The announcements at Google I/O 2026 landed today. I’ve gone through everything and pulled out what I think actually matters for people building products, running technical teams, or making bets on where AI is heading. The short version: Google used this I/O to stake a claim on the agentic layer, and the ambition is widerContinue reading “Google I/O 2026: The Agent Layer Takes Shape”
Author Archives: Ben Lorica
Stop upgrading your LLM. Start fixing your data.
Subscribe • Previous Issues Integration Is the New Moat: Moving Beyond the LLM The AI Agent Conference in New York was one of the better events I’ve attended to get a read on what’s actually happening with enterprise AI. The formal sessions were great, but the hallway conversations was where I got the inside scoop. The consistentContinue reading “Stop upgrading your LLM. Start fixing your data.”
The End of the AI Experiment: Surviving the CFO’s New ROI Demands
Why This Has Become an Executive Issue Why is AI spend no longer just an IT budget problem? AI has crossed a threshold where aggregate spend across every department requires capital allocation discipline, not just software procurement review. Every function now has a case for AI investment, and someone has to decide which requests deserveContinue reading “The End of the AI Experiment: Surviving the CFO’s New ROI Demands”
Why your AI bills are going up (even as tokens get cheaper) 📉💸
Subscribe • Previous Issues The End of the AI Experiment: Surviving the CFO’s New ROI Demands Why This Has Become an Executive Issue Why is AI spend no longer just an IT budget problem? AI has crossed a threshold where aggregate spend across every department requires capital allocation discipline, not just software procurement review. Every function nowContinue reading “Why your AI bills are going up (even as tokens get cheaper) 📉💸”
Why Your AI Agents Fail in Production (And How to Actually Test Them)
In a previous post, I argued that deploying autonomous AI agents reliably is not primarily a model problem. It is an environment problem. The gap between a capable foundation model and a production-ready system is bridged by harness engineering: the discipline of building structured workflows, validation loops, and governance mechanisms around the model rather thanContinue reading “Why Your AI Agents Fail in Production (And How to Actually Test Them)”
Your AI agent looks capable. But can it actually finish the job?
Subscribe • Previous Issues Why Your AI Agents Fail in Production (And How to Actually Test Them) In a previous post, I argued that deploying autonomous AI agents reliably is not primarily a model problem. It is an environment problem. The gap between a capable foundation model and a production-ready system is bridged by harness engineering: theContinue reading “Your AI agent looks capable. But can it actually finish the job?”
Quantum’s Weak Link: Why Supply Chains Will Determine Who Wins
Quantum technologies are entering an industrialization phase. A recent CNAS report argues that over the next three to five years, quantum sensors and computers will begin moving from laboratories into deployable systems with real economic and national-security value. But the United States’ ability to benefit will depend less on science alone and more on whetherContinue reading “Quantum’s Weak Link: Why Supply Chains Will Determine Who Wins”
What mathematicians figured out about AI that most enterprises haven’t
Recent results suggest that research mathematics is no longer a purely speculative test case for AI. A growing set of examples shows AI contributing not just to short contest puzzles, but to open-ended mathematical work that requires literature search, cross-domain connection-making, revision, and verification. The important lesson for enterprise AI teams is not that AIContinue reading “What mathematicians figured out about AI that most enterprises haven’t”
Generation is cheap. Evaluation is everything.
Subscribe • Previous Issues What mathematicians figured out about AI that most enterprises haven’t Recent results suggest that research mathematics is no longer a purely speculative test case for AI. A growing set of examples shows AI contributing not just to short contest puzzles, but to open-ended mathematical work that requires literature search, cross-domain connection-making, revision, andContinue reading “Generation is cheap. Evaluation is everything.”
China’s AI Strengths Are Real. So Are the Structural Drags Behind Them.
China is not out of the frontier AI race. Its open-weight models (models whose parameters are publicly released, allowing anyone to run or adapt them) remain genuinely competitive, and the overall capability lead has changed hands more than once since early 2025. But the more consequential story for anyone building on top of these modelsContinue reading “China’s AI Strengths Are Real. So Are the Structural Drags Behind Them.”