We're all talking about what AI means for software. The real question is what it means for everyone - and everything - else. x.com/af3/status/20177116574…

Sorry, it’s too late. I’ve already pictured you as the feckless, price-taking Haiku market participant, and myself as the shrewd, disciplined Opus market participant.

I’ve seen a drastic rise in ex-McKinsey founders for this exact reason. In SF it’s easy to get trapped in the AI groupthink. The rest of America is different. Consultants were basically FDEs for data/business decades before palantir made it cool

how did Allbirds pivot to AI compute hardware before the shoe company literally called ASICS

What if the Pimiento hats and Map and Flag exclusives are a shibboleth to separate proud corporate partners from true ball knowers

Normal sport cc @KylePorterNS

maybe this is not yet clear, so let me state it plainly: as of right now Anthropic, and really a small number of individuals at Anthropic, has the capacity to directly attack and cause major damage to the United States Government, China, and generally global superpowers. government agencies like the NSA do not have internal models or defense capabilities that outclass frontier models. if they chose to do so, they could likely exfiltrate top secret information from government systems, gain control over critical infrastructure including military infrastructure, sabotage or modify communications between members of government at the highest level, and potentially carry on activities for some time without detection. the thing about having access to a huge number of zerodays your adversaries don't know about is it gives you a massive asymmetric advantage. they did not exploit this to gain power or destabilize the world order. they publicly released the information that they had these capabilities and worked to mitigate these flaws. you should be grateful american frontier labs have proven themselves remarkably trustworthy and concerned with the public good. but it's critical you understand we are in a new regime. private entities now have power that directly rivals and impacts the government's monopoly on influence and violence. and anthropic is certainly not the only one, there's little chance OpenAI's internal models are far behind. this trend will accelerate on virtually every dimension, not slow down. my prediction for how it plays out is the relatively imminent seizure and nationalization of labs by the US government, sometime over the next two years. it's very tough for me to see how they accept the existence of this kind of threat. but this adds a whole new class of governance issues, as then we've handed these extremely wide-reaching capabilities from private entities to public ones.

If I’ve learned anything from working directly with Claude Code in the last 90 days, it’s that the job is the same: “isn’t X a better solution?”

I’m really enjoying the neo-steampunk era that is useful and not purely aesthetic.

My 3yo wanted to use the computer like me so I made him his own terminal. He types whatever he wants, it responds with fun messages. No external deps, no ads, just keyboard practice and cause-and-effect thinking. He thinks he's hacking. github.com/meimakes/tiny-ter…

The movie that Michael Bay is going to make about the Frontier AI Go-to-Market Wars is gonna be sick

It's giving Albert R. Broccoli

Maybe this is intuitive to those of us who build software, so let me crib a line from @johnrichards here: Tools used to build software can be probabilistic, but the software itself — at least for now — needs to be deterministic. In John’s world at @officiallyiru, AI-native SDLC doesn’t just speed up velocity. It can be used in the product to improve threat detection, surface risks faster, and improve policy generation. But the application of all those still has to be applied deterministically. Enterprise software buyers are seeking burden transfer. They are buying dependable outcomes. Could OpenAI and Anthropic build their own ERP? Sure, but I think they know that 1/ there’s far more value to be created building for greenfield and enhancing their own core product (models / inference), and 2/ if they succeed at #1, ASI will let them migrate off of their ERP pretty easily, having inflated away the data portability costs.

The best early stage hires are radically different from a few years ago: multi-hyphenate, commercial generalists. The engineers want to talk to customers and the business people write code. High agency and AI native instead of heads down 10x performers.

This is one aspect of what the Citrini piece does get right. If your profits are mostly predicated on consumers not having enough time to shop around… NGMI

I route my mundane and/or slightly embarrassing queries to ChatGPT or Gemini now so that I keep up appearances with Mr. C

Airlines were fundamental, essential, and transformational to modern life. And yet, owning stock in them over the last half-century hasn’t been a great move. Perishable inventory, commodity pricing, capex that never stops, input costs you don’t control, and customers who’ll switch the moment someone’s a penny cheaper. Consumers won. Shareholders lost. Lesson in there for the inference companies.