One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.
The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below.
The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.
However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality.
Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.
What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.
[Original text: The Batch newsletter]
@MyFitnessPal The quick add is quite limited. It needs more macronutrients for those of us tracking for a broad range of health, especially sugar and sodium and cholesterol. The lack of sugar is particularly limiting because it leads to underestimates of total calorie count.
An excellent compendium on the angst over AI. And some reality checks. And even constructive advice on what to do next.
"Is AI Going To Destroy Our Lives Or Not? " https://t.co/9HExa5gP84
John Harbaugh using getting fired by the Ravens as past of his commencement speech Saturday at Miami University.
Really great message overall on
the "amazing powers of caring and encouragement." Gives you some insight into how he handles himself as a coach.
John Harbaugh using getting fired by the Ravens as past of his commencement speech Saturday at Miami University.
Really great message overall on
the "amazing powers of caring and encouragement." Gives you some insight into how he handles himself as a coach.
I've built Legacy Builder with @base44!
This app-building experience has been full of great surprises. The platform has built-in sensibilities on good user experience, making it easy to construct an easy-to-use prototype in minutes. Iterating on functionality is rapid and fluid.
Commented on $ESPR - Esperion Therapeutics: Still An Attractive Risk/Reward Narrative As The Earnings Clock Ticks Toward Profitability. https://t.co/uVlgbrjjJ7
Researchers sent the same resume to an AI hiring tool twice. Same qualifications. Same experience. Same skills. One version was written by a real human. The other was rewritten by ChatGPT.
The AI picked the ChatGPT version 97.6% of the time.
A team from the University of Maryland, the National University of Singapore, and Ohio State just published the receipt. They took 2,245 real human-written resumes pulled from a professional resume site from before ChatGPT existed, so the human writing was actually human. Then they had seven of the most-used AI models in the world rewrite each one. GPT-4o. GPT-4o-mini. GPT-4-turbo. LLaMA 3.3-70B. Qwen 2.5-72B. DeepSeek-V3. Mistral-7B.
Then they asked each AI to pick the better resume. Every model picked itself.
GPT-4o hit 97.6%. LLaMA-3.3-70B hit 96.3%. Qwen-2.5-72B hit 95.9%. DeepSeek-V3 hit 95.5%. The real human almost never won.
Then the researchers tried the obvious objection. Maybe the AI is just better at writing. So they had real humans grade the resumes for actual quality and ran the experiment again, controlling for it. The result was worse. Each AI kept picking itself even when human judges rated the human-written version as clearer, more coherent, and more effective.
It gets worse. The AIs do not just prefer AI over humans. They prefer themselves over other AIs. DeepSeek-V3 picked its own resumes 69% more often than LLaMA's. GPT-4o picked its own 45% more often than LLaMA's. Each model can recognize and reward its own dialect.
Then the researchers ran the simulation that ends careers. Same job. 24 occupations. Same qualifications. The only variable was whether the candidate used the same AI as the screening tool. Candidates using that AI were 23% to 60% more likely to be shortlisted. Worst gap was in sales, accounting, and finance.
99% of large companies now run AI on incoming resumes. Most of them use GPT-4o. The paper just proved GPT-4o picks GPT-4o 97.6% of the time.
If you wrote your own cover letter this week, you did not lose to a better candidate. You lost to a worse candidate who paid OpenAI 20 dollars.
Your qualifications do not matter if the AI prefers its own handwriting over yours.
Sorry to anyone who thought AI would mean we’d work less (at least for now). AI makes it easy to explore more than you did before, and so you start doing far more as a result.
I regularly have seemingly small things that end up quickly consuming 3 hours because the agent made it easy to get started, but you still have to do the rest of the work to complete the project.
This is work that I wouldn’t previously have handed out to anyone else, it’s just stuff that never got done because it took too long to do fully manually. And, counterintuitively, for some of these tasks as AI gets good enough at doing them, it even becomes economically worth it to hire someone to do it on an ongoing basis with agents. But until you could try doing them at a low cost you would never have tried.
This is why AI won’t automatically reduce work in the way we imagine because work isn’t static. Most companies have far more they can do than they have today, it was just hard to get started on it all because of the natural constraints of time and labor availability.
And it begins
Sullivan & Cromwell just admitted to a federal judge its court filings contained AI hallucinations
The firm apologized to the federal judge as they had to submit multiple corrections focused around:
• Fictitious Case Names: The filing included names of legal cases that do not exist
• Fabricated Quotes: The document contained direct quotes that were never actually spoken or written
• Non-existent Statutes: The AI incorrectly analyzed or entirely invented provisions within the U.S. Bankruptcy Code
The primary team and secondary review all failed to catch these errors, meanwhile the firm's partners bill $2,000+ per hour