Switching FrictionWide moat

Nvidia (NVDA) — moat facet

A rival can be twenty percent cheaper and still lose — the migration cost makes the cheaper chip dearer once everything is counted.

Switching friction is the sum of all the other CUDA threads made concrete at the moment of decision: when a customer weighs a rival chip, they must reckon not with the chip's price but with the total cost of leaving Nvidia's world. Rewriting and re-validating software, retraining engineers, rebuilding tooling, and accepting the risk that something breaks in production together form a wall of cost that a merely cheaper or faster chip must overcome before it wins the sale.

Customer advances received in the first half ($B)$7.5BH1 FY2026$15.6BH1 FY2027NVIDIA Form 10-Q, Q2 FY2027, deferred revenue note
Customers paid $15.6 billion in advance in six months, twice a year earlier: buyers weighing alternatives do not prepay.

The friction is decisive because it reframes the entire comparison. A rival can offer a chip that is twenty percent cheaper and lose anyway, because the customer, totting up the migration cost and the risk, concludes the cheaper chip is not actually cheaper once everything is counted. Nvidia does not have to be the best value on the spec sheet; it only has to be close enough that switching is not worth the upheaval — a far easier bar to clear.

This is why Nvidia's pricing power is so robust in the near term. As long as the friction of leaving exceeds the savings of switching, Nvidia can hold premium prices without losing the customer, and the friction is largest for exactly the biggest, most complex, most deeply invested customers — the ones with the most software, the most trained people, and the most to lose from a botched migration.

For the owner, switching friction is where the abstract moat becomes a dollar figure on a customer's spreadsheet, and it is the mechanism that converts CUDA's accumulated advantages into pricing power. It holds firmly today; the watchful question is whether the various efforts to lower each component of that friction — Nvidia's 2024 licence terms already bar CUDA translation layers1 — can, in time, add up to a wall low enough to climb.

Moat trajectory: Widening

Widening. The cost of moving off Nvidia grows with the size of the codebase built on it — and AI codebases are growing explosively. Porting models, retuning performance, revalidating results, and retraining staff onto another platform is expensive, risky, and slow, and it gets more so every time a team ships more CUDA-based work. Would-be switchers face a bigger bill each year they stay. As the installed base of CUDA software swells, the friction that keeps customers in place only rises. Widening.

The number that tests this moat
Reported
Sanctioned CUDA translation layers
0 (EULA-barred, 2024)

Nvidia raised this wall deliberately: since 2024 its license bars running CUDA code on rival hardware through translation layers, keeping the porting bill — code, revalidation, retraining — fully priced. Watch for the terms being voided by courts or regulators, or porting toolchains getting good enough that the friction stops mattering.

Source: NVIDIA CUDA EULA ↗
⚠ Threats to the moat
References
  1. ReportedNvidia's 2024 EULA bars CUDA translation layers.
    NVIDIA CUDA EULA — licensing terms discouraging translation-layer use of CUDA outputs (2024 terms) — 2024 licensing change · publ. 2024 · source ↗
Sources
Generated September 18, 2026