308 | The craziest new releases week in AI history, the shift towards cheaper AI is intensifying, the new roles of the AI era. And more important AI news ending of week of July 11, 2026

21:32Now, if you haven't used voice mode before, I highly recommend it. It enables you to have actual conversations and brainstorming sessions with ChatGPT or by the way, with any other AI model. And it is very intuitive and allows you to share your thoughts, ideas, feelings, whatever you need in a much more fluent way, at least for me, OpenAI shared at the release that they have 150 million people that are currently using GPT voice and dictation features, and GPT Live is just going to replace the model that does that. Now, another interesting thing that was released this week, again, on the smaller scale, as far as the impact, that's very interesting as far as the feature, Anthropic just launched what they call Anthropic Reflect.

22:08It appears on the left side menu if you're on the latest version of the desktop app. And what it does is, as the name suggests, it reflects on the work that you're doing with Anthropic. It provides visualization and that describe your user habits and tell you what you worked on, how frequently you worked on it, and so on. It's now available on all the different membership levels. And it does two things. As I said, one aspect of it is if you want a visualization of your conversations, the topics, the usage pattern, the task types, but it's also prompting critical reflection through questions like, what is the one thing you want to keep doing yourself, or even that Claude can do it faster than you.

22:46But it also provides suggestions on how to improve things you've already built. So this thing serves two purposes. One purpose is to show you how you're using Claude, and the other is to get you to use Claude even more by providing useful suggestions on what you should do next, and helps you guide yourself through what you want to do on your own or what you want to do with Claude. I find this like a really cool and smart feature that I am definitely going to use regularly, even though I have my own built-in mechanisms that help me decide what to work on next inside of the cloud universe. So what is the bottom line?

23:19The bottom line of this first segment is that the race is definitely still on, but the race is changing dramatically. While OpenAI and Anthropic are pushing the boundaries in the latest and greatest models, more and more of the focus is going to more efficient models that may not be the tip of the spear, but are pretty close to that. And in that new universe, Meta and SpaceX AI can now compete as well together with additional models. And we're going to talk more about that in the next segment where we're talking about the pricing aspect of this, but the race is definitely still on. We have seen crazy amount of releases.

23:55Isar Meitis:Many of them are around the harness in how we use it. If you think about the new ChatGPT work and ChatGPT app, or the new capability to now run Cloud inside of the web app and so on. This comes more critically on the way we're going to do work than which actual model is going to run underneath. I am constantly switching inside of the Cloud environment between Haiku and Sonnet and Opus. And now, while I still have it, Fable as well, which means this has become the focus for me as well. And I'm very, very small. I'm a company of one. If you think about much larger companies, like many of my clients, the math adds up very, very quickly and learning how to effectively switch to the cheaper models and how to use them is becoming a very big deal.

24:38And so using other models and not necessarily from China, and again, more about that in a minute, then you can do that now with the models from Meta and SpaceX. The second topic I want to discuss is the financial aspect of the AI race. We talked about this a lot in the past two weeks. Just a few quick reminders. Lindy, as an example, has moved all their traffic from Anthropic Cloud to DeepSeek. And Flo Crivello, their CEO, told CNBC, we did it and you could see the cost curve go down like crash to the ground. DeepSeek is probably less than 10 % of the cost of Anthropic. So if you're doing it at scale in a company, that adds up very quickly.

25:17Isar Meitis:We mentioned multiple times here in the podcast that Uber has exhausted their entire AI budget in just the first four months and then has put a limit to how much AI each of their engineers and employees can use. Open Router, which is a company that provides multiple AI capabilities through one AI connection. So you connect one API key to Open Router, and then you can choose whichever models you want in the back end. I use them for multiple different things, mostly experimentation. They have learned and shared with the world that Chinese models token usage by US companies was 4.5 % in early 2025 and is now above 30 % since February 8th of 2026, peaking at 40 cost advantage and 60 to 90 % cheaper than the leading Anthropical OpenAI offering.

26:05Isar Meitis:So the reasoning is very, very clear. If you are running the frontier, you are paying huge amount of money that you may not have to pay because you may have cheaper option. And again, in this particular case, we see a very clear move in US. So US-based companies that are moving to use Chinese-based open source models. Now, is this for every company? Do I think that regulated or security or stuff like that industries will switch to Chinese models? Probably not in the near future. And again, maybe they won't have an option in the near future. We're going to talk about that in a second. But the other companies, it is very, very tempting.

26:40Isar Meitis:Another great example of the usage of Chinese models, we mentioned here before that GLM 5.2 is an incredible coding model. And just to prove that, Since it's released in June of 2026, they saw the daily token volume grow 27 and customer count grow X in just the first week of its release. That's the fastest module adoption on Vercel tracked in all of 2026. Harpit Arora, the head of agentic infrastructure at Vercel, told CNBC, and I'm quoting, price is doing the work here. When a task doesn't need the best model, teams are beginning to route it into the cheapest one good enough. And the recent wave of models coming out of China is winning that trade.

27:22Isar Meitis:And I agree 100%. So the picture is very clear. The token maxing era, which was defined by companies basically telling their employees to max out their tokens, is practically dead. Now, are there still maybe companies that do it? Maybe. I doubt it. But it is very clear that companies that want to scale AI are going to implement some kind of model routing capability that will route them to the cheapest model possible that is still good enough to do the work consistently and effectively. And that is not easy to do right now, but I have a feeling, and I shared that with you last week, that in the immediate future, it will become easy through third-party platforms that are already popping up, as well as through the labs themselves.

28:01Isar Meitis:Otherwise, they will not be able to compete. Now, if you don't believe me, you should believe Amazon's CTO, Werner Vogels. Now, again, Amazon, one of the largest hosting platforms on the planet. He knows one or two things about how companies are using AI, and he publicly endorsed enterprise shift towards cheaper open source AI, an interview with Fortune on July 10th. And he said about frontier models, and I'm quoting, come with significantly higher operating costs, particularly when deployed at scale. Now, in addition to the fact that obviously he's exposed to exactly how companies are using it because they're hosting all these platforms on AWS and you can get access to all of them, it is even more critical to hear him say that because Amazon has invested tens of billions of in OpenAI and Anthropic.

28:45Isar Meitis:So when somebody like that, where your company have over$100 billion in investment in closed source companies is saying that the shift is very clear to cheaper Chinese open source models, it tells you that the shift is very real. So the trend is clear. You have more and more companies are shifting to open source models that are significantly cheaper and are close in performance. In some cases, again, GLM 5.2 is on many of the benchmarks, 1 % off from GPT 5.5 and Opus 4.8. So it is practically the same at a fraction of the cost. However, there is an interesting development that was reported this week.

29:24So on July 7th, Reuters reported that potentially the Beijing Ministry of Commerce is in discussions with Alibaba, ByteDance, and Z.ai over potentially blocking overseas access or restricting in different levels overseas access on Chinese most advanced AI models.

29:42Isar Meitis:And they're talking about both closed source and open source models. Now the exact times and the exact implementation is still under discussion, but the potential future of AI models availability around the world, it is very unclear. So we saw the US government blocking international usage of different advanced models, which again, since then was released in one way or another with different limitations and different guardrails. But the fact that the US government is involved and can impose whatever it decides on future models, and they specifically with Fable said that it should be blocked from international users.

30:17They allowed to release it to the US public, just Anthropic did not have a way to do that. And so they blocked it from everybody. Well, it seems that it is going to work the same way the other way around.

30:26Isar Meitis:So which models are currently in discussion with the Chinese government? DeepSeek R1, Alibaba Quinn, Bytan Stubow, ZAIGLM 5.2. And to put things in perspective, these models are currently driving 30 to 46 % of open router share of models that we just discussed a minute ago. Now, it is obviously not the first time the Chinese government intervenes with AI usage or access to Chinese AI technology by the rest of the world. In April, Beijing ordered Meta to unwind its$2 billion acquisition of the Chinese startup Manus. In early June, Beijing tightened the regulation on overseas deals involving Chinese investors, technology, and data that is related, per them, to national security.

31:10Isar Meitis:So this next move perfectly aligns with that. And again, the fact that the US is potentially doing the same thing to them will just increase the likelihood of this is happening. Now, what does that mean? And what does that leave us? Well, it means that the US companies cannot rely on Chinese open source models because they may get blocked at a simple decision of the Chinese government. And then you may be left with something with a lesser model that may or may not perform the tasks of your company in the same level of efficiency as the model that you have right now. Now that puts the other open source providers, the Western world open source providers, and specifically the US open source providers in a very interesting spot.

31:51Isar Meitis:So the one name that I hope was in the back of your head and like, how aren't we talking about them in this whole thing is Google, right? We did not mention Google at all with any new releases for a very long time, definitely not in a competitive way. So if we think about the cycle with Google, Google had failed time and time again in the beginning of the new AI era after the launch of ChatGPT, even though the other ones who more or less invented the technology and had the most advanced lab. It took them a very, very long time to come out of this really bad cycle that they were in, come up with the Gemini models that actually did very well in the beginning.

32:27Isar Meitis:They actually took the lead for a very short time. And now it's been quiet for a very, very long time. I am not sure what is happening in Google. I can give you my two opinions of what's happening. Well, three. One is they just can't get their act together. That would really surprise me because I do think Google has all the different ingredients to lead this race from a compute perspective, from a data perspective, from a advanced lab perspective, talent perspective, even though we told you last week they've been losing a little bit of talent, but they do still have a lot of everything more than probably everybody else.

32:58Isar Meitis:So I don't think that not being able to do this is the problem. I see that there are two potential things could be happening behind the scenes. Those of you who have been following the AI world know that Demis Asabes cares about one thing and one thing only, helping humanity come out on the better side of it, using his brain power to do this. And everything else is a noise or a disturbance to that goal. If you heard any interview with him, that's his main focus is how to solve global hunger, how to solve global warming, how to solve any disease out there, how to et cetera, et cetera, et cetera, improve human lives on this planet.

33:33And he will do anything he can in order to do this. There might be two very conflicting things inside of Google right now. One is that they are allowing Demis to do what he wants. And this is the reason we are not getting newer, better models from Google because that's not what they're focusing on as far as the research and the investment. Option number two is that they are trying to force Demis to do what they wanted to do, which is develop new products that they can sell, but Demis is pushing back and they have to play this very carefully because if I have a feeling that if Demis feels that Google is a stopper to what he's trying to do versus an enabler to what he's trying to do, he will leave.

34:13Isar Meitis:And if you have Demis as your leading researcher, you don't want to make him leave. And so it might be a compromise between what Google wants to go as far as pushing a new product versus what Demis wants to do, which is more pure research for better of humanity. And so they are just moving slower than everybody else because of that compromise. I don't know any of that. This is just my personal assumptions, but it will be very interesting to see what's happening. But the reason I brought this app is because Google have very capable Gemma open source models. They are probably the most capable open source models in the Western hemisphere right now, which means they may get a huge value and benefit from continuing to improve their open source models and releasing those and providing those as a cheaper alternative to the leading models from the US if or when the Chinese models gets blocked.

35:02Isar Meitis:Another interesting approach to this, which is not exactly open source, but delivers a similar capability is Microsoft. Microsoft, in their latest release, in their AI models, their Microsoft AI models, which are their homegrown self-pick models that they're building in order to reduce their dependency on OpenAI and now Anthropic as well, they're not really open source, but they do provide a solution that allows any company to train these models on company data, meaning take the model and make it your own without having access to the open weights and so on, which gets you very close to the same outcome, again at significantly cheaper prices.

35:36Isar Meitis:So very interesting situation right now going on between the push to get cheaper models and potentially having these models that are currently coming from China being blocked. And how would that impact the open source models from the US and other places around the world? I obviously don't know, but I will definitely keep you posted. The last component in the deep dive, and then we're going to go to a few very quick rapid fires, is really interesting. And it comes from Anthropics Boris Cherney. Those of you who don't know who Boris is, he's the guy that started and still running Cloud Code inside of Anthropic.

36:09Isar Meitis:So he definitely knows one or two things about how to build and use AI effectively. So he shared this week what he calls the five archetypes of new roles inside of at least the Cloud Code team in Anthropic and how he believes the future usage of AI will be done. He's saying that they're going to basically eliminate the older type of job functions in the code generation world. And instead of the current roles that we know, he sees what he calls five archetypes, positions, or not even positions, but capabilities that people need to have when working with code. The first one is the prototyper. This is the person that generates new ideas rapidly.

36:52Isar Meitis:One of the biggest benefits of AI that it delivers right now is the ability instead of to think what might be the best solution and then go and build a prototype for a while and then test it, you can prototype almost instantly and then test multiple prototypes at the same time. And then based on actual data, decide how to move forward because it's actually cheaper to do that than the old way of trying to guess and then build one prototype and then see that it doesn't work. So the first archetype is a, that's the person that has great ideas and can show them and present them as a prototype. The second one is the builder.

37:25He is the person that creates the production-grade product. So he takes the prototype that was actually selected to move forward into production and builds the production-grade product that comes with a lot of under levels of this, of different

37:39Isar Meitis:aspects that needs to happen for something to go from a prototype to production. Those of you who have done this know that there's a gazillion things to take into consideration, but that's that kind of archetype that he mentions. The next one is a sweeper. The sweeper optimizes and cleans the code and the UI. So if you think about now you have a working application that is in production, it could still run significantly better and be optimized by reviewing the code and making small changes and updating it over time. And this is what the sweeper does. The next archetype is the grower. The grower is the one that iterates towards product market fit.

38:11Isar Meitis:So you have an initial product, you are now seeing how people are actually using it, and you keep on tweaking it in order to get a better fit to the market, in order to grow the market share of the platform through its user base or maybe find new user bases. So the goal of the grower, as the name suggests, is to grow the usage of the solution based on the actual usage in the real world. And then the last one is the maintainer. It is the person that ensures the system reliability, that it is uptime is correct, that it doesn't have any bugs, that it works well with new platforms that come out, that changes in operating system don't break it, API standards, new features, and so on, all these kinds of things, just making sure the system runs effectively.

38:50Isar Meitis:Now, these are not job descriptions and they are not mutually exclusive, meaning you can have one person doing two, three, or all of the above, depending on the company, depending on the team, depending on the needs. Again, in my universe, I actually do all of those because I prototype and I build and I sweep and I do all these things, even without thinking about it. So I'm glad Boris gave it specific names. Now, under Cherny's models, your actual role is if you want the allocation of time to the different things that you're doing. It's not your job title. So an individual could be spending 60 % of their week iterating on features that drive engagement is basically a grower for the 60 % of the week.

39:32Isar Meitis:But in the other part of the week, they could be doing other things. So it doesn't matter if they're a back-end engineer, a full-stack engineer, a front-end engineer, a system architect, all of that doesn't matter. What matters is how you're using AI in specific segments of time and how that promotes the overall eventual usage and adoption of the new thing that you're developing. Now, the way I look at this after doing this for a while on daily basis for myself and for a huge range of companies that I work with from very small ones to really large enterprises, I can tell you that I think similar thing will go way beyond writing code, which is what Churny is coming from.

40:09Isar Meitis:I do think that more and more people will shift their focus to solution developers of different kinds. And I think different people will hold different aspects of what that means. With all the companies that I work with closely, there are AI champions who are developing solutions. These AI champions, some of them turn into a full-time role as AI champions. That's all they do is they help integrate and develop solutions. The one thing that he didn't talk about that I definitely see as a very important role is an AI integrator. And the integrator is the one that has more technical skills than the average person, even the average coder potentially, and can provide the technical assets, resources, and access that other people, again, the other include.

40:53Isar Meitis:So the integrator is the one that will give you access to a specific database in a secure way. He's the one that will allow the access to be privilege to specific individuals based on their role and the things they should see, despite the fact it's connected to your ERP and theoretically can see the entire data. The integrator is the one that will build for you an API to whatever other platform you want to connect to and will solve all the technical minutia that the average person, and in many cases, the average code developer does not know how to do. So that's the only thing out of the top of my mind that is missing in here.

41:24Isar Meitis:I do see other roles that has to do with customer service that is not a part of that as well. So how do you service customers with AI? So that's a whole other thing. How to do the engagement inside the team together with AI. So that's a whole other archetype. So there's definitely other things that are beyond what Cherni is sharing that is very focused on delivering code and developing software platforms. But I think the concept is real. I think the concept is old job types is something that may fiddle away and disappear, or at least become into a big gray area where it's not exactly clear. And what will be clear is that there are different things that you can do with AI and you spend different segments of your time focusing on different things.

42:06Isar Meitis:I do that all the time, including shifting from strategic uses of AI, or if you want, working on the business, where my businesses should go and using AI to that, to the tactical aspects of using the business, which is all that Cherney is talking about. So he's completely not looking at the strategic aspect of this, which you can also do with AI. So again, great concept by Cherney. I think each and every one of us needs to think about our own teams, our own company, our own goals, our own strategy, and so on, and see what kind of archetypes we can build for ourselves and for other people in our company.

42:36Isar Meitis:And then think about how we actually manage in that universe is a whole other ballgame. Now, we saw aspects of this from other companies as well. So as an example, something of things that I shared with you in the past that connect with this very well, Coinbase announced one-person teams. So inside their company, there are single individuals that are combining the capabilities of engineering, design, product responsibilities, all at the same time, because they can do all these things with AI. And then it falls back to a Cherney's model that they do different archetypes of things in different parts of their time.

43:07Isar Meitis:Another great example that we talked about a few episodes ago is Cloudflare's CEO talked about measures, which are the middle management, the people who manage and coordinate costs across different solutions. He talked about these can be removed, not because he wants to target them, but because AI now enables the coordination overhead to go away and can potentially break down some siloed org structures because more people can do more things and get access to more stuff. We heard the same thing from Jack Dorsey, CEO of Block, who said, we're already seeing that the intelligence tools we're building, creating, and using paired with smaller and flatter teams are enabling a new way of working, which fundamentally changes what it means to build and run a company.

43:51Isar Meitis:So in addition to the huge wave of job cuts, especially in the tech space, what we're seeing is we're finally starting to see where the world is shifting to. It's not necessarily just having less people, it's working in a completely different way. I have a lot of people ask me on how I work and what do I create every single day. And the reality is I create very little. Almost every output that my companies create across the board, I don't create. AI creates it. I ideate, I manage, I help brainstorm, I direct, I make decisions on both strategic and tactical aspects of this, but I don't create anything.

44:30Isar Meitis:It creates everything for me from code to applications, to automations, to PowerPoint presentations, to financial reporting, to planning strategically, all of these things I do with AI, and AI generates the output. And this will definitely, once you understand how to do this, is a very freeing kind of feeling that you can do anything. And I think this will eventually, and eventually might take two years, three years, five years, but somewhere in that timeframe, everybody will learn how to do that. And the structure way of different roles doing different things that we are all working through right now will change dramatically.

45:03Isar Meitis:And the companies will figure out that faster, we'll be able to benefit dramatically. Now let's go quickly into a few really interesting and important deep dive segments. The first one is Claude released their co-work data, basically how people are using Claude Cowork around the world. They anonymously analyzed 1.2 million Cowork sessions from May 2026, and they found the following, about 50 % of that is used to administrative and connective tasks rather than the core job functions. So now let's switch to a few quick but very interesting and important rapid-fire items. First of all, Anthropic released their analysis of 1.2 million cloud cowork sessions from May of 2026, looking into how people are actually using AI or how people are specifically using cloud cowork.

45:50Isar Meitis:And what they found is this. Approximately 50 % of cloud cowork usage falls into connective administrative tasks, status reports, spreadsheets, reconciliation, onboarding checklists, and slide decks that span across roles, but definitely not constitute anyone's specific core responsibility. So it's just the day-to-day things that we need to do that connects very well to what I just said about what I'm doing. I'm not doing the work anymore. Claude Cowork is doing this. I don't know, by the way, how much of those 1.2 million are mine, but I definitely play a role. That's statistics because I use multiple sessions of Claude Cowork at any given minute of the day.

46:26Isar Meitis:So business process and operations tasks account for 33.4%, which is more than double the second largest category, which is content creation and copywriting at 16.4%. Now, very different than Cloud Code that is used mostly for developers and that are building, debugging and shipping code, software development represents only 8.7 of Cloud Cowork. And I assume that is because that everybody that understands that AI can write code goes to Cloud Code or to any of the other Vibe coding platforms in order to ship their code. So when I need to create code, I don't do it in Cloud Cowork. I do it in Cloud Code and sometimes in Cursor and sometimes in Replit and sometimes in Base44, depending in which of my clients I'm working at that particular moment.

47:06Isar Meitis:but I don't do any of it other than sometimes the planning inside of Cloud Cowork. Another thing that I want to touch is there's a very interesting report that actually challenges the AI job loss narrative. And the report is claiming that high intensity AI adopters growing headcount by 10%. So a report by Ramp and Revelo Labs analyzing data from over 21 ,000 and a half US-based companies reveals that significant AI investment is directly correlated with workforce growth and not contraction. So what specifically they found is that companies that are demonstrating high intense AI adoption increased their overall headcount by approximately 10.2 % in the first two years of deployment.

47:47Isar Meitis:Among the high intensity AI adopters, entry-level employment rose by a higher than the average of 12%, leading to a 1.15 % point increase in their workforce share of entry-level workers. This is exactly the opposite of everything that we've been hearing, that entry-level jobs are going away because of AI. As an example, Harvard Business Impact and Education Review indicated substantial employment declines 16 % for early career workers in high AI-exposed fields. Now, what they are stating is that the positive impact on employment doesn't happen immediately, but it gradually happens over time. And it's usually starting to see momentum 6 to 12 months after the initial AI adoption as they require time for integration and so on.

48:31Isar Meitis:So by the time the AI gets deployed and being used, they understand what kind of new roles they need and then they start hiring people. Now, the other interesting thing is that the employment growth was observed across a wide variety of roles. So not just AI engineering. So that includes sales up 10.3%, marketing and administrative positions up 7.8%, finance, customer service up 6.3%, and even managers and officers up 7%. What do I actually think is happening here is something I speculated about, but had no information to support. I do think that AI in the short term creates an incredible opportunity, meaning companies who will go all in on AI in the near future can run significantly faster than those who won't, meaning you have the opportunity to grab market share, meaning you need more salespeople, more customer service people, more administrative people, and so on, because with the same amount of people you had before, you can do 10X, but those 10X inputs cannot be handled by the amount of work you have right now.

49:29Isar Meitis:So you need to increase your manpower by a little bit to capture that 10X. So with a 10 % increase in manpower, you can do X more times the revenue and handle more customers and so on. This requires a highly well-coordinated AI effort that really yields the fruits that you're trying to yield. But I think in the long run, this will not hold. And the reason I think in the long run, it will not hold is because I think in the end, everybody will learn how to use AI effectively. Yes, there's going to be companies who use it more effectively, just like anything else. But the gaps that exist right now between the people who know how to do this and those who don't.

50:03Isar Meitis:And again, I see that every single week when I deliver workshops. I just did a large workshop in Chicago this week, which was really, really amazing. And I appreciate the people who put it together. We had about 70 people and most of them are in the entry stages of using AI. And this is what I see every time I run these workshops. So the vast majority of people and the vast majority of companies still do not know how to use AI effectively. The gains from just what I showed them in this four hour workshops will save them dozens of hours every single month, literally quarter or 30 % or 50 % of entire positions, but they're not going to be saved.

50:35Isar Meitis:It will just allow these people to do twice the amount of work they did before for the same company, which means they can serve twice the amount of clients. That may require adding people in other aspects of the company to support that effort. And that is going to be true in the immediate, which means you need to figure this out because that is an opportunity that will go away. Now, since we mentioned Chinese models, an interesting thing that ByteDance shared this week is that they figured out that ByteDance researchers found that AI agent performance follows a highly predictable mathematical curve when learning from real world environments.

51:08Isar Meitis:What they're suggesting is that if you let AI learn how real life works versus develop them in a lab, they can double their capabilities every three months. So the problem from a learning perspective is that the AI world has more or less consumed the available data, digital data that exists. And so the next thing that needs to be done is learning in real life. And what PyTance developed is what they called Bench, which is a benchmarking suite of 134 ultra long horizon tasks spanning from software engineering, scientific discovery, formal mathematics, professional knowledge work, each requiring 12 plus hours of continuous agent operation.

51:46Isar Meitis:So not human work, but agent operation. The team logged over 38 ,000 hours of environment interaction testing for five frontier models, Anthropic Cloud Opus 4.8, OpenAI's ChatGPT 5.5, GPT 5.4, plus models from AI and DeepSeek in China. And what the ByteDance researchers argue is that post-deployment learning from rich environments may deserve the same systematic scaling attention as pre-training has received so far. that will be shifting the focus from static pre-training towards continuous on-the-job adaptation. This is something we've heard before, but now we have proof, like actual research that proves that this may be the next frontier of scaling laws that we haven't tackled yet.

52:29Isar Meitis:The thing here that aligns well with what we discussed before, this will be company-specific and industry-specific, meaning companies will probably gravitate more towards open source or trainable models so they can do these kind of things in a more effective way. The last two Two topics have to do with some significant changes in the leadership of OpenAI. OpenAI chief officer and AGI deployment leader, Fiji Simo, transitions to a part-time role after her health crisis. So she took a long personal health-related leave, and apparently she's not coming back, or at least not coming back to a full-time job.

53:05So the quote is, three months ago, I had to go on medical leave after a severe exacerbation of a chronic illness. I've lived with for seven years. During this time, it became clear that the road to recovery would be much longer and more complex than I had anticipated, and that I need to focus on it fully. So she's stepping down to a partial role. We'll probably hear in the next few days exactly how that's going to turn out from a new leadership setup. We don't have any, or I haven't seen any news on that yet. And then the other interesting one is that OpenAI hires an investment banking expert to focus on disrupting Wall Street.

53:43OpenAI has opened a new position

53:45Isar Meitis:that is investment backing subject matter expert to enhance Chat2PT's capabilities in high stakes financial transactions, including merger acquisitions and fundraising. So we're going to see more and more of that, of the leading labs focusing on specific areas of the economy, developing capabilities specifically for that, because as we mentioned multiple times, and we focused a lot in the last two weeks, It is now less about the frontier capabilities of the models, but actually how well and effective they behave in specific real-life environments. And that is going to be the focus moving forward.

54:17That is it for today. I hope you found this episode really educational. I really enjoyed making it. I think a lot of interesting things are happening right now, all at the same time, both on the

54:27Isar Meitis:technological capabilities, as well as the financial aspects of this and how that may impact the IPOs that are coming and a lot of other things that are happening in the background, what's happening with the government. and so on. So lots of changes every single day. That being said, on the day-to-day, you still need to learn how to use AI effectively. Come and check out our courses and the multi-agent orchestration course, I think are sold out of the August cohort, which means we're going to open September for registration. So don't wait and go and sign up for that. And there's a link in the show notes.

54:55Isar Meitis:We'll be back on Tuesday with another how-to episode. As I mentioned, potentially something interesting showing you how to use ChatGPT and Claude at the same time to work on whatever thing you're trying to improve in your business or your personal life. And until then, have a great rest of the day.