Here is data I’d love to see more of: have we seen an increase in the quality of economic inputs or new types of outputs with AI? We have seen new AI products, eg muse assistant, but have we seen firms that use AI as an input ship higher quality or different products? Do consumers prefer these products over alternatives? The reason this is important is we are constantly hearing about speed ups in economic inputs, eg lines of code, shipping whole inputs on products, but there’s less discussion of eg, Spotify has reported much more productive coders, with almost all code written by AI, has the app gotten better? Does it have new features that customers value? This is an empirical question that I’d love to see data on, since this is what will ultimately matter for welfare and growth. Will likely require survey data as well.

Really excited to be part of this amazing AI x Economy group that spans across @Google and @GoogleDeepMind. My team will be focused on the economics of AGI, and includes Julian Jacobs (@JulianDJacobs), Andrew Koh (@andrewjkoh), and Maria del Rio-Chanona (@RMaria_drc). We'll be collaborating with @danielrock, @AnuMadgavkar, Zanna Iscenko, @m_codreanu, @arthurturrell, @Dr_Atoosa, @JMateosGarcia. @shree_pandya_x, @davidautor, @professor_ajay @Ph_Aghion, amongst many others, to publish rigorous research on the economics of AI across a broad swath of questions, from the impact on labor markets and the macroeconomy, to AI x Science and economic development. Last week saw the release of our first projects as a team, and much more to come!

Today we are announcing the new DeepMind Institute, a forum dedicated to interdisciplinary, evidence-led debate on the societal and economic questions surrounding AGI. As part of the launch, @JulianDJacobs and I have a new essay and working paper: “Economic Policy for AGI.” bit.ly/economic-policy-for-A… 🧵1/n

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…

Happy 4th of July everyone. I came here as a kid at 9 years old, not knowing a single word of english, lived on food stamps in section 8 housing. My parents cleaned houses while my sister and I collected cans for recycling. Our clothing was all donated and our furniture was things we found on the curb (here I am in front of our "tv set", the broken one being used as a stand for the little working b/w one). But this country gave us an opportunity--to learn, to work hard and earn something for it. "Grateful" does not begin to cover it. I owe this country and its people everything I have. Thank you America, happy 250 to the idea.