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Key takeaways:
- Access to leading agentic coding tools is becoming a hiring filter.
- The divide isn’t only about money. Unpaid time to practice matters just as much.
- Early access compounds fast. Current fixes are a start, but they’re small.
Amid the shift to agentic coding, James Lowman, engineering team lead at trade infrastructure company Starboard, started asking candidates to tell him about the last three Claude Code skills they wrote. It seemed like an effective way to evaluate their fluency with AI agents, and it quickly became his go-to question. Then, he got a reality check.
One candidate, a college student interviewing for an internship, replied “none,” explaining they couldn’t afford anything beyond the free tools.
“It really did catch me off guard,” says Lowman.
In today’s engineering landscape, access to leading AI tools and tokens – and time to get good at using them – is key to staying competitive. They also don’t come cheap. So what does it mean if keeping up and getting hired depends heavily on access to expensive AI?
Some engineering leaders are beginning to grapple with this question, evaluating their hiring processes and working to strike a balance around how to gauge candidates’ agentic proficiency without putting those with less access at a disadvantage. At the same time, the gap won’t simply be fixed with a well-designed hiring process: the compounding effect of who has access now and who doesn’t can have snowballing implications.
“Leaders need to realize that take-home agentic tests and questions where they ask ‘tell me about the skills you’ve written or the agentic workflows you’ve built’ presuppose access to very capable models,” says Matthew Sharp, a research associate at the Oxford Martin AI Governance Initiative who studies digital divides and labor. “If you’re asking for those things, you’re not just testing aptitude; you’re also partly testing who can afford to practice in this way. That risks turning a kind of temporary resource gap into a permanent sorting mechanism.”
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The cost of access to leading agentic AI tools
Several studies suggest generative AI has a leveling-up effect, helping less experienced workers the most. The thing is – the studies all controlled for participants’ access, notes Sharp. Access is an obvious prerequisite to reaping the benefits of AI, which opens up questions on its own. Sharp argues agents complicate the issue of access even further.
In an April paper titled “Agentic Inequality,” Sharp and his co-authors lay out three dimensions of agent access: availability (is an agentic system available to you?), quality (how capable is it?), and quantity (how many agents can you run?). While there have always been inequities that help some people get ahead, agents introduce “scalable delegation,” he says. Put differently, you’re no longer just using a tool, but rather getting access to a kind of workforce that can act on your behalf.
“I think it’s a new inequality, and it’s gated by money, compute, expertise, and time,” says Sharp.
The need for access to leading agentic AI systems is already shaping how engineers make career decisions. Candidates are negotiating token budgets when weighing job offers, for example. Similarly, Lowman describes how continued access to the company’s Claude Code Max Plan was the driving reason an intern took him up on his offer to stay on to do part-time work after he returned to school.
“[It was] almost to the point where he was like, ‘I don’t even care if you’re paying me. I just need that so I can still work on projects,’” says Lowman.
When GitHub removed access to premium models from its Copilot student plan in March, and later instituted a 200 monthly AI credit cap for the plan in June, users loudly voiced their disappointment, explained they need access to premium models to stay on par with industry standards, and pointed out how little they can actually achieve with the newly limited access.
“One prompt and boom all the monthly credits are gone,” commented one user on the post announcing the change, which garnered over 6,700 downvotes and less than 100 upvotes. GitHub did not respond to a request for comment.
Students aren’t the only ones impacted, with experienced engineers also feeling the pressure to pay for multiple agentic coding tools and use their personal time to build projects to impress their bosses and interviewers.
James Rowe, an engineering manager who was recently between jobs and interviewing for his next opportunity, says he was expected to show proficiency in agentic engineering in every single interview he did. He’s been spending $100 per month on various agentic coding tools.
“I find that is the only way to stay current in the industry,” he says.
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The flywheel effect (not the good kind)
In July, a story in The Washington Post went viral over a specific anecdote about an engineering manager in San Francisco who, according to the report, asked his wife to take over nearly all parenting responsibilities for their young daughter because he needed to focus on becoming “AI native.” It describes how he spent nights and weekends locked in his office working on projects, which allowed him to eventually succeed in becoming “a top user of AI at his company.”
The story and the outraged reaction to it capture another, often overlooked aspect of how agentic inequality is showing up in engineering: time. Not everyone can just transfer their responsibilities onto someone else to dedicate themselves to agentic side projects, yet conversations throughout the industry commonly suggest everyone should be experimenting and building with AI agents in their own time. This expectation extends into hiring as well, both with questions like “tell me about the last three Claude Code skills you wrote” and steps in the hiring process that require significant time commitments.
Starboard has moved away from take-home tests and now asks candidates to do a full-day trial with the team, either in person or remotely. This has made it easy to give interviewees access to the same agentic tools, but not everyone can do a full-day trial, Lowman says. In those “special circumstances,” they’ll opt for a take-home assignment instead.
Take-homes still pose a time barrier, too, however. To make sure candidates are all able to use leading agentic tools during the interview process, Peter O’Connor, director of platform engineering at Stack Overflow, has been considering ways to give them access to the team’s AI tools or cover a one-month subscription.
However, he’s found solving for time inequalities among candidates much less straightforward. While he sees a lot of benefits of take-home assignments in terms of his ability to evaluate candidates, he worries about the time required.
“A take-home assignment works beautifully for a junior engineer or someone who is currently unemployed and has the luxury of time. What about a brilliant engineer who is a single parent working a full-time job to support their family?” he says. “If we mandate a massive time commitment outside of hours, have we just eliminated a whole subset of incredible talent simply because they couldn’t afford the time?”
Level the playing field
To start evening the playing field, Sharp suggests engineering leaders ask candidates questions around if they’ve had any disadvantages in accessing agentic tools. Additionally, they should give all candidates access to the same tool to complete take-homes or any other projects they’ll be assessed on.
At the same time, these are only small steps. With previous digital technologies like the internet and mobile phones, Sharp notes there were massive efforts from national governments and international organizations to track lack of access and “close the digital divide.”
Sharp believes governments and researchers need to start working now to measure agentic inequality, including tracking how deployment is different across income groups and geographies. He also suggests thinking about how to track the quality gap between free and paid tiers of agents, as well as exploring public options for agents or subsidized access, especially in education.
The most crucial aspect of this, according to Sharp, is that the inequality can have a flywheel effect. The actions the industry takes or fails to take now risk exasperating the divide and further limiting who can compete, get hired, and take hold of opportunities.
“When you have an early advantage, this feeds back on itself and kind of compounds. I think with agents, it’s especially sharp,” he says. “So the people who can afford the tokens to experiment early don’t just get more work done; they also build the skills to delegate better, which makes them more hirable, which earns them more access.”
