The speed of this erosion is the relevant fact. Historically, skill premiums in software development persist for years. Mobile development commanded a 15–25 percent premium for nearly a decade. DevOps expertise held 20 percent margins well into the 2020s. The shortage dynamics that drive premium pay—scarcity of experienced practitioners, high switching costs, specialized knowledge that doesn’t transfer—typically decay on a five-to-ten-year timeline as more developers retrain, educational programs expand, and tools abstract away complexity.
AI engineering compressed that timeline to six months. And it compressed differently: not by gradual dilution of scarcity, but by simultaneous devaluation of both the skill and the people who retrained to acquire it.
For a solo developer or bootcamp graduate who spent 2023–2024 pivoting into prompt engineering, LLMOps, or entry-level ML roles specifically because the compensation was visibly higher, the situation is now stark. The roles that justified the pivot—the ones where companies were paying 40–50 percent premiums to fill seats—have either vanished, transformed into something that doesn’t require those specific skills, or collapsed into commodity rates where the premium no longer exists. An entry-level AI engineer earned roughly 10.7 percent more than a baseline software engineer in 2024. By the third quarter of 2025, that premium had eroded to 6.2 percent, a 42 percent reduction in the premium itself in a single year. That is the speed of value collapse.
The income floor for people who retrained is the second, more consequential fact. It’s not zero—trained developers still earn money. But it’s lower than the baseline they gave up, and it’s shrinking faster than they can adjust to it.
Why This Is Happening
The collapse has three overlapping causes, none of them mysterious once you look at the underlying dynamics.
First: the scarcity that created the premium was real, but it was scarcity of execution capacity for a specific tool, not scarcity of judgment about how to use that tool. When GPT-4 launched in March 2023, the bottleneck was deployment expertise—how to get models into production, manage inference costs, handle context windows, work around training data cutoffs. That skill was genuinely rare. Companies were willing to pay 40–50 percent premiums because they had capital to spend and no other way to ship.
But once the tool became stable—once the deployment patterns solidified, the inference optimization techniques diffused, the framework maturity increased—the scarcity evaporated almost overnight. By late 2024, a competent software engineer with three months of LLM experience could do the work that had commanded premiums. The tool had become legible. The premium existed because of temporary opacity, not because of permanent structural value.
Second: the supply side was flooded deliberately and at unprecedented scale. Every bootcamp that had a 12-week DevOps program launched a 12-week AI program. Every online course platform pivoted to LLM content. Coursera, Udacity, Lambda School, and dozens of smaller operations began cranking out people trained in the specific skills that were commanding premiums. LinkedIn profile data shows that “Prompt Engineer” titles tripled between 2024 and mid-2025, then contracted sharply as the roles themselves started consolidating back into standard software engineering positions. The supply response to the premium was not measured or gradual—it was a gold rush. And like all gold rushes, it ended when the easy gold ran out and the remaining ore required capital and coordination that solo prospectors couldn’t provide.
Third: AI tool commoditization itself reduced the need for AI specialists. Claude, GPT-4, Gemini moved from esoteric technical infrastructure to API endpoints that any developer could integrate. The surface area for specialized knowledge shrank. A product engineer at a B2B SaaS company no longer needs to hire an AI specialist to add an LLM feature—they integrate an API, tune a prompt, ship it. The work that was scarce in 2023 became a three-day sprint in 2025. And when the work becomes a sprint, the person who spent a year retraining specifically to do it no longer carries a premium.
The result is a bifurcated market. At the top—LLMOps engineers, machine learning infrastructure specialists, people working on training and fine-tuning at scale—premiums still exist. Median LLMOps compensation remains around $165,000; top-tier roles still command $200,000 or more. These positions require both the specialist knowledge and the coordination overhead of building infrastructure at meaningful scale. They are genuinely scarce because they require both the AI skills and the systems thinking, observability discipline, and production hardening experience that comes from years of infrastructure work. But these are maybe 10 percent of the people who retrained into “AI engineering” over the past 18 months.
The other 90 percent—the prompt engineers, the entry-level ML engineers, the bootcamp graduates who learned TensorFlow and PyTorch—they are now competing for work in a market where their specialized training has become a liability. They know the specific tools that are now commoditized. They don’t have the depth of software engineering fundamentals that would make them valuable as general engineers. And the premium that made the retraining decision economically rational no longer exists. Upwork rates for AI/ML work now range from $35–$200 per hour, with clear stratification: the top end is people with 15+ years of systems experience who added AI to their toolkit; the median floor for sub-specialist roles is $75–$125 per hour, indistinguishable from baseline full-stack developer rates.
What Developers Are Actually Doing
In the absence of a functioning upmarket for entry-level AI expertise, developers are making several moves, none of them elegant.
Some are absorbing the loss and repositioning back to general software engineering. The economics force this: if you retrained for AI work in 2023 and now your market rate is 15–20 percent lower than a baseline engineer (because you lack general experience), you have two options: spend another year building general systems skills while earning less, or admit the retraining was a sunk cost and find full-stack or backend work that pays the money you turned down by pivoting. This is happening, quietly, in rejection emails and withdrawn applications. The visible signal is a sharp uptick in “AI engineer” profiles going quiet after mid-2024, with the same people reappearing in general SWE roles by early 2025.
Others are pursuing the infrastructure layer—LLMOps, vector database optimization, prompt management platforms, cost optimization tools. These roles actually do command premiums, because they require both the AI expertise and the systems thinking that comes from building at scale. But these roles are constrained by hiring—they’re not growth roles; they’re optimization roles at companies that have already deployed LLMs and now need to make them cheaper and faster. There are maybe 2,000 of these roles in the US market, not 20,000. For the developers who understand the distinction and have the depth to execute, it’s a viable path. For the person who did a Coursera deep learning specialization, it’s a closed door.
A third group is hedging by building domain+AI combinations. A healthcare developer learns LLMs and pitches themselves as an AI engineer for health tech. A fintech veteran learns prompt engineering and targets compliance-heavy financial services. The idea is defensible: domain knowledge is sticky, harder to commoditize, and when combined with AI skills, it becomes genuinely specialized. But this strategy has a visibility problem. There is no platform, no certification, no credible labor market signal that says “this person has 10 years of healthcare expertise plus 12 months of production LLM experience.” LinkedIn shows skill tags, not skill combinations. Portfolio projects are hard to build in regulated domains. The domain+AI thesis is strategically sound, but the infrastructure to signal it and match it to hiring companies simply does not exist. So developers who are trying this move are either succeeding by luck (network, prior relationships) or spending enormous effort to manufacture the signal themselves through writing, speaking, and direct outreach.
A final cohort is deferring the problem by staying in AI roles while their income floor declines. This is the quietly crushing option. Someone who retrained in 2023 and landed a $160,000 job as a “Machine Learning Engineer” or “AI Engineer” in 2024 is watching their peer wage decline by 15–20 percent in real time as they stay in title. Their salary may not have changed, but the market rate for someone in their role has. Next annual review cycle, next job hunt, next contract negotiation—the compression hits them. For developers with family obligations, mortgage commitments, or geographic constraints, the choice to stay and absorb the decline is not irrational. It’s just visible now, in rejection letters for raises, in the discovery that the next role pays less than the last one, in the realization that the premium was temporary and they didn’t extract it while it existed.
The Build Opportunity
There are two distinct technical infrastructure gaps that this dynamic has exposed. Neither is a band-aid; both reflect structural failures that will create real value for whoever builds them correctly.
The first is a credible signal platform for domain+AI expertise combinations. Developers with deep domain knowledge in healthcare, fintech, supply chain, or other regulated or expertise-heavy sectors want to credibly signal that they have not just learned LLMs in isolation, but have integrated that knowledge into a working understanding of their domain. Right now, this signaling happens through portfolio projects (hard in regulated domains), conference talks (high friction, high cost), and personal network (unfairly random). A platform that could ingest production project work, verify the domain context (contracts, deployments, evidence of shipping—not just coursework completion), and generate a credential that actually carries weight in hiring conversations would solve a real matching problem. This is not a certification body—those are trust-empty when the skill is new. It’s a visibility layer for real work in domain contexts. The technical core is straightforward: GitHub integration for code artifacts, a schema for capturing domain context + LLM work (which technologies, what production constraints, what trade-offs), and a way for hiring managers to query “people who have shipped X in healthcare” rather than just “people with AI tags.” The hard part is the validation mechanism—ensuring the credentials reflect real deployment, not imagined ones. But that’s the value.
The second is a pricing transparency and rate monitoring tool specifically for AI/ML contract work. Upwork, Fiverr, and traditional freelance platforms show rates, but they don’t show stratification—the difference between commodity rates and top-tier rates, or what actually causes the difference. A tool that ingested freelance marketplace data (with consent, via API partnerships), indexed projects by technical depth (model size, inference optimization, production systems vs. prototypes), tracked rate movements month-over-month, and showed developers their percentile position in that landscape would give the market much better information for pricing and positioning decisions. Right now, a developer doesn’t know whether $100/hour is the floor or the median or the 75th percentile until they’re already in the negotiation. A transparent market would force rate discipline, but it would also reveal where the actual value is—what specific combinations of skills and deliverables still command premiums. That data would inform smarter retraining decisions going forward. The technical build is a data ingestion and visualization layer; the political challenge is getting platforms to participate. But the value for developers—the ability to answer “what rate should I be asking for work that requires X”—is real.
Both of these are primarily infrastructure plays. They don’t replace the need for actual skill development. They don’t restore the premiums. But they do address the core problem revealed by the collapse: that the market for AI-specific expertise is now differentiated and local (top-tier infrastructure roles vs. commodity entry-level), and developers navigating it are doing so with almost no real-time information about where the value actually is.