My friend Stephen Messer wrote a really important post last week on his ReloadNYC blog. It was such an interesting insight I immediately texted him to ask if I could re-post it here in full. What follows is an idea that no one has talked about so far, and is worth reading in full. Stephen’s original post is here. Go subscribe.
Every serious conversation about AI infrastructure right now runs into the same wall. Is the buildout real, or is it a bubble? OpenAI‘s revenue against its costs. Anthropic‘s revenue against its compute bill. Oracle‘s spending against its other lines of business. Microsoft, Google, and Meta each running the same calculation with the same numbers.
The debate is fine. It is also not the interesting one.
Where you land depends mostly on how fast you believe AI adoption will happen. I have made the case in The Companies Winning at AI Are Playing a Different Game, in Peak Token, and in The Safest Move You Can Make With AI that adoption is running faster than consensus assumes and that the cautious position is the expensive one. Reasonable people can disagree on the ramp. I am not going to relitigate that argument here. — Read More
Daily Archives: July 26, 2026
Meet Ornith: The Agentic Coding Model That Runs Entirely on Your Laptop
The Semantic Layer is the Ultimate Battlefield in the Era of Agentic AI
The holy grail of enterprise data engineering has always been self-service analytics, the promise that any business stakeholder could ask a question and instantly receive a trusted, accurate answer. To achieve this, the industry spent the last decade building lightning-fast cloud data warehouses, democratizing SQL training, and deploying sleek Business Intelligence (BI) visualization platforms. Yet, the core problem remained unsolved. The moment a user moved beyond a rigidly pre-packaged dashboard, the data stack began to splinter. Different departments presented conflicting numbers for identical metrics like revenue or customer churn.
Enter the Generative AI revolution. — Read More
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