LLMs are built to generate text, but a surprising amount of AI automation does not need text at all. It needs a small decision: Is this spam? Which team should get this ticket? How urgent is it? We can force an LLM to return structured output, but it is still generating that answer token byContinue reading “Not Every AI Task Needs an LLM”
Category Archives: Uncategorized
Stale Data Used to Be Annoying
Imagine a procurement agent checking inventory and deciding that stock has fallen below the reorder threshold. It places another order. The problem is that a large delivery was recorded a few minutes earlier, and the copy of the data the agent queried has not caught up. A dashboard running on stale data might show youContinue reading “Stale Data Used to Be Annoying”
why your data stack is about to get less forgiving
Subscribe • Previous Issues Stale Data Used to Be Annoying Imagine a procurement agent checking inventory and deciding that stock has fallen below the reorder threshold. It places another order. The problem is that a large delivery was recorded a few minutes earlier, and the copy of the data the agent queried has not caught up. AContinue reading “why your data stack is about to get less forgiving”
When Work History Becomes an AI Asset
Google recently agreed to pay $10 million for the internal business data of Spirit Airlines. The airline is bankrupt, but its emails, Teams messages, software, spreadsheets, and operating records apparently still have value. Google plans to use the material for product development and AI. Spirit’s flight attendants objected to the sale and sought additional protectionsContinue reading “When Work History Becomes an AI Asset”
so Google just paid $10 million for an airline’s Teams messages
Subscribe • Previous Issues When Work History Becomes an AI Asset Google recently agreed to pay $10 million for the internal business data of Spirit Airlines. The airline is bankrupt, but its emails, Teams messages, software, spreadsheets, and operating records apparently still have value. Google plans to use the material for product development and AI. Spirit’s flightContinue reading “so Google just paid $10 million for an airline’s Teams messages”
Water, Noise,Power: The Real Costs of Data Centers
I expect AI data centers to become a major issue in the 2028 presidential primaries, particularly on the Democratic side. I recently argued that the case for building more data centers and compute is getting lost amid the backlash, and I have also written about the financial bubble risks surrounding the buildout. But the politicalContinue reading “Water, Noise,Power: The Real Costs of Data Centers”
Self-Improvement Without the Science Fiction
A few weeks ago I wrote about AI systems that keep learning after deployment instead of treating every interaction as a fresh start. That prompted a few readers to ask about recursive self-improvement (RSI). The connection is real, but I think it helps to separate three ideas. Continual learning asks whether experience makes a systemContinue reading “Self-Improvement Without the Science Fiction”
Your AI model is a rental (but the loop is an asset)
Subscribe • Previous Issues Self-Improvement Without the Science Fiction A few weeks ago I wrote about AI systems that keep learning after deployment instead of treating every interaction as a fresh start. That prompted a few readers to ask about recursive self-improvement (RSI). The connection is real, but I think it helps to separate three ideas. ContinualContinue reading “Your AI model is a rental (but the loop is an asset)”
China’s AI Involution
AI has made me pay more attention to a Chinese term that has been circulating for years: involution (内卷). The basic idea is hyper-competition in which everyone has to work harder and improve constantly just to keep up, while the rewards keep shrinking. Companies cut prices, add features, improve service, and adopt new technology, onlyContinue reading “China’s AI Involution”
Your Model Is Not Your Moat
In a recent article, I wrote about companies turning their own data, workflows, and production feedback into specialized intelligence they increasingly control. The deeper idea was compounding. What matters is not simply whether a model performs well today, but whether using it creates assets that make the system better tomorrow. More AI teams are beginningContinue reading “Your Model Is Not Your Moat”