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”
Category Archives: Uncategorized
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”
Your CLI Was Built for Humans, Not Agents
There’s a friendly debate among developers about how to give AI agents reliable ways to use external tools, data, and services so they can do useful work beyond generating text. One side favors CLIs, or Command-Line Interfaces, where agents run text commands against tools like git, aws, jq, duckdb and internal scripts. The other sideContinue reading “Your CLI Was Built for Humans, Not Agents”
What happens when your agent can touch money
Subscribe • Previous Issues Your CLI Was Built for Humans, Not Agents There’s a friendly debate among developers about how to give AI agents reliable ways to use external tools, data, and services so they can do useful work beyond generating text. One side favors CLIs, or Command-Line Interfaces, where agents run text commands against tools likeContinue reading “What happens when your agent can touch money”
Does AI Actually Make Developers More Productive? The Evidence, For and Against
This guide is built from studies and reports published in recent months. Sources include a large matched study tracking more than 100,000 real GitHub developers, a meta-analysis pooling 23 separate productivity studies, telemetry-based reports from engineering-analytics vendors covering thousands of teams and tens of thousands of developers, large multi-country surveys of developers and technology buyers,Continue reading “Does AI Actually Make Developers More Productive? The Evidence, For and Against”
Agents Need Maps, Not Bigger Context Windows
Like everyone else, I’ve been enjoying the steady improvement in coding agents and the tooling around them, from frameworks and harnesses to evaluation suites. But the more I talk with teams actually deploying agents in enterprises, the more I circle back to plumbing. Agents need data integrations and infrastructure built for them, a theme IContinue reading “Agents Need Maps, Not Bigger Context Windows”