129 | $10.6B of cash to OpenAI, ChatGPT Canvas, Priceline's AI travel agent, Liquid AI release it's revolutionary AI model, and many other important news for the week ending on October 4

0:00Hello and welcome to a weekend news episode of the Leveraging AI podcast. the podcast that shares practical, ethical ways to improve efficiency, grow your business, and advance your career. This is Isar Maitis, your host. And like every week, a lot of stuff happened in the AI world, which includes the raise by OpenAI that finally happened, release of some really cool features from OpenAI, new technologies that are very different than AI technologies we had before that was released, and a lot of other good stuff. And in addition, I'm going to share some insights from the AI Realized conference I was speaking at this week.

0:39So let's get started.

0:46Before we dive into company-specific or tool-specific news, I want to share with you some interesting results from a study that was held by the Federal Reserve Bank, plus a couple of universities, including Harvard Kennedy School. And they were looking at the rate of adoption of AI technology. And what they've learned, and it's not surprising, is that generative AI adoption rate is significantly faster than the PC adoption was or the internet. And I'm going to say in a minute why I think it's not surprising. But here's some of the findings. 39.4 % of American ages 18 to 64 reported using generative AI just two years after ChatGPT became publicly available with 28 % saying that they're using it at work.

1:34This adoption rate significantly exceeds the 20 % adoption of PCs that took three years to get to. So a year more and a lower adoption rate. So that shows you how fast this technology is being adopted. Another thing that I found really interesting in the survey is that they found that one in every five blue-collar workers in construction, installation, repair, transportation, and other blue-collar jobs, one in every five uses AI on the job. So that's 20 % already on jobs that are not white-collar office jobs. In white-collar jobs, mostly in management, business, and computer-related occupations, the usage rate on businesses exceeds 40.

2:18Another thing that wasn't surprising, but is very important to pay attention to, is that the AI usage mirrors the workplace inequality quality trends that already exist. So workers with bachelor's degrees or higher are twice as likely to use AIs compared to those without. So 40 % versus 20%. Younger, more educated, and higher income workers show higher adoption rates compared to the rest of the population, which, again, if not addressed, will increase inequalities in our population. Now, what are people using it for? 57 % are using it at work for different writing tasks, which is not surprising.

2:5949 % use it for information search and research. And 25 % said that they're using it for all the other tasks that were listed, such as study, administrative work, data interpretation, and so on. Now, the last interesting point is that they try to do a quick math on the impact of that on the overall market. So their estimate is that between 0.5 % and 3.5 % of all US work hours are currently being assisted by AI. And using that parameter, they were trying to calculate how much is that going to increase the productivity. And they got to the conclusion that it's going to increase the productivity of overall job market between 0.12 % to 0.87%.

3:47Now, that sounds like a very small number, but if you multiply that times the size of the US economy, which is$27.4 trillion at the end of 2023, that by itself is$137 million that are generated in efficiency. But this is just from the small day-to-day tasks that people are using generative AI for. That doesn't take into consideration the big, more significant things that people are using AI to do that bigger companies or even mid-sized organizations are implementing to get efficiencies. As I mentioned, I was in a conference called AI Realized this past week. I was one of the speakers, but the conference as a whole had an amazing list of speakers, including people like Ted Shelton, who is the CEO of Inflection AI, Jeremiah Ouyang, who is one of the managing partners of Blitzscaling Ventures, which is Reid Hoffman's fund, Haling Feng, the head of product at Airbnb, Tolga Kurtogulu, who is the CTO of Lenovo, and many other people at that scale.

4:55And the audience was senior leaders from Fortune 2000 companies who are coming to learn and share about how AI is implemented at the enterprise level. And there were a few very interesting findings in that. The first thing is there's obviously very significant investments. And when I say very significant investments, is tens of millions of dollars in next year's budget in AI implementation. The other thing that was very obvious from everybody's presentations is that the human factor plays a very big role, whether it's training, experimentation, building excitement, or getting buy-in from senior leadership.

5:36All of these play as an important role, and in many cases, more important than the actual technology itself. And touching on senior leadership, many of the participants, again, that are implementing AI at the highest levels in the largest companies are saying that without sponsorship and without buy-in from senior leadership, you are bound to fail to the point that you should pick a different kind of approach and find a sponsor that will support you versus the best use case without support and without sponsorship is not going to be successful. So that's something to take into account. I see that when I work with companies and I work with a very wide variety of companies, whether through my courses and my workshops, as well as the consulting that I'm providing, it is very obvious that the biggest difference in success of implementation of AI in companies is not the size of the company, it's not the industry, it's not the technology, it's how much senior leadership are actually bought into the idea that AI is going to make a difference.

6:34And that is the number one factor on success. So if you're in an organization in where leadership does not see the value in that, either try to convince them by bringing education and providing them additional resources or potentially consider a different workplace because that company is going to stay behind. Another thing that was very obvious, it does these, that there are two kinds of projects. They're small and quick projects that you can get quick wins on and that you can provide many people in the organization the ability to experiment, whether that's building automations, GPTs, low-code and no-code automations, and stuff like that.

7:11You have to define the boundaries. You have to take care of data safety, but beyond that, you can let people experiment. But then there are the big projects that require a lot of infrastructure, a lot of resetting, reshuffling, and cleaning of data, and that require a lot more time. And both are important and both need to happen in parallel. The main thing that everybody said is that regardless of the size of the organization, you have to get started. And the only way to get started is actually to get your hands dirty and experiment with these tools, do it in a safe way, do it without risking the data.

7:45But just learning about this is not going to get you or your company ahead. And the very first thing you got to do is literally pick small, low-hanging fruit use cases and just go for them and try to implement them. That's the way you're going to learn and iterate and start seeing results. And now to the regular. And from that, let's go to OpenAI. OpenAI finally completed their funding round. They have raised$6.6 billion at$157 billion valuation. Now that sounds like a pretty high multiplier, but the reality is as part of the announcement, they released that they're projecting a revenue jump to$11.6 billion in the next 12 months.

8:27So if you consider that as their revenue, then$157 billion is about a 15 multiplier, which now makes a lot more sense. But$157 billion makes them one of the most valuable private companies in the world, less than two years after they've launched the first product that was completely free in the beginning. So that is an incredible valuation that I don't think ever happened before. Some of the investors were existing investors like Thrive Capital and Kostla Ventures and Microsoft, obviously. Some of them were new like NVIDIA. And then Altimeter Capital, Fidelity, SoftBank and Abu Dhabi's MGX fund.

9:13Now, Thrive Capital had a very interesting commitment. They invested$1.2 billion in this round, but they also negotiated the opportunity to invest an additional billion dollars in the next year at the same current valuation if OpenAI hits specific revenue goals. So that means that there might be another billion dollars in that pile in the next 12 months. Now, we've also learned that OpenAI made$3.6 billion this year and that their projected losses is supposed to be over$5 billion. So that tells you that this amount they raised is not enough money for the long run. So if they lost$5 billion a year this year and they're training significantly bigger models in the future and they're hiring more people and there's more inference happening, so all of these things require more budget.

10:01If they raise 6.6, that's just not enough money, which tells us they will need more money. So as a first step, a day after they've made that announcement, they've announced that they've got a$4 billion line of credit, which gets them to just over$10 billion in cash that is available to them right now. that money comes from the obvious aspects that are the major financial institutes, including JP Morgan Chase and Citi and Goldman Sachs and Morgan Stanley and many others. But some of, again, the biggest banks and financial institutions in the world that have provided this line of credit to OpenAI.

10:40So compared to last week, OpenAI has$10.6 billion more to spend on future growth opportunities, which include all the things that I mentioned before, training new models, deployment, customer service, hiring talent, and so on. Now, another interesting report came that one of the requirements that OpenAI has put to the investors is to provide them exclusivity and basically prevents them from investing in their competitors. They named five specific companies, which include XAI, which is Elon Musk's company, Anthropic, Perplexity, Glyn, and Ilya Satskova's new company, SSI, which stands for Safe Super Intelligence.

11:23Elon Musk obviously called them evil for putting these terms in the agreements, and I tend to somewhat agree, but it's not the first time that stuff like that happens in the tech world. Uber has done similar things and other companies has done similar things in the past. That being said, the reason I think it's evil is it prevents competition, which is never a good thing. And it's definitely preventing competition from smaller companies. So if you think about the size of Perplexity and the size of Anthropic and the size of Glean and definitely Ilya's company that hasn't even started yet, that will take away some of their ability to raise money, at least from these specific players, which will reduce their ability to compete and develop other innovative ideas, which we can all benefit from.

12:11So I don't see that as a positive move. Now, the one company that was considered as one of the investors and ended up not investing is Apple, right? So that's a little surprising to me. There were a lot of conversations earlier this year. And as we know, OpenAI is powering some of the new Apple features or presumably supposed to because these were not released yet, even though the new iPhone was released. So something is definitely not right in the relationships between Apple and OpenAI. And I need to assume, and I don't know that for a fact, but I need to assume that the term oil that's happening in OpenAI is not something Apple wants to be involved with, and maybe it's not something they're willing to bet on.

12:54So I got to go back to that. Like they just raised over$10 billion after less than two years of releasing their product and about 10 senior executives, including some of the founders, left in the past few months knowing that this is happening. This is not a good sign for the culture and for the leadership style that is happening in the company right now. If one of two people leave, okay, that happens. When most of your senior leadership departs, when you're making these amazing strides forward, both from a technological perspective and from a financial perspective, it is not a good sign. So Apple is not in this particular game.

13:37But the interesting thing to me is that while this is a huge amount of money and it's going to leave them at the leadership of the AI development in the world, it's a very small amount of money if you're looking at the competition. So while OpenAI themselves have to go out and raise this money in order to basically stay alive, I want to take you to the three other leading companies in the game, which is Microsoft, Google, and Apple. If you take Microsoft's EBITDA in the past 12 months, it's$131 billion, which means they make about$360 million of EBITDA every single day on average. That's$2 billion of new cash every single week.

14:26Alphabet, which is Google, is a little smaller with$274 million a day or$1.5 billion a week. And Apple is at the same ballpark as Microsoft with$344 million a day, which again stands at just over$2 billion every single week. So these companies have a lot more cash than OpenAI has. They have the compute power and they have the distribution. So three things that OpenAI does not have. So they now have cash, they have 10 billion, but again, all these companies make$10 billion in five weeks, every five weeks. So their ability to develop their own models and use them with their own distribution, overlaying it on top of their own data is something that OpenAI doesn't have.

15:15So OpenAI right now is like the dream kid and the superstar of this universe. But I don't think that will last very long. I do think that Microsoft on their own and Google on their own and Apple on their own will surpass the success of OpenAI in the future just because of the things that I said. They have access to the money. They have access to data. Google has access to more data than anybody else. And they have access to talent, compute, all the things you need in order to make this successful, which OpenAI are dependent on these other players and external financial support in order to get.

15:51Now, this whole race is tied to OpenAI changing their structure to a for-profit organization, which might be one or some of the reasons for the turmoil and all the people that are leaving that signed up to work at OpenAI because they were a nonprofit that was working to help humanity make the most out of AI technology. I'm sure there's other stuff and I hope that over time we will learn more about what's happened and what's still happening behind the scenes. But there are specific terms in the money they raised that says what will happen if they cannot make that change. But as it looks right now, that's the direction they're going to go.

16:33There's also been rumors of them changing their logo. So the spiral looking logo that we all know and learn to identify, they're considering changing it to just a round circle, basically the letter O. and we know they have a sweet spot for the letter O because you have the new GPT called O, the new model is supposed to be called Orion, which starts with a no. Whether they're going to do it or not, I don't know. I will be surprised if they would because everybody now knows and are familiar with the ChatGPT and OpenAI logo. And I don't think you want to give that kind of brand equity away, but weirder things have happened, including at OpenAI.

17:12So we need to wait and see whether that's going to happen or not. But there's also been very tactical, practical, exciting news from OpenAI this week. Actually, two. One, they've introduced an API that is a real-time API that allows other companies to basically build conversational models, basically like the advanced voice mode for third parties. And there's already companies are using it. We're going to talk about it in a minute. But the other thing they introduced is Canvas. Canvas is a new user interface for some of the functionality within ChatGPT. It's basically, for those of you who have been using Cloud Artifacts, it's basically the same thing.

17:50It's a side-by-side view where on the left you have the actual chat and on the right you can see the outcome, whether it's the text that you're producing or if you're writing code, the code that you're writing. But they took this idea from Cloud, basically copied Cloud Artifacts, and took it to a whole new level. And what they added is they added these tooltips and snippets that pop up on the right side. So basically on the outcome, if you're writing code, you can, through this little pop-up menu, change the code to multiple coding languages, which I find absolutely magical. So you can change it to Python and to CSS and to HTML and to other and C plus and other coding languages, literally by dragging this slider, you can select different modes, whether you want to debug the code, you want to write comments on the code, you want to optimize the code.

18:40All of these are little pop-up when you touch different areas of the code or as a main thing for the whole thing, but it just pops up on the right side of the screen when you're working with code. This is an amazing functionality that happens within the LLM environment without paying any additional money rather than just the stuff that you're already paying for. And it's going to be later on rolled on into the free functionality as well, probably with limitations on how much you can use it, just like all the free stuff that they've released. So it's an incredibly powerful functionality. In the regular tech stuff, when you're not writing code, it allows you to summarize sections, make them shorter, make them fancier.

19:18So basically make them ready for release and a lot of other cool functionality. You can highlight just specific sections and a tooltip opens just for that. Amazing, amazing. It is really helpful. It's a completely different way to use the large language model to the point that you're thinking, how the hell did we use it before? It looks like the middle ages. And so if you didn't try it yet, go and try it out. If you have the paid version, any of the paid versions, you should have access to it. But like everything with OpenAI, they're rolling it over a few days. So if you don't have access to it yet, you'll probably have access to it by early next week.

19:52But now going back to the real-time API, Priceline, the online travel giant, has released an AI voice assistant called Penny that is powered by this real-time API from OpenAI. So it is basically similar to using the advanced voice mode which they just released last week. And it allows travelers to engage in natural conversational language with anything you want to know about travel from Priceline. And this is an extremely helpful way for travelers to learn about what will be better hotels, what show me just the stuff in this nut rating. I'm looking to go to this conference that is in this convention center, what are going to be the closest hotels that are four stars?

20:34Like literally anything you want, like having your own personal travel agent that can speak in multiple languages. Now, while this is specifically right now for hotels, they're planning to expand it to flights and rental cars and vacation packages later on. But if you broaden this even further, that's going to be the user interface that we're going to use to engage with any company. So if you think about going to websites and visiting them to start to find information about specific companies, it makes no sense. It's a very ineffective way to learn about what a company does, what services are provided, and so on.

21:12And I think this direction of natural speaking, and then later on, you will speak to your agents and your agents will go to visit those websites for you because they can visit multiple websites and come to you with answers, is what's going to become the common way to engage with technology. But as I mentioned, if you want to test it out on Priceline, you can try it right now. Now, the other cool thing is obviously this supports 120 languages. So we talked about many times before that the concept of contact center and call centers is probably a thing of the past. The chances, the concept of having multiple people sitting in a room in front of a computer with headphones and talking to people is going to disappear because these tools can do it extremely well in any language 24-7.

21:58They're never pissed. They're never not focused. and they'll be able to connect to a lot more data than a human can. And so I really think that contact centers will disappear, at least the way we know them, within the next five years, potentially faster. And by the way, if you take that beyond the contact center, you can also use it for internal usage as well, such as training and HR questions that people have and providing reviews to employees based on their actual results that they're providing and so on and so forth. And of course, external stuff other than customer service like outbound sales and inbound sales and BDR work and customer success and data analysis and so on and so forth.

22:42More and more stuff is going to be done via voice just by talking to the computer and getting the answers you want. Now, several interesting pieces of news came from Google this week. One of them that I found really fascinating is they've just released as an open source a new model they called Alpha Chip. And what Alpha Chip does is it designs computer chips, and it can design a new computer chip in hours instead of what will take humans weeks or months. Now, what Google also shared is that's what they've been using to develop Google's Tensor Processing Units, which is their AI chips that is running all their AI capabilities on the Android phones and on data centers that they built and so on.

23:26So the most advanced AI chips that Google has are developed using this technology. The other cool thing about this is that the way this model works is they basically designed a game or a gaming functionality in which the computer is trying to make the most amount of score in that game, placing different chip components in different variations to maximize the score. And that's how they audit to design better, faster computer chips. So I find this to be really interesting. Now, this has already been adopted by other companies like Mediatek, which is another huge chip development and production company.

24:07And so the future of new computer chips is already being assisted by AI to develop the next chip that will allow to develop the next AI and so on and so forth with a perfect flywheel. So everything we're seeing right now that is running extremely fast is going to happen even faster. Now, Google added some new features to Gemini and Google Sheets, such as the ability to create fancier tables than you could before. And you can do this straight from the sidebar on the side. They also keep on updating Notebook LM. Those of you who haven't used Notebook LM yet, and we talked about this in the last show and the show before that, but Notebook LM allows you to take whatever data that you have, and now also YouTube videos, but basically any link, any document, any website, and multiple of them together, connect them to quote unquote notebook.

25:04But all you have to do is literally drag the files or copy the links and that's it. And then you can use Notebook LM to summarize it, to create a learning guide for you, to create an FAQ for you, and also to create a mini podcast that is a conversational podcast between a guy and a girl that are talking about the topics that are within the documents and the websites and so on. I've been using it now almost every single day. And the way I use it is when there's articles that I really want to learn about that I don't have the time to read all of them. and that happens every single day because the AI world moves so fast.

25:38I literally upload them to Notebook LM and I create this podcast. And then the next time I go for a walk with my dog or go for a bike ride or just take my kids to school and I'm in the car, I listen to them one after the other and getting in five or six minutes, something that would have taken me a lot longer, but forget about the a lot longer. The fact that I'm in my car and I cannot read the thing allows me to actually summarize a lot of data very quickly in a really fun way because I like listening to podcasts anyway. So if you did not try this yet, it is going to change the way you consume content.

26:15We 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 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.

26:55Number 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 course. Well, this once a quarter is happening again.

27:34So on October 28th of this month, we are opening another course to the public where anyone can join the courses, four 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 that will 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.