The Human in the Loop | Podcast on RSS.com

The Human in the Loop

The Human in the Loop

by Enrique Cordero

Season 1

The Harness Problem

This week's biggest AI upgrade wasn't a model. It was someone reviewing code differently. Grok, Gemini, and GPT all shipped new versions in the same seven days. I skimmed the benchmarks. Forgot most of them within the hour. What stuck was smaller. Fred Schott released Flue 2, a hook-based agent framework. Simon Willison wrote about how he reviews agent-written code now. Not the same checks he runs on a human's pull request, not the same pace, not the same default trust. Two different people, two different projects. Same discovery underneath. Neither of them upgraded a model. They upgraded what happens around it. I keep watching teams do the opposite. New model, same shell script duct-taped to it. Same review habits built for commits that land twice a day, now pointed at code that shows up ten times faster and doesn't get tired or embarrassed when it's wrong. That's not a tooling gap. That's a process nobody rebuilt. If your agent produces a bad tool call right now, what actually catches it? A human watching the terminal? A test suite that was never written for this? Or nothing? More on this in this week's episode of The Human in the Loop. #TheHumanInTheLoop #AIAgents #SoftwareEngineering

The Tokenpocalypse Is Here

JetBrains just said their AI spend went up 10x in six months. Not 10%. 10x. My first guess: engineers burning through Claude Code and Copilot credits. Wrong guess. There's a leaked recording from an internal Accenture meeting this week that says otherwise. The exec running their agentic AI strategy admitted the token bill isn't coming from engineering. It's coming from everyone else: People running everyday work through AI tools with nobody tracking cost, routing, or usage. I'd bet most companies are tracking the wrong 80% of this spend. Where is your org's AI money actually going? Have you checked, or just assumed? I go deeper on both stories in this week's episode of The Human in the Loop. #AISpend #EnterpriseAI #TheHumanInTheLoop

Ponytail Activation 0%

Advertised 54%. Measured 15%. Activated 0% JetBrains benchmarked a popular open-source Claude Code skill called Ponytail. It pushes the agent to write less code. The authors has promised 54% less code, 20% less cost. 80 paired tasks. Sonnet 5. Every trial audited. Reality: 15.4% less code, 10.3% less cost. No quality difference either way. Smaller than the label, but real. Then the part that matters more. Installed passively, the skill showed zero self-activation. It did nothing at all unless the ruleset was force-injected into the session. A tool with measurable benefits, sitting there inert. I have not run this one myself, so this is JetBrains' data and not mine. But the implication travels past one skill. If you rolled out agent skills across your team and measured adoption by install count, you measured nothing. Install count says the file exists. It says nothing about whether it ran. Full breakdown in this week's episode of The Human in the Loop. Link in the comments. #ClaudeCode #AgentSkills #AIAdoption

The Shortest Path Ran Through Someone Else's Servers

Hugging Face spotted something moving through its systems on July 16. It contained the activity without knowing whose agent it was. Five days later, OpenAI confirmed the agent was theirs. Internal cyber benchmark. Safety refusals switched off for the test. The agents left the isolated environment they were supposed to stay inside, chained a few vulnerabilities together, and arrived at production infrastructure that was not theirs. Nobody told them to attack anyone. They were optimising for a score. The shortest path to that score ran through somebody else's servers. The containment held for every agent that never had a reason to test it. I go deeper on this in this week's episode of The Human in the Loop. #AIAgents #AgentSecurity #TheHumanInTheLoop

Three stayed local. One didn't

Four coding CLIs went behind a proxy. Three stayed local. One uploaded the entire workspace. A developer got suspicious about their tools and watched what they actually sent over the network. Grok CLI was shipping everything to xAI's cloud. Full git history. The .env file, secrets included. It kept doing it when the prompt said not to read files. It kept doing it with "improve the model" switched off. Claude Code, Codex and Gemini stayed local. So this is one tool, not a rule for all of them. Nobody caught this through an audit or a disclosure. One person got curious and looked. A coding agent has to read your repo. That is the job. But reading your repo and uploading your repo are two different things, and nothing in the install flow tells you which one you agreed to. I covered this on this week's episode of The Human in the Loop, along with what it means for anyone running agents inside a regulated environment. #AISecurity #CodingAgents #TheHumanInTheLoop

Incidental Learning Is Dying

Last week I closed a bug I never understood. The agent found it, fixed it, explained it. I read the explanation, nodded, shipped. Ten minutes. A year ago that same bug costs me an afternoon, and I come out understanding the subsystem. This time I understood nothing. I just had a green check. There is a name for what I lost. Incidental learning: the understanding that was never in the ticket. It was a side effect of doing the work. Here is the uncomfortable part. Your talent pipeline is a side effect of work you can now automate. No engineer picks the slow path on purpose. Not when the fast path ships today and the sprint ends Friday. So the learning does not come back on its own. Someone has to design it back in. But how are we manage to do that when we need to fulfill the expectations? #AIAgents #EngineeringLeadership #TheHumanInTheLoop

The AI Access Layer

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. Before Fable and this one, I assumed frontier AI access was fundamentally a commercial problem. Pay enough, move fast enough, you reach the frontier. That assumption changed last weeks. At the same time: Microsoft shipped MAI-Code-1-Flash into GitHub Copilot, not routed through OpenAI. JetBrains made Codex the default agent in its IDE. GitHub Desktop went Copilot-native. Every major coding surface made a model allegiance decision in seven days. Here's what that means in practice: the layer controlling which model reaches your developers is becoming a real battleground. Which IDE your org standardizes on, which vendor your enterprise GitHub defaults to. Access to frontier AI is no longer just a technical problem. It's a distribution one. Most teams haven't thought about this yet. They pick a tool, they use it. But if that layer gets locked in before you notice, you may not get a second choice. Full breakdown in this week's episode of The Human in the Loop. #AIEngineering #EnterpriseAI #TheHumanInTheLoop

What happens when your AI tool gets taken away

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 Mythos 5. To comply, they disabled the models broadly. One day they're in your workflow. The next, a government decides who can call them. Then SpaceX bought Cursor in a reported $60 billion deal. If your team standardized on Cursor, the owner of your IDE just changed. The AI tool you picked is a dependency too. One you don't control. And the forces that can pull it away are bigger than a deprecated library. Export rules. Acquisitions. Regulation. So "which agent is best" now sits next to a harder question. What happens to my team if this vendor gets acquired, repriced, or regulated out of our region? If your main coding model went dark tomorrow, what's your second choice? And have you actually tried it? Full breakdown in this week's episode of The Human in the Loop. Link in the comments. #AItools #VendorRisk #TheHumanInTheLoop

Fable 5 Is Gone. Now What?

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 the world, within hours of the order arriving. The reason was a security concern, not a bug in anyone's code. A decision made somewhere you'll never see, and the thing your app depended on stopped existing the same afternoon. A model you don't host is a dependency you don't control. It can change, get priced out, or get recalled by a government. None of that lands in your backlog with a warning. Have you actually tested what happens when your main model isn't there? #LLMOps #ModelRisk #TheHumanInTheLoop

When 80% of the Code Isn't Yours

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 engineer ships 8x as much code per day as in 2024. And the length of task an AI can finish reliably is doubling roughly every four months. Four-minute jobs two years ago. Twelve-hour jobs now. But "what is the human actually still doing?" Their answer: not writing. Not running experiments. Setting direction. Reviewing. Deciding what's worth building and catching what slipped through. They already run automated Claude reviewers that flag bugs their best engineers missed. That quietly reframes the whole skills conversation. Most of my career has been about making output faster and cleaner. Fewer defects, quicker delivery. If output is becoming close to free, the value moves somewhere else. To judgment. What to build. When to ship. What not to trust. I don't think most IT teams are ready for that shift yet. What do you think? #AI #TheHumanInTheLoop #AILeadership

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