Leveraging AI

162 |  AI profound impact on the job market, the aftermath of DeepSeek R1, AI security and safety, and more AI news you need to know on the week ending on Feb 7, 2025

• Isar Meitis • Season 1 • Episode 162

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Is AI about to take your job—or supercharge your career?

This week, the AI landscape just shifted again with game-changing advancements from OpenAI, DeepSeek, and Google’s Gemini. The result? More powerful models, better automation, and serious implications for the workforce. From millions of jobs at risk to humanoid robots entering factories, the pace of change is staggering.

So what should business leaders do to stay ahead? The answer lies in AI education, strategic implementation, and understanding the risks and opportunities ahead. In this episode, we break down the biggest AI developments of the week and how they impact your industry.

In this AI news session, you'll discover:

  • The shocking AI job impact—Could 300M jobs really be at risk?
  • OpenAI’s new releases and why their most powerful model is now free
  • DeepSeek’s security failures—Why this open-source AI is raising red flags worldwide
  • Google’s AI dominance—How Gemini 2.0 just took over the top AI rankings
  • The rise of humanoid robots and what it means for blue-collar work
  • Why business leaders must invest in AI training now (and how to start)

Don't get left behind in the AI revolution. Take action today by joining the AI Business Transformation Course starting February 17th. Sign up now  https://multiplai.ai/ai-course/ 

 


About Leveraging AI

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Isar Meitis:

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 jam packed week of AI news. It's really becoming ridiculous because every week, I think this is the craziest week we ever had. And then the following week, kind of proves me wrong, but we are going to talk about three main topics today, and then we have a lot of rapid fire things to talk about because it'd been a lot of new releases of new models and features, but these will not make the top three items. So the top three items, we're going to first talk about the potential impact of AI on the global workforce, which is my personal biggest concern on the negative impacts in the short term of AI. And when I say short term, I mean, probably three to five years. The second topic is going to be the aftermath of the DeepSeek release that we spent a lot of time talking about last week. So if you missed last week episode, you can go and check that out. ANd the third big topic is going to be safety and security concerns and measures that have popped up in this past week. Some really big announcements that happened, both good and bad, that impact the safety and security of AI systems. And then, like I said, a lot of new releases and a lot of other good stuff to talk about. So let's dive right in. I'm going to start with a Release of new models, even though I said that's going to wait for later. But that specific release has direct implications on the future of jobs. So OpenAI has released two new functions. One is O3 mini, which is a new reasoning model that they shared with us and showed us in the 12 days of OpenAI back around Christmas time. But we finally got access to it. it's the next variation of all one. So it is the most advanced reasoning model that open AI gives us right now. Now, together with it, they also released deep research, which is a deep research agent that uses the O3 model as the underlying architecture, but it has this agentic capability to go and research the web and write really long and detailed reports based on a huge research that could be hundreds of websites, similar to the same kind of functionality from their competitors, Google. So the first time OpenAI actually comes up with the name of something that actually makes sense, they literally just copied one to one the name of that feature from Google, which I find funny and surprising. But putting that aside, this new capability, the combination of deep research together with a really capable reasoning model, provides extremely powerful capabilities in the hands of Well, every one of us. So the old three mini model is now available for free for all GBT users, which probably was not the plan, but with DeepSeek around and Quinn 2. 5 around, I don't think they had much of a choice. And then this model is really good at coding math and scientific tasks. And as I mentioned, the research version of this also knows how to do serious research and provide results across public data as well as PDFs and other visual information that you can upload to it. So what does that have to do with the future of work other than the fact we got another really capable model? Well, Sam tweeted a lot of things, but two things that caught my attention that are relevant to the future of work and how powerful these models. So if you remember last week, I shared with you that there's this new effort that put together humanity's last exam which was crowdsourced from multiple scientists and really smart people around the world to come up with really difficult questions across any topic that you can imagine. So related to that test after open AI released O3, and shortly after they released deep research, Sam tweeted the following, way back on Friday, the high score on humanities last exam was O3 mini at 13%. Now on Sunday, deep research gets 26.6. To put things in perspective, the previous models before that were in lower single digits. So we went from lower single digits to 26. 6 percent on the hardest exam humans can come up with in a few days. That kind of shows you how powerful this model is, but this still doesn't explain to you why I'm saying it will have impact on jobs. Well, the other thing that Sam's tweeted, he started with congratulating the team who developed it, but then he said, my very approximate vibe is that it can do a single digit percentage of all economically valuable tasks in the world, which is a wild milestone. Now, I want to dissect that for a minute. Sam is obviously a very smart and knowledgeable person. He's saying that right now, O3 with deep research can do single digits percentage of All economically valuable tasks. Now, if we think about how many people are employed in the world, I did a little bit of research for you. the recent numbers for the beginning of 2025 is there about 3. 6 billion people that are employed in the world in 2025. Let's look at the gamut of what he's saying single digits of tasks. So let's put these tasks into jobs. And obviously it's not going to be exactly like that, but just putting things in perspective, if it's 1 percent of jobs that are going to be lost because of the current model, not future development, that's 36 million jobs around the world. If it's 9 percent that's over 300 million jobs that the current AI model can probably do based on Sam's best assessment. Now again, this is not going to be jobs. He's talking specifically about tasks, so it will be across jobs, but if across jobs we can save. This amount of tasks, then we need less people to do other stuff because these people have more time to do other things. So I think the outcome would be the same thing. That kind of gives you an idea where this goes, but let's move on. Let's take another point of reference that can help us in this process. Y Combinator, which is probably the most famous startup accelerator has opened their spring 2025 requests for startups. So those of you who don't know Y Combinator many of the successful companies that you know today came out of Y Combinator. So companies like Airbnb and Cruise and DoorDash Instacart and Dropbox and many, many, many others. So when they have their call for startups, they're looking for startups in specific areas of development to make sure that they're pushing the boundaries and that they can become successful. So they just opened their registration for their spring 2025 cohort of companies. If you look at the type of startups they're looking for, you can see the directions that they're going. One of them is the personal staff revolution. So they are highlighting how AI will democratize access to personal professional services, things like accountants, lawyers, money managers, personal trainers, private tutors. So people who provide services to us and they are looking for startups who can use AI to do these things, which means these jobs will become either less attractive or less needed or obsolete, depending on how far you're willing to push the boundaries with your thoughts on this process. The next one is software engineers. So in their statement pete Cooman, who spoke specifically about this topic, said that Language models can already write code better than most humans. This is going to bring the cost of building software down to zero. That's not me saying that somebody from Y Combinator who is in charge of these types of startups. So they're looking for ways to drive code writing even more than it is today, when today it's already better than most humans. Basically, what they're saying is that they're saying that one developer or one, it's probably not going to be called developer, but software engineer will run hundreds or maybe millions of these code writers that are going to work for him executing stuff on his behalf, More about code execution capabilities in our new releases and features of this week. We're going to talk about this later on in the episode. Compliance and audit automation. So they're looking at any type of compliance and audit work, which by the way is about 4 million people just in the US are doing this kind of work. They're claiming that AI systems We'll replace manual document reviewing, testing, and auditing makes perfect sense. These systems are very good at that. So they're looking for startups in that part of our economy. The other thing that they're looking at is what they call Vertical AI agents. So vertical AI agents are agents that can work in a specific vertical in the industry. So tax accountants, medical billers, phone support agents, compliance agents. So people who do something very specific in the economy. And they're claiming that just this category can generate another 100 unicorns. So companies are worth a billion dollars or more. And so just in that type of the market, they're looking for companies that will build AI that will replace people who do these jobs. The flip side, which is the only positive. impact on potential future jobs is they are saying that there's a huge development and a huge demand in the infrastructure for AI, meaning they need AI optimized hardware and software solutions. And they need companies to build these solutions, which will then employ a lot of people to drive that part of the economy. So the only positive side right now is bringing more minds and more engineers to develop infrastructure that is related to AI deployment. One of the interesting things that they focused in is inference optimized hardware. So because of all of these thinking models, there's a bigger and bigger demand for inference, which is the time the models actually run versus when the times versus the time that these models needs to be trained. And that is still behind compared to the training world, even though there's a few really successful startups in that field. We talked about Grok, several times on this podcast, as an example. So this gives you an idea where the world is going, right? On one hand, we have models that are getting better and better and cheaper and cheaper that can do based on Sam Altman already 1 percent of all tasks in the world. And then you see where Y Combinator are going, but that's just on white collar jobs. But on the flip side, we see huge advancements and big announcement being made by the humanoid robots companies in that entire industry. So as an example, figure AI, Which is one of the leading robotics companies in the world just announced that they're planning to produce 100, 000 humanoid robots in the next four years now They already have strategic partnerships with bmw and microsoft They already have robots being tested and working at actual bmw facilities. Now They are obviously not the only company in this field. You have companies like Tesla and agility robotics and Boston dynamics and unitary, are all developing very capable robots. China in this field as well is being a very strong competitor With eight of the top world's 16 humanoid robot companies coming from China. And so they are not alone, If you remember, we talked about Tesla planning to create millions of these in the next X number of years. Again, that's Elon Musk. And you need to put things in perspective, but yet there's going to be hundreds of thousands of these robots for sure in the next four to five years being deployed and taking over blue collar jobs initially in factories and then in other parts of the economy, including supporting us in our homes, in our yards, in our neighborhoods, and so on. The interesting other announcements from FIGR this week is that they're going to abandon their partnership, with OpenAI, which provided the software to run these robots until so far, and they're developing their own in house capabilities that they believe will be better tailored for the needs of their robots. That might be something that is driven by the fact that open AI are planning to develop their own robotics capabilities, which we talked about in previous episodes. And we're going to talk a little more about today. So before we continue with the news, with all that risk to future of work that will impact people individually, as well as organization, the question is, what can you do? And the very first thing you can do, and you have to focus on this, whether for your own career or for the success of the organization that you're running, whether it's a department or an entire company or a team, the most important aspect is training. And that's why I've been focused on AI education and training since I established. Multiply, which is the company that I'm running. We have trained hundreds of companies so far on AI implementation and the next cohort of our highly successful and sought after AI business transformation course is starting on February 17th So so this is just over a week from the time this episode goes live. So you still have time to sign up and join us. We already have. Multiple people who has joined this cohort. This is probably the last time before May that we're running a public course, because most of the courses that we're running are private to specific organization. We invite us to teach their people. And so if you want to learn on how to implement AI and to learn across the board on tools, system processes, strategies, mindset, everything you need to know in just eight hours, come join us. It's four weeks every Monday. Noon Eastern time, there's going to be a link in the show notes. So you don't forget, or if you're interested opening right now, click on the link and come join us to really prep you much better for the future with AI in the workforce. And now back to the news. another company that is making big waves in this world is Boston Dynamics have been around for a very long time. I'm sure you've seen videos of the robots dancing and doing Christmas celebrations and climbing things and so on. So they just announced a partnership with the Robotics and AI Institute known as our AI, and the goal is to enhance reinforcement learning for their Atlas humanoid robots. Now, both of these organization were founded by the same guy. Mark Railbert, and he's a former MIT professor, and he has been at Postal Dynamics for 30 years, and now he's running the other company. And the goal is to create simulation based environment and other solutions to allow training robots faster, better, and more efficient. Very similar things to what NVIDIA is doing with their robotic infrastructure. So a lot of progress has made in that field, which will accelerate the capabilities of robots to do basically any test that we do, and beyond, in the next few years. Now, in a recent US PTO trademark application, OpenAI is revealing some of their plans for the future. So the one that is related to this particular topic is robots. They are planning and I shared that with you in the past. They already hired people that are robotic engineers and they are developing their plan to build their own humanoid robots. If you remember, I mentioned that in previous episodes, they actually had a humanoid robots department back in the past. They deserted those plans, I think in around 2019 or 2020. And now it's coming back because it's an, obviously a huge economical potential for the future. And so they're going down that path as well. So we're getting more and more companies who are developing robots that are becoming more and more capable that can take off. blue collar jobs as well. I'm personally not optimistic about the outcome of this. I know people are saying that AI like any other revolution will generate more jobs that we can anticipate than the jobs that it's taking. My personal opinion is that it's very, very different than previous revolutions on two different measures. One, it's the first time that the revolution creates intelligence. It creates systems that think, which is the only thing that kept us creating new jobs beyond the jobs that were replaced in previous revolutions, right? So if you think about the industrial revolution, Oh, okay. So now instead of going behind an ox or a horse, plowing my field, I can have a tractor or I can have a factory and I can do the assembly faster. But the thinking part of things, we're still human. So we started doing more and more stuff that has to do with operating our brain than operating our bodies. This is going to change. The thing that allowed us to make up new jobs that we can do in the machines couldn't is going away in this particular revolution. The other thing is speed. The industrial revolution took 200 years. The internet revolution took 10 to 20 years, depending on exactly how you measure it. It gave us time to figure out what's next and to come up with new capabilities. This is happening in days and weeks and months, which does not give us time to adapt. And while it might generate more jobs than it takes away, It will take a lot longer to generate the jobs than to lose the jobs. And then in between, we have a very long period of serious uncertainty when it comes to the livelihood of people and societies and the economy, because if 30 percent unemployment, most of it, white collar jobs of people making a lot of money, the economy comes to a halt. So the only solution for this is some global group of people that involves. Governments, regulators, international bodies, and industry to potentially come up with solutions for this problem. So the only good news I have about this is that next week, there's another major global AI summit. It is going to happen in Paris between February 10th and 11th. And it's bringing a hundred companies who are participating in this and over a thousand private sector and civil society representatives. And It is co chaired by France and India, but there's a lot of really high profile individuals that are going to participate including the French president, the prime minister of India, the U S vice president. So JD Vance is going to be there. China's vice president, open AI, Sam Altman, Google's CEO, Sundar Pichai and many other important people. Interestingly, the UK Prime Minister is not going to be there. And another person who's not going to be there is Elon Musk. I don't know if it's because he's doing other things, or because he wasn't invited, or because Sam Altman is there. Whatever the reason is, Musk is not going to be there. And I'm sure we will hear his opinion about what he thinks about this group. they're going to discuss five different topics. Public interest in AI future of work, which is the topic we just talked about innovation and culture trust in AI and global AI governance. All are really important topics. And I think the more we see this kind of collaborations, and the safer we are, and the higher the chances that we actually going to harness and benefit from AI and hopefully minimize or maybe eliminate, I'm not that optimistic though, the risks of AI usage. And we're going to talk about safety and security, and you'll see why I'm less optimistic about this topic. But I think the fact that they're meeting and it's like the third time that they're meeting in a year is very important. I really hope there's going to be an ongoing body that will include members of all these different organizations. That we'll meet regularly and not just once every six months to discuss these issues and try to come up with solutions or prevention concepts as soon as possible. Our second topic, as I mentioned, is going to be the aftermath of the DeepSeek release that we talked about last week. So quick recap, DeepSeek, Chinese companies released two models. One is called DeepSeek version three and the other one is called DeepSeek is just a thinking, reasoning. version of their model. And that model came out of nowhere to the top 10 in the chatbot arena, overpassing the best reasoning model at that moment, which was OpenAI 01. The other crazy thing was that they claimed that they trained this model on 5. 6 the billions that are being invested in the US. They now released a new aspect to their model that is called Janus Pro, which replaces their previous Janus capability, which is their vision aspect of their model. This new model adds to the multimodality of their DeepSeek capability, and it is very good at generating images, and it's even better at evaluating and understanding images. And it outperforms OpenAI's models in both aspects, both in the generation and understanding of visual content. So in addition to all the capabilities they introduced last week that are very powerful, now it's addressing multi modal aspects as well, which will make their offering even more attractive. That being said, there were big concerns when the model came out that this model comes from China and how safe it is to use and so on. So there's a big backlash on this. And there's two aspects to this. One is several different companies who do vulnerability testing on these models have tested deep six model and it failed 100 percent of vulnerability penetration tests. so in one of the companies that does this regularly to these models, tried 50 different types of malicious prompting, and all 50 went through and were not blocked. So I don't know if you're familiar with the concept of jailbreaking a model, but the idea is all these models come with different safety features and jailbreaking basically means I will find a way to trick your model and still go beyond its limitations to do stuff that the model should not allow you to do. And as I mentioned, DeepSeek failed every single jailbreaking attempt that was thrown at it. And it was in six different categories of general harm, cybercrime, misinformation and illegal activities. You can do all of that with DeepSeek model that is obviously raising very, very serious concerns, especially that this model is open source, meaning you can take this model and run it yourself and make whatever other manipulations you want to it to make it even less restrictive. And then you can do really bad things with it. Now, the testers claiming that beyond the fact it gave these answers, it became, what they're saying. And I'm quoting unusually detailed responses to restricted topic that it's not supposed to respond to. Now. That's just add to the fact that this model is based in China and that it's running on Chinese servers and hence the Chinese government may or may not have access to the data. So major governments and governments bodies around the world has banned the use of DeepSeek and has blocked it in their servers, in their IPs, in their app stores and so on. That includes the Pentagon, NASA, US Navy, the Congress. Italy's data protection authority, Asian governments like Taiwan, which makes sense with their really great relationship with China. And Texas as a state all banned that and there's a list of thousands of companies and corporations who also banned DeepSeek for all these reasons. The EU in addition, has shared their concern over GDPR compliance. And so this model, what it was developed very quickly and shows really amazing capabilities and really took the AR world by a storm has. Serious issues when it comes to data safety. As I mentioned, the solution is obviously learning everything you can from it or using the model, hosting it on your own. And then a lot of these concerns go away. So if you just want to benefit from the capabilities of this model and enjoy the fact that it's significantly cheaper and that it's open source, you can literally host it yourself. Or it's already available on AWS and Azure. So if you're running on those models, you can have your own variation on the model running on your hosted environment on these platforms. Now still staying on the topic on the implications of DeepSeek, one of the questions that was asked is how the hell they did this at 5. 6 million dollars. I shared with you last week that Dario Amadei actually said that's not a big deal and that current models will cost that amount of money, or at least the same ballpark. And that actually distilling a model, meaning training a model and other models, which what everybody believes they have done using open AI's for, Oh, distilling a model and moving it to a reasoning model is not that hard. Stanford university together with. Researchers from the University of Washington were able to prove that. So they just released a new model called S1 that can compete with OpenAI's O1 model and DeepSeq's R1 on math and coding tests. And they've used 50, yes, 50 in computing time to train this model. So they have used Google's Gemini 2. 0 flash thinking model combined with Huen, which is the model that Alibaba released last week, to train this new model, and they've done it in 30 minutes using 16 which ends up being about 30 of compute to train this model. Now, will it be really as capable across the board? Probably not, but their goal was to prove a point. They also released everything that they did, including the code and the training data and the training process as open source, so now other researchers can do the same thing. So this has good news and bad news. The good news is this may help us to save huge amounts of money and compute and power and pollution by using these methodologies. The bad news is it's accelerating the development of new models, even beyond the crazy rate that it is today. But it definitely proves the point that it's possible to use an existing model, an existing powerful model, to train a new thinking variation of this model for almost free. But that being said, the impact on the large companies in the U. S. are developing AI. so Google Anthropic, Meta, et cetera, has been exactly the opposite. You would assume for a minute that they would say, Ooh, we can now save billions by using these methodologies. Let's rethink our spending. Well, exactly the opposite is happening. So Alphabet, Google's holding company has announced that they're planning a 75 billion CapEx investment in 2025 in a AI infrastructure, a 42 percent increase to the crazy AI spending they had in 2024. So each and every one of the companies are going to spend tens of billions of dollars in 2025 to maintain their lead in the race. Pichai, Google CEO said. Part of the reason we are excited about the AI opportunity is we know we can drive extraordinary use cases because the cost of actually using it is going to keep coming down, which will make more use cases visible. So what he's saying, and I heard that from multiple people across the industry and beyond, is that the fact that it's becoming cheaper is not gonna. Reduce the AI usage. It's actually going to make it explode, which means they will need more compute, more models and more everything just because it will become relevant to a lot more use cases and available to practically everybody because the individual use case will be practically free or very, very close to that. Staying on the DeepSeek topic, DeepSeek's researchers actually shared their thoughts about Huawei's Ascend technology 910C, which is the latest AI processor, which is supposed to compete with NVIDIA's offering. And they're claiming that based on their research, it achieves 60 percent of NVIDIA's H100 inference performance. And they're saying that by manipulating how it works, they can actually achieve Even better results. Now, 60 percent may not sound like a lot, but the gap was significantly bigger a year ago. So Huawei, which is Chinese largest cheap manufacturer, is closing the gap on NVIDIA. Again, they're still behind. And moreover. Those researchers also said that while it's not bad on inference, it's still far behind when it comes to training reliability. So at least on the training side, GPUs are still reigning supreme, but the gap is being closing on inference is not as I mentioned before. Inference is becoming a big part of the game because these reasoning models are actually using a lot of compute in the inference time when the model is actually being used and generating tokens versus training. And this development of hardware combined with, we see innovations in software like DeepSeek and Quen are a direct outcome of the U. S. ban of NVIDIA chips being used in China. So first of all, they find ways to use it. As I mentioned, there's rumors that DeepSeek has used 50, 000 GPUs from NVIDIA, but even if they only have 10, 000, like they're claiming, that's still a lot, but putting that aside, it is forcing. China to come up with their own innovation and to develop both hardware and software innovations that they probably wouldn't develop. And they would have been dependent on the U S otherwise. So to summarize the DeepSeek topic, I will mention something from a conversation I had this week. I'm delivering an executive education session in partnership with the university in a couple of weeks. And the person who is in charge of this effort has asked me in an email this week, do I think that DeepSeek changes everything and do we need to change the training data? And what I told him is I think that DeepSeek in the big picture changes very little. If anything, it solidifies the same point of view I had before. We will have access to incredibly powerful intelligence for Free or very, very close to that. And the implications of that are obviously profound, but that doesn't change the trajectory the DeepSeek event doesn't change that, if anything, it accelerates towards that even faster. As I mentioned, the third big topic that we're going to talk about today is security and safety. So OpenAI has done research on how persuasive their models are, and they've done this on the Change My Views forum on Reddit. So it's 3. 8 million members who participate in debates trying to convince one another. They conducted 3, 000 different tests and what they've learned that their models are becoming more and more persuasive to humans. So GPT 3. 5 was 38th percentile persuasive, O1 77th percent, O1 mini 77th percentile compared to other humans, O1 80th percentile and O3 mini 82nd percentile. So O3 mini. Not even the full O3 is better in persuasion than 82 percent of people who participate in this debate, meaning they are people that by definition are better than the average because they want to participate in this process. And so they are becoming very, very persuasive and it's just going to keep on going because of their thinking capability and analyzing capability and reasoning capability that they did not have before. That obviously raises significant concerns. OpenAI shared why they're doing this research and their mitigation strategies and how they're trying to monitor this and make sure that they can prevent and restrict. Political persuasion and other specifically targeted campaigns, but can they really monitor a hundred percent of it? I'm pretty sure they can't, and that obviously raises a lot of risks. So think about foreign governments or terrorist organizations using this to convince Americans or people in the free world with new opinions, because it will be impossible to know what's coming from an AI and what's not. It will be more persuasive than what the humans can write to contradict what the AI is writing. So that's where we are right now. And it's only going to accelerate. And I'm going to go back and forth between good news and bad news on the safety and security thing. So Meta just unveiled a new risk assessment framework for their AI development. And they just announced it on February 3rd and they call it the Frontier AI Framework. And the goal is to establish clear boundaries to AI system development and deployment based on potential risk. and they've identified two levels of risk. One is called high risk systems that could enable, but not guarantee successful cyber chemical and biological attacks. And then the second tier is critical risk systems that could lead to, and I'm quoting catastrophic outcomes, which they didn't label, but based on the fact they're talking about cyber, chemical and biological, you can kind of know where this is going and the biggest difference is, can you mitigate the risks once the model has been deployed? So basically what they're saying is that the critical risk systems once you deploy them, there is no way for you to mitigate what's going to happen versus the other ones that you might be able to prevent it once it starts happening. Their policies go beyond just chemical and so on. So they're talking about things like automated compromise of corporate scale environments creation and deployment of high impact biological weapons and other scenarios that they're deemed most urgent by the company. So the policy defines both internal and external researcher risk assessment for every one of these models. They're including senior level decision makers have the final review authority for everything that is said and done and relying on multiple types of tasks across the different risk levels instead of relying on one specific test. And the outcome is that high risk systems will be limited internal access and deployment only after risk mitigation and critical risk systems will have enhanced security protocols internally and potentially will lead to development suspension. So they're basically saying that they're going to stop the development of AI systems if they deem that it could lead to catastrophic critical risk. I'm very. Happy to hear that. But that being said, that's just one company that decides on its own what the risks are and that are in huge competition with other companies who are doing the same things. And as I mentioned, I'm going to go back and forth between good and bad security announcements. So Google just decided to abandon their 2018 AI weapons ban. So since 2018, they had a restriction. on allowing weapons and surveillance development based on their AI models. And as of February 4th of 2025, they are removing some key components of that. So their previous policy prohibited the usage of AI for technologies causing overall harm, weapons or injury cause causing implementations, surveillance violation, international norms and technologies that could go around international law and human rights. And the justification they're citing for why they're removing this is that the need for democracies to lead AI development involving industry standards, geopolitical AI competition, and that they're saying that they're going to focus on appropriate human oversight rather than just banning the whole thing. Now, multiple employees in Google obviously are opposing and are being very loud against it, but this is the current state and I shared with you that a few weeks ago, we were talking about open AI starting to get into these fields. I think it's inevitable and it's very scary. But I do agree with Google that if China and Russia and other adversaries of the U. S. and the free world will start employing AI capabilities in their military and surveillance capabilities, and we won't, we will fall behind. Now that again can lead to catastrophic outcomes But this seems to be the next cold war or nuclear race or whatever you want to call it for world dominance, and hopefully it will stay balanced and keep everybody intact from doing stupid things. But these systems, AI systems will be deployed and will control deployment of military forces and military capabilities around the world. That could obviously leads to a doomsday scenario, but as I mentioned, the alternative is not necessarily better by letting the other side have it when you don't,. And ending on a positive note on the safety and security before we dive into a lot Of rapid fire items. So Anthropic just announced that the company has launched a new security system designed to protect Cloud AI from universal attempt. Now they achieved incredibly impressive results. Again, very different than what we've heard about DeepSeek. So they reduced success rate from 86 percent to 4. 4 percent using these measures. And they did that while only increasing false positives, basically not breaking attempts that the system thinks are breaking attempts and going to stop this by only 0. 38%. So it basically hasn't prevented any action that is a legit action and was able to prevent 82 percent more jailbreak attempts compared to the previous capabilities that they have. Now that is costing them 23. 7 percent additional compute cost, but they're planning to continue optimize from it. But Claude has always led that direction of safe AI usage. And that's another way for them to prove that we're investing a lot of compute time in making our systems safe. And they're doing this using their constitutional AI frameworks that they've built their systems around, plus a lot of additional new capabilities that they're adding in order to do this. And I don't know if you know that, but they have had a bounty of 15, 000 called HackerOne That is going to be given to a hacker that will be able to develop a universal jailbreak against Claude and that has been around since August and nobody has claimed it yet. So they're definitely doing the right things in order to prevent hacking and jailbreaking their model. And I really hope that every single company that develops these frontier models will follow the footsteps of Anthropic on this particular case, going back to the international development and partnership that I'm dreaming that will happen. I think these kind of capabilities and research must be shared with everybody and every company and every organization who is developing these kind of models to achieve the same results that Claude is achieving. And now let's dive into lots of rapid fire items. We're going to start with a lot of aspects from OpenAI. So we already talked about their big releases with O3 mini and O3 for the professional version and deep research. But they also made a lot of other interesting announcements this week. One of them is that Sam Altman has announced that he's been on the wrong side of history when it comes to open source. Basically, and I don't know if that comes as, again, the aftermath of DeepSeek or anything else, but in a Reddit AMA Ask Me Anything, Sam basically admitted that the fact that they went to completely closed source might have been the wrong decision. And he basically said that they need to figure out a different open source strategy in the same lines. Kevin whale, who is their chief product officer has revealed that their company is considering open sourcing older. Non state of the art models, which they haven't done so far now, is that going to be impactful? I'm not really sure because now we have open source models who are competing with their state of the art models So if they're gonna open source, they're all their models that are not competing. They won't be able to compete in the open source world So I'm not really sure I understand the logic behind it But I think they understand that the open source world is accelerating and it's closing the gap on the closed source world. So the whole point of what they're doing, maybe not be that impactful and effective. And so they're obviously rethinking it. What will that lead to? I'm not a hundred percent sure, but I will update you as there's stuff to update. Now, in the same AMA, Sam touched on a few other things. He did not give a specific timeline on GPT 5. He's saying that they're working on it, but it's unclear when will that become available. There's going to be new features and new capabilities for the O3 model coming in more than a few weeks, but less than a few months. He also mentioned that they're developing a successor to Dal E, which is their image generation tool that is way behind the competition right now. And he's saying, and I'm quoting, it's going to be worth the wait. I assume we're going to get another extremely powerful AI image generation tool. We already have a bunch of those. So right now there's probably five or six models that are very, very good, that can generate completely realistic images of anything you want. And we're just going to get one integrated into the OpenAI environment. To connect it to one of the previous points, OpenAI also defended its collaboration with the U. S. National Laboratories for Nuclear Defense Research. And they were explaining and expressing the fact that they have full confidence that the scientists will have responsible use of the technology. Whether that's true or not, time will tell, but that's their statement, which you wouldn't expect them to have any other kind of statement. A very interesting and positive development from OpenAI is that they announced a rollout of an education specific Chachapiti version for California State University system that is going to reach 500, 000 students. Thousand students and faculty across 23 campuses. Now, as you probably know, OpenAI has created a Chachapiti EDU version back in May of 2024, and it was deployed to some Prestigious institutions such as Wharton and University of Texas at Austin and Oxford University. but this is a much broader deployment of a high education specific model that is going to be deployed across the largest public university system in the U. S. Now, the main features include personalized tutoring for students, study guides generation, as well as administrative task automation for faculty. So it's going to help both professors. And students, I said that many times before, I think AI represents the biggest opportunity education has seen. We still perform education from kindergarten all the way to PhD in the same way it was done in the last hundred years. With a professor or a teacher in front of a classroom, teaching a large, Group of students, assuming they're all the same. It's a horrible way to train people because people have different needs. They progress in different ways across different aspects of the learning. They learn better by leveraging different tools. Some learn better by listening, some by watching, some by doing some by using different games, some by watching videos, et cetera, et cetera, et cetera, and AI will allow us to, for the first time in history, to provide any person, any kid on the planet, personalized tutoring aligned with their needs, aligned with the things they need or want to learn, aligned with their capabilities on different aspects and aligned with how they learn best. And I really hope that this is where this is going to go. First, it will provide education for all, and second, it will make the existing education in places that it's already available significantly more efficient, hopefully making teachers more of mentors than the people who actually teach the material and allowing people to and guiding people or kids through the process in the most effective way. Now I mentioned before that OpenAI has filed some patents. Well, one of the things that Sam Altman confirmed this week in an interview with the ELEC, which is an organization in Japan, that they are indeed developing a device and that they're working in collaboration with the legendary Apple designer, Johnny Ive. I've shared these rumors with you in the past. Well, now they've confirmed, these rumors. Them emphasize the fact that voice will become the user interface medium of the future. I can tell you that right now, I personally use the advanced voice mode. On chat, GPT, as well as the live streaming on Gemini regularly. And it literally changed the way I work with AI models, but I can't wait to basically activate everything with computers and literally just have conversations with them to do. Everything that I need. And I think it will over time become the user interface instead of a keyboard, mouse, or a touch pad on every device that we know, including, microwaves. Why do I need to figure out how much time I need to put in? I can literally tell it, or you will see on its own with a camera, what I'm putting inside of it. And it will just run the microwave based on what's the best outcome that can happen while asking me what my preference is for the level of cooking or the temperature that I want the output to be. But. There will be no need for physical user interfaces on most things interact with right now. Another thing that Sam said about the device is that their plan is not to replace the smartphone, but actually to have an extension of their smartphone. So think about something similar to a smartwatch. So it will connect to the phone. But it will be a device that will allow you to talk to it and use AI in order to then operate the phone or operate through the phone, things that you need to do. So now let's switch gears and talk about rapid fire items as far as new releases of products and features. So we already mentioned open AI with their O3 model in the pre search. Well, Google weren't standing still either. And they just announced on February 5th that the company has made the entire Gemini 2. 0 family generally available. And there's going to be three different models, 2. 0 flash, the smallest. model with 1 million tokens context window, which is way beyond everybody else. Then you have Gemini 2. 0 pro, which is an advanced model that is available through paid members with 2 million tokens context window. Again, now twice what you get in flash. And then there's flashlight, which is built for cost efficiency. 2. 0 flash will become the mid tier model. Think like Claude 3. 5 Sonnet, on the Claude family. And then you'll have the pro and then you have the lighter version that can run faster and probably much, much, much cheaper. And these models have enhanced coding capabilities, improved complex prompt handling, advanced reasoning capabilities, built in Google search integration, code execution capabilities, a lot of stuff that I shared with you in the past before they made it widely available. One of the interesting things that they mentioned about flashlight model is that it's very good at image understanding and that it can process approximately 40, 000 image captions for less than 1 of compute going back to what we already talked about before, we're going to have very high capable intelligence across everything we need for practically three. Now going back to our benchmark, which is the chatbot arena. Gemini 2. 0 flash thinking is now number one. Gemini 2. 0 pro is number two, then Chachapiti 4. 0 latest, which is kind of like a model that they released in late 2024, then DeepSeek R1. So these are the top five models right now. And then number six is another Gemini model, which is Gemini 2. 0 flash. And so what you can see, as I mentioned last week, is that Gemini basically took. Three out of the top six models in the world right now, as far as the success of the results based on usage of actual two people, which is how the chatbot arena works. And I anticipate like I did a year ago when they were still far behind that Google will keep on leading this race because they have access to more of everything you need in order to make this work more compute, more engineers, more money, more data between Google search and YouTube and other platforms that they control and huge distribution across the entire Google ecosystem. Another company that made a big release this week is Mistral. We talked about Mistral many times in the past on this podcast. Mistral are an open source platform from France, and they just released a new multi modal version of their Le Chat model. They just released it on February 6th. And it has faster answers than probably any other open source model right now. They're claiming a thousand words a second, advanced document processing with superior OCR capabilities, code interpreter to create and run code on the platform, image generation powered by Flux. So not their own, but it's integrated into the models and new memory capability similar to what we have now in several different platforms. There's going to be a free tier and a paid tier for 15 bucks a month. And, very interesting, they're more and more enterprise relevant tools, which are not surprising. So secure environment deployment, custom tool integration, tailored model customization. And also they're coming up with new connectors to existing work environments within enterprises. So like all of these companies, they're pushing very, very hard into the enterprise world. And being an open source company, it actually makes a lot of sense because it obviously allows you to host it on your environment while keeping your data secure. Another company that made a big announcement this week is GitHub. GitHub, one of the most commonly used coding environments. They just shared that they're releasing three different AI capabilities. One is called Agent Mode for Autonomous Coding. The other is Copilot Edits. And the third is Project Padawan Reviews. The most interesting one is obviously the Agent Mode. And Agent Mode allows self iterating code generation, automatic error recognition and fixing, terminal command suggestions. Runtime error analysis with self healing, and it is going to be available for everyone who's using Visual Studio, which is the code platform that is it's integrated into. biggest difference here of all these three platforms that it will replace not just. writing short code snippets, but a much bigger portion of the coding ecosystem and process. And as I mentioned in the beginning of this episode, when we're talking about Y Combinator, this is where it's going. Like coders will sometime in the near future, we'll write very little code and they will depend on these platforms throughout code on their behalf, which will allow them to create significantly more code. Faster and much, much cheaper, which will allow us to do future developments better than we can do them right now. Another big release this week that is very interesting and very scary is ByteDance, the company behind TikTok has released what they call OmniHuman, which is a tool that generates videos of people based on a static image of them and a voice sample. So you can take an image of any person and they've shown multiple examples of celebrities, as well as historical characters. And then they took a voice sample of the person and it generates a very realistic motion of the person speaking as if it's the real person. That's just another really amazing deep fake capability, which on one hand is amazing because you can bring to life historical characters for educational purposes. So one of the examples they showed is Albert Einstein speaking about whatever you want. And so you can use it for educational reasons, which is really cool. But on the other hand, it's deep fake and it will allow to replicate any person on the planet in a very highly realistic way in seconds. Now, right now it's still research, but I would not be surprised if this becomes a part of the platform sometime in the very near future. Another interesting announcement on new types of models, but not the models yet, is that a new European AI Alliance has emerged? It's called the Open Euro LLM, and they have launched with 52 million euros in funding. And their goal is to allow an independent open source development of models by Europe in order not to relay on the dominance of U. S. and China and to allow a consortia of European organizations to develop a European based language models and other A. I. capabilities. And as I mentioned, the goal is to make it full open source, including everything models, software, data and evaluation, true open source capabilities. And then the last topic I want to talk about, is the new announcement from the U. S. Copyright Office. They just came out with a second report, and actually with two different parts to it, that is highlighting a significant change from their initial view of AI, but they're still claiming that the law doesn't need to be changed. They're just elaborating on it. The original ruling from a year ago said that basically anything that is AI generated cannot be copyrighted. And now they've changed their opinion a little bit. And they're basically saying that if the process was initiated by human ingenuity. or if the significant part of work was human, then it's still copyrightable and can be protected under copyright laws. One of the things that they made very clear is that prompts alone do not provide sufficient human control to make user of the AI system authors of the output. Meaning even if you wrote a three page prompt to generate an output, the output is not protectable from AI laws in the U. S. But they give other examples where work that is co created or initiated by the human is protectable. I have a very personal example of this. A lot of the content that I create is an output of this podcast. I take the podcast and run it through AI systems, and that generates blog posts, social media posts, ideas for content and other stuff. And that now is protected because it's derived from the content that I'm creating on the podcast. Another example that they gave is that if you're a. Painter and you start with a scribble that you create and then you use AI to improve it. It's still yours and can be copyrighted. I think that will continue to evolve. I think serious prompting versus short prompting and I don't really know how to define that in a legal way. Will also be considered eventually because it's still something that I'm inputting into the system in order to create an output that another person will not be able to do because they did not have my ability to do it, which is the whole point of copywriting. So I think that will continue to change, but the fact that they finally moved from anything with AI is not copyrightable to, okay, some things are, is saying that they understand where the world is going and that they're open to change. That's it for this week. We'll be back on Tuesday with another high two detailed episode that you are going to absolutely love. If you are enjoying this podcast, please rate us on your favorite podcasting platform. It will take you five seconds. So pull up your phone right now and click the five star review and write whatever you think about the podcast or reach out to me on LinkedIn and give me any feedback. I love getting your feedback on LinkedIn and I get a lot of it almost every single day. So good, bad, ugly, whatever you want to say. I really want to hear about it. And while you're at it and you have your phone open, please share it with other people that can benefit from this. I'm sure you know a few people that if you think about it for a second, you can say, Oh my God, so and so has to listen to this podcast as well. and so please share the podcast with those people and until next time have an amazing weekend.

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Isar Meitis

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