Everyone Is Hiring FDEs. Who Are They Going to Hire?
Billions were committed to forward deployment in six weeks. The money was the easy part.
Two announcements, two days apart.
On 30 June, AWS announced a dedicated Forward Deployed Engineering organisation backed by $1 billion, staffed in the thousands, sending pods of five or six engineers into customer environments on 45-day cycles. On 2 July, Microsoft went bigger: $2.5 billion for a new operating business called Frontier Company, embedding 6,000 industry and engineering experts at customers, with $1.2 billion of that earmarked for headcount alone.
That capped a six-week stretch in which OpenAI stood up a deployment organisation with TPG, Advent, Bain Capital and Brookfield behind it at a $4 billion valuation, and Anthropic formed a $1.5 billion services venture with Blackstone, Hellman & Friedman and Goldman Sachs.
A year ago, when I moved into this role, forward deployed engineering was barely known outside Palantir. Explaining my job at a birthday party took a while. Today, four of the largest technology companies in the world have put roughly $5 billion of committed capital behind it in six weeks, with OpenAI’s deployment company valued at another $4 billion on top. Gartner projects that 85% of tech providers will have an FDE programme as a core AI delivery model by the end of this year.
The demand side of this market is now settled. The supply side is the question nobody has answered.
Run the numbers
Microsoft wants 6,000 people embedded at customers. AWS says thousands. Salesforce has reportedly committed to hiring 1,000 FDEs. Postings on Indeed went from 643 in April 2025 to 5,330 in April 2026. The consultancies have piled in too: by one tracker’s count, Deloitte, Accenture, KPMG and BCG together post more FDE roles than any single product company except Google.
Set that against the existing pool.
When I analysed close to 2,000 FDE practitioner profiles for the State of FDE report, one pattern was unmistakable. The population of people with real years in this role is small, and it is overwhelmingly Palantir-shaped. The company had a decade-long head start, and until recently it was more or less the only place you could accumulate this specific kind of experience.
Where are those people now? The evidence here is anecdotal, but it lines up with what leaders in the space keep telling me. The practitioners with genuine depth, meaning multiple deployments across multiple environments and the scar tissue to prove it, mostly fall into two groups: they are already inside the companies that moved early, or they have gone independent and run their own shops. Not every experienced FDE is spoken for. But if your plan requires hiring a thousand of them this year, the maths does not work.
Experience in this role accumulates one deployment at a time. Demand grows by press release.
Manufacturing the role
So companies will do what companies always do when a labour market runs dry. They will manufacture the workers.
That happens in three ways, and all three are already visible.
The first is relabelling. Solutions architects, sales engineers and implementation consultants are being rebadged as FDEs, sometimes with the job description barely touched. The consultancy posting numbers are the clearest evidence. A firm that posts dozens of FDE roles in a quarter did not suddenly discover forward deployment. It renamed a practice it already had.
The second is retraining. This is where the big money goes. Microsoft’s $1.2 billion headcount budget explicitly includes AI-readiness training for existing staff, and the unit will be staffed primarily by people already inside the company. AWS says it will fill roles through a mix of external hiring and internal moves, which lands differently when you remember Amazon has cut more than 30,000 corporate jobs since October. A meaningful share of the new FDE workforce will be existing employees walked across an internal bridge.
The third is new pipelines. Companies in the training business will spin up FDE cohorts and certifications the same way they did for cloud, for data science, and for prompt engineering. Give it a year and there will be a certificate for this.
None of this is scandalous. Every discipline that grows this fast industrialises its own production, and Palantir itself never waited for experienced FDEs to appear. It hired sharp graduates and built them, and Priya Khandelwal made the case convincingly when we spoke about why new grads make great FDEs. Trainability is not the problem.
The problem is definitional. When a 6,000-person Microsoft unit, a Deloitte bench and a rebadged solutions architect all carry the same title, the title stops carrying information. Vlad Shulman warned about exactly this in our conversation earlier this year: once the term detethers from the original intent, candidates self-select against the label, employers hire against the label, and everyone ends up sailing wherever the wind blows.
Worth naming, too, what the industrialised version actually looks like. A pod of five engineers on a 45-day clock is a different animal from an engineer embedded in one customer for a year. The first optimises for repeatable delivery. The second optimises for learning, the thing that made the role valuable to product companies in the first place. Microsoft seems to understand the distinction, because its own announcement pointedly distanced the effort from the FDE label. At some scale, the honest description of what the hyperscalers are building is consulting with better tooling. That is a fine business. It is just not the same job.
The self-serve objection
There is a longer-term question sitting underneath the hiring one, and it deserves a straight answer.
The case against the role’s durability goes like this. FDEs exist to close the gap between a general product and a specific environment. Every deployment teaches the product something. The product absorbs the lessons, becomes more self-serve, and the gap the FDE was hired to close disappears. The role designs itself out of a job.
Per product, that is exactly right. It is also the point. The FDE who makes a product self-serve has done the job well, and the reward for doing the job well is that this particular gap no longer needs them.
Per economy, the logic inverts. Gaps are not a fixed quantity that gets worked down to zero. Every new capability that ships creates a new frontier of environments it does not yet fit, and the practitioners who closed the last gap move to the next one. The role is self-terminating per product and permanent in aggregate. That has been the pattern for every implementation discipline before this one, and nothing about AI suggests it breaks here.
If anything, the coming years look like they will add a category of work rather than remove one. The same Gartner analysts projecting 85% adoption of FDE programmes also project that 70% of enterprises will abandon agentic AI projects born from FDE-led engagements within two years. Read one way, that is an indictment of the whole model. Read another, it is a forecast of the workload. Thousands of systems are about to be built at speed, by pods on 45-day clocks, handed over, and left to drift as models change and upstream systems move underneath them. Somebody will have to go back in. The remediation wave has not started yet, and it will need people who understand why the first deployment decayed.
Which version survives
The question I keep turning over is not whether the role survives. Between the capital committed, the posting curves and even the abandonment forecasts, its survival looks assured for the foreseeable future.
The question is which version of it survives, and whether the word still means anything once the money has been spent. Take the original Palantir model: one engineer, embedded deeply in a single customer, feeding everything they learn back into the product. That version is scarce and slow to develop. The second version is the industrial one now taking shape at the hyperscalers, pods of five or six on 45-day clocks, built for repeatable delivery at volume. It is about to outnumber the original many times over.
There is a third version too, and it is the one I know from the inside: a single practitioner spread across ten or more clients at once, spanning logistics, insurance, finance, manufacturing and wholesale. It trades the depth of the year-long embed for a breadth nobody inside a single vendor gets to see. The same failure modes repeat across industries that have never spoken to each other, and recognising them early is the entire value. This version appears in nobody’s hiring plan, because it cannot be hired in bulk. It gets built one practitioner at a time, and increasingly it operates independently.
All of it will get called FDE. Only the first is what the term was coined for.
A year ago, almost nobody knew what a forward deployed engineer was. A year from now, everybody will have an opinion, and I suspect very few of those opinions will describe the same job.