# AI Bubble Burst? Companies are seeing the problems with AI!

https://www.youtube.com/watch?v=Y7Gapy0HjjE

[00:00] In April 2026, an engineer at Meta built a leaderboard.
[00:02] He called it Claudionics.
[00:04] It ranked all 85,000 of his co-workers by one number, how many AI tokens they burned.
[00:09] Top users got titles like Token Legend and Session Immortal.
[00:11] In 30 days, those employees burned through roughly 60 trillion tokens.
[00:16] The single heaviest user averaged 281 billion tokens by himself.
[00:18] Around the same time, Uber blew through its entire annual AI budget in 4 months.
[00:23] Its CTO told reporters he was back to the drawing board.
[00:29] Microsoft started cancing AI coding subscriptions across whole divisions.
[00:33] And Salesforce's CEO admitted the company's AI bill would hit around $300 million this year.
[00:37] Then said out loud that he was desperate for a tool that would stop sending easy questions to the most expensive AI.
[00:43] This has a name now.
[00:46] The internet calls it token maxing.
[00:48] It got its own Wikipedia page and it is quietly one of the biggest business stories of the year.
[00:52] Here's the twist, and it's why you're going to want to watch this whole thing.
[00:57] Because the exact same companies that spent 2025 screaming, "Use more AI," or
[01:03] You're fired, spent 2026 realizing that using more AI was setting a mountain of money on fire with often nothing to show for it.
[01:12] And whenever a giant expensive problem hits every company on Earth at the same time, it creates two things.
[01:16] A set of skills that make you suddenly very valuable and a wave of brand new businesses for people smart enough to see it coming.
[01:25] So today, three things.
[01:25] What token maxing actually is and why even brilliant companies fell into it.
[01:29] The exact playbook, five specific methods that cuts an AI bill by up to 90% without losing quality.
[01:36] And then the part most videos will never give you.
[01:38] The concrete money opportunities this chaos is creating right now for employees and entrepreneurs before everyone else catches on.
[01:45] This isn't theory.
[01:47] Every example is real.
[01:47] Every number is sourced.
[01:49] Let's get into it.
[01:49] First, the basics fast because the whole story rests on one word.
[01:55] Token.
[01:55] A token is a chunk of text an AI reads or writes, roughly a word or piece of a word.
[02:02] Hello is about one token.
[02:02] A long email, maybe 300.
[02:02] A research report.
[02:04] 10,000.
[02:04] And here's the key part.
[02:07] You pay for tokens both ways.
[02:09] Every word you send in, your prompt, your instructions, your whole chat history, and every word the AI sends back.
[02:15] For years, nobody cared because tokens were basically free.
[02:17] Back in 2023, a top model cost well under a dollar per million tokens, fractions of a penny per task.
[02:24] Who tracks that?
[02:27] Then two things happened at once, and together they created the crisis.
[02:28] Thing one, the top models got expensive.
[02:31] Today, a frontier model's output can run $30, $50, even higher per million tokens.
[02:37] A task that cost a fraction of a cent in 2023 can now cost real dollars.
[02:42] Thing two, and this is the actual villain, the model started running in loops.
[02:46] This is the part everyone missed.
[02:48] Old AI was a chatbot.
[02:48] You ask, it answers done.
[02:51] A few hundred tokens.
[02:53] But the new agents, coding agents like claude code, codeex, cursor, don't answer once.
[02:58] They read your entire codebase, spawn sub agents, run self-debugging loops, reread files over and over.
[03:02] Gartner found that Agentic AI
[03:05] Burns five to 30 times more tokens per task than a chatbot.
[03:09] One agent working on a project for a week can chew through hundreds of millions of tokens.
[03:13] Here's the number that captures it.
[03:15] Per developer token consumption rose roughly 18 times in nine months.
[03:19] Not because prices went up, because the volume per person exploded.
[03:24] Now, here's why smart companies walked right into it.
[03:25] Because this is human nature, and it's fascinating.
[03:30] In 2025, using AI became the way you proved you were ambitious.
[03:32] CEOs put AI usage in performance reviews.
[03:36] Companies built leaderboards like Meta's Claudionics that literally ranked people by how much AI they burned.
[03:42] The message was clear.
[03:42] More tokens.
[03:44] Chukseker is more productive.
[03:44] Spooker better employee.
[03:47] Except that's a lie.
[03:49] And it's the core mistake of the whole era.
[03:51] Token volume measures effort, not results.
[03:53] The exact same hundreds of millions of tokens can represent a hard problem solved brilliantly.
[03:57] Or an AI agent running in circles, rereading the same files, accomplishing nothing.
[04:02] You were rewarding the meter, not the destination.
[04:03] It's like paying a taxi
[04:06] Driver more for taking the longest route.
[04:07] The Phops Foundation, the people who track cloud spending, said that by April, companies were calling them already three times over their full-year AI budgets.
[04:16] Maxios reported one consultant's client spent half a billion dollars in a single month after failing to put any limits on employee AI use.
[04:22] That last one's hard to fully verify, but it landed because everyone suddenly felt how plausible it was.
[04:28] The bill arrived before the value did.
[04:30] That's token maxing.
[04:33] So, here's the good news and the genuinely useful part of this video.
[04:37] The solution is not use less AI.
[04:39] That's like fixing a high electric bill by sitting in the dark.
[04:41] The solution is using AI intelligently.
[04:43] And there are five specific methods that stacked together can cut an AI bill by up to 90%.
[04:49] These work whether you're a solo creator or running a team.
[04:51] Write these down.
[04:54] Method one, change your default model.
[04:56] Think of your office printer.
[04:58] Set it to color by default and people waste expensive color ink on grocery lists.
[05:00] Set it to black and white and they can still print color when they truly need it.
[05:04] But the waste vanishes.
[05:07] Identical.
[05:09] Most people reflexively pick the smartest, most expensive model for everything.
[05:12] Summarizing a meeting, fixing a typo, writing a quick email.
[05:15] But experts call this the big model fallacy.
[05:19] And it's been called the single most expensive architectural mistake in enterprise AI.
[05:23] You do not need a frontier model to summarize notes.
[05:25] A cheaper model or a small open model you can run for pennies gives you a near identical answer at a fraction of the cost.
[05:33] Just change the default.
[05:34] The smart model is still one click away when you need it.
[05:38] Method two, model routing.
[05:41] This is the big one.
[05:42] Picture a hospital.
[05:45] You don't send every patient to the top surgeon.
[05:47] Someone with a headache sees a general doctor.
[05:49] The surgeon is saved for the surgeries.
[05:51] Routing does exactly this for AI automatically.
[05:52] A router is a system that reads each request and decides which model handles it.
[05:55] Fix this grammar.
[05:57] Cheap model.
[05:59] Summarize this transcript.
[06:01] Midmodel.
[06:03] Analyze this 200-page contract for hidden legal risk.
[06:05] Now you call the frontier model.
[06:03] The user notices nothing.
[06:05] They just get their answer.
[06:05] But the company's bill
[06:07] Collapses because 80% of requests were secretly easy and never needed the expensive brain.
[06:12] This is so valuable that Salesforce's CEO publicly begged for a smart router to stop overpaying.
[06:17] It's not a nice to have anymore.
[06:19] It's core infrastructure.
[06:21] And spoiler, it's also one of the business opportunities I'll get to.
[06:26] Method three, caching.
[06:29] Stop paying twice for the same work.
[06:31] Imagine your HR chatbot gets asked all day long, "What's our vacation policy? How many leave days do I get?"
[06:35] Explain the time off rules.
[06:38] Three phrasings, same answer.
[06:40] Normally, the AI processes each one from scratch, paying full price every time.
[06:42] Caching means the system recognizes it, already answered this, and instantly returns the saved response.
[06:48] Near zero cost, and it feels instant to the user.
[06:50] It goes deeper, too.
[06:52] If 10 employees upload the same 500-page handbook, caching lets the AI process it once instead of 10 times.
[06:59] For repetitive, high volume workloads, this alone can gut your bill.
[07:04] Method four, keep your context lean.
[07:06] Almost nobody knows this one.
[07:06] Here's a hidden cost that shocks
[07:08] People.
[07:10] AI doesn't just charge for your latest message.
[07:12] It charges for the entire conversation history you drag along because it rereads all of it every time.
[07:17] So, if you've been chatting for an hour and your thread is now 20,000 tokens long, then you ask, "What's 25*16?"
[07:24] The AI reads all 20,000 tokens again just to answer that tiny question.
[07:27] You're paying for a full suitcase to grab one sock.
[07:28] Three fixes.
[07:31] One, start a fresh chat when the topic changes.
[07:33] Don't let one endless thread balloon.
[07:35] Two, only attach the files actually relevant to this task, not six months of Slack.
[07:40] Three, and this is the pro move for anything repetitive, don't reuse one giant pinned chat.
[07:44] Convert it into a project.
[07:46] Both chat GPT and Claude support these.
[07:48] A project holds your context as compact instructions so you get the same consistency without repaying to resend your whole history every single time.
[07:56] Quick trick, ask your AI, "Turn this chat into an instruction set I can use for a project.
[08:00] Save that and start fresh inside the project."
[08:04] Your usage drops off a cliff.
[08:07] Method five, own the hardware for the boring.
[08:09] Stuff.
[08:11] Every time you prompt a cloud AI, your request flies to a company's servers and you pay per token.
[08:15] But for high volume, repetitive, non-sensitive work, there's another way.
[08:20] Run a smaller open model on your own machine.
[08:22] Then each task costs electricity, not tokens.
[08:25] Using cloud AI for everything is like taking an Uber to work every single day.
[08:29] Fine, occasionally, brutal as a daily habit.
[08:33] You don't even need special gear to start.
[08:35] Tools like LM Studio let you run capable open models like Google's Gemma on a decent laptop.
[08:40] Level up and a well-specced desktop becomes a dedicated inference machine that pays for itself over months of heavy use.
[08:45] You keep the cloud's frontier models for the hard 20%.
[08:50] And stop renting for the easy 80%.
[08:53] Stack all five cheaper defaults, routing, caching, lean context, and owning hardware for the routine stuff and a bill that was hemorrhaging money can drop by an order of magnitude with zero loss in the quality that actually matters.
[09:03] That's the playbook.
[09:06] Now, let me show you how to turn it into money.
[09:07] Here's the pattern that matters, and
[09:09] It's the real reason I made this video.
[09:11] Every technology wave creates two kinds of winners.
[09:15] The people who build the technology, and the people who make it cheaper, faster, and easier to use.
[09:20] In the internet boom, fortunes weren't just made building websites.
[09:24] They were made building the payment systems.
[09:26] The cloud hosting, the tools that made the internet affordable to run.
[09:30] AI just hit that exact phase.
[09:32] The gold rush is ending.
[09:35] The sell pickaxes and plumbing phase is beginning.
[09:37] Here's how to be on the winning side.
[09:39] If you're an employee, become the person who saves the money.
[09:41] Here's my prediction, and it's already starting.
[09:45] In 2025, companies rewarded whoever used the most AI.
[09:48] In 2026 and beyond, they'll reward whoever gets the same work done with the least.
[09:52] The scoreboard is flipping from token legend to token saver.
[09:57] Think about the math.
[09:59] Say a company pays you $80,000 and spends another $60,000 on your AI usage.
[10:01] You cost them $140,000 total.
[10:04] Now, you apply this playbook and cut that AI spend from $60,000 to $20,000.
[10:09] You just saved the company $40,000 a year at no
[10:11] Quality cost.
[10:13] Any smart company will happily share that back as a bonus, a raise, a promotion.
[10:17] You've turned a boring skill into leverage.
[10:19] Learn the five methods in this video.
[10:21] Apply them at your job and become the person who quietly saved the department six figures.
[10:27] That person does not get laid off.
[10:29] That person gets promoted.
[10:29] If you're an entrepreneur, here are three real businesses being born right now.
[10:32] Opportunity one, cost optimization as a service.
[10:35] Every company with a scary AI bill needs someone to fix it to set up routing, caching, and lean context systems.
[10:44] This is a consulting business you can start today.
[10:46] There's even a name for the discipline now, AI Phops, and the going rate for AI agencies is real.
[10:52] Discovery engagements run a few thousand.
[10:53] Custom builds five figures plus monthly retainers to keep optimizing.
[10:59] You're not selling software, you're selling a smaller invoice.
[11:01] That's the easiest sale in business.
[11:03] Opportunity two, run open models for companies.
[11:05] Tons of businesses want to run AI on their own hardware for cost and for privacy, but have no idea how.
[11:13] They need someone to install the open models, tune them for their needs, and keep them running.
[11:18] That's a service.
[11:18] That's a retainer.
[11:20] That's a moat.
[11:20] Because once you're running a company's private AI, they don't switch.
[11:24] Opportunity three, rent out compute.
[11:26] Inference machines are expensive, so many startups won't buy their own, they'll rent.
[11:31] Exactly like companies rent cloud servers today.
[11:33] If you understand this hardware, there's an arbitrage business in owning it and renting it out.
[11:38] And the market backing all this is enormous.
[11:40] The AI inference hardware market, the chips that answer your questions, as opposed to training the models, was valued around $43 billion in 2025 and is projected to grow roughly 10fold over the next decade.
[11:52] Inference now makes up the majority of all AI compute demand, up sharply from a couple years ago.
[11:59] This isn't a niche.
[11:59] It's the plumbing of the entire AI economy and it's being laid right now.
[12:02] Now, let me show you the future because there's a shift happening that will make everything in this video even more valuable.
[12:09] And if you see it early, you're ahead of almost everyone.
[12:11] The biggest change, the industry is abandoning per token pricing entirely.
[12:15] Companies got so burned by unpredictable token bills that the smartest AI businesses are switching to outcome based pricing.
[12:21] You pay per result, not per token.
[12:23] Intercom charges about 99 cents per customer issue actually resolved.
[12:28] Nothing for failed attempts.
[12:30] Zenes charges only when a ticket is fully solved.
[12:34] Even enterprise giants like SAP are moving this way.
[12:37] SAP's CEO said it would be foolish to keep charging per user when AI does the work.
[12:41] The unit of value is becoming the completed task, not the tokens burned getting there.
[12:46] Watch for this everywhere.
[12:49] It's the next phase.
[12:49] Second, the tools are getting radically cheaper underneath.
[12:53] Here's the wild part.
[12:53] Inference costs for a given level of intelligence have fallen dramatically.
[12:57] Stanford's HAI found the cost to run GPT 3.5 level performance dropped nearly 280 fold in 2 years.
[13:06] So the unit price keeps collapsing even as total bills rise because usage explodes faster than prices fall.
[13:13] That paradox cheaper per token, bigger total bill is the defining.
[13:16] Tension of this whole era and it isn't going away.
[13:21] Which means cost discipline isn't a temporary fix.
[13:23] It's a permanent skill.
[13:26] Third, AI cost management becomes a real job title.
[13:28] The way cloud cost engineer became a career after the cloud boom, AI fine is becoming one now.
[13:33] There's even a new industry foundation forming to set standards for measuring AI spend.
[13:38] Early people in a new discipline become the experts everyone hires.
[13:42] That window is open right now.
[13:42] And fourth, my honest take, the companies that win the next decade mostly won't be the ones building the smartest model.
[13:49] They'll be the ones that make AI cheap and reliable enough for billions of people and millions of businesses to use every single day.
[13:55] The intelligence is almost solved.
[13:57] The economics are the real frontier and that frontier is wide open for employees, for founders, for you.
[14:03] We started with Meta's Claudia leaderboard.
[14:06] 85,000 people racing to burn the most tokens.
[14:08] One guy torching 281 billion by himself.
[14:11] Companies treating a runaway meter like a trophy.
[14:15] That whole mindset is already dead.
[14:15] The trophy of 2025.
[14:15] Look how much
[14:18] AI I use became the cautionary tale of 2026.
[14:21] And the people who saw it early, who learned to get more out of AI while spending less, are the ones getting promoted, landing clients, and building the businesses on top of this mess.
[14:31] Here's the one idea to take with you.
[14:33] In a gold rush, you can dig for gold, or you can sell the shovels.
[14:38] Everyone spent the last two years digging, burning tokens, chasing the biggest model, hoping.
[14:43] The real money now is in the plumbing, the routing, the caching, the cost discipline, the tools that make AI actually affordable.
[14:50] That's the boring, unglamorous, wildly valuable opportunity sitting in plain sight.
[14:54] And now you know exactly where it is.
[14:56] So, pick one method from this video and apply it this week.
[14:58] Then decide, are you going to keep watching this wave or ride it?
[15:02] If this gave you a real opportunity to chase, like and subscribe and tell me in the comments, are you going to use this to save money at your job or build a business on?