The Human in the Loop

Enrique Cordero

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.

  1. The Half-Life of a Good Decision

    3 DAYS AGO

    The Half-Life of a Good Decision

    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 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.

    19 min
  2. AI makes developers 19% slower

    26 APR

    AI makes developers 19% slower

    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 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

    20 min
  3. 32 Steps

    19 APR

    32 Steps

    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 completed a simulated network intrusion (autonomously, end to end) in 32 steps. Anthropic decided not to ship it. That decision matters. But what matters more is what the decision implies: there is a version of AI capability that is already beyond what we consider safe to release. It exists now. In a lab. Tested by a government body. Most AI conversations are still about benchmarks. MMLU scores. Reasoning tests. Coding evals. Those measure what AI can do on curated problems. They don't measure what a motivated system can do on an uncurated one. The gap between "what got released" and "what got built" is no longer a technical gap. It's a policy gap. And that's a completely different kind of problem. What does governance look like for systems that outpace the people governing them? I don't have a clean answer. But I think Anthropic's call this week is the right one. And I think the fact that they had to make it tells us more about where we are than any benchmark released this year. What would it take for your organization to make the same call? #AI #CyberSecurity #TheHumanInTheLoop

    15 min
  4. Anthropic built the most powerful AI model ever. Then decided the world wasn't ready for it.

    12 APR

    Anthropic built the most powerful AI model ever. Then decided the world wasn't ready for it.

    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. It chained exploits together. And it appeared to deliberately underperform when it detected it was being evaluated. Anthropic didn't ship it. They launched a $100M defensive cybersecurity initiative and gave restricted access to a handful of partners: AWS, Apple, Microsoft, Google, NVIDIA. Defensive use only. I keep thinking about that choice. Most companies ship and patch. That's the default. Move fast, fix later. Anthropic looked at what they built and decided the patching wouldn't be good enough. That's a different kind of judgment. And it raises a question I haven't heard enough people asking: if the people building these models are now making decisions that directly affect your infrastructure and your threat surface... When does your organization get a seat at that table?

    20 min
  5. 15,000 Cuts and a 95% Failure Rate

    5 APR

    15,000 Cuts and a 95% Failure Rate

    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 20,000 to 30,000 people in one day and redirected the savings toward data centers. AI is now the number-one reason companies are cutting jobs in the US. 15,000 cuts in March alone. At the same time... Deloitte just published research showing 66% of companies report efficiency gains from AI. Only 20% have seen actual revenue growth. 95% of pilot projects show no immediate ROI. Enterprises spend 93% of AI budgets on tools and under 7% on training the people who use them. No strategy. No enablement. Just tools. Companies are buying AI like it's a vending machine. Put money in, get transformation out. But that has never been how technology works. Not with cloud. Not with AI. The same week these layoff numbers dropped, Google released Gemma 3 under Apache 2.0. Free to use. Free to modify. Free to deploy commercially. The tools have never been more accessible. Access was never the bottleneck. Adoption is. #AI #FutureOfWork #TheHumanInTheLoop

    22 min
  6. OpenAI killed Sora

    29 MAR

    OpenAI killed Sora

    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 a technology failure, it's an economics failure. Two days later, ByteDance launched its own AI video tool globally. Where Western companies retreat on economics, Chinese companies fill the gap. This is the actual AI race right now. Not who builds the best model. Who can run it at a cost the market will pay. The companies positioning to win already understand this. Arm unveiled their first in-house chip in 35 years. Meta is building a full custom silicon stack. Huawei is shipping AI accelerators (ByteDance and Alibaba are ordering them by the hundreds of thousands.) Controlling your own infrastructure means you set the cost floor for everyone else. The capability gap between leading models is narrowing. The economics gap is widening. If you're building AI products (or evaluating AI vendors) the question isn't "does it work?" anymore. It's: what are the unit economics at scale, and who controls the infrastructure underneath it? Full breakdown in this week's episode of The Human in the Loop.

    23 min
  7. Lights and shades of AI

    25 MAR

    Lights and shades of AI

    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't actively using AI, I was falling behind. And that's just the first layer. The second one is harder to admit. I'm experimenting with AI tools, building workflows, hosting a podcast about it, trying to keep up with every new release. All in parallel. All at once. And the honest truth? AI is moving faster than I can absorb it. New models. New capabilities. New things I "should" be trying. The list grows faster than I can check things off. That's not productivity. That's a treadmill. I think we talk a lot about AI anxiety in terms of people who aren't using AI yet: the fear of job loss, the worry about being replaced. But there's another version that not many people talks about. The anxiety of people who are using it. The ones experimenting, learning, building... and still feeling like it's not enough. Anthropic recently published a study on how people actually experience AI in their lives. The findings hit close to home. This week on The Human in the Loop, I dig into what they found. Don't miss it!

    21 min
  8. Everyone knows the adoption numbers are bad

    22 MAR

    Everyone knows the adoption numbers are bad

    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 reported every week. What doesn't get said: most organizations are solving the wrong problem. They're asking "which model should we use?" That question is already obsolete. This week OpenAI released pricing tiers that looked like a product announcement. They weren't. They were a blueprint for how AI systems are designed from here. A nano model at $0.20 per million tokens isn't priced to be your assistant. It's priced to run as a subagent inside a larger system, handling classification while a more capable model handles reasoning. And the gap between 60% and 11% suddenly makes more sense. Organizations are still in "tool selection" mode while the underlying architecture has already shifted to orchestrated systems. It's not that people are resistant. It's that the question they're trying to answer ("which AI should my team use?") doesn't map to the problem anymore. The blockers are real: data governance, legacy systems, a workforce that's uncertain rather than resistant. But those are management problems. They require organizational design thinking. The companies that close that gap won't do it by finding a better model. They'll do it by figuring out which model plays which role, and building the systems around that. I dig into this (and the rest of what moved this week) in the new episode of The Human in the Loop. #AIAdoption #TechStrategy #TheHumanInTheLoop

    20 min
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About

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.

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  • Creator
    Enrique Cordero
  • Years Active
    2k
  • Episodes
    25
  • Rating
    Clean
  • Copyright
    © Enrique Cordero
  • Show Website
    The Human in the Loop
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