There is a job in artificial intelligence right now that pays a mid-level person around 385,000 dollars a year, and a senior one past a million. Postings for it jumped roughly 800 percent in nine months. Anthropic, OpenAI, and Google are all hiring for it as fast as they can. It is called the Forward Deployed Engineer, and it is one of the most sought-after roles in the industry.
Here is the part almost nobody mentions. When Palantir invented the role, they modeled it on a restaurant.
Not as a loose metaphor. As the founding image of the whole role. The story inside Palantir is that their CEO watched the way French waiters work, the way the floor is fused to the kitchen, the way a great waiter will tell you no when you order the wrong wine, and built the engineering practice on it. The engineer sits in the client’s office, learns the business the way a server learns a table, and earns the standing to say no to a bad request. They called the first ones Deltas, and until around 2016 they had more of them than they had ordinary software engineers.
I have built my career in the room that job was copied from. So let me tell you what the AI industry just rediscovered, and why it decides who actually gets their money’s worth out of all this.
Intelligence got cheap. Judgment did not.
Start with what changed. For most of computing history, the intelligence was the scarce thing. You paid for the smart system.
That era is over. Every company on earth now has access to roughly the same frontier models. The intelligence is a commodity, priced by the token, available to your competitor at the same rate as to you. When everyone has the same brain for rent, owning the brain stops being an advantage.
So the value moved. It moved to deployment, which is a polite word for a much harder thing: knowing where the intelligence should and should not be pointed inside a real business, and shipping something that actually works there. The numbers are brutal about how rare that skill is. One widely cited MIT study found that 95 percent of enterprise generative AI pilots produced no measurable financial impact. And they almost never fail because the model was not smart enough. They fail because someone automated the wrong thing. A problem that did not matter. Or one that needed a human and never should have been touched.
Picking the right problem is the whole game. And picking the right problem is not an engineering skill.
What the dining room actually teaches
Here is why Palantir looked at a restaurant, and here is what a restaurant taught me before I ever wrote a line of code.
A great server is not taking orders. A great server is running a live diagnostic on a table they met four minutes ago. Is this a celebration or a negotiation. Who is paying, and do they need to look generous, or careful. Is the quiet one bored or just tired. Should I move faster, slow down, disappear, or become the whole evening. None of that is on the menu. All of it decides whether the night works.
And the best ones do the hardest thing of all: they tell you no. No, not that bottle with that dish. No, you do not want the tasting menu tonight, you are too hungry and too rushed, let me feed you properly instead. Saying no to a paying guest, in a way that makes them trust you more, is the highest skill on the floor. It is also, it turns out, the highest skill in deploying AI. The consultant who cannot tell a client “you should not automate this” is the one who sells them the 95 percent failure.
That is what the dining room teaches that a computer science degree does not. How to sit with someone, read what they actually need under what they are asking for, and have the standing to redirect them. Palantir could not find that skill in engineers, so they built a role around people who had it and taught them to ship. The AI labs are doing the same thing right now, at enormous cost, because the skill is rare and they know it is the bottleneck.
I did not have to learn that half. I have been doing it every night on the floor.
The sommelier is the deployment
There is a sharper version of this, and the people building the AI stack keep reaching for it without quite knowing why. When they try to explain what a Forward Deployed Engineer actually does, they land, again and again, on the sommelier.
Think about why. A sommelier does not have one recommendation. A sommelier has a cellar, and a table, and the entire job is the match between them. You read the guest: what they know, what they can spend, what they are eating, whether tonight is a celebration or a Tuesday, whether they want to be led or left alone. Then you find the one bottle, out of hundreds, that fits this person, this plate, this night. The bottle that is perfect for table nine is wrong for table ten. There is no generic pairing. There is only the read, and the match, performed fresh every single time, by someone who does it on the floor, night after night, with real people and real stakes.
That is deployment. That is the whole thing.
Because there is no generic AI deployment either. The workflow worth automating at one restaurant is the one you must never touch at the next. The tool that saves a hotel group two hundred hours is useless to the room down the street with a different system and a different bottleneck. The frontier model is the cellar: vast, powerful, available to everyone at the same price. The value is not the cellar. The value is the person who can read this specific business and make the one match that fits it. A pairing, not a product. Done case by case, by someone who has spent their life learning to read a room.
The AI industry keeps using my job to describe its most valuable one. It is not an analogy. It is the same skill, pointed at a different table.
The two halves, and why almost nobody has both
The Forward Deployed Engineer works because it fuses two things that rarely live in one person.
One half is the build: you can ship a production system, against real data, on the systems a business already runs, with the guardrails and the human approval that make people trust it. I wrote a whole essay arguing that managing AI agents is just managing staff, because I had been doing both. That half I earned at a terminal, after service, building the tools I wished the industry had.
The other half is the read: you can walk into a working business, feel where the money is quietly leaking, know which of its problems is worth solving and which one to leave alone, and say so with enough authority that they believe you. That half I earned on the floor.
Most people chasing this role have the first half and are learning the second the hard way, one failed pilot at a time. A smaller number have the second and cannot ship. The people who have both are the ones getting paid a million dollars, and there are not many of them, and almost none of them come from hospitality, which is strange, because hospitality is where the model came from.
What this means for anyone trying to buy AI
If you run a restaurant, a hotel, a wine business, a group, and you are being sold AI right now, here is the test. The person selling it to you: have they ever worked your floor? Because if they have not, they cannot tell which of your problems is worth solving. They will sell you all of them, because selling you all of them is their business model, and most of it will fail, and it will not be the model’s fault.
The right person does the opposite. They sit in your business first. They find the one workflow that is quietly costing you hours or covers or comps, they build the thing that fixes it on the systems you already have, and then they prove it saved you money in your own numbers. And they tell you, plainly, which of your problems to leave alone. That last part is how you know they are worth hiring. Anyone who never says no is selling.
Where I stand
I am a sommelier and a hospitality operator who learned to build. I built my career in the room the most valuable job in AI was copied from, and I spent my nights learning to ship the tools. I am not an AI consultant. That phrase means nothing now; everyone is one. I am the person you commission to find the one thing worth automating, deploy it, and prove it earned.
The industry spent a fortune rediscovering that the dining room had the answer all along. Some of us never left it.
If your room, your list, or your group is trying to figure out where AI actually pays, that is the conversation I am built for. I will even tell you where it does not.
Woody