Great article, and pretty relevant to what I'm building (cloud developer tooling, including some genai, but also including non-AI tools + an application platform. Email me if interested.).
Obviously I'm not nearly as pessimistic about it. Zoom out for a sec and generalize to SaaS in general, not just AI infra (a subset of Saas) - all the arguments listed apply there too, except the data moat (which honestly doesn't matter to tons and tons of AI infra companies. That's more of an AI application problem). Now of course most startups are doing AI at least a bit, but in the past decade we've seen plenty of SaaS vendors compete with incumbents either head on or by carving out their own niche. In fact, two of the companies the author considers "incumbents" are arguably still challengers, but definitely were in this exact situation just a few years ago: Vercel and Databricks.
Also, competition from incumbents is hardly a deathknell. There's room for multiple products in some market segments - how many RDBMS companies are there? Competition from a huge incumbent in many ways comes with benefits, because it helps grow the overall market and awareness of the product space, including your own product.
I suppose according to this author I'm in the "application layer" even though really I'm in the AI-application-layer-now-but-not-later-layer, software-infrastructure-layer. And that's great because I actually do have experience in that specific application area. But honestly, saying "you ought to have expertise in your domain" is 1) duh 2) in the examples (llamaindex parsing/ocr, langchain llmops + agnetic stuff), there is clearly a big enough twist on doing it "but with AI" that the application/vertical is close to novel. Successful challengers create valuable businesses without prior deep expertise in their domain all the time and I don't really see how this is any different.
Basically, you could repeat this for any SaaS business. Starting a company is hard, but I don't know if AI infra is uniquely hard in the ways laid out.
Obviously I'm not nearly as pessimistic about it. Zoom out for a sec and generalize to SaaS in general, not just AI infra (a subset of Saas) - all the arguments listed apply there too, except the data moat (which honestly doesn't matter to tons and tons of AI infra companies. That's more of an AI application problem). Now of course most startups are doing AI at least a bit, but in the past decade we've seen plenty of SaaS vendors compete with incumbents either head on or by carving out their own niche. In fact, two of the companies the author considers "incumbents" are arguably still challengers, but definitely were in this exact situation just a few years ago: Vercel and Databricks.
Also, competition from incumbents is hardly a deathknell. There's room for multiple products in some market segments - how many RDBMS companies are there? Competition from a huge incumbent in many ways comes with benefits, because it helps grow the overall market and awareness of the product space, including your own product.
I suppose according to this author I'm in the "application layer" even though really I'm in the AI-application-layer-now-but-not-later-layer, software-infrastructure-layer. And that's great because I actually do have experience in that specific application area. But honestly, saying "you ought to have expertise in your domain" is 1) duh 2) in the examples (llamaindex parsing/ocr, langchain llmops + agnetic stuff), there is clearly a big enough twist on doing it "but with AI" that the application/vertical is close to novel. Successful challengers create valuable businesses without prior deep expertise in their domain all the time and I don't really see how this is any different.
Basically, you could repeat this for any SaaS business. Starting a company is hard, but I don't know if AI infra is uniquely hard in the ways laid out.