10 Years Building Vertical Software: My Perspective on the Selloff. Nicolas Bustamante, who has built vertical software on both sides of the LLM disruption (Doctrine for legal, Fintool for equity research), wrote a moat-by-moat analysis of vertical SaaS that is worth reading. In his view, five moats collapse (learned interfaces, custom workflows, public data access, talent scarcity, and bundling), while five hold (proprietary data, regulatory lock-in, network effects, transaction embedding, and system-of-record status).
A few things I think are missing. The biggest threat to vertical software incumbents probably isn't scrappy AI startups building 80% of the features at 20% of the cost (like his new Fintool company). It's that products like Claude Cowork can do 80% of what vertical software does out of the box, with general agents and data access, at marginal implementation cost. Once integrated, enterprises might trust Anthropic, OpenAI, and Google more than they trust a vibe-coded startup.
There's also a scenario Bustamante doesn't address: LLMs themselves will likely commoditize. If that happens, model providers will have to fight for companies and startups to use their tokens. That's precisely why Anthropic, OpenAI, and Google are strongly pushing into the product space themselves, because products might be more defensible than models. This raises an uncomfortable question for Bustamante's own company, Fintool, which he doesn't address. If what they built is, as he describes, essentially markdown skill files integrating with MCPs and foundation model APIs, what's their justification against the model providers doing the same thing?
A popular strategy for bootstrapping networks is what I like to call "come for the tool, stay for the network." The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.
— Chris Dixon, Cited by Ben Thompson in his piece on Sora and Meta's disruption potential.
The old saying in the machine learning community is that "machine learning is really good at partially solving just about any problem." For most problems, it's relatively easy to build a model that is accurate 80–90% of the time. After that, the returns on time, money, brainpower, data etc. rapidly diminish. As a rule of thumb, you'll spend a few months getting to 80% and something between a few years and an eternity getting the last 20%.