Simply this week, Pushmeet Kohli, Google Cloud’s chief scientist, revealed a chunk in a particular AI and science difficulty of the journal Daedalus, writing: “We’re shifting towards AI that doesn’t simply facilitate science however begins to do science.” With autonomous AI scientists on the horizon, it’s tougher to justify huge efforts to develop super-specialized instruments—even one like AlphaFold, for which DeepMind scientists received a Nobel Prize, or a probably life-saving system like WeatherNext. It additionally heralds a far stranger future for science, wherein people and AI methods collaborate as friends—or AI even makes scientific progress by itself.
To be clear, Google doesn’t look like abandoning its work on specialised AI for science instruments. AlphaGenome and AlphaEarth Foundations, that are skilled for genetics and Earth science purposes respectively, have been launched final summer time, and the latest model of WeatherNext got here out in November.
What’s extra, such instruments stay extraordinarily standard amongst scientists. Final yr, as an illustration, Google reported that protein construction predictions from AlphaFold have been utilized by over three million researchers worldwide. And Isomorphic Labs, a Google subsidiary that goals to make use of AlphaFold and associated applied sciences to develop new medicine, simply raised a $2 billion Sequence B funding spherical.
However there are concrete indicators of realignment, in each enthusiasm and assets. Final month, the Los Angeles Occasions reported that Google fellow John Jumper, who received the Nobel for AlphaFold, is now engaged on AI coding, not on science-specific AI instruments. It’s not stunning that Google is assigning its greatest minds to the coding downside, as the corporate has not too long ago taken a reputational hit as a result of its coding instruments don’t presently stand as much as these supplied by Anthropic and OpenAI. However it could additionally sign a prioritization of agentic science on Google’s half, as coding skills are key to the success of a few of these methods.
Throughout the business, agentic researcher methods are displaying actual potential. This week, OpenAI introduced that certainly one of their fashions had disproved an vital arithmetic conjecture—maybe probably the most significant contribution that generative AI has made to arithmetic up to now, in line with some mathematicians.
Importantly, the mannequin utilized by OpenAI shouldn’t be specialised for fixing mathematical issues, and even for analysis; in line with the corporate, it’s a general-purpose reasoning mannequin within the vein of GPT-5.5. If normal brokers could make unbiased contributions to mathematical analysis, they could quickly be capable of do the identical in science (although the truth that concepts in science have to be verified experimentally makes it a harder area for AI).
