Making AI work is getting expensive

Like so many technologies that came before it, it turns out that AI is more complex to implement than we first thought (or at least were led to believe). Survey after survey finds all but a few notable exceptions are stuck in proofs of concept, unable to scale to the broader organization.

As a result, companies are being told that they need help in the form of the forward deployed engineer role. Broadly speaking, FDEs are engineers who work directly with customers, often embedded inside their organizations, to help solve difficult implementation problems. This is an individual who requires not just technical chops, but also softer skills to help get customers to the next level when it comes to AI. That combination is hard to find, yet everyone from the AI labs to the cloud hyperscalers to the systems integrators needs thousands of these people. Good luck.

Customer success teams are not new by any means. Companies selling sophisticated software solutions have learned that effective implementation requires people who work directly with paying customers to guide them. FDEs are an extension of that function, but with these engineers much more directly involved.

Palantir has used FDEs for years, but its version of the role goes beyond deployment support. The company describes FDEs as owning the customer problem and doing whatever is needed to solve it. The question is whether other companies can adopt that model more broadly, particularly when the necessary skill set is both specialized and difficult to find.

Not only do you need someone with deep engineering ability, today's FDEs also need to understand how to transform complex workflows into agents, combined with a deep understanding of agentic AI itself, all while having the people skills and patience required to work closely with customers for long periods of time.

Then consider you might also need a very specific domain understanding like insurance or healthcare and it raises the degree of difficulty even further. It's also expensive. A recent OpenAI posting for an FDE cited a salary of $162,000-$280,000 plus equity. 

The talent problem

The big companies claim they need thousands of these people. Where exactly are they going to find them? OpenAI is asking for five-plus years of customer-facing engineering experience and traveling up to half the time, a profile that could limit the candidate pool even further.

If you put people in place without the necessary skill set, companies are going to balk at paying for an inferior service, especially when they are already paying these companies big money for the AI tooling they are having trouble implementing in the first place.

This comes against a backdrop of companies already admitting they are over-spending on AI. A July report from McKinsey found that 93% of respondents had already exceeded their AI budgets, a stunning admission (especially when you consider the survey was conducted in May), with 46% over by between 10 and 30%. And believe it or not, it's about to get worse with a majority of respondents reporting they expect AI spending to increase by at least 25% in the next 12 months.

It would seem that CFOs have to put on the brakes at some point. You can't just keep throwing money at the problem. If you are lucky enough to get the most elite people to help your organization succeed, then it may be worth the additional cost.

The question is whether there are enough people with the necessary technical and business skills, and whether companies can afford to deploy them at sufficient scale to justify the additional spending. If not, FDEs could be adding cost to a process that is increasingly out of control.

~Ron