Greyball

Super Pumped: The Battle for Uber
by Mike Isaac
Published 2 Sep 2019

On the morning of March 3, the New York Times sent out a push alert to the mobile phones of subscribers: “Uber has for years used its app to secretly identify and sidestep law enforcement officials where it was restricted or banned,” the alert read. The blowback was swift. Attorneys general across the United States began asking Uber whether or not it used Greyball in their cities. Days after the report, Joe Sullivan, Uber’s security chief, prohibited employees from using the Greyball tool to target authorities in the future, and said Uber was reviewing the use of Greyball over Uber’s entire history. The US Department of Justice opened a probe into Uber’s use of Greyball and whether or not it was lawful; the inquiry widened to Philadelphia, Portland, and other cities where it had been used. Uber already had the reputation for being uncooperative and aggressive.

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v=TS0NuV-zLZE. 244 “We will impound the vehicle”: Victor Fiorillo, “Uber Launches UberX In Philadelphia, but PPA Says ‘Not So Fast,’ ” Philadelphia, October 25, 2014, https://www.phillymag.com/news/2014/10/25/uber-launches-uberx-philadelphia/. 244 “UBERX: REMINDER”: Documents held by author. 245 a behavior engineers called “eyeballing”: Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 247 “Uber has for years used”: Isaac, “How Uber Deceives the Authorities Worldwide.” 247 Uber’s security chief, prohibited employees: Daisuke Wakabayashi, “Uber Seeks to Prevent Use of Greyball to Thwart Regulators,” New York Times, March 8, 2017, https://www.nytimes.com/2017/03/08/business/uber-regulators-police-greyball.html. 247 Department of Justice opened a probe: Mike Isaac, “Uber Faces Federal Inquiry Over Use of Greyball Tool to Evade Authorities,” New York Times, May 4, 2017, https://www.nytimes.com/2017/05/04/technology/uber-federal-inquiry-software-greyball.html. 247 the inquiry widened to Philadelphia: Mike Isaac, “Justice Department Expands Its Inquiry into Uber’s Greyball Tool,” New York Times, May 5, 2017, https://www.nytimes.com/2017/05/05/technology/uber-greyball-investigation-expands.html. 248 He called it The Rideshare Guy: Harry Campbell, “About the Rideshare Guy: Harry Campbell,” The Rideshare Guy (blog), https://therideshareguy.com/about-the-rideshare-guy/. 248 directly due to the string of controversies: Kara Swisher and Johana Bhuiyan, “Uber President Jeff Jones Is Quitting, Citing Differences Over ‘Beliefs and Approach to Leadership,’ ” Recode, March 19, 2017, https://www.recode.net/2017/3/19/14976110/uber-president-jeff-jones-quits. 250 “there’s just a bunch of models”: Emily Peck, “Travis Kalanick’s Ex Reveals New Details About Uber’s Sexist Culture,” Huffington Post, March 29, 2017, https://www.huffingtonpost.com/entry/travis-kalanick-gabi-holzwarth-uber_us_58da7341e4b018c4606b8ec9. 253 “I am so sorry for being cold”: Amir Efrati, “Uber Group’s Visit to Seoul Escort Bar Sparked HR Complaint,” The Information, March 24, 2017, https://www.theinformation.com/articles/uber-groups-visit-to-seoul-escort-bar-sparked-hr-complaint. 253 reporter’s cell phone number: Efrati, “Uber Group’s Visit to Seoul Escort Bar.”

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Uber security personnel spied on government officials, looked deep into their digital lives, and at times followed them to their houses. After zeroing in on problematic individuals, the company would deploy one of its most effective weapons: Greyball. Greyball was a snippet of code affixed to a user’s Uber account, a tag that identified that person as a threat to the company. It could be a police officer, a legislative aide or, in England’s case, a transportation official. Having been Greyballed, England and his fellow officers were served up a fake version of the Uber app, populated with ghost cars. They had no chance of ever capturing the rogue drivers. They might not even know if drivers were operating at all.

Humans as a Service: The Promise and Perils of Work in the Gig Economy
by Jeremias Prassl
Published 7 May 2018

Uber contests these allegations: Mike Isaac, ‘How Uber deceives the authorities worldwide’, The New York Times (3 March 2017), http://www.nytimes. com/2017/03/03/technology/uber-greyball-programme-evade-authorities. html?_r=1, archived at https://perma.cc/G48X-RUV7; Julia Carrie Wong, ‘Greyball: how Uber used secret software to dodge the law’, The Guardian (4 March 2017), http://www.theguardian.com/technology/2017/mar/03/uber- secret-programme-greyball-resignation-ed-baker, archived at https://perma. cc/CVR6-BR3R; Amir Efrati, ‘Uber’s top secret “Hell” program exploited Lyft’s vulnerability’, The Information (12 April 2017), http://www.theinformation .com/ubers-top-secret-hell-programme-exploited-lyfts-vulnerability, archived at https://perma.cc/7TQX-UJ4M; Julia Carrie Wong, ‘Uber’s secret Hell program violated drivers’ privacy, class-action suit claims’, The Guardian (25 April 2017), http://www.theguardian.com/technology/2017/apr/24/uber-hell-programme- driver-privacy-lyft-spying, archived at https://perma.cc/35ZK-DVKC 72.

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In the spring of 2017, a series of reports alleged that Uber had modified its soft- ware to nefarious ends: a tool by the name of ‘Greyball’ was designed to defeat law enforcement operatives by rejecting their ride requests; another project, with the cheerful name ‘Hell’, is alleged to have targeted rival operator Lyft by ‘building up profiles of individuals and figuring out who was driving for Uber and Lyft. Uber then prioritized sending rides to drivers who used both apps, hoping to persuade drivers to abandon Lyft.’71 The company has suggested that Greyball was used as an important tool in combatting fraudulent ride requests and investigations into Hell are ongoing.

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L. 176 Chen, Keith 122 Davies, Paul 174 Cherry 38 Davies, Rob 151 Cherry, Miriam 97, 99, 132, 173, 174, 184 Day, Iris 177 chess robots 1, 6 Deakin, Simon 36, 112, 130, 131, 152, 172, China 12, 38, 153 174, 177, 178, 184, 185 Chowdhry, Amit 181 deductions from pay 15, 19, 60, 63, 67 Christenson, Clayton M. 39 Deep Blue 1 ‘churn’/worker turnover 68 Deliveroo 2, 11, 12, 13, 115 Clark, Shelby 46 collective action by drivers 113 classificatory schemes 13, 28–9, 147 contractual prohibitions 66–7 misclassification 95, 96–100 employment litigation 99 Clement, Barrie 162 internal guidelines 43–4 Clover, Charles 153 safety and liability 122–3 Coase, Ronald 19, 94, 101, 172 wage rates 65 Coase’s theory 19, 20 delivery apps 2 Codagnone, Cristiano 150 demand fluctuations 78 Cohen, Molly 36, 37, 152, 157 Denmark 36 ‘collaborative consumption’ 42 deregulation 37, 40 (see also regulation) collective action 113–15 Dholakia, Utpal 150 collective bargaining rights 48, 65, 82 Didi 2, 12, 38 commission deductions 15, 19, 60, 63, 67 differential wage rates 109–11 commodification of work 76, 77, 110 digital disruption 49, 50 competition 88 ‘digital feudalism’ 83 consumer demand 17–18 digital innovation see innovation consumer protection 10, 112, 121, 128–9 digital market manipulation 123 safety and liability 122–3, 128–9 digital payment systems 5 * * * Index 193 digital work intermediation 5, 11, 13–16 borderline cases 100 disability discrimination 62, 121 identifying the employer 100 discriminatory practices 62, 94, 113, easy cases 102–3 121, 180 functional concept of the disputes 66 employer 101–2, 104 disruptive innovation 39–40, 49, 50, 95 genuine entrepreneurs 103 dockyards 78, 79–80 harder cases 103–4 ‘doublespeak’ 31–50, 71, 95, 97–8, 133 multiple employers 103 Doug H 160, 163 platforms as employers 102–3 down-time 60, 65, 76, 77 ‘independent worker’ 48 Downs, Julie 180 misclassification 95, 96–100 Drake, Barbara 168 ‘personal scope question’ 93 drink driving 133, 184–5 employment taxes 125–7 Dzieza, Josh 163 Engels, Friedrich 81, 168 ‘entrepreneur-coordinator’ 101 economic crises 145 entrepreneurship 6, 8, 21, 32, 42, 43, economic drivers 7, 18–24 45–6, 50, 52 (see also micro- Edwards, Jim 146 entrepreneurs) efficiency 7 autonomy 53–5 Elejalde-Ruiz, Alexia 175 algorithmic control and 55–8 ‘elite worker’ status 61, 67 sanctions and 61–3 ‘emperor’s new clothes’ 71 wages and 58–61 empirical studies 28–9 freedom 8, 14, 27, 29, 47, 49, 51, 52, employer responsibility 104 53, 55, 65–8, 69, 85, 96, 108, 110, employment contracts 94 112, 113 bilateral relationships 100 on-demand trap and 68–70 employment law 4, 9, 10, 38, 84 risk and 86 (see also regulation) genuine entrepreneurs 102, 103 continuing importance 139–40 misclassification 96–7, 98, 101 control/protection trade-off 93–4, 95 ‘personal scope question’ 93 European Union 107, 111, 112, 178 self-determination 63–5 flexibility and environmental impacts 21, 26 innovation and 90 Estlund, Cynthia 137, 185 measuring working time 105–7 Estonia 127 mutuality of obligation 174 Estrada, David 41 new proposals 46–9 euphemisms 44–5 rebalancing the scales 107–8 European Union law 107, 111, 112, 178 collective action 113–15 exploitation 26–7 portable ratings 111–13 Ezrachi, Ariel 150 surge pricing 108–11 ‘risk function’ 131, 132 Facebook 35, 57 workers’ rights 105 FairCrowdWork 114, 179 rights vs flexibility 115–17 Farrell, Sean 164 employment litigation FedEx 97 FedEx 97, 173 feedback 5, 15–16 France 99 Feeney, Matthew 35, 151 Uber 45, 48, 54–5, 98, 99, 106, 115 Field, Frank 26 UK 45, 48, 98–9, 106, 115 financial losses 22–3 US 54–5, 97, 98, 99 ‘financially strapped’ 29 employment status 21, 45, 47 Finkin, Matthew 74, 84, 166, 169 * * * 194 Index Fiverr 12, 13, 24, 78 historical precedents and CEO 17 problems 72, 73–85 Fleischer, Victor 20, 147 rebranding work 4–6, 32 flexibility 8, 10, 12, 107, 108 labour as a technology 5–6 vs rights 115–17 market entrants 88 food-delivery apps 12 matching 13, 14, 18–20 Foodora 2, 12 monopoly power 23–4, 28 Foucault, Michel 55, 159 network effects 23–4 founding myths 34–5 overview 2–3 Fox, Justin 182 perils 6, 26–8, 31 fragmented labour markets 83, 84, 86, platform paradox 5 90, 113 platforms as a service 7–8 France 78 consumer protection 10 employment litigation 99 potential 6, 7, 12, 24–6, 31 Labour Code 114, 176, 179 regulation 9–10 (see also regulation) regulatory battles 36 real cost of on-demand services 119, tax liability 126 121–2 (see also structural ‘free agents’ 28–9 imbalances) Freedland, Mark 174, 175 regulation see regulation Freedman, Judith 111, 178 regulatory arbitrage 20–2 freedom 8, 14, 27, 29, 47, 49, 51, 52, 53, size of the phenomenon 16–17, 145–6 55, 65–8, 69, 85, 96, 108, 110, work on demand 11–29 112, 113 gigwork 13 on-demand trap and 68–70 Giliker, Paula 183 risk and 86 global economic crises 145 Frey, Carl 136, 185 Goodley, Simon 173 Fried, Ina 183 GPS 5, 57 Greenhouse, Steven 66, 164 Gardner-Selby, W. 185 Griswold, Alison 164, 181 gender parity 144 (see also Grossman, Nick 46 discriminatory practices) Gumtree 20 Germany Gurley, Bill 161 regulatory battles 36 Guyoncourt, Sally 178 workers’ rights 114 gift vouchers 105 Hacker, Jacob 86, 170 gig economy Hall, Jonathan 60, 162, 165 business models 12–13, 44, 100 Hammond, Philip 126, 182 cash burn 22–3 Hancock, Matthew 46, 166 clash of narratives 8 Handy 18 classification 13, 28–9 Hardy, Tess 176 critics 2, 3, 8 Harman, Greg 163 digital work intermediation 5, 11, Harris, Seth 48, 49, 105, 157, 175 13–16 Hatton, Erin 82, 169 economic drivers 7, 18–24 Heap, Lisa 177 empirical studies 28–9 Helpling 2 employment law and see employment Hemel, Daniel 147, 170 law Hesketh, Scott 181 enthusiasts 3, 4, 8 hiring practices: historical gigwork vs crowdwork 13 perspective 78, 79 growth 17–18 historical perspective 72, 73–85 ‘humans as a service’ 3–6 Hitch 38 * * * Index 195 Hitlin, Paul 162 Internet Holtgrewe, Ursula 169 collective action 113 HomeJoy 132 Third Wave 73 Hook, Leslie 153 Irani, Lilly 6, 114, 142, 162, 179 Horan, Hubert 22, 148 Isaac, Mike 170, 171 Horowith, Sara 144 Issa, Darrell 41 hostile takeovers 111–12 Howe, Jeff 7, 11, 142 jargon 42–5 Huet, Ellen 153 Jensen, Vernon 167, 168, 170 Human Intelligence Tasks (HITs) 60, 93 Jobs, Steve 35 ‘humans as a service’ 3–6 joint and several liability 104 historical precedents and problems 72, Justia Trademarks 143 73–85 rebranding work 4–6, 32, 40–50 Kalanick, Travis 43, 86 Hunter, Rachel 106, 176 Kalman, Frank 16, 144 Huws, Ursula 27, 141, 150 Kaminska, Izabella 22–3, 44, 90, 148, 156, 169, 171, 172 ‘idle’ time 60, 65, 76, 77 Kaplow, Louis 184 illegal practices 57 Kasparov, Garry 1 immigrant workers 77 Katz, Lawrence 16 incentive structures 67–8 Katz, Vanessa 116, 179 independent contractors 21 Kaufman, Micha 17, 145, 149 Independent Workers Union of Great Kempelen, Wolfgang von 1 Britain (IWGB) 113, 179 Kennedy, John F. 135, 185 industrialization 75 Kenya 36 industry narratives 32–3, 49–50 Kessler, Sarah 151 information asymmetries 32, 54, 87, 131 Keynes, John Maynard 135, 185 innovation 3, 6, 8, 9, 10, 31, 32, 42, 45–6, King, Tom, Lord King of Bridgwater 71 110 cheap labour and 89 Kirk, David 133, 184 disruptive innovation 39–40, 49, 95 Kitchell, Susan 166 historical precedents and problems 72, Klemperer, Paul 165 73–85 Krueger, Alan 16, 48, 49, 60, 105, 106, incentives 86–90 157, 162, 165, 175 myths 72, 83 Krugman, Paul 170 obstacles to 88–90 Kucera, David 186 paradox 72, 87 problematic aspects 85–90 labour law see employment law productivity and 87 Lagarde, Christine 86, 170 shifting risk 85–6 Leimeister, Jan Marco 13 workers’ interests and 89–90 Leonard, Andrew 33, 151 innovation law perspective 36 Lewis, Mervyn 168 ‘Innovation Paradox’ 9 Liepman, Lindsay 184 insecure work 9, 10, 12, 27, 42, 107 Lloyd-Jones, Roger 168 historical perspective 80, 81 loan facilities 68 insurance 123 lobbying groups 32, 47, 48 intermediaries 83 (see also digital work Lobel, Orly 11, 37–8 intermediation) low-paid work 9, 26–7, 40–2, historical perspective 79–80 59, 61 International Labour Organization low-skilled work 76, 77, 82 (ILO) 4, 83, 97, 169, 173 automation and 138 * * * 196 Index Lukes, Steven 159 Murgia, Madhumita 182 Lyft 2, 12, 13, 38, 41, 42, 76 mutuality of obligation 174 algorithmic control mechanisms 56 network effects 23–4 regulatory battles 35 Newcomer, Eric 148, 165 Uber’s competitive strategies 88 Newton, Casey 164 Nowag, Julian 183 McAfee, Andrew 137, 138, 185 Machiavelli, Niccolo 93, 172 O’Connor, Sarah 43, 155 machine learning 136, 137 ODesk 60 McCurry, Justin 186 O’Donovan, Caroline 144, 164, 181 Malone, Tom 73 Oei, Shu-Yi 124, 125, 132, 147, 182, 184 Mamertino, Mariano 161, 163 Ola 2, 12 market entrants 88 on-demand trap 68–70 market manipulation 123 on-demand work 11– 29 Markowitz, Harry 184 real cost of on-demand services 119, Marsh, Grace 182 121–2 (see also structural Marshall, Aarian 186 imbalances) Martens, Bertin 150 Orwell, George 31, 151 Marvit, Moshe 142 Osborne, Hilary 164 Marx, Patricia 119–20, 180 Osborne, Michael 136, 185 matching 13, 14, 18–20 outsourcing Maugham, Jolyon 182 agencies 40 Mayhew, Henry 77, 78, 79, 167 ‘web services’ 2 Mechanical Turk 1, 2, 6 outwork industry 74–5, 76–7, 79, 80, 89 mental harm 57–8 Owen, Jonathan 178 Meyer, Jared 149 ‘micro-entrepreneurs’ 8, 21, 46, 49, Padget, Marty 186 52–3, 63 Pannick, David, Lord Pannick 110 ‘micro-wages’ 27 Pasquale, Frank 8, 40, 154 middlemen 80 Peck, Jessica Lynn 26 minimum wage levels 3, 9, 21, 26, 27, 59, peer-to-peer collaboration 42, 43 94, 104, 105 Peers.org 32–3 minimum working hour guarantees 108 performance standard probations 61 misidentification 95, 96–100 personal data 112, 178 mobile payment mechanisms 5 ‘personal scope question’ 93 monopoly power 23–4, 28 Pissarides, Christopher 19, 147 Morris, David Z. 171 platform paradox 5 Morris, Gillian 174 platform responsibility 122–3, 128 MTurk 2, 3, 4, 11, 12, 24–5, 76, 139, platforms as a service 7–8 161–2, 163 consumer protection 10 algorithmic control mechanisms 56 regulation 9–10 (see also regulation) business model 100, 101, 103, 104 Plouffe, David 154 commission deductions 63 Poe, Edgar Allen 1 digital work intermediation 14, 15 Polanyi’s paradox 138–9 matching 19 political activism 114 payment in gift vouchers 105 portable ratings 111–13 quality control 120 Porter, Eduardo 171 TurkOpticon 114 ‘postindustrial corporations’ 20 wage rates 59, 60, 61 Postmates 57, 63, 121 * * * Index 197 Poyntz, Juliet Stuart 168 structural imbalances 130, 131 Prassl, Jeremias 174, 175, 176, 177, robots 136–7 178, 183 Mechanical Turk 1, 6 precarious work 9, 10, 12, 27, 42, 107 Rodgers, Joan 177 historical perspective 80, 81 Rodriguez, Joe Fitzgerald 181 price quotes 121–2 Rönnmar, Mia 175 surge pricing 58, 108–11, 122 Roosevelt, Franklin D. 133, 185 Primack, Dan 148 Rosenblat, Alex 54, 56, 65, 123, 131, 159, productivity 87 160, 163, 164, 182, 184 public discourse 69 Rosenblat, Joel 165 public health implications 27 Rubery, Jill 84, 169 punishment 57 (see also sanctions) Ryall, Jenny 181 quality control 5, 80, 120 safe harbours 47, 49 safety and liability 122–3, 128–9 rating mechanisms 5, 15–16, 53–4 sanctions 61–3 (see also punishment) algorithms 54, 55, 87–8 Sandbu, Martin 87, 170 discrimination 62, 113 Scheiber, Noam 164 portable ratings 111–13 Schmiechen, James 167, 168, 169 sanctions and 61–3 Schumpeter, Joseph 133 rebranding work 4–6, 32, 40–50 self-dealing 123 regulation 9–10 (see also employment law) self-determination 36–7, 47, 63–5 industry narratives 32–3, 49–50 (see also autonomy) new proposals 31, 46–9, 50 self-driving cars 89, 137 opponents 31, 33–4 sexual assaults 121, 180–1 Disruptive Davids 34–7 sexual discrimination 62, 144, 180 disruptive innovation theory ‘sham self-employment’ 97 39–40, 49 sharing economy 7, 20, 51 New Goliaths 37–40 critics 32–3 regulatory battles 35–7, 47–9 disruptive innovation 39, 49 safe harbours 47, 49 enthusiasts 61 self-regulation 36–7, 47 Sharing Economy UK 33, 37 shaping 32–3, 45–9 sharing platforms 116 regulatory arbitrage 20 –2, 147 Shavell, Steven 184 regulatory experimentation 36 Shleifer, Andrei 111, 178 Reich, Robert 108, 176 Shontell, Alyson 161 Relay Rides 46 Silberman, Six 61, 114, 162, 163, 179 ‘reluctants’ 29 Silver, James 156, 158 reputation algorithms 54 Singer, Natasha 43, 155, 156 ride-sharing/ridesharing 2, 21, 38, 41 Slee, Tom 32, 53, 142, 151, 155, 158, 159 (see also taxi apps) Smith, Adam 73 algorithmic control mechanisms 55–6 Smith, Jennifer 170 business model 102–3 Smith, Yves 148 discriminatory practices 62, 121 social media 114 maltreatment of passengers 121 social partners 10, 94 ride-sharing laws 47 social security contributions 21, 125–7 Ries, Brian 181 social security provision 3, 48, 131 Ring, Diane 124, 125, 132, 147, 182, 184 sociological critique 27–8 Risak, Martin 102, 175 specialization 75 risk shift 85–6 Spera 51, 158 * * * 198 Index Sports Direct 40–1 taxi regulation 21, 36, 37, 38, 114 Standage, Tom 141 vetting procedures 121 standardized tasks 76 tech:NYC 33 Stark, Luke 54, 56, 65, 159, 160, 163, 164 technological exceptionalism 6, 128 start-up loans 68 technological innovation see innovation Stefano, Valerio De 84, 169 technology 5–6, 27 Stigler, George 32, 151 unemployment and 135, 137, 140 Stone, Katherine 67, 165 terminology 42–5 structural imbalances time pressure 57 business model 130–2 Titova, Jurate 183 digital market manipulation 123 TNC, see transportation network levelling the playing field 127–32 company platform responsibility 122–3, 128 Tolentino, Jia 166 real cost of on-demand services 119, Tomassetti, Julia 20, 147, 156, 171 121–2 Tomlinson, Daniel 163 safety and liability 128–9 trade unions 65, 113, 114, 178, 179 sustainability 132–3 transaction cost 19 tax obligations 123–4, 129, 131, 132 transport network company (TNC) employment taxes and social regulation 47–8 security contributions 125–7 Truck arrangements 105 VAT 124–5, 129 Tsotsis, Alex 151 Stucke, Maurice 150 TurkOpticon 114, 162, 163, 179 Sullivan, Mike 180 Summers, Lawrence 111, 131, 178, 184 Uber 2, 11, 12, 43 Sundararajan, Arun 36, 37, 41, 73, 74, 75, algorithmic control mechanisms 56, 151, 152, 157, 166, 167 57, 58 Supiot, Alain 130–1, 177, 184 arbitration 165 surge pricing 58, 108–11, 122 autonomous vehicles and 89 survey responses 120 ‘churn’/worker turnover 68 Swalwell, Eric 41, 154 commission deductions 63 competitive strategies 88 takeovers 111–12 consumer demand 18 ‘task economies’ 76, 77, 79 control mechanisms 54 Task Rabbit 2, 12, 13, 46, 143–4, 163 creation of new job business model 100, 101, 160 opportunities 77–8 company law 56 digital work intermediation 14, 15 contractual prohibitions 66 disruptive innovation 39 digital work intermediation 14, 15–16 driver income projections 51 financial losses 22 Driver-Partner Stories 25, 149 founding myth 34–5 driver-rating system 158, 160 regulatory arbitrage 20 employment litigation terms of service 44, 53, 122, 158, 181 France 99 wage rates 64 UK 45, 48, 98, 106, 115 working conditions 57 US 54–5, 99 Taylor, Frederick 52–3, 72, 158 financial losses 22, 23 tax laws 84 ‘Greyball’ 88, 170 tax obligations 123–4, 129, 131, 132 ‘Hell’ 88, 170 employment taxes and social security loss-making tactics and market share 64 contributions 125–7 monopoly power 23 VAT 124–5, 129 positive externality claims 132–3 taxi apps 12, 20 regulatory arbitrage 20 * * * Index 199 regulatory battles 35, 36 Vaidhyanathan, Siva 40, 154 resistance to unionization 65, 178 value creation 18–19, 20 risk shift 86 van de Casteele, Mounia 182 safety and liability 122–3, 180–1 VAT 124–5, 129 sale of Chinese operation 38 Verhage, Julie 147 surge pricing 58, 122 vicarious liability 128 tax liability 125, 126, 127 unexpected benefits 26 wage rates 58–61, 64, 65 wage rates 58, 59, 60–1, 64, 65, 127 Wakabayashi, Daisuke 171 working conditions 113, 178 Warne, Dan 115 UberLUX 14 Warner, Mark 16 UberX 14, 51, 60 Warren, Elizabeth 127, 183 UK Webb, Beatrice and Sidney 80, 168 collective action 113 Weil, David 83, 169 employment litigation 45, 48, 98–9, 106 welfare state 130, 131 tax liability 124–5, 126 Wilkinson, Frank 84, 130, 131, 169, unemployment 135, 137, 140, 145 172, 184, 185 Union Square Ventures 46 Wong, Julia Carrie 170 unionization 10, 65, 113, 114, 178, 179 work on demand 11–29 ‘unpooling’ 147 worker classification 28–9, 147 Unterschutz, Joanna 178 misclassification 95, 96–100 Upwork 12, 76, 144 workers’ rights 105 algorithmic control mechanisms 56 vs flexibility 115–17 business model 100, 160 working conditions 57, 68–9 commission deductions 63, 67 historical perspective 77, 81 US Uber 113, 178 discriminatory practices 121 working time 105–7 employment litigation 54–5, 97, 98, 99 Wosskow, Debbie 157 regulatory battles 36, 47 Wujczyk, Marcin 178 tax liabilities 126–7 taxi regulation 36, 114 Yates, Joanne 73 transport network company (TNC) YouTube 58 regulation 47–8 user ratings 5, 15–16, 53–4, 55 Zaleski, Olivia 165 portable ratings 111–13 zero-hours contracts 40, 41, 107 sanctions and 61–3 Zuckerberg, Mark 35 * * * Document Outline Cover Humans as a Service: The Promise and Perils of Work in the Gig Economy Copyright Dedication Contents Introduction Welcome to the Gig Economy Humans as a Service Rebranding Work The Platform Paradox Labour as a Technology Making the Gig Economy Work Platforms as a Service Exploring the Gig Economy Charting Solutions A Broader Perspective 1.

The Gig Economy: A Critical Introduction
by Jamie Woodcock and Mark Graham
Published 17 Jan 2020

Kalanick has described the rise of the company as analogous to a political campaign in which ‘the candidate is Uber and the incumbent is an asshole called “taxi”’ (Kalanick and Swisher, 2014). Another documented tactic is the use of ‘greyballing’ to evade regulation. This involves the ‘greyball’ tool developed by Uber, which take the data collected by the app through its normal operation in order to ‘identify and circumvent officials who were trying to clamp down on the ride-hailing service’.8 The use of the tool was approved by Uber’s legal team and has been running since at least 2014. For example, in Portland, Oregon, Uber was operating without approval. Uber gathered the details of city officials and ‘greyballed’ them, providing ‘a fake version of the app, populated with ghost cars, to evade capture’, including cancelling any rides they were able to hail.

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To pass the tests (known as appearances), drivers must be able to rapidly compute a route between any given two points in order to pass. 7. See https://www.crunchbase.com/organization/uber/funding_rounds/funding_rounds_list 8. See Isaac, M. (2017) How Uber deceives the authorities worldwide. The New York Times, 3 March. Available at: https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html 9. See https://www.sfgate.com/technology/businessinsider/article/Billionaire-hedge-fund-manager-says-Uber-told-him-6271449.php 10. The taxi medallion is a system of transferable permits used in many US cities. See https://www.washingtonpost.com/news/wonk/wp/2014/06/20/taxi-medallions-have-been-the-best-investment-in-america-for-years-now-uber-may-be-changing-that/?

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Bezos’ letters 87–8, 106 Turkopticon 106–7, 123, 133 Anderson, B. 80 Antunes, Ricardo 36 application programming interfaces (API) 58 apps 5, 51, 52, 133, 138 artificial intelligence 50, 58, 60, 66 Aslam, Yaseen 76 assembly line 24, 94, 117 Australia 127 Australian Independent Contractors Act 128 automation 66–7 Avendano, Pablo 73 B Badger, Adam 86–7 Bangalore (India) 98–9, 102 Barbrook, R. 37 Barry, J. 49 Beck, Ulrich 17 Bent, P. 13, 16 Berg, J. 55 Besant, Annie 14 Bezos, Jeff 87 Bolt 77 Bourdieu, Pierre 17 ‘BrainWorkers’ 33 Braverman, Harry 111 Bryant and May match factory 14 C Californian Ideology 37 call centres 24, 31 outsourcing of 37, 54 Callinicos, Brent 49 Cameron, A. 37 Cant, Callum 40, 96 capitalism cognitive 37 gendered basis of 29 car industry 110 care work 64, 66, 79–83 low barriers to entry 67 and repeat transactions 68 Care.com 80, 80–1 casualization 5, 15 Caviar 73 ‘ChainWorkers’ 33 Cheung, Adora 103 China worker resistance and strikes 100 Christie, N. 73 cleaning work 5–6, 64 low barriers to entry 67 migrant workers 30 cloudworker platforms 6, 43, 53–61, 63, 64, 69, 93 atomization of 92 availability of 56 location of 55, 57 removal of barriers to entry for 69 and resistance 104–8 setting rates of pay 65 and spatial control 63 temporal control 64 cognitive capitalism 37 collaborations 123, 132, 136 collective bargaining 30, 34, 37, 49, 80, 130, 134, 135–6, 143 collective organization 100, 134 commercial content moderation (CCM) 61 computerization 66 consumer attitudes/preferences 27 contingent work 19 Convention on Platform Work, Draft 130, 146–51 cooperatives, platform 138–9 Countouris, N. 129 Craigslist 22 crowdsourcing 58 crowdworkers 55, 90 see also microwork; online freelancing D Dalla Costa, Mariarosa 29 Darcy, Alison 60 data collection 65–6 De Stefano, V. 129 deindustrialization 36, 84 Deliveroo 2, 6, 23, 32, 40, 71–4, 115, 127 experience of working for 7–8, 31, 71, 72–4 self-organization for workers 95 strike action 95–6, 97 delivery work(ers)/platforms 5, 27, 62, 63, 68 and automation 67 and collective organization 134 experiences of workers 71–5 low entry requirements 67 see also Deliveroo democratic ownership 136–40, 141 Denmark 3F trade union 134–5 Desai, Bhairavi 79 developing countries internet penetration rate 25 Didi Chuxing 22, 102 digital divides 25 digital legibility 23–5, 65–7 digital platforms 1, 2, 3, 4, 54–5 Directive on Transparent and Predictable Working Condition in the European Union 129 dock work(ers) 13–14, 15, 38 strike (1889) 15 domestic work(ers) 29–30, 62, 63, 66, 79–83 as central component of capitalism 29 factors determining working conditions 80 numbers 80 positive and negative outcomes for 81 and repeat transactions 68 in South Africa 81–3 Doogan, Kevin 18 E economic crisis (late 1970s) 33 Elance 22 entertainment industries 135 Eurobarometer 40 European Commission 35 Expensify 60 F Facebook 45, 60, 121, 123, 133 factories/factory work 15–16, 94 measuring of factory labour process by Taylor 23–4 Fair Crowd Work website 123 Fairwork Foundation project 121–2, 130, 146–51 Farrar, James 75, 75–6, 77–8, 101 feedback 52, 80, 92, 93 financial crisis (2008) 35 Fiverr 20, 23 flexibility, desire for by workers 4–5, 30–3, 71, 115 flexicurity 35 Flipkart 22 Foodora 127 Fordism 117 fragmented work 5, 40, 114 Freelancer 6, 54, 64, 89 freelancing, online see online freelancing Frey, C.B. 66 G gamification 86 gender and capitalism 29 and relationships of work 28–30 geographically tethered work/platforms 5–6, 7, 34, 50–2, 63 control over workforce 68 forms of resistance in 94–104 setting rates of pay 65 temporal control 64–5 Ghana 8, 64, 92 gig economy advantages 4–5 characteristics 114–15 controversy over classification of people involved 43–4 existence due to digital transformation 114 factors facilitating growth of 19, 114 five principles for ‘fair work’ in 122 future 112–45 governance in 62 meaning of 3–7 numbers working in 1–2 operation of 41–69 origins 11–40 pitfalls 5, 116 preconditions that shape the 19–28 rise of 38–40 ways to bring about change 142–4 gig economy workers barriers to entry for 67–8 communicating with each other 132–4 de-personalization of 118, 120 desire for flexibility 4–5, 31–3, 71, 115 experiences of 70–92 invisibility of 6, 80 lack of collective voice 6, 77 lack of effective regulation for 128–9 misclassified as self-employed 44 numbers 39–40 securing protection through courts 127 working conditions 6, 9 gigs, musical 3 Global North 12, 13, 32, 46 and cloudworkers 55 and microwork 84 and outsourcing 44 size of gig economy 39 Global South 32, 46 internet penetration rate 25 size of gig economy 39 women and online freelancing 90 globalization 19, 37–8 Goodwin, Tom 45, 121 Graeber, David 31 Guru.com 22 H Handy 80 Harvey, David 33, 53 Heeks, Richard 39 Herman, S. 39 Hilfr.dk 134–5 Homejoy 68, 103–4 Howe, J. 58 human intelligence tasks (HITs) 60 Humphries, S. 13–14 Hunt, A. 28, 81, 82 Huws, U. 39–40 I IAEA (International Arts and Entertainment Alliance) 135 Iles, Anthony 32 ILO (International Labour Organization) 16–17, 129 Declaration of Philadelphia (1944) 142 Independent Workers Union of Great Britain see IWGB India delivery drivers 74 strikes by Uber drivers 102 Industrial Workers of the Word see IWW industrialization 16 interface 45 International Arts and Entertainment Alliance see IAEA International Labour Organization see ILO Internet access and penetration rate 25 Irani, Lilly 106 IWGB (Independent Workers Union of Great Britain) 73, 97, 101, 109, 127, 134 IWW (Industrial Workers of the World) 97, 101 J James, Selma 29, 81 job insecurity, growth in 18–19 K Kalanick, Travis 23, 48, 49 Kalleberg, A.L. 18 Kenya Ajira Digital programme 35 Kessler, Sarah 11 L labour law 114, 117, 126, 128, 129 Lagos (Nigeria) 89, 124 Lanier, Jaron 58 LaPlante, Rochelle 60 lean platforms 35, 45 legibility, digital 23–5, 65–7 Li, Qi 100 Limer, Eric 85–6 Living Wage Foundation 122 London taxi arrangement 47 long-term unemployment 18 low-paid work, increase in 35, 139 M Machingura, F. 81, 82 McKinsey 1–2, 39 McKinsey Global Institute 66 Manila (Philippines) 89, 90 Maputo (Mozambique) 26–7 Marsh, Greg 129 Marx, Karl 11–12, 22, 72, 121 Mason, Paul 35 mass connectivity 25–7 Massey, Doreen 63 Matchwoman strike 14 Mateescu, A. 79, 80, 81 Messina, Jim 48–9 microwork 6, 55, 58–61, 62, 83–9, 104 and automation 66–7 experiences of workers 83–9 feelings of alienation 88 numbers engaged in 83–4 wages 84–5 see also Amazon Mechanical Turk 59 migrant workers 30, 80, 90 migration status 30 Mitropoulos, Angela 17, 32 mobile phones 25–6 Mondragon Corporation 138–9 Moody, Kim 40, 111 Moyer-Lee, Jason 98 N Nedelkoska, L. 66 neoliberalism 18, 33–5, 52 characteristics of 34 New York Uber 78–9 NHS (National Health Service) 5 Novogratz, Mike 49–50 O O’Connor vs Uber Technologies Inc. (2015) 124, 126 Ojanperä, Sanna 55 Ola 102 online freelancing 6, 7, 8–9, 43, 55, 62, 141 barriers to entry for workers 67 barriers to organizing 104 experiences of workers 89–92 and feedback 93 reasons for doing 89–90 support forums 104–5 wages 90, 91 and worker resistance 104–5 Osborne, M.A. 66 outcome thinking 118, 124 outsourcing 19, 37–8, 39, 44–5, 51, 54 microwork as extension of 58 P Pandor, Aisha 83 Pasha, Tanveer 102 pay rates, setting of 65 Peck, Jamie 33, 35 Peterloo Massacre (1819) 108 Platform Cooperative Consortium 138 platforms/platform work 2, 4 ability to set pay rates 65 and accountability 125–30 barriers to entry for workers 67–8 as a civic utility 139–40 cloudwork see cloudwork connecting workers and clients 20–1, 22–3, 43, 138 cooperatives 138–9 core functions 23 degree of explicit coordination 68–9 democratic ownership of 136–40, 141 digital legibility 23–5, 65–7 Draft Convention on Platform Work 130, 146–51 early 22 geographically tethered model see geographically tethered model infrastructure 20–3 intermediate function 42–3 lean 35, 45 meaning and operation of 42–6 microwork see microwork negotiation-based matching 22–3 reliance on network effects 45 repeat transactions 68 setting up of ‘counter’ 123 spatial control 62, 63–4 spatiality and temporality of 42–3 spending money on public relations and advertising 28 static-price matching 23 temporal control 64–5 understanding how they work 61–9 Plouffe, David 49 Pollman, E. 49 precariat 18 precarious work(ers) 13–19, 32–3, 38 definition 16–17 two kinds of 33 profitability, crisis of 35, 36, 42 public sector and gig economy 17 and outsourcing 44 Q Quintini, G. 66 R racialization of work 30 racism 30 ratings strategy and transparency 122–3 Ravenelle, Alexandrea 37, 70 Raw, Louise 14 Reagan, Ronald 34 reddit 123 regulation 144 lack of for gig economy workers 128–9 labour law 19, 114, 117, 126, 128 state 19, 33–6 regulatory entrepreneurship 49 repeat transactions and platforms 68 resistance see worker resistance Roberts, Sarah 61 S SAG-AFTRA 135 Samman, E. 28 Schifter, Doug 79 Scholz, Trebor 48, 49, 138, 139 Schor, Juliet 103 Screen Actors Guild (SAG) 135 Second World War 110 self-employment 32, 43–4, 96, 98, 108 Semuels, Alana 84 service industries, growth of 34 Seymour, Richard 18–19 sharing economy 11 Shekhawat, Dushyant 74 ‘shock doctrine’ 34 short term contracts 4 Silberman, Six 106 slavery 30 Slee, Tom 50, 78 soldiering 23 South Africa domestic workers in 81–3 Uber 76, 127–8 worker resistance 99–100 South African Domestic Services and Allied Workers Union (SADSAWU) 82–3 South African Labour Relations Act 128 South Korea 35 South London Gas Workers strike (1889) 14–15 Spain 127 spatial control and platforms 62, 63–4 Srnicek, Nick 4, 42, 45 standard employment relationship 5, 12–13, 16, 18, 32, 33–4 Standing, Guy 17–18, 27 state regulation 19, 33–6 strikes 14–15, 94, 95–6, 99–100, 109, 142–3 preconditions for starting 109 surveillance 24 of delivery drivers 74 Upwork workers’ resistance to 105 Susskind, R. 118 SweepSouth 80, 81–3 Switzerland Notime 102 T TaskRabbit 103 taxi industry 51–2 taxi work(ers) 75–9, 134 and collective organization 134 see also Uber Taylor, Bill 100 Taylor, Frederick 23–4 Taylor, Matthew 129 Taylor Review of Modern Working Practices, The 129 technological changes 19, 21 temporal control and platforms 64–5 temporary work(ers) 3, 17 Thatcher, Margaret 34 Thompson, S. 34 Ticona, J. 79, 80, 81 Tillett, Ben 14 tipping 75 Tolpuddle Martyrs 108–9 trade unions 6, 18, 34, 36, 92–3, 97, 108–9, 134, 135, 143–4 decline of 36, 37 and dock workers 15 early 108–9 and gig economy workers 109–10, 136 and IWGB 97 rise in membership 15 textile 108 Transnational Federation of Couriers 97 transparency 118–24, 141 establishment of ‘counter platforms’ 123 ratings strategy 122–3 Transport for London 28 Turkopticon 106–7, 123, 133 U Uber 2, 4, 20, 23, 25, 32, 44, 45, 46–50, 52, 61, 73–9, 94–5, 108, 115, 121, 124, 139 business model 48 Change.org petition 28 data collection 50, 65–6 drivers’ wages 49–50, 77–8 engagement with regulation and transport policy 48 funding 47–8 and ‘greyballing’ 49 in New York 78–9 O’Connor vs Uber Technologies Inc. (2015) 124, 126 power passengers hold over drivers 75–6 public relations and lobbying campaigns 48–9 rating system 75 safety issues and rising petrol prices for drivers in South Africa 76–7 and self-driving vehicles 50 and tipping 75 Uber International Holding(s) BV 128 Uber Technologies SA 127 UberX 47 worker resistance and strikes 100–2 unfair dismissal 44, 134 United Kingdom employment regulation issues 129 neoliberalism 34 and outsourcing 44–5 worker resistance and strikes 100–1 United Private Hire Drivers (UPHD) 75 United States neoliberalism 34 Uber 47–9 UPHD (United Private Hire Drivers) 76, 101 UpWork 6, 8, 43, 54, 64, 121 resistance of surveillance methods by workers 105 Upwork.com 89, 91 US Chamber of Commerce 108 V van Doorn, Niels 42 Vandaele, Kurt 95, 97 venture capital 36 visibility 136 vWorker 22 W wages microworkers 84–5 online freelancing 90, 91 setting of pay rates 65 Uber drivers 49–50, 77–8 Ward, H. 73 Webster, G.E. 16 Weightman, G.E. 13–14 WhatsApp 95, 99, 123, 132, 133 Williams, Eric 30 women and domestic work 29–30 and online freelancing in the Global South 90 Wood, Alex 95, 104–5, 107 work, transformation of 12–13 worker power 19, 36–7, 130–6, 141 worker resistance 93–111, 113–14 and cloudworkers 104–8 and communication 107 food platform strikes 95–7 formation of networks and meetings 95, 98–9 geographically tethered work 94–104 history of 94 legal battles over employment status 98 and online freelancing 104–5 and self-employment status 98 strikes 14–15, 94, 95–6, 99, 100–1 taking of work off-platform 103 and trade unions 97, 107–11 Uber 101–2 and WhatsApp groups 98, 99, 132 workers’ rights 34, 44, 98, 101, 130, 135, 139, 140, 144 Y YouTube 60 Z Zomato 98–9 POLITY END USER LICENSE AGREEMENT Go to www.politybooks.com/eula to access Polity’s ebook EULA.

Uberland: How Algorithms Are Rewriting the Rules of Work
by Alex Rosenblat
Published 22 Oct 2018

I published an article on Uber’s phantom cabs, which went viral around the world because, until the user manipulation was revealed, people believed that the map represented the accurate location of available drivers. Heather’s discovery turned out to be an indicator of a systematic evasion of regulation through a secret tool at Uber termed “Greyball,” which was reported in the New York Times by Mike Isaac.29 Figure 7. This screenshot of phantom cars was sent to the author in 2015. In the Greyball program, Uber identifies potential law enforcement and municipal actors by various means, such as through the type of phone or credit card they use, and purposefully misleads them about the presence of Uber vehicles by displaying “ghost,” or “phantom,” cars in the app, which do not reflect the actual presence of local drivers.

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In effect, Uber may evade law enforcement’s efforts to regulate the activities of its drivers (by ticketing them, for example), especially in cities where Uber operates illegally. When I reported on the presence of phantom cars in the Uber passenger app in 2015,30 two years before the revelations about Greyball, Uber categorically denied my claim.31 But the fact is, Uber can use its interface and technical tools to control and manipulate how drivers and passengers interact with its platform. Similarly, Uber uses its dispatching function as a tool to control its drivers. Drivers may apply to drive for Uber with the intention of working for a particular service tier (because each tier, such as uberX or uberSUV, requires a specific make and model of car), but Uber often pushes drivers to accept dispatches for lower tiers.

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Benjamin Sachs, “Uber’s Passenger Acceptance Rules: More Evidence of Employee Status,” On Labor, July 14, 2017, https://onlabor.org/ubers-passenger-acceptance-rules-more-evidence-of-employee-status. 29. Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 30. Alex Rosenblat, “Uber’s Phantom Cabs,” Motherboard, July 27, 2015, https://motherboard.vice.com/en_us/article/mgbz5a/ubers-phantom-cabs. 31. Liat Clark, “Uber Denies Researchers’ ‘Phantom Cars’ Map Claim,” Wired, July 28, 2015, www.wired.co.uk/article/uber-cars-always-in-real-time. 32.

New Dark Age: Technology and the End of the Future
by James Bridle
Published 18 Jun 2018

In order to convince users that the system is more successful, more active, and more responsive than it actually is, the map sometimes displays ‘ghost cars’: circling potential drivers who do not actually exist.21 Rides are tracked, without the user’s knowledge, and this God’s-eye view is used to stalk high-profile clients.22 A programme called Greyball is used to deny rides to government employees investigating the company’s numerous transgressions.23 But the thing that seems to bother us most about Uber is the social atomisation and reduction in agency that it produces. Company workers are no longer employees but precarious contractors. Instead of studying for years to gain ‘the knowledge’, as London’s black cab drivers call their intimate familiarity with the city’s streets, they simply follow the on-screen arrows from turn to turn, directed by distant satellites and unseen data.

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It is also a story of the concentration of power in fewer hands, and the concentration of understanding in fewer heads. The price of this wider loss of power and understanding is, ultimately, death. Occasionally, we can glimpse modes of resistance to such powerful invisibility. Such resistance requires a technological, networked understanding: it requires turning the system’s logic against itself. Greyball, the programme Uber used to avoid government investigations, was developed when tax inspectors and police started calling in cars to their own offices and stations in order to investigate them. The company went as far as blacking out areas around police stations, and banning the kind of cheaper phones that government employees picked up to place orders.

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‘Forget the 1%’, Economist, November 6, 2014, economist.com. 17.Thomas Piketty, Capital in the Twenty-First Century, Cambridge, MA: Harvard University Press, 2014. 18.Jordan Golson, ‘Uber is using in-app podcasts to dissuade Seattle drivers from unionizing’, Verge, March 14, 2017, theverge.com. 19.Carla Green and Sam Levin, ‘Homeless, assaulted, broke: drivers left behind as Uber promises change at the top’, Guardian, June 17, 2017, theguardian.com. 20.Ben Kentish, ‘Hard-pressed Amazon workers in Scotland sleeping in tents near warehouse to save money’, Independent, December 10, 2016, independent.co.uk. 21.Kate Knibbs, ‘Uber Is Faking Us Out With “Ghost Cabs” on Its Passenger Map’, Gizmodo, July 28, 2015, gizmodo.com. 22.Kashmir Hill, ‘“God View”: Uber Allegedly Stalked Users For Party-Goers’ Viewing Pleasure’, Forbes, October 3, 2014, forbes.com. 23.Julia Carrie Wong, ‘Greyball: how Uber used secret software to dodge the law’, Guardian, March 4, 2017, theguardian.com. 24.Russell Hotten, ‘Volkswagen: The scandal explained’, BBC, December 10, 2015, bbc.com. 25.Guillaume P. Chossière, et al., ‘Public health impacts of excess NOx emissions from Volkswagen diesel passenger vehicles in Germany’, Environmental Research Letters 12 (2017), iopscience.iop.org. 26.Sarah O’Connor, ‘When Your Boss Is An Algorithm’, Financial Times, September 8, 2016, ft.com. 27.Jill Treanor, ‘The 2010 “flash crash”: how it unfolded’, Guardian, April 22, 2015, theguardian.com. 28.

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation
by Paris Marx
Published 4 Jul 2022

In 2017, New York Times journalist Mike Isaac reported that Uber had been using a tool called Greyball since 2014 that identified authorities using their app and gave them a special designation. The authorities were then shown a different interface where attempts to hail a ride failed and the map was filled with fake cars so they could not easily identify ride-hail vehicles. Greyball was used in cities across the United States, Europe, and beyond to evade regulatory enforcement as it broke local laws in order to operate. Isaac explained that in Portland, Oregon, Uber had launched without permission of the city before getting banned, but it used Greyball to keep operating and stifle the authorities’ ability to shut it down.

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., 167–8 Gore, Al, 51, 68 Gorz, André, 14, 21, 32–3, 71, 143, 157–8 governance practices, of cities, 199 government spending, 38–9, 45–6 Graeber, David, 47 Grand Challenge, 120–1 graphite, 80 Great Depression, 22–3, 95–6 “green” economy, 77–9 Green New Deal, 225 Greens, 209 Greenwich Village, 26, 27 Greyball, 110 Greyhound, 219 gun deaths, 32 Hackett, Jim, 138 Hall, Peter, 14–5 Harvey, David, 199–200 Hermosillo, Carmen, 56 Herzberg, Elaine, 133–5 Hewlett-Packard, 40 Hidalgo, Anne, 210–1 highway systems. See Interstate Highway System Highway Trust Fund, 25 Hill, Steve, 148 Horan, Hubert, 102, 106, 107 horsedrawn carriages, 15–6 The Horseless Age (magazine), 66 housing prices, 128 Humphreys & Partners, 154–5 Hyperloop, 143, 155, 219, 225 IBM, 50 An Inconvenient Truth (documentary), 68–9 individualized transport, Musk on, 188 Infinite Detail (Maughan), 129 Infrastructure Investment and Jobs Act (2021), 127 Instacart, 111 Instagram, 61–2 Intel, 40 Intermodal Surface Transportation Efficiency Act (1991), 119 International Energy Agency, 74–5 International Rights Advocates, 72 internet origins of, 50 privatization of infrastructure of, 55–6 “internet of landlords,” 197–8 Interstate Highway System about, 24, 46, 140, 221 controlled access highways (freeways/motorways), 21–2 development of streets, 11–2 double-decker highways, 151 in Los Angeles, 140–1 in Paris, 210–1 reconceptualization of, 23 slowdown of highway construction boom, 26 traffic and, 142–4 Isaac, Mike, 110 Jacobs, Jane, 230, 232 as campaign leader against urban renewal, 26 The Death and Life of Great American Cities, 26–7 Japan, innovation in, 45 Jarvis, Charlie, 193 jaywalkers, 124–7, 215–6 Jennings, Lois, 42 jitneys, 89–91, 92, 108–9 Jobs, Steve, 36–7, 42, 44 Jump, 166–8 Kalanick, Travis, 5, 92–4, 97, 105, 113, 116 Kamoto Copper Company, 73 Kara, Siddharth, 73 Katy Freeway, 140 Kelly, Kevin, 53 Kenney, Martin, 182 Khosrowshahi, Dara, 133–4 Kirsch, David, 65, 66, 71, 86 Krafcik, John, 138 Kroger, 172–3 Labour Party, 209 Labrador, Canada, 80 Laceese, Francis, 80 Latin America bike lanes in, 171 Pacto Ecosocial del Sur, 225 rapid transit systems in, 215 leasing model, for taxi drivers, 101–3 Le Guin, Ursula K.

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See bicycles Seattle, WA, ride-hailing services in, 99 Securities and Exchange Commission (SEC), 138 Sedran, Thomas, 129–30 self-checkout, 194–5 self-driving cars accidents with, 132–5 Autonomous Land Vehicle project, 119 Brin on, 114–5 challenges of, 126, 129–30 environmental dilemmas and, 131–2 Google, 6 Intermodal Surface Transportation Efficiency Act (1991), 119 Kalanick on, 116 Navlab autonomous vehicles, 119–20 Ng on, 126 pedestrians and, 127 pricing of, 127–8 pulp science fiction and, 118 Radio Corporation of America (RCA) and, 118 software for, 122–3 speed and, 123–4 Tesla’s Autopilot system, 137–8 Tsukuba Mechanical, 119 VaMoRs, 119 Sepulveda Pass, 141 Shanghai Gigafactory (Tesla), 83 Sheffield, UK, docked bikeshare system in, 170–1 Sheller, Mimi, 158, 207 Shell Oil City of Tomorrow, 2 Shill, Gregory, 30 shipping industry, 49 shut-in economy, 196–7 Sidewalk Labs, 228–30 Silicon Valley, 37–8, 44–5 skates (platforms), 146–7 Skyports, 154–5 Small Business Investment Company, 55 smart homes, 60–1 smartphone apps, 55, 181, 194–5 Smiley, Lauren, 196 Social Bicycles (SoBi), 167–8 Socialist Left Party, 209 social media, 61–2 SolarCity, 55, 143, 188 solar panels, Musk on, 188–9 Southern State Parkway, 26 Soviet Union, 39 space program, 48 SpaceX, 55, 144, 148, 150–1 speed limiter referendum, 19–20 speed limits, 18–20 Sputnik I satellite, 39, 45 standardized containers, increasing use of, 49 Standard Oil of California, 21 Stanford Industrial Park, 40 Stanford Research Institute, 54–5 Stanford University, 39–40, 55, 120 Stark, Tony, 70 Starley, John Kemp, 160, 162 Starship Technologies, 172, 173–5, 176–7 Stop de Kindermoord, 205 streetcars, 12–3, 15, 21, 92, 160 “subscriber city,” 197 suburbanization, 23 suburbs, 12–3 superhighway plan (Detroit), 22 supply chains, 50 Surface Transportation Policy Project, 141 surge pricing, for ride-hailing services, 100 Swisher, Kara, 116–7 Taft-Hartley (1947), 112 taxi medallions, 104–5 taxi services about, 95–6, 101–2, 104–5 industry regulation and, 107, 110–1, 185 Taylor, Isaac, 122 TCP/IP protocol, 50 TechGirls Canada, 228–9 tech industry development of, 9–10 growth of, 4, 180–5 speed of technological innovation, 48 technological solutionism, 59 Tesla, 5–6, 55, 63–4, 70, 72, 73, 82–4, 85–6, 116, 137–8, 143, 147, 158–9, 188, 189, 190 Tesla, Nikola, 70 Texas, Interstate Highway System in, 140 Thacker Pass, NV, 79, 226 Thiel, Peter, 46–7 Thrun, Sebastian, 121 Toronto, Canada, 228–30 Toyota, 116, 121, 122 train system in France, 220 in North America, 218–9 transportation bus system, 21, 215, 219 computerized planning systems for, 130 flying cars, 151–2, 159 history of, 7 jitneys, 89–91, 92, 108–9 Navlab autonomous vehicles, 119–20 present-day dominance of, 34–5 taxi services, 95–6, 101–2, 104–5, 107, 110–1, 185 three-dimensional vs. two-dimensional, 145 train system, 218–9, 220 tunnels for, 144–51, 154–5, 158–9, 189 vertical takeoff and landing vehicle (VTOL/eVTOL), 152–5, 157, 158 walking as primary means of, 12 Trudeau, Justin, 79–80, 228 Trump, Donald, 78 Tsukuba Mechanical, 119 tunnels, for transportation, 144–51, 154–5, 158–9, 189 Turner, Fred, 41, 43, 52 Turner, Matthew, 141–2 Uber about, 115 acquisition of Jump, 166–8 Advanced Technologies Group (ATG), 133, 134–5 benefits of, 94 campaigns for, 103 changed from Ford Fusion to Volvo XC90 SUVs, 134–5 compared with taxi services, 95–6 core business of, 93 costs for, 107–8 Covid-19 and, 108 customer base for, 100–1 divisions of, 153–4, 184 driver pay for, 103–4, 107 effect on traffic of, 100 employee classification for, 111–2 founding of, 181 Greyball and, 110 growth of, 97, 105–6 industry regulation and, 101–2, 107, 110–1, 112–3, 156, 174, 185 loss of money by, 106–7, 184–5 marketing by, 158–9 media representation of, 94–5 micromobility services of, 166–9 model of, 102–3 in New York City, 98–9 origins of, 92–3, 109 pricing for, 184 promises made by, 186 pulls out of China, 152 refocus on ride-hailing and food delivery services, 184–5 safety record of, 134, 135–6 in San Francisco, 97–8 walking vs., 191 Uber Air, 153–4, 155, 157, 159 Uber Copter, 155–6 Uber Eats, 184–5 Uber Elevate, 152, 154, 159 unemployment rate, 95–6 unions, for taxi drivers, 101–2 United Kingdom (UK) docked bikeshare system in, 170–1 ecommerce in, 193 University of Technology Sydney, 75 University Paris-East, 169–70 Unsafe at Any Speed (Nader), 27–8 Untokening collective, 218 Urban Challenge, 120 urban renewal strategy, 26 Urry, John, 32–3, 143 US Air Force, 50 US Department of Defense, 50 US-Japan Semiconductor Trade Agreement (1986), 45 US National Labor Relations Act, 102 VaMoRs, 119 Vansintjan, Aaron, 222 Vasquez, Rafaela, 132, 135 Vélib’ bikeshare system, 210 venture capitalists, 186–7, 199 vertical takeoff and landing vehicle (VTOL/eVTOL), 152–5, 157, 158 Very Far Away from Anywhere Else (Le Guin), 202 Vietnam War, 39, 40, 43, 49 VoiceOver, 175 Volkswagen, 77, 78, 129–30 Volocopter, 152 Volvo XC90 SUVs, 134–5 Walker, Jarrett, 59, 142–3, 181–2 walking, as means of transportation, 12, 191 Washington, DC, ride-hailing services in, 99 Waterfront Toronto, 228–9, 230, 231 Waymo, 133, 138, 186 web 2.0, 57 WeWork, 181, 182–3 white people, mortgages and, 29 Who Killed the Electric Car?

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
by Sara Wachter-Boettcher
Published 9 Oct 2017

Everything feels easy—if you avoid looking at the woman with a stroller and a cardboard sign on the corner, or the man sleeping in an alcove over on Howard Street, or the fact that all your friends are leaving this place because their salaries, even the ones inflated by tech jobs, just don’t stack up in a city where people make cash offers at open houses and the average two-bedroom apartment rents for more than $5,000 a month. About a mile southwest, at Uber’s headquarters, another scandal is brewing: a tool called “Greyball,” used to systematically mislead authorities in markets where the service was banned or under investigation, has just been reported in the New York Times.4 Across the street, at Twitter, stock prices fell more than 10 percent in a single month, and the company is scrambling.5 And thirty miles south, in Menlo Park, Facebook has just started rolling out its solution to fake news: stories shared on Facebook that have been debunked by third-party, nonpartisan fact-checking organizations have begun being marked with a red caution icon and the word “Disputed”—a label that’s already being disputed itself, with some calling it censorship and others calling it too milquetoast for news that’s demonstrably false.6 Unrest is brewing at the big tech companies.

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Hatzenbuehler, “The Influence of State Laws on the Mental Health of Sexual Minority Youth,” JAMA Pediatrics, February 20, 2017, http://jamanetwork.com/journals/jamapediatrics/article-abstract/2604254. 4. Mike Isaac, “How Uber Deceives Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 5. Anders Bylund, “Why Twitter, Inc. Fell 10% in February,” Motley Fool, March 3, 2017, https://www.fool.com/investing/2017/03/03/why-twitter-inc-fell-10-in-february.aspx. 6. Hudson Hongo, “Facebook Finally Rolls Out ‘Disputed News’ Tag Everyone Will Dispute,” Gizmodo, March 3, 2017, http://gizmodo.com/facebook-finally-rolls-out-disputed-news-tag-everyone-w-1792959827. 7.

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See also diversity and companies’ personal name policies, 54–59 and default settings, 35–38 devaluing of women’s roles, 20–21, 176 in edge cases, 38 and Etsy, 32–33 and form field design, 50, 71 and game avatars, 35–36 and Google’s use of proxy data, 109–112 in menstrual tracking apps, 28–33 and meritocracy of tech, 173–176, 180 and negging, 91–92 and normalizing TV programming, 47–48 and online abuse and harassment, 147–154, 156–160 and Reddit, 161–163 Slack’s lack of, 190–191 and smartphone personal assistants, 6–7, 7, 36–37 and startups’ venture capital, 175 and team performance, 184, 186 and tech educational pipeline, 21–26, 181–184 in tech industry, 6–7, 7, 13–21 and Twitter’s executive leadership, 157–158 at Uber, 108, 177–181, 187–189 and word-embedding systems, 138–140 and Milo Yiannopoulos, 150–154, 157 gender information, companies’ collection of, 62–66 Ghostbusters II (film), 150–151 GitHub, 175 Gizmodo, 165–166, 169 Glass Room installation, 101–102 Global Positioning System (GPS), 105 Glow app, 29–33, 30 Gmail, and collection of gender information, 62 Gonzalez-Cameron, Aimee, 72 Google and algorithms, 123, 136, 144 design aesthetic of, 143 and pervasiveness of technology, 3 and photo autotagging, 129–130, 129, 130, 132–133, 135–138, 145 photo memories feature, 85 privacy policies of, 109 purchase of YouTube, 2 sexual harassment at, 178 smartphones of, 6 trustworthiness of, 142–143 use of proxy data, 109–112 and Word2vec, 138–142, 145 and workforce diversity, 19–20 Grace Hopper Celebration of Women in Computing, 22–23 Grant, Heidi, 189 Greenshpan, Moshe, 137 Grey, Jacqui, 189 Greyball scandal, 199 Grey’s Anatomy (TV show), 47 Groeger, Lena, 35 the Hacker Way, 170–171 Hampton Creek’s Just Mayo, 187 harassment online, 59, 147–154, 156–164, 170 hashtags, 156 Hatzenbuehler, Mark L., 198 Ho, Ed, 152 Ho, Kevin, 30 Hoffman, Kevin M., 87–88 Holder, Eric, 178, 180 Hon, Calvin, 77–78 Hon, Dan, 77–78, 82 Horseman, Emily, 73 How to Get Away with Murder (TV show), 47 Huffman, Steve, 161 humor.

Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend
Published 15 Jun 2020

But while it’s unclear whether Son will return to the Gulf for more money—he has raised $14 billion from other sources since the killing—for the foreseeable future the House of Saud will remain the prime beneficiary of this new global traction monopoly’s rise. DURING ITS FIRST DECADE, Uber didn’t play nice with cities. Its Greyball program, which ran from 2014 to 2017, scoured user data to identify accounts being used by taxi regulators for sting operations in cities where the ride-hail service was illegal. Once so tagged, Greyball would thwart municipal officials’ attempts at enforcement by spoofing their screens with phantom cabs. But in 2018, the company attempted to turn over a new leaf. Under new leadership, Uber extended an olive branch to cities everywhere.

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Cheape, Moving the Masses: Urban Public Transit in New York, Boston, and Philadelphia 1880–1912 (Cambridge, MA: Harvard University Press, 1980), 1. 175companies merely joined forces: Cheape, Moving the Masses, 172. 175the most powerful, reviled traction monopoly: Walt Crowley, “City Light’s Birth and Seattle’s Early Power Struggles, 1886–1950,” History Link, April 26, 2000, https://www.historylink.org/File/2318. 175enjoyed decades of unrivaled power: Owain James, “We Miss Streetcars’ Frequent and Reliable Service, Not Streetcars Themselves,” Mobility Lab, April 17, 2019, https://mobilitylab.org/2019/04/17/we-miss-streetcars-frequent-and-reliable-service-not-streetcars-themselves/; combination of technological change and federal intervention: “Jersey Trolley Merger,” Wall Street Journal, May 13, 1905, 2. 176$100 billion Vision Fund: Katrina Brooker, “The Most Powerful Person in Silicon Valley,” Fast Company, January 14, 2019, https://www.fastcompany.com/90285552/the-most-powerful-person-in-silicon-valley. 176its total commitment to some $9 billion: Pavel Alpeyev, Jie Ma, and Won Jae Ko, “Taxi-Hailing Apps Take Root in Japan as SoftBank, Didi Join Fray,” Bloomberg, July 19, 2018, https://www.bloomberg.com/news/articles/2018-07-19/softbank-didi-to-roll-out-taxi-hailing-business-in-japan. 177$2 billion into Singapore-based Grab: Yoolim Lee, “Grab Vanquishes Uber with Local Strategy, Billions from SoftBank,” Bloomberg, March 26, 2018, https://www.bloomberg.com/news/articles/2018-03-26/grab-vanquishes-uber-with-local-strategy-billions-from-softbank. 177Ola downloaded $2 billion: Saritha Rai, “India’s Ola Raises $2 Billion from SoftBank, Tencent,” Bloomberg, October 2, 2017, https://www.bloomberg.com/news/articles/2017-10-02/india-s-ola-is-said-to-raise-2-billion-from-softbank-tencent. 17715 percent stake in Uber: Alison Griswold, “SoftBank—not Uber—Is the Real King of Ride-Hailing,” Quartz, January 23, 2018, https://qz.com/1187144/softbank-not-uber-is-the-real-king-of-ride-hailing/. 177Uber picked off Dubai-based Careem: Adam Satariano, “This Estonian Start-Up Has Become a Thorn in Uber’s Side,” New York Times, April 23, 2019, https://www.nytimes.com/2019/04/23/technology/bolt-taxify-uber-lyft.html. 177The damage to consumers: Justina Lee, “Singapore Fine Is ‘Minor Bump’ in Grab’s Ride-Hailing Dominance,” Nikkei Asian Review, September 25, 2018, https://asia.nikkei.com/Spotlight/Sharing-Economy/Singapore-fine-is-minor-bump-in-Grab-s-ride-hailing-dominance. 177Grab cornered more than 80 percent: Ardhana Aravindan, “Singapore Fines Grab and Uber, Imposes Measures to Open Up Market,” Reuters, September 23, 2018, https://www.reuters.com/article/us-uber-grab-singapore/singapore-fines-grab-and-uber-imposes-measures-to-open-up-market-idUSKCN1M406J. 177all launched antitrust investigations: Mai Nguyen, “Vietnam Says Eyeing Formal Antitrust Probe into Uber-Grab Deal,” Reuters, May 16, 2018, https://www.reuters.com/article/us-uber-grab-vietnam-idUSKCN1IH0XNiAikaRey, “Antitrust Watchdog Fines Grab P16 Million over Uber Deal,” Rappler, October 17, 2018, https://www.rappler.com/business/214502-philippine-competition-commission-fines-grab-philippines-over-uber-deal; Yoolim Lee, “Singapore Watchdog Fines Uber, Grab $9.5 Million over Merger,” Bloomberg, September 24, 2018, https://www.bloomberg.com/news/articles/2018-09-24/singapore-fines-uber-grab-s-13-million-for-merger-infringement. 177another fare-slashing battle with Ola: “Steering Group: A Bold Scheme to Dominate Ride-Hailing,” The Economist, May 10, 2018, https://www.economist.com/briefing/2018/05/10/a-bold-scheme-to-dominate-ride-hailing. 177“SoftBank is playing the ride-hailing”: Alison Griswold, “Softbank Has Spread Its Ride-Hailing Bets and Didi Looks Like an Early Win,” Quartz, April 24, 2018, https://qz.com/1261177/softbanks-winner-in-ride-hailing-is-chinas-didi-chuxing-not-uber/. 177“driver incentives, passenger discounts”: Tim O’Reilly, “The Fundamental Problem with Silicon Valley’s Favorite Growth Strategy,” Quartz, February 5, 2019, https://qz.com/1540608/the-problem-with-silicon-valleys-obsession-with-blitzscaling-growth/. 178“locked in a capital-fueled deathmatch”: O’Reilly, “The Fundamental Problem.” 178The Vision Fund’s biggest investor: Brooker, “The Most Powerful Person.” 178the proceeds of an earlier liquidation: Catherine Shu, “Saudi Arabia’s Sovereign Fund Will Also Invest $45B in SoftBank’s Second Vision Fund,” Tech-Crunch, October 2018, https://techcrunch.com/2018/10/07/saudi-arabias-sovereign-fund-will-also-invest-45b-in-softbanks-second-vision-fund/. 178Uber’s multi-billion-dollar quarterly losses: “Aramco Value to Top $2 Trillion, Less Than 5 Percent to Be Sold, Says Prince,” Reuters, April 25, 2016, https://www.reuters.com/article/us-saudi-plan-aramco-idUSKCN0XM16M. 178the House of Saud: Brooker, “The Most Powerful Person.” 178thwart municipal officials’ attempts at enforcement: Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 179“Even if that means paying money”: Dara Khosrowshahi, “The Campaign for Sustainable Mobility,” Uber, September 26, 2018, https://www.uber.com/newsroom/campaign-sustainable-mobility/. 180Five-cent nickel fares: Cheape, Moving the Masses, 174–75. 180cities . . . grant a ride-hail monopoly: “Free Exchange: The Market for Driverless Cars Will Head towards Monopoly,” The Economist, June 7, 2018, https://www.economist.com/finance-and-economics/2018/06/07/the-market-for-driverless-cars-will-head-towards-monopoly. 180“corrupt and contented”: Cheape, Moving the Masses, 177. 180Jay Gould’s Manhattan Railway Company: Terry Golway, Machine Made: Tammany Hall and the Creation of Modern American Politics (New York: Live-right, 2014), 135. 180took over Puget Sound’s streetcar: Crowley, “City Light’s Birth.” 181Public transit was the competition: United States Securities and Exchange Commission, Registration Statement under the Securities Act of 1933: Uber Technologies, April 11, 2019, 25, https://www.sec.gov/Archives/edgar/data/1543151/000119312519103850/d647752ds1.htm#toc. 181deploy predatory pricing: United States Securities and Exchange Commission, Registration Statement. 182“have been created based on cash flows”: “Asset-Backed Security,” Investo-pedia, accessed December 7, 2018, https://www.investopedia.com/terms/a/asset-backedsecurity.asp. 183Amazon’s body-tracking technology: “The Learning Machine: Amazon’s Empire Rests on its Low-Key Approach to AI,” The Economist, April 11, 2019, https://www.economist.com/business/2019/04/13/amazons-empire-rests-on-its-low-key-approach-to-ai. 8.

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See driverless shuttles Shyp, 133 Sidewalk Labs, 209, 210, 211, 222, 232 Silverdome (Pontiac, MI), 196n SilverRide, 95 Singapore, 97, 167, 169, 177, 209, 211 singletons, 237–38 Singularity predictions, 234–35, 236–38 Skype, 56 smartphones, 13, 64, 89, 90, 139, 169 see also mobile phones SoftBank, 176, 176–78, 238 software trains, 70–71, 70–72, 197, 200–201, 202, 204, 206 Son, Masayoshi, 176, 178 Space10 (IKEA), 72–73 specialization overview, 16 shifts in daily travel patterns, 53–54 of taxibot rides, 95–97 of traditional automobiles, 52, 80 vehicular variety increase, 16, 18, 52–55 see also specific types of AVs Speedostat, 24 Sprinter delivery vans (Mercedes), 125 Stae, 247 Standard Oil, 174 Starship conveyors, 55–56, 57, 125, 192 Starship Technologies, 56, 57, 124–25 Starsky Robotics, 46 status quo bias, 49–50, 52 steering wheel introduction, 4 Steffens, Lincoln, 180 store closures in US, 117–18, 121 streetcars, 58, 59, 88–89, 106, 174–75, 180–81, 186 “street furniture,” 77 suitcases, semi-autonomous, 125 Sullenberger, Chesley, 45 Superintelligence (Bostrom), 236–37 supermarket, origins of, 116 Superpedestrian, 66 surge pricing, 17, 87, 181 Swift Nick, 161 task model for computerization of work, 150–54, 151, 155 taxibots (AV cabs) computer models of growth, 97–98, 99 doubts about cost-effectiveness, 97–98 impact on taxi business, 94–95 overview, 60–61, 94–95 price savings, 94, 96 rides for pregnant women, 96–97 specialization of rides, 95–97 traffic congestion and, 99 Waymo self-driving taxi service, 8, 46, 97, 230, 240–41 see also mobility as a service taxis automation predicted by 2030, 10–11 impact of taxibot takeover, 94–95 meters in, 169 number of vehicles, 10 ride-hail push to deregulate the taxi business, 40 Waymo self-driving taxi service, 8, 46, 97, 230, 240–41 Teague, 127 TECO Line streetcar (Tampa, FL), 58, 59 Teetor, Ralph, 24, 26 Tel Aviv’s traffic gridlock, 85–87, 88 Tesla, 26–29, 44, 60, 62, 231 three big stories of the driverless revolution, 16–20, 187–88, 238, 248, 253 see also financialization of mobility; materialization; specialization Thrun, Sebastian, xiv, 7, 8 ticketing in transit systems, 89, 90–91, 93, 109, 110–11 time wasted on commuting, 9, 12, 30–31 Toffler, Alvin, 120 toll roads in Great Britain, 162–63 Toronto, Canada, 209–10, 213–14, 222 traction monopolies investors, 176–78, 182–83 in New York City, 174, 174, 180 in Philadelphia, 180 SoftBank, 176, 176–78, 238 in streetcar era, 174, 174–75, 180 traffic congestion cost of time wasted, 9, 12, 30 driverless shuttles and, 106 predicted effects of AVs, 9 ride-hail and, 168 taxibots and, 99 see also congestion pricing trafficgeddons, 85–86 Trafi, 109, 216 transects of the driverless city, 187–88, 188–89, 194–95, 198–99, 200–201, 206–7, 208 transit oriented development (TOD), 200, 202, 203–4 transit systems autonomist contempt for, 214–15 impact of ride-hail, 215–16 as mobility integrators, 216 response to driverless revolution, 214–17 ticketing in, 89, 90–91, 93, 109, 110–11 as transportation utilities, 216 workforce changes, 216–17 TransMilenio (Bogotá), 69 Trikala, Greece, 102–3 trip chaining, 54 Tron (film), 137 trucks and trucking accident risks, 156 automated fleets impact on economic risks, 156–58 automation and types of truck drivers, 153–54 freight AVs and, 125–26 industrial sprawl and, 12–13 investment in self-driving truck startups, 152 “land trains” and “road trains,” 69 last-mile delivery, 121–29, 154, 218 platoons and platooning, 68–69, 70–71 self-driving tractor-trailers, 68, 122 software trains, 70–71 volatile energy costs, 157 Tsukuba, Japan, 6, 8, 216 turnpike trusts in Great Britain, 162–63 Turpin, Dick, 161 Uber betrayal of cities, 181 Careem purchase by, 177 competition with Lyft, 177–78, 179 congestion pricing, 179, 181 dynamic pricing, 181 fatal AV–pedestrian accident, 231 Greyball program, 178 initial public offering, 97, 177, 181 Jump bike-share platform, 202 limited global footprint, 98 market cap, 97 Micromobility Robotics, 67 number of vehicles, 10 partnerships with public transit, 110–11 relationship with transit, 215 SoftBank and, 177 specialization and variety of rides, 95, 96, 110–11 subscriptions, 244 surge pricing, 17, 87, 181 taxibots, 97 traffic congestion and, 168 Uber Eats, 124 vertically integrated urban-mobility empire, 98 Udelv, 57–58 unmanned aerial vehicles (UAVs), 246 UPS, 116, 120, 127, 130 urban design and driverless cities automation and urban concentration, 186–87 automobiles and urban expansion, 185 complete streets (shared streets), 208–9 core, 187, 188, 188–96, 194–95 desakota, 187, 189, 205–8, 206–7 freight tunnels, 211 fulfillment zone, 187, 188, 196–99, 198–99 infill housing, 204, 253–55 legibility, 229–30, 231 megablocks, 209–10 microsprawl, 187, 189, 200–201, 200–205, 243 parking, 189–93 population growth and home building, 253–54 separation of people and vehicles, 208–12, 210 transects, 187–88, 188–89, 194–95, 198–99, 200–201, 206–7, 208 transit oriented development (TOD), 200, 202, 203–4 urban growth since 1950, 186 Urbanetic, 58 Urban Mobility in a Digital Age, 88 urban ushers, 76–77, 77–79 Vélib system (Paris), 63 VeoRide, 67 Via, 107 Vickrey, William, 165–67, 168, 169, 172 Vinge, Vernor, 233–34 Vision Fund, 176, 178 Vitruvius, 169 von Neumann, John, 234 “Walking City” (Herron), 74 warehouseless distribution systems, 157–58 Waste Management, 142 wayfinding, 229–30 Waymo improvement in rate of disengagement, 42 lidar cost reduction, 35 market cap, 97 market share goal for 2030, 11 remote human safety monitors, 46, 98 self-driving taxi service, 8, 46, 97, 230, 240–41 see also Google Waze, 86–87 Webb, Kevin, 233 Where Do Cars Go at Night?

The Power Law: Venture Capital and the Making of the New Future
by Sebastian Mallaby
Published 1 Feb 2022

BACK TO NOTE REFERENCE 71 Gurley, author interview. BACK TO NOTE REFERENCE 72 Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017. Uber’s general counsel had determined that Greyball could go ahead because there were no specific laws against ride hailing in Philadelphia, where the program was first used. However, when Greyball became publicly known, Uber discontinued its use and the Department of Justice opened a criminal probe. BACK TO NOTE REFERENCE 73 Gurley, author interview. BACK TO NOTE REFERENCE 74 Kolhatkar, “At Uber, A New C.E.O.

…

Coming on top of the sexual harassment allegations, the video of Kalanick sent Uber’s reputation into a tailspin. Google, Airbnb, Facebook, and even Lyft began luring away its demoralized workforce, and in March 2017 the bad news continued. The New York Times broke a story about a hyperaggressive antiregulatory tactic called Greyball. In cities where ride hailing wasn’t authorized, Uber engineers secretly built a shadow version of the app and pushed it to law-enforcement officials. Then, when the law enforcers tried to hail and impound an Uber car, no car arrived to meet them.[73] Meanwhile, a Silicon Valley news site called The Information broke a story about a trip that Kalanick had taken to South Korea.

…

At the top of the tower, Kalanick awaited. Cohler and Fenton lost no time in delivering their message. They told Kalanick they wanted him to go, and they handed him a letter from Team Gurley. The letter cited the disasters of that woeful year: the harassment investigation, the lawsuit with Google, the Greyball deception. “The public perception is that Uber fundamentally lacks ethical and moral values,” the letter said. The company had to “change at its core.” To this end, it required a change of chief executive. Kalanick began to pace the room. “If this is the path you want to go down, things are gonna get ugly for you,” he shouted at his visitors.

Artificial Unintelligence: How Computers Misunderstand the World
by Meredith Broussard
Published 19 Apr 2018

It shows up later in the behavior of tech CEOs like Travis Kalanick, who in 2017 was ousted from his top position at Uber for (among other things) creating a culture of sexual harassment. Kalanick also had the attitude that laws didn’t matter. He launched Uber in cities worldwide in defiance of local taxi and limousine regulations, created a program called Greyball to help Uber computationally evade sting operations by law enforcement, was captured on camera verbally abusing an Uber driver, and looked the other way when Uber drivers raped passengers.10 According to a blog post by former Uber engineer Susan Fowler, Kalanick’s tech managers were almost cartoonishly incompetent at dealing with the harassment complaints Fowler lodged.

…

IEEE Spectrum. “Tech Luminaries Address Singularity.” IEEE Spectrum, June 1, 2008. http://spectrum.ieee.org/computing/hardware/tech-luminaries-address-singularity. Isaac, Mike. “How Uber Deceives the Authorities Worldwide.” New York Times, March 3, 2017. https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. “Jeremy Corbyn, Entrepreneur.” Economist, June 15, 2017. http://www.economist.com/news/britain/21723426-labours-leader-has-disrupted-business-politics-jeremy-corbyn-entrepreneur. Kahan, Dan M., Donald Braman, John Gastil, Paul Slovic, and C. K. Mertz. “Culture and Identity-Protective Cognition: Explaining the White-Male Effect in Risk Perception.”

…

., 180 FEC.gov, 178–179 Film, AI in, 31, 32, 198 FiveThirtyEight.com, 47 Foote, Tully, 122–123, 125 Ford Motor Company, 140 Fowler, Susan, 74 Fraud campaign finance, 180 Internet advertising, 153–154 Free press, role of, 44 Free speech, 82 Fuller, Buckminster, 74 Futurists, 89–90 Games, AI and, 33–37 Gates, Bill, 61 Gates, Melinda, 157–158 Gawker, 83 Gender equality, hostility toward, 83 Gender gap, 5, 84–85, 115, 158 Genius, cult of, 75 Genius myth, 83–84 Ghost-in-the-machine fallacy, 32, 39 Giffords, Gabby, 19–20 GitHub, 135 Go, 33–37 Good Old-Fashioned Artificial Intelligence (GOFAI), 10 Good vs. popular, 149–152, 160 Google, 72 Google Docs, 25 Google Maps API, 46 Google Street View, 131 Google X, 138, 151, 158 Government campaign finance, 177–186, 191 cyberspace activism, antigovernment ideology, 82–83 tech hostility toward, 82–83 Graphical user interface (GUI), 25, 72 Greyball, 74 Guardian, 45, 46 Hackathons, 165–174 Hackers, 69–70, 82, 153–154, 169, 173 Halevy, Alon, 119 Hamilton, James T., 47 Harley, Mike, 140 Harris, Melanie, 58–59 Harvard, Andrew, 184 Harvard University Berkman Klein Center, 195 Data Privacy Lab, 195 mathematics department, 84 “Hello, world” program, 13–18 Her, 31 Hern, Alex, 159 Hernandez, Daniel, Jr., 19 Heuristics, 95–96 Hillis, Danny, 73 Hippies, 5, 82 HitchBOT, 69 Hite, William, 58 Hoffman, Brian, 159 Holovaty, Adrian, 45–46 Home Depot, 46, 115, 155 Hooke, Robert, 88 Houghton Mifflin Harcourt (HMH) HP, 157 Hugo, Christoph von, 145 Human-centered design, 147, 177 Human computers, 77–78, 198 Human error, 136–137 Human-in-the-loop systems, 177, 179, 187, 195 Hurst, Alicia, 164 Illinois quarter, 153–154 Imagination, 89–90, 128 Imitation Game, The (film), 74 Information industry, annual pay, 153 Injury mortality, 137 Innovation computational, 25 disruptive, 163, 171 funding, 172–173 hackathons and, 166 Instacart, 171 Intelligence in machine learning Interestingness threshold, 188 International Foundation for Advanced Study, 81 Internet advertising model, 151 browsers, 25, 26 careers, annual pay rates, 153 core values, 150 drug marketplace, 159–160 early development of the, 5, 81 fraud, 153–154 online communities, technolibertarianism in culture of, 82–83 rankings, 72, 150–152 Internet Explorer, 25 Internet pioneers, inspiration for, 5, 81–82 Internet publishing industry, annual pay, 153 Internet search, 72, 150–152 Ito, Joi, 147, 195 Jacquard, Joseph Marie, 76 Java, 89 JavaScript, 89 Jobs, Steve, 25, 70, 72, 80, 81 Jones, Paul Tudor, 187–188 Journalism.

Surveillance Valley: The Rise of the Military-Digital Complex
by Yasha Levine
Published 6 Feb 2018

Robinson Meyer, “Facebook Is America’s Favorite Media Product,” The Atlantic, November 11, 2016; Alexei Oreskovic, “Facebook Now Gets Almost $20 from Each US and Canadian User, Compared to under $5 at Its IPO,” Business Insider, February 1, 2017. 87. “Uber’s use of Greyball was recorded on video in late 2014, when Erich England, a code enforcement inspector in Portland, [Oregon,] tried to hail an Uber car downtown in a sting operation against the company. But unknown to Mr. England and other authorities, some of the digital cars they saw in the app did not represent actual vehicles. And the Uber drivers they were able to hail also quickly canceled. That was because Uber had tagged Mr. England and his colleagues—essentially Greyballing them as city officials—based on data collected from the app and in other ways.

…

One-night stands and extramarital affairs are a cinch to figure out: two smartphones that never met before suddenly cross paths in a bar and then make their way to an apartment across town, stay together overnight, and part in the morning. They know us intimately, even the things that we hide from those closest to us. And, as Uber’s Greyball program so clearly shows, no one escapes—not even the police. In our modern Internet ecosystem, this kind of private surveillance is the norm. It is as unnoticed and unremarkable as the air we breathe. But even in this advanced data-hungry environment, in terms of sheer scope and ubiquity, Google reigns supreme.

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley
by Corey Pein
Published 23 Apr 2018

In Portland, Oregon, it hired the services of a powerful local political consultant who had managed the election campaigns for the mayor, a key city councilor, and many other statewide power players. After the councilor and the mayor met secretly with Uber reps at the consultant’s home—in violation of city lobbying rules—the city, lo and behold, cut Uber a break. While that was going on, Uber was actively circumventing Portland regulators using special software called Greyball, which it had designed to identify and avoid local taxi inspectors when they tried to hail an Uber car. Similar intrigues followed Uber around the country and the world. To grease the skids in its global expansion, Uber hired David Plouffe, who managed Barack Obama’s 2008 presidential campaign and advised him in the White House as head of public affairs.

…

Michael Fusion Fwd.us Galvanize Gamergate Gandhi, Mahatma Gates, Bill Gawker Genentech General Dynamics General Electric Getty Images Ghostruck Girard, René Glassdoor GM Gmail Goldman Sachs Google Google AdWords Google Express Google Maps Googleplex Google X Gore, Al Government Proposal Solutions Graham, Paul Green, Joe Greender Greyball Greylock Partners GRiD Computers Grossman, Terry Groupon Guardian Hacker News Hagel, John Harper-Mercer, Chris Harvard University Hennessy, John Hewlett-Packard Heyer, Heather Hitler, Adolf Hoffa, Jimmy Hoffman, Reid Hofstadter, Douglas Hogan, Hulk Holmes, Elizabeth Hudson Pacific Properties Humanity+ Hunter, Duncan, Jr.

Unleashed
by Anne Morriss and Frances Frei
Published 1 Jun 2020

Asma Khalid, “Uber Taps Harvard Business School’s Frances Frei to Turn Company in Right Direction,” WBUR, December 21, 2017, https://www.wbur.org/bostonomix/2017/12/21/uber-hires-frances-frei. 22. Mike Isaac, “How Uber Deceives the Authorities Worldwide,” Technology, New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities. 23. Kara Swisher, “Uber CEO Travis Kalanick Says the Company Has Hired Former Attorney General Eric Holder to Probe Allegations of Sexism,” Vox, February 20, 2017, https://www.vox.com/2017/2/20/14677546/uber-ceo-travis-kalanick-eric-holder-memo. 24. Special Committee of the Board, “Covington Recommendations” (Google Doc, 2017), https://drive.google.com/file/d/0B1s08BdVqCgrUVM4UHBpTGROLXM/view. 25.

Hired: Six Months Undercover in Low-Wage Britain
by James Bloodworth
Published 1 Mar 2018

On 22 September 2017, the regulator rescinded Uber’s operating licence – suspending the company from operating legally in the city subject to appeal. In a statement, TFL said that Uber demonstrated ‘a lack of corporate responsibility in relation to a number of issues which have potential public safety and security implications’.32 These included the company’s approach to reporting serious criminal offences; its use of Greyball technology, which could, in theory, prevent regulators from gaining access to the Uber app; and the company’s method of acquiring drivers’ medical papers and criminal record checks. Uber appealed against the decision a few days later – meaning its drivers were not immediately thrown out of work – and it seems likely the company will clean up its act in relation to TFL’s specific concerns.

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies
by Reid Hoffman and Chris Yeh
Published 14 Apr 2018

Some of these issues were due to clearly unethical behavior, including internal problems, such as the sexual harassment reported by the former Uber engineer Susan Fowler, and various external attempts to subvert free competition, regulation, and the press, such as creating fake accounts to poach drivers from its rival Lyft (as reported by The Verge), developing software (Greyball) to prevent law enforcement and regulators from accessing the service, and then-COO Emil Michael suggesting that the company spend money to hire opposition researchers to intimidate journalists. This kind of behavior is unacceptable, regardless of the size or stage of the company undertaking it, and has rightfully been widely condemned.

The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power
by Michael A. Cusumano , Annabelle Gawer and David B. Yoffie
Published 6 May 2019

Nonetheless, the tide of public perception seems to have turned: Media coverage of platforms has become increasingly negative. Calls to break up Alphabet-Google have appeared in major newspapers. The “Delete Facebook” movement was gaining traction among the public. Uber nearly collapsed from internal chaos, failure to properly vet drivers, misuse of digital technology (e.g., “Greyball” software that helped drivers evade law enforcement in markets where Uber was prohibited), and opposition from local governments and taxi industry representatives. Why now? After three decades of explosive growth around the world, why have competitors, users, and regulators started to raise serious questions about the use and abuse of platform power?

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power
by Max Chafkin
Published 14 Sep 2021

to rein them in: Kaushik Viswanath, “How Uber and Airbnb Created a Parasite Economy,” Marker, September 14, 2020, https://marker.medium.com/uber-and-airbnb-are-parasites-but-they-dont-have-to-be-36909355ac3b; Paris Martineau, “Inside Airbnb’s ‘Guerilla War’ Against Local Governments,” Wired, March, 20, 2019, https://www.wired.com/story/inside-airbnbs-guerrilla-war-against-local-governments/; Mike Isaac, “How Uber Deceives the Authorities Worldwide,” The New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. tech founders are godlike: Peter Thiel and Blake Masters, Zero to One (New York: Crown Business, 2014), 23, 168, 183. 1.25 million copies worldwide: Blake Masters (@bgmasters), “Zero to One has now sold more than 1.25 million copies worldwide!” Twitter, January 31, 2016, https://twitter.com/bgmasters/status/693909418321141760.

WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly
Published 9 Oct 2017

Twitter update, retrieved March 29, 2017, https://twitter.com/jkrums/status/1121915133. 43 “We Are All Khaled Said”: Facebook page, retrieved March 29, 2017, https://www.facebook.com/ElShaheeed. 43 “an internal life of its own”: Michael Nielsen, Reinventing Discovery (Princeton, NJ: Princeton University Press, 2011), 53. 44 “A theory is a species of thinking”: Thomas Henry Huxley, “The Coming of Age of ‘The Origin of Species,’” Collected Essays, vol. 2, as reprinted at http://aleph0.clarku.edu/huxley/CE2/CaOS.html. 45 “the way a genome runs on a multitude of cells”: George Dyson, Turing’s Cathedral (New York: Pantheon, 2012), 238–39. 46 income that it can’t deliver: Sami Jarbawi, “Uber to Pay $20 Million to Settle FTC Case,” Berkeley Center for Law, Business and the Economy, January 31, 2017, http://sites.law.berkeley. edu/thenetwork/wp-content/uploads/sites/2/2017/01/Uber-to-Pay-20-Million-to-Settle-FTC-Case.pdf. 46 technology to deflect their investigations: Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 47 Rivals sue over claims of stolen technology: Alex Davies, “Google’s Lawsuit Against Uber Revolves Around Frickin’ Lasers,” Wired, February 5, 2017, https://www.wired.com/2017/02/googles-lawsuit-uber-revolves-around-frickin-lasers/. 47 tolerates sexual harassment: Susan J.