayar/blockchain-2015-analyzing-the-blockchain-in-financial-services. 8. “Ethereum-inspired Clearmatics to save OTC markets from eternal darkness,” Ian Allison, IB Times, http://www.ibtimes.co.uk/ethereum-inspired-clearmatics-save-otc-markets-eternal-darkness-1545180. 5 LIGHTHOUSE INDUSTRIES & NEW INTERMEDIARIES “Fool you are to say you learn by your experience!

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He taught me a lot about blockchains, and I advised him on business matters and growing Ethereum. I may never comprehend a fraction of Vitalik's blockchain dreams on a given night, but one thing I am certain about, is that Vitalik Buterin is emerging as a savvy business person, following the ranks of other bright technologists, while continuing to lead the Ethereum core technology and its Foundation. I proceeded to write 50 blog posts on Bitcoin, blockchains, and Ethereum, and immersed myself with global creators, innovators, pioneers, leaders, entrepreneurs, startups, enterprise executives and practitioners who were at the leading edges of blockchain technology and its implementation.

pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

This incident was not the result of flaws in the Ethereum blockchain itself, but that didn’t matter. As the ruler of the chain, Buterin could not escape the need to intervene. Proponents of Ethereum Classic contend that this arbitrary action undermined the immutability of the database and the principle of decentralization that is the heart of the blockchain. Ethereum Classic has not yet had much influence. Hoskinson has gone on to form Cardano, attempting a blockchain that redresses all the flaws of bitcoin through rigorous functional software. Saifedean Ammous, the author of The Bitcoin Standard, contends: “The fact that Ethereum could be rolled back means that all blockchains smaller than Bitcoin’s are essentially centralized databases under the control of their operators.”5 The key difference between the bitcoin and Ethereum blockchains is that bitcoin focuses on security and simplicity while Ethereum focuses on capability and functionality.

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Again and again he implied that smart contracts could be created on the bitcoin blockchain that critics claimed could not accommodate them. He ended his Arnhem speech by throwing down a gauntlet to his rivals. “I’m not going away. We will scale radically. You’re either with us or against us. We will compete by growing the value through easy connectivity and easy use.” Asked directly what he thought of the rival blockchain from Ethereum, he declared, “I was a bitcoin maximalist in 2013 and I am a bitcoin maximalist today.” A bitcoin maximalist bars all other blockchains. Enter Wright’s nemesis, Vitalik Buterin, the founder of the Ethereum blockchain. It is a platform explicitly designed for smart contracts, token issues, investment vehicles, and autonomous corporations.

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Saifedean Ammous, the author of The Bitcoin Standard, contends: “The fact that Ethereum could be rolled back means that all blockchains smaller than Bitcoin’s are essentially centralized databases under the control of their operators.”5 The key difference between the bitcoin and Ethereum blockchains is that bitcoin focuses on security and simplicity while Ethereum focuses on capability and functionality. Ethereum’s superior functionality is transforming a number of industries. As Buterin puts it, “The Internet tended to displace workers doing routine work on the edge of the system; the blockchain tends to disintermediate executives in the center.” Smart contracts may disintermediate lawyers, accountants, and bankers who do not get aboard.

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Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World
by Don Tapscott and Alex Tapscott
Published 9 May 2016

The intermediary is the ultimate arbiter of everything, including who owns what. The blockchain IPO takes the concept further. Now, companies can raise funds “on the blockchain” by issuing tokens, or cryptosecurities, of some value in the company. They can represent equity, bonds, or, in the case of Augur, market-maker seats on the platform, granting owners the right to decide which prediction markets the company will open. Ethereum was an even greater success than Augur, funding the development of a whole new blockchain through a crowd sale of its native token, ether. Today Ethereum is the second-longest and fastest-growing public blockchain. The average investment in the Augur crowdfunding was $750, but one can easily imagine minimum subscriptions of a dollar or even ten cents.

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CHAPTER 4 RE-ARCHITECTING THE FIRM: THE CORE AND THE EDGES BUILDING CONSENSYS July 30, 2015, was a big day for a global group of coders, investors, entrepreneurs, and corporate strategists who think that Ethereum is the next big thing—not just for business, but possibly for civilization. Ethereum, the blockchain platform eighteen months in the making, went live. We witnessed the launch firsthand in the Brooklyn office of Consensus Systems (ConsenSys), one of the first Ethereum software development companies. Around 11:45 a.m., there were high fives all around as the Ethereum network created its “genesis block,” after which a frenzy of miners raced to win the first block of ether, Ethereum’s currency. The day was eerily suspenseful. A massive thunderstorm broke over the East River, triggering loud and random emergency flood warnings on everyone’s smart phones.

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Just as the blockchain protocol is distributed, a distributed application or DApp runs across many computing devices rather than on a single server. This is because all the computing resources that are running a blockchain constitute a computer. Blockchain developer Gavin Wood makes this point describing the Ethereum blockchain as a platform for processing. “There is only one Ethereum computer in the world,” he said. “It’s also multiuser—anyone who ever uses it is automatically signed in.” Because Ethereum is distributed and built to the highest standards of cryptosecurity, “all code, processing, and storage exists within its own encapsulated space and no one can ever mess with that data.”

pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

But however much one or another of these propositions might appeal to some pie-slice of its target audience, none of them is quite as compelling to the community gathered around Ethereum as its power to act conditionally. This ability to trigger events should certain contingencies arise is something the Bitcoin blockchain could not do, and Buterin had designed it into his creation from the start. You can store data of any nature on the Ethereum blockchain, and it will be validated, agreed upon and permanently accessible to anyone who asks for it, just as records of Bitcoin transactions are accessible to anyone querying the Bitcoin blockchain. But you can also perform logical operations with Ethereum—any operation that a general-purpose computer would be capable of handling.

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Fusing comprehensive technical understanding and a clear vision to a series of crisply articulated value propositions, it swiftly attracted a number of accomplished developers in the cryptocurrency space, and together they more or less immediately set to work on building out the set of tools described in it. The successful launch of a framework called Ethereum just a few months later would demote “the” (implicitly Bitcoin) blockchain to merely “a” blockchain, just one alternative among many. While there has been a groundswell of competing initiatives in the post- or para-Bitcoin space, with cryptic names like Juno and Sawtooth Lake, I have chosen to center the discussion that follows on Ethereum. It remains preeminent among these second-generation blockchain efforts, is now rapidly approaching the size of the Bitcoin network, and seems likely to remain salient when other streams of activity have dried up or come to nothing.2 More importantly, though, Buterin and the substantial community he has attracted to his initiative have generated most of the domain’s fundamentally new ideas, and have otherwise proven adept at translating those already in circulation into their own highly particular idiom.

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And just as the form of a Bitcoin transaction is identical with its content, the terms of a smart contract are articulated unambiguously, in the same code that governs its execution. There is a precise, 1:1 relationship between what it specifies and what it actually does, and those specifications can be retrieved from the blockchain for reference at any time. If the atomic unit of the Bitcoin blockchain is transactions, then, that of the Ethereum blockchain is contracts. This “simplest form of decentralized automation” is key to everything else Ethereum does or proposes to do. Armed with this mechanism, it is capable of binding previously unaffiliated peers in a meshwork of obligation, whether those peers are human, organizational or machinic.

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Cloudmoney: Cash, Cards, Crypto, and the War for Our Wallets
by Brett Scott
Published 4 Jul 2022

All big corporates are part of the complex webs we considered in Chapter 1, operating as oligopolies with transnational supply chains that require co-ordination and co-operation. Microsoft’s Azure cloud computing division now offers corporates the ability to run a ‘consortium blockchain’ among themselves entirely within the Microsoft datacentres. Microsoft is a member of the Enterprise Ethereum Alliance, set up to build a business-friendly version of the anarchic protocol, called Enterprise Ethereum. Our Texan who ended up in Kazakhstan pitching the technology to oil corporations belongs to the same alliance. The explosion of profit opportunities has left the blockchain landscape complex and fragmented, and it is often not clear who is raiding whom: is the corporate world ‘taking over’ crypto, or is crypto taking over the corporate world?

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The Bitcoin movement is not the only community of troubled technological dream-weavers. The Ethereum community, and the broader blockchain movement, continues to forge ahead with its smart contract systems and automated DAOs. We’ve seen how these are easily co-opted by corporate oligopolies who raid the technology, so that what is referred to as ‘blockchain technology’ now contains a confusing amalgamation of disparate agendas. Still, interesting hybrids continue to emerge from that meeting. Like fintech promoters, many blockchain innovators are often pro-digital automation and openly anti-cash, but they claim a desire to build a decentralised version of the finance-tech fusion that is sweeping the world.

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, 49, 72 ‘Cashfree and Proud’, 40 Cashless Catalyst, 127–8 Cashless Challenge, 40 cashless society, 2, 5, 10, 15, 38, 64, 81, 83, 84, 251 inevitability, 10–12, 121–33, 260–61 Cashless Way, 37 casinos, 66–9, 70–71, 83, 236 categorisation, 109, 113–14, 162, 167 Catholicism, 131, 212 Cayman Islands, 111 censorship, 33, 116–18, 250 central banks, 36, 42–5, 51, 84, 254 data surveillance, 115 digital currencies (CBDC), 242–5, 254, 255 international transfers, 79 transfers, 73–4 centralisation of power, 15, 180–83 centralised–decentralised model, 136 Chama, 130 charging up, 22–5 chatbots, 146–8 Chaum, David, 106–7, 117, 183 cheques, 89 Chicago Mercantile Exchange, 158 China, 2, 7, 18, 33, 74–5, 79, 114–15, 254 CBDC plans, 245, 254–5 facial recognition in, 150 leviathan complex, 178 People’s Bank of China, 79, 242 Social Credit System, 115, 245 choice, 124–6, 251 Christianity, 154, 175–6, 212 Christl, Wolfie, 109 cigarettes, 181 Circles, 260 Citigroup, 1, 37, 109, 132, 150, 227 City of London, 6, 135 class, see social class Cleo, 146 climate change, 226 cloakrooms, 66–9, 70–71 cloud, 30 cloudmoney, 82 Coca-Cola, 31, 131 cocaine, 98 code is law, 223, 224 Coinbase, 233 collateralised debt obligations, 26 colonialism, 55, 97, 175–6, 178, 239 Commerzbank Tower, Frankfurt, 18–20, 143, 156 computer boys, 158 conductivity, 179, 249 ConsenSys, 229 conservatism, 7, 131, 155, 184, 192–3, 211 see also right-wing politics consortium blockchains, 231, 233 conspiracy theories, 261–2 constitutional monarchies, 56 consumers, 25 contactless payments, 13, 31, 37–8, 91, 125, 127 core, 28 corporate personhood, 147 Corruption Perceptions Index, 43 counterfeiting, 60–61 countertradability, 209–10, 213, 256–7 Covid-19 pandemic, 2, 10, 16, 34, 36, 181, 249, 254 ATM use, 36 cash and, 2, 34, 40–41, 249, 261 conspiracy theories, 261 Cracked Labs, 109 credit cards, 39, 91, 109 credit creation of bank-money, 70, 72 credit default swap market, 232 credit expansion, 168–9 credit ratings, 17, 114, 160, 162–3, 167, 168, 170 crime cash and, 36, 42–3, 45, 81, 112 cybercrime, 32 financial crime, 111–12 marijuana industry, 102 trust and, 93 Crypto Sex Toys, 13 crypto-anarchists, 183 Cryptocannabis Salon, 101–2 cryptocurrencies, 13–15, 16, 101–2, 103, 184–5, 187–246, 254–60 alt-coins, 217–18 as commodity, 206–10, 213–14, 217, 246, 256 countertradability, 209–10, 213, 256–7 decentralisation and, 14, 15, 189–94, 196, 230, 234, 255, 258 forks, 214, 217 millenarianism and, 212, 213 mutual credit systems and, 260 oligopolies and, 229–33, 246 politics and, 191–3, 211–12, 215–17, 225–6 smart contracts, 220–24, 258 stablecoins, 233–41, 245–6, 255 Currency Conference (2017), 60 Curse of Cash, The (Rogoff), 93 Cyber Monday, 86 cyberattacks, 32, 48 cybercrime, 34 cyberpunk genre, 10 cypherpunk movement, 106, 183–5, 216–17 Dahabshiil, 116 DAI, 235 dark market, 216–17, 259 Dark Wallet, 216 data, 2, 8, 10, 33, 39, 104–19, 156–72 AI analysis, 108, 153–72 banking sector and, 108–9 Big Brother and, 113–15 categorisation, 109, 113–14, 162 panopticon effect and, 118–19, 172 payments censorship and, 116–18 predictive systems and, 105 states and, 110–12, 114–15 Data Bank Society, The (Warner), 106 data centres, 3, 4, 5, 30, 32, 34, 35, 47, 73, 76–7, 149 Davos, Switzerland, 11 debit cards, 39 Decathlon, 40–41 decentralisation, 14, 15, 189–94, 196, 230, 234, 255, 258–60 decentralised autonomous organisations (DAOs), 221–4, 258 DECODE, 236 DeepMind, 8 DeFi (decentralised finance), 258 Delft University of Technology, 31 demand, 29 demonetisations, 43, 44, 93 deposits, 66–7, 69 derivatives, 6, 18, 21, 26, 27, 160 Desparte, Dante, 238 Diamond, Robert ‘Bob’, 38 Diem, 241, 244 DigiCash, 106, 183 digital footprint, 169 disruption, 8, 9, 14, 32, 140–43 distributed ledger technology (DLT), 229–46, 258 Dogecoin, 13, 218 dollar system, 80, 182, 210, 233–6, 239, 240 double spending, 182, 194 doublethink, 143 Dow Chemical, 24 Drakensberg Mountains, 3–4 Dridex, 32 drones, 11 drug dealers, 96 Dubai, United Arab Emirates, 248 Dylan, Robert ‘Bob’, 90 e-commerce, 40, 77 East India Company, 178 eBay, 109, 113 ecological activism, 7 economic syncretism, 175–6 Ecuador, 240 Egypt, 116 El Salvador, 98, 208 elderly people, 126 electricity, 247 Elwartowski, Chad, 216 Emili, Geronimo, 37 employees, 25 enclosure, 86 Enlightenment (c. 1637–1789), 252 enterprise blockchains, 231 Enterprise Ethereum Alliance, 233 entrepreneurs, 1, 15, 129, 155 equivocation fallacies, 85 Erica, 147 Ethereum, 219–24, 257–8 Ethereum Classic, 224 European Union, 14, 37, 42, 254 Central Bank, 51, 74, 79, 242 DECODE project, 236 Eurozone, 51, 74, 79 Evans, Mel, 144 exiting, 39, 48, 61, 63, 68, 83 Experian, 163 F-16 fighter jets, 153 Facebook, 7, 38, 105, 150, 166, 198, 255, 262 Libra, 236–41, 245 Messenger, 237 facial recognition, 10, 138, 150, 181, 245 far-left politics, 7, 215 far-right politics, 7, 14, 215, 225–6, 261–2 fascism, 7, 14, 226 Federal Bureau of Investigation (FBI), 111 Federal Reserve, 32, 35, 36, 234, 242 federated frontline, 136–8, 147 fees, 39, 57, 91, 94 feminism, 226 fiat money, 51–2, 56, 192, 193 Fidor, 142 Financial Crimes Enforcement Network, 111 financial crisis (2008), 6, 8, 17–18, 26–7, 96, 184, 232, 248 financial inclusion, 37, 39, 93–9, 130–32, 167, 238, 262 fingerprints, 150 Fink, Stanley, 38 fintech, 8, 41–2, 140–43 first-world problems, 154 fitness centres, 17 fixed money supplies, 191–3 Floored (2009 film), 158 Florentine Republic (1115–1569), 135, 159 Follow the Money, 112 Fourth Industrial Revolution, 11 fractional reserve banking, 70 France cashless payments strategy, 43 Frankfurt, Germany, 18–20, 143, 156, 248 frogs, slow-boiling, 104 futurism, 1, 12, 86, 122–3, 250, 252 gambling, 105 game theory, 220 Gap, 131 Gates, William ‘Bill’, 44–5, 261–2 GCHQ, 112 Generation Z, 86, 140 gentrification, 128–33 Germany, 7, 18 Bundesbank, 35, 47 cash thresholds, 42–3 Corruption Perceptions Index, 43 Frankfurt, 18–20, 143, 156 honesty boxes in, 91 get-rich-quick investments, 26 Getty Images, 80 giant parable, 52–6, 63–4, 188 global matrix, 12 Gmail, 203 gold, 192–3, 207, 214 Goldman Sachs, 38, 150, 157, 158, 230 Golumbia, David, 225 Google, 2, 5, 7, 262 Cashe, 150 data, 105, 108 DeepMind, 8 Gmail, 203 Maps, 4 Mastercard deal, 109 Pay, 1, 78, 125 Singularity University, 153–6, 252–3 Trends, 84 USAID and, 128, 178 Grassroots Economics, 260 Greece, 42, 43, 62, 131 Green Dot, 150 Greenpeace, 116 growth, 123, 126–7, 249 hackers, 6–7, 101, 184 Hacktivist Village, 101 Halkbank, 131 Handmaid’s Tale, The (Atwood), 117 Hansen, Tyler, 101–2 Harvard University, 47, 93 hawala systems, 179 ‘Here Today.

pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency
by Ian Demartino
Published 2 Feb 2016

As mentioned, Bitcoin doesn’t just bring basic banking to those without banking access; it also has the potential to bring advanced banking abilities to users around the world. Bitcoin 2.0 projects, as they are often called, can involve Bitcoin or other cryptocurrencies. The main idea behind these projects is that the blockchain and blockchain technologies can be used to transfer and keep track of holdings of valuables other than Bitcoin or other digital currencies. Even if a 2.0 project is not built off of Bitcoin, like Ethereum, increased investment and interest in cryptocurrencies as a whole tend to increase Bitcoin’s value as well. Since Bitcoin is currently the most successful, secure, and popular cryptocurrency, any increased interest in cryptocurrencies as a whole has a positive effect on Bitcoin’s price.

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The first example of a “2.0” cryptocurrency was Namecoin, which, in addition to being a currency, acted as a distributed domain name registrar free from the control of any government, individual or group. Users need to download the Namecoin blockchain in order to view sites registered using the Namecoin protocol. Sites on the Namecoin network use the .bit domain extension. Unlike.com, .net, .org, or other domain extensions you normally see on the web, .bit registrations are not issued or controlled by a central entity. Instead, they are issued by the Namecoin network and no entity has the power to seize a .bit website registration. Namecoin was the first example of a Bitcoin 2.0 project but there have been others. Counterparty, Ethereum, Masterparty, and Nxt are on the forefront of crowdfunding and crowd-investing.

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Before you can fund your business or product with cryptocurrencies, you need to know what services are available and how successful other such projects have been. There are four major platforms you can use to issue your own token that represents a small amount of ownership of your company: Counterparty, Omni/Mastercoin, Ethereum, and Nxt. There are others but they are either still in development or are a part of a small community and would limit the number of your potential investors. Counterparty is built on the Bitcoin blockchain so your potential pool of investors includes everyone who uses Bitcoin. Nxt, on the other hand, has its own technology and community, which is significantly smaller than Bitcoin’s. However, Nxt’s asset exchange—its name for its token marketplace—is built right into its core client, while Counterparty requires a special wallet.

pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

The trade-offs are linked to the degree of centralization of the validation mechanism (for instance, more centralization typically means less anonymity), although such trade-offs exist even among fully decentralized cryptocurrencies. Smart Contracts Over time, other virtual currencies added significant new features that updated the blockchain concept so it could handle a wider variety of information. The most valuable virtual currency other than Bitcoin is Ether, which runs on the Ethereum blockchain. In addition to recording virtual currency transactions, the Ethereum blockchain can record and execute automated programs. It is possible, for example, to create a program on the Ethereum blockchain that will move Ether between wallets only after a specific event. This functionality has turned out to have useful applications.

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For more comprehensive (and technical) overviews of these and alternative consensus protocols, see Bano et al. (2019) and Ismail and Materwala (2019). Descriptions of the technical aspects of Ethereum can be found at https://ethereum.org/learn/#improving-ethereums-scalability. The first building block for the Ethereum upgrade, the Beacon Chain, went live in December 2020. See https://ethereum.org/en/eth2. Some back-of-the-envelope calculations (by a company whose blockchain uses Proof of Stake) comparing energy consumption under the Proof of Work and Proof of Stake consensus protocols are reported in ODIN Blockchain, “Going Green: Energy Consumption Evaluation Part 2: Proof of Stake Consensus Algorithms,” Medium, November 12, 2019, https://medium.com/@odinblockchain/going-green-energy-consumption-evaluation-part-2-proof-of-stake-consensus-algorithms-8ce613f1179b.

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But the system is logically centralized—the entire network of nodes that make up such a system is linked and is in a commonly agreed-to state at all times. Bitcoin could be considered an early form of DeFi. The latest wave takes things to a more exalted level. Vitalik Buterin, cofounder of Ethereum DeFi relies on smart contract blockchains, of which Ethereum is by far the most widely used. The Bitcoin blockchain, as noted earlier, does not have smart contract capabilities. Vitalik Buterin, a wunderkind who is a cofounder of Ethereum (and is a college dropout, need you ask?), has argued that decentralization confers many advantages over traditional financial systems. One is fault tolerance—failure is less likely because such a system relies on many separate components.

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The Metaverse: And How It Will Revolutionize Everything
by Matthew Ball
Published 18 Jul 2022

It helps to put aside the idea of NFTs, cryptocurrencies, fears of record theft, and the like. What matters is that blockchains are programmable payment rails. That is why many position them as the first digitally native payment rails, while contending that PayPal, Venmo, WeChat, and others are little more than facsimiles of legacy ones. Blockchains, Bitcoin, and Ethereum The first mainstream blockchain, Bitcoin, was released in 2009. The sole focus of the Bitcoin blockchain is to operate its own cryptocurrency, bitcoin (the former is usually capitalized while the latter is not, in order to distinguish between the two). To this end, the Bitcoin blockchain is programmed to compensate processors handling bitcoin transactions by issuing them bitcoin (this is called a “gas” fee and is typically paid by the user to submit a transaction).

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Ethereum has its detractors, who level three primary criticisms: its processing fees are too high, its processing times are too long, and its programming language is too difficult. Some entrepreneurs have chosen to address one or all of these problems by constructing competing blockchains, such as Solana and Avalanche. Other entrepreneurs instead built what are called “Layer 2” blockchains on top of Ethereum (the Layer 1). These Layer 2 blockchains effectively operate as “mini-blockchains,” and use their own programming logic and network to manage a transaction. Some “Layer 2 scaling solutions” batch transactions together, rather than processing them individually. This naturally delays a payment or transfer, but real-time processing is not always required (just as your wireless phone-service provider doesn’t need to be paid at a specific time of the day).

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Not long after Bitcoin emerged (its creator remains anonymous), two early users, Vitalik Buterin and Gavin Wood, began developing a new blockchain, Ethereum, which they described as a “decentralised mining network and software development platform rolled into one.”1 Like Bitcoin, Ethereum pays those operating its network through its own cryptocurrency, Ether. However, Buterin and Wood also established a programming language (Solidity) that enabled developers to build their own permissionless and trustless applications (called “dapps,” for decentralized apps), which could also issue their own cryptocurrency-like tokens to contributors. Ethereum, then, is a decentralized network that is programmed to automatically compensate its operators.

pages: 247 words: 60,543

The Currency Cold War: Cash and Cryptography, Hash Rates and Hegemony
by David G. W. Birch
Published 14 Apr 2020

Tokens took off with the development of the ERC-20 standard back in 2015. ERC-20 defined a way of creating a standard form of token using ‘smart contracts’ on the Ethereum blockchain. Please note, once again, that smart contracts are not contracts at all because there is no possibility of uncertainty in their execution, and thus no compliance; strictly speaking, they are just automaticity created by the consensus-forming process. The inventor of Ethereum, Vitalik Buterin, says as much: ‘I now regret calling the objects in Ethereum “contracts” as you’re meant to think of them as arbitrary programs and not smart contracts specifically’ (DuPont and Maurer 2015).

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You heard that right: zero. 10 As the economist Diane Coyle pointed out in a Financial Times article (published 26 January 2017), it may be that transparency is the key to making this work, which highlights at least one area where the technology of shared ledgers and machine learning – blockchains and bots – may come together. 11 At the time of writing, the trading of tokens has just overtaken the trading of cryptocurrency on the Ethereum blockchain. 12 They go on to say, and I strongly agree, that this means it is important to achieve a social consensus on how such smart money should be integrated into the existing financial system. Chapter 3 Anyone can make money [Fiat currency] is another 20th century ‘big state ideology’ just like socialism

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According to Alejandro Cao de Benós, president of the Korean Friendship Association, the Democratic People’s Republic of Korea (DPRK) intends to go down the Facebook route: it is planning to create an asset-backed digital currency rather than a digital fiat currency and then use some sort of blockchain with ‘Ethereum-style smart contracts’ to conduct business and avoid sanctions. Why use a blockchain? Well, the regime sees consensus applications as a way of enforcing the deals it makes with foreign counterparties. Since North Korea does not trust the United Nations, it relies on Chinese intermediaries to enforce deals abroad. But sometimes, so sources claim, those intermediaries cheat the North Koreans.

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The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order
by Paul Vigna and Michael J. Casey
Published 27 Jan 2015

Whereas judicial corruption means that low-income people in a developing country can’t rely on watertight contracts to shore up their businesses and unlock de Soto’s mystery of capital, subjecting such agreements to the infallibility of the blockchain could end all that. Jonathan Mohan, who works at Ethereum, the new Bitcoin 2.0 platform that’s seeking to disrupt all sorts of legal and contractual arrangements, offers a compelling explanation for how these “smart contracts,” each designed to be executed on the blockchain via an automated piece of software, would benefit the informal economy. “As long as you render collateral for a contract and the blockchain recognizes the contract, then you know there’s no fraud and you know there’s no need to have to trust a third party,” he said at an Inside Bitcoins conference in New York.

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The pioneer in the field was the Colored Coins project, which launched in the second half of 2012; its purpose: to allow people to exchange digitized securities and fiat currencies directly over the bitcoin blockchain. (Two people could set up a contract to directly exchange a digital claim on euros for a digital claim on gold, for example.) Since then the field has become crowded with Blockchain 2.0 start-ups and projects, including Next, Ripple, Mastercoin, Ethereum, BitShares, Counterparty, and Stellar. Each provides a specially designed blockchain-based platform that allows other entities to create peer-to-peer contracts, to issue and permit trading of digital and digitized assets, or to install special software-driven applications, all of them with decentralized functioning.

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By January 2014, when we caught up with Buterin on the sidelines of a bitcoin conference in Miami, Ethereum, which had been conceived only a few months earlier, already boasted a team of fifteen full-time developers led by Gavin Wood, a noted British programmer schooled in C++ programming language, and had almost a hundred part-time developers adding their input. They established themselves in Zug, Switzerland, and set about building a brand-new, versatile blockchain platform. The team also planned a fund-raiser. Described as a “presale” of ether, Ethereum’s special internal currency—which in compliance with Swiss law was described in the fund-raiser not as a currency or a security but as a piece of software needed to run future applications—the offering raised more than twenty-nine thousand bitcoins, worth more than $14.5 million in late August.

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Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson
Published 26 Jun 2017

The Nakamoto Institute’s withering assessment was that Ethereum was “doomed.” Its combination of poor programming and terms of use that essentially made this lousy programming legally binding spelled disaster. Believers in the dream of decentralizing all the things, however, weren’t yet ready to give up. In July of 2016, Vitalik Buterin, one of Ethereum’s cofounders and the author (at nineteen years old) of the influential 2013 “Ethereum White Paper,” announced a “hard fork” in the cryptocurrency and its blockchain. If a majority of participants in The DAO accepted this fork (which was embodied in a new version of the Ethereum software), all previous transactions that occurred within the decentralized autonomous organization would be essentially forgotten, and all involved ethers would be returned to their original owners.

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Some participants in the original DAO refused to go along with the hard fork, continued to use the original version of the distributed software, and named their system “Ethereum Classic.” As we write this in early 2017, Ethereum and Ethereum Classic continue to exist in parallel. Bitcoin’s Bitter End? Despite ample worldwide enthusiasm for them, Bitcoin and the blockchain have also experienced trouble. In January of 2016 Mike Hearn, who had been a prolific and respected contributor to programming for the blockchain, and who had believed in its promise so deeply that he had quit his job at Google to devote himself full-time to it, sold all his Bitcoins and walked away from the project.

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But firms exist and endure because they work, and they work, in part, because they address the problem of incomplete contracts and residual rights of control that plague markets. The Failure Modes of Decentralized Things These insights help us understand the recent problems of Bitcoin, the blockchain, Ethereum, and The DAO discussed earlier in this chapter. The blockchain was designed from the start to be as decentralized and uncontrollable as possible; it was meant to be the ultimate antihierarchy. But then, what recourse is available to its enthusiasts if it evolves in a direction they don’t like—if, for example, it begins to operate more and more behind the great firewall of China?

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Number Go Up: Inside Crypto's Wild Rise and Staggering Fall
by Zeke Faux
Published 11 Sep 2023

As a teenager, Stone was encouraged to play the stock market by his grandpa. One of his picks was Apple. That trade eventually netted him more than six figures. It was Ethereum—the blockchain that enabled the ICO boom—that got him interested in crypto. He did some Ethereum mining on his laptop around 2016, abandoned it when it was too hard to sell the tokens for real money, and then kicked himself when a friend told him at a poker game that their price had increased tenfold. Stone took his money out of stocks and went all-in on Ethereum, eventually starting Battlestar, which was supposed to help investors earn a return on their crypto holdings through what it called “institutional grade Staking-as-a-Service.”

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What a Bored Ape buyer pays hundreds of thousands of dollars for is not a digital ape cartoon—it’s the ability to prove they are the one who paid hundreds of thousands of dollars for a digital ape cartoon. Think back to that giant Excel spreadsheet in the cloud—the blockchain. What if, in addition to keeping track of how many Bitcoins or Ethereum tokens each person owns, it could also track who owns which ape picture? NFTs did that by adding an additional column: ETHEREUM COINS BORED APE ZEKE 103 #2,735 The blockchain doesn’t even hold the actual image file. It just contains a pointer to the image, which is stored elsewhere on the internet.

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It was as if the Wright Brothers sold air miles to finance inventing the airplane, in the words of Money Stuff columnist Matt Levine. A new programmable blockchain called Ethereum made the process easy. The token sales were called “initial coin offerings,” or ICOs. In 2017, start-ups raised a total of $6.5 billion with them. Everyone wanted a piece of the next Bitcoin. The hype was so powerful, it seemed like anyone could post a white paper explaining their plans for a new coin and raise millions. Brock Pierce, the Tether co-founder, promoted a coin called EOS, which was pitched as “the first blockchain operating system designed to support commercial decentralized applications.”

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Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud
by Ben McKenzie and Jacob Silverman
Published 17 Jul 2023

If you want to get somewhere else, you must run at least twice as fast as that!” That was the basic technological and philosophical framework for Bitcoin, the original cryptocurrency, from which all others sprang. Ethereum, the second largest cryptocurrency as of this writing, was launched in 2015. It offered an alternative open-source blockchain and became notable for offering what are called smart contracts: small computer programs that execute functions automatically on the Ethereum blockchain. A simple example might be to use smart contracts to replicate the escrow process. You could program a transaction that would go through only if two of the three parties said it should, like a buyer, a seller, and a trusted referee.

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Out of these emerged DeFi, or decentralized finance, a vast, unregulated ecosystem of crypto exchanges, lending pools, trading protocols (protocol in this context means a set of rules that allow data to be shared between computers), and complex financial products. Ethereum also led to the introduction of NFTs, which are basically links to receipts for JPEGs stored on blockchains (shh, don’t tell that to anyone who owns one). The number of cryptos exploded around this time, rising tenfold in five years, from less than one hundred in 2013 to more than a thousand by 2017. There are now an estimated 20,000 cryptos, most of them small and insignificant, their ownership concentrated in the hands of a few “whales,” much like penny stocks.

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Kim’s Instagram post from June 2021 read as follows: ARE YOU GUYS INTO CRYPTO???? THIS IS NOT FINANCIAL ADVICE BUT SHARING WHAT MY FRIENDS JUST TOLD ME ABOUT THE ETHEREUM MAX TOKEN! A FEW MINUTES AGO ETHEREUM MAX BURNED 400 TRILLION TOKENS—LITERALLY 50% OF THEIR ADMIN WALLET, GIVING BACK TO THE ENTIRE E-MAX COMMUNITY. SWIPE UP TO JOIN THE E-MAX COMMUNITY. To borrow from the industry’s charming parlance, EthereumMax was a shitcoin. The hastily assembled crypto project seemed purposely designed to be confused with the second biggest cryptocurrency, Ethereum, even though the two were not related. Kim’s post, sent out to her then 251 million (!) followers, was an enormous publicity success for the obscure token.

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Everything for Everyone: The Radical Tradition That Is Shaping the Next Economy
by Nathan Schneider
Published 10 Sep 2018

v=l9dpjN3Mwps; a compelling early analysis of Buterin’s worldview is Sam Frank, “Come With Us If You Want to Live,” Harper’s Magazine (January 2015); for a technical perspective, see Ethereum Foundation, “How to Build a Democracy on the Blockchain,” ethereum.org/dao. 9. Vitalik Buterin, comment on Reddit thread (April 6, 2014), reddit.com/r/ethereum/comments/22av9m/code_your_own_utopia. 10. Follow the ongoing contest of currencies at coinmarketcap.com; for CU Ledger, see culedger.com. 11. Joon Ian Wong and Ian Kar, “Everything You Need to Know About the Ethereum ‘Hard Fork,’” Quartz (July 18, 2016). 12. Duran served as a significant informant, referred to as “Pau,” in Jeffrey S.

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Just a few weeks earlier, a nineteen-year-old Russian Canadian named Vitalik Buterin had published a proposal for what he called Ethereum.7 What Bitcoin was for money, Ethereum would be for everything else. It turns out that the basic idea of an ironclad list with no single caretaker—the blockchain—has an enormous range of potential applications. Rather than listing transactions, for instance, it can list contracts and enforce them computationally, resulting in an autonomous legal system without courts or cops. A blockchain of websites could be the basis of a more secure kind of internet. “It’s an operating system for society,” D’Onofrio said.

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He later interrupted the discussions on technical feasibility and implementation with stories of real-life good deeds done through his lodge, offline. Ethereum went live in 2015. Its underlying currency, ether, soon became second only to bitcoin on the crypto-markets, with a total value in the billions of dollars. Walmart is using Ethereum to manage supply chains, and J. P. Morgan is writing smart contracts to automate transactions. A coalition of US credit unions is building a “CU Ledger” to manage member identities. Some people are trying to craft the perfect co-ops or other sorts of egalitarian DAOs, but they’re not making money like the ones concocting blockchain ledgers for big, old banks.10 A year into Ethereum’s life, the system hit trouble.

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The Bitcoin Standard: The Decentralized Alternative to Central Banking
by Saifedean Ammous
Published 23 Mar 2018

In the aftermath of the DAO hack, Ethereum developers created a new version of Ethereum where this inconvenient mistake never occurred. This re‐injection of subjective human management is at odds with the objective of making code into law, and questions the entire rationale of smart contracts. Ethereum is the second largest blockchain after Bitcoin in terms of its processing power, and while the Bitcoin blockchain cannot effectively be rolled back, that Ethereum can be rolled back means that all blockchains smaller than Bitcoin's are effectively centralized databases under the control of their operators. It turns out code is not really law, because the operators of these contracts can override what the contract executes.

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For any currency controlled by a central party, it will always be more efficient to record transactions centrally. What can be clearly seen is that blockchain payment applications will have to be with the blockchain's own decentralized currency, and not with centrally controlled currencies. Contracts Currently, contracts are drafted by lawyers, judged by courts, and enforced by the police. Smart contract cryptographic systems such as Ethereum encode contracts into a blockchain to make them self‐executing, with no possibility for appeal or reversal and beyond the reach of courts and police. “Code is law” is a motto used by smart contract programmers.

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It would be arguably inaccurate to describe this attack as a theft, because all the depositors had accepted that their money will be controlled by the code and nothing else, and the attacker had done nothing but execute the code as it was accepted by the depositors. In the aftermath of the DAO hack, Ethereum developers created a new version of Ethereum where this inconvenient mistake never occurred, confiscating the attacker's funds and distributing them to his victims. This re‐injection of subjective human management is at odds with the objective of making code into law, and questions the entire rationale of smart contracts. If the second largest network in terms of processing power can have its blockchain record altered when the transactions do not go in a way that suits the interests of the development team, then the notion that any of the altcoins is truly regulated by processing power is not tenable.

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On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

The process is random, though just like buying more scratch-off tickets gives you more chances to win the lottery, having more computing power gives you more of a chance to win the big prize from mining: a block reward, set at 6.25 Bitcoins (approximately $250,000) as I’m writing this plus transaction fees. But how in the world did we go from the first blockchain—a digital ledger for recording Bitcoin transactions—to raucous parties at Art Basel? The through line runs through Vitalik Buterin, a waifish, Russian Canadian computer programmer who created the Ethereum blockchain.[*11] The Ethereum blockchain has a native digital currency, Ether (ETH)—pronounced as “eth” with a long “e” as in the name Ethan. But like an appliance on one of those late-night infomercials—it slices, it dices, it makes julienne fries!—the Ethereum blockchain can do a whole lot more than just record ETH transactions.

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In Hold’em, there is a small blind and a big blind, with the former costing half as much. See also: ante. Blockchain: A digital ledger of transactions in chronological order; for instance, the Bitcoin blockchain records all sales of Bitcoin. A blockchain is decentralized and distributed across multiple computers so as to provide a mechanism to verify transactions without relying on governments or the financial system. There are multiple blockchains—the Bitcoin blockchain and Ethereum blockchain are separate ledgers. Blocker (poker): A card that affects the statistical distribution of your opponent’s hand range.

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Crossroad was purchased for a record $6.6 million, although it would soon be surpassed by another Beeple NFT, Everydays: The First 5,000 Days, which sold for $69 million. To be clear, none of this necessarily justifies the hype that the blockchain received at the peak of the crypto bubble. Still, the Ethereum blockchain clearly has more potential for commercial, technological, and creative applications than the Bitcoin blockchain ever did. And yet, from Ethereum’s inception, Buterin encountered intense resistance from Bitcoin enthusiasts. “You know, it does all these amazing things,” he said. “And, like, we’re all part of Team Cryptocurrency and we’re in this together.

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The Politics of Bitcoin: Software as Right-Wing Extremism
by David Golumbia
Published 25 Sep 2016

One of the main proponents of DAOs and DACs is Vitalik Buterin, author of the passage about “smart contracts” above, “a Canadian college dropout and Bitcoin enthusiast” (Schneider 2014), cofounder of Bitcoin Magazine, and a recipient of one of the US$100,000 Thiel Fellowships funded by the eponymous right-wing technology entrepreneur and PayPal founder Peter Thiel (Rizzo 2014a)—fellowships that specifically promote the rejection of higher education, in a manner harmonious with the rejection by Thiel and others on the right wing of public goods (Lind 2014). Buterin is a cofounder of Ethereum, the best-known project to generalize blockchain technology into applications that go beyond currency-like systems. Buterin (2014) describes DAOs “and their subclass, DACs,” as the “holy grail” of decentralized applications. A DAO “is an entity that lives on the internet and exists autonomously, but also heavily relies on hiring individuals to perform certain tasks that the automaton itself cannot do.”

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In this sense, it becomes a tool for existing power to concentrate itself, rather than a challenge to the existing order: as some better economically informed commentators consistently point out, Bitcoin functions much more like a speculative investment than a currency (Worstall 2013; Yermack 2014), although what one is investing in, beyond Bitcoin itself, is not at all clear. 6. The Future of Bitcoin and the Blockchain BITCOIN IS NOT SO MUCH a single software program as it is software written using a model called the blockchain that is can be used to build other very similar programs (related cryptocurrencies like Litecoin, Dogecoin, and so on), but also less similar ones. The cryptographically enabled distributed ledger, and the blockchain used to implement it, advocates insist, have wide application outside of their current uses.[1] We hear (not infrequently) that the blockchain is as revolutionary today as were “personal computers in 1975, the internet in 1993” (Andreessen 2014).

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“Inside the Fight over Bitcoin’s Future.” New Yorker (August 25). http://www.newyorker.com/. Burdekin, Richard C., and Pierre L. Siklos, eds. 2004. Deflation: Current and Historical Perspectives. New York: Cambridge University Press. Buterin, Vitalik. 2014. “DAOs, DACs, Das, and More: An Incomplete Terminology Guide.” Ethereum (May 6). http://blog.ethereum.org/. Carrico, Dale. 2005. “Pancryptics: Technocultural Transformations of the Subject of Privacy.” Ph.D. diss., University of California, Berkeley. —. 2009. “Condensed Critique of Transhumanism.” Amor Mundi (January 25). http://amormundi.blogspot.com/. —. 2013a. “Futurological Discourse and Posthuman Terrains.”

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The Pay Off: How Changing the Way We Pay Changes Everything
by Gottfried Leibbrandt and Natasha de Teran
Published 14 Jul 2021

Each transaction involves a fee for the sender and mining costs. Ethereum/Ether With 11 per cent of the crypto market’s total value, Ethereum’s Ether is the next largest cryptocurrency after Bitcoin, and the prime example of a utility coin. Ethereum is a ‘distributed computing platform’ and a very different animal from Bitcoin. Its currency, Ether, is not used to pay for goods or services. Instead, it is used to buy computer time from participants. In exchange, these participants run code that you give them. The code is submitted by posting it on a public ledger (much like Bitcoin’s blockchain), for all to see and inspect. The big idea behind Ethereum is that it enables smart contracts.

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In this case, it led to a ‘hard fork’ in the code. The purists branched off into Ethereum Classic, while the pragmatists adapted the code and recovered the funds. The recovered DAO did not live up to its expectations, however, and by the end of 2016 most major crypto exchanges had delisted its tokens from trading. Ethereum Classic still exists, but now ranks #41 on the list of cryptocurrencies, worth only 1 per cent of the value of Ethereum. While Ethereum’s coding language is specifically geared towards smart contracts, few smart contracts have been entered into thus far. But the Ethereum code seems to be quite handy for launching new crypto tokens, as many of the 5,500 cryptocurrencies (mostly ICOs) are built and executed on the Ethereum distributed computing platform.

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The crooks, perhaps sensitive to the fact that their business model rests on their reputation for responding to ransom payments, reportedly unlocked the systems. In Ethereum the contracts are submitted as code and automatically executed or enforced. This takes the thinking behind Bitcoin even further. Bitcoin aims to replace money and much of banking: there is no need to put our trust in institutions like (central) banks when we can trust code and cryptography instead. Ethereum would apply this not only to money and banks but to all contracts and intermediaries. Contracts and data are put on shared ledgers, where their integrity can be inspected and verified by a community of users or miners.

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The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by Kariappa Bheemaiah
Published 26 Feb 2017

A simple, secure swap then takes place, allowing Transferwise to execute the transfer up to 89% cheaper than with a bank ( http://www.telegraph.co.uk/money/transferwise/how-does-it-work-and-is-it-safe/ ). 23On the 30th of April 2016, the founders of SLOCK.IT launched the DAO (Decentralized Autonomous Organization) on the Ethereum platform. 24Gavin Wood is widely known in the Blockchain community as the Co-founder and CTO of Ethereum and the Co-founder of Grid Singularity (a company that uses the Blockchain for decentralized energy data management) 25BRACKETS: Blockchain-based Release of funds, that Are Conditionally Key-signed, and Triggered by Signals. 26EDD: Enhanced Due Diligence. 27Know your business. 28In the UK, there are currently eight such providers approved by the government, including PayPal, the Post Office and Experian. 29A PKC is an identity with some other information (such as an expiry date) that is put together and digitally signed by a third party.

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A smart contract , according to Ethereum’s founder, Vitalik Buterin, “is a computer program that directly controls some digital asset.” Smart Contracts are essentially the same as Apps, except they perform a different kind of automation. While the traditional Apps available on a Google Play Store or Apple App Store are useful for certain operations, Smart Contracts function as Apps that perform value exchange operations when they receive a certain input. Just as the blockchain is a digitally native protocol that is designed for value exchange, Smart Contracts are native to the Blockchain and perform value exchange operations based on the input signals that they receive from the Blockchain.

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Just as the blockchain is a digitally native protocol that is designed for value exchange, Smart Contracts are native to the Blockchain and perform value exchange operations based on the input signals that they receive from the Blockchain. This is currently one of the explosive areas of innovation and protocols developed by platforms like Ethereum are allowing the large scale deployment of Smart Contracts. Whereas a traditional legal contract defines the rules regarding an agreement between multiple counter-parties, Smart Contracts go further and actually administer those rules by controlling the transfer of money or assets under precise conditions. Using Smart Contracts, an asset or currency is transferred into a program “and the program runs this code and at some point it automatically validates a condition and it automatically determines whether the asset should go to one person or back to the other person, or whether it should be immediately refunded to the person who sent it or some combination thereof,” (Buterin, 2016).

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The Code of Capital: How the Law Creates Wealth and Inequality
by Katharina Pistor
Published 27 May 2019

For an excellent summary of Minsky’s thinking of how to stabilize an inherently instable financial system, see Mehrling, “Minsky and Modern Finance.” 47. Described by De Filippi and Wright, Blockchain and the Law, at Loc. 449 (Kindle edition). 48. Gerard, Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contract (Creative Commons, 2017) at Loc 202 (Kindle edition). 49. De Filippi and Wright, Blockchain and the Law, at Loc. 800 (Kindle edition). 50. See also Desan, Making Money, who argues that money is grounded in constitutional law. 51. Mark J. Flannery, “Contingent Capital Instruments for Large Financial Institutions,” Annual Review of Financial Economics 6 (2014):225–240. 52.

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For a survey of the effects of formalizing property rights in the developing world in recent years, see Klaus Deininger, Land Policies for Growth and Poverty Reduction, World Bank Policy Research Reports (Washington, DC: World Bank, 2003). 29. The concept of the decentralized autonomous organization (DAO) is illustrated on the Ethereum website: https://www.ethereum.org/dao. For a useful account of The DAO’s brief existence, see Muhammed Izhar Mehar et al., “Under-standing a Revolutionary and Flawed Grand Experiment in Blockchain: The DAO Attack,” available online at ssrn.com/abstract=3014782 (2017). 30. See the SEC’s press release of July 25, 2017, available online at https://www.sec.gov/news/press-release/2017-131, about its investigative report that concluded that ICOs of the kind The DAO had issued were securities and as such subject to regulations and supervision. 31.

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In the wake of a frenzy in the offerings of tokens or coins in digital ventures to the public, also dubbed “initial coin offerings,” or ICOs after the legacy practice of “initial public offerings” of shares or bonds, the US Securities and Exchange Commission (SEC) intervened. It affirmed that ICOs qualify as “securities” that are subject to standard registration requirements, a decision that reached The DAO only posthumously.30 The DAO was a venture capital fund that was built on the Ethereum blockchain. The DAO was designed to operate without a board of directors or any human officers. Instead, the firms’ investors received voting rights that allowed them to participate directly in developing investment strategies by proposing new investment opportunities to the firm. If agreed by the majority of investors, they would be implemented by the code, an open-source software that was available for everyone to see, but not for everyone to change.

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The Internet of Money
by Andreas M. Antonopoulos
Published 28 Aug 2016

v=jw28y81s7Wo This is going to be a bit more of a philosophical talk about the future of cryptocurrencies and what I’ve learned here at this event. This event is called the Bitcoin Expo 2014. It might have been called the Bitcoin and Ethereum Expo 2014. I don’t know if you noticed, but Ethereum had a pretty big presence here. An interesting question comes up, actually quite a few people have asked me: "Does Ethereum threaten the future of bitcoin? Does it steal some of its thunder?" Those are questions I’ve heard several times, and I’ve heard people refer to that issue in trying to understand altcoins - wondering whether altcoins essentially threaten the dominance of bitcoin, if they make bitcoin weaker, if they distribute the value of the network too broadly. 7.1.

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What they fail to grasp is that this medium is not just for the trivial; it spans the entire range of transactional expression from the trivial to the enormous. "The blockchain can encompass the entire range of transactional expression, from the 10-cent tweet to the $100 billion debt settlement." One day, a country will pay its oil bill on the blockchain. One day, you might buy a multinational company on the blockchain. One day, you might sell an aircraft carrier, hopefully for scrap metal, on the blockchain. The blockchain can encompass the entire range, from the 10-cent tweet to the $100 billion debt settlement. We just haven’t noticed yet.

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I want to sit at my kitchen table every Sunday and balance my checkbook and make sure none of my checks bounced. I don’t like all of this electronic instantaneous global transfer. It scares me,” we can slow it down. This infrastructure inversion will allow us to comfortably run traditional banking applications on top of a distributed global ledger — an open blockchain like bitcoin, the open blockchain, probably bitcoin’s open blockchain and simultaneously open the door for other applications, for applications we’ve never seen before. These new applications will look different from traditional banking. As different as a Segway or skateboard looks to those committed to traditional horse-carriages.

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The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism
by Arun Sundararajan
Published 12 May 2016

Dionysis Zindros, “A Pseudonymous Trust System for a Decentralized Anonymous Marketplace,” GitHub Gist, 2015, https://gist.github.com/dionyziz/e3b296861175e0ebea4b. 11. Melanie Swan, Blockchain: Blueprint for a New Economy (Sebastopol, CA: O’Reilly Media, Inc., 2015). 12. Primavera De Fillipi, “Ethereum: Freenet or Skynet?,” Talk presented at the Berkman Center for Internet & Society, Harvard University, Cambridge, MA, April 15, 2014. 13. Lawrence Lessig, Code and Other Laws of Cyberspace (New York: Basic Books, 1999). 14. Vitalik Buterin, “Decentralized Protocol Monetization and Forks,” Ethereum Blog, April 30, 2014. https://blog.ethereum.org/2014/04/30/decentralized-protocol-monetization-and-forks. 15. It is likely that architecting it “optimally” is impossible, based on a set of results from a branch of economics called mechanism design. 16.

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An alternative might be for each transaction to have a small, voluntary “commission” associated with it, and for this to be added to the payment the customer sends the provider, then shared as a reward to the “miner.”18 This would also suggest that there will be “attention” economies of scale associated with decentralized peer-to-peer marketplaces—the crowd has to notice the blockchain and care enough about it to verify its transactions and maintain the integrity of the ledger. Otherwise, what begins as a decentralized system may simply evolve into a traditional third-party platform that merely uses a glorified blockchain database. Perhaps some of the greatest opportunity from decentralized peer-to-peer systems will come from the emerging decentralized autonomous organizations (DAOs) and decentralized collaborative organizations (DCOs) such as those that are being architected by Buterik’s Ethereum and by Field and DeFillipi’s Backfeed.

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There’s no risk associated with meeting someone unsavory. Not surprisingly, therefore, much of the initial focus of blockchain marketplace development has been on creating new systems for trading assets that are non-physical: digital and financial assets. In a 2015 conversation I had with Adam Ludwin, the CEO of the blockchain startup Chain I mentioned earlier in the chapter, he described the blockchain as a “new database technology, purpose-built for trading assets,” and sees immense potential in new blockchain based marketplaces for loyalty points, mobile minutes, gift cards, and of course, a range of financial assets.

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Blockchain Basics: A Non-Technical Introduction in 25 Steps
by Daniel Drescher
Published 16 Mar 2017

Bitcoin developer reference. Working paper. 2014; Wood, Gavin. Ethereum: A secure decentralized generalized transaction ledger. 2014. http://gavwood. com/paper.pdf. Blockchain Basics 143 Outlook This step explained that the blockchain prevents the history of transaction data from being manipulated or forged by turning the blockchain-data-structure into an immutable append-only data store. The next step focuses on making that data store available to everyone in a distributed peer-to-peer system. Summary • The blockchain protects the history of transaction data from manipulation and forgery by storing transaction data in an immutable data store

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He moved to Oxford to study for his master’s degree and set up Extropy.io, a consultancy working with start- ups to develop applications on the Ethereum platform. Passionate about distributed technol- ogy, he now works as a developer, evangelist, and educator about Ethereum. Introduction This introduction answers the most important question that every author has to answer: Why should anyone read this book? Or more specifically: Why should anyone read another book about the blockchain? Continue reading and you will learn why this book was written, what you can expect from this book, what you cannot expect from this book, for whom the book was writ- ten, and how the book is structured.

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How It Works The idea of selecting a transaction history based on the computational effort that was spent for creating it has led to the following two criteria: • The longest-chain-criterion2 • The heaviest-chain-criterion3 The Longest-Chain-Criterion The longest-chain-criterion is based on the idea that the blockchain-data- structure that comprises the most blocks represents the most aggregated computational effort. In order to study this criterion, let’s consider an initial situation were all the nodes of a distributed system maintain and agree on 2Nakamoto, Satoshi. Bitcoin: A peer-to-peer electronic cash system. 2008. https://bitcoin. org/bitcoin.pdf. 3Wood, Gavin. Ethereum: A secure decentralized generalized transaction ledger. 2014. http://gavwood.com/paper.pdf; Okupski, Krzysztof. Bitcoin developer reference. Working paper. 2014. Blockchain Basics 169 the identical version of the blockchain-data-structure, as depicted in Figure 19-1, which presents a schematic blockchain-data-structure that omits many details for simplicity.

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Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives)
by David Birch
Published 14 Jun 2017

On the one hand there were advocates of the ‘code is law’ school of thought who felt that the investors should take their medicine, and on the other there were advocates of the ‘pragmatic’ school of thought who felt that the transactions should be reversed. Since you can’t go back and edit a blockchain (which is sort of the point of it), this is achieved by ‘forking’ to create a new blockchain. This was done but a significant minority of miners felt that this was the wrong decision so they continued with the original blockchain as Ethereum Classic. At the time of writing, the ‘market cap’ of Ethereum is significantly higher than that of Ethereum Classic. Ripple After Bitcoin and Ethereum, the third biggest cryptocurrency is Ripple, which unlike those first two has its roots in local exchange trading systems (Peck 2013).

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Crypto-alternatives Not only will there be cryptocurrencies beyond Bitcoin and not only will they be better – more powerful and more efficient – there will also be a great many of them as the cost of launching a digital currency falls. Without launching into a treatise on cryptocurrencies, I think it would be useful to take a quick look at a couple of the newer cryptocurrencies on the block (pun intended) to give a sense of the spectrum of possibilities. Ethereum Ethereum is comparable to Bitcoin, in that it uses a blockchain, but it was designed to provide a better platform for shared ledger applications. One of the most interesting users of such applications was the Distributed Autonomous Organization (DAO). The general concept of a distributed autonomous organization goes back a few years, having roots in organizational decentralization theories that have been turbocharged (as William Mougayar puts it) by cryptocurrency technologies and trust-based automation (Mougayar 2016), but the DAO in question was created as a new kind of business: an investor-directed investment fund.

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And let’s have no limit on the number of different currencies that the banking system’s ledger might hold. Here comes the blockchain What might that ledger look like? The emerging consensus, at least in the finance sector, seems to be that the technology behind Bitcoin, the blockchain, will disrupt the sector (Raymaekers 2015), although many commentators are not at all clear how (or, indeed, why). Melanie Swan posits that even if all of the infrastructure developed by the blockchain industry were to disappear, its legacy could persist (Swan 2015). This is because the blockchain has provided new larger-scale ideas about how to organize financial services and, as Swan and other observers have noted, there is a very strong case for decentralized models.

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Magic Internet Money: A Book About Bitcoin
by Jesse Berger
Published 14 Sep 2020

Since there is no other embodiment of value that can be both fully functional and fully verifiable as a purely digital entity, money necessarily rises to the fore as blockchain’s most compelling use. Many public blockchains such as Litecoin, Zcash, and Ethereum (more to come on these in Chapter 8) are attempting to funnel trust and value into their networks through the creative adaptation of blockchain principles, effectively testing the limits of its functionality, but none are more proficient than Bitcoin. Its value has grown faster than any other asset since its inception,10 and it is the most liquid of all blockchains. More importantly though, it is by far the costliest. Costliness is a crucial quality of money because it ensures that money cannot be taken for granted.

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By combining the security of cryptography and the practicality of software, the framework for a new monetary system emerges, which eliminates the need to entrust money to a bank by making banking as trustworthy as money itself. 5.3.1 Blockchain 101: Shared Ledger “We need to come up with use cases for this technology that drive clear benefits for individuals and institutions – these are our customers. Too often we see Bitcoin and blockchain technologies as solutions in search of a problem.” Abigail Johnson, CEO of Fidelity Investments One of the innovations upon which Bitcoin was built has spawned an entirely new class of software-based technology. Originally referred to by Satoshi as the “timechain” in an early version of the software, this technology is known today as “blockchain.” A blockchain is a shared ledger – a shared database – that allows multiple users to inscribe transactions.

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For reference, a node is a computer connected to the network that verifies blocks, and a “full node” goes a step further, saving the complete blockchain – the history of all valid transactions. As a committed ledger of record, blockchain can dutifully underpin a credible monetary system if it standardizes widely known and agreeable protocols (i.e. predetermined issuance schedule and fixed limit of 21 million coins) that are strictly enforced by consensus (i.e. proof-of-work to prevent partisan modifications). 5.3.2 Blockchain 101: Immutable Data “The more people you have to ask for permission, the more dangerous a project gets.” Alain de Botton, Philosopher & Author In recent years, blockchain has become a familiar term in business and technology circles, but its meaning and value proposition have been somewhat blurred.

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Ours to Hack and to Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet
by Trebor Scholz and Nathan Schneider
Published 14 Aug 2017

However, to deliver the AI-powered features that near-future users will demand, applications will need to draw upon sophisticated industrial-strength AI software services and harness powerful clusters of data-mining server farms. This stuff doesn’t come cheap. Free, open, and radically decentralized AI isn’t a thing yet, but blockchain-based platforms like Ethereum and Backfeed could offer decentralized alternatives to the corporate cloud. More libre but not gratis, as you’ll pay for decentralization with cryptocurrency. In its infancy, Ethereum is far more expensive than the Amazon cloud but with laughable performance and capability by comparison. Can you afford to wait for the decentralized solution or do you accept that a corporate cloud is presently your only viable high-performance and affordable option?

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Put differently, cooperatives tended to focus too much on how the value would be shared rather than on a compelling offer to create the value in the first place. Perhaps part of the solution will come from the possibility, created by blockchain technologies, of “distributed collaborative organizations,” or DCOs—new decentralized collectives that, in the eyes of pioneers like Matan Field of Backfeed and Vitalik Buterin of Ethereum, can use rules embedded in computer code to align the incentives of different contributors, of financial capital, of expertise, of labor, and of participation. These DCOs are connected intellectually to a variety of related decentralized ownership models.

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At this point we are re-engineering our initial proof of concepts (developed on Bitcoin) to release ready concepts on Ethereum. We are currently building a global ambassador network through training events in various cities across the globe. We are also currently exploring other projects that might bridge to a mainstream audience and serve as a proof of concepts for both the future of governance and community abundance. As with our original concept, we expect new forms of crowdfunding to have a major role in this, especially around blockchain-hosted organizations. Project Name: Ms., The Madeline System Completed by: Eden Schulz and Brendan Martin Location: New York City URL: http://theworkingworld.org/us The Madeline System networks local, cooperative investment funds, giving them the ability to stay community-controlled and yet gain the benefits of scale.

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The Evolution of Everything: How New Ideas Emerge
by Matt Ridley

Then there is Namecoin, which aims to issue internet names in a decentralised, peer-to-peer fashion; Storj, which plans to allow cloud storage of files hidden inside blockchains; and Ethereum, which is a decentralised peer-to-peer network ‘designed to replace absolutely anything that can be described in code’, as Matthew Sparkes puts it. The digital expert Primavera De Filippi sees Ethereum and its ilk coming up with smart contracts, allowing ‘distributed autonomous organisations’ that, once they have been deployed on the blockchain, ‘no longer need (nor heed) their creators’. In other words, not just driverless cars, but ownerless firms. Imagine in the future summoning a taxi that not only has no driver, but that belongs to a computer network, not to a human being.

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FCC to Congress: U.N.’s ITU Internet plans ‘must be stopped’. zdnet.com 5 February 2013. On net censorship, MacKinnon, Rebecca 2012. Consent of the Networked. Basic Books. On blockchains, Frisby, Dominic 2014. Bitcoin: The Future of Money?. Unbound. On Nick Szabo’s ‘shelling out’, nakamotoinstitute.org/shelling-out/. On Ethereum’s white paper, A Next-Generation Smart Contract and Decentralized Application Platform. https://github.com/ethereum. On private money, Dowd, K. 2014. New Private Monies. IEA. On smart contracts, De Filippi, P. 2014. Ethereum: freenet or skynet?. At cyber.law.harvard.edu/events 14 April 2014. On digital politics, Carswell, Douglas 2014. iDemocracy will change Westminster for the Better.

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One of the pithiest explanations I have come across is in a recent launch by Ethereum, a business built to follow up on bitcoin: ‘The innovation provided by Satoshi is the idea of combining a very simple decentralised consensus protocol, based on nodes combining transactions into a “block” every ten minutes, creating an ever-growing blockchain, with proof of work as a mechanism through which nodes gain the right to participate in the system.’ If you think that’s hard to understand, you are not alone. I have yet to come across a description of blockchain technology in English, as opposed to mathematics, that is really clear.

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Orwell Versus the Terrorists: A Digital Short
by Jamie Bartlett
Published 12 Feb 2015

Bitcoin creates an immutable, unchangeable public copy of every transaction ever made by its users, which is hosted and verified by every computer that downloads the software. This public copy is called the ‘blockchain’. Pretty soon, enthusiasts figured out that the blockchain system could be used for anything. Armed with 30,000 Bitcoins (around $12 million) of crowdfunded support, the Ethereum project is dedicated to creating a new, blockchain-operated internet. Ethereum’s developers hope the system will herald a revolution in the way we use the net – allowing us to do everything online directly with each other, not through the big companies that currently mediate our online interaction and whom we have little choice but to trust with our data.

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fn3 You’ve probably heard of this pseudonymous digital cash because it was, and still is, the currency of choice on the illegal online drugs markets. fn4 And increasingly, I predict, politics. Although no political parties – save the occasional fringe party – have given any thought to what crypto-currencies might mean. What does a modern centre-left party think of crypto-currency, or of blockchain decentralisation? They have no idea. Orwell I’ve interviewed many of the people in the frontline of the battle, the people behind the extraordinary innovation currently taking place. They see the question of online privacy as the digital front in a battle over individual liberty: a rejection of internet surveillance and censorship that they believe has come to dominate modern life online.

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How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs
by Tim Draper
Published 18 Dec 2017

DAOs and ICOs A new form of fundraising is happening around the blockchain architecture. People discovered that the blockchain could be used to raise funds for projects and startups. In effect, people found that they could create their own currencies using Bitcoin as a model. These would be known as decentralized autonomous organizations (DAOs). The first of these, DAO Maker, had an inauspicious beginning. The company used Ethereum (a decentralized currency built using a protocol similar to the Bitcoin blockchain) as its platform. A hacker figured out that when money was moving from one entity to another, they could siphon off the Ethereum currency, called Ether, collected from the sale of the tokens.

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The technology behind Bitcoin is called the blockchain. The blockchain also has some amazing potential. It can be thought of as a giant ledger, keeping track of money, data, inventory, contracts, etc. “Smart” contracts can be designed such that they anticipate eventualities and automatically distribute appropriately. And corporations can use the blockchain to automatically pay employees their wages and benefits, pay shareholders their dividends, and pay noteholders their interest and principal payments, all with precise accuracy and automated accounting. Furthermore, companies can use the blockchain to pay their suppliers and receive money from their customers, handling lay away payment plans and warranties without friction or human influence.

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And the US government (and other governments) can manage social security, welfare, Medicare, worker’s comp, disability and all their data verification of citizens and businesses with Bitcoin and the blockchain, since blockchain is the perfect government service employee. It is honest, incorruptible, secure, and fair. Bitcoin and its underlying technology, the blockchain, are changes that allow us to progress. But change is difficult for those people who don’t have the spark of a Startup Hero in their eyes, and many industries will have to go through fundamental changes to adapt to the advent of this new way of thinking. People will have to learn that the bank, being the trusted third party for centuries, will soon be replaced by computers that now monitor their holdings through the blockchain.

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Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist
by Kate Raworth
Published 22 Mar 2017

Its name derives from the blocks of data – each one a snapshot of all transactions that have just been made in the network – which are linked together to create a chain of data blocks, adding up to a minute-by-minute record of the network’s activity. And since that record is stored on every computer in the network, it acts as a public ledger that cannot be altered, corrupted or deleted, making it a highly secure digital backbone for the future of e-commerce and transparent governance. One fast-rising digital currency that uses blockchain technology is Ethereum, which, among its many possible applications, is enabling electricity microgrids to set up peer-to-peer trading in renewable energy. These microgrids allow every nearby home, office or institution with a smart meter, Internet connection, and solar panel on its roof to hook in and sell or buy surplus electrons as they are generated, all automatically recorded in units of the digital currency.

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Such decentralised networks – ranging from a neighbourhood block to a whole city – build community resilience against blackouts and cut long-distance energy transmission losses at the same time. What’s more, the information embedded in every Ethereum transaction allows network members to put their values into action in the microgrid market, for example by opting to buy electricity from the nearest or greenest suppliers, or only from those that are community-owned or not-for-profit.59 And this is just one example of its potential. ‘Ethereum is a currency for the modern age,’ says the cryptocurrency expert David Seaman. ‘It’s a platform that could be really important to society down the road in ways that we can’t even predict yet.’60 These very different examples illustrate a few of the myriad possibilities of monetary redesign, involving the market, the state and the commons.

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Strassheim, I. (2014) ‘Zeit statt Geld fürs Alter sparen’, Migros-Magazin, 1 September 2014. www.zeitvorsorge.ch/#!/DE/24/Medien.htm 59. DEVCON1 (2016) Transactive Grid: a decentralized energy management system. Presentation at Ethereum Developer Conference, 9–13 November 2015, London, available at: https://www.youtube.com/watch?v=kq8RPbFz5UU 60. Seaman, D. (2015) ‘Bitcoin vs. Ethereum explained for NOOBZ’, published 30 November 2015, available at: https://www.youtube.com/watch?v=rEJKLFH8q5c 61. Trades Union Congress (2012) The Great Wages Grab. London: TUC. https://www.tuc.org.uk/sites/default/files/tucfiles/TheGreatWagesGrab.pdf 62.

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The Curse of Cash
by Kenneth S Rogoff
Published 29 Aug 2016

See also Bitcoin; cryptocurrencies American Hustle (Russell), 71 Amromin, Gene, 238n22 Andolfatto, David, 213 Antràs, Pol, 236n12 Argentina, 44, 82 Ascaria, Guido, 248n5 Australia, 52, 132 Austria: cash, per capita holdings of, 33; cash used for different kinds of purchases, percentage of, 55–56; coinage debasement in, 20; currency held by consumers in, 51–52; deutsche mark currency demand, as a control for estimating, 45; stamp currency experiment in, 164–65 Automated Clearing House system, 103 Bagehot, Walter, 244n9 Bank Act of 1844 (Peel’s Act), 235n25 Bank of England: inflation target, choice of, 153; interest rate hike prior to 2008, impact of, 177–78; nominal policy interest rates, 2000–2015, 130; notes convertible to specie, early issue of, 26; quantitative easing by, 135–36 Bank of Japan: inflationary expectations, challenges faced in lifting, 124; inflation target, choice of, 153; January 2016 policy of, 250n5; museum of, understanding coinage debasement in, 20; negative interest rates, experience with, 1, 161; quantitative easing by, 135–36, 143; zero-bound problem of, lack of international coordination regarding, 206 Bartzsch, Nikolaus, 236n23 Baum, Frank (author of The Wonderful Wizard of Oz), 192 Belgium: cash used for different kinds of purchases, percentage of, 55; currency/GDP ratio, 1995, 46–47; restrictions on the use of cash, 64 Bennett, Paul, 237n4 Bernanke, Ben: financial stability, limits to concern regarding, 176; “global savings glut,” 122; “Helicoper Ben,” advice for Japan from, 155; inflation targeting adopted under, 232; macroprudential regulation, argument for, 177; Perry’s attack on, 191; small interest hikes, limited impact of, 177; “taper tantrum” set off by, 126, 141 Billi, Roberto, 229 biometric method for estimating foreign holdings of currency, 43–44 Bitcoin/bitcoins: “Bencoin” as governmental version of, 209–10, 213–14; blockchain technology pioneered by, 112; as a currency, possibility of, 211; as encrypted digital technology, 208; inflation and, 213; market price of, 212; as payment mechanism for criminal activities, 72; security of using, 67 Black, Fischer, 244n5 Blackburn, David, 253n6 Blanchard, Olivier Jean, 248n2, 252n7 blockchain technology, 112, 210, 213–14 border control, issue of, 75–76 Bordo, Michael D., 234n6 Brazil, 65, 183–84, 191, 205 Breaking Bad (TV series), 68, 240n27 Bretton Woods regime, 30 bribes, 70 Britain.

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Because the technology is evolving so rapidly, I am hesitant to go into much more detail, beyond saying that phasing out paper currency does not really move the needle much on society’s vulnerability to cybercrime. Some of the present-day obstacles to improving security are really more political than economic. Some innovations in security, such as the potentially disruptive distributed-ledger technology embodied in cryptocurrencies like Bitcoin or Ethereum, may eventually lead to major improvements in financial security, at least at the core of the payment system, as discussed further in chapter 14. It is particularly hard to see in any of these arguments why large-denomination notes are important. Probably they would be looked on askance after a power outage, earthquake, or other kind of catastrophe.

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And some applications of distributed-ledger technology offer the promise of cutting out intermediaries in transactions between, say, two banks. This would substantially reduce costs, particularly in international transactions. The approach can also be used to save on legal contracting costs. Some of Bitcoin’s competitors, notably the newer Ethereum platform, aim to offer the possibility of creating secure exchanges for transactions of almost any type. People sometimes ask whether the cryptocurrency Bitcoin could be a currency (supposing that the government does not interfere). The answer is certainly yes, Bitcoin (or perhaps one of its present or future competitors) can fulfill many of the basic functions of currency, including unit of account and medium of exchange, with or without government adherence.3 In fact, digital currencies in some ways offer the capacity for much more complex kinds of transactions and contracts than traditional paper currency offers, precisely because the former embed so much information, including the history of transactions.

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Tribe of Mentors: Short Life Advice From the Best in the World
by Timothy Ferriss
Published 14 Jun 2017

Vitalik Buterin TW: @VitalikButerin Reddit: /u/vbuterin VITALIK BUTERIN is the creator of Ethereum. He first discovered blockchain and cryptocurrency technologies through Bitcoin in 2011, and was immediately excited by the technology and its potential. He co-founded Bitcoin magazine in September 2011, and after two and a half years looking at what the existing blockchain technology and applications had to offer, wrote the Ethereum white paper in November 2013. He now leads Ethereum’s research team, working on future versions of the Ethereum protocol. In 2014, Vitalik was a recipient of the two-year Thiel Fellowship, tech billionaire Peter Thiel’s project that awards $100,000 to 20 promising innovators under 20 so they can pursue their inventions in lieu of a post-secondary institution

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But I do want to get that culty Japanese journal that all the designers use, a Hobonichi Techo. It’s the kind of thing you see in Japan: a notebook turned into a high art. Maybe next year. . . . “No one is qualified to tell you how you experience the world.” Vlad Zamfir TW: @VladZamfir Medium: @vlad_zamfir vladzamfir.com VLAD ZAMFIR is a blockchain architect and researcher at Ethereum, working on blockchain efficiency and scaling. Vlad is interested in governance and privacy solutions, and he was also the first person to introduce me to absurdism. He is a frequent contributor on Medium and lives in Antarctica (or so he wants us to believe). What is the book (or books) you’ve given most as a gift, and why?

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I feel like I have a much better idea about “how society works” now that I understand something about the nature of institutions. Not that I can claim to understand much! I tried to “crystallize” some of my understandings, but I didn’t do a great job. In practical terms, though, I am now able to think much more clearly about blockchain governance. I can see that we already have a handful of nascent blockchain governance institutions! I can understand what it means for an institution to be more or less formal, and more or less tacit/ad hoc. I am now completely open to the possibility that institutionalization can be a reasonable process, rather than one that is inevitably powered by hubris.

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Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets
by Donald MacKenzie
Published 24 May 2021

The sphere to which material political economy unequivocally applies is decentralized cryptocurrencies such as bitcoin and ethereum. (The issues are different for cryptocurrencies such as Facebook’s proposed Libra, which, if it is launched, will be run, at least initially, in a centralized fashion; I don’t propose to discuss those currencies here.) The issue that most clearly makes material political economy applicable is how to motivate at least a subset of the users of a cryptocurrency to check the validity of each transaction (including checking the validity of each other’s checking) and take part in adding it irreversibly to the blockchain, the record of every transaction that has taken place.

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There is no abbot of bitcoin who, like the abbot of St Albans, monopolizes bitcoin ASICs—although there is one dominant designer of them, the Chinese company Bitmain, which has some 90 percent of the market (Liu and McMorrow 2019), and the miners who use Bitmain’s ASICs do tend to be organized in very large pools—but there has been deep unhappiness in the world of cryptocurrencies about the shift to ASICs. Indeed, bitcoin’s main rival, ethereum, was designed to be, in the terminology of the field, ASIC resistant. The hashing algorithm for ethereum was designed to make it hard to develop ASICs that are much more efficient than ordinary computers in ethereum’s equivalent of bitcoin mining. Trying to make a cryptocurrency ASIC resistant is quintessential material political economy, reminiscent (as I’ve said) of the defense of hand-milling, even if in the case of ethereum the effort was less than fully successful. Efficient ethereum ASICs have been developed, although they haven’t yet swept the board as fully as their bitcoin equivalents have.

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I took my rough estimate of the bitcoin network’s fluctuating total hash rate (100 million TH/s) from https://www.blockchain.com/charts/hash-rate, accessed June 5, 2020. (A terahash or TH is a thousand billion applications of bitcoin’s hashing algorithm.) The example of efficient equipment I used was Bitmain’s S17e, which I assumed performed at the company’s specifications (a hash rate of 60TH/s and power consumption of 2.7 kW), taken from https://m.bitmain.com, accessed June 1, 2020. 33. I take the daily numbers of transactions from https://www.blockchain.com/charts/n-transactions, accessed June 10, 2020. 34. See https://newsroom.fb.com/company-info/, accessed June 5, 2020. 35.

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Decoding the World: A Roadmap for the Questioner
by Po Bronson
Published 14 Jul 2020

He had been on 4chan, the dark web, looking for vintage porn to buy and sell for profit. He went on Silk Road and saw people using Bitcoin, and he thought it was more of a PayPal-type thing. But as he learned more, he started buying it, a little every month. One night, here at IndieBio, Tall Joe hosted the first-ever Ethereum Meetup Group for San Francisco. Thirteen people showed up, but one of them was the CTO for the Ethereum Foundation. Ethereum was almost out of money, and at the time nobody knew if Ether would survive. But Joe bought a bunch because he thought smart contracts made sense. Joe took a coding class in San Jose and started buying into ICOs he thought would have a real market.

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Let me revise that; they’re not agreeing to pay your debt long-term, it’s more like your parents would be your escrow company, putting up their own money only for the length of time it takes for the transaction to go through. This method would end the arms race, but making this paradigm shift would be too radical for Bitcoin to go first. So other crypto assets, like Ethereum, will do it first. The Bitcoin community is pretty convinced they need to find a new solution. But the community is very divided over what the new solution should be. So let’s summarize a little bit. 1. Bitcoin has no intrinsic value. 2. But big money needed an alternative to the dollar. 3.

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His girls dropped their piecemeal needlepoints to rush outside. 3 Silicon Valley’s New Obsession: Boring-Ass Startups The Hustle IndieBio erupted just as the Bay Area was kind of getting sick of itself, whining about how everyone was on the make and everybody’s startups were lame. People who worked at Facebook would go out to dinner and express regret that their job was really to sell ads. Everyone made themselves feel better by hosting Social Purpose Parties. This is where you drink, give money, and talk about using blockchain to protect the environment or end poverty. There was a hunger in the Valley for something rad, something uncompromising. Saving the world was everyone’s favorite topic; they talked about it endlessly. But talk grew cheap. IndieBio wasn’t a Think Tank. It was a Do Tank. That’s what was so refreshing about it.

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Bitcoin: The Future of Money?
by Dominic Frisby
Published 1 Nov 2014

And the app catalogue is totally decentralized just like the network. So even we don’t control it – so we can’t take apps down or anything and the government can’t take apps down or something like that. So, that’s what we’re trying to do: mainstream this dis-intermediation technology’. The implications of Ethereum – if it takes off – are clearly enormous. For more information about Ethereum, visit ethereum.org. 10 Should You Buy In? You can’t stop things like Bitcoin. It’s like trying to stop gunpowder. It will be everywhere and the world will have to readjust. John McAfee, computer scientist, founder of McAfee Inc In the 1830s and 1840s a mania gripped the UK.

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‘That is what we’re hoping to do.’ How Bitcoin is just the start of something much, much bigger Ethereum is probably the most talked-about development in cryptography at present. Some call it Bitcoin 2.0. It combines the decentralized mining system central to Bitcoin with a software development platform. Its founders say the potential applications are unlimited: from peer-to-peer betting, to financial derivatives, to identity and reputation systems, to insurance and legal contracts. Some say Satoshi Nakamoto may now even be working for Ethereum. Its former CEO is Charles Hoskinson. A bespectacled, bearded, extremely bright, friendly and fast-talking mathematician.

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This is a system that would work just as well in Africa as it works in North America or Europe, which is a huge step forward for ecommerce and banking the unbanked. And that’s what gets me really excited about the technology and why I came into this space.’ So, what exactly is Ethereum? Everybody’s talking about it, but few seem to understand it. ‘Ethereum is really a continuation of what Satoshi Nakamoto was working on. He wanted to study two things when he released Bitcoin. He’d been working on it for quite some time. The first was this idea of a decentralized database secured by a proof-of-work consensus system, and the second thing was a transaction system – tokens.

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The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

Artificial intelligence does the rest. The entire experience is mobile, simple, and fast. Ninety seconds to get insured, three minutes to get a claim paid, and zero paperwork. Adding more technology to this arrangement, companies like the Swiss firm Etherisc sell “bespoke insurance products” on the Ethereum blockchain. Because smart contracts remove the need for employees, paperwork, and all the rest, all sorts of new insurance products are being created. Etherisc’s first offering is something not covered by traditional insurers: flight delays and cancellations. Individuals sign up via credit card, and if their plane is more than forty-five minutes late, they’re paid instantly, automatically, and without the need for any paperwork.

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Or, at least, until blockchain came along. With blockchain, since trust is built into the system, the system is no longer necessary. Take a stock trade. Right now, to execute that trade, there’s a buyer, a seller, a series of banks that hold their money, the stock exchange itself, clearinghouses, etc.—roughly, ten different intermediaries. Blockchain removes everyone but the buyer and seller. The technology does the rest. In an attempt to hold on to their thinning slice of the pie, every major bank is rushing into blockchain. Yet arguably moving faster are the thousands of entrepreneurs using blockchain to disrupt these same banks.

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See: https://www.alizila.com/how-alipay-users-planted-100m-trees-in-china/. R3: See: https://www.r3.com/. Ripple: See: https://www.ripple.com/. these companies are using blockchain to replace the SWIFT network: The blockchain blog Cointelegraph has a good piece overviewing SWIFT’s relationship to blockchain. You can find the piece here: https://cointelegraph.com/news/swift-announces-poc-gateway-with-r3-but-remains-overall-hesitant-about-blockchain. 4 billion people, the rising billions, will gain access to the internet: The world population is expected to reach 8.2 billion by 2025, as projected by the United Nations Population Division.

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Tracers in the Dark: The Global Hunt for the Crime Lords of Cryptocurrency
by Andy Greenberg
Published 15 Nov 2022

If you don’t get privacy, what do you get?” The temptation was more than Meiklejohn could resist. The blockchain, like a massive, undeciphered corpus of an ancient language, hid a wealth of secrets in plain view. CHAPTER 8 Men with No Names When Meiklejohn began digging into the blockchain in late 2012, she started with a very simple question: How many people were using Bitcoin? That number was much harder to pin down than it might seem. After downloading the entire blockchain onto a UCSD server and organizing it into a database that she could query, like a gargantuan, searchable spreadsheet, she could see that there were more than twelve million distinct Bitcoin addresses, among which there had been nearly sixteen million transactions.

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At one point early in his tenure as an IRS-CI agent, watching the Silk Road’s unchecked growth, he had even gone so far as to suggest to a fellow agent that they try tracing bitcoins on the blockchain. His colleague had laughed at him. “Oh, so we’re going to bring in Satoshi Nakamoto to introduce the blockchain as evidence in court?” the agent had joked. But Gambaryan had read the news coverage on the heels of UCSD’s “Men with No Names” research in late 2013, and it had only reinforced what he’d suspected all along: Despite the prevailing belief of both cops and criminals, cryptocurrency was traceable. So, why not use the blockchain as evidence? If a cryptographically unforgeable, giant ledger displaying every Bitcoin transaction was good enough to prove who owned millions of dollars within Bitcoin’s economy, Gambaryan thought, it ought to be good enough to use as evidence in a criminal indictment, too.

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” * * * · · · It was late afternoon on a fall day in 2014 when Gambaryan got to work tracing Force’s money on the blockchain. Despite having read Meiklejohn’s paper, he possessed none of the data that she’d assembled over months of clustering Bitcoin addresses and identifying them with test transactions. So he simply started copying Bitcoin addresses from Carl Force’s account records—the ones he’d gotten from exchanges such as CampBX and Bitstamp—and pasting them into the search field on Blockchain.info, which displayed the entire blockchain on the web. At first, the collections of garbled character strings seemed meaningless to Gambaryan.

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Radicals Chasing Utopia: Inside the Rogue Movements Trying to Change the World
by Jamie Bartlett
Published 12 Jun 2017

Investors can buy shares in the DAO using Ethereum’s (another blockchain) currency, which gives votes on investments. Anyone anywhere in the world can invest, it’s all transparent, there is no board or employees at all, and shareholders receive any profits directly. One month after it was launched to great fanfare, hackers and/or investors managed to exploit a vulnerability by inserting some code that redirected shares into their personal wallets, allowing them to walk off with millions of dollars of investors’ money. There was no way to change it, except to ‘fork’ the blockchain, which meant making a second copy without that malicious code in it.

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Every time someone sends a bitcoin as payment, a record of the transaction is timestamped to the microsecond, and stored in something called a ‘blockchain’ (each block representing about ten minutes’ worth of transactions). The blocks are ordered chronologically, and each includes a digital signature (a ‘hash’) of the previous block, which administers the ordering and guarantees that a new block can join the chain only if it starts from where the preceding one finishes. A copy of the blockchain—which is basically a record of every single transaction ever made—is kept by everyone who has installed the bitcoin software. To ensure everything is running as it should, the blockchain is constantly verified by the computers of certain key users who compete to crack a mathematical puzzle that allows them to officially verify the blocks are all in order (and in exchange they get to mint a small number of new bitcoins).

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Many people assume bitcoin to be completely decentralised, but if a miner, or a group of miners, controlled over half the computing power that works on verifying the transaction, it could feasibly force a change on the blockchain transaction list however it wished, create a fork of the blockchain, and all the other computers would start to work on the new version (the protocol is written so that all computers work from the longest blockchain). In bitcoin, a few large pools can register most of the new bitcoin blocks, which could push them to the 51 per cent threshold for mining power: which could result in a takeover. Indeed, in 2014 one mining rig took over 51 per cent of bitcoin’s hashing power for twelve straight hours.

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MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them
by Nouriel Roubini
Published 17 Oct 2022

“The concept of renting, the concept of a mortgage, the concept of all this stuff is going to be challenged with this new world because the funding sources are flexible,” says LoanSnap founder Karl Jacob. Using BaconCoin, LoanSnap aims to revolutionize home buying by sharing mortgages via the blockchain that can etch every transaction from day one.20 The first such mortgages changed hands in late 2021. Once inscribed on a blockchain visible to every user, new transactions update the status quo but, in principle, no one can alter or hack what occurred in the past. The rush to DeFi is premature and ultimately misguided. The rapid rise of Bitcoin, Ethereum, Dogecoin, and thousands of fledgling cryptocurrencies since 2010 exposes our collective wilting faith in the ability of governments to back the money they issue.

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Were someone to write a mortgage with principal and interest in bitcoin, a spike in the value of bitcoin would cause the real value of the mortgage to skyrocket. If default then likely occurs, the lender loses money, and the borrower loses her house. A true currency should also be a scalable and widely used means of payment. Bitcoin and Ethereum can handle fewer than a dozen transactions per second, due to the enormous computing resources involved. The Visa network, by contrast, handles fifty thousand transactions per second. The proof-of-work etched into every blockchain transaction may improve its reliability, but it does so at a snail’s pace. Another vital attribute makes money a stable store of value not exposed to dramatic swings in market value.

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A lot of crypto consists of mostly manipulative Ponzi schemes. If we want to revamp a centralized financial system with safeguards and supervision, we don’t need crypto or blockchain. Artificial intelligence, machine learning, big data, 5G, and the Internet of Things can speed transactions, lower costs, and increase reliability. These centralized fintech tools and firms collect and process detailed financial data at blistering speeds without any use of blockchain. Hundreds of firms worldwide have entered the fray with payment systems that handle billions of daily consumer and business-to-business transactions. Companies in the United States and China dominate the industry, but markets have sprouted in other advanced and developing economies.

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Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
by Xiaowei Wang
Published 12 Oct 2020

The first block on the Bitcoin blockchain was created along with the text “THE TIMES 03/JAN/2009 Chancellor on brink of second bailout for banks”—the anti-centralization message of Bitcoin coming through loud and clear. And since 2008, the cryptocurrency and blockchain space has blossomed beyond Bitcoin into other currencies and other blockchains, currencies like Ethereum and EOS, all with slightly different consensus algorithms—ways of ensuring that individual computers, or nodes, have records that agree with each other. Hardin’s original essay in 1968 used the example of the medieval commons, a place where peasants grazed their cows. According to Hardin, the ungoverned nature of the commons led to overgrazing, which is why the commons had to eventually be enclosed and privatized.

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Underneath the large red sign is a woman at a desolate fruit stand rearranging the oranges in her crate over and over. Hustle has come to Sanqiao. 6. Blockchain chicken is not the actual name of the chicken I am here to see. The official name is Bubuji (步步鸡), or GoGoChicken, as some English PR materials call it. The COO of Shanghai Lianmo Technology, the company behind blockchain chicken, says that he explicitly keeps “blockchain” out of the name. To him, overhyped blockchain projects have turned the term “blockchain” into marketing gloss. These blockchain chickens sell for up to RMB 300 (US$40) on JD.com. Typical buyers are upper-class urbanites—people willing to pay a premium on food.

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Despite Ostrom’s work, the belief in innate human selfishness in a world of scarcity had become ingrained outside of ecology—in fields like information science and economics.6 This belief in selfishness and scarcity is one of the core ideologies that gave rise to blockchain. Although blockchain has become synonymous with Bitcoin, they are not quite the same. Bitcoin is one use of blockchain, but it remains separate from blockchain technology. Some have used a biological analogy to illustrate the difference: if blockchain is DNA, Bitcoin is a distinct species. Blockchain is a special kind of distributed record-keeping system that uses cryptography to prevent records from being falsified, eliminating the need to trust a centralized authority to verify records.

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Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind
Published 3 Sep 2018

A ‘smart contract’, for instance, is a piece of blockchain software that executes itself automatically under pre-agreed circumstances— like a purchase agreement which automatically transfers the ownership title of a car to a customer once all loan payments have been made.27 There are early ‘Decentralised Autonomous Organisations’ (DAOs) that seek to solve problems of collective action without a centralized power structure.28 Imagine services like Uber or Airbnb, but without any formal organization at the centre pulling the strings.29 The developers of the Ethereum blockchain, among others, have said they want to use DAOs to replace the state altogether. Blockchain still presents serious challenges of scale, governance, and even security, which are yet to be overcome.30 Yet for a youthful technology it is already delivering some interesting results. The governments of Honduras, Georgia, and Sweden are trialling the use of blockchain to handle land titles,31 and the government of Estonia is using it to record more than 1 million patient health records.32 In the UK, the Department for Work and Pensions is piloting a blockchain solution for the payment of welfare benefits.33 In the US, the Defense Advanced Research Projects Agency (DARPA) is looking into using blockchain technology to protect its military networks and communications.34 Increasingly connective technology is not just about people connecting with other people.

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See Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom (New Haven and London:Yale University Press, 2006) and The Penguin and the Leviathan: How Cooperation Triumphs over Self-Interest (New York: Crown Publishing, 2011). 24. Don Tapscott and Alex Tapscott, Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business and the World (London: Portfolio Penguin, 2016), 7. 25. Tapscott and Tapscott, Blockchain Revolution, 16. 26. Tapscott and Tapscott, Blockchain Revolution, 153–4; Stan Higgins, ‘IBM Invests $200 Million in Blockchain-Powered IoT’, CoinDesk, 4 October 2016 <https://www.coindesk.com/ibm-blockchain-iotoffice/> (accessed 30 November 2017). 27. Melanie Swan, Blockchain: Blueprint for a New Economy (Sebastopol, CA: O’Reilly, 2015), 14. 28. Economist, ‘Not-so-clever Contracts’, 28 July 2016 <http://www. economist.com/news/business/21702758-time-being-leasthuman-judgment-still-better-bet-cold-hearted?

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Schwab, Klaus, The Fourth Industrial Revolution (Geneva: World Economic Forum, 2016), 19; Laura Shin, ‘The First Government to Secure Land Titles on the Bitcoin Blockchain Expands Project’, Forbes, 7 February 2017 <https://www.forbes.com/sites/laurashin/ 2017/02/07/the-first-government-to-secure-land-titles-onthe-bitcoin-blockchain-expands-project/#432b8b494dcd> (accessed 30 November 2017); Joon Ian Wong, ‘Sweden’s Block chain-powered Land Registry is Inching Towards Reality’, Quartz Media, 3 April 2017 <https://qz.com/947064/sweden-is-turninga-blockchain-powered-land-registry-into-a-reality/> (accessed 30 November 2017). Daniel Palmer, ‘Blockchain Startup to Secure 1 Million e-Health Records in Estonia’, CoinDesk, 3 March 2016 <http://www.coindesk. com/blockchain-startup-aims-to-secure-1-million-estonian-healthrecords/> (accessed 30 November 2017).

pages: 273 words: 72,024

Bitcoin for the Befuddled
by Conrad Barski
Published 13 Nov 2014

However, before you build more sophisticated bitcoinJ programs, read “Gotchas When Using Wallets in BitcoinJ” on page 239. Not only does a Bitcoin app need a wallet, it also needs a blockchain. The following lines initialize a new blockchain for us: File file = new File("my-blockchain");➊ SPVBlockStore store = new SPVBlockStore(params, file);➋ BlockChain chain = new BlockChain(params, wallet, store);➌ Because blockchains consume lots of space, we’ll write it to a file named my-blockchain ➊. Next, we create a block store, which is an object that manages the data for our copious blockchain data ➋. BitcoinJ offers several block store types, all with different feature and performance trade-offs.

…

In this case, Crowley and Satoshi will each add a block to the blockchain (each thinking that he is the winning miner for that round). The problem occurs when one part of the network copies Crowley’s block and the other copies Satoshi’s. As a result, now two blockchains disagree! Figure 2-13: Bitcoin miners Crowley and Satoshi find a block at the same time, creating two copies of the blockchain. The resolution to the forked blockchain occurs when Satoshi’s version of the blockchain adds another block before Crowley’s, and Satoshi receives the reward. Recall that your Bitcoin wallet program needs an up-to-date copy of the blockchain to function, but it doesn’t know how to resolve a forked blockchain.

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Well, the biggest performance challenge any app that works with bitcoins has to deal with is that the official Bitcoin blockchain is larger than 10GB in size. Do most Bitcoin apps really need all 10GB of the blockchain? To answer this question, let’s consider why the blockchain exists. At a simplified level, a Bitcoin blockchain is responsible for two main jobs: 1. Figuring out how much money everyone on the network has 2. Figuring out whether new transactions broadcast across the network are valid For the first task, the blockchain allows us to examine all the historical blocks in the blockchain and compile comprehensive data about every Bitcoin address ever used and how much money each contains.

pages: 205 words: 61,903

Survival of the Richest: Escape Fantasies of the Tech Billionaires
by Douglas Rushkoff
Published 7 Sep 2022

After a bit of small talk, I realized they had no interest in the talk I had prepared about the future of technology. They had come to ask questions. They started out innocuously and predictably enough. Bitcoin or Ethereum? Virtual reality or augmented reality? Who will get quantum computing first, China or Google? But they didn’t seem to be taking it in. No sooner would I begin to explain the merits of proof-of-stake versus proof-of-work blockchains than they would move to the next question. I started to feel like they were testing me—not my knowledge so much as my scruples. Eventually, they edged into their real topic of concern: New Zealand or Alaska?

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Chapter 2: Mergers and Acquisitions 25 Tech companies actively sought : Douglas Rushkoff, Cyberia: Life in the Trenches of Hyperspace (New York: HarperOne, 1994). 25 “new communalists” : Fred Turner, From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism (Chicago: University of Chicago Press, 2006). 26 Operation Sundevil : Bruce Sterling, The Hacker Crackdown: Law and Disorder on the Electronic Frontier (New York: Bantam, 1992). 26 “Governments of the Industrial World” : John Perry Barlow, “A Declaration of the Independence of Cyberspace,” Electronic Frontier Foundation, 1996, https:// www .eff .org /cyberspace -independence. 26 fungus and bacteria : Qi Hui Sam, Matthew Wook Chang, and Louis Yi Ann Chai, “The Fungal Mycobiome and Its Interaction with Gut Bacteria in the Host,” International Journal of Molecular Sciences , February 4, 2017, https:// www .ncbi .nlm .nih .gov /pmc /articles /PMC5343866 /. 28 extolled the virtues of the deal : Saul Hansell, “America Online Agrees to Buy Time Warner for $165 Billion; Media Deal is Richest Merger,” New York Times , January 11, 2000, https:// www .nytimes .com /2000 /01 /11 /business /media -megadeal -overview -america -online -agrees -buy -time -warner -for -165 -billion .html. 28 the piece I wrote placed in the Guardian : Douglas Rushkoff, “Why Time Is Up for Warner,” Guardian , January 20, 2000, https:// www .theguardian .com /technology /2000 /jan /20 /onlinesupplement10. 29 People blamed : Seth Stevenson, “The Believer,” New York Magazine , July 6, 2007, https:// nymag .com /news /features /34454 /. 30 hired investment bank Salomon Smith Barney : Tim Arango, “How the AOL–Time Warner Merger Went So Wrong,” New York Times , January 10, 2010, https:// www .nytimes .com /2010 /01 /11 /business /media /11merger .html. 31 probably borrowed : Steven Levy, Facebook: The Inside Story (New York: Blue Rider Press, 2020). 32 stocks quadruple : Lisa Pham, “This Company Added the Word ‘Blockchain’ to Its Name and Saw Its Shares Surge 394%,” Bloomberg , October 27, 2017, https:// www .bloomberg .com /news /articles /2017 -10 -27 /what -s -in -a -name -u -k -stock -surges -394 -on -blockchain -rebrand. 33 “independent, host-led local organizations” : Dave Lee, “Airbnb Using ‘Independent’ Host Groups to Lobby Policymakers,” Financial Times , March 21, 2021, https:// www .ft .com /content /1afb3173 -444a -47fa -99ec -554779dde236. 33 Google was outspending : Shaban Hamza, “Google for the First Time Outspent Every Other Company to Influence Washington in 2017,” Washington Post , January 23, 2018, https:// www .washingtonpost .com /news /the -switch /wp /2018 /01 /23 /google -outspent -every -other -company -on -federal -lobbying -in -2017 /. 33 outspent by Facebook : Lauren Feiner, “Facebook Spent More on Lobbying than Any Other Big Tech Company in 2020,” CNBC , January 22, 2021, https:// www .cnbc .com /2021 /01 /22 /facebook -spent -more -on -lobbying -than -any -other -big -tech -company -in -2020 .html. 33 Numerous studies : Martin Gilens and Benjamin I.

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In the new, improved, post-crash version of Silicon Valley, extreme capitalism rules. Digital technology is valued most for its ability to scale a business without needing to hire many human beings, and to provide the earnings or—as is more often the case—the hype required to boost the share price. (Companies that add trendy words like “blockchain” to their names have seen their stocks quadruple .) Following AOL’s example of mailing free disks, companies scramble to get subscribers at any cost. A company can lose money for years, as long as its user base is rising—preferably at an exponential rate. But it’s not all abstract. Hockey stick user growth leads to hockey stick stock growth.

pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity
by Douglas Rushkoff
Published 1 Mar 2016

Mark Gimein, “Virtual Bitcoin Mining Is a Real-World Environmental Disaster,” bloomberg.com, April 12, 2013. 39. Michael Carney, “Bitcoin Has a Dark Side: Its Carbon Footprint,” pando.com, December 16, 2013. 40. Lawler, “Bitcoin Miners Are Racking Up $150,000 a Day.” 41. Jon Evans, “Enter the Blockchain: How Bitcoin Can Turn the Cloud Inside Out,” techcrunch.com, March 22, 2014. 42. Vitalik Buterin, “DAOs, DACs, DAs and More: An Incomplete Terminology Guide,” blog.ethereum.org, May 6, 2014. 43. David Johnston, Sam Onat Yilmaz, Jeremy Kandah, Nikos Bentenitis, Farzad Hashemi, Ron Gross, Shawn Wilkinson, and Steven Mason, “The General Theory of Decentralized Applications, Dapps,” github.com, June 9, 2014. 44.

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This is a money system that works through protocols—digital handshakes between peers—instead of establishing security through central authorities. Bitcoin is based on a database known as the “blockchain.” The blockchain is a public ledger of every bitcoin transaction ever. It doesn’t sit on a server at a bank or in the basement of a credit-card company’s headquarters; it lives on the computers of everyone in the Bitcoin network. When bitcoins are transacted, an algorithm corresponding to that transaction is “published” to the blockchain. The algorithm is just a description of the transaction itself, as in “2 bitcoins came from A and went to B.” Instead of a list of users and their bitcoin balances, the ledger simply lists the transactions in chronological order.

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Additionally, it is verified by an anonymous peer group, then encrypted so that only those involved in the specific transaction know who participated. This has applications well beyond bitcoins.41 The blockchain can “notarize” and record anything we choose, not just the cash transactions between Bitcoin users. Entire companies can be organized on blockchains, which can authenticate everything from contracts to compensation. Decentralized autonomous corporations, or DACs, for example, are a fast-growing category of businesses that depend on a collectively computed blockchain to determine how shares are distributed. To count as a true DAC, a company must be an open-source endeavor whose operation occurs without the supervision of a single guiding body, such as a board or a CEO.* Instead, a project’s governing rules and mission must emerge from consensus.

pages: 263 words: 92,618

Going Infinite: The Rise and Fall of a New Tycoon
by Michael Lewis
Published 2 Oct 2023

Modelbot was programmed to trade roughly five hundred different crypto coins on thirty or so different crypto exchanges, most of them in Asia, all of them basically unregulated. The tulip-bulb-like explosion in crypto over the previous year had encouraged the creation of hundreds of new coins. Modelbot made no distinction between the better-known coins with deep markets, like bitcoin and ether, the Ethereum blockchain’s token, and the so-called shitcoins that hardly traded at all, like Sexcoin and PUTinCoin and Hot Potato Coin.¶ Modelbot just hunted for any coin it could buy at one price in one place and sell in another at a higher price. Modelbot was maybe the biggest point of disagreement between Sam and his management team.

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Serum was more like a currency in the private board game Sam never stopped playing inside his own mind. Serum was Sam’s bet on blockchains replacing, say, the New York Stock Exchange or, for that matter, FTX. Blockchains were just communally maintained records of who owned what and when they’d owned it. They could keep track of any transaction. It was at least theoretically possible that they could keep track of all financial transactions. The Serum tokens that Friedberg had been paid gave their owner trading discounts on, voting rights over, and a slice of the tiny fee charged for any financial transaction that occurred on the Solana blockchain. Which sounded great. The problem was that there were relatively few financial transactions on the Solana blockchain.

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The Goldman guys and the venture capitalists and the corporate lawyers turned crypto bros—they were all part of this invasion of conventional people who wanted to make fast money without the kookiness that had made the fast money possible. The pseuds would seek common ground with the original crypto religionists by exhibiting their excitement about the technology. The blockchain! The blockchain is going to change . . . everything, they’d say, and hope that would suffice. A religion based on hating banks and government and any sort of institutional authority—well, for the pseuds, that was usually taking things too far. The crypto religionists, the people who had been drawn to the cause for their own reasons, had, at best, mixed feelings about the people who came to it later, and just for the money.

pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

It took the slow-moving, long-term patience of a government to produce the core inventions that make it possible for us to hold a phone and order one of Kalanick’s Uber cars. Nonetheless, the libertarian protestations of a certain set of coders continues apace. In recent years, blockchain technology has been the latest site of tech’s anti-government fervor. That ranges from Bitcoin—a currency specifically designed to create money that couldn’t be controlled by dough-printing central banks—to Ethereum, a way of creating “smart contracts” that, its adherents hope, would allow commerce so frictionless and decentralized that even lawyers wouldn’t be necessary: The instant someone performed the service you’d contracted them to do for you, the digital cash would arrive in their digital wallet.

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There were considerably fewer women working in back-end jobs that involve wrangling servers and databases, or in newly hot areas like blockchain or AI. In those areas, men rule. And the men are paid more for it: Front-end jobs, she found, pay on average about $30,000 less than back-end work. The upshot, Posner notes, is that when women move into an area of coding, it gets devalued. The men leave that area, looking for new cutting-edge areas where they can reestablish artificial scarcity and a tacit no-girls-allowed culture, or at least one where girls are regarded as foreign interlopers. These days, that appears to be Bitcoin—or blockchain tech in general—and AI, where, whenever I go to events, it’s a sea of men, far more than most other fields of coding.

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S., ref1 Ellis, Kelly, ref1, ref2 encasement strategy, ref1 encryption Clipper Chip, ref1 criminal/terrorist use of, cypherpunk views on, ref1 entertainment and copyright law and, ref1 munitions law, ref1, ref2 public/private key crypto and, ref1 Zimmermann’s creation of Pretty Good Privacy, ref1 See also cypherpunks engagement (compulsive use) advertising and, ref1 Like button (Facebook) and, ref1 psychological lures to encourage, ref1 English Electric, ref1 ENIAC computer, ref1, ref2 ENIAC Girls, ref1 Ensmenger, Nathan, ref1, ref2 entertainment industry, ref1 decoding software and, ref1 digital rights management (DRM) software, ref1, ref2, ref3 Erickson, Carolina, ref1 Ethereum, ref1 Everingham, James, ref1, ref2, ref3, ref4 expert systems, ref1 “Exploratory Experimental Studies Comparing Online and Offline Programming Performance” (Sackman et al.), ref1 Facebook, ref1, ref2, ref3, ref4, ref5, ref6 ad tech, civic impacts of, ref1 Cambridge Analytica scandal, ref1 content moderation, ref1, ref2 deep-learning model at, ref1 free-to-use model of, ref1 Like button, ref1 News Feed feature of (See News Feed [Facebook]) purchases Instagram, ref1 Sanghvi hired at, ref1 scale and, ref1 tracking of user activities by, ref1 women and minority coders at, percentage of, ref1 work atmosphere at, ref1 Fancy Bear, ref1 Fan Hui, ref1, ref2 Fast Company, ref1 feature creep, ref1 Ferrucci, Dave, ref1, ref2 file-sharing tools, ref1 Firefox, ref1 Fisher, Allan, ref1, ref2, ref3, ref4 Fitzpatrick, Brad, ref1, ref2 Flatiron School, ref1, ref2, ref3 Flickr, ref1 Flombaum, Avi, ref1 FLOW-MATIC computer language, ref1 flow state, ref1 Foer, Franklin, ref1 Fogg, B.

pages: 262 words: 69,328

The Great Wave: The Era of Radical Disruption and the Rise of the Outsider
by Michiko Kakutani
Published 20 Feb 2024

Others worry that its rapid growth could be destabilizing, that it could facilitate money laundering and criminal exploitation, and that investing in a currency which possesses no intrinsic value is extremely hazardous—a warning underscored by the spectacular crash of the cryptocurrency exchange FTX in the fall of 2022. Blockchain, advocates contend, has the potential to disrupt industries from e-commerce to music sales to health care by creating virtually tamperproof records and allowing retailers to sell directly to customers without intermediaries. Whereas people now tend to place their trust in marketplaces like Amazon and eBay, blockchain will supposedly empower buyers to make trustworthy purchases directly from manufacturers and vendors. For artists, too, blockchain promises tangible benefits—not only making it more difficult, say, to illegally copy and download music, but also enabling musicians to be compensated directly for their work without having to rely upon platforms like Apple Music (iTunes) and Spotify.

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Other proponents of a decentralized web or Web3 are proposing a model based on the sort of peer-to-peer technology employed by blockchain (which powers bitcoin and other cryptocurrencies). In theory, such a decentralized architecture would allow users to bypass the control of tech giants like Facebook, Google, and Microsoft, which currently act as intermediaries, and would also make it more difficult for Big Tech and governments to collect our data and control what we see. Blockchain is a kind of digital ledger where transaction data is stored not in one central location but on servers and hard drives around the world, making that data difficult to alter or hack and circumventing centralized authorities like governments and banks.

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GO TO NOTE REFERENCE IN TEXT “has evolved into an engine of inequity”: Tim Berners-Lee, “One Small Step for the Web…,” Medium, Sept. 29, 2018, medium.com/@timberners_lee/one-small-step-for-the-web-87f92217d085. GO TO NOTE REFERENCE IN TEXT Berners-Lee has proposed a new platform: Solid, solidproject.org; Thomas Macaulay, “Web Inventor Tim Berners-Lee: Screw Web3—My Decentralized Internet Doesn’t Need Blockchain,” TNW, June 23, 2022, thenextweb.com/news/web-inventor-tim-berners-lee-screw-web3-my-decentralized-internet-doesnt-need-blockchain; Peter Verdegem, “Tim Berners-Lee’s Plan to Save the Internet: Give Us Back Control of Our Data,” Conversation, Feb. 5, 2021, theconversation.com/tim-berners-lees-plan-to-save-the-internet-give-us-back-control-of-our-data-154130; Greg Noone, “What Is Web 3.0?

pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

Moreover, as you put money into that machine, you and its other users have a say in what snacks it will order and how often it should be cleaned. It has no managers, all of those processes were pre-written into code.” Developers, leveraging what they have learned in creating cryptocurrencies such as Bitcoin and Ethereum, are pioneering a new generation of decentralized applications that allow organizations to operate like that magical vending machine. Through a series of rules called smart contracts, founders can create, fund, and operate an entire organization independent of hierarchical management. Everything, from paying contributors for their work to making decisions about investment, is managed in a distributed way.

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one study of cooperatives: Virginie Pérotin, “What Do We Really Know About Worker Co-operatives?” Co-operatives UK, no date, www.uk.coop/resources/what-do-we-really-know-about-worker-co-operatives. “pre-written into code”: “What Is DAO,” Cointelegraph, accessed July 31,2016, https://cointelegraph.com/ethereum-for-beginners/what-is-dao#how-daos-work. becomes valuable when it moves: Charles Eisenstein, Sacred Economics: Money, Gift, and Society in the Age of Transition (Berkeley, CA: North Atlantic Books, 2011). producer/philanthropist Jeff Skoll: “A New Global Impact Fund,” The Rise Fund, accessed September 1, 2018, http://therisefund.com

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New forms of universal basic income are being tested for their ability to provide for our basic human needs while also encouraging us to use and share our gifts—through entrepreneurship, service, and community. New forms of currency and means of exchange provide alternatives to the current model of borrowing money lent at interest. Blockchain and cryptocurrencies enable massively distributed collaboration via decentralized autonomous organizations and other alternatives to traditional incorporation or partnership. A new type of thinking is essential if mankind is to survive and move toward higher levels. —Albert Einstein Is a future like that even possible?

pages: 599 words: 98,564

The Mutant Project: Inside the Global Race to Genetically Modify Humans
by Eben Kirksey
Published 10 Nov 2020

The doctor who ran this clinic, Eric Scott Sills, was later arrested and charged with murdering his wife and business partner.4 * * * When Ascendance Biomedical launched their new website, http://ascendance.io, in December 2017, they invited the public to “Help Us Choose Our Next Experiments.” Tristan Roberts designed the front end of the website, plus a back-end cryptocurrency infrastructure that would enable anyone to invest in the project, undetected by the government. Rather than Bitcoin, the best-known cryptocurrency, the group was banking on Ethereum, a newer currency that was growing exponentially in value. One coin (known as an “ether”) was valued at $299 in early November 2017, and the price skyrocketed to $821 just before Christmas. Ascendance offered a diverse menu of options on their website. An estrogen/testosterone experiment topped the list of possible treatments.

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Imagining himself as the next Jeff Bezos or Mark Zuckerberg, Aaron planned to bring a disruptive technology directly to consumers, government regulators and societal concerns be damned. * * * Aaron Traywick’s dreams for enhancing humanity seemed to be getting closer to reality in January 2018. The price of an Ethereum ether peaked at $1,432 as Ascendance Biomedical was about to go public with new findings and research initiatives. Aaron started messaging me on Facebook, saying, “We have confirmation of the first known gene therapy to transfect N6 successfully into human cells and bind to HIV.” He claimed that Ascendance had made “a pretty major breakthrough that no one has been able to do.”

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See also CRISPR; zinc fingers Doudna, Jennifer (biochemist) CCR5 research discovery of CRISPR gene editing encounters with Jiankui He Down syndrome drug pricing for AZT for HIV medicine by Novartis by Sangamo Therapeutics Editas Medicine Edwards, Robert (physiologist) enhancement He, Jiankui, on See also eugenics; genes; genotype Epstein, Steven (sociologist) Ethereum (cryptocurrency) ethics animal welfare and art and China and Christian values commercial enterprises and cyborg disability and environmental genetic engineering and of He, Jiankui human dignity and human enhancement and human subjects and imagination and justice and misconduct mutant queer regulation of relational reproductive medicine and shock art and technology and See also ableism; activists; conflicts of interest; informed consent eugenics (good genes) Cold Spring Harbor Laboratory disabled people and genetic testing and HIV and immigrants and military and Nazis and sterilization and Watson, James and yousheng (“high-quality birth”) See also disability; queer; race Eugenics Record Office euthanasia experimental volunteers Anne (pseudonym, Dr.

pages: 829 words: 187,394

The Price of Time: The Real Story of Interest
by Edward Chancellor
Published 15 Aug 2022

Like the ‘bubble companies’ of 1720 that promised to extract silver from lead or to build an air pump for the brain, several of the new cryptos were spoofs. But that didn’t stop the Useless Ethereum Token, whose logo showed the middle finger and whose website advised ‘don’t buy these tokens’, rising five times faster than Bitcoin in the giddy last months of 2017.25 Just as internet companies were spun off from their parents during the Dotcom bubble, so ‘forks’ in the blockchain created yet more coins. Bitcoin laid two eggs, Bitcoin Cash and Bitcoin Gold. Towards the end of the year, the leading crypto’s market value surpassed many of the world’s largest companies, including Boeing, Toyota and McDonald’s.

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D., 163–4 Röpke, Wilhelm, 97, 100, 299 Rothbard, Murray, 30 Rothermere, Lord, 93 Roubini, Nouriel, 207, 254 Rousseff, Dilma, 258 Rucellai, Giovanni, 21 Rueff, Jacques, 85, 91, 115‡, 251 Ruskin, John, 180–81 Sainsbury’s (British grocery chain), 160 Saint-Simon, Louis de Rouvroy, Duke of, 50–51, 52, 57 Samuelson, Paul, 246–7 Sarkozy, Nicolas, 292 Savills (property consultants), 174 saving: bonus of compound interest, 190; China’s savings glut, 268–9; as deferred gratification, 29, 188–90; and interest, xxiv, 44, 77, 188–93, 194–9, 205–6; interest as ‘wages of abstinence’, xxiv, xxv, 188–91; savings glut hypothesis, 115–16, 117, 126, 128–9, 132, 191, 252; Terborgh on, 125* savings & loan crisis, US, 111, 145 Say, Jean-Baptiste, 99 Sbrancia, Maria Belen, 290 Scandinavian banking crisis (early 1990s), 136 Schacht, Hjalmar, 82, 92, 312 Schäuble, Wolfgang, 299 Scheidel, Walter, 204 Schumpeter, Joseph, 16, 32, 46, 95, 218; Capitalism, Socialism and Democracy (1942), 126, 140, 296–7; ‘creative destruction’ idea, xx, 140–43, 153, 296–7; on deflation, 100; History of Economic Analysis, xviii; view of intellectuals, 297 Schwartz, Anna, 98, 99, 105, 116 Schwarzman, Steven, 207 Sears (department store), 169–70 secular stagnation, 77, 124–8, 131, 132–9, 151, 205–6 Sée, Henri Eugene, Modern Capitalism (1928), 28* Seneca the Younger, 20–21 Senior, Nassau, 188, 191 Senn, Martin, 193 shadow banks: in Canada, 174–5; in China, 266, 270, 282*, 283–5, 286; collapse in subprime crisis, 221, 283; illiquid products, 226–7; re-emergence after 2008 crisis, 221, 227, 231, 233; structured finance products, 116, 227, 283–5; Trust companies as precursors of, 84*; types of, 221; ‘Ultra-short’ bond exchange-traded funds (ETFs), 227 Shaftesbury, Anthony Ashley Cooper, Earl of, 27 ‘shareholder value’ philosophy, 163–6, 167, 170–71 Shaw, Edward, 286 Shaw, Leslie, 83, 83* Shiba Inu (cryptocurrency), 308 Shin, Hyun Song, 254, 263 Shiyan, Hubei province, 275 Silicon Valley, 148, 151, 173, 176, 204 Silver, Morris, 7, 11 Singer, Paul, 185, 246 Smith, Adam, 14, 174; on monopolies, 162, 298; view of interest, 27, 27*, 31, 183; on wealth, 181; The Wealth of Nations (1776), xxii, 27–8, 27*, 31 Smithers, Andrew, Productivity and the Bonus Culture (2019), 152* Smoot–Hawley Act (1930), 261 socialism, 188, 297, 298 Soddy, Frederick, 181, 242 Solon the ‘Lawgiver’, 9, 18 Solow, Bob, 128 Somary, Felix, 94–5, 308 Sombart, Werner, Modern Capitalism, 22* Soros, George, 148*, 273, 283 South Africa, 258 South America: loans/securities from, 77, 79–80; precious metals from, 49, 168; speculation in bonds from, 64, 65–6, 91; trade during Napoleonic Wars, 70 South Korea, 267 South Sea Bubble (1720), 62, 65*, 68, 69, 307 Soviet Union, 278 Spain, 144–5, 147, 168, 213, 253, 279; mortgage bonds (cédulas), 117 Special Purpose Acquisition Companies (SPACs), 307 speculative manias, xxiii; Borio on, 135; and cryptocurrencies, 177–9; ‘hyperbolic discounting’ during, 176–7; in period from 1630s to 1840s, 64–6, 67–72, 73, 74, 75–6, 77–8, 79–80; technology companies in post-crisis years, 176–9; before Wall Street Crash (1929), 91 see also Mississippi bubble Spencer, Grant, 177 Sraffa, Piero, 42 St Ambrose, 18 St Augustine, 18–19, 202 St Bonaventure, 19 Stable Money League/Association, 87, 96 Standard Oil, 157 state capitalism, 280, 284, 292–5, 297, 298 Stefanel (Italian clothing company), 147 Stein, Jeremy, 231, 233 Steuart, Sir James, 53, 273 ‘sticky prices’ theory, 87* Strong, Benjamin, 82–3, 86–8, 90*, 92, 93, 98, 112 Stuckey’s Bank, 63, 66–7 subprime mortgage crisis, xxii, 114, 116, 117–18, 131, 211, 292; produces ‘dash for cash’, 227; unwinding of carry trades during, 221, 227 Suetonius, The Twelve Caesars, 12 Suez Canal, 78 Sumerian civilization, 4, 6, 8, 15 Summers, Larry, 124–5, 127, 129, 185, 230, 230*, 235, 302 Sumner, William Graham, ‘Forgotten Man’, xx, xxii, 198 Susa, Henry of, 25 Svensson, Lars, 247 Sweden, 174, 241, 242, 244, 245, 247, 294 Sweezy, Paul, 156 Swiss National Bank, 172–3, 293–4 Switzerland, 172, 174, 226, 233, 241, 244, 245 Sydney (Australia), 175 Sylla, Richard, 4, 11, 68, 109 Tacitus, 20–21 Tasker, Peter, 271 Tawney, R.H., 201 tax structures, 164; offshore tax havens, 210 Taylor, John, 116–17, 129, 252 Tencent, 283 Tencin, Claudine Alexandrine Guérin, Madame de, 51 Terborgh, George, 125–6, 127 Tesla, 176–7 Theranos, 149 Thiel, Peter, 263 Third Avenue (investment company), 227–8 Thornton, Daniel, 192 Thornton, Henry, 41–2, 66*, 70, 75 Thornton, Henry Sykes, 66* Tiberius, Roman Emperor, 12 time, concept of, xviii; and act of saving, 188–90; canonical ‘hours’, 21; and Lewis Carroll, 309; in era of ultra-low interest rates, 59, 177; Franklin on, xviii, 22, 28; and Hayek, 32; interest as ‘time value of money’, xxiv, xxv–xxvi, 10, 14–15, 16, 20, 22, 26–7, 28–32; Lord King’s ‘paradox of policy’, 194, 230*; the Marshmallow Test, 29, 189; and medieval scholars, 19–20; Renaissance writings on, 21; secularization of, 21–2; speculators’ misunderstanding of, 59; and thought in ancient world, 20–21; time as individual’s possession, 20, 21, 25; ‘time in production’, xxiv, 14–15, 16, 22, 95, 95†, 141; ‘time preference’ theory, xxiv*, 28–32, 42, 95, 188–9; Thomas Wilson’s ideas, 26–7, 28, 30 Time-Warner, 167 Tooke, Thomas, 69 Toporowski, Jan, 167 Torrens, Robert, 66 Toys ‘R’ Us, 169 trade and commerce: in ancient world, 6, 7–8, 12, 14, 15; Atlantic trade, 59; business partnerships (commenda, societas), 26; commercial classes/interests, 35, 36–7, 38–40, 41, 43, 44, 66–7; commercial importance of time, xviii, 15–16, 21, 22; emergence of modern trade cycle, 62–4; expansion of in Middle Ages, 19, 21–3, 25–6; international trade, 6, 15, 23, 24, 59, 252–3, 261–2; and Italian Renaissance, 21; in medieval Italy, 21–3; mercantile/shipping loans, 6, 12, 14, 22–3, 26, 219 TransAmerica Life Insurance, 199* Trichet, Jean-Claude, 239 Trollope, Anthony, The Way We Live Now, 73 Truman, Harry, 84 The Truman Show (Peter Weir film, 1998), 185–7 Trump, Donald, 185, 261, 262, 291–2, 299, 304, 310 trusts/monopolies: in early twentieth century Europe, 159; Lenin on, 159–60; merger ‘tsunami’ after 2008 crisis, 160–63, 161*, 168–70, 182–3, 237, 298; ‘platform companies’, 161; Adam Smith on, 162, 298; in US robber baron era, 156, 157–9, 203 tulip mania (1630s), 68 Tunisia, 255 Turgot, Anne-Robert Jacques, 15, 28–9, 30, 218 Turkey, xxiii, 252, 258–60, 263 Turkmenistan, 262 Turner, Adair, 292 TXU (energy company), 162 Uber, 149, 150 ‘unicorn’ start-up companies, 148–50, 153, 155, 173, 176–7 Union Pacific Railroad, 157, 158 United States: as bubble economy, 184–7; credit expansion of 1920s, 87–91, 92–4, 96–8, 112, 203; Democrats’ Green New Deal policy, 302; economic expansion (1929–41), 143; economy in Bretton Woods era, 291, 302; financial crisis (1873), 157; foreign securities/loans in 1920s, 91; inflation in 1970s, 108–9; Knickerbocker Panic (1907), 83–4; large-scale immigration into, 78; loan of farm animals in, 4; long-term interest rates (1945–2021), 134; loss of manufacturing jobs to China, 261*, 261; low economic vitality in post-crisis decade, 124, 150–53, 191; monetary policy in 1900s, 83–4, 83*; post-Second World War recovery, 126; public debt today, 291–2, 291*; recessions of early 1980s, 109–10, 151; reversal of global capital flows (late-1920s), 93; robber baron era, 156–9, 203; shift from manufacturing towards services, 167–8, 182; and zombification, 146, 152–3, 155 see also Federal Reserve, US United States Steel Corporation, 157–8 Universities Superannuation Scheme, UK, 196 Useless Ethereum Token, 178 usury: attacked from left and right, 17; attitudes to in ancient world, 17–18, 19, 20–21, 219; in Britain, 24, 26–7, 34, 40, 42, 65‡, 65; Church law forbids, 18–19, 23–4; definitions in Elizabethan era, 26–7; etymology of word, 5; Galiani on, 218–19, 220, 221; and Jews, 18; Marx on, 16, 200–201; medieval Church acknowledges risk, 25–6; Old Testament restrictions on, 17; Proudhon-Bastiat debate on, xvii–xix, xxi, xxii, xxv, 9; in Renaissance world, 22–3; scholastic attack on, 18–20, 23–4, 25 Valeant Pharmaceuticals, 161, 168–9 Vancouver, 175 Veblen, Thorstein, Theory of Business Enterprise (1904), 158, 159, 166 Velde, François, 58*, 59 Venice, 22, 23 Vinci, Leonardo da, Salvator Mundi, 208–9 VIX index, 228–9, 254 La Voix du Peuple, xvii–xix volatility, 153, 228–30, 233, 234, 254, 304, 305 Volcker, Paul, 108–9, 121, 145, 184, 240 Voltaire, 57 Wainwright, Oliver, 209 Waldman, Steve, 206 Waldorf Astoria, New York, 285–6 Wall Street Crash (October 1929): Fed’s response to, 98, 100, 101, 108; Fisher and Keynes fail to foresee, 94–5; Hayek’s interpretation of, 101, 105; low real rates in 1920s USA, 87–91, 89, 92–4, 96–8, 203; low/stable inflation at time of, 134; monetarist view of, 98–9, 101, 105, 108; predictions/warnings of, 93–5, 96, 101, 105, 308; reversal of international capital flows (late-1920s), 93, 93*, 261 WallStreetBets, 307, 309 Walpole, Horace, 62–3 Warburg, Paul, 94 Warsh, Kevin, 228 wealth: ‘Buddenbrooks effect’, 216; conspicuous consumption by mega-rich, 54–5, 208–10, 212; definitions of, 179–82, 216; elite displays as signs of inequality, 209–10, 212; virtual wealth bubbles, 179, 180, 181–2, 185, 193–5, 206, 215, 216–17, 217†, 229–30, 237; wealth illusion, 193–5, 198 Welch, Jack, 170, 171 Wells, H.

…

During an outbreak of ‘marijuana madness’ in September 2018, a Canadian cannabis producer was briefly valued at more than American Airlines.21 CRYPTO BUBBLES As the world’s financial system imploded in the summer of 2008, an anonymous software engineer circulated a paper containing a cure for all monetary ills. Commercial banks had shown they couldn’t be trusted with money. Central banks were oiling their printing presses, ready to debase their currencies. The solution was to use the internet to create ‘a new electronic cash system that’s fully peer-to-peer’. Once the distributed ledger, or blockchain, was in place financial trust would be restored and monetary crises come to an end.22 Things didn’t turn out quite as Bitcoin’s mystery creator, Satoshi Nakamoto, envisaged. What he had unleashed was not so much a new type of money, but rather the most perfect object of speculation the world had ever seen.

pages: 330 words: 91,805

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism
by Robin Chase
Published 14 May 2015

Budding companies—LaZooz, Swarm, and Ethereum, to name three—are fleshing out this idea. Any kind of business or personal transaction needing standard contracts, for standard actions, with standard rules could all be sent, appended, and amended using the block chain. Every transaction can have its own special combination built using standard building blocks. Rule-making and standards adoption is all accomplished through decentralized leader-free “voting” based on market signals. All of the transactions on the public ledger are there for all to see, and open source. In the potentiality of block-chain visionaries, the most useful programs, contracts, and methods will be the ones that are most copied, eventually becoming standards.

…

In order to stay compatible with each other, all users need to use software complying with the same rules. Bitcoin can only work correctly with a complete consensus among all users. Therefore, all users and developers have a strong incentive to protect this consensus.22 While the block-chain protocol has necessarily evolved over the last six years, the evolution is driven by consensus, with the most suitable and widely adopted changes being the ones that win out over the alternatives. The block-chain process errs toward consensus and changes only for big improvements. This chapter has been about exploring ways to finance platforms without the involvement of government or the private sector.

…

We often pay for services this way: Cellphone use is paid by the minute or byte and Zipcar by the hour and mile using rates the company sets. Having a reward system that is adopted and applied by a decentralized group is more challenging and therefore more impressive. Can we allow for nuanced circumstances? How do we deal with arguments? Innovators are now repurposing the block-chain methodology for a much wider range of activities and providing rewards dynamically based on more localized circumstances. An Israeli startup, LaZooz, is using the block chain to build a ridesharing network. People sign up and download the app, which measures distances travelled, and provides the reward in Zooz tokens accordingly.

pages: 138 words: 40,525

This Is Not a Drill: An Extinction Rebellion Handbook
by Extinction Rebellion
Published 12 Jun 2019

But instead of being wired with a microphone or taken to a stage, I just sat there at a plain round table as my audience was brought to me: five super-wealthy guys – yes, all men – from the upper echelon of the hedge-fund world. After a bit of small talk, I realized they had no interest in the information I had prepared about the future of technology. They had come with questions of their own. They started out innocuously enough. Ethereum or bitcoin? Is quantum computing a real thing? Slowly but surely, however, they edged into their real topics of concern. Which region will be less impacted by the coming climate crisis: New Zealand or Alaska? Is Google really building Ray Kurzweil a home for his brain, and will his consciousness live through the transition, or will it die and be reborn as a whole new one?

…

It was by far the largest fee I had ever been offered for a talk – about half my annual professor’s salary – all to deliver some insight on the subject of ‘the future of technology’. I’ve never liked talking about the future. The Q&A sessions always end up more like parlour games where I’m asked to opine on the latest technology buzzwords as if they were ticker symbols for potential investments: blockchain, 3D printing, CRISPR. The audiences are rarely interested in learning about these technologies or their potential impacts beyond the binary choice of whether or not to invest in them. But money talks, so I took the gig. After I arrived, I was ushered into what I thought was the Green Room. But instead of being wired with a microphone or taken to a stage, I just sat there at a plain round table as my audience was brought to me: five super-wealthy guys – yes, all men – from the upper echelon of the hedge-fund world.

pages: 297 words: 108,353

Boom and Bust: A Global History of Financial Bubbles
by William Quinn and John D. Turner
Published 5 Aug 2020

Gelderblom, O. and Jonker, J. ‘Public ﬁnance and economic growth: the case of Holland in the seventeenth century’, Journal of Economic History, 71, 1–39, 2011. Gentzkow, M. and Shapiro, J. M. ‘Media bias and reputation’, Journal of Political Economy, 114, 280–316, 2006. Gerard, D. Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum and Smart Contracts, David Gerard (self-published), 2017. Gilmore, N. R. ‘Henry George Ward, British publicist for Mexican mines’, Paciﬁc Historical Review, 31, 35–47, 1963. Gissing, G. The Whirlpool. London: Penguin Classics, 2015. Gjerstad, S. and Smith, V. L. ‘Monetary policy, credit extension, and housing bubbles: 2008 and 1929’, Critical Review, 21, 269–300, 2009.

…

As banks recovered from the effects of the ﬁnancial crisis, credit was becoming more widely available. The continuing development of the Internet meant that the marketability of ﬁnancial assets was increasing. Although levels of speculation appeared to be relatively low, the bubble triangle suggested that a spark could change this very quickly. A spark soon arrived in the form of blockchain technology: an encryption technique that allowed virtual assets known as cryptocurrencies to circulate without being managed by any central authority. The most widely known cryptocurrency was bitcoin. To its advocates, bitcoin was the money of the future: it could not be devalued through inﬂation by a central bank, you could spend it on anything without having to worry about government interference or taxes, and it cut out the middleman, namely commercial banks.

…

Akerlof and Shiller, Animal Spirits, p. 55. 25. Shiller, Irrational Exuberance, p. 105. 26. Gentzkow and Shapiro, ‘Media bias and reputation’. 27. Dyck and Zingales, ‘The bubble and the media’. 28. Dyck and Zingales, ‘The bubble and the media’. 29. See, for example, Gerard, Attack; https://davidgerard.co.uk/blockchain/, last accessed 19 November 2019; www.coppolacomment.com/, last accessed 19 November 2019. One exception to the poor quality of news media coverage was the Financial Times’s Alphaville. 30. On the corrosive effect of television on public discourse, see Postman, Amusing Ourselves to Death. 31. Akerlof and Shiller, Phishing for Phools. 32.

pages: 352 words: 80,030

The New Silk Roads: The Present and Future of the World
by Peter Frankopan
Published 14 Jun 2018

There are real dangers in concentrating only on matters that are of parachial importance when so many other more significant and challenging problems require and demand attention. * The rapid development of new technologies is also a significant difficulty to address, in terms of trying to predict the impact these will have in the coming years – and working out how to prepare accordingly for a world where artificial intelligence (AI), robotics, machine learning, Blockchain, Ethereum and more will change the way we live, love, work and communicate. Then there are cryptocurrencies like Bitcoin, which, while exciting for digital pioneers, seem most obviously of interest to those who seek to keep their transactions secure and away from prying eyes – including those who deal in illicit substances or goods, or who prefer to keep potentially taxable revenue away from the authorities.

…

As well as everything else, the Silk Roads acted as ‘gene corridors’ for humans and for flora and fauna alike.6 Then there is new research that links the origins of Yiddish with commercial exchange across Asia and claims that its evolution was connected to measures designed to protect the security of transactions by devising a language that could only be understand by a select few.7 This has obvious resonance in the world of the twenty-first century, where crypto-currencies and blockchain technology seek to solve the problem of how to enable traders to complete transactions securely. Or there is the startling evidence from new-generation ice-core technology that can be used to shed fresh light on the devastating impact of the Black Death by showing the extent of the collapse in metal production in the mid-fourteenth century.8 Documents declassified in 2017 recording meetings held between the British minister in Washington in 1952, Sir Christopher Steel, and the assistant secretary of state Henry Byroade to discuss a coup to depose the prime minister of Iran help us gain a clearer understanding of how the ill-fated plans took shape.9 The release of previously secret US nuclear strike plans from the early part of the Cold War likewise help reveal important insights into American military and strategic planning – and contemporary assessments of how best to neutralise the Soviet Union in the event of war.10 These are just a small number of examples to show how historians continue to use different techniques to refine and improve their understanding of the past.

pages: 1,237 words: 227,370

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 16 Mar 2017

A transaction log can be made tamper-proof by periodically signing it with a hardware security module, but that does not guarantee that the right transactions went into the log in the first place. It would be interesting to use cryptographic tools to prove the integrity of a system in a way that is robust to a wide range of hardware and software issues, and even potentially malicious actions. Cryptocurrencies, blockchains, and distributed ledger technologies such as Bitcoin, Ethereum, Ripple, Stellar, and various others [71, 72, 73] have sprung up to explore this area. I am not qualified to comment on the merits of these technologies as currencies or mechanisms for agreeing contracts. However, from a data systems point of view they contain some interesting ideas.

…

multi-object transactions, need for, The need for multi-object transactions versus relational modelconvergence of models, Convergence of document and relational databases data locality, Data locality for queries document-partitioned indexes, Partitioning Secondary Indexes by Document, Summary, Building search indexes domain-driven design (DDD), Event Sourcing DRBD (Distributed Replicated Block Device), Leaders and Followers drift (clocks), Clock Synchronization and Accuracy Drill (query engine), The divergence between OLTP databases and data warehouses Druid (database), Deriving several views from the same event log Dryad (dataflow engine), Dataflow engines dual writes, problems with, Keeping Systems in Sync, Dataflow: Interplay between state changes and application code duplicates, suppression of, Duplicate suppression(see also idempotence) using a unique ID, Operation identifiers, Multi-partition request processing durability (transactions), Durability, Glossary duration (time), Unreliable Clocksmeasurement with monotonic clocks, Monotonic clocks dynamic partitioning, Dynamic partitioning dynamically typed languagesanalogy to schema-on-read, Schema flexibility in the document model code generation and, Code generation and dynamically typed languages Dynamo-style databases (see leaderless replication) E edges (in graphs), Graph-Like Data Models, Reduce-Side Joins and Groupingproperty graph model, Property Graphs edit distance (full-text search), Full-text search and fuzzy indexes effectively-once semantics, Fault Tolerance, Exactly-once execution of an operation(see also exactly-once semantics) preservation of integrity, Correctness of dataflow systems elastic systems, Approaches for Coping with Load Elasticsearch (search server)document-partitioned indexes, Partitioning Secondary Indexes by Document partition rebalancing, Fixed number of partitions percolator (stream search), Search on streams usage example, Thinking About Data Systems use of Lucene, Making an LSM-tree out of SSTables ElephantDB (database), Key-value stores as batch process output Elm (programming language), Designing Applications Around Dataflow, End-to-end event streams encodings (data formats), Encoding and Evolution-The Merits of SchemasAvro, Avro-Code generation and dynamically typed languages binary variants of JSON and XML, Binary encoding compatibility, Encoding and Evolutioncalling services, Data encoding and evolution for RPC using databases, Dataflow Through Databases-Archival storage using message-passing, Distributed actor frameworks defined, Formats for Encoding Data JSON, XML, and CSV, JSON, XML, and Binary Variants language-specific formats, Language-Specific Formats merits of schemas, The Merits of Schemas representations of data, Formats for Encoding Data Thrift and Protocol Buffers, Thrift and Protocol Buffers-Datatypes and schema evolution end-to-end argument, Cloud Computing and Supercomputing, The end-to-end argument-Applying end-to-end thinking in data systemschecking integrity, The end-to-end argument again publish/subscribe streams, End-to-end event streams enrichment (stream), Stream-table join (stream enrichment) Enterprise JavaBeans (EJB), The problems with remote procedure calls (RPCs) entities (see vertices) epoch (consensus algorithms), Epoch numbering and quorums epoch (Unix timestamps), Time-of-day clocks equi-joins, Reduce-Side Joins and Grouping erasure coding (error correction), MapReduce and Distributed Filesystems Erlang OTP (actor framework), Distributed actor frameworks error handlingfor network faults, Network Faults in Practice in transactions, Handling errors and aborts error-correcting codes, Cloud Computing and Supercomputing, MapReduce and Distributed Filesystems Esper (CEP engine), Complex event processing etcd (coordination service), Membership and Coordination Services-Membership serviceslinearizable operations, Implementing Linearizable Systems locks and leader election, Locking and leader election quorum reads, Implementing linearizable storage using total order broadcast service discovery, Service discovery use of Raft algorithm, Using total order broadcast, Distributed Transactions and Consensus Ethereum (blockchain), Tools for auditable data systems Ethernet (networks), Cloud Computing and Supercomputing, Unreliable Networks, Can we not simply make network delays predictable?packet checksums, Weak forms of lying, The end-to-end argument Etherpad (collaborative editor), Collaborative editing ethics, Doing the Right Thing-Legislation and self-regulationcode of ethics and professional practice, Doing the Right Thing legislation and self-regulation, Legislation and self-regulation predictive analytics, Predictive Analytics-Feedback loopsamplifying bias, Bias and discrimination feedback loops, Feedback loops privacy and tracking, Privacy and Tracking-Legislation and self-regulationconsent and freedom of choice, Consent and freedom of choice data as assets and power, Data as assets and power meaning of privacy, Privacy and use of data surveillance, Surveillance respect, dignity, and agency, Legislation and self-regulation, Summary unintended consequences, Doing the Right Thing, Feedback loops ETL (extract-transform-load), Data Warehousing, Example: analysis of user activity events, Keeping Systems in Sync, Glossaryuse of Hadoop for, Diversity of storage event sourcing, Event Sourcing-Commands and eventscommands and events, Commands and events comparison to change data capture, Event Sourcing comparison to lambda architecture, The lambda architecture deriving current state from event log, Deriving current state from the event log immutability and auditability, State, Streams, and Immutability, Designing for auditability large, reliable data systems, Operation identifiers, Correctness of dataflow systems Event Store (database), Event Sourcing event streams (see streams) events, Transmitting Event Streamsdeciding on total order of, The limits of total ordering deriving views from event log, Deriving several views from the same event log difference to commands, Commands and events event time versus processing time, Event time versus processing time, Microbatching and checkpointing, Unifying batch and stream processing immutable, advantages of, Advantages of immutable events, Designing for auditability ordering to capture causality, Ordering events to capture causality reads as, Reads are events too stragglers, Knowing when you’re ready, The lambda architecture timestamp of, in stream processing, Whose clock are you using, anyway?

…

In a system with multiple participating organizations, some participants may attempt to cheat or defraud others. In such circumstances, it is not safe for a node to simply trust another node’s messages, since they may be sent with malicious intent. For example, peer-to-peer networks like Bitcoin and other blockchains can be considered to be a way of getting mutually untrusting parties to agree whether a transaction happened or not, without relying on a central authority [83]. However, in the kinds of systems we discuss in this book, we can usually safely assume that there are no Byzantine faults. In your datacenter, all the nodes are controlled by your organization (so they can hopefully be trusted) and radiation levels are low enough that memory corruption is not a major problem.

pages: 903 words: 235,753

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

Speaking of reserve currencies, Bitcoin introduces addressable scarcity not in direct relation to the sum of mined minerals or national currencies, but by the mathematics of solving increasingly difficult problems toward an eventual arbitrary limit of 21 million “coins.” There is much to explore with Bitcoin, blockchains and related initiatives, such as Ethereum, but it is also the monetary platform of choice of secessionist projects for which the metaphysical expulsion of externalities is the paramount program, as important if not more than the disintermediation of central banks. The version of Bitcoin that we have (other currencies may fork or follow) is exemplary of the future-archaic quality of many Stack innovations.

…

See also platform economics Anthropocenic, 58, 103 of borders, 173 capitalist, 56 City layer, 159–160 Cloud model, 137 electronics, mining and trading in, 82–83 zero-sum, 336 economy of additive manufacturing, 202 of cognitive capital, 110, 116 computational, 328 computer-controlled, 58–60 of contemporary warfare, 248 digital, 196 of energy, 92, 106–107 Facebook, 127 Google, 136–138, 159, 444n26 of identity, 270 of information, 199 of mobility, 280 of prostheticization, 273 of reversible partitions, 21 of scarcity, 208 and sovereignty and territory, 114, 316 ecopolitics, 100 ecosystems, 129, 178, 185, 336, 456n6 Ecumenopolis (Dioxiadis), 178 Eisenman, Peter, 410n50 Elden, Stewart, 335, 379n12 electricity, 93, 95, 141 electronics, mining and trading in, 82–83 electronic waste, 83 Elysium (Blomkamp), 311, 323, 444n27 emergency accommodating, 103–104 designing for/designing with, 101–104, 321, 325 ecojurisdictions in response to, 99–100 ecological, 105–106, 295, 305 ecopolitics emerging by, 100 exceptional, 103–104, 173, 321–322 permanent, 104 progress in response to, 321 sovereign decision and, 20, 32, 102–103 state of, 32–33, 99 emergent, the, 9 empty space, 30, 380n20 enclosure and escape, 22, 32–33, 149–150, 172–176, 303 “end of history,” 321 “End of Sykes-Picot, The” 430n65 energy alternative sources, 259 ecologies of, 98–104 economy, 92 efficiencies, 140–141 energy-information network, 93, 95 footprint, of planetary-scale computing, 82–83, 92–96, 106–107, 113, 140–141, 258–260, 303–304 grid, 92–96, 140, 152, 201, 294–295 needs, predicted, 113 political loyalty and availability, 141 polities, subdivided, 99–100 post-Anthropocene, 217 Eneropa, 99 Engelbart, Douglas, 343 Enlightenment, 251, 426n46 entertaining securitization, 156 entertainment identifier registry (EIDR), 207 entrance/exit, 149–150, 313, 315, 317, 371 envelope-interface borders, 172–173 envelopes airports, 156–157 architectural, 23, 165–167, 195, 303, 311–313, 323 cars as, 238 City layer, 12, 70, 148–149, 152, 154–155, 163–171, 173 as interface, 167 mixed, designing for, 168–172 paper, 46, 195 physical, 167 software, 167 urban, 168–172 urban platform, 180 User position in relation to, 252 environmental migrants/refugees, 100–101 Epicureanism, inverse, 358 equilibrium, cybernetic, 59, 158 error detection, 50 espionage, 398n21 Estates of The Oaks, 311 Estonia, 399n36, 446n42 Ethereum, 336 ethics, 258, 285, 362 European nomos of World Wars I and II, 25–26 European Space Agency, 181 European Union, 309 exception in Apple model, 130 automation of, 33 becomes the rule, 102–103, 111 emergency, 103–104, 173, 321–322 emergent order of, 110–111 inside and outside the law, 102 interface, 357 legitimate state of, 104 normalizing, 23, 32–33, 39, 103–104 reversibility of, 21, 32–33, 39, 145 sovereignty over, 20–21, 32, 105, 341 state of emergency as, 99, 105 territories of, 114 exclusion agency and, 173–175 augmented reality and, 236, 241–242 elective, 316–317 societal, 308–309, 311–312, 317 exclusive totalities, 245 exit.

…

We can only anticipate what forms of high weirdness will ensue, as the paired computerization of matter-into-monies (i.e., carbon credits trading, where the value of money is itself measured in carbon) and monies-into-virtuality (i.e., the light pulses of high-speed trading) continues to evolve and accelerate.8 New addressing schemes to locate and coordinate instances of value are multiplying, both as generic currency (bitcoin blockchains) and as platforms for brokering things-with-value (various sharing economy schemes). At stake in all this is also the design of the economy of information itself, from the smallest-scale object or gesture to the largest topological frameworks, and interrelations across scales by drawing and managing an orthodox map in the form of an address table.9 What gets to count and to whom, and who profits from merely counting?

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 17 Apr 2017

A transaction log can be made tamper-proof by periodically signing it with a hardware security module, but that does not guarantee that the right transactions went into the log in the first place. It would be interesting to use cryptographic tools to prove the integrity of a system in a way that is robust to a wide range of hardware and software issues, and even poten‐ tially malicious actions. Cryptocurrencies, blockchains, and distributed ledger tech‐ nologies such as Bitcoin, Ethereum, Ripple, Stellar, and various others [71, 72, 73] have sprung up to explore this area. I am not qualified to comment on the merits of these technologies as currencies or mechanisms for agreeing contracts. However, from a data systems point of view they contain some interesting ideas.

…

The opposite of bounded. 558 | Glossary Index A aborts (transactions), 222, 224 in two-phase commit, 356 performance of optimistic concurrency con‐ trol, 266 retrying aborted transactions, 231 abstraction, 21, 27, 222, 266, 321 access path (in network model), 37, 60 accidental complexity, removing, 21 accountability, 535 ACID properties (transactions), 90, 223 atomicity, 223, 228 consistency, 224, 529 durability, 226 isolation, 225, 228 acknowledgements (messaging), 445 active/active replication (see multi-leader repli‐ cation) active/passive replication (see leader-based rep‐ lication) ActiveMQ (messaging), 137, 444 distributed transaction support, 361 ActiveRecord (object-relational mapper), 30, 232 actor model, 138 (see also message-passing) comparison to Pregel model, 425 comparison to stream processing, 468 Advanced Message Queuing Protocol (see AMQP) aerospace systems, 6, 10, 305, 372 aggregation data cubes and materialized views, 101 in batch processes, 406 in stream processes, 466 aggregation pipeline query language, 48 Agile, 22 minimizing irreversibility, 414, 497 moving faster with confidence, 532 Unix philosophy, 394 agreement, 365 (see also consensus) Airflow (workflow scheduler), 402 Ajax, 131 Akka (actor framework), 139 algorithms algorithm correctness, 308 B-trees, 79-83 for distributed systems, 306 hash indexes, 72-75 mergesort, 76, 402, 405 red-black trees, 78 SSTables and LSM-trees, 76-79 all-to-all replication topologies, 175 AllegroGraph (database), 50 ALTER TABLE statement (SQL), 40, 111 Amazon Dynamo (database), 177 Amazon Web Services (AWS), 8 Kinesis Streams (messaging), 448 network reliability, 279 postmortems, 9 RedShift (database), 93 S3 (object storage), 398 checking data integrity, 530 amplification of bias, 534 of failures, 364, 495 Index | 559 of tail latency, 16, 207 write amplification, 84 AMQP (Advanced Message Queuing Protocol), 444 (see also messaging systems) comparison to log-based messaging, 448, 451 message ordering, 446 analytics, 90 comparison to transaction processing, 91 data warehousing (see data warehousing) parallel query execution in MPP databases, 415 predictive (see predictive analytics) relation to batch processing, 411 schemas for, 93-95 snapshot isolation for queries, 238 stream analytics, 466 using MapReduce, analysis of user activity events (example), 404 anti-caching (in-memory databases), 89 anti-entropy, 178 Apache ActiveMQ (see ActiveMQ) Apache Avro (see Avro) Apache Beam (see Beam) Apache BookKeeper (see BookKeeper) Apache Cassandra (see Cassandra) Apache CouchDB (see CouchDB) Apache Curator (see Curator) Apache Drill (see Drill) Apache Flink (see Flink) Apache Giraph (see Giraph) Apache Hadoop (see Hadoop) Apache HAWQ (see HAWQ) Apache HBase (see HBase) Apache Helix (see Helix) Apache Hive (see Hive) Apache Impala (see Impala) Apache Jena (see Jena) Apache Kafka (see Kafka) Apache Lucene (see Lucene) Apache MADlib (see MADlib) Apache Mahout (see Mahout) Apache Oozie (see Oozie) Apache Parquet (see Parquet) Apache Qpid (see Qpid) Apache Samza (see Samza) Apache Solr (see Solr) Apache Spark (see Spark) 560 | Index Apache Storm (see Storm) Apache Tajo (see Tajo) Apache Tez (see Tez) Apache Thrift (see Thrift) Apache ZooKeeper (see ZooKeeper) Apama (stream analytics), 466 append-only B-trees, 82, 242 append-only files (see logs) Application Programming Interfaces (APIs), 5, 27 for batch processing, 403 for change streams, 456 for distributed transactions, 361 for graph processing, 425 for services, 131-136 (see also services) evolvability, 136 RESTful, 133 SOAP, 133 application state (see state) approximate search (see similarity search) archival storage, data from databases, 131 arcs (see edges) arithmetic mean, 14 ASCII text, 119, 395 ASN.1 (schema language), 127 asynchronous networks, 278, 553 comparison to synchronous networks, 284 formal model, 307 asynchronous replication, 154, 553 conflict detection, 172 data loss on failover, 157 reads from asynchronous follower, 162 Asynchronous Transfer Mode (ATM), 285 atomic broadcast (see total order broadcast) atomic clocks (caesium clocks), 294, 295 (see also clocks) atomicity (concurrency), 553 atomic increment-and-get, 351 compare-and-set, 245, 327 (see also compare-and-set operations) replicated operations, 246 write operations, 243 atomicity (transactions), 223, 228, 553 atomic commit, 353 avoiding, 523, 528 blocking and nonblocking, 359 in stream processing, 360, 477 maintaining derived data, 453 for multi-object transactions, 229 for single-object writes, 230 auditability, 528-533 designing for, 531 self-auditing systems, 530 through immutability, 460 tools for auditable data systems, 532 availability, 8 (see also fault tolerance) in CAP theorem, 337 in service level agreements (SLAs), 15 Avro (data format), 122-127 code generation, 127 dynamically generated schemas, 126 object container files, 125, 131, 414 reader determining writer’s schema, 125 schema evolution, 123 use in Hadoop, 414 awk (Unix tool), 391 AWS (see Amazon Web Services) Azure (see Microsoft) B B-trees (indexes), 79-83 append-only/copy-on-write variants, 82, 242 branching factor, 81 comparison to LSM-trees, 83-85 crash recovery, 82 growing by splitting a page, 81 optimizations, 82 similarity to dynamic partitioning, 212 backpressure, 441, 553 in TCP, 282 backups database snapshot for replication, 156 integrity of, 530 snapshot isolation for, 238 use for ETL processes, 405 backward compatibility, 112 BASE, contrast to ACID, 223 bash shell (Unix), 70, 395, 503 batch processing, 28, 389-431, 553 combining with stream processing lambda architecture, 497 unifying technologies, 498 comparison to MPP databases, 414-418 comparison to stream processing, 464 comparison to Unix, 413-414 dataflow engines, 421-423 fault tolerance, 406, 414, 422, 442 for data integration, 494-498 graphs and iterative processing, 424-426 high-level APIs and languages, 403, 426-429 log-based messaging and, 451 maintaining derived state, 495 MapReduce and distributed filesystems, 397-413 (see also MapReduce) measuring performance, 13, 390 outputs, 411-413 key-value stores, 412 search indexes, 411 using Unix tools (example), 391-394 Bayou (database), 522 Beam (dataflow library), 498 bias, 534 big ball of mud, 20 Bigtable data model, 41, 99 binary data encodings, 115-128 Avro, 122-127 MessagePack, 116-117 Thrift and Protocol Buffers, 117-121 binary encoding based on schemas, 127 by network drivers, 128 binary strings, lack of support in JSON and XML, 114 BinaryProtocol encoding (Thrift), 118 Bitcask (storage engine), 72 crash recovery, 74 Bitcoin (cryptocurrency), 532 Byzantine fault tolerance, 305 concurrency bugs in exchanges, 233 bitmap indexes, 97 blockchains, 532 Byzantine fault tolerance, 305 blocking atomic commit, 359 Bloom (programming language), 504 Bloom filter (algorithm), 79, 466 BookKeeper (replicated log), 372 Bottled Water (change data capture), 455 bounded datasets, 430, 439, 553 (see also batch processing) bounded delays, 553 in networks, 285 process pauses, 298 broadcast hash joins, 409 Index | 561 brokerless messaging, 442 Brubeck (metrics aggregator), 442 BTM (transaction coordinator), 356 bulk synchronous parallel (BSP) model, 425 bursty network traffic patterns, 285 business data processing, 28, 90, 390 byte sequence, encoding data in, 112 Byzantine faults, 304-306, 307, 553 Byzantine fault-tolerant systems, 305, 532 Byzantine Generals Problem, 304 consensus algorithms and, 366 C caches, 89, 553 and materialized views, 101 as derived data, 386, 499-504 database as cache of transaction log, 460 in CPUs, 99, 338, 428 invalidation and maintenance, 452, 467 linearizability, 324 CAP theorem, 336-338, 554 Cascading (batch processing), 419, 427 hash joins, 409 workflows, 403 cascading failures, 9, 214, 281 Cascalog (batch processing), 60 Cassandra (database) column-family data model, 41, 99 compaction strategy, 79 compound primary key, 204 gossip protocol, 216 hash partitioning, 203-205 last-write-wins conflict resolution, 186, 292 leaderless replication, 177 linearizability, lack of, 335 log-structured storage, 78 multi-datacenter support, 184 partitioning scheme, 213 secondary indexes, 207 sloppy quorums, 184 cat (Unix tool), 391 causal context, 191 (see also causal dependencies) causal dependencies, 186-191 capturing, 191, 342, 494, 514 by total ordering, 493 causal ordering, 339 in transactions, 262 sending message to friends (example), 494 562 | Index causality, 554 causal ordering, 339-343 linearizability and, 342 total order consistent with, 344, 345 consistency with, 344-347 consistent snapshots, 340 happens-before relationship, 186 in serializable transactions, 262-265 mismatch with clocks, 292 ordering events to capture, 493 violations of, 165, 176, 292, 340 with synchronized clocks, 294 CEP (see complex event processing) certificate transparency, 532 chain replication, 155 linearizable reads, 351 change data capture, 160, 454 API support for change streams, 456 comparison to event sourcing, 457 implementing, 454 initial snapshot, 455 log compaction, 456 changelogs, 460 change data capture, 454 for operator state, 479 generating with triggers, 455 in stream joins, 474 log compaction, 456 maintaining derived state, 452 Chaos Monkey, 7, 280 checkpointing in batch processors, 422, 426 in high-performance computing, 275 in stream processors, 477, 523 chronicle data model, 458 circuit-switched networks, 284 circular buffers, 450 circular replication topologies, 175 clickstream data, analysis of, 404 clients calling services, 131 pushing state changes to, 512 request routing, 214 stateful and offline-capable, 170, 511 clocks, 287-299 atomic (caesium) clocks, 294, 295 confidence interval, 293-295 for global snapshots, 294 logical (see logical clocks) skew, 291-294, 334 slewing, 289 synchronization and accuracy, 289-291 synchronization using GPS, 287, 290, 294, 295 time-of-day versus monotonic clocks, 288 timestamping events, 471 cloud computing, 146, 275 need for service discovery, 372 network glitches, 279 shared resources, 284 single-machine reliability, 8 Cloudera Impala (see Impala) clustered indexes, 86 CODASYL model, 36 (see also network model) code generation with Avro, 127 with Thrift and Protocol Buffers, 118 with WSDL, 133 collaborative editing multi-leader replication and, 170 column families (Bigtable), 41, 99 column-oriented storage, 95-101 column compression, 97 distinction between column families and, 99 in batch processors, 428 Parquet, 96, 131, 414 sort order in, 99-100 vectorized processing, 99, 428 writing to, 101 comma-separated values (see CSV) command query responsibility segregation (CQRS), 462 commands (event sourcing), 459 commits (transactions), 222 atomic commit, 354-355 (see also atomicity; transactions) read committed isolation, 234 three-phase commit (3PC), 359 two-phase commit (2PC), 355-359 commutative operations, 246 compaction of changelogs, 456 (see also log compaction) for stream operator state, 479 of log-structured storage, 73 issues with, 84 size-tiered and leveled approaches, 79 CompactProtocol encoding (Thrift), 119 compare-and-set operations, 245, 327 implementing locks, 370 implementing uniqueness constraints, 331 implementing with total order broadcast, 350 relation to consensus, 335, 350, 352, 374 relation to transactions, 230 compatibility, 112, 128 calling services, 136 properties of encoding formats, 139 using databases, 129-131 using message-passing, 138 compensating transactions, 355, 461, 526 complex event processing (CEP), 465 complexity distilling in theoretical models, 310 hiding using abstraction, 27 of software systems, managing, 20 composing data systems (see unbundling data‐ bases) compute-intensive applications, 3, 275 concatenated indexes, 87 in Cassandra, 204 Concord (stream processor), 466 concurrency actor programming model, 138, 468 (see also message-passing) bugs from weak transaction isolation, 233 conflict resolution, 171, 174 detecting concurrent writes, 184-191 dual writes, problems with, 453 happens-before relationship, 186 in replicated systems, 161-191, 324-338 lost updates, 243 multi-version concurrency control (MVCC), 239 optimistic concurrency control, 261 ordering of operations, 326, 341 reducing, through event logs, 351, 462, 507 time and relativity, 187 transaction isolation, 225 write skew (transaction isolation), 246-251 conflict-free replicated datatypes (CRDTs), 174 conflicts conflict detection, 172 causal dependencies, 186, 342 in consensus algorithms, 368 in leaderless replication, 184 Index | 563 in log-based systems, 351, 521 in nonlinearizable systems, 343 in serializable snapshot isolation (SSI), 264 in two-phase commit, 357, 364 conflict resolution automatic conflict resolution, 174 by aborting transactions, 261 by apologizing, 527 convergence, 172-174 in leaderless systems, 190 last write wins (LWW), 186, 292 using atomic operations, 246 using custom logic, 173 determining what is a conflict, 174, 522 in multi-leader replication, 171-175 avoiding conflicts, 172 lost updates, 242-246 materializing, 251 relation to operation ordering, 339 write skew (transaction isolation), 246-251 congestion (networks) avoidance, 282 limiting accuracy of clocks, 293 queueing delays, 282 consensus, 321, 364-375, 554 algorithms, 366-368 preventing split brain, 367 safety and liveness properties, 365 using linearizable operations, 351 cost of, 369 distributed transactions, 352-375 in practice, 360-364 two-phase commit, 354-359 XA transactions, 361-364 impossibility of, 353 membership and coordination services, 370-373 relation to compare-and-set, 335, 350, 352, 374 relation to replication, 155, 349 relation to uniqueness constraints, 521 consistency, 224, 524 across different databases, 157, 452, 462, 492 causal, 339-348, 493 consistent prefix reads, 165-167 consistent snapshots, 156, 237-242, 294, 455, 500 (see also snapshots) 564 | Index crash recovery, 82 enforcing constraints (see constraints) eventual, 162, 322 (see also eventual consistency) in ACID transactions, 224, 529 in CAP theorem, 337 linearizability, 324-338 meanings of, 224 monotonic reads, 164-165 of secondary indexes, 231, 241, 354, 491, 500 ordering guarantees, 339-352 read-after-write, 162-164 sequential, 351 strong (see linearizability) timeliness and integrity, 524 using quorums, 181, 334 consistent hashing, 204 consistent prefix reads, 165 constraints (databases), 225, 248 asynchronously checked, 526 coordination avoidance, 527 ensuring idempotence, 519 in log-based systems, 521-524 across multiple partitions, 522 in two-phase commit, 355, 357 relation to consensus, 374, 521 relation to event ordering, 347 requiring linearizability, 330 Consul (service discovery), 372 consumers (message streams), 137, 440 backpressure, 441 consumer offsets in logs, 449 failures, 445, 449 fan-out, 11, 445, 448 load balancing, 444, 448 not keeping up with producers, 441, 450, 502 context switches, 14, 297 convergence (conflict resolution), 172-174, 322 coordination avoidance, 527 cross-datacenter, 168, 493 cross-partition ordering, 256, 294, 348, 523 services, 330, 370-373 coordinator (in 2PC), 356 failure, 358 in XA transactions, 361-364 recovery, 363 copy-on-write (B-trees), 82, 242 CORBA (Common Object Request Broker Architecture), 134 correctness, 6 auditability, 528-533 Byzantine fault tolerance, 305, 532 dealing with partial failures, 274 in log-based systems, 521-524 of algorithm within system model, 308 of compensating transactions, 355 of consensus, 368 of derived data, 497, 531 of immutable data, 461 of personal data, 535, 540 of time, 176, 289-295 of transactions, 225, 515, 529 timeliness and integrity, 524-528 corruption of data detecting, 519, 530-533 due to pathological memory access, 529 due to radiation, 305 due to split brain, 158, 302 due to weak transaction isolation, 233 formalization in consensus, 366 integrity as absence of, 524 network packets, 306 on disks, 227 preventing using write-ahead logs, 82 recovering from, 414, 460 Couchbase (database) durability, 89 hash partitioning, 203-204, 211 rebalancing, 213 request routing, 216 CouchDB (database) B-tree storage, 242 change feed, 456 document data model, 31 join support, 34 MapReduce support, 46, 400 replication, 170, 173 covering indexes, 86 CPUs cache coherence and memory barriers, 338 caching and pipelining, 99, 428 increasing parallelism, 43 CRDTs (see conflict-free replicated datatypes) CREATE INDEX statement (SQL), 85, 500 credit rating agencies, 535 Crunch (batch processing), 419, 427 hash joins, 409 sharded joins, 408 workflows, 403 cryptography defense against attackers, 306 end-to-end encryption and authentication, 519, 543 proving integrity of data, 532 CSS (Cascading Style Sheets), 44 CSV (comma-separated values), 70, 114, 396 Curator (ZooKeeper recipes), 330, 371 curl (Unix tool), 135, 397 cursor stability, 243 Cypher (query language), 52 comparison to SPARQL, 59 D data corruption (see corruption of data) data cubes, 102 data formats (see encoding) data integration, 490-498, 543 batch and stream processing, 494-498 lambda architecture, 497 maintaining derived state, 495 reprocessing data, 496 unifying, 498 by unbundling databases, 499-515 comparison to federated databases, 501 combining tools by deriving data, 490-494 derived data versus distributed transac‐ tions, 492 limits of total ordering, 493 ordering events to capture causality, 493 reasoning about dataflows, 491 need for, 385 data lakes, 415 data locality (see locality) data models, 27-64 graph-like models, 49-63 Datalog language, 60-63 property graphs, 50 RDF and triple-stores, 55-59 query languages, 42-48 relational model versus document model, 28-42 data protection regulations, 542 data systems, 3 about, 4 Index | 565 concerns when designing, 5 future of, 489-544 correctness, constraints, and integrity, 515-533 data integration, 490-498 unbundling databases, 499-515 heterogeneous, keeping in sync, 452 maintainability, 18-22 possible faults in, 221 reliability, 6-10 hardware faults, 7 human errors, 9 importance of, 10 software errors, 8 scalability, 10-18 unreliable clocks, 287-299 data warehousing, 91-95, 554 comparison to data lakes, 415 ETL (extract-transform-load), 92, 416, 452 keeping data systems in sync, 452 schema design, 93 slowly changing dimension (SCD), 476 data-intensive applications, 3 database triggers (see triggers) database-internal distributed transactions, 360, 364, 477 databases archival storage, 131 comparison of message brokers to, 443 dataflow through, 129 end-to-end argument for, 519-520 checking integrity, 531 inside-out, 504 (see also unbundling databases) output from batch workflows, 412 relation to event streams, 451-464 (see also changelogs) API support for change streams, 456, 506 change data capture, 454-457 event sourcing, 457-459 keeping systems in sync, 452-453 philosophy of immutable events, 459-464 unbundling, 499-515 composing data storage technologies, 499-504 designing applications around dataflow, 504-509 566 | Index observing derived state, 509-515 datacenters geographically distributed, 145, 164, 278, 493 multi-tenancy and shared resources, 284 network architecture, 276 network faults, 279 replication across multiple, 169 leaderless replication, 184 multi-leader replication, 168, 335 dataflow, 128-139, 504-509 correctness of dataflow systems, 525 differential, 504 message-passing, 136-139 reasoning about, 491 through databases, 129 through services, 131-136 dataflow engines, 421-423 comparison to stream processing, 464 directed acyclic graphs (DAG), 424 partitioning, approach to, 429 support for declarative queries, 427 Datalog (query language), 60-63 datatypes binary strings in XML and JSON, 114 conflict-free, 174 in Avro encodings, 122 in Thrift and Protocol Buffers, 121 numbers in XML and JSON, 114 Datomic (database) B-tree storage, 242 data model, 50, 57 Datalog query language, 60 excision (deleting data), 463 languages for transactions, 255 serial execution of transactions, 253 deadlocks detection, in two-phase commit (2PC), 364 in two-phase locking (2PL), 258 Debezium (change data capture), 455 declarative languages, 42, 554 Bloom, 504 CSS and XSL, 44 Cypher, 52 Datalog, 60 for batch processing, 427 recursive SQL queries, 53 relational algebra and SQL, 42 SPARQL, 59 delays bounded network delays, 285 bounded process pauses, 298 unbounded network delays, 282 unbounded process pauses, 296 deleting data, 463 denormalization (data representation), 34, 554 costs, 39 in derived data systems, 386 materialized views, 101 updating derived data, 228, 231, 490 versus normalization, 462 derived data, 386, 439, 554 from change data capture, 454 in event sourcing, 458-458 maintaining derived state through logs, 452-457, 459-463 observing, by subscribing to streams, 512 outputs of batch and stream processing, 495 through application code, 505 versus distributed transactions, 492 deterministic operations, 255, 274, 554 accidental nondeterminism, 423 and fault tolerance, 423, 426 and idempotence, 478, 492 computing derived data, 495, 526, 531 in state machine replication, 349, 452, 458 joins, 476 DevOps, 394 differential dataflow, 504 dimension tables, 94 dimensional modeling (see star schemas) directed acyclic graphs (DAGs), 424 dirty reads (transaction isolation), 234 dirty writes (transaction isolation), 235 discrimination, 534 disks (see hard disks) distributed actor frameworks, 138 distributed filesystems, 398-399 decoupling from query engines, 417 indiscriminately dumping data into, 415 use by MapReduce, 402 distributed systems, 273-312, 554 Byzantine faults, 304-306 cloud versus supercomputing, 275 detecting network faults, 280 faults and partial failures, 274-277 formalization of consensus, 365 impossibility results, 338, 353 issues with failover, 157 limitations of distributed transactions, 363 multi-datacenter, 169, 335 network problems, 277-286 quorums, relying on, 301 reasons for using, 145, 151 synchronized clocks, relying on, 291-295 system models, 306-310 use of clocks and time, 287 distributed transactions (see transactions) Django (web framework), 232 DNS (Domain Name System), 216, 372 Docker (container manager), 506 document data model, 30-42 comparison to relational model, 38-42 document references, 38, 403 document-oriented databases, 31 many-to-many relationships and joins, 36 multi-object transactions, need for, 231 versus relational model convergence of models, 41 data locality, 41 document-partitioned indexes, 206, 217, 411 domain-driven design (DDD), 457 DRBD (Distributed Replicated Block Device), 153 drift (clocks), 289 Drill (query engine), 93 Druid (database), 461 Dryad (dataflow engine), 421 dual writes, problems with, 452, 507 duplicates, suppression of, 517 (see also idempotence) using a unique ID, 518, 522 durability (transactions), 226, 554 duration (time), 287 measurement with monotonic clocks, 288 dynamic partitioning, 212 dynamically typed languages analogy to schema-on-read, 40 code generation and, 127 Dynamo-style databases (see leaderless replica‐ tion) E edges (in graphs), 49, 403 property graph model, 50 edit distance (full-text search), 88 effectively-once semantics, 476, 516 Index | 567 (see also exactly-once semantics) preservation of integrity, 525 elastic systems, 17 Elasticsearch (search server) document-partitioned indexes, 207 partition rebalancing, 211 percolator (stream search), 467 usage example, 4 use of Lucene, 79 ElephantDB (database), 413 Elm (programming language), 504, 512 encodings (data formats), 111-128 Avro, 122-127 binary variants of JSON and XML, 115 compatibility, 112 calling services, 136 using databases, 129-131 using message-passing, 138 defined, 113 JSON, XML, and CSV, 114 language-specific formats, 113 merits of schemas, 127 representations of data, 112 Thrift and Protocol Buffers, 117-121 end-to-end argument, 277, 519-520 checking integrity, 531 publish/subscribe streams, 512 enrichment (stream), 473 Enterprise JavaBeans (EJB), 134 entities (see vertices) epoch (consensus algorithms), 368 epoch (Unix timestamps), 288 equi-joins, 403 erasure coding (error correction), 398 Erlang OTP (actor framework), 139 error handling for network faults, 280 in transactions, 231 error-correcting codes, 277, 398 Esper (CEP engine), 466 etcd (coordination service), 370-373 linearizable operations, 333 locks and leader election, 330 quorum reads, 351 service discovery, 372 use of Raft algorithm, 349, 353 Ethereum (blockchain), 532 Ethernet (networks), 276, 278, 285 packet checksums, 306, 519 568 | Index Etherpad (collaborative editor), 170 ethics, 533-543 code of ethics and professional practice, 533 legislation and self-regulation, 542 predictive analytics, 533-536 amplifying bias, 534 feedback loops, 536 privacy and tracking, 536-543 consent and freedom of choice, 538 data as assets and power, 540 meaning of privacy, 539 surveillance, 537 respect, dignity, and agency, 543, 544 unintended consequences, 533, 536 ETL (extract-transform-load), 92, 405, 452, 554 use of Hadoop for, 416 event sourcing, 457-459 commands and events, 459 comparison to change data capture, 457 comparison to lambda architecture, 497 deriving current state from event log, 458 immutability and auditability, 459, 531 large, reliable data systems, 519, 526 Event Store (database), 458 event streams (see streams) events, 440 deciding on total order of, 493 deriving views from event log, 461 difference to commands, 459 event time versus processing time, 469, 477, 498 immutable, advantages of, 460, 531 ordering to capture causality, 493 reads as, 513 stragglers, 470, 498 timestamp of, in stream processing, 471 EventSource (browser API), 512 eventual consistency, 152, 162, 308, 322 (see also conflicts) and perpetual inconsistency, 525 evolvability, 21, 111 calling services, 136 graph-structured data, 52 of databases, 40, 129-131, 461, 497 of message-passing, 138 reprocessing data, 496, 498 schema evolution in Avro, 123 schema evolution in Thrift and Protocol Buffers, 120 schema-on-read, 39, 111, 128 exactly-once semantics, 360, 476, 516 parity with batch processors, 498 preservation of integrity, 525 exclusive mode (locks), 258 eXtended Architecture transactions (see XA transactions) extract-transform-load (see ETL) F Facebook Presto (query engine), 93 React, Flux, and Redux (user interface libra‐ ries), 512 social graphs, 49 Wormhole (change data capture), 455 fact tables, 93 failover, 157, 554 (see also leader-based replication) in leaderless replication, absence of, 178 leader election, 301, 348, 352 potential problems, 157 failures amplification by distributed transactions, 364, 495 failure detection, 280 automatic rebalancing causing cascading failures, 214 perfect failure detectors, 359 timeouts and unbounded delays, 282, 284 using ZooKeeper, 371 faults versus, 7 partial failures in distributed systems, 275-277, 310 fan-out (messaging systems), 11, 445 fault tolerance, 6-10, 555 abstractions for, 321 formalization in consensus, 365-369 use of replication, 367 human fault tolerance, 414 in batch processing, 406, 414, 422, 425 in log-based systems, 520, 524-526 in stream processing, 476-479 atomic commit, 477 idempotence, 478 maintaining derived state, 495 microbatching and checkpointing, 477 rebuilding state after a failure, 478 of distributed transactions, 362-364 transaction atomicity, 223, 354-361 faults, 6 Byzantine faults, 304-306 failures versus, 7 handled by transactions, 221 handling in supercomputers and cloud computing, 275 hardware, 7 in batch processing versus distributed data‐ bases, 417 in distributed systems, 274-277 introducing deliberately, 7, 280 network faults, 279-281 asymmetric faults, 300 detecting, 280 tolerance of, in multi-leader replication, 169 software errors, 8 tolerating (see fault tolerance) federated databases, 501 fence (CPU instruction), 338 fencing (preventing split brain), 158, 302-304 generating fencing tokens, 349, 370 properties of fencing tokens, 308 stream processors writing to databases, 478, 517 Fibre Channel (networks), 398 field tags (Thrift and Protocol Buffers), 119-121 file descriptors (Unix), 395 financial data, 460 Firebase (database), 456 Flink (processing framework), 421-423 dataflow APIs, 427 fault tolerance, 422, 477, 479 Gelly API (graph processing), 425 integration of batch and stream processing, 495, 498 machine learning, 428 query optimizer, 427 stream processing, 466 flow control, 282, 441, 555 FLP result (on consensus), 353 FlumeJava (dataflow library), 403, 427 followers, 152, 555 (see also leader-based replication) foreign keys, 38, 403 forward compatibility, 112 forward decay (algorithm), 16 Index | 569 Fossil (version control system), 463 shunning (deleting data), 463 FoundationDB (database) serializable transactions, 261, 265, 364 fractal trees, 83 full table scans, 403 full-text search, 555 and fuzzy indexes, 88 building search indexes, 411 Lucene storage engine, 79 functional reactive programming (FRP), 504 functional requirements, 22 futures (asynchronous operations), 135 fuzzy search (see similarity search) G garbage collection immutability and, 463 process pauses for, 14, 296-299, 301 (see also process pauses) genome analysis, 63, 429 geographically distributed datacenters, 145, 164, 278, 493 geospatial indexes, 87 Giraph (graph processing), 425 Git (version control system), 174, 342, 463 GitHub, postmortems, 157, 158, 309 global indexes (see term-partitioned indexes) GlusterFS (distributed filesystem), 398 GNU Coreutils (Linux), 394 GoldenGate (change data capture), 161, 170, 455 (see also Oracle) Google Bigtable (database) data model (see Bigtable data model) partitioning scheme, 199, 202 storage layout, 78 Chubby (lock service), 370 Cloud Dataflow (stream processor), 466, 477, 498 (see also Beam) Cloud Pub/Sub (messaging), 444, 448 Docs (collaborative editor), 170 Dremel (query engine), 93, 96 FlumeJava (dataflow library), 403, 427 GFS (distributed file system), 398 gRPC (RPC framework), 135 MapReduce (batch processing), 390 570 | Index (see also MapReduce) building search indexes, 411 task preemption, 418 Pregel (graph processing), 425 Spanner (see Spanner) TrueTime (clock API), 294 gossip protocol, 216 government use of data, 541 GPS (Global Positioning System) use for clock synchronization, 287, 290, 294, 295 GraphChi (graph processing), 426 graphs, 555 as data models, 49-63 example of graph-structured data, 49 property graphs, 50 RDF and triple-stores, 55-59 versus the network model, 60 processing and analysis, 424-426 fault tolerance, 425 Pregel processing model, 425 query languages Cypher, 52 Datalog, 60-63 recursive SQL queries, 53 SPARQL, 59-59 Gremlin (graph query language), 50 grep (Unix tool), 392 GROUP BY clause (SQL), 406 grouping records in MapReduce, 406 handling skew, 407 H Hadoop (data infrastructure) comparison to distributed databases, 390 comparison to MPP databases, 414-418 comparison to Unix, 413-414, 499 diverse processing models in ecosystem, 417 HDFS distributed filesystem (see HDFS) higher-level tools, 403 join algorithms, 403-410 (see also MapReduce) MapReduce (see MapReduce) YARN (see YARN) happens-before relationship, 340 capturing, 187 concurrency and, 186 hard disks access patterns, 84 detecting corruption, 519, 530 faults in, 7, 227 sequential write throughput, 75, 450 hardware faults, 7 hash indexes, 72-75 broadcast hash joins, 409 partitioned hash joins, 409 hash partitioning, 203-205, 217 consistent hashing, 204 problems with hash mod N, 210 range queries, 204 suitable hash functions, 203 with fixed number of partitions, 210 HAWQ (database), 428 HBase (database) bug due to lack of fencing, 302 bulk loading, 413 column-family data model, 41, 99 dynamic partitioning, 212 key-range partitioning, 202 log-structured storage, 78 request routing, 216 size-tiered compaction, 79 use of HDFS, 417 use of ZooKeeper, 370 HDFS (Hadoop Distributed File System), 398-399 (see also distributed filesystems) checking data integrity, 530 decoupling from query engines, 417 indiscriminately dumping data into, 415 metadata about datasets, 410 NameNode, 398 use by Flink, 479 use by HBase, 212 use by MapReduce, 402 HdrHistogram (numerical library), 16 head (Unix tool), 392 head vertex (property graphs), 51 head-of-line blocking, 15 heap files (databases), 86 Helix (cluster manager), 216 heterogeneous distributed transactions, 360, 364 heuristic decisions (in 2PC), 363 Hibernate (object-relational mapper), 30 hierarchical model, 36 high availability (see fault tolerance) high-frequency trading, 290, 299 high-performance computing (HPC), 275 hinted handoff, 183 histograms, 16 Hive (query engine), 419, 427 for data warehouses, 93 HCatalog and metastore, 410 map-side joins, 409 query optimizer, 427 skewed joins, 408 workflows, 403 Hollerith machines, 390 hopping windows (stream processing), 472 (see also windows) horizontal scaling (see scaling out) HornetQ (messaging), 137, 444 distributed transaction support, 361 hot spots, 201 due to celebrities, 205 for time-series data, 203 in batch processing, 407 relieving, 205 hot standbys (see leader-based replication) HTTP, use in APIs (see services) human errors, 9, 279, 414 HyperDex (database), 88 HyperLogLog (algorithm), 466 I I/O operations, waiting for, 297 IBM DB2 (database) distributed transaction support, 361 recursive query support, 54 serializable isolation, 242, 257 XML and JSON support, 30, 42 electromechanical card-sorting machines, 390 IMS (database), 36 imperative query APIs, 46 InfoSphere Streams (CEP engine), 466 MQ (messaging), 444 distributed transaction support, 361 System R (database), 222 WebSphere (messaging), 137 idempotence, 134, 478, 555 by giving operations unique IDs, 518, 522 idempotent operations, 517 immutability advantages of, 460, 531 Index | 571 deriving state from event log, 459-464 for crash recovery, 75 in B-trees, 82, 242 in event sourcing, 457 inputs to Unix commands, 397 limitations of, 463 Impala (query engine) for data warehouses, 93 hash joins, 409 native code generation, 428 use of HDFS, 417 impedance mismatch, 29 imperative languages, 42 setting element styles (example), 45 in doubt (transaction status), 358 holding locks, 362 orphaned transactions, 363 in-memory databases, 88 durability, 227 serial transaction execution, 253 incidents cascading failures, 9 crashes due to leap seconds, 290 data corruption and financial losses due to concurrency bugs, 233 data corruption on hard disks, 227 data loss due to last-write-wins, 173, 292 data on disks unreadable, 309 deleted items reappearing, 174 disclosure of sensitive data due to primary key reuse, 157 errors in transaction serializability, 529 gigabit network interface with 1 Kb/s throughput, 311 network faults, 279 network interface dropping only inbound packets, 279 network partitions and whole-datacenter failures, 275 poor handling of network faults, 280 sending message to ex-partner, 494 sharks biting undersea cables, 279 split brain due to 1-minute packet delay, 158, 279 vibrations in server rack, 14 violation of uniqueness constraint, 529 indexes, 71, 555 and snapshot isolation, 241 as derived data, 386, 499-504 572 | Index B-trees, 79-83 building in batch processes, 411 clustered, 86 comparison of B-trees and LSM-trees, 83-85 concatenated, 87 covering (with included columns), 86 creating, 500 full-text search, 88 geospatial, 87 hash, 72-75 index-range locking, 260 multi-column, 87 partitioning and secondary indexes, 206-209, 217 secondary, 85 (see also secondary indexes) problems with dual writes, 452, 491 SSTables and LSM-trees, 76-79 updating when data changes, 452, 467 Industrial Revolution, 541 InfiniBand (networks), 285 InfiniteGraph (database), 50 InnoDB (storage engine) clustered index on primary key, 86 not preventing lost updates, 245 preventing write skew, 248, 257 serializable isolation, 257 snapshot isolation support, 239 inside-out databases, 504 (see also unbundling databases) integrating different data systems (see data integration) integrity, 524 coordination-avoiding data systems, 528 correctness of dataflow systems, 525 in consensus formalization, 365 integrity checks, 530 (see also auditing) end-to-end, 519, 531 use of snapshot isolation, 238 maintaining despite software bugs, 529 Interface Definition Language (IDL), 117, 122 intermediate state, materialization of, 420-423 internet services, systems for implementing, 275 invariants, 225 (see also constraints) inversion of control, 396 IP (Internet Protocol) unreliability of, 277 ISDN (Integrated Services Digital Network), 284 isolation (in transactions), 225, 228, 555 correctness and, 515 for single-object writes, 230 serializability, 251-266 actual serial execution, 252-256 serializable snapshot isolation (SSI), 261-266 two-phase locking (2PL), 257-261 violating, 228 weak isolation levels, 233-251 preventing lost updates, 242-246 read committed, 234-237 snapshot isolation, 237-242 iterative processing, 424-426 J Java Database Connectivity (JDBC) distributed transaction support, 361 network drivers, 128 Java Enterprise Edition (EE), 134, 356, 361 Java Message Service (JMS), 444 (see also messaging systems) comparison to log-based messaging, 448, 451 distributed transaction support, 361 message ordering, 446 Java Transaction API (JTA), 355, 361 Java Virtual Machine (JVM) bytecode generation, 428 garbage collection pauses, 296 process reuse in batch processors, 422 JavaScript in MapReduce querying, 46 setting element styles (example), 45 use in advanced queries, 48 Jena (RDF framework), 57 Jepsen (fault tolerance testing), 515 jitter (network delay), 284 joins, 555 by index lookup, 403 expressing as relational operators, 427 in relational and document databases, 34 MapReduce map-side joins, 408-410 broadcast hash joins, 409 merge joins, 410 partitioned hash joins, 409 MapReduce reduce-side joins, 403-408 handling skew, 407 sort-merge joins, 405 parallel execution of, 415 secondary indexes and, 85 stream joins, 472-476 stream-stream join, 473 stream-table join, 473 table-table join, 474 time-dependence of, 475 support in document databases, 42 JOTM (transaction coordinator), 356 JSON Avro schema representation, 122 binary variants, 115 for application data, issues with, 114 in relational databases, 30, 42 representing a résumé (example), 31 Juttle (query language), 504 K k-nearest neighbors, 429 Kafka (messaging), 137, 448 Kafka Connect (database integration), 457, 461 Kafka Streams (stream processor), 466, 467 fault tolerance, 479 leader-based replication, 153 log compaction, 456, 467 message offsets, 447, 478 request routing, 216 transaction support, 477 usage example, 4 Ketama (partitioning library), 213 key-value stores, 70 as batch process output, 412 hash indexes, 72-75 in-memory, 89 partitioning, 201-205 by hash of key, 203, 217 by key range, 202, 217 dynamic partitioning, 212 skew and hot spots, 205 Kryo (Java), 113 Kubernetes (cluster manager), 418, 506 L lambda architecture, 497 Lamport timestamps, 345 Index | 573 Large Hadron Collider (LHC), 64 last write wins (LWW), 173, 334 discarding concurrent writes, 186 problems with, 292 prone to lost updates, 246 late binding, 396 latency instability under two-phase locking, 259 network latency and resource utilization, 286 response time versus, 14 tail latency, 15, 207 leader-based replication, 152-161 (see also replication) failover, 157, 301 handling node outages, 156 implementation of replication logs change data capture, 454-457 (see also changelogs) statement-based, 158 trigger-based replication, 161 write-ahead log (WAL) shipping, 159 linearizability of operations, 333 locking and leader election, 330 log sequence number, 156, 449 read-scaling architecture, 161 relation to consensus, 367 setting up new followers, 155 synchronous versus asynchronous, 153-155 leaderless replication, 177-191 (see also replication) detecting concurrent writes, 184-191 capturing happens-before relationship, 187 happens-before relationship and concur‐ rency, 186 last write wins, 186 merging concurrently written values, 190 version vectors, 191 multi-datacenter, 184 quorums, 179-182 consistency limitations, 181-183, 334 sloppy quorums and hinted handoff, 183 read repair and anti-entropy, 178 leap seconds, 8, 290 in time-of-day clocks, 288 leases, 295 implementation with ZooKeeper, 370 574 | Index need for fencing, 302 ledgers, 460 distributed ledger technologies, 532 legacy systems, maintenance of, 18 less (Unix tool), 397 LevelDB (storage engine), 78 leveled compaction, 79 Levenshtein automata, 88 limping (partial failure), 311 linearizability, 324-338, 555 cost of, 335-338 CAP theorem, 336 memory on multi-core CPUs, 338 definition, 325-329 implementing with total order broadcast, 350 in ZooKeeper, 370 of derived data systems, 492, 524 avoiding coordination, 527 of different replication methods, 332-335 using quorums, 334 relying on, 330-332 constraints and uniqueness, 330 cross-channel timing dependencies, 331 locking and leader election, 330 stronger than causal consistency, 342 using to implement total order broadcast, 351 versus serializability, 329 LinkedIn Azkaban (workflow scheduler), 402 Databus (change data capture), 161, 455 Espresso (database), 31, 126, 130, 153, 216 Helix (cluster manager) (see Helix) profile (example), 30 reference to company entity (example), 34 Rest.li (RPC framework), 135 Voldemort (database) (see Voldemort) Linux, leap second bug, 8, 290 liveness properties, 308 LMDB (storage engine), 82, 242 load approaches to coping with, 17 describing, 11 load testing, 16 load balancing (messaging), 444 local indexes (see document-partitioned indexes) locality (data access), 32, 41, 555 in batch processing, 400, 405, 421 in stateful clients, 170, 511 in stream processing, 474, 478, 508, 522 location transparency, 134 in the actor model, 138 locks, 556 deadlock, 258 distributed locking, 301-304, 330 fencing tokens, 303 implementation with ZooKeeper, 370 relation to consensus, 374 for transaction isolation in snapshot isolation, 239 in two-phase locking (2PL), 257-261 making operations atomic, 243 performance, 258 preventing dirty writes, 236 preventing phantoms with index-range locks, 260, 265 read locks (shared mode), 236, 258 shared mode and exclusive mode, 258 in two-phase commit (2PC) deadlock detection, 364 in-doubt transactions holding locks, 362 materializing conflicts with, 251 preventing lost updates by explicit locking, 244 log sequence number, 156, 449 logic programming languages, 504 logical clocks, 293, 343, 494 for read-after-write consistency, 164 logical logs, 160 logs (data structure), 71, 556 advantages of immutability, 460 compaction, 73, 79, 456, 460 for stream operator state, 479 creating using total order broadcast, 349 implementing uniqueness constraints, 522 log-based messaging, 446-451 comparison to traditional messaging, 448, 451 consumer offsets, 449 disk space usage, 450 replaying old messages, 451, 496, 498 slow consumers, 450 using logs for message storage, 447 log-structured storage, 71-79 log-structured merge tree (see LSMtrees) replication, 152, 158-161 change data capture, 454-457 (see also changelogs) coordination with snapshot, 156 logical (row-based) replication, 160 statement-based replication, 158 trigger-based replication, 161 write-ahead log (WAL) shipping, 159 scalability limits, 493 loose coupling, 396, 419, 502 lost updates (see updates) LSM-trees (indexes), 78-79 comparison to B-trees, 83-85 Lucene (storage engine), 79 building indexes in batch processes, 411 similarity search, 88 Luigi (workflow scheduler), 402 LWW (see last write wins) M machine learning ethical considerations, 534 (see also ethics) iterative processing, 424 models derived from training data, 505 statistical and numerical algorithms, 428 MADlib (machine learning toolkit), 428 magic scaling sauce, 18 Mahout (machine learning toolkit), 428 maintainability, 18-22, 489 defined, 23 design principles for software systems, 19 evolvability (see evolvability) operability, 19 simplicity and managing complexity, 20 many-to-many relationships in document model versus relational model, 39 modeling as graphs, 49 many-to-one and many-to-many relationships, 33-36 many-to-one relationships, 34 MapReduce (batch processing), 390, 399-400 accessing external services within job, 404, 412 comparison to distributed databases designing for frequent faults, 417 diversity of processing models, 416 diversity of storage, 415 Index | 575 comparison to stream processing, 464 comparison to Unix, 413-414 disadvantages and limitations of, 419 fault tolerance, 406, 414, 422 higher-level tools, 403, 426 implementation in Hadoop, 400-403 the shuffle, 402 implementation in MongoDB, 46-48 machine learning, 428 map-side processing, 408-410 broadcast hash joins, 409 merge joins, 410 partitioned hash joins, 409 mapper and reducer functions, 399 materialization of intermediate state, 419-423 output of batch workflows, 411-413 building search indexes, 411 key-value stores, 412 reduce-side processing, 403-408 analysis of user activity events (exam‐ ple), 404 grouping records by same key, 406 handling skew, 407 sort-merge joins, 405 workflows, 402 marshalling (see encoding) massively parallel processing (MPP), 216 comparison to composing storage technolo‐ gies, 502 comparison to Hadoop, 414-418, 428 master-master replication (see multi-leader replication) master-slave replication (see leader-based repli‐ cation) materialization, 556 aggregate values, 101 conflicts, 251 intermediate state (batch processing), 420-423 materialized views, 101 as derived data, 386, 499-504 maintaining, using stream processing, 467, 475 Maven (Java build tool), 428 Maxwell (change data capture), 455 mean, 14 media monitoring, 467 median, 14 576 | Index meeting room booking (example), 249, 259, 521 membership services, 372 Memcached (caching server), 4, 89 memory in-memory databases, 88 durability, 227 serial transaction execution, 253 in-memory representation of data, 112 random bit-flips in, 529 use by indexes, 72, 77 memory barrier (CPU instruction), 338 MemSQL (database) in-memory storage, 89 read committed isolation, 236 memtable (in LSM-trees), 78 Mercurial (version control system), 463 merge joins, MapReduce map-side, 410 mergeable persistent data structures, 174 merging sorted files, 76, 402, 405 Merkle trees, 532 Mesos (cluster manager), 418, 506 message brokers (see messaging systems) message-passing, 136-139 advantages over direct RPC, 137 distributed actor frameworks, 138 evolvability, 138 MessagePack (encoding format), 116 messages exactly-once semantics, 360, 476 loss of, 442 using total order broadcast, 348 messaging systems, 440-451 (see also streams) backpressure, buffering, or dropping mes‐ sages, 441 brokerless messaging, 442 event logs, 446-451 comparison to traditional messaging, 448, 451 consumer offsets, 449 replaying old messages, 451, 496, 498 slow consumers, 450 message brokers, 443-446 acknowledgements and redelivery, 445 comparison to event logs, 448, 451 multiple consumers of same topic, 444 reliability, 442 uniqueness in log-based messaging, 522 Meteor (web framework), 456 microbatching, 477, 495 microservices, 132 (see also services) causal dependencies across services, 493 loose coupling, 502 relation to batch/stream processors, 389, 508 Microsoft Azure Service Bus (messaging), 444 Azure Storage, 155, 398 Azure Stream Analytics, 466 DCOM (Distributed Component Object Model), 134 MSDTC (transaction coordinator), 356 Orleans (see Orleans) SQL Server (see SQL Server) migrating (rewriting) data, 40, 130, 461, 497 modulus operator (%), 210 MongoDB (database) aggregation pipeline, 48 atomic operations, 243 BSON, 41 document data model, 31 hash partitioning (sharding), 203-204 key-range partitioning, 202 lack of join support, 34, 42 leader-based replication, 153 MapReduce support, 46, 400 oplog parsing, 455, 456 partition splitting, 212 request routing, 216 secondary indexes, 207 Mongoriver (change data capture), 455 monitoring, 10, 19 monotonic clocks, 288 monotonic reads, 164 MPP (see massively parallel processing) MSMQ (messaging), 361 multi-column indexes, 87 multi-leader replication, 168-177 (see also replication) handling write conflicts, 171 conflict avoidance, 172 converging toward a consistent state, 172 custom conflict resolution logic, 173 determining what is a conflict, 174 linearizability, lack of, 333 replication topologies, 175-177 use cases, 168 clients with offline operation, 170 collaborative editing, 170 multi-datacenter replication, 168, 335 multi-object transactions, 228 need for, 231 Multi-Paxos (total order broadcast), 367 multi-table index cluster tables (Oracle), 41 multi-tenancy, 284 multi-version concurrency control (MVCC), 239, 266 detecting stale MVCC reads, 263 indexes and snapshot isolation, 241 mutual exclusion, 261 (see also locks) MySQL (database) binlog coordinates, 156 binlog parsing for change data capture, 455 circular replication topology, 175 consistent snapshots, 156 distributed transaction support, 361 InnoDB storage engine (see InnoDB) JSON support, 30, 42 leader-based replication, 153 performance of XA transactions, 360 row-based replication, 160 schema changes in, 40 snapshot isolation support, 242 (see also InnoDB) statement-based replication, 159 Tungsten Replicator (multi-leader replica‐ tion), 170 conflict detection, 177 N nanomsg (messaging library), 442 Narayana (transaction coordinator), 356 NATS (messaging), 137 near-real-time (nearline) processing, 390 (see also stream processing) Neo4j (database) Cypher query language, 52 graph data model, 50 Nephele (dataflow engine), 421 netcat (Unix tool), 397 Netflix Chaos Monkey, 7, 280 Network Attached Storage (NAS), 146, 398 network model, 36 Index | 577 graph databases versus, 60 imperative query APIs, 46 Network Time Protocol (see NTP) networks congestion and queueing, 282 datacenter network topologies, 276 faults (see faults) linearizability and network delays, 338 network partitions, 279, 337 timeouts and unbounded delays, 281 next-key locking, 260 nodes (in graphs) (see vertices) nodes (processes), 556 handling outages in leader-based replica‐ tion, 156 system models for failure, 307 noisy neighbors, 284 nonblocking atomic commit, 359 nondeterministic operations accidental nondeterminism, 423 partial failures in distributed systems, 275 nonfunctional requirements, 22 nonrepeatable reads, 238 (see also read skew) normalization (data representation), 33, 556 executing joins, 39, 42, 403 foreign key references, 231 in systems of record, 386 versus denormalization, 462 NoSQL, 29, 499 transactions and, 223 Notation3 (N3), 56 npm (package manager), 428 NTP (Network Time Protocol), 287 accuracy, 289, 293 adjustments to monotonic clocks, 289 multiple server addresses, 306 numbers, in XML and JSON encodings, 114 O object-relational mapping (ORM) frameworks, 30 error handling and aborted transactions, 232 unsafe read-modify-write cycle code, 244 object-relational mismatch, 29 observer pattern, 506 offline systems, 390 (see also batch processing) 578 | Index stateful, offline-capable clients, 170, 511 offline-first applications, 511 offsets consumer offsets in partitioned logs, 449 messages in partitioned logs, 447 OLAP (online analytic processing), 91, 556 data cubes, 102 OLTP (online transaction processing), 90, 556 analytics queries versus, 411 workload characteristics, 253 one-to-many relationships, 30 JSON representation, 32 online systems, 389 (see also services) Oozie (workflow scheduler), 402 OpenAPI (service definition format), 133 OpenStack Nova (cloud infrastructure) use of ZooKeeper, 370 Swift (object storage), 398 operability, 19 operating systems versus databases, 499 operation identifiers, 518, 522 operational transformation, 174 operators, 421 flow of data between, 424 in stream processing, 464 optimistic concurrency control, 261 Oracle (database) distributed transaction support, 361 GoldenGate (change data capture), 161, 170, 455 lack of serializability, 226 leader-based replication, 153 multi-table index cluster tables, 41 not preventing write skew, 248 partitioned indexes, 209 PL/SQL language, 255 preventing lost updates, 245 read committed isolation, 236 Real Application Clusters (RAC), 330 recursive query support, 54 snapshot isolation support, 239, 242 TimesTen (in-memory database), 89 WAL-based replication, 160 XML support, 30 ordering, 339-352 by sequence numbers, 343-348 causal ordering, 339-343 partial order, 341 limits of total ordering, 493 total order broadcast, 348-352 Orleans (actor framework), 139 outliers (response time), 14 Oz (programming language), 504 P package managers, 428, 505 packet switching, 285 packets corruption of, 306 sending via UDP, 442 PageRank (algorithm), 49, 424 paging (see virtual memory) ParAccel (database), 93 parallel databases (see massively parallel pro‐ cessing) parallel execution of graph analysis algorithms, 426 queries in MPP databases, 216 Parquet (data format), 96, 131 (see also column-oriented storage) use in Hadoop, 414 partial failures, 275, 310 limping, 311 partial order, 341 partitioning, 199-218, 556 and replication, 200 in batch processing, 429 multi-partition operations, 514 enforcing constraints, 522 secondary index maintenance, 495 of key-value data, 201-205 by key range, 202 skew and hot spots, 205 rebalancing partitions, 209-214 automatic or manual rebalancing, 213 problems with hash mod N, 210 using dynamic partitioning, 212 using fixed number of partitions, 210 using N partitions per node, 212 replication and, 147 request routing, 214-216 secondary indexes, 206-209 document-based partitioning, 206 term-based partitioning, 208 serial execution of transactions and, 255 Paxos (consensus algorithm), 366 ballot number, 368 Multi-Paxos (total order broadcast), 367 percentiles, 14, 556 calculating efficiently, 16 importance of high percentiles, 16 use in service level agreements (SLAs), 15 Percona XtraBackup (MySQL tool), 156 performance describing, 13 of distributed transactions, 360 of in-memory databases, 89 of linearizability, 338 of multi-leader replication, 169 perpetual inconsistency, 525 pessimistic concurrency control, 261 phantoms (transaction isolation), 250 materializing conflicts, 251 preventing, in serializability, 259 physical clocks (see clocks) pickle (Python), 113 Pig (dataflow language), 419, 427 replicated joins, 409 skewed joins, 407 workflows, 403 Pinball (workflow scheduler), 402 pipelined execution, 423 in Unix, 394 point in time, 287 polyglot persistence, 29 polystores, 501 PostgreSQL (database) BDR (multi-leader replication), 170 causal ordering of writes, 177 Bottled Water (change data capture), 455 Bucardo (trigger-based replication), 161, 173 distributed transaction support, 361 foreign data wrappers, 501 full text search support, 490 leader-based replication, 153 log sequence number, 156 MVCC implementation, 239, 241 PL/pgSQL language, 255 PostGIS geospatial indexes, 87 preventing lost updates, 245 preventing write skew, 248, 261 read committed isolation, 236 recursive query support, 54 representing graphs, 51 Index | 579 serializable snapshot isolation (SSI), 261 snapshot isolation support, 239, 242 WAL-based replication, 160 XML and JSON support, 30, 42 pre-splitting, 212 Precision Time Protocol (PTP), 290 predicate locks, 259 predictive analytics, 533-536 amplifying bias, 534 ethics of (see ethics) feedback loops, 536 preemption of datacenter resources, 418 of threads, 298 Pregel processing model, 425 primary keys, 85, 556 compound primary key (Cassandra), 204 primary-secondary replication (see leaderbased replication) privacy, 536-543 consent and freedom of choice, 538 data as assets and power, 540 deleting data, 463 ethical considerations (see ethics) legislation and self-regulation, 542 meaning of, 539 surveillance, 537 tracking behavioral data, 536 probabilistic algorithms, 16, 466 process pauses, 295-299 processing time (of events), 469 producers (message streams), 440 programming languages dataflow languages, 504 for stored procedures, 255 functional reactive programming (FRP), 504 logic programming, 504 Prolog (language), 61 (see also Datalog) promises (asynchronous operations), 135 property graphs, 50 Cypher query language, 52 Protocol Buffers (data format), 117-121 field tags and schema evolution, 120 provenance of data, 531 publish/subscribe model, 441 publishers (message streams), 440 punch card tabulating machines, 390 580 | Index pure functions, 48 putting computation near data, 400 Q Qpid (messaging), 444 quality of service (QoS), 285 Quantcast File System (distributed filesystem), 398 query languages, 42-48 aggregation pipeline, 48 CSS and XSL, 44 Cypher, 52 Datalog, 60 Juttle, 504 MapReduce querying, 46-48 recursive SQL queries, 53 relational algebra and SQL, 42 SPARQL, 59 query optimizers, 37, 427 queueing delays (networks), 282 head-of-line blocking, 15 latency and response time, 14 queues (messaging), 137 quorums, 179-182, 556 for leaderless replication, 179 in consensus algorithms, 368 limitations of consistency, 181-183, 334 making decisions in distributed systems, 301 monitoring staleness, 182 multi-datacenter replication, 184 relying on durability, 309 sloppy quorums and hinted handoff, 183 R R-trees (indexes), 87 RabbitMQ (messaging), 137, 444 leader-based replication, 153 race conditions, 225 (see also concurrency) avoiding with linearizability, 331 caused by dual writes, 452 dirty writes, 235 in counter increments, 235 lost updates, 242-246 preventing with event logs, 462, 507 preventing with serializable isolation, 252 write skew, 246-251 Raft (consensus algorithm), 366 sensitivity to network problems, 369 term number, 368 use in etcd, 353 RAID (Redundant Array of Independent Disks), 7, 398 railways, schema migration on, 496 RAMCloud (in-memory storage), 89 ranking algorithms, 424 RDF (Resource Description Framework), 57 querying with SPARQL, 59 RDMA (Remote Direct Memory Access), 276 read committed isolation level, 234-237 implementing, 236 multi-version concurrency control (MVCC), 239 no dirty reads, 234 no dirty writes, 235 read path (derived data), 509 read repair (leaderless replication), 178 for linearizability, 335 read replicas (see leader-based replication) read skew (transaction isolation), 238, 266 as violation of causality, 340 read-after-write consistency, 163, 524 cross-device, 164 read-modify-write cycle, 243 read-scaling architecture, 161 reads as events, 513 real-time collaborative editing, 170 near-real-time processing, 390 (see also stream processing) publish/subscribe dataflow, 513 response time guarantees, 298 time-of-day clocks, 288 rebalancing partitions, 209-214, 556 (see also partitioning) automatic or manual rebalancing, 213 dynamic partitioning, 212 fixed number of partitions, 210 fixed number of partitions per node, 212 problems with hash mod N, 210 recency guarantee, 324 recommendation engines batch process outputs, 412 batch workflows, 403, 420 iterative processing, 424 statistical and numerical algorithms, 428 records, 399 events in stream processing, 440 recursive common table expressions (SQL), 54 redelivery (messaging), 445 Redis (database) atomic operations, 243 durability, 89 Lua scripting, 255 single-threaded execution, 253 usage example, 4 redundancy hardware components, 7 of derived data, 386 (see also derived data) Reed–Solomon codes (error correction), 398 refactoring, 22 (see also evolvability) regions (partitioning), 199 register (data structure), 325 relational data model, 28-42 comparison to document model, 38-42 graph queries in SQL, 53 in-memory databases with, 89 many-to-one and many-to-many relation‐ ships, 33 multi-object transactions, need for, 231 NoSQL as alternative to, 29 object-relational mismatch, 29 relational algebra and SQL, 42 versus document model convergence of models, 41 data locality, 41 relational databases eventual consistency, 162 history, 28 leader-based replication, 153 logical logs, 160 philosophy compared to Unix, 499, 501 schema changes, 40, 111, 130 statement-based replication, 158 use of B-tree indexes, 80 relationships (see edges) reliability, 6-10, 489 building a reliable system from unreliable components, 276 defined, 6, 22 hardware faults, 7 human errors, 9 importance of, 10 of messaging systems, 442 Index | 581 software errors, 8 Remote Method Invocation (Java RMI), 134 remote procedure calls (RPCs), 134-136 (see also services) based on futures, 135 data encoding and evolution, 136 issues with, 134 using Avro, 126, 135 using Thrift, 135 versus message brokers, 137 repeatable reads (transaction isolation), 242 replicas, 152 replication, 151-193, 556 and durability, 227 chain replication, 155 conflict resolution and, 246 consistency properties, 161-167 consistent prefix reads, 165 monotonic reads, 164 reading your own writes, 162 in distributed filesystems, 398 leaderless, 177-191 detecting concurrent writes, 184-191 limitations of quorum consistency, 181-183, 334 sloppy quorums and hinted handoff, 183 monitoring staleness, 182 multi-leader, 168-177 across multiple datacenters, 168, 335 handling write conflicts, 171-175 replication topologies, 175-177 partitioning and, 147, 200 reasons for using, 145, 151 single-leader, 152-161 failover, 157 implementation of replication logs, 158-161 relation to consensus, 367 setting up new followers, 155 synchronous versus asynchronous, 153-155 state machine replication, 349, 452 using erasure coding, 398 with heterogeneous data systems, 453 replication logs (see logs) reprocessing data, 496, 498 (see also evolvability) from log-based messaging, 451 request routing, 214-216 582 | Index approaches to, 214 parallel query execution, 216 resilient systems, 6 (see also fault tolerance) response time as performance metric for services, 13, 389 guarantees on, 298 latency versus, 14 mean and percentiles, 14 user experience, 15 responsibility and accountability, 535 REST (Representational State Transfer), 133 (see also services) RethinkDB (database) document data model, 31 dynamic partitioning, 212 join support, 34, 42 key-range partitioning, 202 leader-based replication, 153 subscribing to changes, 456 Riak (database) Bitcask storage engine, 72 CRDTs, 174, 191 dotted version vectors, 191 gossip protocol, 216 hash partitioning, 203-204, 211 last-write-wins conflict resolution, 186 leaderless replication, 177 LevelDB storage engine, 78 linearizability, lack of, 335 multi-datacenter support, 184 preventing lost updates across replicas, 246 rebalancing, 213 search feature, 209 secondary indexes, 207 siblings (concurrently written values), 190 sloppy quorums, 184 ring buffers, 450 Ripple (cryptocurrency), 532 rockets, 10, 36, 305 RocksDB (storage engine), 78 leveled compaction, 79 rollbacks (transactions), 222 rolling upgrades, 8, 112 routing (see request routing) row-oriented storage, 96 row-based replication, 160 rowhammer (memory corruption), 529 RPCs (see remote procedure calls) Rubygems (package manager), 428 rules (Datalog), 61 S safety and liveness properties, 308 in consensus algorithms, 366 in transactions, 222 sagas (see compensating transactions) Samza (stream processor), 466, 467 fault tolerance, 479 streaming SQL support, 466 sandboxes, 9 SAP HANA (database), 93 scalability, 10-18, 489 approaches for coping with load, 17 defined, 22 describing load, 11 describing performance, 13 partitioning and, 199 replication and, 161 scaling up versus scaling out, 146 scaling out, 17, 146 (see also shared-nothing architecture) scaling up, 17, 146 scatter/gather approach, querying partitioned databases, 207 SCD (slowly changing dimension), 476 schema-on-read, 39 comparison to evolvable schema, 128 in distributed filesystems, 415 schema-on-write, 39 schemaless databases (see schema-on-read) schemas, 557 Avro, 122-127 reader determining writer’s schema, 125 schema evolution, 123 dynamically generated, 126 evolution of, 496 affecting application code, 111 compatibility checking, 126 in databases, 129-131 in message-passing, 138 in service calls, 136 flexibility in document model, 39 for analytics, 93-95 for JSON and XML, 115 merits of, 127 schema migration on railways, 496 Thrift and Protocol Buffers, 117-121 schema evolution, 120 traditional approach to design, fallacy in, 462 searches building search indexes in batch processes, 411 k-nearest neighbors, 429 on streams, 467 partitioned secondary indexes, 206 secondaries (see leader-based replication) secondary indexes, 85, 557 partitioning, 206-209, 217 document-partitioned, 206 index maintenance, 495 term-partitioned, 208 problems with dual writes, 452, 491 updating, transaction isolation and, 231 secondary sorts, 405 sed (Unix tool), 392 self-describing files, 127 self-joins, 480 self-validating systems, 530 semantic web, 57 semi-synchronous replication, 154 sequence number ordering, 343-348 generators, 294, 344 insufficiency for enforcing constraints, 347 Lamport timestamps, 345 use of timestamps, 291, 295, 345 sequential consistency, 351 serializability, 225, 233, 251-266, 557 linearizability versus, 329 pessimistic versus optimistic concurrency control, 261 serial execution, 252-256 partitioning, 255 using stored procedures, 253, 349 serializable snapshot isolation (SSI), 261-266 detecting stale MVCC reads, 263 detecting writes that affect prior reads, 264 distributed execution, 265, 364 performance of SSI, 265 preventing write skew, 262-265 two-phase locking (2PL), 257-261 index-range locks, 260 performance, 258 Serializable (Java), 113 Index | 583 serialization, 113 (see also encoding) service discovery, 135, 214, 372 using DNS, 216, 372 service level agreements (SLAs), 15 service-oriented architecture (SOA), 132 (see also services) services, 131-136 microservices, 132 causal dependencies across services, 493 loose coupling, 502 relation to batch/stream processors, 389, 508 remote procedure calls (RPCs), 134-136 issues with, 134 similarity to databases, 132 web services, 132, 135 session windows (stream processing), 472 (see also windows) sessionization, 407 sharding (see partitioning) shared mode (locks), 258 shared-disk architecture, 146, 398 shared-memory architecture, 146 shared-nothing architecture, 17, 146-147, 557 (see also replication) distributed filesystems, 398 (see also distributed filesystems) partitioning, 199 use of network, 277 sharks biting undersea cables, 279 counting (example), 46-48 finding (example), 42 website about (example), 44 shredding (in relational model), 38 siblings (concurrent values), 190, 246 (see also conflicts) similarity search edit distance, 88 genome data, 63 k-nearest neighbors, 429 single-leader replication (see leader-based rep‐ lication) single-threaded execution, 243, 252 in batch processing, 406, 421, 426 in stream processing, 448, 463, 522 size-tiered compaction, 79 skew, 557 584 | Index clock skew, 291-294, 334 in transaction isolation read skew, 238, 266 write skew, 246-251, 262-265 (see also write skew) meanings of, 238 unbalanced workload, 201 compensating for, 205 due to celebrities, 205 for time-series data, 203 in batch processing, 407 slaves (see leader-based replication) sliding windows (stream processing), 472 (see also windows) sloppy quorums, 183 (see also quorums) lack of linearizability, 334 slowly changing dimension (data warehouses), 476 smearing (leap seconds adjustments), 290 snapshots (databases) causal consistency, 340 computing derived data, 500 in change data capture, 455 serializable snapshot isolation (SSI), 261-266, 329 setting up a new replica, 156 snapshot isolation and repeatable read, 237-242 implementing with MVCC, 239 indexes and MVCC, 241 visibility rules, 240 synchronized clocks for global snapshots, 294 snowflake schemas, 95 SOAP, 133 (see also services) evolvability, 136 software bugs, 8 maintaining integrity, 529 solid state drives (SSDs) access patterns, 84 detecting corruption, 519, 530 faults in, 227 sequential write throughput, 75 Solr (search server) building indexes in batch processes, 411 document-partitioned indexes, 207 request routing, 216 usage example, 4 use of Lucene, 79 sort (Unix tool), 392, 394, 395 sort-merge joins (MapReduce), 405 Sorted String Tables (see SSTables) sorting sort order in column storage, 99 source of truth (see systems of record) Spanner (database) data locality, 41 snapshot isolation using clocks, 295 TrueTime API, 294 Spark (processing framework), 421-423 bytecode generation, 428 dataflow APIs, 427 fault tolerance, 422 for data warehouses, 93 GraphX API (graph processing), 425 machine learning, 428 query optimizer, 427 Spark Streaming, 466 microbatching, 477 stream processing on top of batch process‐ ing, 495 SPARQL (query language), 59 spatial algorithms, 429 split brain, 158, 557 in consensus algorithms, 352, 367 preventing, 322, 333 using fencing tokens to avoid, 302-304 spreadsheets, dataflow programming capabili‐ ties, 504 SQL (Structured Query Language), 21, 28, 43 advantages and limitations of, 416 distributed query execution, 48 graph queries in, 53 isolation levels standard, issues with, 242 query execution on Hadoop, 416 résumé (example), 30 SQL injection vulnerability, 305 SQL on Hadoop, 93 statement-based replication, 158 stored procedures, 255 SQL Server (database) data warehousing support, 93 distributed transaction support, 361 leader-based replication, 153 preventing lost updates, 245 preventing write skew, 248, 257 read committed isolation, 236 recursive query support, 54 serializable isolation, 257 snapshot isolation support, 239 T-SQL language, 255 XML support, 30 SQLstream (stream analytics), 466 SSDs (see solid state drives) SSTables (storage format), 76-79 advantages over hash indexes, 76 concatenated index, 204 constructing and maintaining, 78 making LSM-Tree from, 78 staleness (old data), 162 cross-channel timing dependencies, 331 in leaderless databases, 178 in multi-version concurrency control, 263 monitoring for, 182 of client state, 512 versus linearizability, 324 versus timeliness, 524 standbys (see leader-based replication) star replication topologies, 175 star schemas, 93-95 similarity to event sourcing, 458 Star Wars analogy (event time versus process‐ ing time), 469 state derived from log of immutable events, 459 deriving current state from the event log, 458 interplay between state changes and appli‐ cation code, 507 maintaining derived state, 495 maintenance by stream processor in streamstream joins, 473 observing derived state, 509-515 rebuilding after stream processor failure, 478 separation of application code and, 505 state machine replication, 349, 452 statement-based replication, 158 statically typed languages analogy to schema-on-write, 40 code generation and, 127 statistical and numerical algorithms, 428 StatsD (metrics aggregator), 442 stdin, stdout, 395, 396 Stellar (cryptocurrency), 532 Index | 585 stock market feeds, 442 STONITH (Shoot The Other Node In The Head), 158 stop-the-world (see garbage collection) storage composing data storage technologies, 499-504 diversity of, in MapReduce, 415 Storage Area Network (SAN), 146, 398 storage engines, 69-104 column-oriented, 95-101 column compression, 97-99 defined, 96 distinction between column families and, 99 Parquet, 96, 131 sort order in, 99-100 writing to, 101 comparing requirements for transaction processing and analytics, 90-96 in-memory storage, 88 durability, 227 row-oriented, 70-90 B-trees, 79-83 comparing B-trees and LSM-trees, 83-85 defined, 96 log-structured, 72-79 stored procedures, 161, 253-255, 557 and total order broadcast, 349 pros and cons of, 255 similarity to stream processors, 505 Storm (stream processor), 466 distributed RPC, 468, 514 Trident state handling, 478 straggler events, 470, 498 stream processing, 464-481, 557 accessing external services within job, 474, 477, 478, 517 combining with batch processing lambda architecture, 497 unifying technologies, 498 comparison to batch processing, 464 complex event processing (CEP), 465 fault tolerance, 476-479 atomic commit, 477 idempotence, 478 microbatching and checkpointing, 477 rebuilding state after a failure, 478 for data integration, 494-498 586 | Index maintaining derived state, 495 maintenance of materialized views, 467 messaging systems (see messaging systems) reasoning about time, 468-472 event time versus processing time, 469, 477, 498 knowing when window is ready, 470 types of windows, 472 relation to databases (see streams) relation to services, 508 search on streams, 467 single-threaded execution, 448, 463 stream analytics, 466 stream joins, 472-476 stream-stream join, 473 stream-table join, 473 table-table join, 474 time-dependence of, 475 streams, 440-451 end-to-end, pushing events to clients, 512 messaging systems (see messaging systems) processing (see stream processing) relation to databases, 451-464 (see also changelogs) API support for change streams, 456 change data capture, 454-457 derivative of state by time, 460 event sourcing, 457-459 keeping systems in sync, 452-453 philosophy of immutable events, 459-464 topics, 440 strict serializability, 329 strong consistency (see linearizability) strong one-copy serializability, 329 subjects, predicates, and objects (in triplestores), 55 subscribers (message streams), 440 (see also consumers) supercomputers, 275 surveillance, 537 (see also privacy) Swagger (service definition format), 133 swapping to disk (see virtual memory) synchronous networks, 285, 557 comparison to asynchronous networks, 284 formal model, 307 synchronous replication, 154, 557 chain replication, 155 conflict detection, 172 system models, 300, 306-310 assumptions in, 528 correctness of algorithms, 308 mapping to the real world, 309 safety and liveness, 308 systems of record, 386, 557 change data capture, 454, 491 treating event log as, 460 systems thinking, 536 T t-digest (algorithm), 16 table-table joins, 474 Tableau (data visualization software), 416 tail (Unix tool), 447 tail vertex (property graphs), 51 Tajo (query engine), 93 Tandem NonStop SQL (database), 200 TCP (Transmission Control Protocol), 277 comparison to circuit switching, 285 comparison to UDP, 283 connection failures, 280 flow control, 282, 441 packet checksums, 306, 519, 529 reliability and duplicate suppression, 517 retransmission timeouts, 284 use for transaction sessions, 229 telemetry (see monitoring) Teradata (database), 93, 200 term-partitioned indexes, 208, 217 termination (consensus), 365 Terrapin (database), 413 Tez (dataflow engine), 421-423 fault tolerance, 422 support by higher-level tools, 427 thrashing (out of memory), 297 threads (concurrency) actor model, 138, 468 (see also message-passing) atomic operations, 223 background threads, 73, 85 execution pauses, 286, 296-298 memory barriers, 338 preemption, 298 single (see single-threaded execution) three-phase commit, 359 Thrift (data format), 117-121 BinaryProtocol, 118 CompactProtocol, 119 field tags and schema evolution, 120 throughput, 13, 390 TIBCO, 137 Enterprise Message Service, 444 StreamBase (stream analytics), 466 time concurrency and, 187 cross-channel timing dependencies, 331 in distributed systems, 287-299 (see also clocks) clock synchronization and accuracy, 289 relying on synchronized clocks, 291-295 process pauses, 295-299 reasoning about, in stream processors, 468-472 event time versus processing time, 469, 477, 498 knowing when window is ready, 470 timestamp of events, 471 types of windows, 472 system models for distributed systems, 307 time-dependence in stream joins, 475 time-of-day clocks, 288 timeliness, 524 coordination-avoiding data systems, 528 correctness of dataflow systems, 525 timeouts, 279, 557 dynamic configuration of, 284 for failover, 158 length of, 281 timestamps, 343 assigning to events in stream processing, 471 for read-after-write consistency, 163 for transaction ordering, 295 insufficiency for enforcing constraints, 347 key range partitioning by, 203 Lamport, 345 logical, 494 ordering events, 291, 345 Titan (database), 50 tombstones, 74, 191, 456 topics (messaging), 137, 440 total order, 341, 557 limits of, 493 sequence numbers or timestamps, 344 total order broadcast, 348-352, 493, 522 consensus algorithms and, 366-368 Index | 587 implementation in ZooKeeper and etcd, 370 implementing with linearizable storage, 351 using, 349 using to implement linearizable storage, 350 tracking behavioral data, 536 (see also privacy) transaction coordinator (see coordinator) transaction manager (see coordinator) transaction processing, 28, 90-95 comparison to analytics, 91 comparison to data warehousing, 93 transactions, 221-267, 558 ACID properties of, 223 atomicity, 223 consistency, 224 durability, 226 isolation, 225 compensating (see compensating transac‐ tions) concept of, 222 distributed transactions, 352-364 avoiding, 492, 502, 521-528 failure amplification, 364, 495 in doubt/uncertain status, 358, 362 two-phase commit, 354-359 use of, 360-361 XA transactions, 361-364 OLTP versus analytics queries, 411 purpose of, 222 serializability, 251-266 actual serial execution, 252-256 pessimistic versus optimistic concur‐ rency control, 261 serializable snapshot isolation (SSI), 261-266 two-phase locking (2PL), 257-261 single-object and multi-object, 228-232 handling errors and aborts, 231 need for multi-object transactions, 231 single-object writes, 230 snapshot isolation (see snapshots) weak isolation levels, 233-251 preventing lost updates, 242-246 read committed, 234-238 transitive closure (graph algorithm), 424 trie (data structure), 88 triggers (databases), 161, 441 implementing change data capture, 455 implementing replication, 161 588 | Index triple-stores, 55-59 SPARQL query language, 59 tumbling windows (stream processing), 472 (see also windows) in microbatching, 477 tuple spaces (programming model), 507 Turtle (RDF data format), 56 Twitter constructing home timelines (example), 11, 462, 474, 511 DistributedLog (event log), 448 Finagle (RPC framework), 135 Snowflake (sequence number generator), 294 Summingbird (processing library), 497 two-phase commit (2PC), 353, 355-359, 558 confusion with two-phase locking, 356 coordinator failure, 358 coordinator recovery, 363 how it works, 357 issues in practice, 363 performance cost, 360 transactions holding locks, 362 two-phase locking (2PL), 257-261, 329, 558 confusion with two-phase commit, 356 index-range locks, 260 performance of, 258 type checking, dynamic versus static, 40 U UDP (User Datagram Protocol) comparison to TCP, 283 multicast, 442 unbounded datasets, 439, 558 (see also streams) unbounded delays, 558 in networks, 282 process pauses, 296 unbundling databases, 499-515 composing data storage technologies, 499-504 federation versus unbundling, 501 need for high-level language, 503 designing applications around dataflow, 504-509 observing derived state, 509-515 materialized views and caching, 510 multi-partition data processing, 514 pushing state changes to clients, 512 uncertain (transaction status) (see in doubt) uniform consensus, 365 (see also consensus) uniform interfaces, 395 union type (in Avro), 125 uniq (Unix tool), 392 uniqueness constraints asynchronously checked, 526 requiring consensus, 521 requiring linearizability, 330 uniqueness in log-based messaging, 522 Unix philosophy, 394-397 command-line batch processing, 391-394 Unix pipes versus dataflow engines, 423 comparison to Hadoop, 413-414 comparison to relational databases, 499, 501 comparison to stream processing, 464 composability and uniform interfaces, 395 loose coupling, 396 pipes, 394 relation to Hadoop, 499 UPDATE statement (SQL), 40 updates preventing lost updates, 242-246 atomic write operations, 243 automatically detecting lost updates, 245 compare-and-set operations, 245 conflict resolution and replication, 246 using explicit locking, 244 preventing write skew, 246-251 V validity (consensus), 365 vBuckets (partitioning), 199 vector clocks, 191 (see also version vectors) vectorized processing, 99, 428 verification, 528-533 avoiding blind trust, 530 culture of, 530 designing for auditability, 531 end-to-end integrity checks, 531 tools for auditable data systems, 532 version control systems, reliance on immutable data, 463 version vectors, 177, 191 capturing causal dependencies, 343 versus vector clocks, 191 Vertica (database), 93 handling writes, 101 replicas using different sort orders, 100 vertical scaling (see scaling up) vertices (in graphs), 49 property graph model, 50 Viewstamped Replication (consensus algo‐ rithm), 366 view number, 368 virtual machines, 146 (see also cloud computing) context switches, 297 network performance, 282 noisy neighbors, 284 reliability in cloud services, 8 virtualized clocks in, 290 virtual memory process pauses due to page faults, 14, 297 versus memory management by databases, 89 VisiCalc (spreadsheets), 504 vnodes (partitioning), 199 Voice over IP (VoIP), 283 Voldemort (database) building read-only stores in batch processes, 413 hash partitioning, 203-204, 211 leaderless replication, 177 multi-datacenter support, 184 rebalancing, 213 reliance on read repair, 179 sloppy quorums, 184 VoltDB (database) cross-partition serializability, 256 deterministic stored procedures, 255 in-memory storage, 89 output streams, 456 secondary indexes, 207 serial execution of transactions, 253 statement-based replication, 159, 479 transactions in stream processing, 477 W WAL (write-ahead log), 82 web services (see services) Web Services Description Language (WSDL), 133 webhooks, 443 webMethods (messaging), 137 WebSocket (protocol), 512 Index | 589 windows (stream processing), 466, 468-472 infinite windows for changelogs, 467, 474 knowing when all events have arrived, 470 stream joins within a window, 473 types of windows, 472 winners (conflict resolution), 173 WITH RECURSIVE syntax (SQL), 54 workflows (MapReduce), 402 outputs, 411-414 key-value stores, 412 search indexes, 411 with map-side joins, 410 working set, 393 write amplification, 84 write path (derived data), 509 write skew (transaction isolation), 246-251 characterizing, 246-251, 262 examples of, 247, 249 materializing conflicts, 251 occurrence in practice, 529 phantoms, 250 preventing in snapshot isolation, 262-265 in two-phase locking, 259-261 options for, 248 write-ahead log (WAL), 82, 159 writes (database) atomic write operations, 243 detecting writes affecting prior reads, 264 preventing dirty writes with read commit‐ ted, 235 WS-* framework, 133 (see also services) WS-AtomicTransaction (2PC), 355 590 | Index X XA transactions, 355, 361-364 heuristic decisions, 363 limitations of, 363 xargs (Unix tool), 392, 396 XML binary variants, 115 encoding RDF data, 57 for application data, issues with, 114 in relational databases, 30, 41 XSL/XPath, 45 Y Yahoo!

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Gray and Catharine van Ingen: “Empirical Measurements of Disk Failure Rates and Error Rates,” Microsoft Research, MSR-TR-2005-166, December 2005. [65] Annamalai Gurusami and Daniel Price: “Bug #73170: Duplicates in Unique Sec‐ ondary Index Because of Fix of Bug#68021,” bugs.mysql.com, July 2014. [66] Gary Fredericks: “Postgres Serializability Bug,” github.com, September 2015. [67] Xiao Chen: “HDFS DataNode Scanners and Disk Checker Explained,” blog.clou‐ dera.com, December 20, 2016. [68] Jay Kreps: “Getting Real About Distributed System Reliability,” blog.empathy‐ box.com, March 19, 2012. [69] Martin Fowler: “The LMAX Architecture,” martinfowler.com, July 12, 2011. [70] Sam Stokes: “Move Fast with Confidence,” blog.samstokes.co.uk, July 11, 2016. [71] “Sawtooth Lake Documentation,” Intel Corporation, intelledger.github.io, 2016. [72] Richard Gendal Brown: “Introducing R3 Corda™: A Distributed Ledger Designed for Financial Services,” gendal.me, April 5, 2016. [73] Trent McConaghy, Rodolphe Marques, Andreas Müller, et al.: “BigchainDB: A Scalable Blockchain Database,” bigchaindb.com, June 8, 2016. [74] Ralph C. Merkle: “A Digital Signature Based on a Conventional Encryption Function,” at CRYPTO ’87, August 1987. doi:10.1007/3-540-48184-2_32 [75] Ben Laurie: “Certificate Transparency,” ACM Queue, volume 12, number 8, pages 10-19, August 2014. doi:10.1145/2668152.2668154 Summary | 549 [76] Mark D.

pages: 454 words: 127,319

Billionaires' Row: Tycoons, High Rollers, and the Epic Race to Build the World's Most Exclusive Skyscrapers
by Katherine Clarke
Published 13 Jun 2023

At 111 West 57th Street, there was a contest between two wealthy buyers for one of the building’s most expensive units, a 7,130-square-foot aerie on the 72nd floor asking $66 million. The developers had already accepted an offer on the unit when they got a second offer from Gavin Wood, one of the founders of Ethereum, the blockchain-based computer network. (The unit went to the first, unidentified buyer.) Tim Gong, an executive whose firm owned a major stake in the parent company of social media giant TikTok, bought two units at the building for $34 million. At Central Park Tower, new buyers included Nicole Mendelsohn, the vice president of the global business group at Meta, Facebook’s parent company, which was then in the midst of doubling down on the digital realm known as the metaverse.

…

See also 432 Park Avenue Children’s Investment Fund and, 80–81 CIM and, 73–78 Citigroup Inc.’s private bank and, 79 CMZ Ventures and, 39–41, 43–46 Macklowes and, 13–14, 34, 35, 37–38, 44–45 Dubai, 49, 346 Dyson, James, 266 E East 60th Street between Park and Madison avenues, 190 EB-5 visa program, 89, 270 Ecclestone, Petra, 263, 264 Edward Durell Stone & Associates, 3 Ehrsam, Fred, 336 Eichner, Ian Bruce, 37, 314 80/20 program, 152 800 Park Avenue, 21–22 834 Fifth Avenue, 20 11 Madison Avenue, 78 Emaar Properties, 38 Emery Roth & Sons, 3, 101 Empire State Building, xxvii, xxviii–xxx, 48 Ethereum, 336 Extell Management and Investment Company basic facts about, 56 Calvary Baptist Church and, 65 Central Park Tower and, 271–72 Andrew Cuomo and, 151–52, 153, 154–55 financial status of, 83, 335 hundredth birthday of Iris Apfel, 334–35 One57 and, 100–101, 138–40, 142, 150, 152–53 Vornado and, 134, 136–37 Zuckerman and, 154 F façades Carnegie Hall, 24, 30 53 West 53rd Street, 190 One57, 83, 143 111 West 57th Street, xxi, 212 Steinway Hall, 30 fair housing laws, 205, 251 Fascitelli, Mike, 129 Fayed, Ali, 35, 75 Fegel, Gary, 290 Feldman, Ziel, 57, 62, 170 Fertitta, Frank, 266 Field, Nikki, 65, 101–3, 260 15 Central Park West Blackmon and, 88–89 design, 142 history of, 31–32 marketing of, 94, 117 projected sales of units at, 210 purchase prices, 242, 252 residents, 32, 86–87, 88–89, 90–91, 209, 242 residents moving to 220 Central Park South from, 208–9 restaurant at, 31, 110 Sukenik and, 130–31 53 West 53rd Street, 147–48, 190, 266 financial crisis of 2008 American housing market and, 52 Barnett’s plans for One57 and, 82–83 Extell and, 83 Macklowe and, 3, 5 traditional commercial banks and, 42 Financial District, high-rise condos in, 262 Finkelstein-LeBow, Jacqueline, 322 Firtash, Dmytro, 43–44, 45–46 Fiscal Policy Institute, 307 520 Park Avenue marketing of, 237 residents and prices, 266 sales of units at, 301 550 Madison Avenue, 191 Flaunt, 259 floor area ratios (FARs), 59, 60–61 Florida income sources of wealthy residents in, 205 mansion listings, 240, 263 Mink allegations against Stern and JDS in, 333 supertalls in, 346 ultra-wealthy in, 233, 315 Floyd, George, 314 Forbes, 89 Forrell & Thomas, 62 Fortress Investment Group, 343 Foster, Norman, 296 Foucault, Michel, xiii 421-A tax abatements, 150–55 432 Park Avenue.

pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

In the same way that Internet communications have seen costs drop to near zero, we expect to see internal organizational and transactions costs also fall to near zero as we increasingly information-enable and distribute our organizational structures. Ultimately, in the face of such low transaction costs, we anticipate what we’re calling a Cambrian Explosion in organizational design—everything from community-based structures to virtual organizations (see Ethereum) that will be small, nimble and extensible. It is also becoming increasingly clear that, like the Internet, the ExO paradigm is not just for business. It can just as easily be applied to all sorts of enterprises and organizations, from academia to non-profits to government. In short, it is not just a system of commerce, but also a philosophy of action.

…

Oculus Rift, High Fidelity and Google Glass drive new applications. Implications: Remote viewing; centrally located experts serving more areas; new practice areas; remote medicine. Bitcoin and block chain Description: Trustless, ultra-low-cost secure transactions enabled by distributed ledgers that log everything. Implications: The blockchain becomes a trust engine; most third-party validation functions become automated (e.g., multi-signatory contracts, voting systems, audit practices). Micro-transactions and new payment systems become ubiquitous. Neuro-feedback Description: Use of feedback loops to bring the brain to a high level of precision.

pages: 252 words: 78,780

Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us
by Dan Lyons
Published 22 Oct 2018

They’ve seen other big old companies get killed off by Silicon Valley, and they would rather not have this happen to them. They seem to believe that some magic elixir exists here, some recipe for innovation that floats in the air and can be absorbed if you drive around with your windows open, smelling the eucalyptus trees. They see people getting rich on things they don’t even understand. Blockchain? Ethereum? Initial coin offerings? So they fly out and have drinks at the Rosewood Hotel on Sand Hill Road in Menlo Park, where venture capitalists hang around, as do expensive “companions,” many with Eastern European accents. They eat lunch at the Battery, a members-only private club for social-climbing parvenus in San Francisco.

pages: 611 words: 130,419

Narrative Economics: How Stories Go Viral and Drive Major Economic Events
by Robert J. Shiller
Published 14 Oct 2019

Narrative economics, the study of the viral spread of popular narratives that affect economic behavior, can improve our ability to anticipate and prepare for economic events. It can also help us structure economic institutions and policy. To get a feel for where we are going, let’s begin by considering one such popular narrative, recently in full swing. Bitcoin, the first of thousands of privately issued cryptocurrencies—including Litecoin, Ripple, Ethereum, and Libra—has generated enormous levels of talk, enthusiasm, and entrepreneurial activity. These narratives surrounding Bitcoin, the most remarkable cryptocurrency in history as judged by the speculative enthusiasm for it and its market price rather than its actual use in commerce, provide an intuitive basis for discussing the basic epidemiology of narrative economics (which we explore in detail in chapter 3).

…

In reality, Bitcoin was meant to function as a monetary weapon, as a cryptocurrency poised to undermine authority.5 Most Bitcoin enthusiasts might not describe their enthusiasm in such extreme terms, but this passage seems to capture a central element of their narrative. Both cryptocurrencies and blockchains (the accounting systems for the cryptocurrencies, which are by design maintained democratically and anonymously by large numbers of individuals and supposedly beyond the regulation of any government) seem to have great emotional appeal for some people, kindling deep feelings about their position and role in society.

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See also gold standard Bitcoin narrative, xviii, 3–11; anarchism and, 5–7; bimetallism and, 108, 161–62, 171; cause of increased value and, 72; contagion of, 21–23; cosmopolitan culture and, 4, 11, 87; cryptocurrencies competing with, 92; epidemic theory applied to, 21–23; fading by 2013, 76; fascination with narratives about money and, 173; fear of inequality and, 8–9; the future and, 9–10, 87; geographic pattern of spread, 299; history of, 4; as human-interest story, 7–8; key features of, 87; mathematical concepts underlying, 5, 302n3; membership in world economy and, 11; as mystery story, 7, 8, 162; in news articles by year, 22, 22f; in news articles compared to relevant algorithms, 9–10; sale of Bitcoin in convenience stores and, 10; similarity to gold standard and bimetallism narratives, 108–9; as successful economic narrative, 3–4; technocracy movement and, 193; uncertain truth of, 96; volatility of value in, 5, 10. See also Nakamoto, Satoshi Bix, Amy Sue, 186–87 Blade Runner (film), 203 Blanc, Louis, 102 Blinder, Alan, 281 blockchains, 6 blue jeans, 147–48, 149 blue sky laws, 220, 221 Booker, Christopher, 16 book jackets, 60–61 Boulding, Kenneth E., xv–xvi Box, George E. P., 295 Boycott, Charles C., 239–40 The Boycott in American Trade Unions (Wolman), 241 boycott narrative, 239–43; in 1973–75 recession, 256–57; contributing to 1920–21 depression, 249; going viral, 241; during Great Depression, 254; origins of, 239–40; profiteer stories in World War I and, 241–42, 246; recurring periodically, 241; during world financial crisis of 2007–9, 257; after World War II, 255.

pages: 589 words: 147,053

The Age of Em: Work, Love and Life When Robots Rule the Earth
by Robin Hanson
Published 31 Mar 2016

“Time Spent in Primary Activities and Percent of the Civilian Population Engaging in Each Activity, Averages per Day by Sex, 2012 Annual Averages.” Bureau of Labor Statistics Economic News Release. June 20. http://www.bls.gov/news.release/atus.t01.htm. Buterin, Vitalik. 2014. “White Paper: A Next-Generation Smart Contract and Decentralized Application Platform.” April. https://www.ethereum.org/pdfs/EthereumWhitePaper.pdf. Caplan, Bryan. 2008. “The Totalitarian Threat.” In Global Catastrophic Risks, edited by Nick Bostrom and Milan Ćirković, 504–519. Oxford University Press, July 17. Caplan, Bryan, and Stephen Miller. 2010. “Intelligence Makes People Think Like Economists: Evidence from the General Social Survey.”

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Very secure and anonymous communications between willing parties can be arranged via “public key cryptography,” wherein each person publishes a public key for which they can prove only they know the matching private key. In addition, robust systems of secure anonymous decentralized transactions may be built on the recent innovation of block-chain based cryptographic systems, where a public record of all transactions between public key labeled accounts prevents double-spending of assets. Such systems could support digital currencies, token systems, safe wallets, registration, identity, decentralized file storage, multi-signature escrow, consensus via rewarding those who best guess a consensus, financial derivatives including insurance and bets, and more general decentralized autonomous organizations (Nakamoto 2008; Buterin 2014).

pages: 497 words: 144,283

Connectography: Mapping the Future of Global Civilization
by Parag Khanna
Published 18 Apr 2016

With the Bali Trade Facilitation Agreement of 2013, the harmonization of customs administration (cutting red tape) could add $1 trillion to world GDP and create twenty million jobs. A study undertaken by the World Economic Forum and Bain estimates that further aligning supply chain standards would boost world GDP by an enormous 5 percent, while implementation of all current WTO accords would deliver only 1 percent growth. The Ethereum blockchain platform will allow for standardized and transparent contracts between trading parties beyond any single jurisdiction and, when combined with real-time data sharing on supply chain transactions, can substantially reduce the cost of insuring trade. Open trade and open borders further reorganize the world into functional circuits.