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AIPROPX ReportFortune · 3h ago
This CEO was out to dinner when he caught his AI agent wasting $1,000 in tokens. He says ‘insecurity’ is a bigger problem
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 that agentic AI models can require between 5x and 30x more tokens per task than a standard chatbot exchange, and a WitnessAI survey 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 , CEO of Hebbia, put it memorably when he wrote that using agents means “ you just hired a million bad employees .”
Uber reportedly burned through its entire 2026 AI coding budget within four months, and Amazon 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 easi...
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