- Enterprise AI sells like middleware, not SaaS. You’re not dropping an API into Slack; you’re rewiring 20-year-old ERP systems. Procurement cycles are long and bespoke scoping kills product velocity. Then there’s the potential for things to go very wrong. “Small deals are just as much work as larger deals, but are just way less lucrative,” Falk says. Yep.
- Systems integrators capture the upside. By the time Accenture or Deloitte finishes the rollout, your startup’s software is a rounding error on the services bill.
- Maintenance is greater than innovation. Enterprises don’t want models that drift; they want uptime, and AI’s non-deterministic “feature” is very much a bug for the enterprise. “Enterprise processes have countless edge cases that are incredibly difficult to account for up front,” he says. Your best engineers end up writing compliance documentation instead of shipping features.
These aren’t new insights, per se, but they’re easy to forget in an era when every slide deck says “GPT-4o will change everything.” It will, but it currently can’t for most enterprises. Not in the “I vibe-coded a new app; let’s roll it into production” sort of way. That works on X, but not so much in serious enterprises.
Palantir’s “told-you-so” moment
Ted Mabrey, Palantir’s head of commercial, couldn’t resist dunking on Falk: “If you want to build the next Palantir, build on Palantir.” He’s not wrong. Palantir has productized the grunt work—data ontologies, security models, workflow plumbing—that startups discover the hard way.
Yet Mabrey’s smugness masks a bigger point: Enterprises don’t buy AI platforms; they buy outcomes. Palantir succeeds when it shows an oil company how to shave days off planning the site for a new well, or helps a defense ministry fuse sensor data into targeting decisions. The platform is invisible.