Tim O’Reilly
AI, Algorithms, Antitrust, and the Economy
An archive, in reverse chronological order, of essays, interviews, and talks relating to technology and the economy
The Alignment Problem Is Not New: Lessons for AI Governance from Corporate Governance (O'Reilly Radar, June 15, 2023)
"Corporations are nominally under human control, with human executives and governing boards responsible for strategic direction and decision-making. Humans are "in the loop," and generally speaking, they make efforts to restrain the machine, but as the examples above show, they often fail, with disastrous results. The efforts at human control are hobbled because we have given the humans the same reward function as the machine they are asked to govern: we compensate executives, board members, and other key employees with options to profit richly from the stock whose value the corporation is tasked with maximizing. Attempts to add environmental, social, and governance (ESG) constraints have had only limited impact. As long as the master objective remains in place, ESG too often remains something of an afterthought."
The first step to proper AI regulation is to make companies fully disclose the risks (The Evening Standard, June 15, 2023)
"We are a long way from knowing what all of the best practices are, but much as accounting standards are based on a consensus view of what good financial reporting contains, adopted widely by industry and then codified into formal reporting and auditing, understanding what companies are actually doing (or not doing) to manage the risks of the AI models they develop is a good place to start."
Regulating Big Tech through digital disclosures, co-authored with Ilan Strauss and Mariana Mazzucato (UCL Institute for Innovation and Public Purpose, June 15, 2023)
"The current disclosures framework for public companies - the annual 10-K financial report in the U.S. and related IFRS-governed filings in the European Union - was designed for industrial economies based primarily on physical assets and in-person consumption. By contrast, today's technology companies derive their value from intangible digital marketplaces and platforms. Since technology shares account for 27.3% of total US market capitalization - roughly equivalent to materials, energy, utilities, and industrials combined - the failure to update disclosure regulations for these radically different businesses is a glaring omission."
You Can't Regulate What You Don't Understand (O'Reilly Radar, April 14, 2023)
In which I propose that accounting standards might suggest a governance model for AI regulation: "What we need is something equivalent to GAAP for AI and algorithmic systems more generally. Might we call it the Generally Accepted AI Principles? We need an independent standards body to oversee the standards, regulatory agencies equivalent to the SEC and ESMA to enforce them, and an ecosystem of auditors that is empowered to dig in and make sure that companies and their products are making accurate disclosures. But if we are to create GAAP for AI, there is a lesson to be learned from the evolution of GAAP itself. The systems of accounting that we take for granted today and use to hold companies accountable were originally developed by medieval merchants for their own use. They were not imposed from without, but were adopted because they allowed merchants to track and manage their own trading ventures. They are universally used by businesses today for the same reason. So, what better place to start with developing regulations for AI than with the management and control frameworks used by the companies that are developing and deploying advanced AI systems?"
Web3—the latest Silicon Valley buzzword (The Economist’s Babbage podcast, February 8, 2022)
Is the promise of Web3 a lot of money for a handful of grifters or a way to return power to the people? Is it a complete reinvention of cyberspace or just another phase of decentralization followed by a new wave of centralization? In this segment of the Economist’s Babbage podcast, host Kenneth Cukier and guests Tim O’Reilly, Benedict Evans, Rachana Shanbhogue, Jutta Steiner, David Rosenthal, Ludwig Siegele, and Tim Cross explore the hype and the potential of a decentralized Web3.
It's Time to Open Big Tech's Financial Black Box (The Information, December 16, 2021)
My op-ed for The Information on why we need improved disclosures by tech companies on how they monetize the data they collect and on the operational metrics that they use to guide. their business decision making. This op-ed summarizes the argument of a much longer report that I co-authored with Ilan Strauss, Mariana Mazzucato, and Joshua Ryan-Collins of University College London, called Crouching Tiger, Hidden Dragons: how 10-K disclosure rules help Big Tech conceal market power and expand platform dominance.
Why It's Too Early To Get Excited About Web3 (O'Reilly, December 13, 2021)
I place Web3 in the context of previous bubbles, and ask the question of what will be left behind when the bubble pops. As both Bill Janeway and Carlota Perez have pointed out, there are both productive and unproductive bubbles. In a productive bubble, speculative excess builds out lasting infrastructure that can be capitalized on by the future. Is Web3 like the real estate mortgage bubble that popped in 2009, leaving only destruction in its wake, or like the dotcom bubble, which left a legacy of billions of miles of high speed fiber, data centers, and the infrastructure that led to the subsequent boom. Only time will tell.
Two Economies, Two Sets of Rules (O'Reilly, June 22, 2021)
Why is Elon Musk so rich? The answer tells us something profound about our economy: he is wealthy because people are betting on him. But unlike a bet in a lottery or at a racetrack, in the vast betting economy of the stock market, people can cash out their winnings before the race has ended. A lot falls into place when you realize that there are really two economies at work today: an operating economy in which people have jobs, create products, and deliver services to each other, and a betting economy in which people gamble on the future worth of various financial assets, including company stocks and cryptocurrencies, which may be only loosely tied to the operating economy.
This betting economy, within reason, is a good thing. Speculative investment in the future gives us new products and services, new drugs, new foods, more efficiency and productivity, and a rising standard of living. Tesla has kickstarted a new gold rush in renewable energy, and given the climate crisis, that is vitally important. A betting fever can be a useful collective fiction, like money itself (the value ascribed to pieces of paper issued by governments) or the wild enthusiasm that led to the buildout of railroads, steel mills, or the internet. As economist Carlota Perez has noted, bubbles are a natural part of the cycle by which revolutionary new technologies are adopted.
Sometimes, though, the betting system goes off the rails.… Silicon Valley is awash in companies that have persuaded investors to value them at billions despite no profits, no working business model, and no pathway to profitability.
Checking Jeff Bezos's Math (O'Reilly, April 2021).
In his final shareholder letter, Jeff Bezos touted the value that Amazon creates for each of the company's stakeholders, including its shareholders, its employees, its customers, and its suppliers. While this is a welcome nod to a fuller stakeholder capitalism, the metrics that Jeff used to measure value creation were different for each group, sometimes ambiguous, and sometimes just plain misleading. In this essay, I use Jeff's letter as the occasion to call for consistent metrics explaining "who gets what and why."
The End of Silicon Valley As We Know It? (O'Reilly, March 2021).
Understanding four trends that may shape the future of Silicon Valley is also a road map to some of the biggest technology-enabled opportunities of the next decades. I take a look at AI in the life sciences, the opportunity of climate change, internet regulation, and our overheated financial markets.
Reimagining Government and Markets (The Bridge: National Academy of Engineering, January 2021).
I've been writing for more than a decade about what government can learn from Silicon Valley. This essay reflects on the urgency of the challenges the world will face over the next 50 years, and the role of Silicon Valley in overcoming them: “The struggles of social media companies notwithstanding, the information management capabilities of the Silicon Valley giants are truly staggering. What if these capabilities could be put to work on stuff that matters more than getting people to click on provocative content and the ads that accompany it? What if government had the kind of capabilities, information flows, and partnerships between humans and machines that distinguish the best of technology companies?”
What's Wrong With Silicon Valley's Growth Model (University College London MPA Lecture, October 2020).
I have recently taken on a side-hustle as a Visiting Professor of Practice at University College London, where I'm leading a research project on rent-seeking algorithms used by the big tech platforms. As part of the job, I gave a lecture to students at the UCL Institute for Innovation and Public Purpose. Here are the slides for the long three-part lecture. The slides are fairly self-explanatory, especially if you download the ppt so you can look at the speaker notes, which pretty much recap what I intended to say along with each slide. (I will have to see if there is any video.)
We Have Already Let The Genie Out Of The Bottle (Rockefeller Foundation, July 2020).
In many ways, this piece is a highly compressed recap of one of the central arguments of my book WTF?, that our economy and markets are an example of the same kind of algorithmically-controlled human-machine hybrid that is at the heart of platforms like Google and Facebook. The failures of corporate governance at these platforms are a harbinger of our inability to govern even more powerful algorithmic systems and artificial intelligences in the future. These companies are doing exactly what our financial markets tell them to do; our attempts to rein them in will fail unless we change the objective function of our economic algorithms.
Antitrust regulators are using the wrong tools to break up Big Tech (Quartz, July 2019).
I take aim at what I call “the illusion of free markets,” in which platforms like Amazon and Google first increase our economic freedom, and then restrict it in pursuit of increased profits. Rather than focusing on breaking up the big platforms, I urge regulators to look more deeply at the way they compete with their ecosystem of suppliers. “These giants don’t just compete on the basis of product quality and price—they control the market through the algorithms and design features that decide which products users will see and be able to choose from. And these choices are not always in consumers’ best interests.”
The fundamental problem with Silicon Valley’s favorite growth strategy (Quartz, February 2019).
This critique of Reid Hoffman's book Blitzscaling became a manifesto against Silicon Valley's quest for monopoly. In it, I lay out an argument for why sustainable growth funded by customers is better for most entrepreneurs (and for society) than winner-takes-all growth funded by a vast influx of capital, why the capital-fueled blitzscaling model will eventually come to an end, and the responsibility of those who do win their way to a monopoly position.
Shaping the Stories That Rule Our Economy (O'Reilly, September 2018).
My review of Mariana Mazzucato's book The Value of Everything. Mariana's explanation of how economists (and society) have come to see different sectors as the source of value while leaving others out of the accounting has become fundamental to my thinking. Her explanation of how economic rents (excess income derived from control over a limited resource) are overlooked in the diagnosis of inequality has shaped my thinking on antitrust and big tech. Mariana and I have since begun working together to develop a theory of what we are calling “algorithmic rents.”
Evolving the New Economy: Tim O’Reilly and David Sloan Wilson
Evolutionary theory meets artificial intelligence and the management of algorithms (Evonomics, August 2018).
I've become fascinated with the overlap between the ideas of evolutionary biologist David Sloan Wilson and my own thinking about business ecosystems, so I was delighted that he saw the overlaps too. In this conversation, we explore our mutual fascination. David's ideas about altruism and multilevel selection are especially eye-opening as a lens through which to view our businesses, our economy, and human societies.
Tim O’Reilly Says Don’t Eat the Ecosystem, a Lesson for Voice Platforms (Interview on the VoiceFirst Roundtable podcast with host Bradley Metrock, September 2017).
"The book is really a meditation on what we learn from the great technology platforms about the future of the economy. And one of the key things that we learn is that these platforms… can’t just serve their users. They have to actually create a rich ecosystem of suppliers."