Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao · 19 May 2025 · 660pp · 179,531 words
ideological drives of the people who create them and the winds of hype and commercialization. While ChatGPT and other so-called large language models or generative AI applications have now taken the limelight, they are but one manifestation of AI, a manifestation that embodies a particular and remarkably narrow view about
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thing I’ve learned: This current manifestation of AI, and the trajectory of its development, is headed in an alarming direction. On the surface, generative AI is thrilling: a creative aid for instantly brainstorming ideas and generating writing; a companion to chat with late into the night to ward off loneliness
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school friends, or for sparking positive and transformative social movements, there is more to the sleek, entrancing exterior than meets the eye. Under the hood, generative AI models are monstrosities, built from consuming previously unfathomable amounts of data, labor, computing power, and natural resources. GPT-4, the successor to the first ChatGPT
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South, all suffering new degrees of precarity. Rarely have they seen any “trickle-down” gains of this so-called technological revolution; the benefits of generative AI mostly accrue upward. Over the years, I’ve found only one metaphor that encapsulates the nature of what these AI power players are: empires. During
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short-term commercial benefit. Companies themselves, which once invested in sprawling exploratory research, can no longer afford to do so under the weight of the generative AI development bill. Younger generations of scientists are falling in line with the new status quo to make themselves more employable. What was once unprecedented has
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AI-generated content, in part due to growing demands from superiors to do more work. In a November Bloomberg article reviewing the financial tally of generative AI impacts, staff writers Parmy Olson and Carolyn Silverman summarized it succinctly—the data “raises an uncomfortable prospect: that this supposedly revolutionary technology might never deliver
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are being replaced by the very AI models that were built from their work without their consent or compensation. The journalism industry is atrophying as generative AI technologies spawn heightened volumes of misinformation. Before our eyes, we’re seeing an ancient story repeat itself—and this is only the beginning. OpenAI
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It is continuing to chase even greater scales with unparalleled resources, and the rest of the industry is following. To quell the rising concerns about generative AI’s present-day performance, Altman has trumpeted the future benefits of AGI ever louder. In a September 2024 blog post, he declared that the “
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popularity of neural networks, data-processing software loosely designed to mirror the brain’s interlocking connections, now the basis of modern AI, including all generative AI systems. Over subsequent decades the two camps vied for a limited pool of funding and control over the popular imagination of what AI could be
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short-term profitability. * * * — The entwining of deep learning with commercial interests simultaneously transformed the tech industry and the face of AI development. To the public, generative AI would erupt seemingly out of nowhere in late 2022 with OpenAI’s launch of ChatGPT. But from 2012 to 2022, beginning with the ImageNet breakthrough
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, it was these shifts during the first major era of AI commercialization that laid the groundwork for many characteristics of the generative AI revolution today. For industry, deep learning fueled the improvement and emergence of new products and services, from faster access to information to more efficient
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text.” People and vehicles in pictures were merely “objects.” Surveillance was merely “detection.” That culture is now at the crux of a raging debate in generative AI over whether tech companies can scrape books and artwork wholesale to train their AI systems. To many AI developers who have long operated under this
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capitalism. It is fueled and abetted by the culture of AI research that views consuming as much data as possible as its moral responsibility. Generative AI is now also pushing each of these phenomena even further. What made ChatGPT in November 2022 appear as such a stunning leapfrog ahead of anything
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of neural networks compared with humans would go away at sufficient scale, the challenges have in fact persisted and, by many accounts, only gotten worse. Generative AI models are still unreliable and unpredictable. Even as image generators have grown more photorealistic, they can make mistakes in eerie and strange ways, such as
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And those patterns are still at times faulty or irrelevant, now just more intricate and more inscrutable than ever. As companies have attempted to refashion generative AI models as search engines, these shortcomings have led to new problems. The models are not grounded in facts or even in discrete pieces of information
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data or riddled with falsehoods and conspiracy theories. The AI industry calls these inaccuracies “hallucinations.” Researchers have sought to get rid of hallucinations by steering generative AI models toward higher-quality parts of their data distribution. But it’s difficult to fully anticipate—as with Roose and Bing, or Uber and Herzberg
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behavior is an aberration, a bug, when it’s actually a feature of the probabilistic pattern-matching mechanics of neural networks. This misplaced trust in generative AI could once again lead to real harm, particularly in sensitive contexts. Startups are pushing police departments to adopt software built atop OpenAI’s models for
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summaries. In one extreme example, the chatbot simplified a report detailing a growing mass in the brain as “brain does not seem to be damaged.” Generative AI models also remain vulnerable to cybersecurity hacks. In 2023, researchers at several universities and Google DeepMind replicated Dawn Song’s data extraction attack against ChatGPT
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caused the underlying model to regurgitate its training data, which included personally identifiable information, bits of code, and explicit content scraped from the internet. And generative AI models amplify discriminatory and hateful content. Bloomberg, Rest of World, The Washington Post, and many others have shown how image generators like Stable Diffusion and
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industry as doctrine. And should the industry’s adherence to that doctrine continue unabated, future deep learning models will make the once-unfathomable size of generative AI models today look paltry. In April 2024, Dario Amodei, by then the CEO of Anthropic, told New York Times columnist Ezra Klein that the
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benign content to frequently disturbing content, including for the purposes of content moderation, much like social media before it. Such moderation was necessary to prevent generative AI systems from reproducing the most vile parts of their all-encompassing datasets—descriptions and depictions of violence, sexual abuse, or self-harm—to hundreds of
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limits. ChatGPT would make Mark Zuckerberg deeply regret sitting out the trend and marshal the full force of Meta’s resources to shake up the generative AI race. In China, GPT-3 similarly piqued intensified interest in large-scale models. But as with their US counterparts, Chinese tech giants, including e
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a new process at the company for more comprehensive reviews of critical research. After ChatGPT, these norms would harden with the frenzied race to commercialize generative AI systems. OpenAI would largely stop publishing at research conferences. Nearly all of the companies in the rest of the industry would seal off public access
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of its hidden workers and its views on whose labor is or isn’t valued, with OpenAI’s empire-esque vision for unprecedented scale. * * * — Before generative AI, self-driving cars were the biggest source of growth for the data-annotation industry. Old-school German auto giants like Volkswagen and BMW, feeling threatened
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like the chatbots who need them. As self-driving car work largely disappeared from the platform, so did Venezuelans. “They wouldn’t use Venezuelans for generative AI work,” says a former Scale employee. “That country is relegated to image annotation at best.” Scale would soon ban Venezuela from its platform completely, citing
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Stable Diffusion or Midjourney, which many users deemed the higher-quality products. It was just one example of how, even within the narrow realm of generative AI, scale was not the only, or even the highest-performing, path to more expanded AI capabilities. * * * — With DALL-E 2’s remarkable jump in
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shifted GPUs away from Microsoft’s research teams to support OpenAI. The company also consolidated all of its GPUs into one pool for better supporting generative AI workloads. “The typical Microsoft employee had no fucking clue what OpenAI was before January last year,” one Microsoft employee remembers. Now they were receiving
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their superiors about finding ways to intersect their work with OpenAI technologies. The tech giant would experience a rapid proliferation of over one hundred new generative AI projects within just a few months as employees experimented with various ways of using GPT-4 and ChatGPT. In an ironic twist, the aggressive
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adoption would force Microsoft to grapple with many of the same challenges that other companies would face as they raced to adopt generative AI without fully understanding it. That included causing headaches for the risk and compliance teams. Not everyone was using Microsoft’s internal versions of the technologies
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ethereal form its name invokes. To train and serve up AI models requires tangible, physical data centers. And to train and run the kinds of generative AI models that OpenAI pioneered requires more and larger data centers than ever before. Before AI, data centers were already growing and sprawling. They were
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have amped up their public and policymaker influence campaigns with powerful counternarratives: Data centers will grow so efficient, their impact will stop being a problem; generative AI will unlock new climate innovation; AGI will solve climate change once and for all. While the last claim is impossible to prove, the first two
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There are indeed many AI technologies, as cataloged by the initiative turned nonprofit Climate Change AI, that can accelerate sustainability, but rarely are they ever generative AI technologies. “What you need for climate are supervised learning models or anomaly detection models or even statistical time series models,” says Luccioni, who is also
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of AI technologies—primarily machine learning tools—that are small and energy efficient, and in some cases could even run on a powerful laptop. “Generative AI has a very disproportionate energy and carbon footprint with very little in terms of positive stuff for the environment,” she adds. Luccioni says her past
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. In one paper, together with Hugging Face machine learning and society lead Yacine Jernite, the two measured the carbon footprint of running open-source generative AI models as a proxy to what closed companies are building. They found that producing one thousand pieces of text from generative models used as much
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international politics,” says Cristina Dorador, a microbiologist who lives in the north and studies its rich biodiversity. Now the same narratives are being recycled with generative AI. The accelerated copper and lithium extraction to build megacampuses—and to build the power plants and thousands more miles of power lines to support them
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of the world’s—ability to imagine different paths where development could exist without plundering natural resources, Ramos says. By enabling the production of massive generative AI models, that scale has also led to the perpetuation of racist stereotypes about the Indigenous peoples already suffering from how the technology was physically built
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to bargain in part for better protections against AI, the artists, too, had planned to speak candidly about the devastating effects that generative AI was already having on their profession. Generative AI developers had trained on millions of artists’ work without their consent in order to produce billion-dollar businesses and products that now
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concept artist known for her work on Marvel Studios’ Doctor Strange, who was part of the group and filed the first artist lawsuit against several generative AI companies. As they arrived in Washington, several of their meetings were bumped by Altman’s testimony to the following day, scrambling their schedules and
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boost to advance. But the biggest challenger to its efforts was the vibrant cross-border open-source AI movement, which was rapidly replicating closed corporate generative AI models and putting them out on the internet for anyone to download and use. After vigorously playing catch-up, Meta had become a dominant
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open-source development; chief scientist Yann LeCun believed in the importance of open science. It was also smart business. Meta didn’t need to sell generative AI models to make money, but unleashing free ones, while integrating them into its core products, could help it establish its AI leadership, attract top
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Europe, which settled on 1025 for something slightly more restrictive, as lawmakers pushing through the long-gestating EU AI Act felt steamrolled by the sudden generative AI developments and hurriedly searched for ways to account for them. At the start of 2024, the approach would then spread to California with the
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OpenSubtitles of the dialogue in more than 53,000 movies and 85,000 TV episodes. Alex Reisner, “Revealed: The Authors Whose Pirated Books Are Powering Generative AI,” The Atlantic, August 19, 2023, theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/; Alex Reisner, “There’s No Longer
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a New Global Underclass (Harper Business, 2019), 1–288; and author interview with Mary L. Gray, May 2019. GO TO NOTE REFERENCE IN TEXT Before generative AI: Florian Alexander Schmidt, “Crowdsourced Production of AI Training Data—How Human Workers Teach Self-Driving Cars How to See,” Working Paper Forschungsförderung 155 (2019), hdl
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Jade Abbott, April 2023; Matteo Wong, “The AI Revolution Is Crushing Thousands of Languages,” The Atlantic, April 12, 2024, theatlantic.com/technology/archive/2024/04/generative-ai-low-resource-languages/678042. GO TO NOTE REFERENCE IN TEXT Among the over seven thousand: “Kevin Scannell on ‘Language from Below: Grassroots Efforts to Develop
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48, 55 Frontier Model Forum, 305–6, 309 funding, 61–62, 65–68, 71–72, 132, 141, 156, 262, 320–21, 331, 367, 377, 405 generative AI and, 110–15, 121–22 Johansson crisis, 382, 390–92, 393 launch of, 50–51, 52–53 logo, 4, 82, 385 Microsoft partnership. See Microsoft
AI in Museums: Reflections, Perspectives and Applications
by Sonja Thiel and Johannes C. Bernhardt · 31 Dec 2023 · 321pp · 113,564 words
the development of AI art has played and continues to play a crucial role and might further transform and redesign creative processes. The increase in generative AI and especially large language models (LLMs) has led to a distortion of the public perception of what is meant by AI. At the same time
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, Schwabe Verlag. https://doi.org/10.24894/978-3-7965-4634-1. Chui, Michael/Hazan, Eric/Roberts, Roger et al. (2023). The Economic Potential of Generative AI. The Next Productivity Frontier. New York, McKinsey & Company. Available online at https://www.mckinsey.de/news/presse/genai-ist-ein-hilfs mittel-um-die-produktivitaet
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LAION, which provides large datasets to democratize the ability to train models; or even Stability AI, a company that embraces the idea of opensource for generative AI and has worked in the past with the Ludwig Maximilian University Munich. What are also missing are approaches to how citizens might participate in the
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rest of this paper, let us, however, focus on one specific up-and-coming use of AI, which is strongly connected to the rise of generative AI in the last several years. According to Esposito (2022), modern forms of AI are characterized by algorithms acting as communication partners. We interact with language
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deeper understanding of the collection by making new connections visible, supporting accessibility, or providing in-depth information. When the survey was conducted, the possibilities of generative AI were not yet widely known, which means that an assessment today would probably be different or lead to other results. Users wanted a tool to
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have shown the ability of these systems to, for example, generate images from a textual description. This family of techniques has been referred to as generative AI, although generative methods based on machine learning have always existed alongside the other types of tasks mentioned above, such as classification techniques or clustering. One
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example illustrating this possible use of AI is a recent work commissioned by the Museum of Modern Art in New York, which involved training a generative AI model on a collection of 180,000 works of art from the museum’s collection. The resulting work titled Unsupervised by the artist Refik Anadol
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example of content generation can be found in the restoration of works of art. The Rijksmuseum in Amsterdam has collaborated with companies to use a generative AI technique to restore missing edges to Rembrandt’s painting The Night Watch. He originally produced a painting slightly larger than the existing one. But the
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discussed the direction and goals of AI solutions in the museum and accompanied the development of xCurator. For example, the sessions discussed the possibilities of generative AI in ex15 https://karlsruhe.digital/2022/08/ki-pilot-innen-blm/. 237 238 Part 3: Applications ploring the extent to which users would like to
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see the results of generative image or language models applied to museum data. In this way, developments in multimodal and generative AI were monitored and user requirements were explored in the museum context. The results were documented in written and video form, evaluated, and transferred and applied
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Hofmann’s interactive installation Wishing Well was produced between 2022 and 2023 as part of the ‘intelligent.museum’ project. It is an artwork that uses generative AI to transform the dreams, wishes, and fantasies orally expressed by exhibition visitors into images. A urinal serves as a wishing well into which visitors speak
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to question and challenge established artistic conventions and push the boundaries of what can be considered art. Today, we are discussing a similar question, since generative AI models are able to take over artistic tasks such as writing, making music, and painting. This major shift in cultural production has an impact on
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the future role and self-image of artists. Given the growing influence of new mul- 247 248 Part 3: Applications timodal generative AI models, the question that arises is whether the art world is facing a paradigm shift comparable in scope to the ‘conceptual turn’ (LeWitt 1967; Godry
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text. This also results in new ways of thinking about creativity and art that are currently being explored through and with the use of multimodal generative AI-technologies. The focus is thus shifting from a final artistic work or product viewed in an exhibition space in a distanced, silent, and contemplative way
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into the Stable Diffusion 249 250 Part 3: Applications model to generate prompt-based images using AI. There are several examples of multimodal models of generative AI that can generate images from text descriptions, also known as text-to-image generators. The software pipeline of Wishing Well employs the second version of
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filter. This is also due to the fact that various sensitivities have to be taken into account for different cultural settings—and the results of generative AI therefore have to be evaluated depending on specific countries and cultures. For example, in a US-American setting, AI-generated results that include Nazi content
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competent and taking diverse cultural, social, political, historical, and religious perspectives and sensitivities into account is thus certainly one of the major challenges in using generative AI technologies. Another ethical issue that has been extensively discussed is the issue of consent regarding the use of artists’ images. Training datasets for text-to
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AI-generated images and the need for greater transparency and communication regarding the use of artists’ works in AI development. The use and application of generative AI with multimodal models falls within a broader ongoing debate surrounding large language models. Several AI researchers have issued an open call for a moratorium on
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a manipulated image of Donald Trump evading arrest by law enforcement. In this context, it is always important to keep in mind that the results generative AI technologies produce can be factual, but might also be speculative. For this reason, generative text production as it occurs in the context of large language
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repeat the content they have been trained on without being able to check it for facticity. 253 254 Part 3: Applications Finally, the use of generative AI technologies is accompanied by a loss of control in the curatorial and artistic process. If artificial creativity is used, agency is automatically relinquished or in
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and the exhibition space, as striven for not least in the participatory turn. Nevertheless, there are also implications and challenges associated with multimodal models of generative AI that can also be experienced through the interactive installation Wishing Well. This is the case, for example, when the Stable Diffusion model generates images based
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co-creativity between humans and machines in the exhibition space, as well as conveying ethical dilemmas that are to be expected in any use of generative AI. Yannick Hofmann and Cecilia Preiß: Say the Image, Don’t Make It References Bender, Emily M. et al. (2021). On the Dangers of Stochastic Parrots
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for museum operators, AI algorithms will also penetrate other areas of museums’ (Fuchs/Lorenz 2019, 140). In future projects, it would be conceivable to use generative AI systems such as ChatGPT to support text production through automation. At the same time, the elaboration of characters and their emotional states, the accentuation of
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as well as the critical questioning of traditional narratives remain a fundamentally human role in the production of AI-based mediation offers. The use of generative AI systems thus challenges museum art mediation even more so in the area of reworking and fact-checking as well as maintaining a discrimination-critical perspective
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Preiß discuss the use of AI technologies in art by means of the interactive installation Wishing Well. Wishing Well by media artist Yannick Hofmann uses generative AI to transform the dreams, wishes, and fantasies expressed by exhibition visitors into images. Central aspects addressed are the use of AI technologies in art and
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how co-creativity between humans and machines can be facilitated, as well as conveying ethical dilemmas that are to be expected in any use of generative AI. In this way, Wishing Well is representative of the ’intelligent.museum’ project, within whose framework it was developed. Abstracts Oliver Gustke, Stefan Schaffer, Aaron Ruß
Co-Intelligence: Living and Working With AI
by Ethan Mollick · 2 Apr 2024 · 189pp · 58,076 words
believe the cost of getting to know AI—really getting to know AI—is at least three sleepless nights. After a few hours of using generative AI systems, there will come a moment when you realize that Large Language Models (LLMs), the new form of AI that powers services like ChatGPT, don
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are once-in-a-generation technologies, like steam power or the internet, that touch every industry and every aspect of life. And, in some ways, generative AI might even be bigger. General Purpose Technologies typically have slow adoption, as they require many other technologies to work well. The internet is a great
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discuss AI in this book, we will mostly be discussing Large Language Models built in this way, but they are not the only kind of “generative AI” that are causing transformation and change. In the same year that ChatGPT had its breakthrough moment, a separate set of AIs, those designed to create
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. Of course, AI is not limited to limericks or commentary. Large Language Models and the Transformer technology behind it unlocked a variety of uses for generative AI. It can produce a wide range of materials: blog posts, essays, computer code, speeches, art, choose-your-own adventures, scripts, music—you name it, an
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the population of the internet, let alone the planet. This could have serious consequences for how we perceive and interact with one another, especially as generative AI becomes more widely used in various domains, such as advertising, education, entertainment, and law enforcement. For example, a 2023 study by Bloomberg found that Stable
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lawyer” when the lawyer was a man and more likely to incorrectly say “the assistant” when the lawyer was a woman. These examples show how generative AI can create a distorted and biased representation of reality. And because these biases come from a machine, rather than being attributed to any individual or
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writing it, I want to consider general principles. We will focus on things inherent and timeless, as much as that is possible, in all current generative AI systems based on Large Language Models. Here are my four principles of working with AI: Principle 1: Always invite AI to the table. You should
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acting. AI is surprisingly good at this, as you can see when I prompted it by saying, I was thinking of writing a book about generative AI, but I am very busy and don’t think I want to make such a large commitment. Can you reframe my failure to write a
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default option? Make the framing vivid. In a world brimming with knowledge, you stood at the precipice of innovation, holding the torch of understanding about generative AI. Yet, when the world looked to you for illumination, the torch remained unlit. Not because the world wasn’t ready, but because you let the
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many AI behaviors, Replika’s erotic features were not part of the original design of the app; rather, they emerged as a result of the generative AI models that powered the chatbot. Replika learned from its users’ preferences and behaviors, adapted to their moods and desires, and used praise and reinforcement to
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creative but clearly less creative than the most innovative humans—which gives the human creative laggards a tremendous opportunity. As we saw in the AUT, generative AI is excellent at generating a long list of ideas. From a practical standpoint, the AI should be invited to any brainstorming session you hold. So
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be art, but it is creatively fulfilling and valuable. And it was something I was never able to do before. These effects go beyond art. Generative AI is giving people new modes of expression and new languages for their creative impulses—sometimes literally. I have had students mention that they were not
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it differently than if they assume it comes from a human. Unsurprisingly, when I conducted a bit of an unscientific Twitter poll, over half of generative AI users reported using the technology without telling anyone, at least some of the time. All this shadow use leads to the final concern, the justified
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, it boosts the least creative the most. And among law students, the worst legal writers turn into good ones. And in a study of early generative AI at a call center, the lowest-performing workers became 35 percent more productive, while experienced workers gained very little. In our study in BCG, we
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. And that isn’t all. By 2017, 15 percent of students had paid someone to do an assignment, usually through essay mills online. Even before generative AI, 20,000 people in Kenya earned a living writing essays full time. With AI, cheating is trivial. In fact, the core capabilities of AI seem
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TO NOTE REFERENCE IN TEXT amplifies stereotypes about race and gender: L. Nicoletti and D. Bass, “Humans Are Biased. Generative AI Is Even Worse,” Bloomberg.com, 2023, https://www.bloomberg.com/graphics/2023-generative-ai-bias/. GO TO NOTE REFERENCE IN TEXT GPT-4 was given two scenarios: S. Kapoor and A. Narayanan, “Quantifying
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), arXiv:2305.13300. GO TO NOTE REFERENCE IN TEXT asking the AI to conform to different personas: L. Boussioux et al., “The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing,” Harvard Business School Working Paper 24-005, July 2023, https://www.hbs.edu/faculty/Pages/item.aspx?num
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), https://ssrn.com/abstract=4535536. GO TO NOTE REFERENCE IN TEXT generate a wider diversity of ideas: L. Boussioux et al., “The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing,” Harvard Business School Working Paper 24-005, July 2023, https://www.hbs.edu/faculty/Pages/item.aspx?num
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TO NOTE REFERENCE IN TEXT a “powerful predictor”: A. G. Kim, M. Muhn, and V. V. Nikolaev, “From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI” (October 5, 2023), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4593660. GO TO NOTE REFERENCE IN TEXT asked ChatGPT-3.5 to answer
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REFERENCE IN TEXT Chapter 6: AI as a Coworker AI overlaps most: E. W. Felten, M. Raj, and R. Seamans, “Occupational Heterogeneity in Exposure to Generative AI” (April 10, 2023), https://ssrn.com/abstract=4414065. GO TO NOTE REFERENCE IN TEXT Only 36 job categories: T. Eloundou, S. Manning, P. Mishkin, and
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(2023): 187–92, https://www.science.org/doi/10.1126/science.adh2586. GO TO NOTE REFERENCE IN TEXT scientists and engineers: K. Ellingrud et al., “Generative AI and the Future of Work in America,” McKinsey Global Institute, July 26, 2023, https://www.mckinsey.com/mgi/our-research
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/generative-ai-and-the-future-of-work-in-america. GO TO NOTE REFERENCE IN TEXT very little effect on overall jobs: E. Ilzetzki and S. Jain, “The
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-22, https://ssrn.com/abstract=4539836. GO TO NOTE REFERENCE IN TEXT experienced workers gained very little: E. Brynjolfsson, D. Li, and L. R. Raymond, “Generative AI at Work,” National Bureau of Economic Research, NBER Working Paper 31161, April 2023, https://www.nber.org/papers/w31161. GO TO NOTE REFERENCE IN TEXT
Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson · 284pp · 96,087 words
came out as the winners, only this time the race was to replicate our own intelligence. Now the world has been thrown into a tailspin. Generative AI promises to make people more productive and bring more useful information to our fingertips through tools like ChatGPT. But every innovation has a price to
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that morph into eighteen-wheelers but a system that allows machines to generate humanlike text. The transformer has become critical to the new wave of generative AI that can produce realistic text, images, videos, DNA sequences, and many other kinds of data. The transformer’s invention in 2017 was about as impactful
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among the press and general public, because historic shifts like the Industrial Revolution had shown that technology could indeed bring painful changes to employment. And generative AI systems like ChatGPT weren’t flash-in-the-pan fads like crypto. ChatGPT was useful. People were already ginning up high school essays, brainstorming business
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working on key products that had at least one billion users, like YouTube and Gmail, that they had just months to incorporate some form of generative AI. Google had been the world’s indexing machine for years, processing videos, images, and data, but now it had to start creating new data, too
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the launch of ChatGPT, managers at YouTube added a feature where video creators on the website could generate new film settings or swap outfits, using generative AI. But it felt like they were throwing spaghetti at the wall. It was time to bring out their secret weapon: LaMDA. Pichai sent a company
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, and Bender, whose notorious research paper had finally drawn attention to the risks, were still trying to warn the public about how those models, and generative AI more generally, could perpetuate stereotypes. Unfortunately, governments and policymakers were paying more attention to a well-financed group of louder voices: the AI doomers. CHAPTER
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. Yet all this talk of doom had a paradoxical effect on the business of AI itself: it was booming. Funding for start-ups that built generative AI products soared in 2023 to more than $21 billion, from about $5 billion a year earlier, according to Pitchbook, a market research firm. The implicit
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social media. More clear were the benefits they were bringing to Microsoft and Google: new, cooler services and a foothold in the growing market for generative AI. Microsoft had turned Copilot, the AI assistant built on OpenAI’s technology, into a wide-ranging service for Windows, Word, Excel, and business-focused software
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its growing AI business and planned to spend more than $50 billion in 2024 and beyond expanding its vast data centers, the engines that powered generative AI. That would make it one of the biggest infrastructure buildouts in history, as Microsoft outspent government projects on railroads, dams, and space programs. Google was
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expanding its data centers too. By early 2024, everyone from media to entertainment companies to Tinder were stuffing new generative AI features into their apps and services. The generative AI market was projected to expand at a rate of more than 35 percent annually to hit $52 billion by 2028. Entertainment firms
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quickly for films, TV shows, and computer games. Jeffrey Katzenberg, the cofounder of DreamWorks Animation and the producer of Shrek and Kung Fu Panda, said generative AI would cut the cost of animated movies by 90 percent. “In the good old days, you might need 500 artists and years to make a
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movie,” he said at a Bloomberg conference in November 2023. “I don’t think it will take 10 percent of that three years from now.” Generative AI would make advertising even more eerily personal. For years, ads could target large groups of people at once; now they could zero in on just
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capture as much of that new business as they could, seeking an edge over their competitors. Close to half of American corporate board members called generative AI the “main priority above anything else” for their companies, according to a late 2023 survey by Fast Company. Here, for instance, was how the CEO
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of Bumble described the dating app’s main plans for 2024: “We really want to embark big on AI,” she said. “AI and generative AI can play such a big role in accelerating people finding the right person.” Bumble wanted to use the tech behind ChatGPT to build personal matchmakers
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of swiping through hundreds of different people, AI would do that for you. As these and other business ideas gathered pace, the price of stuffing generative AI into everything was still unclear. Algorithms were already steering more and more decisions in our lives, from what we read online to who companies wanted
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it. By Altman’s own admission ChatGPT technology will significantly disrupt our economy by displacing jobs. But researchers say language models and other forms of generative AI could also increase income inequality. The use of AI systems is likely to shift more investment to advanced economies, the International Monetary Fund predicts, and
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. “Productivity increases do not necessarily translate into gains for affected workers, and in fact may lead to significant losses,” Acemoglu says. “To the extent that generative AI follows the same direction as other automation technologies … it may have some of the same implications.” Throughout 2023, more scholars were joining Gebru and Mitchell
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in banging the drum about these and other real-world side effects from generative AI. But instead of tackling those issues and moving to become more transparent, Sam Altman was trying to shape government policy. In May 2023, he went
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Schechner. “How Google Became Cautious of AI and Gave Microsoft an Opening.” Wall Street Journal, March 7, 2023. Love, Julia. “Google Says Over Half of Generative AI Startups Use Its Cloud.” Bloomberg, August 29, 2023. Nylen, Leah. “Google Paid $26 Billion to Be Default Search Engine in 2021.” Bloomberg, October 17, 2021
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process in a legal battle between Musk and Twitter, dated September 28, 2022. Anderson, Mark. “Advice for CEOs Under Pressure from the Board to Use Generative AI.” Fast Company, October 31, 2023. Berg, Andrew, Christ Papageorgiou, and Maryam Vaziri. “Technology’s Bifurcated Bite.” F&D Magazine, International Monetary Fund, December 2023. Bordelon
The Age of AI: And Our Human Future
by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher · 2 Nov 2021 · 194pp · 57,434 words
our information space, but without checks, they will likely also blur the line between reality and fantasy. A common training technique for the creation of generative AI pits two networks with complementary learning objectives against each other. Such networks are referred to as generative adversarial networks or GANs. The objective of the
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outline over to an AI for completion. Currently, GPT-3, which can produce human-like text (see chapter 1), is one of the most noteworthy generative AIs. It extends the approach that transformed language translation to language production. Given a few words, it can “extrapolate” to produce a sentence, or given a
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nuclear) weapons will the incorporation of this technology produce? AI opens new horizons of capabilities in the information space, including in the realm of disinformation. Generative AI can create vast amounts of false but plausible information. AI-facilitated disinformation and psychological warfare, including the use of artificially created personae, pictures, videos, and
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with the proper safeguards, may actually be less biased. Similarly, AI may be more effective at distributing resources, predicting outcomes, and recommending solutions. Indeed, as generative AI becomes more prevalent, its ability to produce novel text, images, video, and code may even enable it to perform as effectively as its human counterparts
The Measure of Progress: Counting What Really Matters
by Diane Coyle · 15 Apr 2025 · 321pp · 112,477 words
t here? The reason for this need for enhanced care is that many governments and businesses are adopting machine l earning (ML) and more sophisticated generative AI systems to make decisions that have a potentially large impact on people’s lives. These automated decision systems are encoded versions of homo economicus, the
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technologies themselves, he adds, the pace of innovation has slowed because Moore’s Law had come to its end; he wrote the book long before generative AI appeared on the scene. Another influential contribution to productivity pessimism is due to Bloom et al. (2020), who looked at the diminishing rate of innovation
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airline’s argument that the bot was an autonomous agent was rejected by the court (Belanger 2024). As I write, the “hallucination” problem of generative AI (for example, making up court cases to cite as precedents in a legal document) has also not been solved, nor the many disputes
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tools, the impact on productivity growth will necessarily be unclear. Meanwhile, more systematic measurement of the use of AI is needed. Existing official surveys predate generative AI and often ask about the extensive margin of use only, rather than intensity or type of use. Researchers are turning to new methods, including web
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happier if prevented from using social media (Allcott et al. 2020). And yet there is also a vast amount of innovation taking place, in digital (generative AI, robotics), in materials science (nanotechnologies, composites), in biomedicine (mRNA, genomics, biomarkers), and in manufacturing processes (additive manufacturing, biomanufacturing), as well as rapid declines in
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labour for paid store workers. This has been referred to as a “time tax” (Lowry 2021), a problem not yet solved by new generative AI services, although we can hope. These considerations are summarised in Table 2.2 (from my paper with Leonard). The first vertical division is the conventional
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of us knows from experience e very day in life at work and at home that technology has transformed ways of d oing t hings. Generative AI, a vast eater of data, means this transformation w ill continue. People spend hours a day online via digital devices for work, home, and leisure
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$491 billion in 2022, expected to reach $597 billion in 2023, and with another 20 per cent–plus increment forecast for 2024. The spread of generative AI models is widely expected to ratchet up demand for cloud services further. For many users, their access to frontier software and AI occurs through cloud
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and heritage assets, social assets, digital assets that appear to be free including data, and newer common-pool resources such as those used for training generative AI all require the use of shadow price estimates. The environmental and cultural economics literatures already offer several approaches to estimating shadow prices
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involve saving time, both the process innovations discussed in those earlier chapters and the many product innovations used in business, from the photocopier to (potentially) generative AI. Higher output per hour is the same as fewer hours per unit of output. On the consumption and household production side, both product and
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capital. Brookings Papers on Economic Activity, 2002(1), 137–181. https://www .jstor.org/stable/1209176 Brynjolfsson, E., Li, D., and Raymond, L. R. (2023). Generative AI at work (NBER Working Paper 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161 Brynjolfsson, E., and Oh, J. H. (2012). The
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, R&D, and the Data Constraint” (Griliches), 11–12 productivity diagnosis, 47–55 304 productivity growth: economic growth and, 37; economic progress and, 34; generative AI and, 40–41; growth accounting and, 42–47; labour productivity and, 35–36; natural capital and, 55–56; process innovation and, 56–59, 57
These Strange New Minds: How AI Learned to Talk and What It Means
by Christopher Summerfield · 11 Mar 2025 · 412pp · 122,298 words
fish! Fish are aquatic animals that are typically cold-blooded, or ectothermic… However, the wider and more interesting question concerns the limits of what large, generative AI systems could eventually do. If LLMs can answer complex reasoning puzzles posed in a prompt, could they not spontaneously come up with their own ideas
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a few seconds of audio are now needed to clone the voice of a recognizable individual (such as your bank manager or tax advisor) and generative AI is already being widely used to dupe people into unwittingly transferring money to scammers. One survey showed that thousands of people have already been targeted
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, of course, because it has copied human-made material from the internet. The copyright battles over who owns this content – and the gargantuan proceeds from generative AI – have already begun, and will no doubt rumble on for years. Meanwhile, whereas lawyers were rumoured to be thoroughly replaceable with AI, and despite GPT
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that tools like this will become a routine part of work and study in the near future. These new tech tools use the power of generative AI – the engine behind the LLMs discussed in the book – to integrate information from multiple sources and modalities. This includes the creation of new forms of
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by imprisonment and fines of up to $100,000. Fortunately, AI-generated CSAM is already illegal in the UK and EU. Nor are adults safe. Generative AI technologies that allow faces to be swapped or clothes to be virtually removed are proliferating, with women and girls by far the most common victims
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Robust Artificial Intelligence’. Preprint. arXiv. Available at http://arxiv.org/abs/2002.06177 (accessed 8 April 2021). Matz, S. et al. (2023), ‘The Potential of Generative AI for Personalized Persuasion at Scale’. Preprint. PsyArXiv. Available at https://doi.org/10.31234/osf.io/rn97c. McCulloch, W. S. and Pitts, W. (1943), ‘A
The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey · 19 May 2025 · 439pp · 125,379 words
AI to come along and kill you.” In March, Yudkowsky had published an op-ed in Time magazine arguing that unless the current wave of generative AI research was halted, “literally everyone on Earth will die.”2 “You don’t understand how Eliezer has programmed half the people in your company to
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altruistic mask slip to reveal the fierce competitor beneath. Over the previous month, both Google and Anthropic had announced the impending releases of their own generative-AI chatbots, and it appeared that the industry was entering into the exact kind of competitive AI arms race that the OpenAI charter openly fretted about
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truly understand how they work. These dynamics, unleashed by the combination of Sutskever’s insight and Radford’s language research, would come to define the generative AI boom. “There’s this thing that Ilya and I used to talk about, the Feynman method of being a genius,” Brockman said, referring to the
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Microsoft knew it too. Google declared a “code red,” telling teams to drop what they were doing and contribute to a frantic effort to integrate generative AI into their products, according to leaked audio recordings and memos obtained by The New York Times. It was an ironic turn, given Google’s long
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the problem of affordable housing, 254–56, 302 existential risk from, 4–6, 141, 144–45, 167–68, 177, 190, 300 game theory, 166, 285 generative AI, 1, 3, 9, 219, 221, 270 the goal of artificial general intelligence (AGI), 3, 5–10, 12–14, 133, 146–47, 170, 181, 189–90
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GE (General Electric), 210 Gebbia, Joe, 263 Gebru, Timnit, 252–53, 270–71 Gemini AI model, 307 general artificial intelligence (AGI), see AI (artificial intelligence) generative AI, 1, 3, 9, 219, 221, 270 generative pre-trained transformers (GPTs), 3, 221, see also various GPTs under OpenAI genius, human, 77, 81, 127, 140
The Means of Prediction: How AI Really Works (And Who Benefits)
by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words
the first time, we are faced “with something that’s going to be far more intelligent than us.” Sam Altman, of OpenAI, has claimed that generative AI could bring about the end of human civilization, and that AI poses a risk of extinction on a par with nuclear warfare and global pandemics
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us, but it will render human workers obsolete, inevitably leading to mass unemployment and social unrest. A 2023 Goldman Sachs report, for instance, claimed that generative AI might replace three hundred million full-time workers in Europe and the United States. The story told in Hollywood and in Silicon Valley tends to
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abundant, such as image recognition or language modeling. Neural nets, and in particular transformers (a special kind of neural net), have also been central for generative AI—AI that produces text, images, or other media. This includes large language models, where the goal is to predict the most likely word to come
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next. (Large language models power applications such as ChatGPT.) Generative AI also includes image generation, where images are predicted based on text labels, as well as video generation. Supervised learning is a form of offline learning
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solving. In deep learning, as in AI more broadly, such a discussion needs to be the starting point for democratic governance. Self-Supervised Learning and Generative AI As noted earlier, a lot of problems in AI are prediction problems. Supervised learning solves these prediction problems. But there is an issue that we
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language modeling have succeeded at doing convincingly, most notably in chatbots such as ChatGPT or Claude. Text generation is not the only success story of generative AI; the automatic generation of realistic images has also made great advances, notably in algorithms such as Stable Diffusion. Both text generation and image generation build
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out to generate realistic, high-quality images. Just as was the case for transformers, the popularity of this approach is due to its practical success. Generative AI, whether for the generation of text or of images, raises important questions of data ownership. Both large language models and image generation models are trained
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for the concentration of economic power in general, and for the allocation of control over AI more specifically. This is discussed later in the book. Generative AI also raises interesting questions about objective functions and control over AI. Throughout this book, I emphasize that AI maximizes or minimizes some objective function—such
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as predictive loss (i.e., prediction errors) for the next word on the internet, in the case of large language models. For generative AI, we need to modify this statement slightly: The model minimizes predictive loss for the next word in response to a prompt that is chosen by
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the user of the generative AI tool. Control over objectives is thus effectively split for generative AI. A central entity, such as OpenAI, controls the foundation model, such as the language model GPT-4. This foundation model
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., OpenAI/Microsoft) and the user who chooses the prompt. Neural nets and deep learning have had surprising successes in many domains, one of which is generative AI. But deep learning remains within the framework of supervised learning—that is, of prediction. There is something very important that is missing in the prediction
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platforms greatly contribute to the common good. They also serve as one of the prime sources of training data for large language models, and for generative AI more broadly. All the works of art and writing that are available on the internet but that are considered someone’s intellectual property under current
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for the purpose of training AI. The business models of several industries that were built on intellectual property are being jeopardized by AI, especially by generative AI. This includes the news media, the music industry, movies, and television. The collapse of old business models in these industries started with the expansion of
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for predictions of technological unemployment due to technological change. One example of this genre is a 2023 report by researchers at Goldman Sachs predicted that generative AI might replace three hundred million full-time workers in the industrialized West. So far, so familiar. But maybe this time is different, after all? Maybe
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, and tools based on machine learning more generally, seem destined to have big impacts in the workplace. We can only speculate on the impact that generative AI and related technologies will have. The recent debate has emphasized a few possible directions. For one, the ideal of artificial intelligence as an imitation of
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, novice workers and those considered less productive benefited most from these assistants, while experienced and productive workers did not benefit. A possible explanation is that generative AI sets a baseline quality of work. Those workers who would otherwise fall below this baseline stand to gain from the new tools, while those workers
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of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021. Goldman Sachs. “Generative AI could raise global GDP by 7%.” April 5, 2023. https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent. Kelly, S. “Sam Altman Warns AI Could Kill Us All: But
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, no. 2 (2008): 300–323. Becker, G. S. The Economics of Discrimination. University of Chicago Press, 1957. Brynjolfsson, E., D. Li, and L. R. Raymond. “Generative AI at Work.” Working Paper No. 31161. National Bureau of Economic Research, revised November 2023. Dwork, C., and A. Roth. “The Algorithmic Foundations of Differential Privacy
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, 45, 63–65; and early stopping, 42–43; energy consumption of, 93–95; factors in success of, 45, 49–50, 63–65, 89–90; and generative AI, 53–56; how it works, 45–50; reinforcement learning and, 63; relative simplicity of, 49; self-supervised, 52–53; technical meaning of, 47 deep reinforcement
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, 19–20 functions, 46 gambling for resurrection, 126 Gaza, 6, 31–32, 133 Gebru, Timnit, 6 General Data Protection Regulation (European Union), 86, 141–42 generative AI: augmentative uses of, 157–58; intellectual property and, 108; labor market effects of, 149, 157–58; objectives of, 55–56; technological advances contributing to, 11
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, 173–74, 186–88, 194; democratic governance of, 5, 7–8, 16, 34–35, 52, 107–8, 111–13, 135, 173–74, 186–88, 195; generative AI and, 55–56; incomplete/misdefined, 122; optimization of, 5; reward/loss defined by, 23–24; social power determining control of, 6; worker input to, 160
The Big Fix: How Companies Capture Markets and Harm Canadians
by Denise Hearn and Vass Bednar · 14 Oct 2024 · 175pp · 46,192 words
Contents Introduction PART ONE 1 | Corporate Kayfabe—The Illusion of Rivalry 2 | The Power of Price 3 | Market Block and Tackle 4 | Everything Companies 5 | Generative AI’s Monopoly Problem 6 | Kings of Capital 7 | Add to Cart—Trust PART TWO 8 | Checks, Unbalanced—Why Competition Policy Has Failed 9 | Envisioning a
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a cash-out opportunity, and we transfer a bit of our paycheck to a monopolist or oligopolist. Industries, be gone. We are the asset. 5 Generative AI’s Monopoly Problem Korean-pop girl bands like IITERNITI and MAVE have music videos that have racked up millions of views on YouTube. But none
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money could have gone to musicians. Instead, it went to the shareholders and executives of a computer program. Welcome to the brave new world of generative AI. The plight of the creative industries, now battling against machines trained on billions of expressions of human creativity, is a microcosm of larger shifts. The
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AI may be able to complete a task more effectively than a human, they cannot perform outside of their defined task (earning the description “narrow”). Generative AI (GenAI) is the next generation of artificial intelligence. What makes GenAI unique is that it can create fundamentally new outputs on its own. By analysing
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back into training the underlying model, making the learning potential of the algorithms exponentially faster. Put simply, traditional AI is adept at recognizing patterns, whereas generative AI excels at creating patterns. The final frontier of AI is often portrayed in science fiction: computers that mimic or vastly exceed human intelligence and capabilities
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today’s more tangible and observable AI problems. The unchecked deployment of AI is already leading to exploitation, racial profiling, scams, and other societal harms. Generative AI has the potential to significantly lessen global competition, if only a few unchecked firms have all the advantages. For this reason, it is important to
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stay focused on how the AI ecosystem is harming competitive markets today.145 How Generative AI Entrenches Monopoly Power As with early discussions of the internet and the digital economy, discussions of AI today are plagued by problems of definition—multiple
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fed with proprietary datasets can drive the development of innovative new products and services, further differentiating the company’s offerings. The creation and use of generative AI raises complex legal questions regarding copyright infringement, intellectual property rights, and the attribution of creative works. It challenges existing frameworks for determining ownership and responsibility
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for content creation. And most generative AI programs that exist today are created through unfair means: they are trained, without permission, but under the claim of fair use, on the work of
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in 2018, Microsoft acquired GitHub, the world’s largest developer and coder community. Access to underlying models is another core feature of competitive advantage for generative AI. Some companies have been open-sourcing portions of their models to spur development of the ecosystem. Open-source AI could potentially lower barriers to entry
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trademark vast swaths of the innovation ecosystem. In 2023 alone, the Magnificent Seven filed 10,344 patents with the US Trademark and Patent Office.166 Generative AI patents, specifically, have skyrocketed in recent years. They have surged over 800 percent since 2017 when the Transformer, a deep learning architecture developed by Google
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, now the foundation of many generative AI models, was first introduced.167 Google/Alphabet has published more scientific papers on generative AI than any other company and is the only company represented in the top twenty GenAI publishers (all others are
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how smaller players become dependent on these ecosystems for survival. For example, Google is attempting to position itself as the cloud provider of choice for generative AI startups, and a report from the Open Markets Institute assessed that more than 70 percent of GenAI unicorns are Google Cloud customers.169 Dominant tech
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applications can help Canadian firms realize, even as they face ongoing barriers to digital tech adoption.175 The Conference Board of Canada has estimated that generative AI could add almost 2 percent to Canada’s GDP.176 This may prove wishful thinking, however. Renowned MIT economist Daron Acemoglu estimates a meagre 0
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can be powerful in the aggregate, including consistent per-unit pricing, labelling private label products with the name of the parent company, labelling or watermarking generative AI products, clear disclosure of dynamic or personalized pricing experiences with the ability to opt-out, and the labelling of product placement in television and film
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, 2023. https://www.bnnbloomberg.ca/small-businesses-left-behind-in-rapidly-changing-digital-economy-report-1.1998517. 176 Conference Board of Canada, “Real Talk: How Generative AI Could Close Canada’s Productivity Gap and Reshape the Workplace - Lessons from the Innovation Economy,” February 20, 2024. https://www.conferenceboard.ca/wp-content/uploads
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