Leveraging AI

137 | GPTsearch and Meta are going after Google, Apple Intelligence finally released, a new king of image generation and many more AI news for the week ending on November 1st

• Isar Meitis • Season 1 • Episode 137

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Are We Witnessing the Search Wars of the Future?

In this weekend news roundup, we unpack the latest shakeups from AI’s top players—OpenAI, Meta, Google, and more—who are outdoing themselves in the quest to rule your search bar and digital workspace. Is this a sneak peek into a world where search giants are dethroned, and AI tools redefine the way we access information? This episode covers it all, from bold moves to the behind-the-scenes rivalries that are reshaping search engines and productivity tools alike.

OpenAI, for instance, just dropped a Search GPT feature that could be a serious wake-up call for Google. With a seamless desktop integration, advanced citation display, and location-based queries, it’s a sleek alternative that’s already challenging the search status quo. Meanwhile, Meta and Google are hustling on similar innovations, and Apple is finally entering the fray—albeit a few years behind, according to their own insiders. 

On the professional front, we touch on how businesses and individuals can harness these tools right now. With new options for on-device data privacy, AI-enhanced image generation, and low-code assistants from the likes of GitHub and Salesforce, today’s tools are designed to revolutionize how we search, create, and operate at work. 

In this session, you’ll discover:

  • How OpenAI’s Search GPT is challenging Google and Perplexity—and what’s coming next.
  • Why Meta’s low-profile integration of AI tools in Instagram and Facebook is a potential game-changer for search.
  • Apple’s “Intelligence” features in iOS 18.1: what’s included and why Apple feels they’re lagging.
  • Why LinkedIn’s new AI hiring assistant might transform recruiting—and disrupt the HR job market.
  • How GitHub’s “Spark” and Salesforce’s AgentForce open up no-code development and smarter workflows.
  • What to know about AI in government and the international strategies shaping our digital future.

About Leveraging AI

If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Speaker:

Hello and welcome to a weekend news episode of the Leveraging AI podcast, a podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business and advance your career. This is Isar Meitis, your host, and we have another week with a lot of interesting news. If last week we discuss the release of, I don't know, like eight or nine different new models. Today there's less releases of models, but a lot of new capabilities that are all really important. And in the end, we're also going to talk about one new model that is an image generator that came out of nowhere and took the lead in image generation by a big spread. So let's get started. The first and biggest piece of news of this week is OpenAI just released search GPT to everyone. Rumors about OpenAI releasing a search engine started popping out early this summer, and then they released a version of it to a small audience to get tested. And now this week it became available to everyone with a paid subscription. Now, those of you who have been following what's happening with aI assisted search. Google introduced something like this a while back and it wasn't great, and they took it back and then they brought a different version of it. Again, In parallel, Perplexity, which is the company that probably figured it out the best way, is growing very, very fast with more and more users every single day, and which forces Google, obviously, to get their act together as far as the search. But Google didn't make significant changes. You still have the old traditional Google search, just with a snippet on top that has the AI summary, while Perplexity and now OpenAI are providing you just a summary. With the option to see the sources on the side, open AI took a similar approach to perplexity where the main screen shows you the results while you can choose to open another kind of like tab on the right side That shows you the sources where the information comes from. So you can verify the information accurate. Some of the key features of the new open AI capability. It's it has an interactive stock graph with real time market data. It has location based search. With interactive maps, it has clickable citations that you can go and see the different sources, and it has a source sidebar, as I mentioned before, and it is available on the desktop as well as the Android and iOS apps, it is built into the regular chat GPT, so you don't have to open a different application or anything. And it's pulling information from multiple sources, many of them that we spoke about in the past few months of the licensing deals that OpenAI has signed with Hearst and Condé Nast and Axel Springer and News Corp and a bunch of other publishers that are providing them access to real time news and information. Now, currently, there are no ads and there's no clear business model to this, other than the existing paid licensing that anybody can pay to use ChatGPT, pre users will have limits on amount of searches that they can do. And where that's going to go in the long run, as far as the business model is obviously unclear yet, but the trophy is very, very obvious. And the risk for Google is also very, very obvious because Google reported 49. 4 billion in search revenue in just Q3 alone. So the amount of money that is as risk or the pieces of cake that can be distributed differently. They are very significant. I do not know if OpenAI are going to go into an ad model or something else in order to monetize this. But for now, it's definitely a risk for Google that will have to push Google to do more with AI in their searches. We're going to talk more about Google later on in this episode. Another interesting thing that OpenAI released with this new capability is a new Chrome extension for this tool. And if you install this Chrome extension, it makes chat GPT the default search engine on Chrome replacing Google. I installed it and it's actually working really, really well. When you're typing something in the top bar of Chrome, It actually opens open AI and runs the search on chat GPT and shows you the results. I must admit that after testing it for a couple of days right now, I still prefer as of right now, the Google search because there's some things it just does better, but I'm definitely going to continue using both just like I've been using perplexity more and more. And so I will report as time goes by, My preferences right now, I still do most of my searches on perplexity, then Google, and now open air as well. This may change as I see what provides me better and more accurate and more helpful results. In parallel, Meta, the company behind Facebook and Instagram and so on, has announced that they are developing their own web crawling and search engine that will allow them to provide real time answers for their AI models to replace Google search and Microsoft Bing, which are driving these capabilities right now. So a little bit of background, Meta released, Meta AI capabilities, all as open source for the past couple of years. They've been releasing more and more capabilities that are more and more advanced, but they're also having the AI functionality running natively in Facebook and Instagram and WhatsApp and Messenger, which means right now, because people are using it as part of the other tools that they're already using regularly, they're claiming they have 185 million weekly active users on their AI tools. That's just not too far behind ChachiPT's 250 million weekly active users. I don't think it's a fair comparison because as I mentioned, I think a lot of these people do not even know what they're using. The meta AI tools, but that's the beauty of it, right? They've integrated it into their existing distribution seamlessly. And AI is just providing some of the functionality, but they're definitely growing and they're having more capabilities. Meta is known for having issues with being dependent on other platforms. Their most known issue is obviously with Apple, but the amounts of money that they have to pay the app store through any sales that happen on their platforms or any interactions that happen on their platforms. And they're not happy about it. And they were trying to fight it several times in the past. So now they are trying to potentially get away from the shackles of Google and Bing when it comes to providing real time information to their platforms. And if they get it right, they We'll be able to do a lot more with real time searches, which is another risk to Google's search, because a lot of the younger generation spends significantly more time on Instagram and on messenger than they spend on Google. And if they'll be able to search. The web and get answers to anything they want within Instagram. I think they will do that. That's obviously my personal opinion, but based on the amount of money and efforts that they're putting into this at meta, it seems that they believe that that's a potential outcome. So the team now has been working on this for eight months. It's been led by a senior engineer that has been meta for many years. And as we shared recently, they just signed a deal with Reuters to get feed to their live event coverage. And so this will be another interesting aspect of the search game that was so far completely held by Google. And now there's more and more competition from different sources. But going back to open AI. Another small search capability actually happens within chat GPT. And now there's a little magnifying glass on top of the regular chat GPT interface that allows you to search your history of chats. So if you're like me and you're using it regularly, you have X number of chats per day, this could be six to 20 for me, which means there's thousands of them in the past two years. And this is a helpful tool to go and find. So I can go back to older chats and either continue them or find the information that was in them. I started using it. I must admit it's not working great. It's still not sometimes finding me exactly the chats that I'm looking for. And there's some specific chats I like to go back to because they're yielding specific results. Or they're a part of demos that I'm doing when I speak on stages or as part of my courses. And it's still not finding them in some cases. So this is a very helpful functionality. I'm very happy that it's there. But it's still not working perfect as of now. In an interesting article on ZDNet this week, there were some interesting metrics on the current status of OpenAI. So they have 250 million weekly active users. 5 to 6 percent read to paid conversion rates. So I don't know if that's high or low, but that's not bad. If you're thinking about the pool of 250 million active users. They have 1 million enterprise or team paying users, which is significant. And 75 percent of the revenue comes from consumer subscriptions and not from the enterprise subscription. And I. Assumed with all the money they invested in going to the enterprise realm, that the percentage of enterprise revenue is going to be higher than 25%, but that's not the case right now. Now I would like to give some of my personal opinion to what's happening with chat GPT in general. When people ask me what tool they should pay for, if they don't have the money and they want to commit only to one tool, chat GPT is an obvious winner. Now, to be fair, I pay all of them. I have perplexity pro and I pay for Gemini and I pay for Claude and I pay for chat GPT, but that's perplexity pro. What I do for my business, right? I help companies implement this. I need to know exactly how these tools work. And yes, there are some things that Gemini does better. There's something that Claude does better. There's some things that perplexity does better, but if you look in one tool that does some of the things better than all the others, but the combination of capabilities is by far better than everybody else, ChachiPT is a very, very obvious winner. So if you add to that, the fact that now they have Canvas that in many things does a much better job than. Claude artifacts, definitely means of data analysis and presentation of data, it's doing a better job. And now with the addition of search, they're opening more and more gaps on more and more aspects that provides as a one tool that does it all just a better tool than any other tool out there. So if you are one of those people that is currently not paying for any of the tools and you're sitting on the fence and you're not sure which one you want to use right now, ChachiPT is a very obvious winner. And in another very interesting piece of news from this week that has to do with changing world orders, in addition to search, OpenAI just announced that they're starting to develop its own in house AI inference chip in a partnership with Broadcom and TSMC. So Broadcom is going to help in chip design and optimization, and TSMC is going to be the manufacturing capacity. They also announced that they're adding AMD chips. To assist in some of the aspects that they're doing with Nvidia while they're maintaining their strong relationship with Nvidia. So it's a new department within OpenAI that is going to work on chip development. There are two different kinds of operations when it comes to OpenAI. large language models or AI in general. There's the training side in which NVIDIA GPUs is still a very dominant player. And there's more and more new companies and new chip capabilities that are focusing on inference, which is the generation of tokens, basically us using the model versus the companies who develop the models, training the models. And in there, there's some very big success stories. The one that we talked about Many times it's Grok with a Q, G R O Q, that developed what they're calling an L P U, a Language Processing Unit, versus a GPU of a Graphics Processing Unit, which is the chips that are sold by NVIDIA. And they're able to generate inference significantly faster. Now, if you connect the dots together, OpenAI is projected to lose 5 billion in 2024 on a 3. 7 billion revenue. The biggest expense that they have out of that 5 billion is compute, meaning they're spending a lot of money on other people's computing power. And if they're developing their own inference chip, they can focus their expense on compute on something that they control that is presumably, or If it works, runs better, faster, and cheaper for them that can save them significant expenses moving forward. And what I project, and again, I think a lot of people would agree with that as these models gets better and better, we'll get to the point that we won't need the best frontier models for most tasks. What we will need is to run older models in the most efficient way. And if you control the entire ecosystem of hardware, software. and algorithms, you can be significantly more competitive in this new world. The production of these chips is supposed to start in 2026. So about a year and a little bit out and probably installation of them, probably at the later part of 2026. And so this is not immediately, but it's definitely in the future. And I definitely understand the move that open AI is making in this direction. Now, we talked about meta earlier because we were talking about the topic of search, but going back to meta, they also made another interesting release this week, and they released models that are based on their existing models, which are just faster and smaller and are designed to run on low power devices, basically to run On our phones and tablets and potentially watches, and these new models are 56 percent reduction in size compared to the previous model size. They have a 41 percent decrease in memory usage on Android devices. They run 2 to 4 times faster on inference, and they're per meta maintaining the performance comparable with the larger models. The only big limitation of these is that they support a context window of only up to 8, 000 tokens, which is very small compared to what we used to. But if you're thinking about the use cases, which are the things that you want to run on your device, which are usually short and quick queries, this should be more than enough. This is the next frontier, right? Is the Two huge benefits. One is data privacy, right? Because your information is not going anywhere. It's staying on your phone or on wearables, which is the next thing that is coming. But the other benefit is obviously speed because you don't have to relay the information to a server and then back to the device. So you win on both these aspects. And these new models are available both on Lama's website, as well as on HuggingFace and they're open source, just like all the other stuff that Meta is doing, so anybody can use them to develop on device solutions. We're going to talk more about on device AI capabilities once we talk about Apple later in this episode.

We have been talking a lot on this podcast, on the importance of AI education and literacy for people in businesses. It is literally the number one factor of success versus failure when implementing AI in the business. It's actually not the tech, it's the ability to train people and get them to the level of knowledge they need in order to use AI in specific use cases. Use cases successfully, hence generating positive ROI. The biggest question is how do you train yourself? If you're the business person or people in your team, in your company, in the most effective way. I have two pieces of very exciting news for you. Number one is that I have been teaching the AI business transformation course since April of last year. I have been teaching it two times a month, every month, since the beginning of the year, and once a month, all of last year, hundreds of business people and businesses are transforming their way they're doing business because based on the information they've learned in this course. I mostly teach this course privately, meaning organizations and companies hire me to teach just their people. And about once a quarter, we do a publicly available horse. Well, this once a quarter is happening again. So on November 18th of this month, we are opening another course to the public where anyone can join the courses for sessions online, two hours each. So four weeks, two hours every single week with me. Live as an instructor with one hour a week in addition for you to come and ask questions in between based on the homework or things you learn or things you didn't understand. It's a very detailed, comprehensive course. So we'll take you from wherever you are in your journey right now to a level where you understand. What this technology can do for your business across multiple aspects and departments, including a detailed blueprint of how to move forward and implement this from a company wide perspective. So if you are looking to dramatically impact the way you are using AI or your company or your department is using this is an amazing opportunity for you to accelerate your knowledge and start implementing AI. In everything you're doing in your business, you can find the link in the show notes. So you can, you just open your phone right now, find the link to the course, click on it, and you can sign up right now. And now back to the episode.

Speaker:

Now, in general, Meta is going all in on AI. We talked about their new search, but they have made some serious announcements about stuff that they're developing and haven't released yet. And we talked about some of them in the past few weeks, like the MovieGen video creation and new AI based advertising features and SpiritLM for emotional voice generation. So there's A lot of things that are either partially released or not released yet that are going to become a part of Meta's AI ecosystem. And one of the interesting things about Meta is they're one of the only, maybe the only player in this industry that does not need revenue coming in from AI. Hence, they can release really advanced capabilities as open source just to reduce the competitiveness of their competitors like OpenAI and Anthropic, and they can release very powerful capabilities that they integrate into other things that is generating revenue for them. I think we're going to continue seeing MetaPush in that direction and taking bigger and bigger part of the AI universe while monetizing it through their existing ecosystem. Another great example of them going after their competition directly, but with open source capabilities is that they just released Notebook Llama, which is an open source alternative to Google's Notebook LM that we talked about in several different episodes, including one dedicated episode where I explained exactly how this can be used. But those of you who don't know, Notebook LM is a very powerful Google tool that existed for a while, but then Caught like wildfire when they released a feature in it that allows you to generate mini podcasts from information you upload to it. So Meta just released a similar tool that allows you to upload files and links such as articles and PDFs and create an interaction of a podcast of two people speaking to one another. Now, I must admit, I listened to some of these examples. Their text to speech models are definitely not as good as Google's. As of right now, it sounds way more robotic than Google's Notebook LM voices, but it's definitely a move in the right direction. There's also an open source model, not from Llama, called Open Notebook LM that does something very similar. So while it was very cool when Google released it, now there's other functionality, as I must admit that I'm. Very happy with the Google tool. And I'm not interested in looking at other ones unless they're going to provide some significant additional value, which right now they are not. And the Google tool is still free. So there's really no reason for me to switch to something else, but this is just my personal opinion. Now we already mentioned Google a lot as far as competition to Google. From multiple sources. So let's talk about Google themselves. So a lot of information is being leaked about Google's next release, and it is now mentioned that is Google is creating an agent platform called project Jarvis that will control the Chrome web browser on your behalf and complete tasks autonomously. So if you remember last week, we talked in depth about the new release from Anthropic of a cloud function that can take over the computer as a whole and do things for you on anything that computers have access to, including local software. This is Google's variation of this that is supposed to be released in December with the new Gemini models, which 2. 0. And. In it, it will be able to control everything within the browser. So from a security benefits, it reduces the risks because it can access stuff outside of the browser in your computer. But if you think about the browser and specifically Chrome, it has access to your passwords in many cases that can access anything you can access to. So there's still a lot of risks. But as I mentioned, this new project Jarvis is planned to be released in December as more. Information comes out, we will share it with you, but the use cases that have been discussed are research gathering. So obviously going to multiple sources to find information on your behalf and summarize it for you product purchasing. So go and find the cheapest X based on whatever characteristics, then you will look through multiple sources will provide you options and can actually complete the purchase booking flights and travel, processing returns of different items that you bought on different platforms and other everyday tasks that we do on our browsers, you'll be able to basically tell it what you want it to do, and it will go and do it for you, which will be very useful. This goes back to the same concerns I talked about last week. And every time I talk about these agents, A, these tools are not consistent right now. They do weird things and they don't always complete the task. B, there's security concerns, as I mentioned. And C, I think the biggest information will be trust. When will we feel comfortable enough to give these tools real access to do stuff on our behalf, knowing that they will complete the task the way we wanted it. And if it's not possible, they will raise a flag and stop and tell us what's happening and ask for permission or additional guidance. I think we're still. Months away from that, but it's definitely the direction that everybody's going. And hence I see this as an inevitable future where these AI agents will act on our behalf across multiple aspects, both personal and business. Now, another interesting piece from Google and AI related topics, Google reports that. AI is now generating between 25 to 30 percent of the code that Google generates. Now, I don't know how many software engineers Google has today, but it has to be a really large number. And they're generating a lot of code. And if 25 to 30 percent of that is generated with AI, it's a huge I'm out of code that they're generating with AI. Now this came as part of their quarterly reports. They are also reporting that their cloud revenue has reached 4, 11. 4 billion, which is 35 percent increase year over year search revenue has grown as well with 12. 3 growth year over year. And the stock is up this year, almost 30%. So while I see. Clouds in Google's future when it comes to this revenue, because there's going to be more competition on search and on cloud and on other aspects right now, they're doing very well with leveraging the AI capabilities. Now, if you remember, I said that multiple times before, I think Google is probably the best position company to win this overall game because they have everything that is required. They have the talent, they have the compute, they have everything. Unlimited amount of money and resources to invest in this they have deep mind, which is Probably the best development lab out there right now, but if not, definitely one of the top three and they have the distribution and the data. So they have more stuff than anybody else. They are obviously also banking on their full stack approach to this, that it integrates to all their existing services and they're growing their enterprise customer usage of AI. I'm using Gemini Within the Google suite of tools, more and more every single day. And if you want to learn on ways on how you can do that, just go and check episode one 26 of this podcast, where we dove deeply into how you can use Gemini within Docs, Gmail, and other Google Suite tools. Now, I mentioned briefly earlier that Gemini 2. 0 is also planned to be released in December of 2024, and There haven't been enough leaked rumors to know what Gemini 2. 0 will do different than Gemini 1. 5. Right now, the two biggest benefits of Gemini 1. 5 is that it has the largest context window by far compared to the competition with a very accurate retrieval percentage from that. So if you're asking questions about the data that you provided, you're getting a 97% accuracy in the answers based on the information, which is higher than all the other competitors right now. What's going to be the difference to 2. 0? Nobody knows exactly, but the interesting thing about this is that it's roughly a year out from the release of Gemini 1, which was released in December of 2023, In the middle, but really early in the year, in February, they released Gemini 1. 5 and then they released Gemini 1. 5 Pro in May. And the interesting thing about this is this starts to resemble more and more a normal cycle of version releases of traditional software versus the complete madness we know from the AI world so far. Which means from an enterprise perspective, it gives more time for planning and deployment and so on. As a new version comes out, a big version once a year and a small version once every six months. I think this is a lot more attainable and controllable if you're running an IT department in an organization. And it will really allow companies to implement these AI capabilities in a much more structured way. Now we spoke about OpenAI, we spoke about Google, we spoke about Meta, the other really big piece of news this week is from Apple that has finally released its Apple intelligence AI capabilities. It is coming through iOS 18. 1 and iPad OS 18. 1 and Mac OS Sequoia 15. 1, which has been anticipated for a very long time. The capabilities that are released right now is writing tools that are going to be available Through everything writing. So system wide AI writing assistant enhanced Siri. So being able to ask Siri more things from a more natural conversation and get answers that will just improve the context and understanding of Siri to what you're asking it and be able to provide better answers. Smart photo search will allow you to search your photo with natural language and find photos that you took in the past, a. Photo cleanup tool that allows you to remove objects or people from an image that you want to remove. That's a functionality that exists on Android for the last two or three years. It's finally coming to Apple as well. Priority messaging will allow you to use AI to organize your emails and summarize your emails in a more effective way. The biggest difference between Apple intelligence and other platforms is maybe the focus on privacy, like everything else, Apple. And the main thing is that most of the processing is done on device, meaning none of your data is getting sent anyway. And the other aspect is what they call private cloud compute, which is an instance of a cloud that is open to run a specific request from a user. Which once it's done, it gets deleted and none of the data gets stored on the cloud, which provides significantly higher levels of privacy than any of the other AI capabilities. Now, some of the functionalities that they have demoed earlier this year, about six months ago, are not released yet. One of them is the integration with chat GPT, which is. rumored to be released in December that will provide users free access without an account to ChatGPT. It will protect your IP address from ChatGPT, but it will also provide an option for users to connect to their ChatGPT account so they can continue the conversations in other places and see the history and so on. All this functionality is going to be available on iPhone 15 Pro, 15 Pro Max and the new 16 series iPad, a 17 Pro or M one or later, and on Mac M one or later MacBook computers. Now, other few features that they haven't released yet is visual AI features like Genmoji that they released an image playground, enhanced writing capabilities, camera based visual intelligence and expanding to languages other than English. So right now they're releasing only in English, so there's. A few things they haven't released, even though This release was delayed several different times, and according to Bloomberg India, there are internal memos within Apple that are stating that they're two years behind their competitors in means of their capability. They're claiming that chat GPT is 25 percent more accurate when compared to the new GPT. Upgraded Siri, that it can handle 30 percent fewer queries than its main competitors in the AI world, and that they feel internally that they're far behind Google OpenAI and Meta when it comes to their in house AI capabilities. So this has been Not very Apple like this whole process. They did a demo months before they released the capabilities. They did a launch of the iPhones without releasing that capability. Now they're releasing just some of the functionality that they promised. And that functionality is not as good as the competition. Overall, doesn't look very good to Apple. There's obviously going to be the Apple lovers that are going to love it. Go crazy and say, Oh my God, look how amazing this is when in reality, it's not even close to probably what the competitors are doing. I do think that Apple will eventually close the gap one way or another, either by giving up on their internal efforts and doing deeper integrations with existing models, maybe open source platforms. but right now this is a situation and Apple intelligence is finally available in some shape or form to all the more advanced Apple devices. If you remember last week, we reported on the intensifying competition between the frontier models on coding tasks. Well, this week, GitHub Copilot has announced that they're now supporting the big three models. So far, they only supported open AIs, Models initially just GPT 4. 0, then a few weeks ago, they offered the support for GPT 0. 1, and now they added Anthropic Cloud 3. 5 Sonnet plus Google Gemini 1. 5 Pro, which means GitHub users will be able to pick different models for different tasks, because some of these models are better in specific aspects of coding or in specific coding languages that obviously provides a lot more flexibility to users. They made that announcement during the GitHub universe conference in San Francisco and they made a few other announcements, like the release of spark, which I'll talk about in a minute. The interesting thing about I see about GitHub launching this multi model functionality is the fact that I believe that's the direction the world is going where in multiple tools we will have access to multiple models. And over time, I think that tools themselves will pick the right models. For us, right? So if right now, if I even go just to chat, GPT, I need to pick which model I want to use. Do I want to use four? Oh, do I want to use a one? Do I want to use 3. 5 sonnet in Claude? Do I want to use three Opus on? Claude and so on. And I think what's going to happen over time is there's going to be more of like a user, higher level interface that will understand the task, and then it will break it into smaller tasks, agentic style, and we'll divide it through different. Models that will do each of the tasks better than other models. And so I think GitHub is just doing the first step in that direction. Now, as I mentioned, GitHub also released Spark. So GitHub Spark allows users to create simple web applications using plain English, knowing nothing about coding. So you open it and you have a chat with it, just like you have with any other chat interface, and it will write the code for you automatically. And we'll be able to execute and create applications that you can then run and use them for multiple use cases. The interesting thing from this particular implementation is that it allows you to also edit the code within GitHub Copilot. So it doesn't just generate the code, it's also a code management platform, just like GitHub knows how to do, and it also provides you a preview within seconds from the moment you run the prompt. Now, based on GitHub CEO, Thomas Domke, the Spark is positioned to support rapid prototyping, creating micro applications, personal productivity tools, and learning software development. Now you heard me say that many times before on this podcast. I think the SaaS world and definitely the application world are at risk, meaning In the future, and I don't know if that future is coming out in a year or five years, but somewhere within that timeframe, I think the concept of an app store will cease to exist. And instead of an app store, we will have an app creator where you'll be able to come in and request whatever game or application or tool that you need. And this will be created on the fly. tested by the tools themselves and deployed in the relevant environment that you need it in, whether it's a cloud environment, your local computer, or your handheld device. And you'll be able to use these tools for specific functionality that you need tailored to your needs without the need to buy and pay for expensive software that has 6, 700 other features that you don't necessarily need. So I definitely see that's the direction this is going. I started creating different small applications for my day to day usage. I know nothing about coding. I don't understand how to read code. And yet I can do that right now. And I'm doing this with these tools. And the more advanced and capable the tools become, the more day to day tasks we'll be able to complete it by these applications that anybody can develop. So right now, yes, I can do this, but I'm more technical than the average person, even though I don't understand code. And by watching a few YouTube videos, I learned how to deploy them and how to run them and spin server from them and run Python on my computer and stuff like that. For some of you, that sounds very basic for somebody that sounds like rocket science, but that's As these tools get better, you won't have to know any of these things because the tool themselves will do all those steps for you. And as I mentioned, I think that's the direction this is going. If you've been following this podcast and you've been following what's happening in the development world, the latest darling in the code generation world is Cursor. Relatively younger and small startup that provides very similar functionality to what Spark is now providing. And I assume Spark is GitHub's answer to Cursor that will allow similar functionality. I've already seen some examples of comparisons between Cursor and GitHub. And right now there's benefits to each sides. And I assume it's going to be a continued competition where they're going to keep on adding functionality and capabilities that will keep this competition going where people have to pick the tool that they want to use. And since we started talking about agents and creating autonomous capabilities, Salesforce just released AgentForce to general availability. So the big announcement by Salesforce happened in their annual conference a few weeks ago, but they released that functionality only to selected enterprise companies, and now they're releasing it to everyone. So, AgentForce allows you to create low code or no code chat platform developments, automate multiple tasks based on different triggers, connected to different business rules, build custom agents using different templates that they're providing and use specific self service customer support capabilities that they have already released that everybody can use. Now, in addition to all the templates, they also providing an agent builder functionality that allows you to build your own agents and not just use templates. Templates. This is a very aggressive move by Salesforce to go into enterprise AI and not just stay within the CRM space. CEO Mark Benioff Is very clearly going after Microsoft. And in addition to just going and playing in their field, he also made the claim that the new Microsoft AI tools are Clippy 2. 0. Those of you don't remember Clippy, it was kind of like an assistant On the early Microsoft office systems that looked like a paperclip that was supposed to be helpful and nobody ever used. So he's obviously playing off of that. He's claiming that Microsoft tools are not really agents and that they're very limited with information that they have on the companies and so on. And he's claiming that their benefits running. Agents, as well as having access to all the information that they have about the company through the CRM platform is a completely different universe, completely compared to what Microsoft has. I'm not going to get into that battle. If you know the history of Salesforce, not the first time they're making that move. That's how they started Salesforce. And they are today. So they're just making the same play again. But that being said, they have some points that are relevant. And to me, the interesting thing is now everybody's calling everything agents, whether it's actual agents or not. So I think we're going to see literally any platform, any company, any tool that we know, suddenly having agents. The definition of agents is going to be murkier and murkier. But in general, I think most of the things that people that are releasing right now as agents are not real AI agents. And what I mean by real AI agents are AI platforms that can make their own decisions, that can take a task and analyze it and break it into smaller tasks. And I'll sign each task to a specific AI agent. Mini agent that can execute report and evaluate and then complete much more sophisticated and complex tasks that are not scripted by a human user. Are we there yet with all the agents that are out there now? Absolutely not. But I think that's going to be the direction. And we're going to hear the word agents more and more, whether it's accurate or not. Speaking of agents, LinkedIn just unveiled an AI hiring assistant, which is their AI agent that is aiming to transform the recruiting business on LinkedIn. So LinkedIn is currently driving 7 billion in revenue just from its recruiting business, and they just developed an agent which allows people in the recruiting side of business to use this agent for multiple aspects in the recruiting life cycle, including converting rough notes and inputs into very detailed and accurate job descriptions, including evaluating candidates and including recruiting. handling messaging and scheduling interviews with candidates all within the LinkedIn platform. They're claiming that there's several companies are already using these capabilities, including AMD and Canva and Siemens. So some very big companies that are already testing that. Now that makes perfect sense From LinkedIn's perspective, LinkedIn has over 1 billion users, 16 million companies, and 41, 000 skills that are being tracked behind the scenes. This tool is powered by OpenAI Chachapiti through partnership that they have with Microsoft. And so a very interesting development that will allow HR departments and companies who are helping with talent acquisition to do skill based candidate matching. And as I mentioned, all the other stuff that I said before, but what this also means is that you will need less people in HR departments. It also means that you will need less people in talent acquisition companies, because these tools will allow you to do some of that work in a much more efficient way. And that goes back to a conversation we had In several different episodes of the risk that these tools are putting on job displacement across multiple aspects in multiple industries. Now, speaking of risks to jobs or society and so on, the White House has issued a comprehensive AI national security framework, defines different policies and different concepts for AI. Federal agencies to deal with AI risk. So the core policy objectives are to establish US leadership in safe, secure, and trustworthy AI development, harness AI for national security with appropriate safeguards, and foster responsible international AI governance frameworks. All very important. I'm going to talk specifically about number three for a second. You heard me say several times in the past that the only way to control AI before it controls us is to have a very strong international governance that will involve both academia and government. With the leading companies, as well as governments to figure out how to do this the right way. And I'm very happy that the U S government is finally taking this seriously and making the right steps. Some of the key organizational aspects of this, it requires agencies to appoint a chief AI officer per agency. It establishes AI governance boards within the agencies, creates AI national security coordination group for dealing with AI risks and forms a national security AI executive talent committee. So multiple steps that are done by the government to increase Innovation on one hand, but in a safe way, on the other hand, they're also talking from a strategic perspective that the AI Safety Institute will lead pre deployment testing of frontier AI models that has already started voluntarily with cloud and open AI, but that's going to be hopefully mandatory by the government. And as I mentioned, presumably later on through collaboration with other agencies and other countries to any advanced model that will be released anywhere in the world. So part of this, it directs the State Department to develop a strategy for international AI governance, with an emphasis on collaboration with allies on AI development, specifically pushing for democratic values as part of AI deployment. So lots of great initiatives. The only bad thing in all of this is that the initial deliverables are due within 180 to 270 days. So it's not something that's going to happen tomorrow. And a lot of stuff can happen between now and when these things starts to shape. But just the fact that government is making very aggressive moves and the fact it's going to happen within less than a year. Is very promising from my perspective. I just wish to see more governments jump into this. And as I mentioned, I'm very eager to see this international collaboration and when it will start to take place and be significant in its application to anybody who's developing AI. Another one of the AI giants in the risk is XAI from Elon Musk. And there are rumors that they're looking for their next funding round that will value the company at 40 billion. Now to explain how crazy this is, XAI came out of nowhere, raised 6 billion on a 24 billion valuation in the spring of this year. So we're six months away and they're looking for an increased valuation of 16 billion more than the previous valuation. That being said, in that timeframe, they have built a 100, 000 NVIDIA chips. A GPU data center in Memphis that is the. Most powerful data center that exists with GPUs in the world today. And they built it in a record time that nobody thought is possible. So now they're training the next X. AI model with the strongest training capabilities ever, Sam Altman, himself sounded concerns of what might be the outcome of all of that. And to remind you, they have X previously Twitter as a huge data source for it, combined with a lot of other data sources coming from multiple directions, including Elon Musk's other companies, such as Tesla, SpaceX, et cetera. So it will be interesting to see how that evolves. I assume they will be able to raise that money. I assume it will be at the valuation that they want. And I assume their next model is going to be finally something that will be seriously competitive to the offerings from open AI, anthropic meta and Google. And I promised with you to share with you something about a new image generation tool. So on. The artificial analysis text to image model leaderboard last week, a new model showed up called red Panda. The red Panda model that nobody knew where it came from or who it belongs to secured an impressive 72 percent arena win rate. So those of you who don't know how the arena works, you put in a prompt and it gives you two answers and you need to choose which one is the better answer. And based on that, it ranks multiple types of models, whether image generation, large language models, and so on. And so this model took the top position of the leaderboard when it came out of the blue, literally before anybody knowing who it is or what it is. It also achieved an 1172 ELO rating, which is better than Mid Journey and Flux 1. 1. So who is this? So that was a mystery for about a week where a lot of conversation was happening about who released that. There were rumors that maybe it's a new model from open AI, but it's not. It is a model called reCraft V3, which is developed by a UK based company called ReCraft AI. It's the first AI image generation model that is offering unrestrictive Image size generation. So you can generate much larger images than you can do with any of the other tools. It has a unique capability to handle much longer text in their images. So as these tools are really bad at generating text and those who are getting text Better like flux 1. 1 and like ideogram are limited with a length of text. So this tool can generate significantly longer text accurately in its images. And it's specifically designed for professional designers, allowing them to have a lot more control on the outcome. Per their CEO, anna Veronica Durgash. And I hope I'm not butchering her name. The company is going to focus on providing designers precise control rather than just prompt based generation. So time will tell what the models are, but they raised an interesting amount of money already. And as I mentioned, as of right now, they have the best image generation model out there. It will be interesting to see what Midjourney and Flux are doing to get back on top. And that's in addition to the fact that last week I shared with you that Stable Diffusion released a new model. So there's a huge amount of tools to choose from. The pricing for this new platform is going to be between free with limitations to 48 a month for premium subscriptions using this new platform. That's it for the news this week. If you enjoyed this episode and if you find this podcast valuable, please open your phone right now and write a review on your favorite platform, whether it's Apple or Spotify. And while you're at it, click the share button and share it with a few people who you think will benefit from this podcast. This is the podcast. your way to help with AI literacy for everyone. And we'll be really grateful if you do that. We'll be back on Tuesday with another how to episode. In this time, it will be how to create consistent characters when you want to create images. One of the biggest problems that people have when it comes to generating images with AI for specific campaigns is that they It generates a different image of a different person every single time. And there is a way to actually create the same person with the same clothing, with specific control on their poses. And that's what we're going to share with you this Tuesday. And until then, enjoy your weekend, test AI, and share with us what you learn and have an awesome rest of your day.

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Isar Meitis

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