Almost every viral money take is built on one of four simple mix-ups: a total confused with a yearly rate, the headline number confused with earnings, how tax slabs work, and a multiple confused with a real return. Here's how to spot all four.
A fintech engineering handbook is making the rounds, and the debate around it is more instructive than the document itself: three principles survive every context, and organizations still break them anyway.
The US government has moved from checking who uses frontier AI to deciding which institutions get to use it at all. Annex A is the permanent structure, and it changes the competitive picture for anyone not on the list.
OpenAI announced Jalapeño, a Broadcom-built inference chip, and claimed their own AI helped design it. Whether that is real or IPO marketing is the interesting question, and OpenAI is the one company that should know the answer.
AI agent loops have made code fast and cheap to generate. The hard part, knowing what to build and staying in ownership of it, is still entirely yours.
Mitchell Hashimoto pledged $700k of his own money to a programming language he partly disagrees with. It's a rare honest answer to who actually pays for the tools the software industry depends on.
Anthropic now requires a government ID before accessing its most capable models. What looks like a safety measure is export control, and it is nudging developers toward the Chinese AI alternatives the US is trying to contain.
Norway just banned AI in elementary schools. The research behind that decision is more uncomfortable than the headlines suggest.
The companies building giant AI compute for themselves have started renting it out: SpaceX leased all of Colossus to Anthropic, and Meta says a cloud business is on the table. Apple is about to have the most efficient, most private inference fleet on earth. Can it keep that to itself?
Google invented the transformer, paid $2.7B to buy back its co-author, and just lost him to OpenAI. The pattern says something about why large organizations keep giving away the people who built them.
SpaceX just paid $60 billion for a coding tool. The price is interesting. The justification -- a $26 trillion addressable market -- reveals more about how we price AI right now than about what it's actually worth.
Serious developers are dropping paid AI subscriptions for local models and reporting it actually works. The 'it's free' pitch is real - but it obscures a different set of costs.
Almost every team now codes with AI; only a third governs it. The teams pulling ahead review AI's output as seriously as they once reviewed their own code.
Anthropic's Fable 5 shipped with two guardrails: one that refused honestly, and one that quietly degraded the answer without telling you. The second is the dangerous kind, and Anthropic's own reversal shows why.
For a while we treated generative AI as a parlour trick with bad hands and a habit of making things up. Five kinds of story ended that phase for me, and raised a harder question than 'will it take my job?'
AI coding tools have stopped failing loudly. The new failure compiles, passes the tests, and is confidently wrong, and our review process was built for the old kind of mistake.
Working code is becoming cheap. What stays scarce is judgment about what to build and whether it was worth building. That is the engineer worth becoming.