What's the trillion dollar opportunity for AI?

Some more thoughts on the state of AI in 2025 and why we need AI governance

TL;DR

This newsletter is about the opportunities and challenges with building and deploying applications built on top of foundation models. It looks Sequoia's thesis for where value will accrue in AI development, the relevant counter-arguments and the importance of governance for AI engineers.

Here are the key takeaways:

• Sequoia believes that the value of AI will accrue in the application layer. This includes all the companies building applications on top of foundation model provided by the frontier developers.

• This is done through the process of AI engineering. This includes a range of techniques for augmenting general capabilities of foundation models specific domains or use cases.

• Sequoia seems to be of the opinion that there is an opportunity to harness the power and capabilities of LLMs to build tools that solve problems for others. And if AI engineers manage to do this well, then there is latent demand to tap into and generate lots of revenue from.

• However, there are a few major difficulties with this as a business model:

  • The foundation model developers themselves are, and may continue to, creep into the application layer themselves, bringing with them greater access to compute infrastructure and VC funding.

  • The physical infrastructure for AI is scarce and therefore distributes control over the development of models and systems away from model developers and, crucially, AI engineers.

• Dealing with the physical infrastructure problem is that most difficult. The barriers to entry for those wanting to build their own foundation models from scratch are so high.

• But to compete with model developers building applications using their own models, AI engineers may need to find ways to make themselves meaningfully distinguishable from model providers. This might involve taking advantage of proprietary data to power products that serve a valuable niche.

• However, to ensure longevity, AI engineers will also need to focus on AI governance. This is important for three key reasons:

  • It helps deal with the complexity of AI engineering

  • They can use legal compliance as a USP

  • It becomes easier to build a community that trusts them

• If AI engineers can better deal with the complexity of LLMs, ensure legal compliance by default and be attentive to user needs, then they could deepen their moat.