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?

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

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.

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!

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

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, companies are quietly shifting back to CLI and REST APIs.

Not because MCP failed. Because LLMs are surprisingly fluent in terminal. CLI workflows can cut token usage by 35x. That's a lot of money by the end of the year.

That’s typical pattern with new technologies. A new abstraction layer arrives, gets widely adopted, then specialists find where it leaks... and the pendulum swings back toward what actually scales.

The teams getting it right aren't picking sides. They're building hybrid stacks: CLI for cheap local execution, REST APIs for volume, MCP where governance and auditability actually matter.

The abstraction wars never end. They just find their right level.

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 Anthropic over OpenAI for its most important new product, what a rogue AI that started mining cryptocurrency tells us about the real deployment risks nobody's talking about, and why Meta spent more than most countries on AI and still had to delay its flagship model.

We also dig into the robotics funding surge (over $1.1 billion in a single week) and a technical breakthrough that may have just solved the hardest problem in teaching robots to move. The pattern across all of it is the same: building smart AI is no longer the hard part. Governing it, securing it, and making it economically sustainable, that's where the real race is being run.

Press play if you want to understand what's actually happening beneath the headlines.

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-generated code contains 1.7x more bugs than human-written code
• Copy-pasted code is up 48%. Refactoring is down 60%.
• Pull request sizes have grown 154%. Review times up 91%.
• Only 29% of developers trust the quality of AI output

If developers don't trust what they're producing, what does that mean for the engineering leaders managing the downstream impact?

The problem isn't the AI. We optimized for output. We forgot to optimize for outcomes.

The teams that get this right won't necessarily be the fastest. They'll be the ones who still treat AI-generated code as a starting point (not a finished product) and keep senior engineers in the loop as reviewers, not just approvers.

Measuring PR volume and lines of code tells you how fast the machine is running.

It doesn't tell you where it's going.

For engineering leaders: are your review processes built for this volume? Or have your senior engineers quietly become the quality layer nobody planned for?

Everyone says they're doing AI. Almost no one has moved past the pilot stage. This week we dig into why that gap exists.

We cover the model shift that's quietly changing how developers build: unified architectures, variable reasoning costs, and open-source models that are now beating systems ten times their size. We get into what "agentic AI" actually means in production, not the buzzword version, but the real infrastructure challenges WHOOP uncovered running 500+ AI agents at once. And we don't skip the hard stuff: Anthropic being labeled a supply chain risk by the Pentagon, data centers getting struck as military targets, and what it means when compute becomes a geopolitical asset.

If you want to understand where AI is actually going this is the episode.

Three massive forces collided this week in AI, and the fallout is just starting. First, the unprecedented standoff: Anthropic gets blacklisted by the US government for refusing to remove safety guardrails, while OpenAI steps in. Second, the money: OpenAI's record-breaking $110 billion raise. Third, the workforce: Block's explicit AI-driven layoffs and the market's enthusiastic reaction. We break down why safety principles are becoming commercial liabilities, what the capital deluge means for competition, and how developers should prepare for the new era of 'agentic' layoffs. Press play to get caught up on the week that changed everything.