Monthly Archives: May, 2026

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During Tuesday’s Google I/O keynote, Demis Hassabis, the CEO of Google DeepMind, proclaimed that we are currently “standing in the foothills of the singularity.” It was a striking statement—the singularity is the theoretical future moment when AI rapidly exceeds human intelligence and dramatically transforms the world. But what struck me as I listened in the audience was the context in which he said those words. 

He was on stage to close out the session with a segment on scientific AI, the centerpiece of which was a video detailing how the company’s weather prediction software provided an advance alert about Hurricane Melissa’s catastrophic landfall in Jamaica last year—and potentially saved lives. If that software, called WeatherNext, helped anyone escape the storm or better fortify their home, that’s an enormous and meaningful achievement. But it’s hardly evidence of an impending singularity.

The juxtaposition of Hassabis’ lofty rhetoric with the real-world results of WeatherNext highlighted the tension between two very different approaches to AI for science. The first focuses on AI tools, like WeatherNext, that are designed and trained to solve specific scientific problems. The second is agentic, LLM-based systems that could one day execute cutting-edge research projects without human involvement.

This second vision powers a great deal of AI enthusiasm right now, including recent excitement around recursive self-improvement, or the idea that AI systems could eventually become the primary drivers of AI advancement—a process that would get faster and faster as the AI systems grow smarter. And agentic systems are now making real research contributions, sometimes with limited human guidance.

Just this week, Pushmeet Kohli, Google Cloud’s chief scientist, published a piece in a special AI and science issue of the journal Daedalus, writing: “We are moving toward AI that doesn’t just facilitate science but begins to do science.” With autonomous AI scientists on the horizon, it’s harder to justify massive efforts to develop super-specialized tools—even one like AlphaFold, for which DeepMind scientists won a Nobel Prize, or a potentially life-saving system like WeatherNext. It also heralds a far stranger future for science, in which humans and AI systems collaborate as peers—or AI even makes scientific progress on its own.

To be clear, Google does not appear to be abandoning its work on specialized AI for science tools. AlphaGenome and AlphaEarth Foundations, which are trained for genetics and Earth science applications respectively, were released last summer, and the newest version of WeatherNext came out in November.

What’s more, such tools remain extremely popular among scientists. Last year, for instance, Google reported that protein structure predictions from AlphaFold have been used by over three million researchers worldwide. And Isomorphic Labs, a Google subsidiary that aims to use AlphaFold and related technologies to develop new drugs, just raised a $2 billion Series B funding round.

But there are concrete signs of realignment, in both enthusiasm and resources. Last month, the Los Angeles Times reported that Google fellow John Jumper, who won the Nobel for AlphaFold, is now working on AI coding, not on science-specific AI tools. It’s not surprising that Google is assigning its best minds to the coding problem, as the company has recently taken a reputational hit because its coding tools don’t currently stand up to those offered by Anthropic and OpenAI. But it may also signal a prioritization of agentic science on Google’s part, as coding abilities are key to the success of some of those systems. 

Across the industry, agentic researcher systems are showing real potential. This week, OpenAI announced that one of their models had disproved an important mathematics conjecture—perhaps the most meaningful contribution that generative AI has made to mathematics so far, according to some mathematicians.

Importantly, the model used by OpenAI is not specialized for solving mathematical problems, or even for research; according to the company, it’s a general-purpose reasoning model in the vein of GPT-5.5. If general agents can make independent contributions to mathematical research, they might soon be able to do the same in science (though the fact that ideas in science must be verified experimentally makes it a tougher domain for AI).

Google is certainly devoting a lot of attention toward an agent-driven scientific future. The big scientific announcement at I/O was the new Gemini for Science package, which unites several of the company’s LLM-based scientific systems under one brand.

This includes the hypothesis-generating AI Co-Scientist and algorithm-optimizing AlphaEvolve, which are still not publicly available—but as Google is now allowing any researcher to apply for access to Gemini for Science, they may soon see wider adoption in the scientific community. Scientists who were involved in early testing are enthusiastic about their potential: Gary Peltz, a Stanford geneticist, compared using the AI Co-Scientist to “consulting the oracle of Delphi” in a Nature Medicine article.

Gemini for Science isn’t incompatible with specialized tools; to the contrary, agentic systems can be designed to call on such tools when they might be useful. And no agentic system can predict the structure that a protein will fold into without AlphaFold’s help (at least not yet). But the company seems to be shifting its public image—and at least some resources and personnel, such as Jumper—away from specifically developing those kinds of tools. Though it has only been five years since AlphaFold solved the protein-folding problem, both the technology and the discourse have quickly moved beyond that once-revolutionary achievement.

Google has been careful to position this new set of scientific agents as an accelerant for human scientists, rather than a replacement for them—the choice of the name AI Co-Scientist as opposed to AI Scientist, for instance, appears quite deliberate. Hassabis uses that same human-centric framing when he talks about changes in the landscape of scientific AI. “For the next decade or so, we should think about AI as this amazing tool to help scientists,” Hassabis said in an interview published in the Daedalus issue. “Beyond that timeframe, it is hard to say with any certainty, but perhaps these systems will become more like collaborators.”

But no one can be an effective scientific collaborator without also being a skilled scientist in their own right. And if Hassabis is anywhere near the mark when he talks about the “foothills of the singularity,” then AI scientists could eventually exceed the capabilities of their human counterparts.

In a discussion with the journalist Mike Allen at I/O, Hassabis spoke of how he was initially inspired to pursue AI when he observed how progress in physics had stagnated since the 1970s; he wondered whether the human mind had reached its limits in that domain, and if AI could help to overcome that barrier. Superhuman agentic scientists would certainly fit that bill. We might not ever get anywhere near there, but Google seems to be aiming itself toward that summit.

 During Tuesday’s Google I/O keynote, Demis Hassabis, the CEO of Google DeepMind, proclaimed that we are currently “standing in the foothills of the singularity.” It was a striking statement—the singularity is the theoretical future moment when AI rapidly exceeds human intelligence and dramatically transforms the world. But what struck me as I listened in the… 

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Californians would have to take a four-hour course with live-fire training to buy a gun if a bill advancing through the Legislature gets signed into law.

Senate Bill 948, by Berkeley Democratic Sen. Jesse Arreguín, also would require gun owners moving to California to obtain a firearm safety certificate and register their firearms within 180 days of their arrival. Beginning in 2028, obtaining that certificate would require completing the training.

It’s the latest effort by California Democrats to add more restrictions on firearm ownership in a state that already has some of the toughest gun laws in the country. However, it’s hardly certain the bill will become law. A similar measure died in the Legislature last year.

This year’s proposal advanced from the Senate Appropriations Committee Thursday on a party-line vote with Republicans opposed. Committee members offered no comment on the measure and did not take any public testimony, which is typical for that committee.

But in March, when an earlier version of the bill would have required eight hours of training, Arreguín told the Senate Public Safety Committee the proposed training requirements would reduce gun violence and prevent accidental shootings.

“Firearm safety is essential in preventing firearm-related incidents, especially those involving children,” he said. “By strengthening training requirements and closing gaps in current law, SB 948 will ensure responsible gun ownership to keep Californians and communities safe.”

Rebecca Marcus, a lobbyist for the Brady Campaign, told the committee there were more than 69,000 shootings resulting in death or requiring urgent medical care in California from 2016 to 2021. Around one in three of those shootings were accidental, she said. Many involved children.

Gun rights advocates said the bill would be challenged in court if it becomes law. 

Adam Wilson of Gun Owners of California called the proposed requirements “an insurmountable barrier to exercising a constitutional right.”

Clay Kimberling, a lobbyist for the National Rifle Association’s Institute for Legislative Action, said that’s especially true for the estimated 115,000 gun owners who move to California each year.

“Whether they move into the state on a new job, a new military assignment, or family obligations such as helping a sick or elderly family member, lawful firearm owners would now have to search out an instructor, pay for the class … and take eight hours out of their day … for simply wanting to continue to practice their constitutional right to keep and bear arms in a new state,” Kimberling said.

That original version of the bill also would have required new California arrivals to register firearms and take the course within 60 days. 

Will the bill make it to Newsom?

Under current law, Californians are required to pass a written test and pay $25 to obtain a five-year firearm safety certificate to purchase a gun, but no formal training course is required.

Licensed hunters are required to take a mandatory hunting-safety course and aren’t required to get a certificate when buying rifles or shotguns. Also exempt are those who’ve obtained a concealed weapons permit, which is issued after 16 hours of mandatory training that includes live-fire at a gun range.

Those exemptions would still apply.

For everyone else, the proposed four hours of training would include coursework on state and federal gun laws, secure firearm storage, safe handling, the dangers of guns, use-of-force laws, how to sell firearms legally and conflict resolution. The live-fire portion of the course would need to last at least an hour.

Second Amendment groups say paying a Department of Justice-certified firearms instructor would add at least $400 to the cost of buying a firearm. Applicants also would have to pay for ammunition, gun rentals and range fees. Fees and firearms taxes already can add more than $100 to the cost of a firearm in California. 

The training requirements would take effect July 1, 2028.

Until then, beginning on Jan. 1, gun owners moving to the state would be required to pass the current written test and register their firearms with the Department of Justice within 180 days.

Violating the proposed law would be a misdemeanor.

The bill now moves to the full Senate. It will then have to advance through the Assembly by this summer if Gov. Gavin Newsom is to sign it. He hasn’t taken a position on the legislation.

Last year, a bill with eight-hour training requirements died in the Assembly Appropriations Committee.