Jeff Dean's Discovery Loop Should Automate Chip Design First<br>FutureSearch is live<br>Research plan half price for your first month<br>Run a forecast →<br>← Back to Research<br>Discovery Loop is one day old and, as someone who worked at Google (2014-2022), in my view the most interesting "neolab". Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, and Quoc Le left Google this morning to found it, to automate machine learning, science, and engineering. Jeff's announcement says they will automate the experimental loop itself, proposing parallel experiments, running them, evaluating results, and iterating. Their first target is, of course, ML research and engineering, with a stated ambition, "important subproblems in nearly every one of the fourteen NAE Grand Challenge problems."
So what type of science should they automate? I forecasted, conditional on them focusing on one of the 14 domains they mention, their odds of a breakthrough by 2028. My theory is that our model of AI 2027 timelines forecast, e.g. how AI speeds up R&D feedback loops, will be useful here.
My conclusion is that Discovery Loop should focus on chip design, with a 38% chance of a major breakthrough by 2028. Second is secure cyberspace and third are tools of scientific discovery.
This is broadly consistent with my and other forecaster's views on AI 2027. FutureSearch predicted that superhuman coding and other types of AGI would not come until 2031-2032, because of how hard automating these feedback loops can be. Discovery Loop will be a direct test of some of this theory.
First, though, since these are all former Google Brain / DeepMind people, we should think about their priors on this based on how well DeepMind did when it tried to apply AI to speed up scientific discovery.
DeepMind using AI for science
Before that actually, some forecast-relevant facts about Discovery Loop. Their seed round was co-led by Radical Ventures and Khosla Ventures, with Lightspeed, Kleiner Perkins, Doerr Capital, and Alphabet participating, terms undisclosed. Unsurprisingly Google Cloud supplies compute. No employees, no office, just a plan to hire a lean in-person founding team. I forecast the seed valuation in my companion piece about the post-Jeff Dean Google on the reorg at a median of $5.8 billion in its first non-Google-led round.
Back to DeepMind. The best reference class for a lab automating science is the applied-science record of the lab these four just left. DeepMind's fast wins do in fact seem to have come in domains where the experimental loop closes, as per our AI 2027 modeling. AlphaGo of course. But even AlphaFold had a public archive of over a hundred thousand solved protein structures to learn from, and CASP, a biennial blind contest, to judge it. GraphCast and GenCast had decades of reanalysis weather data and clean skill scores. AlphaTensor, AlphaDev, and the IMO systems had formal verification. Data-center cooling had a thermostat.
The slow projects and the failures ran through the physical world or through institutions. DeepMind Health's Streams app was shut down after the NHS data controversy. GNoME predicted millions of crystals, and independent labs disputed how many were real. Isomorphic Labs was founded on AlphaFold in 2021 and reached its first clinical trials only in 2026, with no approved drug.
I seeded FutureSearch with this research and my view on this. Here are the resulting forecasts, unconditional on whether they focus on chips/cyber as I suggest:
MilestoneForecast dateFirst loop-authored ML research resultAug 2027 (Jan 2027 to Oct 2028)Reaches 20 employees beyond the foundersJun 2027 (Dec 2026 to Dec 2028)First recognized research contribution outside MLAug 2030 (Feb 2028 to Mar 2037)First commercial offering or named paid partnershipJan 2029 (Jul 2027 to Aug 2033)Publishes a formal safety framework for automated AI researchMar 2029 (Jul 2027 to Sep 2034)
FutureSearch thinks the cold start is hard. Even OpenAI took years to get going. Thinking Machines, which launched with a sizable team already assembled, took about seven months to publish its first technical blog post, and SSI took close to a year to reach twenty employees. The August 2027 median on a first loop-authored result, with a 90th percentile stretching to late 2028, also factors in attribution of the result. Claiming your system found the result, rather than your researchers, is a tall order, see Sakana with its AI CUDA Engineer. And the median headcount a year from now is 28 people beyond the founders.
The January 2029 commercialization median is a statement about strategy, not capability. Discovery Loop calls itself its own first customer, the careers page lists no sales or partnerships roles, and free Google compute removes the revenue pressure that forces early deals. If a named paid deal shows up anyway, I would update. By the way, FutureSearch found there is only a 21% chance their first published artifact carries Google's name. The forecasts expect the break from...