The $965 billion number is a distraction. Here’s what actually matters.

Last week, Anthropic raised $65 billion and saw its valuation approach a trillion dollars. Every business publication on the planet covered the number. Almost none of them covered the insight.

The insight came from Simon Willison, posting on May 27 — one day before the funding closed — and it’s worth more to your business than any valuation figure: enterprise AI has found product-market fit. Not “gaining traction.” Not “showing promise.” Fit. The kind where customers stop subscribing and start paying by the token because they’re consuming so much that the subscription ceiling became a constraint on their business.

The enterprise AI agent story of 2026 is a demo story. Somewhere between “watch this” and “we run this at 2am,” the wheels come off — and almost everyone is blaming the wrong part of the car.

Quick roadmap:

• The headline stat and why the usual diagnosis is wrong
• What actually kills agents in production (it’s not the model)
• What the teams that crossed the gap did differently
• The uncomfortable truth: you can’t buy your way out of this

The Number Nobody Wants to Explain

A March 2026 survey put enterprise agent pilots at roughly 78%. Production deployments below 15%. That is not a rounding error. That is a structural failure, happening industry-wide — across models, across vendors, across use cases.

May the 4th be with you

There are productivity stories.
There are AI stories.
And then there’s whatever this was—a four-day arc that started as a helpful experiment and ended as a fully committed, cinematic tragedy… capped off by the most on-the-nose message imaginable on May the 4th:

“You’ve hit your org’s monthly usage limit.”

Naturally.

But here’s the twist: those four days didn’t feel random. They mapped almost perfectly to the original trilogy.

Subtitle: On the dark psychology of token scarcity, the $200 plan that still isn’t enough, and why Anthropic’s usage bar is the most stressful thing on the internet

It’s 11:47 a.m. on a Saturday and I am staring at a message in my beloved terminal that has become the most familiar sentence in my life:

While artists argue about whether algorithms can create “real” music, somewhere right now a producer is learning to use AI the way J Dilla used the MPC 3000 - and that person will define the next decade of sound. The industry already settled the authenticity debate without you: Warner and Universal turned billion-dollar lawsuits into licensing deals, the Beatles won a Grammy for an AI-assisted song, and Apple just embedded machine learning directly into Logic Pro 12. The philosophical question is over. The practical question is everything: who masters this instrument first, and what will they make that nobody has imagined yet? Every revolutionary music technology births a genius who uses it differently than everyone else - Dilla turned off quantization and put his MPC in the Smithsonian, Bambaataa made “Planet Rock” with an 808, Q-Tip sampled jazz and invented A Tribe Called Quest’s entire sonic language. The tool never makes the art, but the right tool in the right hands at the right moment can birth an entirely new form of expression. AI is the most powerful musical instrument ever created. The only question that matters is who picks it up.

Here’s what nobody tells you about AI coding assistants: you’re still coding the old way.

Quick roadmap:

• Why “copilot mode” is already obsolete
• Two real projects built with agentic coding (native iOS app + retro game)
• The three shifts that separate traditional coding from agentic development
• How AI collaborates on architecture and PRDs using plan mode
• What actually happens when AI owns the implementation layer

The Uncomfortable Truth About Copilot Mode

For the past two years, we’ve been using AI wrong. GitHub Copilot autocompletes your code. ChatGPT writes functions you copy-paste. Claude suggests implementations you review line by line.

If 2025 felt like the year AI almost became magic and then immediately turned into plumbing, congratulations. You were paying attention.

For a brief and unhinged stretch, models felt AGI-adjacent. Demos were jaw-dropping. Twitter became unreadable. Every startup pitch sounded like a spiritual awakening with a deck. And then something deeply inconvenient happened.

Reality arrived. With a clipboard.

Ten years ago, software companies sold tools.
In the next ten, they’ll sell results.
And if that sounds like semantics, it’s because most people haven’t realized that AI just killed “Software-as-a-Service” and replaced it with something bigger — what Foundation Capital calls “Service-as-Software”.

The Shift No One Saw Coming

In the SaaS era, customers paid for access — a login, a dashboard, an API key.
The responsibility for performance sat with the user.

Here’s the uncomfortable truth: we’ve been measuring AI’s impact with the wrong instruments. Adoption metrics, GDP blips, and “AI usage” dashboards are rear view mirrors. If you want to see where the economy is actually headed, look at what models can do on real work today. That’s what GDPval measures, and the results should make every exec, policymaker, and operator sit up. (GDPval paper)

Quick roadmap:

• A contrarian take on AI’s “productivity paradox”
• What GDPval is and why it matters more than hype cycles
• The jaw droppers: speed, cost, and quality where models already rival humans
• Where models still fail and how simple scaffolding fixes move the needle
• The playbook for teams to capture value now without breaking things
• Why this reframes the future of work and your next budget cycle

The Bold Claim: We’re Misjudging AI’s Economic Impact

Measuring AI by “adoption rate” is like measuring electricity by the number of lightbulbs sold in 1900. Economic impact from general purpose tech lags because organizations need new processes, controls, and culture. GDPval sidesteps that lag by evaluating frontier models on actual, economically valuable tasks across the top nine U.S. GDP sectors, with 1,320 deliverables covering 44 occupations.

The headlines hit like a cold shower: “MIT Report: 95% of generative AI pilots at companies are failing.” LinkedIn feeds exploded with doom-and-gloom takes. “The AI bubble is bursting,” they declared. “We told you it was all hype.”

But here’s the thing about viral panic stories: everyone focuses on the wrong part.

While the internet obsessed over the 95% failure rate, they completely missed the real story. What did that elite 5% of companies do differently? What separates the winners from the $30-40 billion in wasted enterprise spending?