The past year has seen a dramatic shift in the landscape for the economics of AI. Artificial intelligence has made remarkable progress, particularly in the space of large language models, and this progress has been faster than many (including myself) expected. As we enter 2023, I’d like to share some facts, thoughts and opinions on the implications of these developments.
- Cognitive automation: This is set to be a major trend in 2023 and beyond. As the capabilities of large language models continue to expand, cognitive workers (such as myself) are increasingly at risk of being automated. This means that economists must abandon the notion that automation only affects routine jobs, and that human creativity is somehow — miraculously — immune to automation. To keep up with this new reality, economic models must be adapted accordingly.
- Exponential growth in compute: Progress in AI is continuing relentlessly, propelled by a combination of advances in hardware and software as well as ever-growing training budgets. This has resulted in doubling times in compute of about six months for cutting-edge models — much faster than Moore’s Law — a regularity that has held for almost a decade now. Note that the dollar cost of training runs is also growing exponentially for cutting-edge models — and currently in the realm of eight-digit dollar amounts. Since expenditure on compute is growing much faster than the overall economy, an ever-growing portion of our economy’s resources is devoted to compute — the beginning of an AI takeoff!
- Economic growth: Compared to the macroeconomy, investment in AI is still relatively small, and it will take several more rounds of doubling for the macroeconomic effects to be felt. The economy is like a large vessel that takes a long time to turn, so I don't anticipate that the recent advances will be reflected in investment, productivity and growth numbers at the macro level in 2023.
- Growing public attention: OpenAI's ChatGPT, released in November, gave the public a firsthand experience of the power of advanced AI and significantly increased public awareness of the abilities of large language models. (If you haven’t already, try it out to get a glimpse of what the personal assistants of the future will look like.) Technically, the system is just one customization of one large language model (GPT3.5) among several others that have demonstrated a growing level of general intelligence. But it represents yet another small step forward toward artificial general intelligence (AGI), i.e., toward AI systems that can perform all cognitive tasks that humans can perform. The next generation of language models are rumored to be released soon and will display even greater general intelligence.
In the above episode of American Public Media's Marketplace, Korinek comments on the potential of AI to revolutionize nearly everything in the next decade.
- Expanding Overton window and AGI governance: As conversations about AGI become more commonplace, the Overton window — the range of ideas and policy options that people view as reasonable to discuss — is rapidly expanding. This will bring AGI governance to the forefront of the public discourse. Important questions include both how AGI will interact with existing governance structures and how AGI itself should be governed. Economists have much to contribute to these topics, providing ample opportunities for cutting-edge research papers and dissertations. Our Oxford Handbook of AI Governance, set to be published in early 2023, will make an influential contribution — and many of the chapters are already available online.
- Preparing for the nonexistent future of work: In the medium term, our society will have to adjust to a world in which human labor is largely redundant. And this may happen sooner than many expect, perhaps even within the current decade. Cognitive automation is making policies such as a universal basic income more urgent and more appealing. To better understand how to prepare for this nonexistent future of work, I recently published a report on the topic. What is more, if cognitive work becomes redundant, we must also reevaluate the purpose of education at a fundamental level.
- Life after cognitive automation: Finally, although it’s crunch time for research on how to govern AGI, I am also taking time to reflect on life after cognitive automation has made me redundant as an economist and to prepare myself mentally so that I'm ready when the time comes.