Moats

Plain English. A moat is whatever stops competitors from competing away your margins. In AI the claimed varieties are data (proprietary training material), speed (shipping faster than rivals can copy), supply chain (locked-up compute, memory or power), and distribution — against the standing null hypothesis that at the model layer there are no moats at all.

Why it moves money. Where the moat lives decides which layer keeps pricing power. Tunguz's distinction: application companies can win with lagging moats built after product-market fit, while infrastructure needs leading moats — and the model layer in between is where defensibility is weakest, since every API is a free teacher for imitators (see distillation). The data moat has fresh empirical support — one decomposition attributes 12x of six years' pretraining gains to data against 3.7x for architecture — while even CUDA, the canonical software moat, now attracts serious arguments that it thins as AI writes the kernels.

What to watch. The price premium frontier models sustain over near-frontier — the live measure of model-layer defensibility — and whether any claimed moat survives being tested by a competitor's price.

From the signals. What if there is no moat. Mark's gut call: CUDA's moat could thin faster than the chip cycle. Data drove 12x of pretraining gains, model tweaks 3.7x.