Second, no single policy is a silver bullet; there are serious tradeoffs across every dimension and durability scenario; policies that are easier to implement and command broad support are not durable to more transformative scenarios. At the same time, the analysis identifies policies that lay on the Pareto frontier of the dimensions we study, which points to a sequence of ``least-regret'' policies, which are triggered based on different economic scenarios. This sequence includes an expanded Unemployment Insurance, modernized Earned Income Tax Credit, and employee-led retraining programs for Mild scenarios; the EITC transitioning to a Negative Income Tax for Moderate scenarios; and a Universal Basic Capital backstop for more extreme, full transformation scenarios. 4/n

I've had the most wonderful time working on this project for the last few months. This was (equally) co-led w/ @JMateosGarcia , @alexolegimas and a fantastic team.

Today @Google, we published work led by @m_codreanu on AI in Science, drawing on 15 million Gemini interactions, 2,600 specialised AI models & a 600-scientist survey. What did we find about how AI is changing science? Read on...

Today we have set out how we’re building AI to accelerate science and improve people’s lives. Just some examples in the last week or so: - Mapped all 9B possible single letter genetic changes across the human genome with AlphaGenome Atlas and made it openly available to researchers. - Billions of decisions depend on weather predictions so we introduced WeatherNext 3, our most accurate and capable global weather AI model to date. - We published AI & Economy ATLAS, a comprehensive open-access look at how people are using AI globally. - AI has enabled extraordinary advances in language translation. Today our services are available in nearly 300 languages, spoken by 7B people We’re focusing our efforts on four key areas: health, natural disaster and weather resilience, learning, and economic opportunity. blog.google/innovation-and-a…

Today @Google, we published work led by @m_codreanu on AI in Science, drawing on 15 million Gemini interactions, 2,600 specialised AI models & a 600-scientist survey. What did we find about how AI is changing science? Read on...

In July we launched the first edition of our AI & Economy ATLAS, a deep dive into how people are using Google’s AI products and tools at work and in day-to-day life. Now we’re making ATLAS’s millions of global data points easier to explore. blog.google/innovation-and-a…

Thrilled to release AI in Science: Early Insights into the world. This was a project co-led with the excellent @JMateosGarcia and @m_codreanu and an incredible team of co-authors. This collaboration between @Google and @GoogleDeepMind teams is the first, initial look, representing the beginning of a research agenda for us. The promise of AI for economic growth and flourishing moves directly through its impact on science, and this paper is the start of a brand new effort in our group on the study of AI's impact on science. Lots of results, lots of insights, lots of questions still to answer. There is great excitement -but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three complementary data sources: 1) a sample of 15 million Gemini interactions, 2) an inventory of over *2,600* specialized AI models (e.g., AlphaFold) across disciplines, 3) a new survey of over 600 scientists. But what does this tell us about how scientists actually use AI in their workflow, and is the impact of AI? To answer these questions, we map these data to: a) a new taxonomy of scientific tasks from @ProfNeilT and his lab; b) bibliometric data tracking scientific publications, and citations (including to specialized AI models). Four main findings emerge. 1) We find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have huge disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. 2) There is evidence that LLMs and specialized models act as *economic complements*: LLMs are used for general analysis, coding, and manuscript preparation, while specialized models push the frontier through domain-specific predictions, data generation and classification. The figure below illustrates this nicely: Each model class reinforces the other in the scientific process. 3) Surveyed scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily reinvested in more research. Scientists also report having better access to interdisciplinary insights and increased capacity for synthesis. 4) But as some stages of scientific research become easier, bottlenecks shift downstream to non-automated tasks. Scientists report an increased backlog of untested hypotheses and substantial time spent on output verification. Half of scientists also report AI is pushing them towards safer, more incremental question, evidence of a potential “streetlight effect”. What do we make of this? Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into eliminating emerging bottlenecks. Investment into infrastructure and the organization of scientific production is necessary to realize AI’s full scientific potential for economic and societal gains. Finally, this is work in progress. There are many limitations, which we discuss in section 6 of the paper, at length. Link: ai.google/static/documents/A…

New essay on @alexolegimas's blog: Will AI Soon Deliver Double-Digit Growth? Probably not. Here is why. aleximas.substack.com/p/will… 1. We outline the economics behind oft-discussed predictions that AI will soon deliver double-digit GDP growth in advanced economies. We list the assumptions that need to all hold in order for double-digit growth to happen and explain why we think they won’t. 2. To be clear: we are extremely bullish on AI and think the capabilities explosion predicted by technologists is already happening (e.g. yesterday's Navier-Stokes news!). But predictions of GDP growth in the 2030s of 15%, 30% or even 100% per year are off the mark. What we take issue with is the timeline. To paraphrase Milton Friedman's dictum on monetary policy, AI will affect GDP growth with "long and variable lags." 3. Start with some growth rate arithmetic. It is often much more useful to first think in levels rather than growth rates. Ask yourself: how much richer will we be in, say, 15 years? If you think twice as rich, that implies 4.7% annual growth, which would already be massive. Ten times as rich requires 16.6% per year; it would also imply that we are 100 times as rich 30 years from now! Asked in levels, we bet that most people would come up with much lower growth rates. 4. It's important to be clear: there is absolutely nothing in standard growth theory that constrains growth rates to be in the single digits. In fact, it's pretty easy to write down theoretical models that deliver explosive double-digit growth. We show this by writing down a standard textbook growth model of the type we routinely teach our undergrads (a souped-up Solow model), plug in some seemingly innocuous parameter values, and get AI-driven double-digit growth by the mid-2030s benjaminmoll.com/task_based_…. The basic logic is that, by replacing labor with capital, automation alleviates / eliminates diminishing returns and removes labor as a bottleneck on growth. Fancier models, in which AI also automates R&D, deliver even wilder numbers. 5. But just because something is possible in theory doesn't mean it will happen in practice. The explosion rests on five assumptions, and each is unlikely to hold within the next 10-15 years. These assumptions are: Assumption 1: Fast, economy-wide automation, with machines doing two thirds of all tasks by 2035. Historically, automation has proceeded at about 2% of tasks per year. Most work is physical, not cognitive. And politics will slow things down. Assumption 2: People keep spending on whatever gets automated. They don't. As things get cheap, their share of spending falls, as it did for agriculture and manufacturing. Messy jobs, relational goods and scarce physical inputs like energy, chips and land become the new bottlenecks. Assumption 3: Someone buys the new output and firms invest to produce it. Automation shifts income from workers to capital owners, who spend a smaller share so demand may not keep up with supply. Assumption 4: No AI-driven cyber incidents destroying economic value. AI can also destroy output, and every incident slows deployment and investment. Assumption 5: Explosive technology growth because AI automates R&D. The wildest scenarios in which the economy doubles each year all rest on this feedback loop. There is no evidence for it so far. Why do many people who are closest to the technology (and who have been consistently right about the capabilities explosion) consistently predict double-digit growth? Our best guess: they extrapolate from their own sector to the rest of the economy. This reminds me of the 2022 German gas debate: industry insiders were right about their own firms and very wrong about the economy as a whole. Our bottom line: a much more likely outcome is a large increase in the level of GDP spread over a decade or two, which is what 4-5% growth is. If you remain unconvinced and still believe in double-digit growth, we are still looking for counterparties for our bet benjaminmoll.com/growth_bet/ 😃