Who should do
your AI work, now that the labs sell it too.

On May 4, Anthropic announced a new AI services company with Blackstone, Hellman & Friedman and Goldman Sachs. The announcement says exactly who it is for: companies “from community banks to mid-sized manufacturers and regional health systems” that “lack the in-house resources to build and run frontier deployments.” Applied AI engineers from Anthropic will work inside the new firm. Fortune, citing the Wall Street Journal, reported about $1.5 billion in committed capital.

A week later OpenAI launched the OpenAI Deployment Company, “majority-owned and controlled by OpenAI,” with more than $4 billion of initial investment from 19 investment firms, consultancies and system integrators. To staff it from day one, OpenAI agreed to buy Tomoro, an applied AI consulting firm that brings about 150 forward-deployed engineers and deployment specialists.

I work with exactly the companies that sentence describes, and the two labs whose models I use every day now want to sell them people as well as software. Anthropic’s CFO, Krishna Rao, explained why in one line.

Enterprise demand for Claude is significantly outpacing any single delivery model.Krishna Rao, CFO of Anthropic, May 2026

Put plainly, the labs have more customers who want to use the models than people who can install them. That is an admission about where AI value gets stuck, and it comes from the company that sells the model.

Everyone moved toward the work in the same five months

The labs were not alone. In January Accenture agreed to buy Faculty, a London firm of more than 400 AI specialists, including data scientists and AI engineers, and made its CEO, Marc Warner, Accenture’s chief technology officer. The deal closed in March. A month earlier Accenture and Anthropic had set up a joint business group in which about 30,000 Accenture people would be trained on Claude and a new role, the “reinvention deployed engineer,” would embed Claude inside client systems. In February OpenAI signed multi-year “Frontier Alliances” with BCG, McKinsey, Accenture and Capgemini, pairing its own forward-deployed engineers with the consultants, as TechCrunch reported.

Forward-deployed engineer is the job title of the year. Palantir made it famous, and nearly every firm in this note now uses some version of it. Patrick Heffernan, principal analyst at TBR, expects it to spread through IT services in 2026 “primarily as a marketing term but also backed by actual organizational changes.” Both halves of that sentence are true, and a buyer has to work out which half they are being offered.

6:1

Services spend to software spend, per Sequoia, March 2026.

$4B+

Initial investment in the OpenAI Deployment Company, May 2026.

400+

AI specialists Accenture added by buying Faculty, closed March 2026.

Investors say the money is in the work

The investors explained the logic before the labs acted on it. In March Julien Bek, who invests at the seed and early stage for Sequoia, published “Services: The New Software.” His example is an accounting department: a company might pay $10K a year for QuickBooks and $120K for the accountant who closes the books. “For every dollar spent on software, six are spent on services.” The founders he wants to back sell the closed books.

If you sell the tool, you’re in a race against the model. But if you sell the work, every improvement in the model makes your service faster, cheaper, and harder to compete with.

In April Y Combinator published a call for AI-native service companies. The post, by general partner Gustaf Alströmer, says it in one sentence: “We’re excited about companies that don’t sell a tool to help you do the work: they just do the work.”

The part of Bek’s essay I keep coming back to is a caveat he adds near the end. He splits work into intelligence, which follows complex but learnable rules, and judgment, which “requires experience and taste.” On his map, management consulting is a “huge market but the work is mostly judgement.” Medical coding and standard NDAs sit at the other end. For a mid-sized company that tells you something specific. The narrow, rule-heavy tasks you already outsource will be sold to you as a finished product soon, if they are not already. Deciding how your company should run around AI is the judgment work, and the investors betting most heavily on AI services expect that part to stay with people.

A mid-sized company has four ways to get the work done

So a company of a few hundred people now has four serious options for its AI work. Each one is the right answer for someone.

1. Buy a product that does the task. If the job is narrow and countable, like drafting NDAs, coding medical bills or adjusting standard insurance claims, a vertical product that sells the outcome is usually the cheapest and fastest route. Bek’s essay is a shopping list of them. The catch: the product does the task its own way, and your process bends to fit it. It also changes nothing around the task. Your people still hand work back and forth the way they did before.
2. Hire a large consultancy. If you run thirty sites in twelve countries, need a regulator to sign off and want a firm with a balance sheet behind the program, this is the honest choice. Accenture now has Faculty’s engineers in-house, and McKinsey and BCG work alongside OpenAI’s. The catch is the one buyers already know: price, and the distance between the partner who designs the change and the team that builds it.
3. Bring in a lab-backed deployment team. If you have chosen one model and the work is mostly engineering, nobody knows that model better than the people who made it. Anthropic’s own example is a group of physician practices, where engineers sit down with clinicians and build tools around documentation, coding and prior authorizations. The catch is that one lab staffs it and that lab’s models come with it. One post on X put the longer-term risk sharply: “organizations are effectively outsourcing key learning curves and domain knowledge to the AI labs.”
4. Work with a small senior team that redesigns and builds. This is what I run at VANDAM. The same few people map how the work flows, decide which steps go, rewrite the roles with HR and finance, and build the agents. We are model-agnostic with Claude by preference, and the client owns what we build. The catch: we have little capacity, no global footprint and no balance sheet for a forty-country rollout.

The options overlap more than the list suggests. A lab team can talk to your HR director, and a consultancy can write code. What separates them is who owns the redesign, and who is still around once the engineers leave.

When I would send you elsewhere

VANDAM is the wrong choice in at least three situations, and I would rather say so up front.

If your problem is a single outsourced task with a clear output, buy the product. Paying a team to redesign a process that a vendor already sells as a finished service wastes your money and my time. If you are a portfolio company of one of the investors behind the Anthropic venture, already standardized on Claude, with a clear engineering backlog, that firm was built for you, and it has Anthropic’s own engineers inside, which I do not. If you need the same change in many countries at once, with a program office and procurement rules that only a large firm can satisfy, hire the large firm and hold it to measurable results.

The same post that warned about learning curves also makes the strongest case against firms like mine: “If consulting services are truly a value add and competitive advantage, then in the long term you want to in-house.” I agree with it. The goal of an engagement should be a company that can carry on without us. That is why our work includes education, why the client keeps the code and the company context we build, and why the first deliverable is a plan your own people can argue with.

Where a small senior team fits best is the company in the middle: a few hundred people, a process that crosses finance, operations and HR, nobody inside with time to redesign it, and a leadership team that wants to understand what changes before anything is built. In that company, the expensive mistake is to automate today’s process as it stands.

The labs, the consultancies and the investors all agree that the work is where the value sits. Whoever you choose, ask them to name the person who will redesign it, the person who will build it, and what the people doing it today will be doing a year from now. If one name answers the first two questions, you are probably talking to a team like ours.