Palantir CEO Alex Karp — "Something Has Gone Completely Wrong In AI"
I watched Karp's interview and I couldn't stop nodding. Not because I'm a Palantir fan — I'm not particularly — but because he named something I'd been trying to articulate for a while and hadn't found the right words for.
His argument is blunt: enterprises are feeding their most sensitive data into AI systems they don't control, getting limited operational value back, and in the process handing their competitive intelligence to companies whose business model is to learn from that data. He called it a wealth tax. I think that's exactly right.
In IT operations specifically, the stakes are higher than most people realize. When you connect your monitoring stack to an external AI provider, you're not just sharing business data — you're sharing a detailed map of how your infrastructure fails. The failure signatures, the remediation patterns, the timing, the blast radius. That's extraordinarily sensitive information. It's the kind of thing a sophisticated attacker spends months trying to reconstruct.
What bothers me about most AIOps tools is that nobody even frames this as a tradeoff. You sign up, you connect your monitoring, your incidents start flowing out. The value proposition is capability — the AI can detect things, suggest things, correlate things. The question of where your data goes and what happens to it doesn't come up until procurement, and by then teams are already committed.
Karp's point is that this model is broken at a structural level. The value should accrue to the enterprise, not to the model provider. And the way to fix it is the architecture he describes: you control the model, you control the application layer, your data stays inside your perimeter.
That's the architecture I built Axiometica AIR on. Not because I predicted Karp would say this — I didn't — but because after years of implementing monitoring and event management for large IT teams, it was the only approach that made sense to me. You can't ask a security-conscious organization to route their infrastructure telemetry through a third-party API and expect them to trust the result.
Bring your own model. Data stays in your environment. The AI reasons about your incidents without your incidents leaving your network. That's not a differentiating feature — it should be the baseline. I think Karp is right that the industry is going to have to reckon with this.