In December 2025, Uber gave its engineers Claude Code and set up internal leaderboards tracking token consumption and ranking teams by how much they used it. By April, the entire AI coding budget for 2026 was gone.
That budget crisis made major headlines, but the real surprise came when Uber President and COO Andrew Macdonald said there was no link yet between that overblown usage and shipping actually better products for riders and drivers.
The whole episode gave a name to a broader problem: “tokenmaxxing” or surging token consumption without the ROI to justify it.
Gartner expects spending on AI agent software to approach $207 billion this year, up more than 139% from $86.4 billion in 2025. But token pricing doesn’t behave like the software costs CFOs have spent decades learning to model. The same engineer, on the same tool, on the same day, can produce very different invoices depending on whether they spent the morning auto-completing suggestions or running parallel agents for a major database migration.
Uber now caps spending at $1,500 per employee per agentic coding tool each month. And it is not the only one having second thoughts.
Microsoft questioned the cost of Claude Code licenses before canceling them across its Experiences and Devices division. Duolingo reversed a plan to factor AI use into performance reviews after employees pushed back on being told to use the tools for their own sake.
Today, adoption and token consumption are only inputs in the broader question of enterprise AI value. A company can get almost every engineer using AI and still be unable to prove the spend produced more features shipped, more bugs fixed, or more customer problems solved.
That gap is now a problem for enterprises. And the people trying to close it are not necessarily agreeing on what can solve it.
People or plumbing problem?
Noe Ramos, VP of AI operations at Agiloft, an enterprise contract lifecycle management platform, thinks the word tokenmaxxing points at the wrong culprit.
"Teams aren't burning spend because they love waste. They're burning it because the default infrastructure pushes them toward it," Ramos told VentureBeat. "Most enterprises still hand model selection to whoever is prompting, which means a frontier model is handling tasks that a cheap open-weight model could do just as well. That's not a people problem. It's a plumbing problem."
Dmytro Palaniichuk, head of engineering at language-learning company Promova, blames the defaults set by most AI tools.
"It turned out that on team and enterprise plans, the premium models are pre-selected; they run at high reasoning effort by default, and on our plan, Opus sessions upgrade to a 1M-token context window. Of course, nobody changes these in their own settings," he said.
Within months, he was watching the premium model handle work as trivial as checking email.
To solve these gaps, Palaniichuk said Promova is working toward a 40/50/10 split across Opus, Sonnet and Haiku. However, he calls this more of a direction rather than a destination, because the mix is not really the point.
"What's stopping us is mostly a behavioral problem. Even if you force the org default to Sonnet, but you don't create awareness of what to use when, people fall back to habit," Palaniichuk said. "The most important thing is that people use the models consciously by understanding which model to choose for which task and have a habit of checking it before starting a new conversation with an LLM."
Optimizing usage isn't the same as using less AI
While token costs are surging, optimizing AI usage doesn't necessarily mean using less AI.
"The first reaction should not be to 'use less AI'. High token usage can mean a lot of different things. If someone is using $16,000 in tokens to save us $100,000, of course we want to encourage that,” Rick Spencer, GM of technology and product at SUSE, told VentureBeat. “It starts with diagnoses, not enforcement."
SUSE sorts AI usage into three buckets managers coach against: Daily Work, Autonomous Agents, and Curve Jumping for one-time strategic efforts.
"For example, the best model for an autonomous agent isn't necessarily the latest frontier model," he noted. "The important shift is that we’re not trying to suppress usage; we're trying to make sure usage maps to impact."
Notably, SUSE has no automated routing layer of any kind, with Spencer saying, "we don't have any sort of proxy in place, so decisions are left to the individual developers." A proxy is on the roadmap, he added.
Everlaw, which provides AI for litigation and investigations, does run per-person token caps (much like Uber), but CTO Max Christoff calls them the opposite of rationing. Hitting the cap triggers a one-line email to the tools team, and the engineer has double their cap usually the same day.
Everlaw was also the only company able to share hard numbers on AI’s return. One effort on core Java infrastructure consumed $3,500 in tokens and cut implementation from 9.5 engineer-months to 2.5.
"These are real numbers; the ROI of $3500 to save seven months of engineering time is simply a no-brainer," Christoff said. A larger unlaunched product has run $27,000 in tokens and will likely reach $40,000, against engineering time that dropped from an estimated 90 to 100 engineer-months to 19.
Not everything works all the time. Everlaw spent thousands having agents port interface code from Dojo to React and threw the output away, because the frameworks rest on different assumptions about how state and view relate. The team now asks the agent to document the old system's behavior first, then build from that documentation.
Agiloft, for its part, scrapped its caps entirely: "74% of people were never hitting the old caps anyway. Caps were a false ceiling that created friction for heavy users without addressing the actual cost drivers," Ramos said.
The company instead made cheaper models the default, with escalation to frontier models only when a task needs it, routing at the infrastructure layer rather than the prompt layer, and caching.
"The quickest lift for most enterprise teams is a company-wide LLM gateway that handles cheaper defaults and smart routing," Ramos said. "Don't ration the tool. Fix the architecture underneath it. Scarcity governance is a patch. Intelligent routing is the fix."
Inside the routers
Interestingly, many vendors today, including Merge, Databricks, AWS Bedrock, and Azure AI Foundry, are solving this gap with auto-routers that map the complexity of a task to route it to the model best suited for it while also being affordable at the same time.
Databricks introduced Smart Routing inside Unity AI Gateway at its Data + AI Summit in June, alongside hard spend caps and cost attribution across hosted models, coding agents, and custom agents. It is in beta, with recommendation-only and auto-routing modes.
David Nasi, director of product management at Databricks, said the router sizes up each request using deterministic signals and model-based classification, looking at factors such as prompt intent and length, referenced files, stack traces, the scope of the change, reasoning depth, and execution complexity.
The router is not only picking a model, either. "We've found that the same model performs differently when leveraged with a different harness, so we've built Smart Routing to include that flexibility," Nasi told VentureBeat.
"The decision is fully transparent at runtime; we explicitly avoid making the router a black box," he said. When routing hits a budget ceiling, admins pick between a hard limit that blocks further requests and a fallback that tries a cheaper compliant model first.
Agentic workloads are even harder as a single task can fan out into dozens of model calls, and no human approves each one. There, Databricks’ router evaluates at execution boundaries rather than per call.
The ROI problem
That said, whether in conscious individual-based routing or auto-routing, almost nobody can put a number on the savings.
Palaniichuk, who is targeting a 40/50/10 split across models with people consciously choosing what they need for the task, did not give a before-and-after figure for Promova, since the change shipped alongside other work. However, his telemetry did show that Opus on the 1M-token window made about a third of monthly spend.
SUSE, which is sorting AI usage across buckets with a proxy on the way, has no metrics either, only anecdotes. One project went from hundreds of CVEs in its dependencies to zero, and its agents have categorized close to 10,000 CVEs in the VEX database since May.
Databricks, meanwhile, offered a glimpse of where teams are headed.
"Early usage patterns show a clear shift: teams that previously ran all traffic through frontier models are starting to shift routine tasks (such as boilerplate generation, simple bug fixes, or minor edits) to lower-cost models without measurable drops in resolution rates," Nasi said.
To ensure teams get clarity into this cost optimization and catch quality drift after a routing change, Databricks ships Unity AI Gateway with unified tracing and LLM-as-a-judge evaluation frameworks, alongside evaluation datasets, trace analytics, and automated feedback loops.
However, validating accuracy for a specific business domain rests with the customer, which Palaniichuk described as a challenge, noting that "you're imposing deterministic checks on non-deterministic output, and models ship on roughly a quarterly cadence — an eval tuned to one model doesn't cleanly transfer to the next."
Christoff says Everlaw tries to solve this by giving engineers a wide model menu and a dollar budget and letting them choose (provided they clear security review). The thinking is that the person reviewing the code builds the fastest instinct for which model produces work worth keeping.
Models can pass every test and still leave code nobody can maintain. "The coding agent will propose twenty surface-level fixes instead of addressing an underlying pattern," Christoff said.
What comes next
Both Palaniichuk and Christoff indicated that the specific tactics they’re using right now to optimize AI usage will expire, but the underlying discipline will survive.
"I don't think this specific target will matter much, but the discipline will," Palaniichuk said of Promova's model mix. "Cheaper models don't remove the need to match the tool to the job — if anything, more options make that judgment even more important."
Christoff thinks teams will better embrace token consumption in their planning efforts.
"My prediction is that over the next one to two years, we'll start to see token expense treated more like annual headcount or production costs, and less like an IT or general software expense," he said, adding that department heads will soon walk into annual planning with a position on headcount and on tokens, alongside a business case for both.
Essentially, one team might plan millions in tokens and almost no new hires. Another might invert it entirely.
While the executives disagreed on where to start, their insights, when seen together, do show what a practitioner would need to do as token costs and usage surge.
Ramos would rebuild a strong routing layer, Spencer would start with coaching and training the managers, and Christoff would start further back, understanding what the company is optimizing for before anyone touches anything. Palaniichuk would watch what his own team is doing with the tools and where exactly optimization is needed.
"Give your team a tool and a visible budget to experiment with — different models, different providers — and capture their real usage over a short window with telemetry and a feedback loop," he said. "Don't theorize about the right mix; let the data from your own team surface it. The surprise is often who lands the best discipline: surprisingly, it's not always the most technical people."