A few weeks ago, OpenAI launched something called the Deployment Company. Most of the headlines I saw read it the same way: "OpenAI starts a consulting firm." A handful of people argued about whether it would hurt McKinsey.
I read it differently, because I work inside the exact problem it's trying to solve.
For the last few years, part of my job has been driving AI adoption inside a large energy company. And the thing almost nobody outside of enterprise understands is this: the models are no longer the bottleneck. The tools available today can already do far more than most large organizations are actually using them for. The gap isn't capability. The gap is everything between a capable model and a real workflow that real people use on a Tuesday afternoon under real constraints.
I think about it as the deployment gap. And once you've seen it from the inside, you can't unsee it.
What the gap actually looks like
It's not glamorous. It's a brilliant model sitting next to a twenty-year-old ERP system that nobody wants to touch. It's a workflow that technically could be automated, except the three people who understand why it works the way it does are busy, and the person excited about AI doesn't have the domain context to know what would break.
The missing link is almost never the technology. It's judgment. It's someone who understands the business deeply enough to know what's safe to change, and understands the tools well enough to know what's now possible — sitting in the same chair, at the same time. That combination is rare. When I've managed to be that person on a project, things move fast. When that person doesn't exist, the AI initiative becomes another slide deck.
That's the whole story of enterprise AI right now. Capability is abundant. Translation is scarce.
Why $14 billion just validated that
So when OpenAI put real money behind the Deployment Company, what struck me wasn't the consulting angle. It was the model of it: they're putting engineers directly inside the customer. Not advising from the outside — building on the inside. They even acquired a firm to get a hundred and fifty of these "forward-deployed engineers" on day one.
That's not a consulting firm. That's an admission that the value is no longer in the model alone — it's in the last mile. The hardest, least glamorous, most human part of the chain.
A few things clicked for me reading the partner list.
Why would McKinsey and Bain invest in something that could cannibalize them? Because the part that gets eaten is the generic strategy deck. The part that survives — the C-suite trust, the relationships, the muscle to actually change how an organization operates — that's their real moat, and now it gets a frontier-model engine bolted onto it. You either own a piece of the wave or you get hit by it. They chose to own it.
Why is private equity all over this? This one took me a second, because PE already hires consultants. But PE brings something the labs can't buy: they own the companies. They can mandate adoption across fifty portfolio companies at once, with no sales cycle and a direct line from "margin improvement" to "bigger exit." It's the perfect testing ground for deploying the same playbook over and over. The old model was a consultant writing a hundred-day plan. The new model fuses the advice and the execution into one embedded team.
Strip away the names and the dollar figures, and the signal is simple: the market is reorganizing itself around the last mile. Around the exact thing I've been doing in my corner of the energy world, quietly, for a couple of years.
What I think this means
I'll be honest that part of why I'm writing this is selfish. I'm heading into an MBA, thinking hard about where to point the next decade, and this reframed the question for me.
For a while I was asking the standard MBA question: consulting, or tech, or finance? After sitting with this, I think that's the wrong frame. The advice layer is the part getting automated. The deployment layer is the part exploding. So the better question is: which seat puts me closest to actually deploying AI — with real domain depth behind me?
Because here's the thing the gap taught me: domain expertise is the moat nobody can fake. A model can generate a strategy. It cannot, yet, be the person who knows that this valve, this contract, this team's workflow is the one you don't touch. The people who win the next decade won't be the ones who can talk fluently about AI. They'll be the ones who can walk into a messy, real-world business and make the technology actually work — and who understand that business well enough to be trusted with it.
That's not a flashy skill. It doesn't demo well. But it's the entire game right now, and a $14 billion company was just built to industrialize it.
I don't have all of this figured out. I'm working it out in real time, from inside the problem. But if you're trying to build a career in AI and you're staring at the same fork I am, I'd offer one filter: don't chase the layer that's getting automated. Chase the last mile — and bring something to it that the model can't.
For me, that something is energy. For you it might be healthcare, or law, or manufacturing, or whatever world you actually understand from the inside. The frontier needs translators. Be one.
I write these as I think through them — issued for review, not for construction. If you're working in this gap too, I'd genuinely like to compare notes.
