287 | GPT 5.5, GPT Images 2.0, GPT Chronicle, GPT Privacy Filter, Claude Design, Kimi 2.6, all in one week 🤯 20% of companies gain 74% of AI financial benefits, Tim Cook is Stepping Down and more important Ai news for the week ending on April 24th, 2026

28:13It is much better at creating extremely realistic images, including things that look like errors that would happen in actual photography, which makes it look even more realistic. It is also really good at rendering complex detailed text, user interfaces, elements, condensed compositions, anything that requires significantly more detail, it is very good at. It is also really good at editing existing images and upgrading them and making changes without losing the granularity and the focus and the crispness of images. Any of you who tried to take a nano banana image and manipulate it several times, you end up with something that is not in the same quality and not in the same resolution as the original image.

28:59And this model doesn't do that. It allows you to keep the quality of the image while making changes and edits with simple text prompts. It is extremely good, as I mentioned, in creating highly detailed images that include text in them, which opens the door to a huge variety of use cases. Examples are creating a restaurant menu, including the images of the food with whatever background you want, and it knows how to nail that, including all the details in it, which was almost science fiction before that. Like if you try that with any of the previous models, you know that you get weird artifacts every now and then, and with some models more than every now and then.

29:35It also knows how to create multi-panel comic strips straight out of the box. It knows how to create UI elements, actual user interface elements for software, more about that in a minute, and many other capabilities that were very hard and very inconsistent with previous models, including the latest Gemini Nano Banana Pro. This model also is very good in non-Latin text rendering. So Japanese, Korean, Hindi, Bengali, etc., which was very inconsistent in previous models. And now it actually does it very well, including very small font and print, again, with a lot of details, with a lot of text on a single page.

30:11Now, the other interesting thing about this model, the fact that it's a thinking model, it is connected to the thinking capability of ChatGPT, and it can understand your needs and understand what you mean and go and perform research on a specific topic to come back with what it needs in order to generate the really incredible outputs that it generates. Now back to the user interface use case. This is one that I find very, very interesting. Any of you who try to generate user interface through code using Codex knows that Codex is really good at coding and really bad at coming up with user interface.

30:45If you give the same prompt to CloudCode and to Codex from a code quality perspective, they will both do great. Some people prefer that. Some people prefer the other. It doesn't really matter. They're very close and they will do an awesome job. But from a user interface look and feel, Claude will look great and polished and Codex will look not great and not polished. And what this new functionality enables you to do is to go and experiment and iterate on really advanced, complex, and well done user interfaces for whether it is a mobile app, desktop, website, whatever it is that you're trying to develop, using the new image generation tool, and then taking that into Codex and asking Codex to build the components that it sees on the image, and it will be able to do that.

31:29This is probably a use case that we're going to see a lot of people experimenting with and potentially using regularly moving forward. Now, on that aspect, when it comes to building user interfaces, we're going to talk about shortly when it comes to the new really cool tool released by Anthropic this week. But for now, let's focus on the image generation capability of ChatGPT. From my perspective, the three biggest things in this model, one is the quality of the output. The second is the realism. If you need to create photorealistic images, it is practically indistinguishable from real life. And the third, that for my workflows and probably for many other workflows is the most critical, is the consistency and maintaining resolution and details across changes, which is something I was struggling with with previous models.

32:17If you want to iterate four or five times, you have a significantly degraded quality of output, and this is not happening with this model, so I'm very, very excited about it. The moment I heard about it, I asked Dispatch, which is the ability to talk to Claude through the phone. I actually saw the news while I was on the treadmill at the gym, and I asked Dispatch to add another path to the tools that generate images for my LinkedIn posts and other social media, as well as the path that generates the thumbnails for my YouTube channel. And now it's going to run both of them in parallel. So in a few weeks or maybe a couple of months when I run it more times, I'll be able to tell you whether it consistently generates better results in real life usage compared to Nano Banana Pro or not.

33:03Now, I want to touch on a specific negative feedback that I've seen in one of the articles. It was a specific article from TechRadar who was specifically talking about the use case of creating magazine covers. And what it said is that the model currently can create professionally designed magazine covers that has the images and the graphics and everything you want on a really, really high quality. The problem that they're running into is that is a flat image, which means you cannot take it to Adobe tools or any other tools and then edit the different components, resize the text, move things around, control whatever you want to control, which obviously is a limitation when you're just generating the final output.

33:39However, I have two answers to that. One is maybe you don't need to. If you can generate the final output accurate enough and exactly the way you want it, maybe the extra steps of doing the fine tuning is probably not needed. That being said, I know very little about the design of covers for magazines, so that is probably a necessity. But that technology already exists. Those of you who do not know and ever tried this in Canva, and I assume it's the paid version because that's what I have, but maybe it's also available in the regular Canva tools. If you go to edit of an existing image, one of the functions is called magic layers.

34:16And what magic layers does is it magically separates a flat image into the different layers. So you got a background, you get the foreground, you get the different components, you get the text. Each and every one of them, you can drag, resize, relocate, delete, change the text, edit, change the colors, whatever you want. It becomes its own entity as if it was created separately as a separate layer in Canva. This is one of the coolest, most incredible capabilities that in my workflows and in my team's workflows complement the generation of AI. In many cases, the easiest way to get from a 90 % solution to 100 % solution is to bring the AI image into Canva, click on the magic layer thing, wait for two minutes until it does the separation, and then make the small edit that you needed instead of trying to convince AI to do exactly the change that you wanted without changing anything else in the image.

35:05I am certain that this same capability that exists in Canva will come to the professional tools as well. And I'll be somewhat surprised if Gemini and the image generation from ChachiPT does not offer that functionality out of the box. But that being said, I assume that is coming many months ago and we still didn't get it from the leading labs. But in the professional tools, I'm certain we're going to get it across the board from everyone. But for now, you can definitely go and use it inside of Canva. If you have such a need, I'm sure you will find it very effective. But wait, there is more from OpenAI.

35:36This is not the last thing they announced this week. They also announced Chronicles. And what Chronicles is, it is an opt-in tool that you can turn on when you are using Codex. And what it does is it takes screenshots off your screen on whatever cadence, I'm not 100 % sure. And then it takes the screenshots and analyzes them and creates summaries in MD file format that then the codex tool can use as reference to understand what you're doing and have a deeper knowledge and context on what you're developing, how the process works, and so on. So from a conceptual perspective, this is absolutely fantastic.

36:09Again, any of you who have developed software either on your own or Vibe coding tools understand that giving the model context of what you're working on and what other tools and what your process is extremely powerful and helpful. The problem with that right now is that it saves the images unencrypted on your desktop for a short amount of time, but then it saves the MD files that describe what was on the screen unencrypted on your computer forever until you delete them. So while the tool may provide value, it also exposes a very significant risk because you have data on everything that was on your screen available in simple text on your computer that anybody who gets access to your computer can get access to.

36:52Now, if this reminds you of things we talked about in the past, you are correct. If you remember, Microsoft came up with a feature that they called Recall when they came up with the new computers. And that was a huge disaster from a data security perspective and nobody wanted to use it. And I think eventually they canceled that capability altogether because of the backlash on, I don't want the computer to be able to see everything that I'm doing and save that. And that is despite the fact that their data was encrypted and it was running completely locally on the computer versus Chronicles, which does not encrypt the data and actually runs it against the API and sends information to Chachapiti to get process and then saves it not encrypted on your computer as text files.

37:31So I don't know how many people are going to use it in its current state. I'm hoping for OpenAI, because I do see the value in that, that they're going to make the required changes in order to make this safer to use. And the last thing that I want to mention, which probably wouldn't have made the cut, but because this past week I did a workshop in Europe and GDPR was a big thing, I want to mention that, especially for the people who are listening from Europe or that work in industries that care a lot more about data security, such as legal or healthcare and so on. So OpenAI just launched an open source privacy filter.

38:03It is currently available on Hugging Face and GitHub under Apache 2 license, meaning you can go and train it yourself for your specific needs and specific use cases. And what it is, it basically allows to use AI, deep knowledge, context awareness, and understanding of text to find sensitive information, including private address, private email, private phone, private URL, private date, account number, et cetera, et cetera, secret keys, API keys, and so on. It knows how to find these in data that you're running through it, which means you can connect it to your pipeline and save you a lot of problems.

38:44Now, they do not guarantee the results, but if this is your first pass, it reduces the chances of humans missing more things. And it allows you to then just verify the outputs of the AI rather than going over the entire data set in a manual way. And the fact that it is open source and allows to train on specific data allows you to make it domain specific and relevant to your thing. In a test they're running, they showed that fine tuning improved the accuracy of domain specific tasks from 54 % to 96%. And I'm willing to bet that 96 % is higher than humans doing this manually. As I mentioned, when I did the workshop last week, it was in Europe, so GDPR was a big deal.

39:26That was before the announcement by OpenAI. But what I suggested to them, and I'm suggesting now to all of you, is you can build a skill in whichever platform you're using, whether it's Claude or Chachapiti or other, that will be GDPR aware or whatever other level of data aware. And the way I suggested to do this is send the AI to do the research on the rules of GDPR and then build a skill that will know how to score and evaluate and filter to GDPR-related content. And then you can decide in your universe what you want the skill to do. Does it block the entire pipeline? Does it highlight specific fields?

40:01Does it try to anonymize the relevant fields and just change the client name to practically client name or John Doe or whatever you tell it to replace it with and anything like that. So you can decide what's going to be the output. But the other cool thing that I suggested to my client that did the workshop with me is if you're running on a Teams license, at least on the Anthropic universe, you can force that scale onto every computer that uses Cloud, meaning everybody in the organization can have that on their computer. And you can force a rule on the Cloud.md on every single computer to force that scale to run every time you get data from any source.

40:39So again, this is not a firewall that guarantees that nothing will go through. It's not bulletproof, but it dramatically reduces the chances of human errors or just missing specific things. So I highly recommend to anybody who is in a data secure environment to implement something like this in your universe, either by using the OpenAI open source tool or by building your own skills around the specific needs that you have or a combination of both. The last thing that I will say about OpenAI that wasn't very positive. They had a small size, but relatively long outage on April 20th that took down several thousands of users out of tens of millions.

41:19So it's not a big amount, but for those thousands of users, that was a very big deal. There's obviously a very strong negative sentiment on social media that shows how dependent we are becoming on these tools. And the only thing that I can say, which I said multiple times, and we had a whole conversation about it on the AI Friday Hangouts earlier today. So I'm recording this on Friday, April 24th, and we run the Friday AI Hangouts every Friday at 1 p.m. Eastern time. It's an incredible group of people who care about how to leverage AI effectively in business. And we just talk AI every Friday. It's kind of like an open mic scenario where people share ideas, share thoughts, share things that they build.

41:56And if you want to join, you're more than welcome. There's a link in the show notes and you can come and join us whenever you are available. But in that, in today's conversation, we had a whole conversation about the need to have redundancy and the need to build your processes that in a way that is independent of the model, meaning running the right structure of files and folders and instructions and MD files and so on that are independent of the model is going to run on. And that allows you to switch the model either because the model is down or because there's a new model coming out from a different supplier that is now doing better for your use cases.

42:30And you want to switch and you don't want to spend the next four to five weeks in figuring out what is going to break. And after talking a lot about OpenAI, let's talk about something really cool that Anthropic released this week. So Anthropic just released Cloud Design on April 17. What is Cloud Design? It is a new tool that is currently in research preview, meaning it's not perfect yet. And I can tell you it's not perfect, but it is really cool. It is currently available to anybody with Pro, Max, Teams, and Enterprise licenses. It will probably flow to everybody else later on. But what this tool does is it helps you design user interface for applications.

43:04And it does it in a really effective and really cool way. You can provide it access to whatever you want as reference materials. This could be screenshots. This could be the URL to your website. This could be your actual code base of your existing application on GitHub or other sources. And it uses all of that to analyze what are the brand guidelines. You can obviously upload the brand guidelines as well. But from that, it actually builds the codes of all the different components and all the different knowledge it needs to rebuild, reconstruct, or create new user interfaces based on what it has learned.

43:40So step one, it does the research on the information you give it. Step two, it creates components. Step three, it can create any kind of new design based on what it has created. And it gives you multiple choices and you can select how many. Give me four variations of this. And then it gives you four variations of whatever you want. So different color swatches, different sizes of fonts, different styles. And you can just click through and move sliders around to see the different variations and pick the one you want. And you can iterate from there. The very cool thing about this is, as I mentioned, it is not just generating the view of how it looks like.

44:14It generates the actual code components. This is a button. Here are the two different states of the button. Mouse over, not mouse over. When I'm clicking the button, how does it look? All of that gets created through code that then you can import into whatever coding platform you want. You can also export that because this can become a design brief. You can export that as an HTML page. You can export that as a PowerPoint presentation. You can export that as a PDF. And you can obviously push this to Cloud Code. You can also push this straight to Canva. So it is not replacing Canva, at least not yet.

44:47or it will probably replace Canva on people who are trying to create designs on Canva, which would be weird to me because it's not what the tool is built for. But you can push that into Canva to turn this into a presentation or whatever other thing you want to turn this to. Now, you can edit your designs in more or less every way you can imagine. You can click around on the interface. You can give it commands. You can use the sliders and buttons that it generates for you in order to try the different functions and the different options and versions and so on. It is really fun to use. It is really easy to use.

45:16It is really flexible. to use and it makes the design of user interface significantly easier than it was before. If you are following what's happening in the stock market, Figma's stock has declined 49 % year to date. So they lost 50 % of their value because practically you don't need Figma anymore and tools like this just make it more and more extreme. And as the announcement happened on the release of Cloud Design, Adobe, Wix and other relevant shares had dropped 2 % or more at the moment that Anthropic announced that capability. To tell you how clear it is that this is a competitor to Figma, Anthropic Chief Product Officer Mark Krieger was sitting on Figma's board and he resigned on April 14th from the board three days before the launch, which tells you that there's a clear conflict of interest and not the best relationship now between Claude and Figma.

46:07Now, just a few weeks ago, I shared with you a similar tool called Google Stitch, which does something very similar. It allows you to provide it access to whatever reference materials you want and then create user interfaces that turn into the code of the components that can then be imported into whatever tools you want. They're not doing exactly the same thing and they're definitely not doing it the same way, but they are doing very similar things. They're both really powerful. I really enjoyed using Google Stitch. I actually used it with my son to create a website he wanted to create because he wanted to be able to sell his Perler Beads creations.

46:43so I gained the opportunity to A, test a new tool and B, teach my son how to use AI to do design. That was a very fun experiment. And I also tested Cloud Design on two different projects. One was more simple and straightforward and Cloud Design did a great work. And the other one was a little more complex because I was comparing two different websites and was trying to apply the user interface and the guidelines of one to the other, which is not an easy task to do. And it actually did okay. I would give it a seven out of 10 on that task. but that 7 out of 10 took me about 5 minutes to do and if I would have done this in a traditional way it would have taken me a few days at least if not a week and a half so despite the fact that the outcome is not exactly what I wanted it allowed me to explore how the second website can look in different variations of the guidelines of the first website and I was able to do that and explore different variations of that in just a few minutes and in a one-shot process so I literally gave it all the information explained what I wanted to do and got a decent output.

47:45I'm sure if I would invest a little more time and iterations, it will do even better. But Anthropic also had a snafu this week, actually a much bigger boo-boo than OpenAI had. And the incident that happened is that an unauthorized group got access to Mythos, which is the latest model that they said is not safe to release to the public, that they're only going to give to a short list of companies in order to make the world a safer place before they release it to the world. That model that is potentially has the ability to break into more or less any digital system out there, any operating system out there, any server system out there has leaked to a Discord group that was able to gain unauthorized access to it by getting a contractor credentials and guessing the URL that they need to put in order to access the model.

48:40Now, apparently through this leak, thousands of people across multiple organizations potentially got access to this very dangerous, from a cybersecurity perspective, model that Anthropic were trying to keep as safe as possible in order to keep the world safe. Now, we know for sure that multiple organizations, including partner organizations and beyond, had access to this. We know for sure that people in the Discord group and other people who got access to the people in the Discord group had access. And because of that, we need to assume that nation-state actors most likely obtain access to the model.

49:20Now, this wasn't a five-minute thing. The model was available long enough for a widespread distribution across multiple networks. And it was definitely enough time and spread wide enough that people with malicious intentions would get access to the model. It also took Anthropic quite a while to notify the relevant government agencies of the breach. Now, what Anthropic did is obviously it reissued recommendations to all its partners to revoke access credentials immediately and to assume the model was breached and compromised and to take the relevant precautions to do this. But this is obviously all reactive and the damage, if is done, and we need to assume it is already done.

49:56So here are my thoughts on this topic. Just a few weeks ago, Anthropic accidentally released the entire source code of Claude Code, which is their best kept secret and the hand that lays golden eggs for them. And that was accidentally released to the public and basically anybody who wanted to, definitely their competitors and definitely nations from the other side of the world got access to it. Now, after that, they made a very big deal out of Mythos saying it is potentially dangerous and that the world shouldn't have access to this because it generates a significantly high level of cybersecurity to more or less every critical system that we know.

50:36And they've defined an entire program of partnering companies to increase the safety of the release of that model and future models. And within less than two weeks, we learned that this model that was really dangerous and that shouldn't be available to the public leaked out to a large number of unauthorized organization. Now, this does not look good for Anthropic at all, and it definitely does not look as a company that has good control over the data that they get access to and or the advanced AI models that they are building and releasing in a safe or non-safe way. Now, combine that with the fact that Anthropic has planted their flag on developing AI in a safe way and coming from a company that is trying to get back on the positive side of the government that designated them as a supply chain risk.

51:33So as much as I like Anthropic and their products, and I'm building incredible, really mind-blowing, insane solutions with their tools, and I'm teaching others how to do this, and I'm teaching workshops and courses and really doing stuff that would have been science fiction just a few weeks ago. This kind of level of security is literally unacceptable. I have no other way to say this. And they must find ways to reduce these kind of instances to zero. Otherwise, they will not be able to compete at the highest levels because people will not be willing to take the risk. If this is what happens with the most secure, scary model that you ever developed, and within a few days, anybody who wants access to it gets access to it.

52:17Why should I trust you with my company's data? Not to mention secure, highly sensitive data from governments or companies, etc. And so I really hope for Anthropic that they'll be able to figure this out and turn this around. Now back to the model releases from this week. Moonshot AI, the company behind the Kimi models, just released an open source Kimi 2.6, which is a 1 trillion parameter mixture of experts model that demonstrates really incredible performance capabilities, mostly on, as you can expect, long horizon coding tasks and multi-agent orchestration. Now, this new model, K2.6, achieves higher scores on many of the leading benchmarks than GPT 5.4, Cloud Opus 4.6, and Gemini 3.1 Pro.

53:04So, maybe not the latest and greatest because we just got GPT 5.5 and Cloud Opus 4.7, but it is beating the previous models, which were just recently released from the leading companies in the world. And they were able to achieve some really remarkable real-life examples of what this model can do as far as long horizon tasks. As an example, the K-Point, this new model autonomously overhauled an eight-year-old financial matching engine, so an old piece of software, and it did it autonomously on its own over 13 hours, executing over 1 ,000 tool calls across 12 different strategies and modifying over 4 ,000 lines of code, delivering 185 increase in throughput and 133 % performance gain, again, totally autonomously, 13 hours straight.

53:54In addition, K2.6 scales 300 sub-agents that can execute up to 4 ,000 coordinated steps simultaneously. That is 3x what the previous model was able to do, and it is the highest number from any model in the world right now. Now, they also introduced some other capabilities that allows to run multiple other agents together with their agents in a unified claw groups. That's what they call it. That allows them to all work together to achieve specific tasks and other capabilities that make it one of the leading models in the world. Significantly cheaper than the Western Hemisphere models and open source, so you can host it and run it on your own.

54:31So that wraps up the model releases for this week. A lot to test and work with. Everybody's pushing in the same direction. Really advanced, extremely capable models that can run for hours, autonomously spin up other sub-agents to accelerate their work even further and achieving above-human results across multiple different benchmarks. And on the rapid fire, we're going to do just one quick summary of things that were announced on the Google Cloud Conference this week. As you can imagine, the focus was all agents, agents, agents, and agents in the enterprise, if you want to be more specific. Google launched or expanded what they call the Gemini Enterprise Portfolio, which includes the Gemini Enterprise Agent Platform, which is a new platform to develop, run, and evaluate agents that is an expansion of Vertex AI.

55:18This new environment provides a secure full stack connective tissue needed to build, scale, govern, and optimize agents with confidence. That's their words. So if you want, this becomes the mission control or the centralized point of contact to everything agents that you can develop for anything. And even more, obviously, for the Google ecosystem. This environment also includes their model garden, which currently includes over 200 models. Obviously, the leading models from Google themselves, Gemini 3.1 Pro, Gemini 3.1 Flash, image generation Lyria 3, as well as their open source models from the Gemma family, but also third-party models, including their top competitors, such as Claude Opus, Sonnet, and Haiku, and lots and lots and lots of open source models coming from any aspect of the world.

56:07So not locked into the Google Gemini environment while running on this agentic management platform. They're also expanding the capabilities of Workspace Studio, which is a tool that existed before that I actually already use in several of my automations. And what it is, it is a no-code automation platform that lets you build and deploy agents that automate processes across Gmail, Docs, Sheets, Drive, Meet, Chat, etc., all the different Google tools. So think about NA10 or Make.com or Zapier that works just in the Google Workspace universe. So on one hand, it does not connect to the rest of the tools.

56:44So that's a big disadvantage compared to NA10 or Make or so on. But on the other hand, it is free to use within the Google universe. So if you just need to build automations in the Google universe, it is really easy to build. And as I mentioned, I'm already using it and it's working extremely well. They also announced that 75 % of all code at Google is now AI generated and engineers just approve most of the code. That's up from 50 % just less than a year ago. Now, Google also addressed their collaboration with Apple when it comes to using Gemini as the foundation models for anything Apple intelligence.

57:19And they also announced that the new Siri that has been talked about and delayed for many, many, many, many months is finally coming later this year based on a custom made Gemini model. there are rumors that OpenAI are going to pay$1 billion annual fee to get this custom 1.2 trillion parameter model to run iOS intelligence and Siri. Now, to me, the most interesting aspect of that piece of the news is that whether you're interacting with Siri through iOS or with Gemini as the assistant on Android phones, you are using a Gemini Google model, which means more or less every personal assistant interaction that anybody in the world is going to have is going to go through Google's technology, which will give them a gap that I don't know if anybody will ever be able to close when it comes to understanding the day-to-day needs of people and how to serve them in the best way.

58:17Now, the expected reveal of this partnership as a product within Apple is probably going to be in the worldwide, in the WWDC conference of Apple on June 8th with the launch on probably iOS 27 later this fall. From a real-world usability and partnership perspective, they are pushing very hard their partnership program. They're planning to invest$750 million to deliver new resources to their wide network of partners. And they're working very closely with the leading consulting companies, including Accenture and Deloitte and KPMG, to deliver, test, and deploy agents for their clients. Accenture has already built 450 plus agents on this platform.

59:00Deloitte committed its largest investment yet to that environment. KPMG pledged 100 million, PWC 400 million. You can kind of see where this is going. Really big players with really deep pockets are all committing to investing in this environment. From a hardware perspective, they announced the 8th generation of their TPUs, which is their version of GPUs. That's their models. And they're delivering two versions for the first time. One is called TPU-8T, that is optimized for training new models, and the other one is called TPU-8I for inference, which is going to be optimized for inference. As you can imagine, the world is shifting very, very rapidly to a world in which inference is significantly more important as AI gets embedded into more and more processes, especially in the agentic world where you have 300 agents running in parallel for 13 hours doing everything on their own.

59:51you need a lot more inference and a lot less training. And that will allow them to compete with companies like Cerebras and Grok with a Q that was Acqui hired by NVIDIA that already has these capabilities. That's the future. We're going to have two types of chips, some of them for training, probably significantly less for training and a lot more chips for inference. Now, the last thing that I want to touch on, Google introduced workforce intelligence, which is a unified real authentic workforce in a secure and dynamic way connected to everything in the Google universe, which again makes a lot of sense.

1:00:29You want your agents to be able to have access to everything in your environment in a safe, secure, and effective way. And this is exactly what Google is planning to deliver. So the bottom line of this very quick summary of the major things that Google announced, vertical integration wins, right? They are offering the models, they're offering the hardware, they're offering the runtime environment, they're offering the productivity suite, they're partnering with the relevant companies to deploy this at scale. They have everything they need. And if you remember, if you go back in this podcast, when they were doing miserably in the beginning of the AI era, when OpenAI just launched their tools, and it seemed like Google doesn't know what to do next, and they were chasing their tails, I said all the time that they will win because they have everything they need in order to win.

1:01:11And we're now seeing more and more signs of that. The other thing that is very clear is that the agentic enterprise is inevitable and everything is going to go through agents and it's just going to grow both in means of its ability to access data in an effective way as well as in its ability to coordinate large complex sophisticated processes across every aspect of the organization and that Google's partner channel is massive and it is going to help them grow and deploy this at a very large scale and what all of that tells us is the dominant position that Google has right now in the world is probably going to get even more dominant rather than the other way around.

1:01:49The last thing that I will say, because I have to mention that, is that Tim Cook, the CEO of Apple, is stepping down. His last day at the job is going to be September 1st of this year. And he has been at the company for 15 years. In those 15 years, he achieved incredible financial growth for Apple. He took them from a market cap of$350 million to over$4 trillion, making them the third most valuable company in the world. He was able to take their service business from$3 billion quarterly in 2011 to$30 billion quarterly at the end of last year and really made Apple what it is today, one of the most loved and successful companies on the planet.

1:02:30But he failed miserably in leading the company into the AI era. They had one failure after the other. There were many, many voices from large investors calling from him to step down to bring in somebody who's going to be focused on product development and new ingenuity versus just efficiency and operations. And the person who's stepping in is John Tennis, who has been with the company for 25 years, was involved in building, developing, and deploying some of the most iconic products ever built, including different variations of iMacs and MacBooks and iPhones and AirPods and other different things that Apple has built through the years.

1:03:09and he is going to take the position of CEO. How will that evolve and how will that impact the success of Apple in the AI era together combined with what we report previously as far as their tightening partnership with Google and Gemini? Only time will tell, but these are the last news that I'm going to report today. A lot of other really interesting things happened this week. There are two other really interesting reports, one from McKinsey, which released their AI transformation manifesto with 12 themes that separate AI winners from the rest. Another report from Forbes that released their 2026 AI50 list, additional announcements on interesting partnerships, such as SpaceX and Cursor, which is an interesting combination, which might turn into a$60 billion M &A outcome, new plugins from Anthropic for Word, and other interesting news that you should probably check out.

1:04:01And you can find all of these in the newsletter, and you can sign up to the link in the show notes. that's it for today if you find this podcast helpful please rank us and rate us on your favorite platform whether it's apple podcast or spotify and click the share button you can do this right now it'll take you a few seconds unless you're driving don't do this if you're driving but anything else that you're doing you can stop for a second click the share button and share this with other people who can benefit from this podcast they will appreciate the fact that you're giving them such a resource i would really appreciate it and you will feel good about helping people learn about AI and hopefully helping the world end up on a better state because of AI rather than the other way around.

1:04:39We'll be back on Tuesday with an interesting episode in which I'm going to share with you my Claude universe, how I'm actually working and building things through my different companies right now. And I'm going to open the kimono and show you everything that I'm doing so you can do the same. That's it for today. Have an awesome rest of your weekend and we'll be back.