Last week I argued that open models will absorb most of the money and compute the world spends on AI. A week later, open weights are even more central to the conversation. Recent releases have made the gap between capable and affordable harder to ignore, and a broad coalition of technology companies is now publiclyContinue reading “Specialized AI Is Getting Easier to Build”
Author Archives: Ben Lorica
The Big AI Labs Are Suddenly Competing with Your Own Data
Subscribe • Previous Issues Specialized AI Is Getting Easier to Build Last week I argued that open models will absorb most of the money and compute the world spends on AI. A week later, open weights are even more central to the conversation. Recent releases have made the gap between capable and affordable harder to ignore, andContinue reading “The Big AI Labs Are Suddenly Competing with Your Own Data”
Multiple Dials, Not One: Reading an Open Model Release
The most useful thing I’ve learned watching this latest round of open model releases is that “open” has stopped predicting anything. It used to be a reliable shorthand for cheap and second-tier. Now the top-ranked open coding model in the world costs roughly thirteen times more per million tokens than the cheapest credible open modelContinue reading “Multiple Dials, Not One: Reading an Open Model Release”
AI Can Win While Data Centers Lose
I keep coming back to the same uncomfortable math on AI data centers: the money going in is piling up faster than any economic proof that it should. Usage of AI systems has reportedly been climbing seven to twenty times a year, while the revenue that usage produces has grown maybe three or four times,Continue reading “AI Can Win While Data Centers Lose”
Three New Models, One Signal About Where AI Spending Goes Next
Three frontier level models landed within weeks of each other this fall, GLM 5.2 from Zhipu, Kimi K3 from Moonshot, and Gemini 3.6 Flash from Google, and I wanted to capture early developer reaction so I can monitor how feelings about these models change over time. The individual verdicts differ, but together they hint atContinue reading “Three New Models, One Signal About Where AI Spending Goes Next”
Open Models Will Absorb Most of the AI Spend
Here is my bet: open models (open weights and open source alike) will end up absorbing most of the money and compute the world spends on AI. The proprietary frontier models get the headlines and the IPO valuations, but developers and AI teams see something different up close. Open models are improving fast, the gapContinue reading “Open Models Will Absorb Most of the AI Spend”
we need to talk about where AI spend is actually going
Subscribe • Previous Issues Open Models Will Absorb Most of the AI Spend Here is my bet: open models (open weights and open source alike) will end up absorbing most of the money and compute the world spends on AI. The proprietary frontier models get the headlines and the IPO valuations, but developers and AI teams seeContinue reading “we need to talk about where AI spend is actually going”
What We Actually Know About China’s AI Export Controls
Since June 2026, Chinese officials have been in closed door talks about restricting foreign access to the country’s most advanced AI models, both open and closed. The Ministry of Commerce is leading the discussions, China’s state planning agency is also in the mix, and the companies involved include Alibaba, ByteDance, and Zhipu. Nothing has beenContinue reading “What We Actually Know About China’s AI Export Controls”
What Startups Taught Me About the Next Layer of AI Infrastructure
A little while back I wrote about how teams use reinforcement learning (RL) to make agents reliable. Since then I keep bumping into startups where RL is not a research footnote or a feature buried in the stack. It is central to what they are building. I know of more than 25 at last countContinue reading “What Startups Taught Me About the Next Layer of AI Infrastructure”
25+ startups all solving the same missing piece
Subscribe • Previous Issues What Startups Taught Me About the Next Layer of AI Infrastructure A little while back I wrote about how teams use reinforcement learning (RL) to make agents reliable. Since then I keep bumping into startups where RL is not a research footnote or a feature buried in the stack. It is central toContinue reading “25+ startups all solving the same missing piece”