A 10x increase in compute may only yield a one-tier improvement in model performance. This appears inefficient but can be the difference between a useless "6-year-old" intelligence and a highly valuable "16-year-old" intelligence, unlocking entirely new economic applications.

The immense size of companies like Meta isn't due to constant innovation but from the unexpected, massive scalability of their single core concept (the feed). Founders often mistakenly chase a "second act" when the greatest value lies in maximizing the orders of magnitude still available in their primary business.

Traditional valuation models assume growth decays over time. However, when a company at scale, like Databricks, begins to reaccelerate, it defies these models. This rare phenomenon signals an expanding market or competitive advantage, justifying massive valuation premiums that seem disconnected from public comps.

The market struggles to price exponential growth, creating opportunities to buy dominant tech companies at low forward earnings multiples (e.g., Nvidia at 4x). An understanding of S-curve adoption reveals this underappreciated earnings power before the market catches on.

NVIDIA's revenue growth is speeding up even as its revenue base expands massively, a rare feat that defies the "law of large numbers." This suggests strong network effects and a dominant market position are creating a self-reinforcing cycle of demand for its AI hardware.

Big Tech's sustained outperformance presents a portfolio anomaly. These companies are simultaneously the largest market components and among the fastest-growing, a rare combination that breaks historical patterns where size implies maturity and slower growth, forcing managers to adapt.

Meta's business model is so efficient that its profit in a single quarter ($27 billion) is greater than the total annual revenue of a global giant like McDonald's. This stark comparison highlights the unparalleled scalability of digital platforms that monetize attention at near-zero marginal cost.

The massive investment in AI mirrors the HFT speed race. Both are driven by a fear of falling behind and operate on a logarithmic curve of diminishing returns, where each incremental gain requires exponentially more resources. The strategic question in both fields becomes how far to push.

The current wave of AI companies is growing at unprecedented rates, far outpacing the growth curves of the mobile, social, or SaaS eras. They are becoming larger and more consequential much faster, a phenomenon described as "speed running the process of company growth."