Unreal Intelligence

Ninety-eight percent of organizations report unsanctioned AI use. Only about thirty-seven percent have any AI governance policy at all. That's not a compliance gap — that's a canyon. And the reflex every leadership team reaches for — ban the tools, send the all-staff email — is the single most expensive mistake you can make.

This week I make the case that shadow AI is two things at once: your biggest ungoverned risk AND the clearest signal you have about where real productivity demand is hiding inside your company. Miss the second one and you'll make the wrong call. I walk through the actual scale (Verizon's 2026 DBIR — 22,000+ breaches, shadow AI now the third most common non-malicious insider action, a fourfold jump in a year, and source code as the #1 data type walking out the door), the new leak vectors most leaders aren't watching (browser AI extensions, OAuth agents, MCP servers), and a first-hand story from an HR analytics team that built a leadership-selection AI on its own — stripped the names, felt responsible, and still exposed real people's identities in under ten minutes of prompting.

Then the part that matters: why prohibition trades a visible risk for an invisible one, and a four-move govern-don't-ban playbook you can start Monday — see it before you police it (discovery + amnesty survey), sanction a fast path that actually beats the shadow option, govern the new vectors (OAuth scopes, agents, extensions) by name, and tier your rules by data sensitivity, not by tool. Plus the sequel to that HR story — the acceptable version we got called back to build — and the uncomfortable lesson underneath both: your process being slower than the technology is the real disease.

Entry-level job postings are down 35% in eighteen months. Unemployment for recent college grads just hit one of the highest levels in over a decade. And every executive says the same thing: “AI does what our juniors used to do.” Fine. Then answer one question — in ten years, who’s running your company? 

This week I dig into the quiet collapse of the bottom rung of the career ladder, and why I think cutting junior hiring is the most expensive tech debt companies will ever take on. The entry-level job was never really about the work — the work was the tuition. AI took the tasks, but it didn’t take over the learning function those tasks were secretly carrying. Meanwhile, JLL’s brand-new Future of Work survey (2,200+ C-suite leaders, 21 countries) shows the most AI-advanced organizations doing the exact opposite of everyone else: growing headcount, investing in entry-level talent, and reporting — for the first time in fifteen years of that research — that their #1 constraint isn’t money. It’s future-ready talent. 

Then I get practical: four plays you can start Monday morning — redesign junior roles around AI supervision, compress the ladder into a deliberate 18-month apprenticeship, hire for learning velocity instead of tool checklists, and measure pipeline health like a supply chain. Build the

Remember when Walgreens closed stores and blamed shoplifting — and then their own CFO admitted “maybe we cried too much”? The real

Anthropic shipped Fable 5 — its most powerful public model to date — on June 9th. Three days later the U.S. government switched it off. By June

In one week this June, the United States took its two most powerful AI models off the table — and China gave its best one away for free. Same week, two countries, two opposite bets on the future. This episode is about the phrase we all grew up on — “better safe than sorry” — and why it may be the most expensive instinct we have right now.  I walk it straight, no spin. First, the week America hit the brakes: a June 12–13 export-control order forced Anthropic to suspend Fable 5 and Mythos 5 — even for its own foreign-national employees — after a reported jailbreak of its top cybersecurity model, plus the June 2 executive order standing up a voluntary, 30-day pre-release review with the power to anoint “trusted partners.” I steelman the security case honestly — frontier cyber-capability really is dual-use — before asking the uncomfortable question: when pulling a model becomes a tool of policy, what does that do to the appetite to push the frontier at all? 

Everyone’s buying AI to fix email overload — and nobody’s asking why we have this many emails in the first place. This week I’m going after the most expensive open secret in corporate tech: most enterprise AI deployment is theater. Companies are pouring billions into licenses pointed at surface-level text work while the broken processes underneath keep generating the noise. MIT’s research says 95% of GenAI pilots fail to deliver ROI. The models aren’t the problem. The thinking is.  I walk through the two human failure modes wrecking AI initiatives — overtrust and refusal. On the overtrust side: the Air Canada chatbot ruling that made “the AI said it” legally your problem, the lawyers sanctioned for ChatGPT-invented case law, the Bard demo error that wiped $100 billion