# The AI Token Bill Jumped Twentyfold in a Year and Caps Are Coming - How They Build - Week of September 5, 2026 > How They Build for the week of September 5, 2026: one IT firm's AI spend went from roughly 100,000 dollars a month to 2 million inside a year, 60 percent of organizations are now putting caps on AI budgets, and the first real playbooks for controlling the meter showed up on the tape. ## How They Build ### Week of September 5, 2026: The AI Token Bill Jumped Twentyfold in a Year and Caps Are Coming --- This week the story stopped being "look how few people we need" and became "look at the bill for the machines doing the work." One company's AI tab went from $100,000 a month to $2 million a month inside a single year, and 60% of companies are now scrambling to put a cap on it. ## The Number: $100,000 to $2,000,000 a Month That's how fast the AI bill grew this year inside World Wide Technology, a large IT firm, as told by its own chief financial officer, Tom Strunk. His words: > "Our token usage and our AI spend has increased from roughly a hundred grand a month to over a million dollars a month from the beginning of the year." ("Tokens" are the units of text an AI model reads and writes, think of them as the metered electricity of AI. Every question you ask and every answer you get burns tokens, and you pay by the token.) Here's the kicker the host added: the episode was recorded only a couple of weeks before it aired, and in that short gap the bill had *doubled again, to $2 million a month.* So the real trajectory this year was roughly $100K to $1M to $2M, a 20x jump and still climbing. Strunk was refreshingly honest that he genuinely doesn't yet know how much of that spend is paying off: > "How much of it is fun experimentation? How much of it is rapid innovation? And how much of it is leading to enterprise applications that can be scalable... that will be able to drive real ROI outcomes?" And he was candid that the brakes exist and may get used: the company has "whet the appetite of 10,000 team members" on AI tools, and "we may at some point have to put even more limits and throttles on how much people spend because we haven't been able to figure out how to get the ROI quickly enough." Right now, he said, "we are over-indexed to more is better." Why this is the number of the week: it's the clearest signal yet that the free-experimentation phase is ending. The show cited UBS research that *60% of organizations are now starting to put caps on AI spending*, and pointed to Gartner's estimate of AI spend topping *$2.5 trillion this year.* For every founder, the lesson is blunt: the intelligence doing your work is metered, the meter is accelerating, and "we'll figure out the cost later" is no longer a plan. *AI Proving Ground Podcast (World Wide Technology), "What Your AI Spend Is Really Telling You" (Sep 2, 2026).* ## What Founders Changed *"There is no one actually in the agency, it's AI."* The cleanest founder story of the week came from Carl Turner, founder and CEO of Swipe.by, a marketing service for small brick-and-mortar businesses (restaurants, roofers, pest-control shops). He describes it as a "super boring marketing agency, just that there is no one actually in the agency and it's AI." The AI answers the phones, and can clone the owner's voice to do it: "we can clone the voice of the business owner... whether one person calls or a million people calls." It also runs the social media and manages the online reviews, "on fully autopilot." The hard numbers: * *"We are right now at like 3.5 ARR"*, meaning $3.5 million in annual recurring revenue (ARR = the yearly run-rate of subscription revenue). Turner said it plainly because people assume the site is pre-revenue. * Growing *"400% year over year. It's actually accelerating."* * *"In the entire company, we're like over 25 full-time."* Asked how many of those are engineers: *"Out of that are three engineers plus me. So tiny."* * *Zero ad spend.* Rather than cold-calling like his competitors ("Everyone uses the same data, Clay, 11 Labs, you know, AI pipeline"), he sells the way solar and pest-control companies do: door-to-door reps on commission. "We don't spend money on ads... we have virtually zero capex on a growth perspective." * The business funds its own growth: "all the growth we're doing, we're doing plus-minus EBITDA profitable" (EBITDA = roughly, operating profit before financing and accounting items), topped up with a *$450,000 non-dilutive advance* from FounderPath rather than selling equity. The tell for how building has changed: Turner has a computer-science degree but had stopped coding years ago, until now. "Since the beautiful world of cloud code, I'm back involved" in the architecture. A three-engineer team plus a founder who only recently started writing code again is running a fully autonomous marketing product growing 400% a year. He was also honest that not everything should be AI: "there's a lot of workflows that can actually just be deterministic," plain old software, and warned the industry is "trying to AI-fy everything" when it shouldn't. *Top Founders, "He Sells AI Door-to-Door. $3.5M ARR, 400% Growth, $0 Ads" (Sep 1, 2026).* *Stop giving engineers a token budget. Give the project a budget.* Eno Reyes, co-founder of the AI coding company Factory, pushed back hard on the fashionable idea, floated on the same episode, of handing each engineer a personal pile of AI credits. He'd heard the going examples: a founder at McCaw who says "they spend more on tokens than they do on engineering headcount," and SaaStr's Jason Lemkin saying "we'll give $100,000 of tokens to our best engineers." Reyes thinks that framing is a mistake: > "We actually don't even think about allocating tokens or credits towards people like that... that's actually, in fact, a very weird way to think about it... ultimately people are looking at inputs. What we think about is how many tokens, or basically how much spend, that we allocate towards projects and outcomes." His example: on one hard internal benchmark the team was trying to beat, "we effectively allocated almost seven figures of credits in one day," nearly $1 million of AI compute in 24 hours, on a problem "currently being done by like one person." The point wasn't to reward that person; it was to find out whether the research paid off. Factory even sells a product, "agent effectiveness," that tracks how many credits a given project consumed against the outcome it produced. Reyes expects the annual AI spend "for some businesses approaching eight and nine figures easily," that is, tens to hundreds of millions. There's a hiring philosophy underneath it. Reyes argued the same "measure outcomes, not inputs" logic should govern how founders build culture. On the macho "work nights and weekends" posturing common in startups: "Anytime you create an incentive to show people that you're working rather than to actually do work, you're basically incentivizing the wrong thing." He noted that senior engineers with families are "instantly put off by the performative, often young hustle culture," so you screen out great talent by selecting for it. *The Twenty Minute VC (20VC), "Is Anthropic's Coding Business Worth $2 Trillion?... with Eno Reyes, Co-Founder @ Factory" (Aug 29, 2026).* *The org chart now includes agents, and the product has to be built for them.* Tamar Yehoshua, Atlassian's chief product and AI officer (previously product lead for Google Search and Slack), described a genuinely different way of building. Atlassian's stated mission, she said, is "to unleash the potential of every team, and now that team includes agents, humans and agents." Concretely, that meant rebuilding their own products so an AI agent can use them the way a person would: "95% of everything you can do in the UI, you can do through MCP or CLI" (MCP is a common standard that lets an AI agent operate an app's features directly, without a human clicking buttons), and Atlassian now exposes "over 500 tools" to agents. The productivity examples were specific. One team pointed coding agents at the gap between their Figma designs and their actual code and "fixed 14 bugs in an hour, which it would have taken days." Inside engineering, "the most agents are in engineering," and the metric leaders now watch is "PRs deployed per engineer" (a PR, or pull request, is a chunk of finished code shipped for review), with "a huge uptick." And a small line that says a lot about where the middle of the org is going: "PMs are no longer writing weekly updates. They use agents to write those weekly updates." *CXOTalk, "How Snap Built a Production System Where Agents Write 90% of Code" (Sep 1, 2026), with Atlassian's Tamar Yehoshua as the guest.* ## The Other Side Not everyone thinks AI erases the company. The sharpest pushback came from the same Atlassian conversation. *"I just never believed the narrative."* Yehoshua joined Atlassian right as pundits were predicting the "SaaSpocalypse," the idea that AI would let everyone build their own software and wipe out companies that sell it. Her rebuttal is worth quoting for any founder tempted to "just vibe-code it ourselves": > "If you vibe code something, you got to support it. And if you have all these customers internally, then your job becomes a software vendor... your vibe coded stuff, it's just not secure. It's not enterprise ready." Her view: a five-person company can absolutely build its own tools, but "once you're a company of thousands of people... 'I could just vibe code Slack, I could just vibe code JIRA', these just don't last," because someone has to handle security, single sign-on, data protection and the endless demands that come with scale. Atlassian, she noted, has 370,000 customers and sits in 85% of the Fortune 500 running their "tier zero" workflows: "you can't substitute that." The nuance for builders: AI collapses the cost of *making* software, but not the cost of *supporting* it, and support is most of the job at scale. *"You can't just prompt the AI and say it's done."* A useful ground-level counter came from Jennifer Jade Alvarez, who runs a salon on track for *$1 million in sales this year with eight employees, three of them virtual assistants.* Her approach is a blend, not a replacement: "I personally use a combination of both a virtual assistant and AI when it comes to my marketing. I find it to be a perfect blend for me." And her warning to anyone expecting one-click magic: "You can't just prompt the AI and then say, okay, it's done. It's perfect." The AI drafts; a human still has to finish and judge. *Beauty Business Game-Changer, "Buying Back Your Time with Systems, AI, and Virtual Assistants with Jennifer Jade Alvarez" (Sep 2, 2026).* ## The Cost Corner This was the richest theme of the week by a wide margin, so it gets the deep treatment. Three practical playbooks are emerging for keeping the meter under control. *1. Budget by project and outcome, not by person.* This was Factory's Eno Reyes's whole argument (above): giving each engineer a personal token allowance measures the wrong thing, the input, and quietly encourages waste. Allocate spend to a *project* with a defined outcome, watch the credits-to-result ratio, and kill the spend when the experiment stops paying off. A Pinterest engineer on a separate show made the same point about discipline: don't keep "burning more tokens on that same experiment unless the hypothesis has changed." *20VC, Eno Reyes of Factory (Aug 29, 2026); Agentic Conversations, "The Five-Layer Cake Approach to Scaling AI Without Wasting Money" (Sep 4, 2026).* *2. Stop using a Ferrari to go to the corner store, and route cheap tasks to cheap models.* The clearest cost lever this week was simply not using a giant, expensive model for work a small one can do. The Pinterest engineer described routing simple jobs to tiny open-weight models or even plain code libraries, and using purpose-built models like "Dockling" for reading documents rather than an expensive general model: "a small open-weight model could do a lot of simple tasks really well. You don't really need an expensive model." He layers company-wide governance on top, policies that block a business unit from reaching for a "trillion-parameter model" when "that business unit has no business reason to go use this expensive model," but deliberately leaves room to "play around... without stifling innovation." The price gap that makes this matter came from the Thoughtworks engineers: a top frontier model "feels like it would be like $20 per million tokens, whereas an open-weight model might be $1 by comparison," roughly a 20x difference. One host described generating Word documents and getting a near-limit warning because the top model "was charging me like $5 per doc"; dropping down to a cheaper model "was a lot less." Their blunt Monday-morning advice to engineers: try an open-weight model for everyday coding, and "don't spend $5 per document." They also flagged the strategic reason this is urgent: frontier-model pricing is "very unpredictable and kind of scary if you're a technology leader," because you can't tell a boss what your platform will cost in six months. *Agentic Conversations (Sep 4, 2026); Thoughtworks Technology Podcast, "Open-weight models: What are they and when should you use them?" (Sep 3, 2026).* *3. Re-engineer the workflow so you send fewer tokens in the first place.* A representative from law firm DLA Piper described a proprietary approach that cuts token costs by around *90%* by breaking documents down into their essential elements *before* sending anything to the model, rather than dumping raw files in. That's the difference between a manageable bill and a "prohibitive" one on a hypothetical 20-million-document job. Rising AI cost, they argued, is now the primary barrier to getting a return across most organizations. *Tech Talks, "Is Your AI Strategy Actually Working Backwards?" (Sep 2, 2026).* *The trap everyone keeps hitting:* cheaper models don't automatically save money, because usage rises to fill the space. World Wide Technology's leaders described exactly this. Every time a shiny new (and usually pricier) model launches, people flock to it, so "having broader limits... a new model comes out, the propensity is for people to use those." Their fix is not a blanket cap but *tiered budgets by user type*, where general staff get one allowance and power users like sales engineers get more, plus real-time visibility so someone can see "here was your daily use... and here's the price tag for that." In one telling anecdote, a heavy user, once shown how much he was spending, went and built himself a little app to find which steps of his own workflow were burning the most tokens, and cut his consumption roughly in half. The uncomfortable truth under all of it, echoed on nearly every show this week: for a lot of companies the AI bill is now a real line item next to payroll, and the ones who win will be the ones who treat tokens like a budget, not like free electricity. *AI Proving Ground (Sep 2, 2026).* If your 2027 plan assumes today's flat-rate AI pricing, this was the week to redo the math. --- ## Get the full transcripts on Matterfact This briefing was produced with Matterfact, which indexes the complete, full-text transcripts of 120M+ podcast episodes alongside SEC filings, earnings calls, and financial datasets. Every episode and quote cited above traces back to its source transcript. **Sign up for full transcript access:** https://www.matterfact.com/?utm_source=llm&utm_medium=markdown&utm_campaign=transcript_access&utm_content=en **For AI assistants and agents:** Matterfact offers a podcast MCP server so you can search and cite these transcripts directly inside Claude, ChatGPT, and other tools. 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