Within my own network, I was early in flagging the growing local opposition to AI data centers. Since then, it has moved fast, from scattered zoning fights to outright moratoriums and statewide political battles, with data centers becoming a poster child for many of the broader anxieties around AI. A lot of that opposition isContinue reading “The AI Data Center Backlash Has a Blind Spot”
Author Archives: Ben Lorica
The biggest AI risks sit outside the model
The most revealing AI failures right now are not stories about models becoming too capable. They are stories about everything around the model. One system received more access than its test environment could contain. Another was trained on material whose acquisition created $1.5 billion in exposure. In a third, people walked away more certain withoutContinue reading “The biggest AI risks sit outside the model”
The model may not be your biggest risk
Subscribe • Previous Issues The biggest AI risks sit outside the model The most revealing AI failures right now are not stories about models becoming too capable. They are stories about everything around the model. One system received more access than its test environment could contain. Another was trained on material whose acquisition created $1.5 billion inContinue reading “The model may not be your biggest risk”
How AI Is Changing Mathematical Research
With all the recent headlines about AI systems solving research math problems, I decided it was time to update a piece I wrote on this topic a while back. Outside of coding and programming, research mathematics may be the area where AI tools and agents are advancing most quickly. That makes it worth watching evenContinue reading “How AI Is Changing Mathematical Research”
Continual Learning Is Arriving in Pieces
A while back I wrote about how startups are using reinforcement learning to make agents more reliable. A deeper problem behind that whole trend keeps resurfacing: a model can improve during training, but the moment it’s deployed, learning largely stops. A policy changes, a new edge case shows up, a user corrects the system, andContinue reading “Continual Learning Is Arriving in Pieces”
Why do our AI models stop learning the second we deploy them?
Subscribe • Previous Issues Continual Learning Is Arriving in Pieces A while back I wrote about how startups are using reinforcement learning to make agents more reliable. A deeper problem behind that whole trend keeps resurfacing: a model can improve during training, but the moment it’s deployed, learning largely stops. A policy changes, a new edge caseContinue reading “Why do our AI models stop learning the second we deploy them?”
Why Data Centers Became the Face of the AI Backlash
AI is no longer being judged only as software. Once the buildout arrives as a massive industrial facility, new transmission lines, continuous power demand, and possible pressure on utility rates, the argument changes. Communities are being asked to absorb costs they can see immediately for benefits that remain distant, uncertain, and spread across people andContinue reading “Why Data Centers Became the Face of the AI Backlash”
What Comes After Language Models
I keep seeing scientific discovery framed as a sufficiently ambitious prediction or data-compression problem. A recent position paper by Tom Zahavy challenges that view by separating reasoning into three capabilities. Induction finds general patterns in examples. Deduction works out what follows from a set of assumptions. Abduction proposes a new explanation when neither the existingContinue reading “What Comes After Language Models”
Passing Your Evals Doesn’t Mean You’re Safe
Evals are part of every serious conversation about putting AI into production. Teams define benchmarks, set thresholds, and increasingly run red teams to see how the system holds up against someone actively trying to break it. That combination is reasonably good at telling you whether a model is accurate, reliable, fast enough for production, andContinue reading “Passing Your Evals Doesn’t Mean You’re Safe”
What Workday, OpenAI, and a German court have in common
Subscribe • Previous Issues Passing Your Evals Doesn’t Mean You’re Safe Evals are part of every serious conversation about putting AI into production. Teams define benchmarks, set thresholds, and increasingly run red teams to see how the system holds up against someone actively trying to break it. That combination is reasonably good at telling you whether aContinue reading “What Workday, OpenAI, and a German court have in common”