Ep 813: AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem (Start Here Series Vol 31)

AI’s all-you-can-eat era is ending. 🍲

For years, one subscription felt like unlimited access to frontier models.

But that business model for the AI labs apparently breaks when agents can now run for days, use tools, retry work and burn through tokens.

And with Anthropic's powerful Fable 5 model exiting subscription tiers today and moving to API only pricing, it's as imperative of a time as ever to figure out your AI spend strategy. 

Frontier AI is becoming a metered utility. On today's show, we teach you how to deal with it. 

AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. End of Unlimited AI Subscription Plans
2. Anthropic Fable Five Subscription Removal
3. Copilot and Grok Switching to Pay-Per-Use
4. Enterprise AI Cost Control Challenges
5. Token Consumption in Agentic AI Models
6. Board-Level AI Spending Concerns
7. Strategies for AI Spend Optimization
8. Fine-Tuning and Multi-Model Routing Solutions
9. Seven-Step AI Cost Reduction Playbook

Timestamps:

00:00 Rising AI costs and usage

05:18 AI service cost challenges

10:18 Cost of AI and OpenAI's Future

14:18 Chatbot costs becoming a big issue

15:10 Automating work with desktop agents

19:26 Hidden costs of automation loops

24:13 The future of model mixtures

25:16 Microsoft Foundry's fine-tuning service

31:20 Fine tuning AI models

32:13 Closing thoughts on AI future

Keywords: 

AI cost control, chatbot bill, AI spend, token efficiency, metered AI, agentic models, AI subscription plans, Fable Five, Anthropic, API pricing, OpenAI, GPT-5.6, Copilot cowork, GitHub Copilot, Google Gemini, AI credits, usage limits, credit-based system, Grok, NeoCloud, board-level AI concerns, token maxing, spending limits, enterprise AI, SMB advantage, API token pricing, token-based billing, model routing, open source AI models, GLM 5.2, Kimmy 2.7, caching, difficulty-based routing, fine-tuning models, Microsoft Foundry, fine-tuning as a service, Thinking Machines Lab, tuned specialists, mixture of models, AI routers, perplexity, Merge, spend routers, AI budgeting, overage alerts, default model selection, AI model compaction, automation, human-in-the-loop AI, context length limits, token burn rate, Jovan’s paradox, AI tool escalation

Transcript

SPEAKER_00 0:16

For the past three and a half years, AI has almost felt too cheap to be real, especially for power users during early 2026. I mean, you pay a monthly fee, you open Chat GPT or Claude or Codex, Gemini Copilot, whatever, and then you have agents run for hours completing your actual work with your contacts without paying an additional penny on top of that subscription. But that version of AI is ending and it's gonna hit even harder later today. Why? Well, that's because Anthropic's Fable 5 only has hours left until it's no longer included in paid subscription plans. And that's not just an anthropic story. And it's the clearest signal yet that Frontier AI is becoming a metered utility. I mean, Microsoft GitHub moved co-pilot into AI credits. Google changed Gemini limits around compute complexity features and chat length, and Microsoft started pricing co-pilot cowork through credits, not included subscriptions, and even NeoClaude XAI's Grok has been moving users toward share weekly pools and extra usage credits. So with the explosion of agentic models that can now work for hours with expert level artifacts, the question is no longer how do we get everyone in our company using AI? The board level question is how do we use more AI without bankrupting our budget in the process? And that's exactly what we're going to be tackling today. So here is the big picture. Frontier AI has definitely now become a metered utility. And the wild west days of using whatever model you wanted around the clock and just paying a $20 or $200 a month subscription fee and nothing more, those days are pretty much gone. Um like I said, for three years we've been doing this seemingly unlimited use of AI. And if you go back to literally back in 2023, even when the models were pre-agentic, I even said eventually we're gonna see 200, $2,000 subscription plans. And I think even at that time, it's still gonna be a steal. And now when I throw some numbers out, you're gonna see that even these $200 a month plans, or if there isn't even a plan on top of that someday, will still be a steal considering what you're gonna start paying tomorrow. That's because even though the price per token has fallen by literally like 98% over the past three years, depending on what tier you're looking at, the agentic capabilities and the amount of tokens that these models are using has legit skyrocketed. And over the past two or three months, AI has quickly gone from let's use as much as we can in token max to putting strict spending limits. And sometimes, you know, developers at huge companies aren't even to use AI as much as everyday non-technical people like you and me. And I think the fable issue today in Thropix Fable V leaving subscription uh in a couple of hours only heightens that chatbot bill problem. So on today's show, here's what you're gonna learn. You're gonna learn why Fable Five subscription exit is the cleanest and loudest warning shot for subscription AI and your team's AI strategy. You're gonna know why GitHub, Copilot, and Grok swapped unlimited access for pay-per-use meters. You're gonna understand why companies are capping spend, but still trying to use more tokens and how they're doing it. And I'm gonna leave you at the very end with our seven-step playbook to control runaway AI spend. Let's get into it. Welcome to Everyday AI and our Start Here series. This is the essential podcast series to both learn the AI basics and to double down on your AI knowledge. So if this is helpful and if that's exactly what you're trying to do, make sure you go to starthierseries.com. That is going to give you exclusive access to our inner circle community. You're gonna be thrust straight into our Start Here Series space where you can go listen uh to every single Start Here series, the simplest way to do it on the playlist that we have there, as well as you can go read about it, connect with other people who are trying to do the same thing. And if you missed our last Start Here series episode, we talked about the desktop agent lingo simplified, going over goals, loops, plans, sub agents, and how it works in codex and clawed code. And well, if you're doing all of those things, chances are yeah, your bills might start getting more and more expensive, which brings us to today's episode AI cost control 101 and why your chat bot bill is now becoming a board level problem. So over the past couple of months, we've seen this, and it's not just Anthropic's Fable Five. I mean, let's go back to May, right after Google's I.O. conference, their big yearly conference, for the first time, Google started putting actually pretty strict restrictions uh on their Google Gemini. So it's actually the first time I'd ever uh run into Google Gemini limits on their $20 plan. Up until then, it had been pretty generous. And, you know, after they made this shift of actually starting to count longer, uh, you know, taking things like complexity, the model you use, and chat length, all of a sudden that $20 plan that used to feel pretty great started feeling not super useful. Uh, then in June, you had Microsoft's GitHub Copilot replaced their premium request with token-based AI credits. So the unlimited plan was gone for Microsoft's GitHub Copilot. The same thing with Copilot Cowork. Microsoft is starting to go with task-level credit pricing and no longer including essentially unlimited co-work usage after they moved it to general availability. Uh, then a couple days ago, we also saw Grok uh from XAI Space XAI, I think is technically their new name, as of a couple hours ago. Uh, right, they started rolling out uh the credit base system as well, which is pretty telling considering, well, they're kind of like a neo-cloud leader now, right? They are uh one of the companies that has technically extra compute and they're even charging for it. And like I've already said, today is maybe the day that all of this gets thrust more squarely into focus because of Anthropic's Fable 5. And why is that important? Well, right now, at least, I mean, we'll see what happens over the next couple of hours because we've heard uh rumors that we might get a GPT-5-6 from OpenAI today, uh, sometime between today and Thursday, but uh, at least for the next couple of hours, uh, Fable Five is the most powerful AI model in the world. And at least for a couple more hours, it is still available in a subscription. But after today, you are going to be paying the full API price for that. So we've gone over the uh Fable Five drama kind of enough, but you know, they put it out there, they had to take it away because of some problems with the US government. Uh, and they brought it back a couple of days ago, but they said only included in plans through July 7th. And they said, hey, maybe one day when we have enough compute, maybe we'll bring it back, but no guarantee. And why does this matter? Because, like I said, for the past three and a half years, we've been able to, you know, whether you have a $20 plan, $100 pro max plan, $200 pro max, right? But for a very low price, and I know that's all relative, right? For many people out there, I understand a $200 a month plan, especially if you're paying it for it yourself, doesn't seem like the cheapest thing. But let me just show you why it actually is. So now, starting tomorrow, uh technically 11:59 p.m. Pacific time, you're gonna be paying uh $10 per million input tokens and $50 per million output tokens for uh Fable 5. Let me just put this into comparison now. So you're probably saying, all right, what's the big deal? All right, like Jordan, if you're paying, you know, $200 uh, you know, a month for, you know, which I am, I'm paying $200 a month uh for the Claude 20X Max plan, I'm paying $200 a month for the ChatGPT Pro plan. Uh, you know, I paid the $200 a month previously for the Google uh Ultra plan, so I paid for all of these. So you're saying, okay, what's the big deal? You know, if it just moves to API only, can't you just use that money for the API credits? Yeah, no. Okay. So granted, OpenAI has still ridiculously generous limits. All right. So let me put that out there. You're normally not getting that level of spend on these other plans, but I've been averaging uh over the past couple of weeks about two-ish billion tokens a week on the $200 a month uh OpenAI codex plan. Okay, uh, which is included with ChatGPT, right? So it's the same plan. Uh so that at a at a very generous blended rate, right? Of uh a blended rate of input and output tokens, that is a $200,000 a month anthropic bill. If I were to take everything I'm doing inside of Codex and I'm only paying $200 a month, and if I were to say, in theory, move all of that into um Fable, that is $200,000 a month. Okay, now you see why this has been a such a big discussion in the AI world about no longer having this democratized AI uh access to AI. We'll see uh, you know, what it means with uh OpenAI's models moving forward. Uh, they did say that their, you know, new GPT-5.6, uh kind of the different tiers, uh, Seoul, Terra, Luna, and you know, Soul Ultra would be included in subscription plans. So maybe OpenAI may be one of the only few players left that has yet to um, you know, kind of knock, knock out that subsidy, but that is still the reality uh because you have to assume that one day that's gonna be the status quo. And maybe open AI might be able to do it for another three months, three quarters, three years. We're not sure. But eventually, AI is going to get more and more expensive because the models themselves, in theory, are going to be eating up more and more tokens. And this is exactly why we're seeing that token maxing to token efficiency shift. And we did cover that on the start here series, volume 27 or episode 789. So go check that out. But we've seen it in some of the biggest companies in the world. So, as an example, uh, we saw the very uh you know popular story, Uber reportedly burned through its 2026 AI coding budget in just four months. Tesla, we recently saw capped employee AI tool spend to just $200 a week. And then UBS says that 60% of interviewed enterprises are throttling AI spent already, right? So I do assume that number will probably go up if UBS does the same survey quarterly. I don't know if they do. I would assume that's gonna go up to about 70 to 80 percent uh by the end of the third quarter, because I don't think the uh kind of the token efficiency uh wall has hit the majority of enterprises just yet. So, and this is actually everywhere, right? I even had a conversation with uh a friend who is in software development at one of the Mag 7 companies, you know, and he said his spend is $100 a month, right? So that $200 a week at Tesla. Although when you're looking at API credits, you're like, that's like nothing. Yeah, imagine having like $100 a month. So that is the reality. So I also want to tell this to people out there who maybe don't talk to a lot of people at Fortune 100 type companies and you know, big mag 7 type companies. We are seeing actual real spend limits on the amount of AI that these employees can use. And that actually, I think, for SMBs and medium-sized enterprises might create a strategic advantage, right? If the biggest players are maybe uh, you know, having to reel in their spend, and maybe part of that was their own fault for pushing these token leaderboards and all these other things. I think there is an advantage to be had for everyone else. All right, let's take a quick break for a word from our partners. Here's what most AI tools still can't do: work outside their own little box. The all new Slack bot just changed that. It's your AI teammate inside Slack, and now it can read, write, and act across the other apps your team already uses. No more tool switching, no more re-explaining yourself every time you open a new tab. One ops team at engine says the summary feature alone saves them 15 to 20 minutes of use. See what Slackbot can do at Slack.com. Okay, so why now? Right? Why are we seeing this uh, you know, your chatbot bill becoming a boardroom problem now? It sure wasn't at this time last year. It wasn't really at the end of 2025. And I think to make a very short story of the last six to eight months uh of AI developments, if you listen to the show, it's no surprise. Maybe if you're newer here, let me fill you in. And Tropic set the pace at the end of 2025 uh with Claude Code, bringing that to the desktop uh as well as Claude Co-work in early 2026. And then I think the rest of the way has been blazed uh by Codecs, Cursor, and some others. But we are now having extremely powerful AI models, even for non-technical work, right? When I talked about spending, you know, two-ish billion tokens a week, I'm not doing a ton of traditional coding. Yes, I'm building a bunch of programs that I use all the time, but a lot of my token spend is just doing right scheduled work that I wouldn't normally be doing, right? Doing a bunch of research, creating a bunch of just documents. Um, and the reason why we are seeing this explosion is because these capabilities are becoming more powerful and they're becoming easier for non-technical people to use. So you have more and more people starting to automate their everyday work with these desktop agents that can work 24-7 and can work for hours, right? I remember even in the earlier days in 2026 with Claude Code, I would have to really push the models uh to work for maybe like an hour or so. But now with the combination of subagents and plan mode and goal and all those things that we talked about in the last start here series, every single day I'm usually having multiple threads running for eight to 16 hours and creating amazing outcomes. So it's all about the amount of tokens being spent. So a Stanford Digital Economy Lab study this year showed how much more impactful and how much more token hungry today's models are than the old chatbot only non-reasoning models. So essentially, they found that agenc coding tasks used a thousand times more tokens than a code chat or just a normal reasoning chat. So the same task runs varied up to 30x, making forecasting brutally hard. That's the other thing. And I think that you know, say what you want about your model of choice, but certain models aren't exactly token efficient, right? If you look at the artificial uh artificial analysis leaderboard, the anthropic models are extremely token inefficient. Yes, they're some of the best in the world, but if you are not keeping a close eye on your AI spin and you're just giving these very powerful models access to all your data, your repos, your uh code, uh you know, your code base, whatever it is, and you're giving it this big goal and then just not really watching it, right? We've seen models like Fable, like Opus be two to three X uh more token inefficient than some of their closest competitors from the other labs, right? From um, you know, Google, OpenAI, you know, and then even some of the open source models as well. And then there is the rumored story, right, that a single enterprise wasn't keeping a close watch on their API bill and accidentally spent $500 million uh with Anthropic. I still don't know if that is true or not, but there were some reputable uh you know news organizations reporting on it. But that is the risk, and that is why this essentially AI bill has gone all the way up to the boardroom, and it used to be a couple of months ago, right? The um the message being pushed down was let's increase our seats, right? Hey, we're paying for you know 5,000 seats, but only 2,500 are being used. We need to increase that, and now that has shifted because a lot of those seats, quote unquote, that they were paying for before now have meter billing, maybe on top of a very limited uh, you know, monthly or weekly quota. So now, you know, there was all of this push over the end of 2025 and early, early 2026 to let's get people using this AI as much as possible, right? We all got this taste of the gateway drugs, right? The 2025 models were fantastic. The harnessing improved, the tool calling improved, models reasoned and thought by default, right? So all of a sudden, you had companies that were smart and doing things the right way, they showed measurable ROI and they're like, let's get this thing, let's use this as much as humanly possible. And then the whiplash effect is hitting us all in the face, and that's why we have Javon's paradox, and that explains kind of this concept of cheaper, cheaper tokens but larger bills. So if you don't know what that is, that's essentially the idea that as a resource becomes more efficient to use, its consumption often increases rather than decreases. So Javon's paradox means cheaper units can drive more total consumption. Simple, right? But cheaper tokens also created those longer contexts in more agents, more retries, more automation, and sometimes more loops of tool calling in a bad way, right? Just models calling tools when you're not looking at them and they're growing over and over and over, and you're maybe not monitoring that. And that's why these bills are growing because of demand expanding faster than the unit prices fell. And also sometimes the human in the loop being a little lazy and not keeping an eye on because they're like, Hey, I can give this thing a goal and give this all this data, and it's going to work for 16 hours and it's going to get the job done. Maybe not looking at how much that job is actually costing or how many you know, times a certain model is trying to go fetch a website and failing and it's just trying over and over and over. And maybe each of those tries, maybe there's hundreds or thousands of tries, and it's costing you maybe a couple of dollars every single attempt. So there's use cases already we've seen of companies maybe doing this the right way. And I think one of them is Coinbase. So what they did, and they shared about it a little bit online, is they swapped their default models uh from essentially anthropic to GLM52 and Kimi 2.7. And they shared that the combination of that, right? Not every single model, uh, right, but a lot of their uh, you know, orchestration. So, you know, still handing off uh some of the heavier lifting to maybe an anthropic model, but a lot of that, you know, middle grunt work, the orchestration, the summarization, the writing, some of those things that you maybe are fine with a you know one B tier model. Well, they were just going open source with that and using something like ZAI's GLM52 or Kimmy27. So combining that with caching, proper caching, and then difficulty-based routing. Well, they were able to cut spend to nearly half of what it was at its peak. Yet they increased their actual token usage. And in terms of output, it didn't fall off, at least according to the company. And that's also led to a surge of a new breed of model routers. Uh, right. And what's funny is well, I told you guys about this more than 18 months ago. That's why, uh, especially those, you know, uh year-end uh prediction series. That's why you got to listen to these and put these things into practice. Because I told you guys this was coming, uh, right? The mixture of models and some uh some other things. But we've seen a lot of I think great innovations, some of them like open routers fusion, uh perplexity computer, uh computer, although I wish it was a little more affordable. Um, you know, and you've seen some other ones as well. Uh merge, uh another great example. But essentially now you have these services that are kind of plug and play and will do this for you. So similarly, how Coinbase went in and did this all manually, right? You are having some quote unquote third party services that are going to do a lot of the heavy lifting for you, right? You enter in your API keys, uh, you know, connect your data as you normally would. Via, you know, easy one click, easy uh, you know, porting over connectors. And then, well, you can just hopefully reduce your API spend. There are obviously things you have to keep in mind and do your due diligence on in terms of privacy, data sharing, and all of those things. Uh, right. I'm not gonna be vouching for all of these other companies and how they do that. You have to go do your own uh kind of digging on those. But routing is just gonna pick the model, but tuning is I think where we're gonna start to see a lot of moats being built. Yes, that's right. Fine tuning models. I don't know if we'd say it's making a comeback because it didn't really go away, right? But I think fine tuning was all the rage back in like, you know, late 2023 and 2024, before we had a handful of you know, frontier AI labs to choose from. And before, you know, bringing your data was difficult, right? Back when you know, we were talking about rag pipelines a lot, also fine-tuning models was a huge deal. Um, and I think that again, this is something that we talked about 18 months ago, right? Several models kind of teaming up, uh, not just mixture of experts, right? Where you're looking at sparse versus dense models uh in giving a query to a big model and a big model just activating the uh, you know, the experts that it needs within those parameters that it carries. No, I think we're looking at the mixture of models as being something that is going to become increasingly popular in the near future. And I actually hope that we see that from the actual Frontier Labs themselves. I would love to see that from uh, you know, Anthropic, OpenAI, Microsoft, Google, etc., right? Where even within something like Claude Desktop or uh, you know, codecs where you can go into a certain mode and it will do it for you. We've kind of seen previews of this that I don't think were super well done, where you had these auto modes and it decided how much reasoning it would want to use. But that was really a little bit more, at least for our non-developers, non-technical people. Uh, you know, it it was more of I think just an experiment uh to maybe um, you know, keep compute in check around new model launches. But I do think that the combination of mixture of models and fine-tuning models um is going to help companies not just uh keep spend in check, but actually build a moat. So this kind of flew under the radar. Um, you know, I saw this out when I was out at Microsoft Build Conference, but now Microsoft Foundry offers fine tuning as a service, right? So, as an example, you know, Foundry has a ton of models from all of these different providers, right? If you just need one model and if it's 80% of your usage, let's just say, as an example, is doing some financial modeling around a certain sector, instead of taking one of the big state-of-the-art trillion parameter models, right? You can work with now Microsoft in Foundry. They do this as a service. Uh, they can find the right, maybe a smaller or medium-sized model and then just fine-tune that or essentially create a version of the of a medium-sized model that's more cost efficient, faster, and well, just better because they're essentially going to train a version of the model just around your specific tasks. Speaking of that, that seems to be a uh key area where Thinking Machines Labs is starting to play. Uh, right, former uh, you know, OpenAI CTO and co-founder Mira Marathi uh with Thinking Machines Lab, they just came out with a study that showed Bridgewater and Thinking Machines Lab reported a tuned specialist beat a frontier model at about 14x lower cost. Uh so that does seem something that Thinking Machines uh is doing as well, essentially offering fine-tuning API as a service. But when you have the big labs, right, and sure, we'll throw Thinking Machines in as a big lab in terms of uh market cap valuation money raised, right? You they're at least in that consideration. Microsoft obviously one of the biggest and best in the country or sorry, in the world, right? When you are starting to see fine tuning as a service, that is not um about model specialization, that is about the uh the impending uh reality of subscription models going away in enterprise companies having to look at that, right? If you were paying for, as an example, if you were paying for 10,000 licenses of Microsoft GitHub Copilot, and then all of a sudden they switched over to us at billing, right? Are you gonna continue to do that? I don't know, right? This is where you're gonna have to start looking at these alternatives. So here is your Monday move. You have to build the spend router, right? Whether you look at some of those other services, uh, whether you start doing this and duct taping it yourself internally, or whether you are just preparing for when the big labs offer this, you have to start putting this in place. You don't just cut AI blindly. That is the absolute worst thing you can do. And unfortunately, a lot of companies are doing this. So don't do that. Uh, right. You have to start classifying work by value, risk, and complexity and start using some of these cheaper open source models. Again, go through all your due diligence on the privacy data, uh, you know, IP side, all that. Uh, right. But you should be looking at these tuned specialists, routers, caps, and frontier escalation because the winners ultimately are going to be the ones, I think, like Coinbase that are finding ways to actually spend more tokens and to use more tokens. I don't think there was anything wrong per se with the token maxing era, but it was more of doing it irresponsibly on the API side and using the most expensive models, right? I do think that you have to have some experimentation on these models that are a fraction of the cost, and you still get maybe, you know, 85 to 95 percent of the performance of the state-of-the-art models. So here is your seven-step playbook uh to control runaway AI spend as we wrap today's show. Number one, you have to meter first. So you have to get token spend visibility by team, task, and model, right? So, similarly, how I said, hey, right now I'm going through personally 2.5 billion tokens a week. I can break that down by project. I actually build a tool inside of Codex that helps me do that, right? You can be doing these things as well. You have to start seeing where your tokens are going, who is spending them, uh, what task, what model, and what are the outcomes as well. Number two, set budgets. After you do that, you have to be able to cap spend per team, user, and workflow with overage alerts. That doesn't mean you cut use, right? So let's say you first meter and you say, here's what we're doing on our subscriptions. What is that uh, what is that spend that you're gonna have to do to get the same outcomes, right? So don't cut budget without protecting your outcomes, all right. Number three, you have to start learning to default to cheap, right? I'm not saying the cheapest models, but now we do have models that are extremely capable, open weight models like GLM 5.2, uh, you know, like uh Kimmy um 2.7, right? We're gonna, I'm sure, have some other things from Quen. Uh, they have some great models. Deep Seek, I'm sure we'll come up with another good one. Again, go through all your proper uh IP privacy security, but you have to start looking at these maybe open source, open weight models, whether you're running them locally or paying for them on the API side. They're normally a fraction of the price as the closed uh frontier models. Number four, you have to route by difficulty. You shouldn't be, as an example, having Fable five on extra ultra reasoning, whatever it's called inside Cloud Code. You shouldn't be doing that to help you write better emails, right? You have to send the right type of tasks to the right models. Uh, then you need to understand your get proper uh caching. You need to trim and reuse repeated prompts and start lean sessions per task, right? Even simple things like if you're working on the front end using things like projects, right? Uh certain uh, you know, certain providers do things better than others, right? As an example, Codex is great at compaction, auto compaction, it's really good. Um, you know, Claude recommends as chats get longer to start new chats, uh, to save on cost, but you have to start doing those kind of non-technical best practices as well as caching and trimming. Number six, fine-tune the repeats. Yes, if especially I think if you are a uh Microsoft organization, I do assume that we're gonna start seeing fine-tuning as a service, right? I think Thinking Machine Labs and Microsoft uh offering this leads me to believe we're gonna see this probably within uh nine months from all of the big players. You have to start looking at and understanding what it's what are we gonna fine tune, right? What type of model is going to do 80% of our AI use for certain teams? And well, if you fine-tune a model, it might be a little bit of work and a little bit of cost up front, but you have to be able to math the math because chances are it's gonna save you a lot in the long run. And then last but not least, escalate on purpose. Call the frontier model only to close hard problems, especially if that frontier model is not included in a subscription. And I say that as a very timely reminder. Yes, Fables, uh Fable Five, a couple more hours, and it's gone. We'll see what happens with open AI, uh, GPT-5.6, Soul, uh, and Soul Ultra, they did say it's gonna be included uh inside codex. So who knows what's happening? But all I know right now, as we wrap up today's show, the future of AI is token efficiency. And it is no longer uh, if you are the decision maker, six months ago, regardless of what tool, what system, the right move was to use as much AI as possible. And that still may be the case, especially if you're an open AI organization, because they are pretty much the only player right now that hasn't drastically uh either cut uh the subscription limits or that hasn't gone to true metered access. But chances are some of your team, some of your organizations most needed, most used AI tools have converted to metered pricing. So that doesn't mean stop. That just means be smarter. Don't stop experimenting and well, follow us daily because we're gonna be helping you every step along the way. I hope this one was helpful. If so, please make sure to go to starthirseries.com, go sign up for our community, uh, go network with other people who are leaders in the AI industry, uh, figuring it all out there. Uh also, if this was helpful, please subscribe to the show on Spotify and on Apple Podcasts. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.