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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?”

Subscribe • Previous Issues How to Stay Employable When AI Is Coming for Your Job Over the past few weeks, I have had a lot of conversations with people who are genuinely worried about what AI means for their careers. Not just developers, but marketers, analysts, lawyers, and others who are starting to wonder how much ofContinue reading “Why the heaviest AI users actually produce worse results 🤯”

Subscribe • Previous Issues The AI Bubble Is Real. Enterprise Usage Is Even More Telling. The existence of an AI bubble is beyond dispute. What remains unclear is when or how it deflates. As investors know all too well, the most costly mistake in business is often being correct prematurely. The infrastructure layer has already booked revenues.Continue reading “The margin paradox threatening every AI company”

Subscribe • Previous Issues AI Reliability Patterns That Generalize Beyond Medicine The gap between pilot projects and production deployments has emerged as a defining challenge for enterprise AI teams. Recent surveys indicate that only a small percentage of generative AI initiatives reach full production, with most stalling due to brittle workflows and integration failures. At last year’sContinue reading “Your AI passed benchmarks. Why is it failing in production?”

Subscribe • Previous Issues Boom, Bubble, or Bust? How to Build a Resilient AI Business Comparisons to the dot-com bust are common but this AI boom rests on short-cycle hardware. Frontier training chases each GPU generation, rendering last year’s chips economically obsolete for training even as they stay serviceable for inference — forcing relentless reinvestment. This dynamicContinue reading “How to build an AI business that survives the bubble”

Subscribe • Previous Issues Escaping Prototype Purgatory: A Playbook for AI Teams We’re living through a peculiar moment in AI development. On one hand, the demos are spectacular: agents that reason and plan with apparent ease, models that compose original songs from a text prompt, and research tools that produce detailed reports in minutes. Yet many AIContinue reading “Think Smaller: The Counterintuitive Path to AI Adoption”

Subscribe • Previous Issues How Leaders Are Using RL to Build a Competitive AI Advantage I have long been fascinated by reinforcement learning (RL), but have always viewed it as complex and beyond the reach of most enterprise AI teams. That perception began to shift slightly earlier this year after a conversation with Travis Addair, co-founder ofContinue reading “The data flywheel effect in AI model improvement”

Subscribe • Previous Issues Time Bought, Advantage Lost? The Limits of Semiconductor Sanctions The technological and economic competition between the United States and China has increasingly centered on AI capabilities, with semiconductor access becoming the critical battleground. Since 2022, these export controls have evolved from targeted restrictions to a complex regulatory regime with far-reaching implications. I haveContinue reading “Why AI Efficiency Outruns Hardware Shortages”

Subscribe • Previous Issues Taming the Wild West of AI Agents: Addressing the Challenges of Real-World Deployment AI agents are autonomous systems that combine language (and multimodal) understanding with the decision-making prowess of foundation models to interpret complex inputs, reason through multifaceted scenarios, and execute tasks autonomously. The business landscape is abuzz with excitement, as industry analystsContinue reading “From Prototype Purgatory to Production-Grade AI Agents”