Milk and Cereal

It turns out that a certain company possesses a technology that can detonate the internet. We should probably all get familiar with how that technology works.

On Tuesday, the AI company Anthropic announced that they had built a model so capable of finding and exploiting software vulnerabilities that they couldn’t release it.

A model called Claude Mythos — an instance of nominative determinism up there with “Altman” and “Amodei,” some dental-hygienist uncle taking pot shots at shaping destiny — had autonomously discovered thousands of zero-day flaws in every major operating system and web browser. Some of these had survived twenty-seven years and millions of automated security tests previously undetected. In one case, the model chained together four separate vulnerabilities to escape a browser sandbox entirely on its own. No human involved. No weird guardrail caveats. The capability just emerged, the way a child who learns to write his name eventually learns to forge a signature.

Anthropic’s response was to launch something called Project Glasswing: give the model to Apple, Microsoft, Google, AWS, and about forty other organizations and say, essentially, patch everything you can before someone else builds something like this and doesn’t tell you about it. They committed a hojillion dollars in credits. They called it “urgent.” They weren’t playing.¹

I read all this while eating a bowl of cereal. The Axios headline had little sirens in it: 🚨🚨🚨

“Huh,” I said, and sent a Tweet or something.

Judging by the relative lack of headlines beyond Axios, our collective reaction as a nation was similar. It was like we heard an asteroid landed in our neighborhood, but decided not to look out the window to see for sure because we needed to go clip our toenails.

This is not a column about cybersecurity or frontier-model regulatory policy. I don’t know enough to write one, and people who do — Dean Ball, Seb Krier, Mikko Hypponen, literally anyone at Glasswing — should be the ones you listen to. I defer to them with great enthusiasm and zero shame.

What I want to talk about is the cereal and the “huh.”

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Your average Senator didn’t understand the internet in 1996. By 2006, Ted Stevens was describing it as “a series of tubes” and Congress was still trying to figure out what a hyperlink was. By 2016, Mark Zuckerberg was explaining to a roomful of legislators what a cookie is and why it didn’t involve any Monsters. This was all cute and cringe and funny. It generated a lot of memes. It was also, in retrospect, quite bad — bad enough that a generation of teenagers got psychologically rewired by algorithms nobody in power understood well enough to regulate.

But the internet was never going to delete anyone’s operating system overnight. We had time to be stupid, and we used all of it — fifteen years of getting blindsided by things a subreddit mod could have explained to us, while a lot of people got hurt by our sluggishness. It was bad, but it wasn’t existential.

With Mythos-class capabilities, appropriately enough, the stakes have become…mythic. A similar distance exists between what the technology can do and what the people who govern it understand — but compressed from a decade into months. It’s like our dental hygienist uncle just figured out how to shut down every computer on earth. And a society that can’t even locate its own reaction to that news — that reads about it over cereal and shrugs — is a society that is not equipped to navigate whatever comes next.

So how do we close that kind of distance? Not by pushing our glasses up our noses and ranting about Mythos over dinner, or giving talks in conference rooms about how transformative AI is going to be, or convening blue-ribbon commissions.² That’s not going to cut it.

The way you close any distance between an abstraction and a reality is the same: you make the abstraction concrete, put it in someone’s hands, and let the understanding grow from the inside out.

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Almost exactly two years ago, my wife Maria decided to open a nail salon. She had never run a business. She speaks English as a second language. She is not a person you would describe as “extremely online” (that would be me). What she had was a vision, a laptop, and a ChatGPT subscription.

So, over three weeks, she used it to write her business plan, draft her lease emails, design her price list, figure out what kind of LLC to form in D.C., write her website copy, compare ventilation systems — everything. As she buzzed along she would sing a little song — “G-P-T, GPT,” to the tune of that Rosé/Bruno Mars number.

She did not do any of this because she was abstractly excited about AI. She was not trying to prove some kind of concept. She did it because she had a problem, and the tool solved it, and so she kept using it. By the end, she didn’t just have a nail salon (which — please drop by!). She had what I would conservatively describe as a better working understanding of these models’ strengths and weaknesses than most members of Congress. Because she’d used the thing, and using it taught her something.

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Matt Yglesias recently ran an experiment along similar lines, but pointed at something debatably more ambitious than “elevated beach shack chic” waterless nail hygiene. When the Washington Post was imploding in February, he got interested in whether AI plus open data could produce cost-effective local journalism. He launched a Substack called Ten Miles Square, fed local D.C. datasets to Claude, and had it generate analytical stories. He did a whole debrief on the ups and downs.

The results weren’t perfect. But a single person with no coding background, working with publicly available municipal data, generated in a few weeks the kind of unglamorous, civically vital, base-hit local reporting that used to require a whole newsroom. Test scores broken down by demographic shift. Police overtime driven by recruiting shortfalls. Metro ridership patterns that showed commuting hadn’t recovered from Covid even though weekend traffic had. Stories that matter to actual communities — but that weren’t being written, because dozens of person-hours would otherwise be spent on each one, and those kinds of business models aren’t viable anymore.

Now imagine that experiment run not by a Substack celebrity but by a community organization in Jackson, Tennessee. Or a legal aid clinic in East St. Louis. Or a volunteer fire department trying to figure out evacuation routes from a county with a paper map and a phone tree.³ Imagine it in two hundred cities across America. Imagine all that being built in a year.

The technology exists right now. It’s happening in pockets all across the country. But it isn’t happening everywhere, at scale, because nobody has built the connective tissue between the people who make these models and the communities that need them — and funded the small, scrappy, technically curious teams that would actually do the work.

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This kind of work is a far cry from Mythos. But that’s exactly the point: it doesn’t feel like sci-fi. We can start to wrap our heads — and our hands — around it right now.

The people who need to develop a felt, operational understanding of these tools aren’t just the legislators who’ll vote on regulating them — though God knows they need it too. It’s the whole ecosystem that shapes how a society relates to a technology: the county administrators who decide what software to buy, the school superintendents who set policy for ten thousand kids, the philanthropists whose funding decisions determine which problems get worked on, the nonprofit directors and journalists and thought leaders whose framing determines whether the public sees AI as a toy, a threat, or a tool.

These communities learn from each other. A funder who has watched a team process a decade of benefits data in an afternoon thinks differently about what’s possible than one who’s merely read an op-ed about it. And right now, across all of these categories, a staggeringly small number of people have reps — the kind of repeated, load-bearing, this-is-part-of-my-actual-job engagement that teaches you not just what AI can do but how it fails, where it hallucinates, when to trust it and when to check it twice.

People had computers in their offices for well over a decade before it was normal to have them at home. And it was the office — not the living room — that taught them not to click on the email from the Nigerian prince.

Now, that scammy Prince is a model that can crack the entire internet.

I’ve talked to enough friends in enough industries to know that corporate America is struggling with this too. I know people whose organizations have spent more on AI strategy decks than on AI itself. The distance exists there also, and it’s not closing on its own. But I don’t know enough about enterprise deployment to fight that particular fight in corporate America.

What I know here in civil society, on my home turf, our lack of reps is kneecapping us.

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My contention is that our collective cereal-bowl reaction happened because this stuff still doesn’t seem real. It feels like sci-fi even as it’s happening, and so it outpaces our collective ability to process it.

That won’t change until a vastly larger circle of people possess operational knowledge — the kind that comes from deploying the technology, repeatedly, in contexts where the stakes are real and going through the process teaches us something. Whether a court filing summary needs a human to double-check it before it goes to a judge, or whether it’s fine to flag the edge cases and let the rest through. What “good enough” means for a benefits enrollment form versus a zoning analysis, because when the consequences are different, the tolerances should be too.

This kind of operational knowledge is what will build the institutional muscle memory that lets organizations use these tools the way they use spreadsheets: imperfectly, but competently, with an earned sense of when the output is solid and when it’s going to get someone in trouble.

We do not have a decade to figure this out. The internet took roughly that long to go from Yahoo’s homepage to our moms’ Facebook accounts, and the basic infrastructure of society was never at risk of being compromised overnight. With Mythos-class capabilities, every month we fail to build this knowledge is a month where the task in front of us gets even harder. We weren’t prepared this past Tuesday. We can’t afford not to be prepared tomorrow.

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I keep coming back to the name. Claude Mythos. This “aw-shucks” everyman grafted onto Aristotle’s term for the structure of a tragedy. The incomprehensibly powerful dressed in the absurdly ordinary. Milk and cereal. Cereal and milk.⁴

But it’s also where the opportunity lives — in the ordinary. To deal with a challenge on the scale of “Mythos”, we need deployment on the scale of “Claude”. My wife didn’t develop her understanding of AI by reading Works in Progress (alas). She developed it by opening a business and stumbling through.

To be clear, the answer to the many Cereal Moments we’ll have over the course of our lives — because this is not the last time we’ll read about something cataclysmic over breakfast and shrug — is not to feel worse about them. It’s not to generate more anxiety, or more white papers, or more takes.

It is to get in the reps. All of us. To build things. To fund and support the work. To talk about it. To learn, by doing, how to do it well — so that when the next Mythos arrives (and it will) we greet it with a felt relationship with the technology rather than a blank stare into a bowl of cereal.

The asteroid didn’t miss us. It landed. But we can go outside and check it out.

1. I want to flag, because I think it matters, that Anthropic's decision to withhold the model and fund defensive patching rather than race to market is exactly the kind of morally-serious thing you want a frontier lab to do. It would have been very easy — and probably very profitable — to release Mythos and let the chips fall. They didn't.

2. This is the part where I acknowledge that I, personally, have both given talks about how transformative AI is going to be and participated in the convening of things that could be described as commissions. Glass houses, etc.

3. This is a real use case. One awesome dude named Luke Alvarez built a volunteer firefighter evacuation tracking app using an off-the-shelf frontier model and publicly available geographic data. It took weeks, not years, and it replaced a system that was — I am not exaggerating — a paper map and a phone tree.

4. My point in resurrecting this ancient meme is to remind us how comparatively long-cycle the adjustment period to the upheaval of the internet was. We ain’t got that kind of time no more.