The Organizational Immune System Is Real, and It's 44% of Your Gen Z Staff
Your AI rollout has a failure mode that no model card, eval suite, or vendor SLA will catch: the people you asked to use it are quietly feeding it garbage.
Posts
Two People Could Replicate Your Best Line in 90 Days
Here is the question to ask about your own company before anyone else asks it for you: is there a high-margin line of your business that two people with off-the-shelf AI tools could rebuild in 60 to 90 days? Not the whole company. One line. The profitable one. The one that funds everything else and that nobody on the leadership team worries about because it has always just worked.
Vibe Coding a Turbine Blade
The claim that snaps everything into focus is small and absurd: “it allows two engineers to design an entire jet engine.” The mechanism behind it is even more provocative — “the hardware engineers can vibe code their pieces.” If that holds, then hardware engineering quietly becomes a branch of software engineering, and the headcount math that has governed aerospace for fifty years stops applying.
Waste Tokens, Save Time — When Brute Force Is the Rational Choice
This piece was sparked by The AI Industrial Revolution with Naval Ravikant, where one of the builders at the table offered a deceptively simple rule: “just waste tokens, save time.” Don’t optimize the inputs or the outputs, he argued — optimize your own time and the final result, because “they’re still way cheaper than a human.”
What a 73x Year Actually Proves (and What It Doesn't)
Pick the number that makes you uncomfortable. Cognition’s Devin went from $1M in ARR in September 2024 to $73M by June 2025 — a 73x rise in nine months, the steepest revenue ramp documented in coding-agent history at that point (AgentMarketCap). That figure gets thrown around as proof that going “fully AI-native” is a license to print revenue. It isn’t, and treating it that way will get you the wrong lessons. But there’s a real signal buried in it, and it’s worth digging out carefully — because the parts that are true are more useful than the headline, and the parts that are oversold are where teams burn their first year.
What Dies First: The Five-Year Plan, the Org Chart, the Quarterly Review
Run a test this week. Find your five-year plan, open it to the section on AI, and ask whether the version written twelve months ago would tell you anything useful about where you are today. For most of us the answer is no — and that’s the whole problem with static planning. The artifact didn’t fail because it was badly made. It failed because the thing it predicts moves faster than the cadence on which you refresh it.
Why 80% of Corporate AI Projects Fail
Here is the uncomfortable pattern. A company buys the models, hires the team, runs the pilot, and eighteen months later the dashboard shows nothing. Not a smaller P&L line, not a slower one. Nothing. The default outcome of an enterprise AI project is failure, and it is not close.
Why China Is Going All-In on Open-Source AI
If you have a structural lead in hardware and supply chains, the rational move is to give software away. Make it free, make it ubiquitous, and let it erase the one place your competitor is ahead. That is the logic behind China’s open-weight AI strategy, and it is worth taking seriously even if you build for the other side.
You Always Want the Smartest Model
The cheaper model’s mistakes are the ones you never see. That is the whole argument, and it is worth sitting with before you reach for the dropdown and pick the model that costs a fifth as much.
You Can't Fix the Old System — Build at the Edge
There is a failure mode every operator who has tried to “transform” a company from the inside knows in their bones: the work is sound, the demo lands, the pilot ships — and then it quietly dies. Not killed by a villain. Starved, re-scoped, reabsorbed, out-argued by a more defensible line item on someone else’s spreadsheet. The instinct afterward is to try harder next time: better change management, more executive air cover, a crisper deck. That instinct is the mistake. You cannot patch a system whose own logic is rejecting you. You build the replacement next to it, at the edge, and let the new thing become the center of gravity.