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.

This Sunday, a group of 42 athletes will gather in Las Vegas to compete in a somewhat unusual sporting competition. Participants in the inaugural Enhanced Games are being encouraged to take performance-enhancing drugs. The goal is to “push the boundaries of human performance.”

The games’ organizers have said that competitors will only be taking substances that have been approved by the US Food and Drug Administration, and that they are all being medically monitored and supervised. But they have also said they expect to see world records broken—and are offering substantial prizes to athletes who succeed in doing so.

As you might expect, the event is generating a mix of curiosity, excitement, and condemnation from various quarters. To me, it feels like very much a reflection of where we are today—an era of peptide-crazed looksmaxxing in which consumers are being encouraged to get thinner than ever, optimize for longevity, and have their “best baby.” It’s 2026, and if you’re not enhancing, what are you even doing?

So, these games. They’ll feature competitions in four categories: swimming, track and field, weightlifting, and strongman (which also involves lifting weights). Many of the competitors already hold national and world records, and some are Olympic medalists. They’ve been paid a salary and will compete for prizes from a $25 million pot. The money has been a major draw for at least some of the athletes.

Another draw is the opportunity to openly experiment with drugs that might boost their performance. In the world of elite sport, every microsecond and every millimeter counts. Athletes—most of whom arguably have genetics on their side already—follow meticulous diet, training, and recovery protocols and wear specially designed gear that allows them to reach for those performance bests.

But within most sporting communities, there are limits. The World Anti-Doping Agency—an international outfit that fights the use of drugs in sports—maintains a lengthy list of “non-approved substances” that are banned in international sporting events. It features many anabolic steroids (which can build muscle), hormones (such as those that stimulate testosterone production or increase the ability of blood to carry oxygen), growth factors (which can stimulate muscle growth and repair, among other things), and more.

Some of these substances have been FDA approved to treat health disorders. And that means they can be used by participants in the Enhanced Games, according to the organization’s rules.

I’ll briefly point out the obvious here—just because a drug has been approved by the FDA doesn’t mean it’s totally safe for everyone and anyone. The risks associated with use of anabolic steroids, for example, include high blood pressure, acne, depression, and liver tumors. Growth hormone use can cause weak muscles, affect vision, and even lead to diabetes.

“Technological doping,” or using improved equipment to gain advantage, has also been supported by the games’ organizers. Last year, participating swimmer Kristian Gkolomeev was reported to have broken a record in a 50-meter freestyle time trial while wearing a polyurethane “super” swimsuit. Such suits have been banned for use in the Olympics since a slew of record-breaking performances in 2008 and 2009. Back then, the swimming governing body ruled that they gave athletes an unfair advantage. But hey, this is the Enhanced Games, where the word “unfair” seems to have a completely different meaning.

Can we expect more records to be broken on Sunday? Maybe. In addition to prize money for winning an event, any athlete who manages to beat a record stands to win up to $1 million, the sum also awarded to Gkolomeev last year following his time trial. But those performances won’t be recognized by official sporting bodies.

Plenty of concerns have been raised about these games. Some argue that they are unsafe and promote risky drug use. Others see them as a “clown show,” and a slap in the face to “clean” athletes who train hard without the use of prohibited drugs. World Athletics president Sebastian Coe has said that anyone who takes part is “moronic,” and World Aquatics, which oversees international competitions in water sports, has banned Enhanced Games participants from its events and activities.

But. The games—and the participating athletes—will still get a huge amount of attention. As a result, so will performance-enhancing drugs. Enhanced, the company behind the games, also runs an online store. There, you can buy a $52 T-shirt emblazoned with the message “I am Enhanced.”

There is also a range of prescription drugs on offer, including peptides “to support recovery, vitality, and longevity.” One of these is a growth hormone that the FDA approved in 1997 for the treatment of children with “growth failure.” The compounded version offered on the Enhanced website, which is not FDA approved, is marketed for longevity, supporting deep sleep and “overall wellness and vitality.” (“Marketed” is the key word here. The drug has, again, not been approved for that purpose.)

It all fits very well with the zeitgeist. Sure, we don’t yet have any drugs that are designed to extend human lifespan. But the search for anti-aging drugs is getting more attention—and funding—than ever. People, particularly women, are seemingly not allowed to visibly age anymore—we have filters and facelifts for that now. The idea that “death is wrong” is gaining acceptance.

And self-experimentation is rife. “Biohacking” was shortlisted for Collins Dictionary’s Word of the Year in 2025. Peptides are everywhere, despite all the unknowns surrounding their safety and effectiveness. So are longevity clinics, despite the fact that most are selling unproven treatments. US states like Montana are making it easier for people to get hold of unapproved “therapies.”

Companies are even offering would-be parents the option to choose the potential future children expected to live longest. Yep—you can supposedly optimize your embryos now, too.

In this climate, the Enhanced Games don’t feel so radical. They feel entirely fitting for our era of questionable optimization despite the risks —an era when, apparently, being human is no longer enough.

The vibes were strong at Code with Claude, Anthropic’s two-day event for software developers in London that kicked off on May 19, the same day as Google’s I/O in Palo Alto. (A coincidence, not a flex, Anthropic staffers assured me.)

“Who here has shipped a pull request in the last week that was completely written by Claude?” Jeremy Hadfield, an engineer at Anthropic, asked from the main stage. Almost half the people in the packed room—many sitting with laptops on their knees, coding or prompting as they watched the talks—raised their hands.

Pull requests are fixes or updates to existing software that are submitted for review before they go live. They are the bread and butter of software development, the chunks of code that most professional developers spend their lives writing—or did until now.

“Who here has shipped a pull request that was completely written by Claude where they did not read the code at all?” Hadfield asked next. Nervous laughter. Most of the hands stayed up.

It’s not news that LLM-powered tools like Anthropic’s Claude Code and OpenAI’s Codex have upended the way software gets made. Top tech companies now like to boast of how little code their developers write by hand. (“Most software at Anthropic is now written by Claude,” Hadfield said. “Claude has written most of the code in Claude Code.”) OpenAI, Google, and Microsoft make similar claims. Many others wish they could.

Even so, it is striking how normal this new paradigm already seems, and how fast it has set in. This was the second year that Anthropic has put on developer events, which also run in San Francisco and Tokyo. This time last year, the company had just released Claude 4. It could code, kind of. But with Anthropic’s latest string of updates—especially Claude 4.6 and then 4.7, released in February and April—Claude Code is a tool that more and more developers seem happy to hand their work off to.   

ANTHROPIC (GRAPHIC) / WILL DOUGLAS HEAVEN (PHOTO)

Anthropic says its goal is to push automation as far as it will go. Instead of using AI to generate code and then having humans clean it up and fix the mistakes, it wants Claude to check and correct its own work. “The default isn’t ‘I’m going to prompt Claude’—the default is now ‘I’m going to have Claude prompt itself,’” Boris Cherny, who heads Claude Code, said in the opening keynote.

If all goes well, human developers shouldn’t even see the error messages when something doesn’t work. That will all be handled by Claude, which will test and tweak, test and tweak, until everything runs as it should. As Ravi Trivedi, an engineer at Anthropic, put it in another talk: “The key principle is getting out of Claude’s way. We like to say: ‘Let it cook.’”

Trivedi presented a new feature in Claude Managed Agents, Anthropic’s cloud-based setup for building and running multi-agent systems, announced two weeks ago, which the company calls dreaming. Claude agents write notes to themselves, recording and saving useful information about specific tasks. When another coding agent, say, starts to work on the same code that others have worked on, it can use the notes they left behind to get up to speed faster and learn from any errors those previous agents may have made.

Dreaming is a system that Claude agents can use to read through the notes and consolidate the information they contain, spotting patterns and common issues across different tasks. In theory, dreaming should help coding agents learn about a particular code base and get better and better at working on it.

Success stories

Code with Claude is an event aimed at developers. As well as product showcases and hands-on workshops from Anthropic, there were how-tos from a range of companies that have reshaped their software development teams around Claude Code, including Spotify and Delivery Hero as well as Lovable, Base44, and Monday.com—three startups vibe-coding apps that help people vibe-code apps.

There were no signs of unease at Code with Claude. Everybody I met wanted in.

And yet outside the conference there have been a number of reports that many coders are starting to question this bright new future. Some gripe in online forums like Reddit and Hacker News that AI coding tools are being pushed by managers chasing productivity gains, when in practice the technology makes software development harder because of all the extra code developers now have to review. “The only people I’ve heard saying that generated code is fine are those who don’t read it,” a user called pron posted on Hacker News last week. 

Others claim that their coding abilities have fallen off as they hand more tasks to AI. And researchers have warned that AI tools can produce unsafe code that will make software more vulnerable to attacks.  

I sat down with Claude engineering lead Katelyn Lesse and Claude product lead Angela Jiang and asked them what they made of the concerns that a sudden flood of code generated (and shipped) without proper human oversight was kicking serious security and maintenance problems down the road.

“All of the old software development best practices still apply. They’ve applied this entire time,” said Lesse. “I think there are a lot of people and teams that may have lost sight of them in this moment.” 

And yet as Anthropic and others push for greater automation and tools like Claude Code improve, the temptation increases to offload more and more tasks, including oversight. Lesse told me that some of the technical managers at Anthropic are exhausted by keeping up with all the code their teams now produce. “Part of things happening so much more quickly is just managing your time,” she said.

“I think that right now Claude is probably as good as a midlevel engineer at writing code,” she added. You still need expert engineers to design a system and troubleshoot harder problems, she said. “But over time we want Claude to get better and better at all different types of engineering.”

Jiang agreed: “I think the absolute end state we’re trying to get to is Claude basically being able to build itself.”

Correction: Dreaming is a feature of Claude Managed Agents not Claude Code. The article has been updated.