Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online.

According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk.

In an effort to address this, Google DeepMind—which made agent-based tools a centerpiece of Google I/O last month—has teamed up with several other organizations to announce a $10 million funding pot for researchers to study the behavior of multi-agent systems and come up with ways to prevent unsafe scenarios. Joining Google DeepMind are Schmidt Sciences, a philanthropic foundation set up by Eric and Wendy Schmidt; ARIA, the UK government’s moonshot agency; the Cooperative AI foundation, a UK-based nonprofit research outfit; and Google’s charitable arm, Google.org.

I asked Shah and James Fox, who leads the Science of Trustworthy AI program at Schmidt Sciences, what they hope to achieve with that $10 million. It’s no small sum, but it’s dwarfed by the budgets commanded by Google DeepMind’s own research teams.

The aim is to kick-start research outside tech companies, says Shah: “The strength of academia is that it can look really quite far into the future and do the kind of work that isn’t top of mind at industry labs.”

“The main issue is that there just isn’t really a field of research for multi-agent safety yet,” he adds. “And we would like there to be.”

The concern is that as more and more AI agents get deployed and begin working together, we could hit a tipping point where imagined scenarios become real. “We see this with humanity, too,” says Shah. “Our institutions can accomplish things that no individual human can.”

Shah thinks we have a few more months to go before agents are deployed throughout the economy in numbers that make potential risks a real concern. He wants to get ahead of that moment.

Risky business

What risks are we talking about, exactly? The possibilities that Shah and Fox have in mind mostly boil down to supercharged versions of bad things that happen on the internet already: scams, prompt injections (where an AI agent is fed malicious instructions, turning it into a self-guiding piece of malware), other forms of cyberattack. We look at what humans do now and ask what the agent version of that would be, says Shah.  

“We’ve got this digital commons that is integral to how society works, and you really want to ensure that this doesn’t descend into just absolute anarchy,” says Fox.

(I asked Shah if they were considering any worst-case scenarios more on the doomer end of the spectrum, such as widespread economic collapse. “Certainly not if we’re talking by the end of the year,” he said. That’s only six months away! He laughed. “Okay, a while after that.”)

Shah and Fox both think that the only way to understand what might happen when large numbers of multi-agent systems interact with each other is to run realistic simulations. They want researchers to drop AI agents into sandboxes and study what they do.

You can’t predict what’s going to happen by studying single agents, or even small groups of agents, in isolation. You can’t assume that AI agents underpinned by LLMs will always act rationally, says Fox. And the complexity comes from having huge numbers of interactions at once.

Some researchers, including a team at Google DeepMind, have argued that artificial general intelligence (if possible at all) could come not from a single super-smart model but from a kind of agent hive mind, where the capabilities of the whole add up to more than the sum of its parts.  

Lack of trust

Google DeepMind is not the only top AI firm warning about the risks of the technology it is building. A couple of weeks ago, Anthropic published guidelines for deploying AI agents based on an approach to cybersecurity known as zero trust, which starts with the assumption that a computer system is vulnerable, an agent is an attacker, and a breach will happen.

Refael Angel, cofounder and CTO of Akeyless, a cybersecurity firm based in Tel Aviv, agrees that understanding the new risks introduced by agent-based systems is crucial.  

Every approach to security in the past has assumed that the machine in question was software written by a human, doing fixed things on fixed paths, says Angel: “An agent breaks all of those assumptions. It reasons, it improvises, and it can be hijacked by a single sentence buried in a document it was asked to read.”

Angel welcomes this new funding. “No single lab should author the safety standards everyone else has to trust,” he says. But he cautions that safety researchers can overlook boring problems that are already here in favor of more exotic hypothetical ones.

And yet, Fox notes, risks that were hypothetical a few years ago are now very real: “The future’s come more quickly than perhaps expected.”

At SXSW London last week I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now.

I pulled a few things from our first AI10 list, an annual guide to the most important trends in this buzzy world, but I also veered off on a number of tangents. In my half-hour slot, I tried to cover the key talking points that I think help to make sense of what’s going on in tech—and thus the economy—today.  

(I gave a talk with the same title at SXSW London last year with five different things you needed to know. A lot has happened since then!)

So: This is how I’m thinking about AI midway through 2026. Let me know if you would pick different points!

1. Strictly speaking, I didn’t need to show up to give this talk.

Tongue in cheek? Maybe. But generative AI tools have already become mundane, used by millions to automate everyday office tasks (including producing and delivering talks). It’s no surprise that one of the biggest questions out there right now is what this all means for jobs. People are confused and scared.

The frustrating answer is that despite the hype coming from the top about the potential for AI to join the workforce soon—and viral social media posts yelling that something big is happening—there is almost no data to say either way what kind of effect this technology will have on employment and the economy overall. That’s not to say it won’t have an impact, even a huge one, but it’s just too soon to tell.

In theory, teams of agents working together toward common goals could become assembly lines for white-collar work, doing to offices this century what Henry Ford’s innovations did to factories in the 20th century.

In theory. Because in order to know what will happen to jobs, we need to know what will happen inside the companies that create those jobs. But most companies are still figuring that out.

 2. AI is getting scary (for real this time).

There have been scary stories about AI for years—claims that it will kill us all or bring about the end of civilization. There’s still a loud crowd of doomers, but those scenarios remain dystopian science fiction.

What’s happened instead is that many of the worst near-term, real-world fears have come true.

Take deepfakes, AI-generated images or videos of people doing things they didn’t actually do. Deepfakes have been used to incite violence, swing votes, and sow distrust. Trump’s White House is among those creating and publishing fake images.

Many deepfakes are also used to abuse women and girls. One study found that 98% of deepfakes are pornographic and 99% involve women.

Another concern is the rise of dangerous and delusional relationships with chatbots. Many people turn to chatbots to seek private advice and to feel heard. But there are now multiple lawsuits against AI companies alleging that the technology encouraged or aided suicides and other forms of self-harm.

AI is also being used in warfare in new and worrying ways. LLMs are now giving advice, not just being used for analysis. One US defense official told my colleague James O’Donnell that you could now give a military chatbot a list of targets and ask which one to hit first. Anyone who uses AI knows that its output needs to be reviewed carefully. In fact-paced, high-stress active conflict, the risk that corners get cut is high.

3. A lot of people really hate AI.

I checked out an anti-AI protest in London earlier this year and found a very broad mix of complaints. Banners proclaiming the end times bounced along to chants of “Stop the slop! Stop the slop!” Protests are getting more organized and drawing larger crowds.

There’s pushback from fans of films and video games, who object to the use of generative AI in their favorite titles. In one notable case, the acclaimed 2025 game Clair Obscur was stripped of an award when the developers admitted to using AI in just one small, specific part of its production.

And there’s the data center backlash. The US has more than 5,400 data centers and counting. With the energy demands of AI growing, people are unhappy about the environmental impact and their rising electricity bills. Activists are managing to stall development in a number of places.

Regulation is becoming politically popular. Grassroots movements like QuitGPT have gained momentum. A small number have turned to violence; a few weeks ago somebody threw a Molotov cocktail at Sam Altman’s house. It’s not clear where all this leads. But the apocalyptic hype from tech leaders is not helping people stay calm.

4. AI for science is a very big deal.

It’s early days yet, but the potential for AI to help make a genuine and important scientific discovery is greater than ever.

Google DeepMind has developed Co-Scientist, a multipurpose tool that can help researchers dig up and compare previous results, generate hypotheses, and devise experiments to test them. OpenAI told me this year that its North Star is the goal of building a fully automated researcher by 2028.

Mathematicians are excited too. Fundamental math underpins many everyday technologies, from internet security to video streaming. The last few months have seen a string of claims that AI has cracked unsolved math problems. And software that can solve really hard math problems will be able—so the argument goes—to solve more general-purpose real-world problems too.

What are the downsides? Some scientists are warning that an overreliance on AI tools could narrow the scope of research because scientists may choose problems that are most suited to AI assistance. There are also concerns that AI-assisted research will lead to a flood of inaccurate or fake results: science slop.

5. AI is everywhere all at once.

So where does that leave us? There are a lot of exciting things, a lot of worrying things, and a lot of hot air. It can be exhausting to keep up, and yet it all feels inescapable. Some people will tell you we’re in a race to the top; some will tell you we’re in a race to the bottom. But it’s really not clear where we’re headed.

AI companies want us to march to their tune and buy into the propaganda about artificial general intelligence, whatever that means. They are selling a vision that feels inevitable, but it isn’t.

We’ve built a technology that can do humanlike things, and I think that makes it hard to get our heads around the fact that it is still just a technology.

Something is happening. Maybe even something comparable to the invention of electricity or the internet. But technologies like that take time to settle and bring lasting change.

Get ready for a marathon, not a sprint.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.