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?

The wait is over. After months of speculation, leaked benchmarks, and Sam Altman’s cryptic tweets, GPT-5 dropped on August 7, 2025, and OpenAI isn’t holding back on the bold claims. They’re calling it “a team of PhD-level experts in your pocket” – but does it live up to the hype, or is this just another case of AI marketing running ahead of reality?

Let’s dig into what GPT-5 actually brings to the table, cut through the noise, and explore whether we’re witnessing a genuine breakthrough or just an incremental upgrade with a PhD certificate slapped on it.

Read the full paper here (arXiv 2507.18074)

Welcome to the Age of AI That Designs AI (While We Watch, Slightly Nervous)

Remember that time AlphaGo made Move 37 and all the Go grandmasters collectively spat out their tea? Well, buckle up, because the latest paper from Shanghai Jiao Tong University and friends claims we’ve just had an “AlphaGo moment” in AI research itself. Yes, you read that right: the machines are now inventing their own architectures, and apparently, they’re pretty good at it.