Silicon Valley has long competed for talent with ever-richer pay packages built around salary, bonus, and equity. Now, a fourth line item is creeping into the mix: AI inference.
As generative AI tools become embedded in software development, the cost of running the underlying models — known as inference — is emerging as a productivity driver and a budget line that finance chiefs can’t ignore.
Software engineers and AI researchers inside tech companies have already been jousting for access to GPUs, with this AI compute capacity being carefully parceled out based on which projects are most important. Now, some tech job candidates have begun asking about what AI compute budget they will have access to if they decide to join.
“I am increasingly asked during candidate interviews how much dedicated inference compute they will have to build with Codex,” Thibault Sottiaux, engineering lead at OpenAI’s Codex, the startup’s AI coding service, wrote on X recently.
He added that usage per user is growing much faster than overall user growth, a sign that AI compute is becoming even scarcer and more valuable.
That scarcity is reshaping how engineers think about their work and pay. OpenAI President Greg Brockman put it bluntly: “The inference compute available to you is increasingly going to drive overall software productivity.”
In other words, access to AI may soon matter as much as access to a fat salary and juicy equity awards. As a coder in the AI era, if you don’t have access to massive compute, you might end up producing far less software than your colleagues, threatening your career prospects.
Hakeem Shibly, a data specialist at Levels.fyi, recently spotted a compensation submission from a software engineer that listed “Copilot subscription” as part of the pay and benefits, a small but symbolic step toward AI access as a standard perk.
Getting paid in AI tokens
Some in the AI community see an even more explicit future.
“OpenAI and Anthropic should create recruitment sites where their clients can advertise roles, listing the token budget for the job alongside the salary range,” said Peter Gostev, AI capability lead at Arena, a startup that measures the performance of models. Those startups didn’t respond to requests for comment on Monday.
Investors are taking note. Tomasz Tunguz of Theory Ventures said companies are effectively adding AI inference as a fourth component of engineering compensation: salary, bonus, equity, and now tokens.
Tokens are the economic language of generative AI. Models break down words and other inputs into numerical tokens to make them easier to process and understand. One token is about ¾ of a word. They’re also used to price AI model use, via an industry-standard cost per million tokens.
“Will you be paid in tokens? In 2026, you likely will start to be,” Tunguz said.
CFOs are watching
For CFOs, this potentially big new expense must be tracked as closely as other headcount-related costs, Tunguz said.
“It is starting to happen,” Tunguz told me, as employee use of AI increasingly contributes to total cash burn. “It is a consideration for the Office of the CFO.”
With Levels.fyi pegging the 75th percentile software engineer salary at $375,000, Tunguz estimates that adding $100,000 in annual inference costs brings the fully loaded cost to $475,000 — meaning just over 20% of the compensation cost could come from AI usage in the future.
The key question for finance leaders: what’s the return on that AI spend? If cloud infrastructure performance is judged by gross profit per hour of GPU use, Tunguz suggests the employee equivalent is productive work per dollar of inference.
Tunguz has been building AI tools and models into his daily workflow and is automating 31 tasks a day at a cost of about $12,000 a year in inference.
“The engineer still burning $100k? They’d better be 8x more productive!” he wrote in a recent LinkedIn post.
If this trend continues, 2026 may mark the year engineers don’t just negotiate pay in dollars and equity, but in tokens.
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