Podcast
The Human in the Loop
Hosted by Enrique Cordero · EN
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.
20episodes
Episodes
Newest firstAll episodes
What happens when your AI tool gets taken away
4d ago00:23:19Tap to summarizeOne 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
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Fable 5 Is Gone. Now What?
2w ago00:21:49Tap to summarizeThe 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
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When 80% of the Code Isn't Yours
2w ago00:22:00Tap to summarizeYou'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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The $36,000 Engineer: When Agentic AI Stops Being a Subscription
3w ago00:23:37Tap to summarizeUber 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 the math. Two tools, and one engineer runs about $36,000 a year.For years AI was a flat subscription. You paid once a month and you knew the number. Agentic coding turned that into a metered bill. And a metered bill does not warn you politely. It surprises you.This is the cloud invoice all over again. A team turns something on, forgets it is metered, and finds out at the end of the month.A paper this week put numbers on the risk. 63 real cases where agents blew past their limits. Often a single retry loop, quietly burning thousands before anyone looked.A cap you cannot enforce in code is just a wish.So before you scale agents across a team, the real question is not what the budget is. It is what happens, automatically, the second someone hits the ceiling. Most teams can answer the first. Almost none can answer the second.Full breakdown in this week's episode of The Human in the Loop.
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AI Ships Faster Than Anyone Can Review It
4w ago00:22:09Tap to summarizeMeta 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 review has declined." Their words. AI-generated code outpaced human review capacity.Read that with the rest of the week's news.Cognition says Devin merged 7x more PRs year-over-year. AI-written commits inside customer codebases jumped from 16% to 80%. Anthropic shipped Opus 4.8 on Wednesday, and every IDE, gateway, and agent runner had it the same day. They also disclosed a $47B revenue run-rate. The "is this a real business" debate is over.But here is what keeps coming back to me:Shipping more code faster is only a win if the systems that catch problems scale at the same rate. This week, the evidence says they aren't.A new arXiv study of 20,574 real coding-agent sessions documents how often agents do something other than what was asked. ITBench-AA, the first serious benchmark for agentic IT work, scored every frontier model below 50%.Adoption is real. The guardrails are not.This week's episode of The Human in the Loop covers all of it: the shipping wave, the cost-control backlash starting inside eng departments, and why ITBench-AA matters more than the score suggests.
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Why Your Coding Agent's PRs Keep Getting Rejected
May 2400:20:10Tap to summarizeThe 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 test, or wrote a test that would have passed either way. 4 fixed the bug but broke something else in the process.I used to assume model quality was the main driver. It isn't.The agent doesn't ship a one-line fix. It opens a change touching twelve files, half of them unrelated to the bug. It writes the code and skips the test. Or it writes a test that proves nothing.But notice that none of these are model problems. They're the same review failures a junior would ship. Just at higher volume and with more confidence.The practical move: stop logging "the agent failed" and start logging why. Counting changed how I prompt and how I scope tickets. It told me more about my codebase than any benchmark score ever has.If your top three match mine, you're seeing what everyone else is seeing. If they differ, that's signal about your codebase or your prompts.What's your top rejection reason?Full breakdown in this week's episode of The Human in the Loop. Link in the comments.
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Counting Keystrokes to Prove the Team Can Write
May 1700:24:49Tap to summarizeCounting 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 Brooks split software work into two parts. The accidental: syntax, boilerplate, scaffolding. The essential: what to build, why, for whom, what to trade off.The accidental is what AI tools are good at. That is why the dashboards look spectacular. Lines generated. Suggestions accepted. Prompts sent. The tools were always going to win that part.The numbers that should actually move sit one layer deeper. Cycle time. Change failure rate. Time to first PR review. Defect density.These were already telling you whether the team was shipping good software, long before AI showed up. AI either bends them or it does not.If cycle time has not moved, suggestion-acceptance is a vanity stat. If change failure rate has not dropped, you are not shipping faster. You are writing more code, faster. If time to first review has not shortened, your reviewers are the bottleneck and Copilot cannot fix that.GitHub shipped team-level Copilot metrics this week. It made the wrong question easier to ignore, not harder.Which second-order metric has actually moved on your team since you rolled out an AI coding tool?Full breakdown in this week's episode of The Human in the Loop.
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The Vulnerability Your Agent Merged
May 1000:13:27Tap to summarizeThe 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 agents write code, they pin library versions that trained well. Not versions that are safe.The mechanism is simple. A model has seen one popular version of a library thousands of times. It reaches for that version because it minimizes prediction loss. Pin-by-popularity and pin-by-safety are different jobs. The model only knows one of them.The GitHub Actions paper found the same shape. Right syntax. Wrong threat model.So the code looks clean. The tests pass. The PR merges. And six months later a security audit finds a CVE that was public before the agent ever touched the file.This is not a model problem. It is a workflow problem.Human PRs go through SCA. Agent PRs often don't. That gap is where the bill arrives.The fix is not complicated. Put pip-audit, npm audit, or OSV-Scanner between the agent and main. Same gate you'd use for any contributor. The agent has not finished the work when it merges. It has finished its part.Your security pipeline was designed for human contributors. Has anything changed since you started using agents?
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The Half-Life of a Good Decision
May 300:18:35Tap to summarizeThe 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 defend it when someone wants to cut corners.In AI, six months is enough to flip one into an antipattern.A paper published this week tested multi-agent orchestration frameworks against plain in-context prompting on procedural tasks. The orchestration lost. Same accuracy. More cost. More complexity. More failure modes.Six months ago, multi-agent was the answer you gave when someone asked how to handle complex workflows. Not because it was always right. Because models could not yet follow a long, careful prompt. That was the constraint. The scaffolding was built around it.The constraint changed. The scaffolding stayed.This is the part of AI adoption nobody talks about enough. It is not just that things move fast. It is that yesterday's correct decision becomes today's drag. And you cannot always feel it happening. The system still runs. The agents still coordinate. Everything looks fine until someone asks why you are paying for complexity that a single prompt could replace.We have approval processes built for risk. We do not have processes built for expiry.What is the half-life of an AI architectural decision right now? Six months? Three?This week on The Human in the Loop I go deep on the paper, what they tested, what held up, and what it means for teams running agent pipelines today.
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AI makes developers 19% slower
Apr 2600:19:37Tap to summarizeThe 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 times.The diff is bigger than we would have written. The tests cover things we did not ask for. The names drift across files.So we clean it up. And while we're cleaning, the next prompt is already queued.Here's what nobody warned us about: the bottleneck didn't disappear. It moved. Off of writing. On to reviewing. Testing. Deploying. Understanding code that isn't yours is harder than writing your own.So I changed how I work. Smaller prompts. Fewer tools loaded. One agent at a time. Read the diff before the next ask.It feels slower. The PRs go out faster.The productivity gain is real. But so is the cognitive load of reviewing at scale. I'm not sure we're talking enough about that second part.What's slowing you down: the generation or what comes after it?#claudecode #aiengineering #devproductivity
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