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Welcome to The Human in the Loop , a weekly look at what’s going on in the world of AI. Every week, I go through the biggest stories, the weird experiments, and the stuff that might actually matter in our day-to-day lives.
Unknown Host hosts The Human in the Loop.
The most powerful AI model in general release this week requires government approval to access. OpenAI launched Sol, Terra, and Luna (restricted to around 20 companies). That's not a GTM decision. That's a policy one. Be
One export rule. One acquisition. Either can pull your AI tool out from under you before lunch. This week both happened. A US export-control directive forced Anthropic to cut off foreign-national access to Fable 5 and My
The model you built on can disappear. Not crash. Disappear. That's not hypothetical. Last week the US government ordered Anthropic to pull Claude Fable 5 and Mythos 5. Not throttle them. Pull them. For every customer in
You're still measuring AI by whether it writes good code. That's already the wrong question. Reading Anthropic's latest numbers, more than 80% of the code merged into their codebase is now written by Claude. The typical
Uber blew its whole 2026 AI budget in four months. Then it set a $1,500 monthly cap on each coding tool, per engineer. Claude Code, Cursor, a dashboard to watch the spend, an approval step to go over. Simon Willison did
Meta says AI writes 80% of new code. Their own reviewers can't keep up with their own AI. Straight from their engineering blog. They built RADAR to auto-review low-risk diffs because "the share of diffs receiving timely
The model isn't the problem. I went back through 20 of my agent's pull requests and the failures looked exactly like a junior's first month. 3 of them tried to rewrite things nobody asked them to rewrite. 5 skipped the t
Counting accepted Copilot suggestions to prove AI works is like counting keystrokes to prove the team can write. It is the cleanest number on the dashboard. It is also the one that tells you nothing. Forty years ago Fred
The unit tests pass. The PR merges. And you won't find the problem for six months. Two papers landed this week — one on LLM-generated code, one on GitHub Actions workflows. Different researchers. Same finding. When agent
The best practice you followed six months ago might be the technical debt you're cleaning up today. In traditional IT, a best practice can survive a decade. You study it. You argue for it in architecture reviews. You def
The agent doesn't slow down. We do. We generate code in seconds. Then we spend an hour reading what it wrote. We trust the output less than we trust what we would write ourselves. So we read it twice. Sometimes three tim
32 steps. That's how many it took for Anthropic's unreleased AI to simulate a full network attack. They buried that number in a release note. The model is called Mythos. The UK AI Security Institute tested it. It complet
Anthropic built the most powerful AI model ever. Then decided the world wasn't ready for it. Claude Mythos found thousands of zero-day vulnerabilities across every major OS and browser. On its own. Without being asked. I
We just spent $300 billion building a car nobody taught anyone to drive. Q1 2026. The four largest venture rounds in history all closed in a single quarter. 80% of capital went to AI companies. Oracle cut an estimated 20
OpenAI just proved $15 million a day isn't enough to make an AI product work. Sora got shut down last week. The technology worked. The economics didn't. $15M a day in compute costs. No viable path to revenue. That's not
I caught myself staring at my Claude usage quota thinking: "I need to use this. But for what?" Not because I had a problem to solve. Not because I had an idea to explore. Just... pressure. A quiet feeling that if I wasn'
Everyone knows the adoption numbers are bad. Nobody's saying why they're actually bad. 60% of the workforce now has sanctioned AI tools. Only 11% of organizations have moved agentic pilots into production. That gap gets
MCP was supposed to be the USB-C of AI. One protocol. Everything connected. Then developers ran the numbers. Connecting GitHub's MCP server alone burns 55,000 tokens (before your agent does a single useful thing). So, co
The AI industry just quietly crossed a threshold, and most organizations aren't ready for what comes next. This week, we cover the pivot from capable AI models to autonomous agents operating at scale: why Microsoft chose
AI is helping us write code faster. But I'm not sure it's helping us ship better software. These two things are not the same. And right now, I think we're confusing them. The data is starting to show the gap: AI-generate
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