298 | $500M token charge?! 🤯 The AI race is changing, Claude Opus 4.8, a robotic house cleaning service and more AI news for the week of June 5, 2026

22:53Isar Matis:by Meta, all of them are very, very close together from a capabilities spread perspective. And yes, they vary on coding and they vary on image generation. But if even on those areas, the spreads are not that big. And by the way, even where the spreads are bigger, and there are cases like in the agentic side where the closed models are significantly better than the open source models, the open source models are good enough. They can do most work that is required in the knowledge work today really well or good enough in order to replace people or at least dramatically increase their capabilities and their throughput.

23:30Now, if you want to look at it not from a quantitative perspective of the different benchmarks, but from a qualitative perspective,

23:36Isar Matis:Adina Jakub from Hugging Face related to the latest release from Minimax and said their really solid work on mixture of agents, efficiency, agent-oriented design, and she praised on how they are now coming very, very close on, in some cases, doing better than an OpenAI and Anthropic on a few different things after the gap between them was significantly higher not too long ago. So she continued and said, beyond the benchmarks, they've done some really solid work, which tells you that they are delivering a very good model. And she was specifically talking about Minimax M3, which was just released, which is doing some really cool things, which we're going to talk about in a minute.

24:12Isar Matis:Now, the second aspect of this conversation, beyond the fact that the models are getting closer and closer together, and like I said, most of them, including the open source ones, have the capability to do most jobs in a pretty solid way, even if some doing it better than others. There is the budget question of this, and the costs companies are paying right now are going through the roof. The most viral announcement this week that caught fire and appeared on almost every publication you can imagine was that a company that wasn't named got to 500 million of spend in just one month to Anthropic because they did not put any caps or limits on what employees can use in that.

24:57Isar Matis:But to be fair, that wasn't confirmed by anyone. This appeared as one line in a Axiom newsletter that then was quoted by everybody else. But the name of the company wasn't published and in Shepanthropic didn't say anything about this and tried to do the math in order to see if that even makes sense. So let's do the math together so we'll understand how insane that number is. Let's say the average price they paid for the output tokens is$10. There's a big variety. it can be$5, it can be$0.60, it can be$25, but let's take$10 as an average just to make the math. If we take$10 as an average to get to 5 trillion tokens in a single month, if you want to understand what that means, that is 19 million tokens every single second, non-stop, 24-7 for the entire month.

25:46Now, if you think about when you just run Claude regularly, it generates about 50 to 80 tokens every single second. It means you got to run hundreds of thousands of sessions every single minute across multiple users not getting that number. Now, if you want a different benchmark, Uber, which we're going to talk about in a minute, said that their heavy users are using about 1 ,000 to month in token to hit 500 million, even at the$2 ,000 mark, you need 250 ,000 engineers running. Again, these numbers just don't add up. They don't make any sense. I'll be really surprised if that number is real. And I think if that number was real, we would have known by now who the company is or exactly what happened and stuff like that.

26:27None of that became available. Again, it was one line in Axiom that everybody jumped on and shared. That being said, they admitted that they burned through their entire annual budget to Claude by the end of April. And they shared that Claude code adoption jumped from 32 % of employees to 84 % of employees through its 5 ,000 engineers in just a few months. The per engineer monthly cost now varies between$500 on the lower end to$2 ,000 on the higher end per month. Their CTO said, the budget I thought I would need is blown away already. But in addition, what they're finding is that it's very, very hard to quantify the value of the tokens that they're using.

27:06So Andrew McDonald, their COO, said it is very hard to draw a line between one of those stats and okay, now we're actually producing 25 % more useful consumer features. We also talked about the fact just a couple of weeks ago that Microsoft is canceling internal cloud code licenses, pushing everybody to use their own GitHub Copilot CLI and that the deadline for all of that to cancel all of the cloud capabilities is June 30th. And that again is obvious because it looks bad when you are using the competitor's tool instead of your own internal tool. But in addition, we talked about the fact that they're about to close their fiscal year and hence cutting cost could play a very big role in showing efficiencies.

27:48And so again, they share that their per engineer cost is$500 to$2 ,000 for Claude Code's tokens. So what does that tell us? It tells us that companies are spending more and more money on tokens without necessarily knowing exactly what they're spending it on, what the results that it's generating, which is very, very hard to measure. And that is, by the way, happening while the actual token cost, the cost per token is dropping dramatically. So dropped over 80 % in the past year. So we're talking about almost an order of magnitude decrease in the cost per token because the technology is getting better, but with costs per usage are dramatically increasing, mostly because more and more employees and more and more organizations are jumping into the energetic universe and the agents are consuming crazy amount of tokens.

28:35So despite the fact that tokens are becoming cheaper, the overall cost is growing very high very, very quickly. So this is the current situation. And this is why I think the conversation is changing and it's changing fast from being a conversation about which model is better to a conversation about what value we can get and how fast we can get it and how easy we can get it. And that becomes more important than this benchmark on that benchmark that this model or that model can get a higher score on. So we got two interesting examples about this week. Google just released Gemma 4. Gemma 4 has a capability that's called multi-token prediction or MTP, which drives three times faster results than the previous Gemma model at the same quality.

29:21So how does MTP work? MTP has two separate components. One of it is called rafters, which basically try to speculate across multiple tokens at the same time, trying to drive the speed higher. But the second layer is the primary GEMA 4 model that verifies the suggestions and in parallel to the generation of them, which is overall, as I mentioned, generating three times faster token generation. And it can do this across all the different model sizes that GEMA 4 comes in. Now, if you look at what's happening recently in the open space, model, you would see that in the past two months, roughly every open source model has included such capability in it.

30:03So MTP is becoming a standard, which is allowing AI to provide results significantly faster without changes in the hardware and without changes in the actual results from a quality perspective. Another approach list is Minimax M3 that we talked about earlier. Minimax M3 is achieving 15.6 % faster decoding for a 1 million token context window. They're using a technology that they call Minimax Sparse Attention, but it does something similar where it performs block level selection on real uncompressed keys, meaning they are also running things in parallel versus token by token, which is how AI worked until very recently.

30:41And they're achieving also 99.7 % faster pre-filling at the full 1 million context window. What does all of this technical mumbo-jumbo mean? It means that M3 achieves better results than M2 while reducing the compute cost by 80 % and running faster. To put things in perspective, Minimax M3, which is not at the frontier level, but it's just slightly behind, costs$3.30 for every million entry tokens and$1.2 for million output tokens. And that model will be open source by the time you listen to this episode.

31:21Isar Matis:To put things in perspective, compared to the 30 cents of input tokens, Claude 4.8 costs$5 for million input tokens. And if you compare the output tokens, you will be paying versus$1.2 on M3. That is almost 25 the cost for some benefit in the value. definitely not 25x value. Now, on the same topic, we heard Perplexity's CEO, Arvind Srinivas, quoting his new view on who's going to win in the AI race based on the value per token, or specifically the way he phrased it is token value per watt per user. And he said that this parameter, token value per watt per user, is going to be, and I'm quoting, whoever is able to maximize this particular objective really will, by balancing accuracy, latency, cost, privacy, and intelligence altogether, they're going to win.

32:12That's what's going to win in the long run. And I agree with him. It is very, very clear that currently all the leading models are just good enough. What is going to make the difference are three things. How easy it is to get to the outcome, and that combines all the things he said, like the ease of use, the latency, the cost, the privacy, the stuff like that, together with how cheap and quickly can I get it. This is going to be more important by a very big spread than the final capability of the model itself, because the not leading models are also going to be good enough. And I believe we are already there, which leads us to the last component of what is going to make a difference.

32:49And you heard me say that before, I'm just putting everything together, which is the harness. So what the hell is a harness? The harness is how you are actually going to work with the AI model. If you think about it, the AI model is the engine and the The harness is the car, right? And you're going to pick a car, not necessarily just because of its engine, just like you do today, right? You don't really care which engine is in the car. You want this specific car because it serves you better. You can afford it better. It does what you want it in a better way.

33:16Isar Matis:And it doesn't have to have the best engine inside. It just has to serve your needs. And it is exactly the same thing with a harness and the model. The model is the engine. It's the technology that runs the old and the intelligence inside of it. but how you can approach it how you it makes a very big difference the better than the device has lesser capabilities from a moral perspective absolutely not on the benchmark gpt 5.5 is winning on most cases however the ease of use to make the best out of this technology in the cloud co-work environment is just a lot easier for me to get the results i'm looking for than doing the same exact thing in the chat gpt environment are they're making changes in order to catch up a 100%.

33:59If you're now on the enterprise level, there's an agent capability built into the app. They are going to release codecs into the ChatGPT app, just like you have Cloud Code inside the Cloud app. So they're closing the gap. But as of right now, and in the last six months, they were ahead, not by the model's capabilities, even though in some cases it is, but mostly by the ability to make value out of these capabilities, which is what really matters. Combine that with the fact that the leading software companies are doing everything they can to embed into their platforms. That is true for Salesforce, the most out of the platform you're working with is what's going to make a difference, not necessarily caring about which model is running behind the scenes.

34:41So far, we understand that the speed makes a very big difference. The cost makes a very big difference. The harness makes a very big difference. And then we have the last component, which is the orchestration across different platforms or different compute capabilities is also going to make a difference because it is going to combine a lot of the things that we talked about before. So we see more and more companies that are enabling AI to run across different platforms. If you have tried Perplexity's computer capabilities, which are really, really awesome and really amazing, they use some of the capabilities of your operating system, running some of it locally, and then automatically decides what to actually send to the cloud.

35:19Or like And Arvind Srinivas, the CEO of Proplexity, said the data center is coming to your laptop. Another good example that we're expecting in the next couple of weeks is Apple is supposed to finally introduce the new Siri that is powered by Google Gemini. But it is also going to use on-device capabilities and jump to the cloud just when it needs to do so. Microsoft just launched a new family of products, which we're going to talk about in a minute in the next topic in their latest event.

35:47Isar Matis:and they are able to run locally to some extent on your computer hardware without going to the cloud. And then we're going to see the same thing for new devices. We've heard a lot of new rumors on devices as well. So we're going to see more and more models that know how to run on your wearable device, whether it's a watch or glasses or a pendant or whatever it is that you're going to be wearing and can run on your local computer, PC, et cetera, and can run on the cloud and jumping back and forth between them will give more speed, more privacy, and cost savings that can be transferred to I can now create or gain more value without paying the high price or the latency that we are used to or willing to accept right now and will not be willing to accept in, I think, the very near future.

36:34Isar Matis:So what does that mean if you are running a business and you need to decide which models to use? One thing that we didn't talk about is what the companies are trying to do in order to lock you in to their environment. As an example, if you want to use any of the enterprise level platforms, they demand you commit for a year and they demand for at least 50 licenses to be included in that full year. So you're already locked in for a full year for at least some of your employees. That's one lock-in. The other lock-in is obviously the integrations to different tools and different systems that you're going to build.

37:06And over there, it is your choice. You need to be smart about this and to try to build the layer that integrates your data into the AI as generic as possible. Because if you build it specifically for Chat UPD or specifically for Claude or specifically for something else, you will be locked in forever instead of just for the full year you're committing to versus being able to switch whenever you want with relatively little effort in order to gain the benefits from another platform that is now cheaper, faster, more efficient, better harness, and so on. So why am I sharing all of this with you, this makes a very, very big plan, your AI tools, how to plan your budget and how to plan your training.

37:43Because if training your employees on a specific platform versus generic use of AI, and then you can use whatever tools, then again, you are increasing your lock into one platform over the other. All tools out there today are good enough for most white collar knowledge work. If you train your people and you have the right cutters and the right data access to clean data in a safe way. What I just said in one sentence is very not obvious. Again, neither the training nor the access to clean data in a safe way. But if you do this path connected to we are committed just to OpenAI or just Anthropic or just Gemini or just Grok, doesn't matter, you will see that later on you will pay a higher price than you need to.

38:24And if you think about it right now and you try to build it as generic as possible, you will be able to gain significantly more value per dollar in the future. And like I said, I think the race is changing. I think the race is going from we need better models to we need easier, faster, cheaper access, safer to generating real value from these models. And I think the race, while it is definitely on, if you just look at the releases from Anthropic the past six months, they released, I don't know, like six different new models. And I don't see that stopping, but I think the bigger difference is going to be how it integrates to our real life, a real business and do this quicker, cheaper, faster, and safer for the users.

39:06And as users, we need to learn how to switch and make it as painless as possible so we can enjoy these benefits. Now let's jump to rapid fire of this week. The first rapid fire item is Microsoft held their build conference this week. They have announced seven new in-house MAI models. MAI stands for Microsoft AI. So these models span across more or less anything you can imagine.

39:27Isar Matis:It includes MAI image 2.5 plus a flash variant of the same thing, speech to text, which is called transcribe 1.5, reasoning, which is called thinking 1, text to speech, which is called voice 2, and coding MAI code 1 flash, which is already live in Copilot and VS Code. So this is a really wide range of tools. And you can imagine that this becomes a very important part of their strategy as they're trying to break away from their dependency on OpenAI. So we've seen the few first steps as their partner with Claude for more and more things. But now building their own models is obviously going to give them more freedom.

40:05They also has upgraded Microsoft IQ, which is a context layer for agents.

40:10Isar Matis:And it's now generally available. So that was available before, but just to select companies. And now it is available across GitHub Copilot, Foundry, and Copilot Studio. And it has three different layers. It has WorkIQ, which basically knows everything in your organization and how it actually works. Email, meetings, docs, whatever you want to connect to it. FabricIQ, which is a structured business data and Work and WebIQ, which is fast AI first web grounding to verify facts and so on. And this is going to be publicly available in the next couple of weeks. So WorkIQ APIs go to general availability on June 16th.

40:43They also introduced Microsoft Scout, which is a proactive personal work agent that is built on top of WorkIQ and handles everything from meeting prep to scheduling conflicts to routine tasks without being even a Microsoft

40:59Isar Matis:environment and can help you do your job or anticipate what you need and help you with that. They also introduced Project Solara, which are agent-first devices. And interestingly, they're built on Android and not Windows. So they're talking about agents that will run locally on the device. We heard that from the rumors about where OpenAI is going with their device. But the idea is that instead of running apps on devices, we will run agents on devices to do the things that we need them to do. And Microsoft are going to be one of the players in that game. Again, running specifically on Android. They also clearly pushing Foundry as an agent factory.

41:35So they introduced Agent Framework 1.0, which is now generally available. And the idea is you can now host and run long-running agents and procedural capabilities inside of Foundry native connected to everything else Microsoft, including publishing it to Teams and 365 Copilot. So it will run in Foundry, but you will have access to it in your regular day-to-day tools. They also introduced local AI on Windows. So this is a small, fast model that can run entirely on an NPU for email summaries, formatting, basically basic tasks that doesn't need the cloud at all. Going back to the topic we just talked about before in where this is all going.

42:15And this is going to be available on the new versions of devices and models that they're sharing. and they're upgrading their governance and money layer. They're calling it Agent 365 that allows companies to govern agents and co-pilot credits to meter their work. So going back again to the topics we started this conversation with on how do you manage your organization? How do you manage how many credits are people using? Where are they using it? What are the value that it's generating? It's starting to get built into the platforms themselves. The next rapid fire that I already hinted to, Anthropic gave us Opus 4.8.

42:46It is their most capable model that they've ever released. Not surprising. It has a 1 million tokens context window, and it is designed, again, not surprising, for advanced coding, AI agents, and enterprise workflows. And also, it is scoring higher on most benchmarks than Opus 4.7 and GPT 5.5. Again, not surprising. Otherwise, I don't think Anthropic would have released it. The model automatically adjusts computational effort between different tasks, depending on the complexity of different steps of the different tasks. So it is going to think harder on more complex problems and it's going to think less on less sophisticated or needing problems, which is going to save you money as a user and give you fast results when it doesn't need to think very hard.

43:26It is priced exactly the same as Opus 4.7, so$5 input tokens and$25 for Vemillion output tokens. Also similar to before, caching prompts, which helps a lot, is going to save you up to 90%.

43:39Isar Matis:If you do that, you need to remember these expire, so you can only use them during a specific session. Once it's over, if 30 minutes pass, you lose those caching, unless you use really sophisticated tools out there that enables you to save those in different ways. And as I hinted before, this is a crazy release cycle by Anthropic. If you think about it, they released Opus 4.1 in August of 2025. And then in November, we got 4.5. In February, we got 4.6. In April, we got 4.7. And now we got 4.8. And they seem to be releasing them faster and gaining more and more capabilities. But as I mentioned previously, that doesn't necessarily matter.

44:14But that being said, if you look at benchmarks that actually may be connected to real life, such as GDPVal, which is trying to evaluate actual work that needs to be done across different aspects of the organization, Opus 4.8 scored 90 ELO points, 137 points above Opus 4.7, 121 points ahead of GPT 5.5 extra high. So very capable model, another very capable model, I should say from Claude. But the interesting thing is connecting it back to our previous topic, it is doing this while consuming 35 % fewer output tokens, which means you're going to pay less and you're going to get the results faster than you did in Opus 4.7, which is great for all of us.

44:56On the other side of this race, we have OpenAI, who just announced on June 4th a new version of its memory. It's called Dreaming 3.0, which is supposed to provide much better, more effective memory inside of the OpenAI models. It is supposed to be a completely new infrastructure to how their memory works and allows to provide better, cheaper. They're also going to roll it out later on to all free users.

45:24Isar Matis:Now, as part of this release, they shared something I found interesting, which is how they evaluate memory success, which is on three different dimensions, following user preferences and constraints consistent by staying current as time passes, like update two weeks before the party, and now three days before the party. And you'll be aware of that. They will be able to provide the right guidance. OpenAI also announced that they are going to be integrating codecs into the app. This hasn't happened yet, but it is going to happen in the next few weeks. On my application, it's still not showing up.

45:57This rollout is accompanied by new capabilities. And Codex now has sites, which I find really exciting. So what is sites? It allows you to deploy whatever you develop from Codex directly inside the OpenAI environment. So if you've been using any web coding tools, you know that there's two separate approaches. The more, if you want, professional tools like Codex or VS Code require you to get a deployment environment. You need somewhere to deploy the code that you generate the same way that professional software companies are doing it. I use mostly Railway and Google Cloud, but I also have some stuff on AWS.

46:33So you need to learn how to create this deployment mechanism. There are tools on the outside like Replit and like Base44 that allow you to deploy on their environments, meaning you need to know nothing about how to deploy software and so on. You just click deploy and then it's available to whoever you want to make it available to. And now this functionality is going to be available inside of your ChatGPT application because Codex is going to be available on your ChatGPT application. And Codex now has sites, which is the ability to do the deployment straight from the ChatGPT environment. This is a shot directly towards competitors like Replit and like Base44 and definitely for Anthropic.

47:12Anthropic has the capability to share on a limited way. I think this new functionality from OpenAI is going to push Anthropic to do the same thing. It will be very interesting to see how much that is going to cost over time from a hosting and usage perspective. But the functionality is definitely helpful from small patients to dashboards and stuff like that. You can now generate in minutes and then deploy and make it accessible to other people in your organization and beyond. As part of this release, they also released six new agent plugins that are covering different aspects of work. They include data analytics, creative production, sales, product design, public equity, investing, and banking.

47:51So each and every one of these is available inside the open environment. We're seeing very similar things from Anthropic. We'll talk about this in a minute. What they also shared, which is not surprising because the same exact thing happened on the Anthropic side, is that currently after their crazy spike in usage that they've seen since the launch of Codex, 20 %-ish of Codex users are not software engineers and they're using it to do other things, which hence makes a lot of sense that they're adding this to the regular open air application and that they're adding sites to allow people to deploy what they're actually building.

48:23They also announced that their latest models, so GPT 5.5 and 5.4, and Codex software capability for engineers are now generally available on Amazon AWS via Amazon Bedrock. This is a interesting strategic move that really is a great partnership that helps both parties, because from OpenAI's perspective, they're breaking away from the exclusive agreement they had with Microsoft, which we shared with you a few weeks ago. So there's no exclusive agreement anymore. But in parallel, this is a great opportunity for Amazon, because as part of this agreement, OpenAI are going to use their Tranium chips instead of NVIDIA.

48:58So Amazon is getting a huge growth to its Iranian chip deployment and OpenAI is getting another delivery channel through AWS Bedrock. Both companies are winning and us as users are winning as well, because if you're on AWS, you can now have access to these models that were not accessible before. We talked about launching new capabilities and we said that OpenAI released their capabilities while Anthropic also released a lot of new plugins and new tools. So they just announced they're dramatically expanding their cloud for legal capabilities. So beyond the original 12 plugins that they released, there are now 90 specialized AI agents designed for specific granular legal work.

49:35So Claude for Legal now provides many different aspects of law work, trying to cover end-to-end workflow of a legal person, including vendor agreement, DSAR sponsor, claim chart builder, and so on and so forth. We're not going to dive into this. Now, in addition, Claude for legal includes over 20 new MCP connectors to more or less any tool you want to use in the legal work, such as DocuSign and Ironclad and iManage and NetDocuments and LexisNex and Thomson Reuters and many, many others, which means you can do more and more professional legal work either as a law firm or as internal legal teams in companies with Claude versus going to the professional tools like Harvey, which may be more capable.

50:18But again, is that more capable worth the extra cost is a question that still needs to be answered. It is very, very clear that both these companies are going hardcore after specific tasks in specific organizations. And they're going to keep on pushing in that direction because, again, that's the next frontier.

50:34Isar Matis:We talked about the speed and you out of the box, the capabilities to do the work you need to do to get the value you need, paying them versus somebody else and overall saving you money because you don't need two or three or four services. Now, a few interesting news about hardware side of it. Meta is reportedly developing an AI-powered pendant and it is testing it for a next year release. This is following their acquisition of the company called Limitless, which is just acquired recently. And this is based on an internal memo that was published by The Information. Now, this initiative is a part of a broader strategy by Meta to expand AI Glass's lineup and introduce what they call wearable for work, which is going to include a business subscription.

51:19In order to be able to use this, you will need to pay them an annual subscription in order to enjoy the benefits of this. And this is trying to compensate for the crazy losses they currently have at the Reality Labs division. So Reality Labs has lost$4.03 billion just in the first quarter of 2026, accumulating to over$80 billion in losses since its inception and$19 billion just in 2025. So the idea here, like we've seen OpenAI and Anthropic, push from the consumer market to the business professional market and then be able to charge higher prices and per seat if you want licenses. And that's the direction that they're trying to go.

51:57So apparently it's going to be a pendant that will be used mostly for business use cases. And in order to use it, you will need to pay a monthly or an annual fee to enjoy the benefits. I anticipated this before, and I'm going to say this again. I think sometime in the not too far future, we are all going to be wearing AI devices because it makes perfect sense. And that raises a lot of questions from privacy relationships, security, data access, and a lot of other stuff that I don't think anybody has answers for. But I think the hardware will start being more and more available and more and more worn and used by more and more people.

52:34And we'll have to figure that out as we go. And another interesting, cool, scary, weird, whatever you want to call it, piece of news from this week is that San Francisco-based startup called Gatsby has completed the first consumer home cleaning by a humanoid robot in the United States. So if you're wondering what the hell this is, Gatsby provides a robot that you can book from an iOS app for a dollar flat fee per apartment in San Francisco. So it doesn't matter how big your apartment, a robot is going to show up at your doorstep and invest X amount of time in cleaning and doing everything you need as far as cleaning.

53:07That includes dishes, surfaces, floors, bed making, laundry, folding, all without a human person there watching it. It completed the first task in about three hours. What does that tell you? Not much. I'm not sure how well the cleaning went. There are also a lot of open questions, including privacy or what happens if it breaks something or harms someone, what it is doing, what it is doing. It does have remote access of humans. So if it gets stuck in anything complex, there's a human remotely who can access and see what the robot is doing and control the robot, which raises even more concerns from a privacy and control perspective.

53:44but the bottom line is very, very simple. This is a robot that just did a house cleaning for a fee that is at the very, very low end of what humans charge in the San Francisco area, which is about$150 to$300 to do the same exact work. So while this robot may not be perfect, it is the canary in a coal mine that tells you what is happening. We are going to see more and more robots take blue-collar work and do it at a rate that humans will not be able to sustain or not be able to sustain themselves if that's what they get paid. It's going to push a lot of uncertainty into the lives of people. It's the kind of work it is going to be doing.

54:23If this time it is just a house, the next thing is going to be baristas and different service providers and cashiers or tellers or customer service at railway stations and so on and so forth. We are going to see more and more of these showing up in the next few years, which is going to drive more people out of their existing jobs, which, as I mentioned, I'm not very optimistic on the white-collar knowledge work. This is just the next wave, and it is coming, and even this is not going to grow very, very fast. It will grow in the next three to five years to scales that will start to make a difference.

54:56Before I let you go, I want to remind you of our courses. The next multi-agent orchestration course is starting at the beginning of August. We sold out of every course between May and the end of July, so this is definitely something that you want to do if you want to understand where the world is going how to build multiple agents and how to make them work together to automate more or less anything you can imagine in your business I'm also doing this as workshops for companies so if you're in a leadership position in a company and you want to teach this to the relevant group of employees in your company I will gladly talk to you about this you can grab time on my calendar with the link on the show notes or just find me on LinkedIn and send me a message both ways are perfectly fine We will be back on Tuesday with another how-to episode that will teach you how to use AI in your business right now.

55:45And until then, have an amazing rest of the day.