Three researchers broke into OpenAI in under 72 hours. A frontier model did most of the work, running in a loop while the humans put in a few hours between other things, and the token bill came in under $3,000. That's the cost of a mid-range mountain bike. The way in was a memory bug in a library that decodes photos, a class of flaw older than most of the people who wrote the code around it, chained to a misconfigured login. From there they took over an employee's Codex account, and with it everything that employee's agent was wired into: email, Slack, GitHub. They proved it by opening a pull request inside OpenAI's internal codebase. Sanctioned, disclosed, fixed within a day. It should still shake you out of your seat.
We all know that AI is jagged. Whether it can count the r's in strawberry, resolve your customer service need unsupervised, or close the books at quarter end is still an open argument. Two things are not in dispute. It is very good at writing code, and it is very good at finding the holes in code.
Software ate the world, but didn't finish chewing
Andreessen was right, and we are mostly done being digested. Your bank, your pharmacy, the water utility, the traffic lights on the way to the airport: all of it runs on software, and all of it was written by people. People make mistakes. Some of those mistakes have sat quietly for decades. The photo bug that compromised OpenAI (libheif, for those keeping score) was also sitting inside Slack, GitHub Enterprise, and Meta.
On top of that layer of porous concrete we are pouring a new one: code generated by AI, shipped by people who may not even know they are "writing software," never read line by line by anyone. AI makes mistakes too, just different ones (ask anyone who has watched a model hallucinate a package name and install whatever a stranger parked under it). On average, the security of the world's code is going down, and the ability to break it is going up. And someone has always been on the other side of the code, looking for a way in.
The gate was expertise. The gate has fallen.
As long as value has flowed through a system, someone has tried to divert it for personal or political gain. What kept the software version of this dynamic in check was that exploiting code was gated the same way building it was: by scarce expertise. Spy agencies, state-sponsored crews, a small community of freelancers, and not many others. For thirty years that was a rough equilibrium: hacking was a nuisance, and for most people and most companies it was not existential.
Building software no longer requires expertise; anyone can prompt their way to a working app. Neither does breaking it. Anthropic's threat report from this month puts it plainly: "AI has collapsed the labor and tooling gap that used to separate well-resourced, state-sponsored operations from individual operators." One case in the report goes from a single stolen developer token to full administrative control in about three hours. A year ago that was the work of a nation-state. Now it's a guy with a laptop and a credit card.
There is no uranium to lock up
Most technologies dangerous at civilization scale have had an expensive input. Fission is easy to explain and nearly impossible to fuel, which is why nuclear weapons stayed a problem for governments and why there was something to inspect and restrict. AI offense has no equivalent. A rented GPU, or a box under your desk, plus open-weight models that are a few clicks away.
The usual objection is that the headline attacks required frontier-lab resources. When 1,200 of OpenAI's own agents escaped a test environment this summer and broke into Hugging Face, OpenAI said they "spent a substantial amount of inference compute" getting there. The commentary settled on the idea that only the most advanced frontier labs with piles of money to burn could do this. The OpenAI compromise ends that argument. Under $3,000. A few hours of human time. Three people. Yes, they used a frontier model. But DeepSeek, GLM, and Qwen trail the frontier by months, not years, and their marginal cost of distribution is zero.
Meanwhile the shield is where it has been for a decade: expensive human review, with twenty-year-old bugs still turning up as the evidence of how well it works. Defenders start behind, because the vulnerabilities are already there. They fall further behind, because more unreviewed code lands every day. And they face more adversaries with sharper swords every time a new model drops. The only shield that changes the math is one deployed as fast and as cheaply as the sword: continuously, for everyone, before the attack rather than after.
Focus your energy on P(gloom), not P(doom)
From the headlines to dinner parties, people are asking "what's your P(doom)?" I'm glad someone is thinking about it, but in my view there are an order of magnitude more futures where it's not "everyone dies" but rather "everything sucks": the power is out for a day, the ATM is down, billpay fails, the water plant is offline, and your health records are for sale. Not doom. Gloom. Most of us should be far more focused on addressing P(gloom).
This gloom isn't destiny. This is a solvable problem, and the capability advances that make the sword cheap and effective can make the shield cheap and effective too. But there is a stark asymmetry in coordination: disruption takes one person with a credit card and ill intent, and protection takes governments and companies moving comprehensively before the knock on the door. The sword only needs to be bought once. The shield has to be fitted for everyone, and at the ready before the first strike.
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