Why the AI Bubble Matters Less to Builders Than People Think (www.kerno.io)

The piece argues that talk of an AI “bubble”—media warnings about overinvestment and a potential crash—matters less to builders than to investors because product-market fit and unit economics ultimately determine survival. For engineers and founders the central question is value vs. cost: will users pay enough for the value you deliver, and how expensive will inference be? Many current costs are being heavily subsidised (notably foundation-model inference subsidised by companies like Microsoft), which masks true marginal costs today and creates a fragile pricing baseline if subsidies ebb. Technically, the author highlights that straightforward scaling is reaching diminishing returns (logistic growth of capability), pushing teams toward expensive multi-pass “reasoning” approaches rather than raw scale. Practical levers include model distillation, lower-precision math, router architectures that pick smaller specialist models, and task-specific small models to cut inference cost. But LLM-based software has linear marginal cost per user (unlike most traditional software), and physical limits—especially electricity and datacenter capacity—are tightening (data centers’ share of US electricity could grow sharply). The takeaway: builders should optimize for token efficiency, realistic future pricing of inference (possibly tied to electricity), and architectures that reduce per-token cost rather than assuming cheap, ever-scaling compute.