AI business
Business POV on AI
AI business
When Generative AI first emerged, SaaS firms’ playbook was to use foundation models from hyperscalers or OpenAI/Anthropic, then train and fine-tune them for specific use cases. The market now believes that, rather than augmenting software, Agentic AI may directly perform tasks for users ( bypassing the need for traditional software ). Since SaaS... See more
Notes on SaaS
The story unfolding here means that rather than incumbents, the native AI company's disruptors are the market leaders.
who has better domain knowledge, the vendor or the customer?
🔮 Exponential View #559: coherent agents; goodbye SaaS; orchestrators needed; space phages, immortality & Kimi's …
A a strong case for the death of SAS, which means a reduction in enterprise spend on SAS. Traditional SAS drops because the tone, specificity, and configurations can be defined by the user directly while the user builds software engine types.
The Rule of 40 is now the Rule of 50+. Investors demand profitability paths, not growth-at-all-costs narratives. Unit economics must work from early stages—companies cannot “grow into” profitability by raising more capital.
The SaaS Defensibility Framework
AI infrastructure sits uncomfortably close to that boundary. Chips, data centers, and models are expensive, capital-intensive assets with useful lives that are hard to forecast. Their economics depend on demand that may accelerate, stall, or shift as the technology evolves.
🫧 Investing in a Bubble
So even without the distillation, historically in AI, we've seen this perverse economy of scale where the leaders take a lot more investment to make reasonable gains relative to challengers.”
Brian Chau • Why You’re Wrong About AI Data
Does another layer form the moat then?