Ep 789: Tokenmaxxing is over: The New Era of Token Efficiency and how Your Company Should Adapt

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life.

There's been this pseudo badge of AI honor over the past 6 months that makes no sense, and I think it has led a lot of enterprise companies astray when choosing the right AI strategy. What is that thing? It's token maxing. So token maxing is the corporate practice where companies push AI token consumption and correlate that AI usage as a direct metric for employee productivity, or pointing to the fact that they're doing something positive with their AI spend. Like, if you're using billions of AI tokens a day, that means that you're living inside of large language models, and clearly you are creating huge value for the company. Because more prompts has to mean more ROI, right? But that strange AI power flex of early 2026 has quickly revealed itself to be a false and sometimes dangerous metric by mid-2026, because it turns out all that token maxing leads to is a competition to see who can run the longest agentic loops regardless of if it actually accomplishes a company's goal. So what's the new metric? What should your company be looking at? That's token efficiency. In other words, getting the most bang for your AI token usage buck means not just burning AI usage for the sake of using it, but instead aligning your company's AI strategy with the right model used the right way for the right job. Weird. Common AI sense pays off more than a leaderboard showing who is using the most tokens. So on today's show, we're gonna break down the rise and fall of token maxing, not only showing you what tokens are and how they work, but also uncovering the proper best practices to put your tokens toward actual measurable outputs versus just lighting millions of dollars on fire. So here's the big picture. For the past 6-ish months, companies have treated heavy AI token use as proof of productivity. And that's because token prices themselves have fell like 200 times, right? If you remember back, you know, the earlier versions of the GPT technology were like $30 per million, you know, input and like $60 per million token output. And now it's like 3 and 8, right? So token prices have fallen significantly., but the token usage has also gone up like 100 to 200x as models get more agentic. And now that models are reasoning by default, AI agents make the number of tokens per task absolutely explode. So I think now in mid-2026 and through the rest of the year, I think the goal, if it hasn't already in your organization, shifted away from token maxing and toward token efficiency. So stick with us on today's show and you're going to learn what token maxing is and how it led companies to waste a ton of money. You're going to learn even more fundamentally what a token is and the 4 main ways that AI models gobble those token ups— tokens up. You're going to know why companies are shifting from token volume to token efficiency and how to turn AI spending into real measurable results. Let's get into it and start here with the basics. Yes, this episode is part of our Start Here series, the essential series to both learn the AI basics and to double down on your AI knowledge. So if that's what you're trying to do, make sure you go to starthereseries.com. That is going to give you free access to our exclusive inner circle community. It's free. There's no other way to get access aside from going to starthereseries.com. And once you sign up, you will be inserted into our Start Here series space where every single episode is there for you. You can digest them all very easily. We have a playlist there as well that we keep updated. So Start Here series. If you missed our last episode, we talked about the 2026 LLM cheat code and 10 essential steps to get the most out of any AI chatbot. And if I'm being honest, that's probably one of the best shows we've ever done here out of 700 90 episodes. So make sure you check, uh, that one out. That's volume 26 and episode 786. But today, let's talk token maxing. So token maxing is, well, thinking that however many tokens you spend means something. In, in rare cases, it might, but I'd say in most cases, it just means that employees have been given a green light to use as much AI as possible. They're probably not closely monitoring what their agents are doing. And in some cases, it's more of, you know, people trying to pound their chest and talk about how many tokens they're using. That's because, yeah, companies, I think especially when it came to, you know, Claude Code in Claude Cowork of late 2025 and early 2026, that was kind of the ushering in of the new era of these agents that can run all the time. So, you know, you saw people sharing, oh, I had my agent run for 30 minutes or 60 minutes. For 5 hours. And then there was this rush for enterprises to, well, let's do that because clearly that has to mean something. And it was the assumption that if you used a lot of tokens or, you know, if your agents were constantly running around the clock, that means that the company was making money. But obviously those two things do not correlate. And I think some of the big companies set a bad example. One of the most prominent examples, well, we saw some reporting that Meta reportedly built an internal leaderboard ranking employees by tokens used. So one engineer reportedly ran 281 billion tokens in a single month. And some employees, well, they wanted to climb this internal leading board, leaderboard, because, you know, not just that Meta, but a lot of other companies that were doing something similar. You know, if you weren't using a lot of tokens, developers and, well, you know, people that were being pushed to use AI models felt if they weren't using enough tokens, then maybe their job was at risk. So you had stories come out that employees were just running empty agentic loops, right? Having it, you know, intentionally trip up, you know, do some of their personal errands, but giving it impossible tasks just to make sure that they were using those tokens. So they weren't actually creating new business value for their company. They were just trying to light tokens on fire. All right, so now you know the trend. Let's double-click into the basics, right? This is the Start Here series. This is for both beginners and for, uh, you know, experts trying to double down. And depending on when you're listening to this, right, as of the recording, it's early June 2026. So if you're listening to this in December, maybe the tokenization story has changed. But in simple terms, here's what a token is. It's essentially a small piece of text that an AI model reads or writes. So essentially, if you, you know, put a big prompt to ChatGPT or Gemini or Claude or Copilot and you insert some of your company's documents, right, these models don't technically read it. They convert everything into tokens. So they think in tokens, they technically read in tokens, and they output or talk back in tokens. So one token is about 3/4 of an English word, right? So, uh, you could say a million tokens is about 750,000 words, give or take, right? And models charge a different way. We're gonna talk about some of these model subsidies and how they're kind of going away, uh, right? But your usage, or what you're actually paying for, is ultimately converted into tokens. So now that you know that that is kind of the currency of a token, let's break down how these models use tokens, right? And that's obviously changed because, you know, 2 or so years ago it was a very straightforward transaction, right? It was literally, you could measure it in those words, right? Hey, I, you know, I pasted 100 words to, you know, ChatGPT, 75 tokens. It gave me 100 words back, another 75 tokens. Now, It's not as easy because the models are getting more and more capable. So let me just give you an example, right? So Codex and OpenAI recently ran a promotion. I'm on the $200 a month plan. This part's kind of important. And they had a couple incentives going where, you know, essentially they gave some multiples to some people. And I was one of those people. So I was able to essentially go through maybe about a billion tokens a day, right? On some days where I was running certain processes a little more heavily, not even a ton of coding projects. I'd just been automating a lot of my manual knowledge work, which has been great, right? So think of a billion tokens and it depends on your input output and how much reasoning you're using, you know, how agentic your task is. But think of about a billion tokens somewhere from like $12,000 to maybe $17,000 if you were paying for those straight via the API. All right? And this part's important because early on in the early days of AI, and we're getting out of those nice days, essentially companies would subsidize those tokens via a subscription plan. So I wasn't paying $12,000 to $17,000 a day for these tokens, obviously, right? We don't have, you know, 15 ads on our daily podcast like other podcasts do, right? We're not making $17,000 a day in ads. So a lot of companies have been subsidizing those tokens and, you know, as a way to attract and retain customers, you know, it's kind of the dance, especially I think OpenAI, Google, and Anthropic have been taking different stances on it, especially as of recently, because many companies Specifically Anthropic, they've been kind of the strictest on those usage limits. Google, after their I/O conference on their $20 a month plan, they started to implement some actual usage limits, whereas before they really didn't. And Microsoft, even with their Microsoft GitHub Copilot, they're starting to do that too. So before, on certain paid plans, you really had some wide open usage. Now they're really starting to meter it., right? So, you know, before it's kind of like, well, taking a Lyft, right? It's almost like you had a monthly subscription to Lyft and you can just take it from any point. Now some of the companies are more taking it to be the metered taxi approach, right? OpenAI, I think, is still probably the most generous in subsidizing those subscriptions, but eventually a lot of those subscriptions or those subsidies are going to change because someone's paying for it. And that's essentially a customer acquisition or customer retention costs that companies are ultimately paying to keep their millions or, you know, eventually billions of customers happy. But many tier— so that's a personal plan, right? Personal plans work a little bit different than, you know, big business or enterprise plans, because many enterprise plans, you know, you're— you may get a certain allotment of tokens, uh, per seat, but once an individual surpasses that, then they're charged at the normal taxi rate, right? So it's like, all right, well, you get, you know, 10 free Lyft rides a month or 10 free Waymo rides a month. But then after that, not only are you paying the metered taxi rate, but the meter's gonna be running all the time because if you're running agents, those things are running around the clock. So let's talk about how these tokens are fully spent. I wanna break this down in hopefully a very easy and simple to understand way. So there's, depending on how technical we want to get, I'm going to keep it easy. There's essentially 4 different types of tokens, right? Because everyone's like, well, how do I know how I'm using tokens? Well, basically anytime you or the model is working, tokens are essentially being counted up. All right. So you don't really have to worry about it from the cost side. So if you are paying strictly via API, so if your company is, you know, working just on the backend via the API, you're paying strictly API costs. If you're on a personal subscription, for the most part, you're just, you know, you just have your allotment and your usage. And if you're on a team plan, it might be a hybrid of those things. But every input that you send, every message is converted to tokens, right? So longer prompts and larger files obviously raise those inputs, those input costs. And even how different models tokenize or understand things really vary from model to model, or sorry, not just model provider to model provider, but also the actual model to model within the same provider. You know, some, you know, models cache things a little differently, right? So like a browser cache that remembers things so it doesn't have to reload that webpage every time. Sometimes, you know, depending on how you have things set up, how you have your connectors set up, if you have a very long chat thread going, it might rerun those tokens every single time, right? Like if you have a browser that crashes and you had, you know, 50 windows open and you reload it, it might load every single one of those 50 windows and it could be very slow. So depending on how a model caches things, it might actually impact how many tokens are being used. And that's important because the token usage also impacts the context window. So that's essentially how much information a model can remember fairly accurately before it starts to lose information at the top of the context. Okay, so that's, that's driven by tokens, right? So the most common way and the easiest way to explain is your input tokens. The second type is output. So that is what a large language model gives back to you. Sometimes that's text, sometimes it's code, right? Sometimes in theory it's a video, right? Sometimes it's an image. So, you know, in those cases, it's a little bit harder to count and understand the token usage there. But AI moves too fast to follow, but you're expected to keep up. Otherwise, your career or company might lag behind while AI-native competitors leap ahead, but you don't have 10 hours a day to understand it all. That's what I do for you. But after 700+ episodes of Everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's 3 ways to jump in. Number 1, go scroll back to the first one in episode 691. Number 2, tap the link in your show notes at any time for the Start Here series, or you can just go to starthereseries.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. This is where it starts to get expensive, right? And I remember being at the, uh, uh, NVIDIA GTC event, uh, might've been last year. Yeah, I think, you know, uh, NVIDIA CEO Jensen Huang kind of had a graph up there and saying, yes, the price of tokens is going down., but the amount of tokens used is going up. So yes, we've seen roughly a 10x decrease in the cost of tokens. So you may say, oh, well, what's the harm with token maxing then? Well, that's because of token type 3, that is reasoning. And because of these models that now reason by default, whereas before they were kind of just like, you know, super smart next token predictors, right? It was text in. Text out, now not so much. Obviously, the type of inputs can cost more to tokenize. The type of outputs, right? If it builds audio, video, an interactive website, right? But now these models think by default, and that's where we sometimes see what Jensen Huang and others have talked about, 100x or more, um, kind of output that an agentic model requires versus a, you know, traditional non-reasoning, non-thinking transformer model. And these new models reason internally. Sometimes you can see these models reason by looking at their chain of thought, and you can observe and see all these different tools it calls and things like that. Sometimes it doesn't, but these models actually think. And even though you might not be able to count in theory the words of those thinking, it is eating those up. Those tokens are being consumed. And then last but not least, I kind of just alluded to this because you can sometimes trace this in the chain of thought, and that is the tool use, right? That's the harness. That's, you know, as an example, you know, if you're running something like Codex or Claude Code, you know, it's not just the model that thinks. It's thinking and it's using tools, you know, if you give it special access to skills, sometimes those skills will run multiple apps. They'll run, you know, different plugins, you know, calling different tools, searching things on the web, using computer vision, using computer use. All of these different tools are obviously very token intensive. That's because AI agents can use tools, they can read files, they can run code, and then they can repeat steps automatically. And then when we talk about scheduling, agents, right? And you talk about running agents on an automation. So many times business leaders aren't even always looking at these things, right? As an example, if you have a scheduled agent, you know, and it's pulling from your dynamic data in one of its jobs, and I do this all the time, but luckily I'm on, you know, Codex. I love it. I'm on the $200 a month, uh, plan, so I don't usually have to really worry about my limits, right? But a lot of times people, they'll set up powerful agentic loops and it's working with your dynamic data. So maybe you have something every single day that, you know, triages your company's, you know, SharePoint, OneDrive, maybe there's a team inbox and then it creates a live dashboard, right? And it updates every day or maybe it updates every single hour, right? That's an agent going in there and ingesting a lot of input tokens it's exerting, I guess, a lot of output tokens. It is using a lot of thinking tokens, and it is calling a lot of tools. So it is doing all of those things on a loop, scheduled, and let alone being efficient, which we're going to get to here in a minute, right? Because especially as humans get more hands-off, right, which I think is the wrong approach, as models get more autonomous and more powerful, the expert-driven loops should be expert-driven, not the lazy human-in-the-loop that I absolutely despise, right? But well, humans take their hands off, so a lot of these processes are happening on repeat in a loop, and humans just look at the output and they don't necessarily see, oh well, maybe this did cost us a billion tokens a day, right? And costs like that can really add up. So that was kind of the story, the token maxing story, because it became this bragging right. You know, you saw these leaderboards, people would post, you know, oh, I used, you know, this many tokens this week. And a lot of times it was the big labs, right? The people actually building these things kind of bragging or showing how many tokens they're using. And well, a lot of times, you know, your Fortune 500 type companies, are looking at what big tech is doing and assuming that they just have to follow suit. And that doesn't always turn out that well. There's actually an Axios report, uh, that just came out talking about an unnamed company. You know, there's a lot of, uh, thought out there or talk out there on if this story is actually true or not, but Axios is generally a pretty good media outlet, uh, better than a lot of the others. And they said that a company reportedly wasn't monitoring or setting spending limits on Claude, and they reportedly used $500 million in AI usage. One company, one month, by not properly monitoring, uh, this specific model that is very token hungry, right? Claude is like me at 4 PM if I haven't eaten. I'm angry and I want to eat everything. All right. So here's kind of what I want to talk about. Shifting toward token efficiency. So don't get me wrong, I am not anti using a lot of tokens. I'm not right. Because I think that there is obviously a certain amount of experimentation in token burn that needs to be done. Right? A good example, if you're in marketing or advertising, let's say you're starting a new campaign on Google Ads or something like that, or, you know, an advertising campaign on Meta. A lot of times you have to just burn a lot of money, right, to, you know, kind of train the algorithm on what you want, what you don't want, you know, types of conversions for your company to have enough data to know what works and what doesn't. And technically using tokens is the same, right? Because you don't know, as models' capabilities are constantly changing, you know, where your company or your department is going to be able to derive the most value unless you are experimenting, unless you're, well, maybe burning through a lot of tokens. But if your strategy, if your company's strategy is token maxing, right, it's not going to work. Right? You have to start looking at a certain point at token efficiency because even if you've been able to afford it, right, even if your company, you know, everyone's on a highly subsidized plan and you're not having to pay a lot of API overages, the thought is all these companies eventually the free ride is going to be a little less free. And as these models become more capable, they're going to cost more to run because they're going to think more. They're going to become more and more powerful, right? So token efficiency means getting the most useful work per token spent, right? My thought is I like to think that there's a certain price that you would pay for a certain output. Let's say you're going to hire consultancy. Right? Maybe you have to think, okay, are we going to hire a Big Four consultancy? Are we going to hire a small team of full-time employees? Or are we maybe going to hire an agency or a group of contractors for a certain project? Right? That's a normal decision that a lot of companies have to go for. And they probably say, this is the output. The output is X. We have 3 different routes that we can go. But no matter what, we have to monitor our cost. And, you know, essentially that output is a certain piece of intelligence or an artifact that our company needs. You need to start looking at your AI usage in the same way. Not just, all right, let's give everyone Claude Desktop. Let's give everyone Codex and go to town. Don't get me wrong, your company should be doing those things, but doing it in a responsible way. Uh, because the future of work, I think, is in these extremely powerful desktop harnesses that can be always on and take advantage of your company's dynamic data. But you have to understand that it's not just about running through. It's about what those tokens are actually doing, measuring what they're creating, knowing and being able to equate what those tokens create in new economic value for your company versus how much it costs a human to do the same thing without any AI, right? What did that artifact, what did that output, what did that client deliverable, what did that RFP cost in 2020, right? And then being able to put a price on what a human would cost pre-AI, you know, knowing how your, um, whatever AI system you're using, how much it costs from a token or a usage perspective to do the same thing. You have to start looking at it in a very pragmatic and measured out way. We've actually had a couple of episodes, including one in the Start Here series, specifically talking about measuring ROI, but that is the accepted output. So the measure is the accepted output against the total cost, time, and rework, right? So you have to measure it, what it costs for a human to do it, no AI, what it costs for an augmented, uh, you know, kind of a best human with the best non-agentic AI. And then you need to measure it with the agentic AI, with the expert-driven loop. So you have to know there's going to be a different mix, but you have to know the right model used the right way just beats simply using more AI every single time. So a couple things. I'm not going to say use this model for this, but I think there's been a lot of, um, I don't know if it's disinformation necessarily, but a lot of misinformation floating out there around what model is the best to use for what reasons, right? And I think we've seen a shift and I'm probably going to do a Start Here series episode on this very soon about the shift away from models to the harness because I don't think models are as important as they were anymore because especially these, you know, point upgrades, you know, going from, you know, GPT-5.3 to 5.4 and Opus 4.7 to Opus 4.8. Are the models better? Yes. Are the benchmarks better? Sure. Right. But ultimately it's the harness, I think, that makes the biggest difference. Anyways, you still have to look at the price per intelligence, right? Are those tokens worth it? Or if you give a model a certain task, is it efficient? Is it just going to run in loops all day? Or right, if you give a model a task and it gets it done in one pass without misfiring 20 times, without having to, you know, unnecessarily call 7 tools that it didn't really need because it was rushing to the conclusion instead of breaking it down in a proper way. Is that the right model? And I think there's a couple important metrics to look at. We're going to tackle 2 here. So if you listen to the show at all, you know, a lot of times I talk about the artificial analysis. So there's the artificial analysis intelligence. Score. So that's essentially a series of tests, and then models get a score. And one of the most important, you know, metrics to look at undoubtedly is just that score. So that's ultimately how intelligent is a model, nothing else considered, which is a good starting point, right? Especially if you're on a highly subsidized plan that you don't have to worry about anything else. But that's not the reality. And well, that You know, fantasy AI setup that we all had in mid-2025 through, uh, maybe like 5 months ago is gonna slowly start to go away. So you have to now start looking at what does that model actually produce, uh, per token, right? So that's why the Artificial Analysis, that same exact test that I talked about, uh, right where it's essentially a conglomerate in Artificial Analysis, the company, runs these, you know, these, you know, roughly 10 or 12 different tests and they count how much money, because they're running this via the API. So they know not just the score, the intelligence, because that's important, but how much money did they have to spend to get that level of intelligence. So artificial analysis also has this, uh, in intelligence versus cost. And so one thing you'll see The new Opus 4.8, uh, did beat out the new, the, uh, OpenAI 5.5. Not by a lot, but by a solid point. One point is not a big jump in the artificial analysis intelligence index, but it's, you know, big enough to say yes, when it comes to just strictly intelligence and intelligence only, Claude Opus 4.8 is the best across all of these benchmarks, which is an important thing to talk about. But the cost, it is absolutely bonkers because the Anthropic models are, and you can see this by tests and people always, this is annoying y'all. Like people always accuse me of being, oh, anti-Anthropic. You love Google. You love OpenAI. No, Claude's a great model, right? But it is incredibly token inefficient. And if you are on a, a paid plan, Anthropic's plans are the least subsidized, right? That's maybe why they're the most profitable AI company per user right now, presumably. But in terms of if you are out there, you know, on a subsidized plan, not only is it you're going to get the least for your dollar, but if you're paying on it through the API, all you're doing, you, your company is saying, eh, let's just go ahead and flush this money down the toilet because it doesn't matter. Because the facts say, the stats say, the unbiased third parties say that you are literally lighting your money on fire if you are choosing to use the Anthropic models via the API, right? Yes, maybe there's a capability that the Harness, you know, Claude desktop has that no one else has. Sure. Right. But if you have built your company's AI strategy in a modular way, which is the right way to do it, right? And you are still using Anthropic's models. You are essentially saying by all measures that you just want to waste money, right? Here's another example also from Artificial Analysis. This is the total cost, right? The total cost to get to this. So Anthropic's models are the most expensive. So to run that test, about $5,100 for Opus 4.7. Comparatively, OpenAI's latest model, uh, $3,300. So I'm not the best at math, but that is 50% more expensive to get that same level of intelligence, right? That's like if you're saying, okay, I'm, am I going to take an Uber or a Lyft? Well, the Lyft is 50% more expensive and they're going to get you there. In roughly the same time. Well, not actually, because there's also a speed factor, but I don't want to focus on speed today. But from a cost per intelligence ratio, right? And this is where, when we talk about token efficiency, this is why you have to keep up. This is why you have to build in a smart way. You don't want to lock your company into a certain vendor if you don't have to, right? Obviously, if you're using something on the desktop side, but if you were building on the API side, Or, you know, maybe you're using Cursor, right, where you can use a number of models. You're using, you know, Copilot that can use the GPT models. You can use the Claude models. You can use the, you know, the Microsoft models, right? You have to know these things. You have to understand how much does the intelligence cost, not only which model is the most intelligent. Another great metric to look at, and I'll probably be talking about this a little bit more, is DeepSuite. So DeepSuite is a new, and I'd say it's probably one of the better, uh, agentic, um, coding benchmarks. So there's an older benchmark called SuiteBench that I think is actually antiquated, um, and DeepSuite is actually a really good benchmark. Maybe I'll do an entire show on that. Let me know in the comments if you want me to do it. Just DeepSWE. So that's DeepSWE if you care about this, right? But this is a great benchmark that shows something similar to the artificial analysis. But the reality is, at least when it comes to token efficiency, right, I'm not going to tell you use this model for this, right? But in the same way that the artificial analysis, actually Gemini 3.1 Pro one of the best models out there in terms of, you know, getting for what you pay for because it costs even a fourth, just about a fourth of what GPT-5.5 costs, right? And it costs a fraction, you know, not even 20%, you know? Yeah, it's, let me, Let me do my math here. So that is about more than 5x, uh, more to use Opus 4.7. You're going to pay 5x more, not 50%, 5x more to get the same level of intelligence out of Opus 4.7 versus Gemini 3.1 Pro. So if we get back to DeepSuite, the same thing shows true, right? GPT 5.5. Gets a, not only a higher score, much higher than the Opus models, but it does so at, again, a fraction of the cost, right? So, uh, it's more than, uh, double, right? So you're getting not only a higher level of intelligence, uh, but you're about half the cost. So when it comes to token efficiency, it's not just about what your company gives you access to. Because like I said, you always need to be building modularly. Because what happens if a model, you know, if you've, uh, set up a big chunk of your company, whether it's running somewhat autonomously, you know, a bunch of, you know, cron AI jobs, whatever it may be, if GPT-5.6 breaks your flow, right? If, if, you know, Mythos comes out and nothing works, you have to have a fallback, right? So you should always be testing these things, but Not just what is the most intelligent model. That's a good place to start, but the cost per intelligence is an extremely important thing. And that is the prudent and the responsible, and I think will be the rest of the story. The telling takeaway through the rest of 2026 is setting your, setting your company up to not only be AI native, but to be token efficient because the AI subsidy days are gonna be over fairly soon. And you have to be ready. So your company needs to measure the accepted outputs and the AI costs, not just the number of tokens used. You have to match the model to the job. You can use smaller, less expensive models for certain things, parsing PDFs, you know, doing simple text summarization. And you can use those bigger, more expensive models like, you know, Opus and GPT-5.5 for those more complex tasks. You need to start monitoring., you know, these agents that are stuck in a loop. You need to start maybe kicking out certain models that just constantly are calling tools that they maybe don't need to, right? You have to be paying attention to the chain of thought. And then you need to review your AI cost because these long-running agents, it is so easy. It's so easy as these models become autonomous like you can in a, in a Waymo or, you know, in that version of Tesla's Cars that, I don't know if they ship, but they keep saying, right? It's almost like your hands off the wheel. Right? You can completely hands off the wheel doing something else. You can't do that right now with agentic AI. You can't just be a passive human in the loop because that is going to cost your company probably millions of dollars in the long run. You have to have an expert-driven loop that is constantly monitoring. Not just the inputs and the outputs, but monitoring the efficiency of those tokens as well. Start doing that now and you are not going to be behind for the rest of the year. You are going to be ahead. And that is where I want you to be. You know, one quick tidbit to wrap this up was actually, you know, just got out of a great session last night with the Microsoft CTO Kevin Scott, and he kind of ended, you know, one of his, you know, it's just a little, little gathering at a bar, but, you know, really just talking about how activity does not equal value. And I think that's a great takeaway to end this on, because just because you're using the best models and you have all of these scheduled prompts and you have all of these agents running around the clock, don't confuse activity for value, and especially pay attention to what model you are having be active for you and the value that it is actually creating. All right. I hope this one was helpful. If so, let me know if you could like and subscribe to the show. If you're listening on Spotify or Apple Podcasts, I'd really appreciate that. And then make sure you please go to starthereseries.com to get exclusive access to our inner circle community so you can start here series along with everyone else that's doing it. So thank you for tuning in. I hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.