(This was originally published in the Artificial Intelligencer newsletter, which is issued every Wednesday. Sign up here to learn about the latest breakthroughs in AI and tech.)
Call it the Anthropic effect.
Over the past month, a rapid-fire series of product releases from the AI lab has triggered flash selloffs across the stock market, rattling enterprise software names in particular.
The tremors began earlier this month when Anthropic unveiled a legal plugin for its Claude Cowork agent. Capable of automating contract review, the tool wiped billions off companies like Thomson Reuters and RELX, the parent company of LexisNexis. The pressure continued last week with the launch of Claude Code Security, an AI vulnerability scanner. The tool sent cybersecurity players, including CrowdStrike, Cloudflare and Okta, down 8% to 10% in a single session. Then this week, IBM suffered its steepest daily drop in more than 25 years after Anthropic said its Claude Code tool could help modernize legacy programming languages that run on IBM systems.
Some of the anxiety is justified. The software economy has long depended on per-seat licensing and billable hours. If AI agents can autonomously execute tasks once handled by junior coders or paralegals, incumbents face real pressure to defend their core revenue models. But much of the turbulence also reflects broader market unease that investors have been telling me about for months — a structural debate about whether AI enhances enterprise software or compresses it.
The reality is more nuanced. Anthropic itself cautions that its legal tool does not provide legal advice, and its security scanner still requires human approval for patches. To make these AI agents truly work inside enterprise workflows, the labs themselves are hiring aggressively for a specialized role to bridge the gap.
In this week’s issue, we dive into that hiring spree behind AI’s enterprise push — and why the latest job and wage data tell a split story about AI’s impact. Scroll on.
OUR LATEST REPORTING IN TECH AND AI:
THE HOTTEST JOB IN AI RIGHT NOW
The enterprise AI boom has an open secret: buying access to a powerful model is easy — integrating it into a messy corporate system is not.
To bridge that gap, leading labs and AI startups are aggressively hiring a hybrid “special ops” role: the so-called Forward Deployed Engineer (FDE).
First popularized by Palantir to embed engineers with government and military clients, the FDE approach has become AI’s hottest go-to-market strategy. According to a recent LinkedIn report tracking global AI jobs from 2023 to 2025, demand for FDEs and similar roles has grown 42-fold. While only about 9,000 FDE roles were created globally, they address the industry’s biggest bottleneck: ensuring AI actually works in the real world.
These aren’t just sales engineers or IT support. FDEs are versatile engineers who embed with clients, navigate internal politics and write production-grade code to make models deliver results.
Two years into this enterprise push, both OpenAI and Anthropic have converged on the same conclusion: AI needs FDEs to fulfill its promises. OpenAI treats them as a parachute consulting force, with job listings emphasizing up to 50% travel to embed directly with strategic customers. Anthropic, meanwhile, focuses heavily on complex deployments in regulated industries like finance and healthcare, hiring applied AI engineers to build custom workflows. Hyperscalers like Google and Amazon are also pairing their cloud platforms with teams that work closely with enterprises to lock in AI workloads and expand their dominance.
To secure this scarce talent — engineers who can debug an agent one minute and brief a Fortune 500 executive the next — both labs are paying top-tier compensation. OpenAI lists base salaries up to $325,000, while Anthropic’s roles can reach $400,000, with stock-based pay pushing total compensation well above $500,000.
It’s “your traditional, really strong senior engineering talent that’s used to working with models, used to working in enterprises at scale, with really good context of business knowledge and technology,” OpenAI’s chief revenue officer Denise Dresser told me, adding that communication skills are critical because FDEs act as a bridge between product teams and corporate customers.
While adapting models to specific processes, FDEs also feed lessons back into their AI lab’s model and product development. In one case, she said, embedding AI into workflow returned account executives about 90% of their time — the kind of measurable change enterprise customers are looking for.
But even as pay soars and demand spikes, there’s an open question: how long will the FDE boom last? OpenAI’s Dresser believes the model is transitional. The goal, she said, is not to permanently do the work for customers but to guide them through early, complex stages of adoption until they can become self-sufficient.
For now, the AI arms race isn’t just about building better models. It’s about building the human bridge that makes those models usable. Whether that bridge becomes a permanent layer of the AI economy — or fades as tools mature — will tell us a lot about how durable this enterprise AI cycle really is.
CHART OF THE WEEK
The more an industry has embraced AI, the more its workforce has shrunk, according to U.S. employment data. That uncomfortable correlation jumps off this chart plotting adoption rates against unemployment changes since 2022.
The information industry (telecom, media, publishing) reports roughly 70% AI adoption and about a 75% rise in unemployed workers, while other AI-intensive fields such as professional services and finance also sit above the trendline. Research from the Dallas Fed this week also finds employment has declined 1% in the 10% of sectors most exposed to AI, even as total U.S. employment has grown approximately 2.5% since late 2022.
The wage data might offer some reason for optimism. Over the same period, compensation is still rising in occupations that rely heavily on tacit knowledge, judgment and experience. Among the top 10% of AI-exposed industries, wages grew 8.5%, surpassing the average wage increase of 7.5%. The pattern is a widening gap: More jobs are disappearing in AI-heavy industries, but workers with skills that AI cannot replace are still getting raises.
