AI spending won't pay off until companies have engineers who know how to turn powerful models into working systems, and investors are losing patience waiting. I shared my perspective with CNBC, explaining why experienced FDEs should lead smaller teams and pass on what they've learned from real deployments.
Investors Want to See Results
AI stocks have had a rough week, with Dell, CoreWeave, AMD and Micron all posting short-term losses. I told CNBC, "The Street is starting to become impatient with these billions of companies who are spending on AI without getting any business outcomes."
Major tech and consulting companies are investing heavily in deployment talent. AWS has put $1 billion into forward deployed engineering. OpenAI launched a deployment business with more than $4 billion behind it and agreed to buy Tomoro, an applied AI firm with roughly 150 deployment specialists. Anthropic committed $100 million to train 10,000 FDEs by the end of 2027, and Accenture is building a 1,000-person FDE team with Google Cloud.
These companies already have the AI technology. Now they need enough engineers to make it work inside customers' existing systems and security requirements.
Demand Has Outrun Experienced Talent
Lightcast data cited by CNBC shows U.S. FDE postings climbing from fewer than 200 in 2023 to more than 7,300 in the first nine months of 2026. Deloitte and Accenture each posted more FDE roles than Google during the period. For companies adopting AI, deployment experience has become a major hiring constraint.
Christian & Timbers' Elite FDE Scarcity Study estimated that of roughly 17,000 FDEs in the U.S., fewer than 2,000 have a demonstrated record of deploying AI into production and delivering measurable business outcomes. Those are the engineers companies need at the head of their deployment teams, and there aren't enough of them to staff every project. Companies need a way to stretch the ones they have.
Experienced FDEs Should Lead Pods
My answer is to organize around experience. An FDE who has completed 10 to 15 successful deployments has seen enough projects go wrong to supervise a pod of five or six less experienced engineers. That senior engineer spots problems early and passes on lessons from difficult customer deployments.
This changes the hiring math. A company doesn't need an elite engineer in every seat. It needs a handful of proven leads, each running a team that gets better with every deployment.
It also changes who you recruit first. One engineer with a long record of shipping into customer environments is worth more to a growing team than several strong engineers who have only built inside their own company.
Can Training Close the Gap?
Anthropic and Accenture are betting that it can. Anthropic's residency program is modeled on medical training, pairing engineers with practitioners and assessing them on realistic enterprise cases. Accenture retrains experienced software engineers for deployment work in 30 to 90 days, depending on background. Lightcast found that FDE shares about half its skills with software engineering, which means the pool of people who could be trained is far larger than the pool already doing the job.
These programs could expand the available talent pool. The question is how quickly newly trained engineers can develop the judgment to work independently inside complex customer environments. Caylent CEO Val Henderson told CNBC that customer EQ is the hardest part of the role to teach.
That's why training works best paired with the pod model. A newly trained engineer learns faster working under someone who has already handled a dozen difficult deployments than working alone on their first one.
What This Means for CEOs and Boards
The first hire that matters is the leader who owns deployment. Depending on the company, that's a VP of Deployment, Head of Forward Deployed Engineering, VP of AI, Head of AI or a CTO who has shipped AI into production. Without that person, an FDE budget tends to become a series of pilots.
Pay expectations have moved too. Median advertised FDE pay reached $195,456 this year, up 40% from 2023. Companies hiring against outdated salary benchmarks may struggle to compete for experienced FDEs.
Boards should be honest about the evidence so far. CNBC cited company case studies, including an AWS example in which some engineering tasks at PennyMac were completed up to six times faster. Those productivity gains still need to translate into verified returns on AI spending. Directors should ask management who owns deployment and how quickly projects are reaching production.
As I told CNBC, "The only way you can get AI to work is by human-to-human interaction." The companies that get the most from AI will be those that build deployment teams around engineers who have already delivered working systems.
Christian & Timbers is an executive search firm specializing in AI and technology leadership, recruiting CTOs, CPTOs, VPs of Engineering, VPs of AI, Heads of AI and board members. Its searches also include executives responsible for scaling enterprise AI deployment.
