This CEO was out to dinner when he caught his AI agent wasting $1,000 in tokens. He says ‘insecurity’ is a bigger problem

Maxio CEO Branden Jenkins discovered his AI agent had burned $1,000 in tokens during a weekend coding session, charged automatically in $1,000 increments to an auto-renewing card. Jenkins said the surprise invoice is less concerning than his employees' 'insecurity' about being outpaced by AI, a problem he sees as more significant than the cost. The incident highlights how agentic AI tools can rapidly consume budgets, with Gartner estimating they require 5x to 30x more tokens per task than standard chatbots.

Branden Jenkins was out to dinner when he pulled out his phone, glanced at his AI usage dashboard, and realized his weekend coding session had just cost him $1,000 — charged automatically, in $1,000 increments, to a card set on auto-renew. Jenkins is the CEO of Maxio, a private-equity-backed software company headquartered in Atlanta that’s on a path toward $100 million in annual revenue over the next couple of years. He’s also, by his own admission, near the top of his company’s internal AI spending leaderboard — an odd place for the chief executive to land. “A thousand is not that much, I would say, but for one weekend, it’s pretty annoying,” he said in an interview with Fortune . Describing his agent as “cooking away,” he described his response as “Wow, I just got here quickly.” The episode has become something of a parable inside his company — and inside corporate America more broadly — for how quickly “agentic” AI tools can consume money without anyone quite noticing until the bill lands. But Jenkins said the surprise invoice isn’t what’s keeping him up at night. The deeper problem is something messier and more human: his own employees’ “insecurity” about being outpaced by the technology — and by him. How a weekend turned into a $1,000 lesson Jenkins, a self-described technical CEO who builds his own agents and automations, said he can write code from his phone using Claude even while away from his desk — which is how he ended up debugging and iterating on a project at dinner. The token wallet he’d set up to fund those sessions was configured to auto-refill by $1,000 every time it ran dry, silently recharging his card without requiring a second thought — until he saw the total. “I don’t have governors where a lot of my staff hits limits, and they have to ask for approval,” Jenkins said, describing his own unlimited internal budget as both a perk and a liability. “So I started leaning in and going, ‘What does this look like?'” What he found, he said, is that a lot of the waste comes down to model selection and runaway conversational drift — an AI system wandering a user down paths they never intended to go. “A lot of times it’s the agent’s own mistakes that’s burning your money,” Jenkins said. “You kind of find yourself just chatting, and things getting away from you.” Casting his mind back to his dialogues with his bots, he said, “You’re like, ‘Yeah, yeah, I like it, more of that, more of that.’ All of a sudden you end up in who-knows-where, and you’re like, ‘No, I don’t want that at all.’ So some of that money is just wasted because it took you there.” His experience mirrors a pattern now well documented across the industry. Gartner has estimated https://www.gartner.com/en/newsroom/press-releases/2026-03-25-gartner-predicts-that-by-2030-performing-inference-on-an-llm-with-1-trillion-parameters-will-cost-genai-providers-over-90-percent-less-than-in-2025 that agentic AI models can require between 5x and 30x more tokens per task than a standard chatbot exchange, and a WitnessAI survey https://www.prnewswire.com/news-releases/new-witnessai-report-reveals-the-financial-cost-and-risk-factors-of-enterprise-ai-adoption-302831518.html found that 68% of U.S. companies say at least some of their AI initiatives ran over budget in the past year, with a third saying overruns happen “mostly or always.” George Sivulka https://www.a16z.news/p/the-next-ai-goldrush-tokens-loops , CEO of Hebbia, put it memorably when he wrote that using agents means “ you just hired a million bad employees https://finance.yahoo.com/technology/ai/articles/just-hired-million-bad-employees-070000900.html .” Uber https://fortune.com/company/uber-technologies/ reportedly burned through its entire 2026 AI coding budget within four months, and Amazon https://fortune.com/company/amazon-com/ reportedly spent $500 million on AI in a single month after rolling out access without usage caps. That was the month “tokenmaxxing” died. Jenkins’s $1,000 weekend is a rounding error by comparison — but the point is, that’s real money. “There’s no refund button. There’s no dispute button in Claude,” Jenkins said, adding that maybe there should be. Tricks of the trade After the dinner incident, Jenkins said he looked for ways to cut his own token burn — much of it, by his account, learned from AI-optimization tips circulating on TikTok rather than from his own engineering team. He started routing different tasks to different models based on complexity: lighter models like Claude’s Haiku for basic math, mid-tier models for routine coding, and reserving the most expensive, highest-reasoning models for genuine strategic planning. He also adopted what he called orchestration layers — third-party tools, often distributed as free GitHub repositories, designed to compress AI output and cut wasted tokens. One, which he called “Caveman mode,” forces an AI assistant to reply in short, blunt sentences instead of long, elaborated answers, which Jenkins estimated cuts token use by 70%. Another mode he described, “grunt mode,” compresses replies to a word or two: “It’s very trite.” He named other tools, including “Superpowers” and “Ponytail,” as part of the same underground ecosystem of cost-saving hacks. The catch, Jenkins said, is that none of it is accessible to a typical employee. “These are nerdy things,” he said. “Do we need sales leaders and service leaders and marketers finding this stuff?” That gap—between what power users like himself know and what the rest of the workforce can access —is where he says the real organizational risk begins. Why he warns insecurity, not cost, is the bigger threat Asked to rank the problems he’s encountered rolling out AI across his several hundred employees, Jenkins didn’t lead with cost. He named three forces he says he has to actively manage: inefficiency, inequality, and — the one he returned to repeatedly — insecurity. Jenkins explained that he’s proud that everyone’s becoming a builder with vibe-coding permissions in his company, but it’s disruptive in a very human sense. “ Token overspend is really not the problem, but it could easily be the excuse,” Jenkins said. He described building tools and automations inside his own leadership team’s departments, unprompted, simply because he’d learned how — and watching the reaction turn uneasy. “One of them came to me and said, ‘This put me on edge. I should be coming to you with these things. I’ve got to catch up. I feel so behind.” Jenkins said the dynamic repeated itself down through multiple layers of management: “Am I doing my job? Am I keeping up? Will this replace my job? Will this replace my team members?” He also pointed to a version of the same anxiety playing out at the departmental level, rooted in unequal access to tools. His company initially rolled out ChatGPT company-wide, then began licensing the pricier Claude for a smaller group of roughly 50 employees concentrated in sales and marketing — prompting pushback from teams left out. “People were like, wait a minute, why don’t I have Claude? Why do they get that and we don’t get that?” he said. “That’s an inequality.” Jenkins argues that the anxiety is ultimately more corrosive to a company’s culture than any single runaway invoice, because it shapes whether employees engage with the tools at all — or quietly resist them out of fear. Building an org chart for humans and agents Jenkins said his company’s response has been structural. His entire executive leadership team went through a formal org-design exercise in which each executive mapped out their department not just in terms of the people who report to them, but the AI agents those people now manage directly. The result, he said, is a literal hybrid org chart — human names and agent functions layered together — that the company treats as a living management document. He’s also expanded his DevOps organization specifically to govern the growing number of employee-built, informally coded internal tools — what he and others in the industry call “vibe-coded” software — that have become load-bearing parts of the business despite originating as side projects. “It can’t just be with Tim that vibe-coded it on the weekend,” Jenkins said, citing continuity risk if the employee who built a critical internal tool leaves or gets sick, along with unresolved questions about security and scalability. The bigger financial story, in his telling, isn’t the occasional four-figure token overrun but a shift in his company’s underlying labor math. Jenkins said his company’s headcount has stopped scaling with revenue the way it once did, driving up its ratio of annual recurring revenue per employee — a metric he calls the clearest signal of what AI is actually doing to his cost structure, as opposed to the more visible but comparatively minor token bill. “I’m not arguing that we want to reduce a whole bunch of headcount because of AI, but we should not be growing the headcount at the same rate that we were before,” Jenkins said. “That’s a big change in our business, and it’s all attributed to AI.” He also agreed that the occasional ping of $1,000 token burn at dinner is like a tax you pay — an AI agent colleague just got the wrong idea of what the job was. Jenkins said he doesn’t see any of that as a reason to pull back. “Right now there’s so much good that outweighs a lot of this,” he said, arguing that the efficiency gains from AI adoption are large enough that occasional waste, hallucination-driven detours, and even four-figure surprise bills are simply the cost of getting there. His head of engineering, when first told about Jenkins’s token-saving tricks, waved them off, telling him the company was “not at the level of we’re spending more on A.I. than on people, because there are examples of that out there. We’re nowhere close to that.” Jenkins said he didn’t disagree — but he’s convinced that day is coming. “It probably will be a thing.” For this story, Fortune journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing. breaks the traditional barrier between audience and newsroom. 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