The AI Wildfire Is Coming. It's Going to Be Painful and Healthy (ceodinner.substack.com)

Think of the current AI cycle not as a bubble waiting to pop but as a wildfire that will prune an overgrown ecosystem. The piece argues that abundant capital has created dense “brush” of lookalike startups—AI wrappers, commodity infra clones, and consumer apps with weak retention—that will be the most flammable. Historical parallels to the 2000 and 2008 crashes show the pattern: a brutal correction burns away hype but leaves durable infrastructure (servers, fiber, cheap bandwidth) and redistributes talent. Survivors fall into three groups: fire-retardant incumbents with deep moats (Apple, Microsoft, NVIDIA, Google, Amazon), resprouters—teams with IP/data/talent that can pivot and rebuild—and fire followers, the later-stage founders who scale cheaply on the ashes. For practitioners and builders, the technical implications are clear. The next phase will favor companies that deliver intelligence cost-effectively in production—the inference layer, not just bigger training runs. As compute commoditizes and agentic tools proliferate, efficiency, distribution, and inference latency/cost become the real battlegrounds. There’s also a “canopy problem”: capital and GPU demand are concentrated in a few bilateral relationships (hyperscalers and NVIDIA), so a normalization of AI demand could trigger a sharp pullback in GPU utilization and ripple across the stack. In short: expect painful churn, but also cheaper infrastructure, richer talent pools, and a more robust foundation for the next generation of AI-native companies.