“Tokenmaxxing” – the idea that AI coding success comes down to using as many tokens as possible – is an appealing metric.
Tokens are the fundamental unit that AI coding tools use to read, write, and reason. So on the surface, more tokens should mean more output, more productivity, and more impact.
But when we analyzed 12,000 developers across 200 companies, the data revealed that while more tokens do correlate with more output, they come at a significantly higher price per unit.
Head of Research at Jellyfish.
Some organizations are pushing software engineers to use as many tokens as possible, using leaderboards to promote the biggest AI users. But that’s not a sustainable strategy. CFOs are starting to push back on uncontrolled AI spending and asking coders to show receipts.
Leaders may be willing to spend money to move fast, but they can’t do it without proving their engineering teams are having an impact.
The best approach to “toxenmaxxing” isn’t to blindly push for AI adoption. Instead, the best path forward for companies is to push AI coding adoption more broadly, moving more engineers into the middle of the curve while avoiding both underuse and expensive overconsumption.
Why ‘tokenmaxxing’ doesn’t scale
We found that the top 10% of Claude Code users consumed about 10 times as many AI tokens as the median developer but produced only about twice the output. In other words, increasing token consumption does increase output, but not proportionally.
The research also shows a small but growing group of power users dominating total token consumption. At the 90th percentile, users are burning around 225M tokens per week, about 3x what they were using six months ago, and about 7x the median.
Many engineering leaders are now looking at their highest adopters and trying to figure out how to get the rest of the organization to the same level. That approach is misguided. With the cost per merged PR increasing from $0.28 at the lowest adoption tier to $89.32 at the highest, scaling extreme token usage simply cannot drive value.
Instead, engineering leaders should focus on smoothing the curve. Broad, moderate token consumption is far more cost effective than having a small group of power users at one end of the spectrum and everyone else lagging behind. When most of the organization is operating in the middle of the curve, AI becomes a durable advantage: enough to drive real productivity gains but not so much that engineering teams burn money chasing marginal output.
Maximize impact, not token consumption
The organizations that burn through the most tokens aren’t necessarily getting the furthest with AI. When token consumption is high, most are spent on automating manual tasks with tools like Claude, Copilot or Cursor. Developers essentially have a better tool to do the same kind of work as they did before.
To really drive impact with AI, engineering organizations need to move towards new, truly agentic modes of working. However, agentic systems require major investments in IT infrastructure, including context engineering, orchestration, and sandboxed environments. Until organizations address these issues, the productivity gains will remain blocked by an “agentic barrier” that no amount of tokens can overcome.
How established enterprises can follow the AI-native lead
Conversations around AI and software development focus on coding, but writing code is just one part of an engineer’s role. Taking a product to market also involves roadmap work, deployment, go-to-market enablement, and more. If engineers are spending tons of tokens on writing code as fast as possible, everything else needs to catch up.
Changing roadmap cadence and accelerating sales enablement requires major cultural shifts that many organizations aren’t prepared for. As a result, extra cadences are often poured into the backlog or other things that may deliver value down the line but won’t move the revenue needle in the short term. Teams can consume millions of tokens every week but have little to show for it by the end of the quarter.
AI-native companies are more likely to see an immediate return on their AI investments. While established enterprises may not be able to start from scratch, adopting AI-native principles can help remove bottlenecks and turn token spend into measurable business returns faster. By designing workflows with automation in mind, they can continue to accelerate coding without creating technical debt.
“Tokenmaxxing” is having a moment, but engineering leaders need to move beyond token count and start finding ways to prove value. By measuring how AI impacts delivery, quality, and productivity across the software delivery life cycle, leaders can demonstrate ROI and make sure every token counts.
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