Open Source AI Business Models & Brand Moats
How business models differ in Open-Source AI versus traditional OSS + thoughts on how Brand Moats are increasingly important in AI broadly.
The full post with footnotes can be read on my personal website.
Typically in open-source software the business model for venture-backed companies has been to release a public good and build managed services on top of it. This has worked with low capex and a set of contributors working for long periods of time. These companies and individuals maintain the core repo and build a community, both of which builds structural resilience amongst a group of core developers and becomes some form of a standard.
With open-source AI (OSAI) labs you are running super high capex in order to build a public good (model) for some period of time, that you hope the community perhaps then goes to iterate upon. While certain labs create small communities, very few have broken out in language, instead coalescing around subreddits like r/LocalLLaMa and Hugging Face. However as models become more modular and hot-swappable at the inference layer, it’s likely that a given model (of which we can assume $100k-$1M of capex each at least) will become stale and be swapped out.
While this dynamic can and will likely push forward the overall rate of progress within AI, it’s unclear if it’s a dominant economic strategy. And depending on how horizontal your model is, these dollar figures can creep higher and result in 7-8 figures to “maintain” your place in the OSAI ecosystem if playing at the model layer.
This brings obvious complexity issues as to how a variety of labs predicated on open-source last long-term unless the goal is to do radical things like start open-source and then move closed-source, or build new forms of appstores where a series of models are commercialized and the OSAI lab becomes a take-rate driven model at the API layer. Both of these feel like a losing battle vs. OpenAI, Anthropic, Meta, Apple, Google, MSFT etc. especially as Meta allows free commercial usage of LLaMa-2 up until ~700 million monthly active users.
The obvious other answer is of course to build tools around your models and own the infra/ middleware layer and/or building custom models for enterprises or governments. This is compelling as we see more “novel” approaches to performance pop up that can increase inference quality and speed, including architectures like Mixture of Experts or deployment choices like Agents, each of which have basically no discernible and approachable infrastructure at scale today.
That said, the enterprise approach is one that a variety of proprietary, closed-source model providers will go after, especially as the Frontier Model companies start to see divergence in success. One can easily imagine a possible scenario where if OpenAI and/or Anthropic steamroll everyone on horizontal models, the next pitch for second-tier “Frontier” language model companies will be the custom-trained enterprise path in order to grow into multi-billion dollar valuations.
I’m net incredibly bullish on the open-source side of AI and my answer to many of these questions comes down to me perpetually screaming that this becomes a view on who can build the best process to advance proprietary model performance, the infrastructure to be malleable on the model side, and the best (and most opinionated version defining “best”) product to utilize both proprietary and finetuned open-source models over time.
In this world, I’m not sure the labs doing open-source model development win at scale, but maybe I’ll be wrong, so please challenge me on this.
BRAND MOATS
This leads me to my second view on OSAI models and broadly all AI labs and companies today, which is the increasing importance of brand moats…