@thealexker: .@chamath on building a moat in 2026: If you are a reasonable company, why are you not finding an independent way to ac…

X AI KOLs Following News

Summary

Chamath argues that companies should build their own AI intelligence using models like GLM to avoid leaking competitive advantage, emphasizing cost efficiency and control.

.@chamath on building a moat in 2026: If you are a reasonable company, why are you not finding an independent way to access intelligence in a way that doesn't leak your edge away? To [not] do so at this point now is kind of becoming derelict and irresponsible… With post-training and telemetry collected from usage, you can take GLM, control it entirely, soup to nuts, on your own hardware, inside the United States, and have it be much, much cheaper. Start building your own intelligence today.
Original Article
View Cached Full Text

Cached at: 07/05/26, 12:57 AM

.@chamath on building a moat in 2026:

If you are a reasonable company, why are you not finding an independent way to access intelligence in a way that doesn’t leak your edge away?

To [not] do so at this point now is kind of becoming derelict and irresponsible…

With post-training and telemetry collected from usage, you can take GLM, control it entirely, soup to nuts, on your own hardware, inside the United States, and have it be much, much cheaper.

Start building your own intelligence today.

Similar Articles

Moats Need Models (6 minute read)

TLDR AI

The article argues that AI defensibility comes from owning the full feedback loop—custom models post-trained on proprietary data, tuned to specific workflows, and evaluated by user-defined standards—rather than renting frontier APIs from suppliers who can change terms. It emphasizes model customization as key to differentiation and margin control.