Data Needs a Day Job
Why every enterprise AI bet I'm watching get made right now is half a bet.
The closest people in my life will not go to the airport with me. A few friendships have not survived it. The ones that have, have survived in spite of it.
The rule I live by is this: if I don’t miss at least one flight a year, I am wasting too much time waiting at airports. I will defend the rule after I have made whoever is flying with me sweat through a shirt sprinting to the gate. I will defend it after I have, in fact, missed the flight.
When I miss it, and I do, I always have the boarding pass in my hand. Sometimes I am watching the plane push back from the gate. The ticket is necessary to fly. It is not, as it turns out, sufficient.
Almost every AI strategy I am looking at right now, from Fortune 500 boardrooms to founder pitches, is the ticket in their hand with the plane pushing back from the gate.
The big answer engines have eaten the public internet. GPT, Claude, Gemini, and the rest have all read the same Wikipedia, scraped the same Reddit, ingested the same blog posts and code repositories. The differentiation game has moved on. The new race is for proprietary data: the data the models couldn’t get to.
That’s why you’re seeing the rise of Scale, Mercor, and a wave of more specialized data plays. Radiology read-outs from working physicians. Gameplay traces from elite competitive players. Field maintenance logs from industrial fleets. The companies doing this well will mint money, because they’re feeding the necessary input that AI cannot synthesize for itself.
So far, so consensus. Proprietary data is the new oil.
But that’s only the necessary condition.
The sufficient condition for an agentic AI business is something else, and much harder. It’s the engineering know-how to build an autonomous agent that does useful work in production: giving it the right context to make good decisions, the orchestration to run for hours or days instead of seconds, the wherewithal to recover from its own mistakes and know when to ask for help, and the guardrails to keep it from going off the rails when reality doesn’t match the training data. That work is hard for anyone. It is hardest for the companies who assume their existing teams can just pick it up.
The best data engineers I know, even the ones who are virtuosic with data, are not by default the best agentic engineers I know. A career spent building a clean, performant, well-governed data pipeline does not automatically produce the instincts required to ship an autonomous software agent into production. Different sport.
And yet I keep hearing the same pitch. “We have the best data, we have the best engineers, the agents will follow.”
The Publicis acquisition of LiveRamp is the cleanest current example. Publicis has the right instinct, and Arthur Sadoun deserves credit for taking the shot. The strategic rationale, as Publicis itself tells it, isn’t about advertising. It’s all about agents. LiveRamp’s identity data is supposed to be the fuel for an agentic transformation of media planning, buying, and optimization. The data LiveRamp brings to the transaction is absolutely necessary.
But the open question is whether Publicis has the second thing: the agentic muscle, the software discipline, the cultural appetite to ship and break things at the pace this work demands. Legacy advertising holding companies are not natural lily pads from which to hop into agentic software development. The engineering teams at LiveRamp are excellent at what they’ve always done; that’s not the same as being excellent at what they now need to do. Just because Publicis has the data doesn’t mean it has the agents.
This deal, and a hundred others like it being announced this quarter, is half the answer to a two-part question. The other half is going to require partners, more acquisitions, hard hiring choices, and a willingness to rewire how the engineering org works. None of that is in the press release.
The Arab states are awash in oil. But if it can’t get through the Strait of Hormuz, it’s a stranded asset.
Data needs a day job.
Pure data businesses are rough and rarely valuable. On their own, they’re potential energy: oil under the surface, sitting in a tank. The software that converts the data into a customer outcome is the kinetic energy. That’s what gets paid for. And in 2026, “we have the data” is no longer the winning pitch. It’s table stakes. The winning pitch is “we have the data and we have the team that has actually shipped autonomous agentic software into production and made a customer’s life better and somebody’s CFO richer.”
I’ve been building data and AI companies for a long time. Every company I build has proprietary data capture designed in from the first whiteboard session. That part wins admiring nods from investors. I’ve always wanted extra credit for the necessary condition, and I’ve learned the hard way that the market only ever pays for the sufficient one: the software that harnesses the data and gets it successfully into market.
That is the picture I keep in my head when I hear a CEO tell me they have the data, they have the engineers, and the agents will just... follow. I see myself standing at the window with the boarding pass in my hand, watching the plane push back from the gate.
You’re either on the plane or you’re not. There’s no partial credit.