How Meta open sourcing its Llama models helped make Mark Zuckerberg popular again with Silicon Valley developers; Llama 2 has 180M+ downloads since July 2023
After some trying years during which Mr. Zuckerberg could do little right, many developers and technologists have embraced … X: @mikeisaac and @tomseymour X: Rat King / @mikeisaac : amid the jockeying for artificial intelligence supremacy in Silicon Valley over the past 18 months, developers have found a new (if somewhat unlikely) hero: Zuck. he is the highest profile tech leader to espouse open source AI, something that has earned him enormous goodwill [image] Tom Seymour / @tomseymour : Interesting on the distinction between open source and closed source AI https://www.nytimes.com/...
Context & Ripple Effects
Meta’s Llama push followed an earlier effort to make the next model commercially usable, shifting the family beyond its initial research-only positioning. The 180M-plus download count shows that broader access was translating into developer reach, not just a product announcement.
The story also fits Meta’s stated strategic bet that wider model availability could push down rivals’ AI prices and broaden adoption of Meta’s approach. Later coverage of Llama nearing 350M downloads and gaining enterprise users suggests that developer goodwill could compound into distribution.
First-order effects
- Meta gains stronger credibility with developers, while Zuckerberg personally benefits from being associated with a high-profile open-model strategy.
- Developers can build around a widely distributed Llama ecosystem rather than relying exclusively on closed-model providers’ hosted access.
Second-order effects
- The download base gives Meta more leverage in attracting tools, integrations, and commercial users around Llama; its later move to permit Llama outputs to help improve other models further lowers friction for that ecosystem.
- Closed-model vendors face added pressure to defend pricing and developer value when an alternative model family can be obtained and adapted more broadly.
Third-order effects
- If adoption continues, AI competition may be shaped as much by ecosystem distribution and developer allegiance as by proprietary model access alone.
- The divide between open and closed models becomes a strategic fault line: broader access can build a constituency for a platform, while raising enduring questions about how model access should be governed.
The trend: Foundation-model providers are using model access and developer ecosystems as competitive distribution channels, not merely as licensing choices.