Dario Amodei has published a lengthy post arguing that frontier AI development must be paced. There are already plenty of people disagreeing with the proposal he makes, and many are speculating about ulterior motives. Personally, I simply find the argument deeply unconvincing.

Bias alert: I’ve been working in the clinical IT space for over 15 years, including more than a decade in environments where advanced AI systems are developed to assist with clinical diagnosis or, in some cases, make diagnoses directly. I’m entirely sympathetic to arguments around risk management, safety engineering, validation, and objective performance measures.

Read More

Earlier this year, the Leiden Declaration was published, addressing how mathematicians ought to integrate AI tools (primarily LLMs, if we’re being honest). It has since been joined by a letter from Fields Medalists warning of a Severe Misalignment of AI in Mathematics.

Taken together, I think these are some of the most consequential things written about the future knowledge work in the face of AI.

What interests me is that the debate is often framed as being about whether AI is becoming more capable of doing mathematics. I think that misses a deeper question: to what extent does AI progress itself depend on mathematicians? And, further, what role do the current mathematical LLM users contribute to progress?

My thinking here is straightforward. Frontier users are not merely consumers of frontier AI systems: they are increasingly part of the process by which those systems improve. If that relationship weakens, some of the apparent pace of AI progress could weaken with it.

Read More

Apple’s Watch Series 12 launch yesterday has one headline feature that’s generating a lot more discussion than the usual battery/screen/chip incrementalism: Siri can now listen continuously, in the background, and produce recaps and transcriptions of conversations that happened nearby. You don’t have to say “Hey Siri” first. You don’t have to open an app. The watch just… hears things, and later on you can ask it what was said.

Read More

Most teams already let production shape what they do next, but we still think in these relatively linear cycles from idea to production. An incident changes a feature requirement. A cost spike forces a redesign. A support pattern triggers a new feature flag or rate limit. These issues get ticketed, included into the backlog, and the development team attempts to address this growing list in priority order.

The software industry is still grappling with novel AI technology. There are a range of opinions on how to best incorporate these systems - from giving the AI narrow and specific roles within the existing org structures, through to tearing the whole thing up and running a software factory with swarms of “agents” doing relatively unreviewed work. But even in the most radical approaches, this linear “we design something, make it, deploy it and then run it” thinking still dominates.

Read More

Everyone wants to go faster; this has always been true, and it is especially true in the context of LLM deployment. Teams using AI in development are not just using it to write code more quickly, but, being frank, AI has not yet demonstrated that it is good at many of the other tasks. Teams therefore have to decide where human attention buys the most safety and quality for the least overhead and the most speed.

Read More

I left Twitter not long after it rebranded to X. At the time, I didn’t write about why — I simply walked away. But recent developments have prompted me to finally put my thoughts down.

Read More