Editorial articles on Tech, AI, Startups, Business and Science. Looking for founder narratives? See Founder Stories →
The narrative that big always wins is a partial narrative. We map the class of small, task-focused models that quietly took a growing share of production AI work — where they win, where they still lose, and what changed.
For a decade the Rule of 40 was the single benchmark that SaaS boards used to judge growth-versus-profitability trade-offs. It survived the ZIRP era. It did not survive the AI-cost era. Here is what replaced it.
Zero-trust is no longer a buzzword — it is the default security architecture for serious organizations. We map what production deployments actually look like, where they still fail, and what the emerging identity-first models add.
Two years after GLP-1 receptor agonists went from diabetes drugs to cultural phenomena, we audit the public data — CDC prescription trends, NEJM outcomes trials, and the second-order effects showing up in retail and payer numbers.
Design systems have quietly become plumbing. We surveyed the largest public systems, tracked what changed in the tooling, and mapped where the interesting design-engineering work is happening now.
The Inflation Reduction Act's clean-energy provisions were billed as the largest climate investment in US history. Three years in, we audit what the IRS, Department of Energy, and Energy Information Administration say has actually happened.
Google's third-party cookie deprecation in Chrome — long-delayed, much-mocked — finally moved in 2025. We map what actually replaced the cookie for measurement, targeting, and retargeting, and where the seams still leak.
Most founder equity guides stop at the first paragraph of useful detail. This one keeps going — the mechanics of cliffs, what happens at acquisition, how the IRS treats acceleration, and the cases nobody warns you about until you live them.
The AI infrastructure conversation is bad because the layers blur. Most arguments about 'the AI stack' confuse five separate markets with five separate competitive dynamics. Here is the framework we use to keep them apart.
Eighteen months ago the AI conversation was wall-to-wall predictions. We pulled the most-cited public claims about 2026 from late 2024 — AGI timelines, job displacement, autonomous agents, capital returns — and graded each against what actually happened.
Both extremes of the remote-versus-office argument were wrong. We pulled four years of public engineering data — incident rates, code review depth, onboarding time — to answer what actually happened to software when the office emptied out.
Cursor, Claude Code, Replit Agent and the rest reframed the act of programming inside twelve months. We audited the shipping data — pull request size, review depth, incident rates — to ask whether the productivity gains are real, and what they cost.