biz.mdIn a nutshell, the new AI infra startups will struggle to succeed because they lack significant differentiation and capital to crack the enterprise segment. It’s not the startups’ fault, the real problem is competitive dynamics. There’s simply too many entities offering the same table stakes features within 1-3 months apart from each other, which creates a collective tarpit dynamic, where only the incumbents can keep swimming.
The argument goes:
For AI infra startups to be “venture scale”, they will eventually need to win over enterprise customers. No question. That requires the startups to have some sustainable edge that separates their products from the incumbents’ (GCP, AWS, as well as the likes of Vercel, Databricks, Datadog, etc).
Unfortunately, most cutting edge innovation either comes from the incumbents or the research / OSS community - and incumbents are in a better position to commercialize the innovations because they have more usage data than startups, as well as the relationships.
To add salt to the injury, any good ideas that originate from startups get benchmarked and copied quickly. For example, I was quite surprised how quickly Databricks and Datadog caught up to the leading LLMOps products from the startup world (e.g. Arize AI).
Furthermore, OSS community can’t help but create OSS versions of other AI infra startups’ products - perhaps a testament to how easy it has become to write software.
Thus, startups struggle to maintain a sustainable lead over the incumbents to buy them time to win enterprise contracts.
And enterprise customers are incentivized to “hold off” on onboarding new vendors, because vendor products diminish in value so quickly because AI landscape changes every few months.
This ultimately lengthens sales cycles, and increases churn, which hurts startups more than the incumbents.
There are also some other dynamics at play (to be discussed in the next section) - but essentially the AI infrastructure space becomes a grind that favors players with the longest runways.