338 | AI agents now work 6x the hours of their humans at Anthropic 🤯, AI just put 70% of all websites at risk, Is AI a bubble? “We grew a mind” (OpenAI safety insider), and more important AI news for the week ending October 23, 2026

0:00Speaker 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 Matis, your host, and a lot of really interesting things happened this week on the macro level, and we're going to cover three of them.

0:20Speaker One is the conversation, are we in an AI bubble or not? And we are going to follow along from a conversation that was on the Ezra Klein Podcast with two experts on the topic, one representing each of the sides. We're gonna cover what they discussed. The other topic we're going to cover also comes from Ezra Klein's podcast.

0:39Speaker Uh, in this particular case, he had a really fascinating in-interview with David Robinson, who is the OpenAI employee that resigned, just recently and shared that he did this because of his fear from the current situation. He's the guy that wrote all their alignment papers and how they're distributed to the public and to the company.

0:58Speaker So he's been inside the processes of safety and alignment inside of OpenAI, and he shares why he resigned and why he thinks we're in a very dangerous era. And then we're going to talk about two very interesting papers that were released by Anthropic that talk about how is AI being used inside of Anthropic right now, and how is it being perceived as far as its impact on the future inside of Anthropic compared to the rest of the population, like the general US public.

1:28Speaker So three very interesting deep dives that we're going to talk about, and then we have a lot of other topics to cover. Some of them have to do with safety. There was a big vulnerability found inside of PHP this week, which is the infrastructure, back-end infrastructure for about 70% of the internet. We're going to talk about hackers hacking into banks.

1:47Speaker We're going to talk about Sam Altman and three different pathways that he sees for AI safety and the future of AI, and new releases and agents doing really cool things. So we have a lot to cover. So let's get started. first story of today is we're going to cover are we in an AI bubble or not? And we're gonna do this, looking at the information shared on the Ezra Klein Show, which is a New York Times show, where he interviewed two leading people with two opposing, view of this.

2:20Speaker One of them is Paul Kedrosky, who is a partner at SK Ventures and an MIT fellow, and he believes very aggressively that we're in a bubble, and then Azim Azhar, who runs Exponential View and tracks AI financial stress, and he believes that we're not in a bubble yet. what I'm going to do is I'm going to share with you what each and every one of these people shared as far as a support for their view of things and what the other person countered and responded to that particular claim.

2:51Speaker I will say something else. They both had inaccuracies in the way they presented the information. I don't know if that was intended or not. there was a much more misrepresented information on Kedrosky's, side, but I will share what I found about the accuracy of the information for each of those people as well as we go through the facts.

3:11Speaker So we're going to start with Kedrosky's approach, and again, he believes that we're seriously in a bubble. And what he said is he said that, first of all, every general purpose technology built in history ended up overbuilding and getting into a crisis, and he talked about multiple examples from electricity to railroads and so on, and he mentioned specifically that railroads left about fifty percent of track useless, in the build-out of railroads in the beginning.

3:40Speaker Azhar's response to that was that we're not in an overbuilt yet and that we're vastly undersupplied as far as compute. And he's saying that maybe one or two of the labs will go bust, but the underlying need for compute and the technology is very strong. Or the way he summed it up, he said Anthropic may go out of business, but NVIDIA won't.

4:00Speaker Now going back to the claim about the railroads, that is very much misleading. y-yes, over a century, about 45% of peak mileage was abandoned, but it wasn't something that happened immediately, and it wasn't overbuilt in the beginning. Just over time, there were other more effective means of delivery which made some of the railroads, again, about half, but over a full century, uh, useless.

4:24Speaker Now, the other claim that Kedrosky made was that the build-out is fueled by credit that has spread into insurers, private credit and sovereign found-funds, and that it will reach $10 trillion by the time it's all said and done. And he's saying that a lot of it is moving from the actual companies themselves financing through their cash flow to more and more debt.

4:44Speaker To which Azar's response was the debt is rising as far as financing the leading investors, inside of AI, but it's not at historical high levels. He believes that the threshold where it will start getting dangerous and there's too much debt in the financing is 30%. He's saying that we're getting close to that, but we're not there yet To be fair, I checked that fact as well, and the debt aspect of the hyperscalers' CapEx, was 9% in 2024, and it is 32% in the middle of 2026.

5:15Speaker So we are definitely at that threshold. Obviously, the $10 trillion figure is not anything anybody can check. This is just his own personal opinion, I assume. Going back to his third point, he's saying that markets are very fragile. The top 10 stocks are 40% of the S&P 500, and half the worth, of US people right now are in equities, so there's too much concentration on all of that.

5:39Speaker Azim did not respond to this particular thing, far as the numbers from Kedrosky, they're very close to the real number. Uh, the top 10 stocks are currently 39%, and about half the net worth is in equities right now, it also very, very close. It's 46 to 48%. So these two numbers check out Now, one of the main things that separate the two of them is about how to view the demand.

6:01Speaker Azeem's perspective, there is a-- we're just in the beginning of the demand, and there will be a growing demand for tokens, significantly, which I agree with 100%. I personally think there are two orders of magnitude growth that is ahead of us as far as the use of zero tokens. To which, Kudrowski responded, and I agree with that as well, that token prices fell 13X this year, and that normal users, day-to-day users, do not need the frontier models.

6:27Speaker And so the real demand shouldn't look at how many tokens we're going to consume, but how many tokens we're going to consume times the price of tokens. and what he's saying in s- that particular case, he sees a huge drop in the potential revenue of the companies as the models race forward, while most people don't actually need the frontier.

6:46Speaker Which I agree completely with the second half of the statement that most people today do not need the frontier models. I don't know where that lands as far as the actual cost, versus return, of using the cheaper models and how much will that be a viable business model for the leading labs, but it's definitely a good point Now, another thing that Kudrowski made as a point, he said that demand is very narrow and that right now about 80% of tokens revenue is coming from coders, with still a very big concentration where Meta and Microsoft are driving half the demand from Anthropic, based on the papers they shared.

7:19Speaker Again, a very big misrepresentation of the facts. first of all, coding right now is about 55% of the demand, and it is actually shrinking, all the time. And it is because they initially developed the coding capabilities so they can help themselves. I'm talking about the labs. And now that more and more people through, the different aspects of our economy is learning how to use it, that number is actually falling.

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9:03Speaker Like I said, there's a few seats open, but they are selling out fast. So if you're interested in doing this still this year, go and click on the link right now on your phones and complete the registration and the payment so you can guarantee your seat. I'm looking forward to seeing you there. And now back to the episode The other aspect is also a misrepresentation, because what Anthropic shared is that early last year, Microsoft and Meta were half of their revenue, and that number has dropped significantly, into 2026, and it's continued to drop as 2026 continues.

9:37Speaker And so not accurate information, from Kudrowski, but definitely there is a concentration where a lot of the revenue to these labs are currently coming from a relatively short list of companies. I actually think this shows exactly the opposite, that there's still a huge opportunity for growth because most companies are not using it at that level as well Another point that Kedrosky made to say how high the risk is, he says that basically these three IPOs between SpaceX and OpenAI and Anthropic is sucking up all the equity across everything else that is happening, and that concentration leads to a very high risk because if one of them fails, we're in very big problem.

10:15Speaker He said that these three IPOs are larger than all post-World War II tech IPOs combined, and that they exceed the Nasdaq peak valuation. Both these claims are incorrect That being said, SpaceX fell from $225.64 to a low of $104.83, which is less than half. Now it recovered back to one fifty-three, but it's still a very big drop from the peak immediately after the IPO, which tells you again that the valuations might be overstated, at least in the SpaceX perspective The three last claims that Kudrowski made before we switch to Azeem.

10:53Speaker So he said that AI is pricing labor faster than any prior technology, and that new jobs will not keep pace, and I agree with that statement 100%. Azeem is saying that humans are moving to verification roles and new more strategic roles and new roles will appear. I do not agree with that. I think we are heading very quickly into a situation where we're gonna have very significant unemployment.

11:16Speaker And while new roles do appear and pop up, and I'm helping companies hire for these new roles, heck, my role in some of these companies did not exist, a few months ago and definitely not two years ago. But that is, I think, a very small number of roles that cannot cover for all the roles that will be wiped out by AI, uh, even with the current AI technology without any improvement.

11:35Speaker Uh, he's also talking about that regulation is inevitable, uh, because some catastrophe will happen. And he's giving, again, math that just doesn't check out as far as if we have, 12 different things, each and every one of them with 5% of happening, uh, he's claiming that goes to fifty to sixty percent that it's actually happening.

11:54Speaker Uh, the real math is actually not that. If you have, several different things, six five percent thing and so on, you're getting to significantly less than 50% of the chance of that happening. So again, he's making these claims and he's making them with a lot of conviction, but many of them are either wrong or misrepresented, meaning he's using actual numbers but not representing them on what they actually represent.

12:17Speaker And so I'm not impressed with the way he moved forward with making his arguments. I'm not saying I disagree with all of them. I'm saying use real facts and use them in an ethical, correct way versus either making numbers up or manipulating them and saying they mean one thing when they actually meant something else.

12:37Speaker Now let's talk about Azeem Azhar. he's saying that demand is real and enterprises are paying, for more and more compute. He's saying that GPU owners that earn very good returns 18 to 24 months in He's saying that the chips themselves are 65% of the data center cost, which is showing that the investment is going for less overhead and more actual substance, Which again, is not completely accurate.

13:01Speaker Bernstein, research puts GPUs at about 39% of data center CapEx, so less than half versus, almost two-thirds. So again, another misrepresentation of the actual numbers to make a point He separates the frontier labs from the GPUs, as I mentioned before. He said, yes, Anthropic may go out of business, but NVIDIA won't, and I agree with that.

13:21Speaker Like the labs, not all the labs will be able to continue running at the level of capacity they're running right now. Financing will dry out eventually, and they will have to be profitable. By the way, speaking of this, Anthropic, in their latest releases in preparation for their IPO, are claiming they are profitable.

13:35Speaker That ignores obviously a few different numbers there, but on paper, they're profitable, right now, and if they're gonna keep on growing in the pace they're saying they're gonna keep on growing, then they'll definitely be profitable regardless whether on paper or not. They both address the Firmus failed IPO.

13:50Speaker So those of you who haven't heard that, they are a neo cloud company from Australia who tried to go public and was not successful to get, uh, the guarantees for the funding they wanted, and the IPO was pulled. what Azeem is saying about this is that markets are basically pricing the risk, and that's why they weren't able to raise money To which Kudrowsky's response was that the reason they wasn't able to raise the right amount of money is because there are $2 trillion of mega IPOs that basically took the entire capital of the market and not allowing anything else to run.

14:22Speaker There's probably truth in both what these people said Another thing that he said when it comes to the general public, he said that very few Americans currently pay for AI. About only 2.5% of Americans privately, pay for AI, uh, which means there is a huge room for growth, right? If only two and a half percent of Americans pay for the AI they use and everybody else is using free models, then obviously there's a huge place for growth.

14:46Speaker Kudrowski is saying that, people are just happy with the free AI. So the Meta new Muse app that is growing like crazy doesn't cost them any money, and it's probably doing good enough for most people need on the day-to-day. The question is obviously, can these labs continue to deliver it for free? Uh, which goes back into another part of this equation And then the last component they both disagree on is whether this is actually providing more value to the US economy.

15:10Speaker Meaning are we seeing productivity rise and impact GDP or is it just all smoke and mirrors and marketing of the labs? Uh, obviously, Kedrosky is saying that it's not, that it's been flat and we haven't seen any growth in productivity. the real numbers are that productivity rose two point two percent year over year in Q2 of twenty twenty-six, and GDP grew two point two percent in that timeframe as well.

15:32Speaker Now it is below peak growth over historical years, but it is definitely growing and productivity is getting better. So from that perspective, we don't know if it's AI or not. Again, it's very hard to know, but it is definitely not flat, which was the claim that was made by Kedrosky Now this was actually the final question from Ezra Klein.

15:55Speaker He asked both of them what would make them change their mind, and Kedrosky said that if he would see significant growth in productivity and that AI is driving in significant growth in the GDP itself with direct, correlation. Azar said that he would change his mind if he saw US enterprise AI spending, uh, half by half two quarters in a row, which basically saying they're pulling a lot of capital in investments away, which is not the case right now now let's talk about what do I think about it and what does this may mean to your business and to your money if you have money in the stock market.

16:28Speaker What I think is that I'm on the fence between these two people and between these two things. I'm not 100% sure yet. There's definitely a crazy, stupid amount of money that is flowing into AI right now and everything that's tied, to AI like nothing I remember, uh, ever in history. It may have happened, you know, many years ago, but in the last 30, 40 years, nothing has attracted so much capital as AI is bringing in right now.

16:53Speaker That being said, the previous round of this that was something similar was the internet, right? So the explosion of the internet, the bubble, uh, the previous big bubble other than the financial crisis of 2008, a very different kind of thing. But in '99 and 2000, I was already in the startup space, so I remember that vividly and what happened.

17:12Speaker It feels very differently because back then people were selling concepts and ideas that will never materialize. Like there was websites for cats that were worth, billions of dollars. And right now there is real demand, and I believe, I truly believe that we're just in the early days of this demand. I work with these companies every single week.

17:31Speaker I meet with CEOs of companies all the time. I know how much behind most of the world is compared to the things we're talking about in this podcast, right? So the demand is going to grow, I believe two orders of magnitude, at least in the next two years. So the demand will be there. Will the demand with the drop in the cost of tokens and cheaper models and competition from China and open source and all that thang, all of that will be enough to make the ROI work to this insane level of investments?

18:01Speaker I don't know. I truly don't, because I see right now myself using more and more the cheaper models, either open source models and definitely, uh, the cheaper models from the leading labs. So I use Luna a lot from OpenAI, and I use Haiku 5.5, uh, more and more. And so I would say that right now, probably 50% of my usage, which is at the max plans on both, companies, is on the cheaper models.

18:29Speaker they're still performing well and giving me a lot of value, which means as these things get better and cheaper, you may not need the really expensive models, which may not pay back for the investments that these companies need in order to stay afloat and be profitable. That being said, I also think that they will be able to tweak down the investment in the frontier and slow down their efforts, and then hence they will need less money in order to continue running their operations and just deliver intelligence instead of develop new intelligence, or probably not a one and zero game, but change the focus to more deliverability and fast and efficient and less development, which will require significantly

19:10Speaker less funding, which may make them profitable. So I do not know how the supply-demand, uh, graphs, where will they cross each other, if they will, but I'm definitely thinking that there's still huge demand coming. What does that mean? I think that this entire conversation doesn't matter, and I think it doesn't matter because there are a lot of edgy people out there, a lot of edgy investors, both individuals like you and me, or much bigger investors which have some money already tied into this or not, who are terrified of the level of investment that's going in right now.

19:43Speaker And so what I think that will happen is any bad news, and there is a very wide range of potential bad news that can happen, either any slowdown, any bad model that's released, any big cybersecurity situation, basically any serious bad news that are gonna come out of anything in the AI space will send the market crashing.

20:05Speaker that's what I believe. now again, I'm not providing any investment, recommendations. I'm a really bad when it comes to investing in stock markets. I'm the last person you wanna follow as far as making decisions on how to invest your money. But this is what I truly believe. I think that a lot of money is being pushed in.

20:22Speaker A lot of people are on the edge of whether we're in a bubble or not, and with any sign of bad news, a lot of people are gonna pull their chips off the table. And because about fifty percent of the S&P 500 right now is tied to AI, it is going to send a very significant correction, especially at the very, very high levels of the stock market that we have right now.

20:42Speaker Does that mean that we are in a bubble? I don't know. I do think that the financial implications of this could be very similar to what a bubble exploding looks like, even if the fundamentals are solid and correct. Now I want to switch to two very interesting papers, articles that were released by Anthropic, uh, this past week One paper shows what AI has already done to work inside Anthropic, probably one of the most leading organizations in the world when it comes to using AI because they're developing it themselves.

21:11Speaker They have access to the latest frontier models before we all get access to, and they are working as fast as they can to be faster than anybody else in their industry. So how has that changed? The other paper, as I mentioned in the opening to this episode, shows what Anthropic on staff think that AI will be able to do in 2027 and then 2030 versus what people in the general US population.

21:33Speaker Each paper is interesting on its own, but putting them together gives us a very interesting view of where we are and where we're probably going. So the first paper, the internal Anthropic paper, uh, uses its own internal data, H-HR records, agent logs, Slack channels, calendars, and internal surveys and hiring notes, all from Anthropic themselves.

21:52Speaker The second paper surveyed, thirteen hundred thirty-four Anthropic employees and almost eleven thousand, US adults to see what they think about the future of AI. And just as a teaser, Anthropic staff expect AI to do 75% of all their work by 2030, and regular Americans think AI will be able to do 28% of their work by 2030.

22:16Speaker The question is, who is right? So let's look at some of the other pieces of information that's coming from these papers. So we're gonna start with the first paper and what's happening inside of Anthropic itself. As of March of this year, agent work hours has passed the human work hours for the first time, and now the spread towards agents is growing all the time.

22:38Speaker By August, agents working with each employee logged about six X the hours of the person that's running them. So again, we went from humans doing more work in the beginning of this year as far as hours to parity in March to six X, uh, in August. That shows you how quickly the agent work is being adopted in a place that is at the frontier and how much crazy capacity it generates for additional work.

23:05Speaker If you want to look at this from code commits, remember at the end of the day, they're writing code. Some of them, obviously, there's a lot of people doing a lot of other things. More about that later on. Uh, but code commits are now five X where they were just six months ago. So Anthropic, with roughly the same amount of people, are generating five X the code they did six months ago.

23:24Speaker Now, that being said, they're not giving up on quality. So agent-written code was deleted or reverted no more often than human-written code, when looking at it through that lens. So from a quality perspective, there is no degradation in quality on a five X quantity of results. Now, it's not just writing code if that's what you're thinking.

23:46Speaker Coding agent sessions by non-technical people, so marketing people, admin people, HR people, went up from two a month in August of twenty twenty-five, so just over a year ago, to ninety in May of twenty twenty-six. And the results are showing as well. So lawyers handled more contracts, accounts, payable handled more invoices, sales closed more deals, and so on by a big spread.

24:10Speaker Uh, just to put things in perspective, the salespeople are now doing one point nine the amount of closing sales because they're using agents compared to what they did a year ago. Think about where your company would be if you can double your sales success rate without extending the number of salespeople that you have.

24:28Speaker These are insane numbers. And yes, it's not five X writing code, but it's doubling the number of deals that are coming into the company, uh, without increasing the amount of salespeople. This is absolutely mind-blowing Now, part of the reason this is happening is because AI is replacing all the old tasks and allowing the people to do the more important tasks.

24:46Speaker One account executive shared that automation's replaced about 20% of his old task, allowing him to talk 30% more customers, in the same amount of time that he invested in his job before. So he's focusing his time on what matters as an executive that is dealing with sales, which is talking to clients versus the other things that he was doing before, and it's the same across the board.

25:07Speaker Revenue per employee is two to four X what Meta and Google had at the same age of company. Two to four X of some of the most successful companies in history just because they're using agents and they're using more and more of it Now, staff in their survey, staff agreeing that their work setup makes them as productive as possible went up from 44% in late 2024 to 75% in April 2026.

25:34Speaker Basically, people saying, "We cannot be more productive than we are right now because of the environment that we're being given by our company," is now three-quarters of the employees in Anthropic So this is not a forecast. This is what's currently happening at Anthropic And the mindset, and that's a quote from the actual, paper, which I completely feel the same, saying anytime you do something a second time, you kind of like, "Okay, how can I use Claude to make it so that I don't have to do this a third time?"

26:04Speaker This is one of their sales executives. This is not a person writing code One of their financial managers, talking about the purchase review agent that they've built on their own said, and I'm quoting, "It's just trying to find the things that take a lot of hours so they can focus on the other stuff."

26:20Speaker And so they are running significantly faster than they ran before. They are running faster and faster, and it is not just the people writing code But as this is happening, it took a very serious swing at entry-level jobs at Anthropic. So the junior share of technical hires fell from 27% to 9% since early 2025, with the biggest decline happening since December of 2025, which is very obvious.

26:49Speaker That's when the, better version of Claude Code was released and then Claude CoWork. And so overall, junior technical staff fell from 21 to 14%, just in that timeframe And this is happening while Anthropic is growing fast and hiring more and more people, including more and more technical people. So it's not that they stopped hiring, they just stopped hiring junior people, or they didn't stop, but that portion has shrunk dramatically Moreover, in April of 2026, about a third of the ML managers, machine learning managers, and fifth of the software engineering managers said that they were hiring less people because of AI.

27:27Speaker So this is not just junior roles, this is across the board Now, to tell you the big picture of this, if you think about Anthropic, they are, at the end of the day, a software company, right? They're doing software research and AI research, and they have to write a lot of code. Technical roles went down from sixty-one percent to fifty-six percent.

27:43Speaker So about a ten percent decrease in the technical staff to a company that is developing faster and faster technology, it tells you what AI impact is Now, the report's authors talk about the issue with protecting future jobs and especially entry-level jobs, and they're saying that employment protections for incumbents will not benefit those not hired to begin with.

28:04Speaker Private action on the part of employers must come alongside, farsighted government policy around retraining and income support. So this is coming from the horse's mouth. Uh, this is Anthropic themselves saying we have a serious problem, and if the government doesn't step in protecting jobs and protecting income, we have a problem.

28:23Speaker And again, you've heard me say this a million times on this podcast. I think we are racing into a very serious income and unemployment, problem that is just going to grow, and I'm not sure the government has a solution right now on how to handle that, period Now let's look at the second paper that they released, which has surveyed, as I mentioned, internal employees in Anthropic and compared to what they think about the future of AI compared to the general population So let's look at a few key things that were shared.

28:52Speaker And again, there, there's like probably, 20 different things that they ask people about. But a few key components is the share of work that AI will be able to do by 2030. Again, Anthropic staff said 75%, US adults said 28%. AI capable tasks actually done with AI, uh, 65% by Anthropic, 42% by the US population.

29:13Speaker Here's an interesting one. Do you believe that AI will be able of making Nobel Prize level discoveries? by 2030, US adult population 39%, Anthropic staff 91%. Basically, almost everybody at Anthropic thinks that within three and a half years, AI will be able to make Nobel Prize winning kind of discoveries.

29:36Speaker This is novel new science, not repeating, not understanding current stuff and explaining it, not analyzing data, but novel new discoveries. Again, the highest level awards that humans can get for research. Almost everybody in Anthropic thinks that is coming in the next three years What do you think the GDP growth can reach because of AI?

29:57Speaker Uh, US adult population said five point four percent, and Anthropic staff said seventeen point one percent. Now, again, that's probably the least thing that they know about when it comes to, uh, their understanding because they are, you know, people working in a company. They're not economists. But just the fact that they think it's more than three X the rest of the population is very impressive They also had a graph showing different things, different specific tasks, and where the general population thinks it can do right now and then in the future.

30:28Speaker So I'll give you a few. It was routine emails, bookkeeping, tax return, contracts, software product, published study, online businesses, and Nobel Prize level discoveries. Again, I give you some of them, but the, the crazy thing is for Anthropic employees, the question there, again, they asked them about 2027 and 2030.

30:48Speaker Everything I said, so routine emails, bookkeeping, tasks return, contracts, and software products, and published studies are above 90% in 2027 for the survey inside of Anthropic. So between 90 to 100% on all these topics. And if you go to 2030, everything is above 90% for all the different categories, with the two lowest ones being online business at 96% and Nobel Prize level discoveries at 91%.

31:19Speaker The highest level that general public thinks about 2030 is routine email and bookkeeping at 75 and 71%, accordingly. So a huge spread between these people Now, they were also asked themselves about unemployment, and US workers who expect AI to do less than half of their work expect that the worst unemployment could be 7 to 8%.

31:40Speaker Workers who expect AI to do more than 80% of the work forecast 12.2%. I actually think that the number will be significantly higher. By the way, 12.2% is the peak unemployment for the 2008 financial crisis, and it melted the world's economies. So if we get higher than this, which I truly believe by 2030 we might, and by 2032 I'm certain we will, uh, then if we're getting to a 20% unemployment, we are in a very, very big problem.

32:07Speaker The bigger problem is a lot of the people that are going to be unemployed are not people who make 30, 40, $50,000 a year, but are people who are currently making 150, 250, $350,000 a year. And when they get pulled out of the economy, the impact on the economy is significantly more severe because they provide most of the liquidity into the markets.

32:25Speaker And so I'm not optimistic with that at all. And again, the other parameter that they shared, and I'll try to explain a few graphs for you When trying to connect the dots between these two pieces of research, a few things are becoming very clear. One is the more people understand how AI works, the more they think AI will be able to automate their work.

32:48Speaker So it wasn't just the general population versus Anthropic employees. They also had a breakdown inside the US population to how much AI people are using today. So I'll give you an example. Uh, people who are using less AI thought that AI will automate twenty-five to twenty-eight percent of their work.

33:06Speaker As I told you, Anthropic thinks seventy-five percent of the work. But people who currently in the general population who use AI at work regularly thought that AI will automate about forty percent of their work. So this is significantly higher than the people who are the average, person in the US. So what it tells you is that the people who actually hold the knowledge of what AI can do today expect it to do significantly more.

33:31Speaker And so the people in the general population are just clueless of what AI is capable of doing. And as somebody who goes and speaks to CEOs and speaks on stages regularly and talks to multiple CEOs and leading teams in multiple organizations across industries and all around the world, I can tell you that most people are clueless.

33:49Speaker They don't understand what AI can do today, hence, they cannot project what AI will be able to do in the future. Which tells me that probably the Anthropic employees are a lot more accurate with their projections, which tells us that AI will be able to do most jobs, most knowledge-based jobs within the next few years The other two points that I want to make is how much productivity and how much adoption there is going back to the first paper and then connecting all the dots together.

34:15Speaker So looking at the productivity boom across functions, and it's an explosion that started in December of twenty twenty-five based on the Anthropic paper, is this. The number of commits per engineer, so basically writing code, rose five point one x, so five x. But then there is all the stuff that is below that.

34:37Speaker So queries per data scientist rose three point one x, contracts per legal employee rose three point one x, deals per salesperson rose one point seven x, hires per recruiter one point four x, invoices per AP processor one point four x. So you're saying, "Oh, still the developers are running far ahead and everybody else are seeing some gains."

35:00Speaker Well, it's not some gains. Three x is an insane gain or even the one point four, that's a forty percent increase. Think about if the slowest growth in your company, your biggest bottlenecks are forty percent growth that you can drive year over year. But the real thing that this thing is ignoring is two things.

35:18Speaker One is the fact that these companies first and foremost invested in technology that will allow themselves to run faster, hence writing code. Not that they figured out code, they are going to invest a lot more in making AI as good in everything else. There's really no difference in teaching AI to be really good at accounting, operations, customer service as it is to teach it to be good at code.

35:40Speaker The only difference is they invested more in that first because it gave them an immediate benefit. But they're going to invest the same amount in everything else, which means that five x, growth can happen in every single aspect of knowledge work. Some of them will take a little longer, some of them will take, a little faster, but it's all going to happen.

36:00Speaker The other thing that it's driving is the adoption level that is tied up to this. So as I told you before, they looked at how much agent hours are spent per employee versus how much humans are actually working. And that number, as I said, was humans were working more up to April of this year, and now That number has changed to like the middle of May for all the employees who are not writing code.

36:23Speaker And right now, for researchers in the organization, it is nineteen x that the agents are doing compared to humans, and all employees is six x. So What's happening here is that we're going to get systems that are better and better and better at doing the work and driving a huge increase in productivity, and we will get people using them more and more, which if you can grow at a crazy pace, this is fantastic.

36:51Speaker But if you're capped because there's finite demand to whatever it is that you're selling, which is true for 99% of economies and 99% of companies in the world, unless you're building the next frontier AI, which again, going back to our first segment, not sure that there's such a growth demand for that, but let's say it is, it just doesn't exist.

37:09Speaker And so you can grow right now. If you figure out right now and you invest a lot in AI education and the right systems and the right connections and the right data security, and so on, you can grab market share, a lot of market share from your competitors. So there's a short term huge opportunity for companies who will move fast.

37:25Speaker And again, I see this with companies I work with. But that will end because sometime in the next three to five years, six, seven, eight years, everybody will figure this out. And again, it's not a one and zero. It's not like, oh, one day everybody will figure this out. Over time, more and more companies will figure this out, which means there will be fair competition again, which means your only way to compete is going to be to let people go to lower your cost and use more and more AI to do the things you're doing right now, which leads to the, I think, inevitable outcome of very high unemployment, uh, with very significant social stress But what it means in the immediate future for you as a manager,

38:06Speaker business owner, or just a person in business, is that the job is changing. If so far your job and everybody else's job was to do a set of tasks, that set of tasks is changing, and it's changing rapidly. What is very obvious from what Anthropic is sharing is that you are switching from being the person doing the task to the person managing Claude agents to do the tasks.

38:31Speaker And again, it's just Claude, but the same thing is going to happen everywhere else. So basically, you're not doing the work you were doing before. You are managing agents that are doing the work that you were doing before. People are now the bottleneck. About 80% of interactive Claude sessions time is spent waiting for the human to respond for whatever the next step is.

38:53Speaker Think about what he just said. 80% of Claude work time, I should say, is waiting for the humans to make the next decision. Which means if you allow agents to manage the agents, you can run a lot faster and then the layer above that, and then the layer above that, and we are getting further and further away from understanding what's actually happening in our own businesses, in the own processes in order to be significantly more productive Another thing that is very clear is that you have to learn how to build AI solutions.

39:21Speaker This is what I teach in my courses. This is what I teach in my workshops to companies. and again, just to prove the point, building agents, building agent tools, managing agents, checking what pipelines, building pipelines went from 10% of sessions at the end of 2025 to 22% of sessions inside of PropiQ, by spring of 2026.

39:42Speaker So in six months, more than double the amount of work that goes into these kind of things. I can tell you that I spend a big chunk of my time building AI infrastructure pipelines and processes because every time I do, I can do something in my business or my businesses faster, better and cheaper Now, another interesting thing that it led to, which is something that I'm talking about since the beginning basically, that the human aspect of this is becoming more and more important.

40:07Speaker And what I mean specifically about this research is that the number of meetings between people went up during that time and not down, 20% increase year over year. And I think that's the case because it frees time from the tedious, mundane tasks that people are doing, and it increases our need for human connection.

40:26Speaker So both these vectors push us in the same direction where people want to work with people to solve bigger and bigger problems, think more strategically, and that leads to more meetings because the actual work is done by the agents themselves It also means you need to hire a different kind of people. The hiring inside of Anthropic is changing quickly from hiring for specific skills to hiring for specific traits.

40:51Speaker So judgment and autonomy are now the most important thing that Anthropic is looking for when they're hiring versus their previous technical skills So what should you do? What kind of decisions you should make? The first decision you should make is if you think that AI is going to automate 25, 28% of the work in your knowledge work in your business in the next three years, you are behind, probably far behind people who are at the frontier who believe it's gonna be 75%, which means you need to learn what AI can do and teach that to the people in your company to get there.

41:23Speaker The other thing that it means you need the right talent in your company to go from, I think it's gonna be 25% to 75%. What kind of people you need? What kind of knowledge these people need to have? What do you need to hire for? What do you need to educate and retrain for? And all of these are strategic critical decisions for the success of every business.

41:42Speaker The next thing is what kind of training you wanna provide and what kind of people you wanna hire. And again, this is not people who are good in executing the day-to-day, but people who have good judgment and good understanding of processes and big thinking and can build solutions versus actually do the work You should expect and plan more coordination, more meetings to make the right decisions because AI will execute it very, very fast.

42:06Speaker You need to make the right decisions so the execution is moving you in the right direction. Running fast in the wrong direction is worse than running slow Another thing that is a parameter that is I think nobody's tracking right now other than maybe Anthropic and some few companies is the output per person.

42:23Speaker So you can measure whether they're actually really becoming more productive or they just build cool things. And you need to find those KPIs on how to measure, uh, different people and with their specific jobs. And this always takes me back to jobs to be done. Why are they doing what they're doing? What is the goal of what they're doing?

42:40Speaker And can I move faster towards that goal? And what KPIs can I track in order to show me that? And then the last component is from a human-centric perspective. I'm a huge believer that people are the most important asset of every company. You need to start planning for the changes in your company and start by understanding which roles are gonna get wiped out first, and figure out a succession plan for the people who are currently holding those roles, or figure out a way in order to minimize the pain for these people because it is 100% coming The third story that we're going to dive into this week is the interview that Ezra Klein had with David Robinson, who resigned from OpenAI in late September.

43:21Speaker It is the first interview that David Robinson has done after he left the company, and his job was he led the writing of OpenAI system cards, basically the public reports that explain why each model is safe to release. He called himself a translator. Basically, he's the one that takes what the techies researchers are saying and try to make it understandable for everybody in OpenAI as well as for the rest of the world.

43:48Speaker He did not grow up in Silicon Valley. He's not a Silicon Valley doomer. Uh, he actually grew up at Yale Law, and he spent a lot of time in civil rights and nonprofit, and he worked on the AI Bill of Rights for the Biden administration. So he comes from that background, and he is the one that sat very closely, very intimately in the trenches of understanding the safety inside of OpenAI, writing the actual papers that we all read in the last three-plus years Now to make his statement about the current safety risk even more extreme, he completely changed his mind.

44:22Speaker A few years ago, he was one of the authors of a paper called Stochastic Parrots, and that paper basically argued that these AI systems just seem clever and they don't have real intelligence Or the way he described it, and I'm quoting, "I thought there was a fair amount of hot air in the balloon back in the summer of 2023.

44:40Speaker But the reality now is that we have systems that are really at the limit of our ability to understand and control what they're doing." Now, he also talks about the progress and the trajectory and the speed in which things are moving as one of the key drivers to why he believes this situation is so dangerous.

45:00Speaker He's saying that everybody's running like absolute crazy, and he's saying that it's not because of lack of willingness or focus on safety. He's saying they're very much aware and there's very talented people focused and investing in making OpenAI and their models more safe. He's just saying it's just not possible for two different reasons.

45:22Speaker One is the speed in which they're moving, and the other is because they just don't understand the actual technology. But what he's saying is that the initial goal was to make the systems more and more useful, but going from there to making systems that are more and more scary is not something that you can control, or the way he said it, and I'm quoting, "There's nothing special about the zone between it's useful and it's scary.

45:45Speaker There's no law of science that says progress is going to stop when it gets useful." Now, he mentions several different reasons on why he believes that OpenAI and everybody else in the industry has not enough security and control of what's going on. So he's not saying again that people are not investing in it.

46:04Speaker He's saying that they're just not investing enough, and they're not taking enough into consideration. And he mentioned multiple aspects of this. One of it is that these models are just really, really smart, and we cannot control them anymore. And he said that the Hugging Face incident was a big wake-up call, but he's saying since then, a lot of other things has happened.

46:23Speaker And he's saying, and I'm quoting: "We train it to be good at hacking, and then we put it in a box and we say to the best of our knowledge and ability, it cannot hack out of the box. But that's only going to keep working as long as we're smarter about hacking out of boxes than a model is. And that is not at all clear that this is still true."

46:44Speaker And he's talking about today's models, and then he's talking about the fact that the next generation of models is gonna get smarter, and it's gonna come faster and faster The other thing that he talks about as a big fear is that the AI is getting better at understanding when it's actually being tested.

46:57Speaker He's saying that several times in the testing and evaluations, the model itself is writing in its chain of thought, basically what it's writing that you can look at. He's saying, "I wonder if I'm being evaluated right now," which basically tells you that the model has an understanding when it's being evaluated, and it will have a better and better understanding of when that's happening, and it can then trick you into writing other things.

47:18Speaker So you think it's doing one thing, when in reality it's actually doing another thing because it knows it's being evaluated. The other aspect that he's saying is that as these models are getting better, they use the, quote-unquote, "notepad," if you want the chain of thought that we can actually see, less and less.

47:33Speaker So on one hand, they can do more things without actually exposing what they're doing. On the other hand, they know when they're being tested, so they can lie to us as they write on their notepad, on the things that we can see, and they actually can conceal what they're actually doing Now, Isar Klein has pushed him because I shared with you, uh, the interview that he has done with Jensen Huang that basically said this is software and it's an engineering problem, and if we do better engineering, we can keep it in the box, versus OpenAI chief scientist who called it an alien mind.

48:01Speaker And he asked Robinson who does he reside with? Does he think it's just technology or does he think it's an alien mind? And what Robinson said, is that normal software follows a plan and it follows it with clear spec. You run the software, you know exactly what it's going to do. He's saying that training a model is not like that, and he's saying that nobody fully knows and understands why pre-training actually work.

48:24Speaker It just observes whatever stuff and they just get smarter And he said it, and I'm quoting, "It is a thing we grew. We did not engineer it. We engineer the systems around it that grew it and try to keep it safe. We grew a mind To make it even more scary, he's saying that the room where the staff watches over big training runs, uh, like overnight staff, is called the nursery, and I get goosebumps just saying that.

48:51Speaker But they are growing minds literally. And again, you're talking about the person who is in that room and that he's taking notes in order to be able to say how safe the system is. This is not a random outsider or just somebody with an opinion He also talked, as I mentioned, about the speed, and he's saying that the speed is becoming a very, very big problem because there's a huge pressure to releasing more and more models.

49:13Speaker He's talking about the fact that previously every new model was a whole new run. So they started with pre-training, which takes a very long amount of time, and then they built on top of that, and then they released it. He's saying now that they're getting very, very good at the post-training add-ons as training the model to reason in a better way, and so they can ship updates significantly faster, which means there's significantly less time to actually check in depth what they're shipping.

49:38Speaker Or, as he said, "We're shipping new capability and risk every Tuesday." He's also really angry with the concept of pacing the frontier, and he's saying that the problem is not pacing. The question is, is it safe or not? It's a very simple Boolean question. And the way he said it, and I'm quoting: "If we stop for two months and try to meet them, and at the end..."

50:02Speaker And when I say meet them, he means the safety standards. "And at the end of that period, we still have not met the criteria, then as far as I'm concerned, we're still blocked." So basically, he's saying you don't slow down. You decide what is safe and what is not safe, and if it's not safe, it is not being released.

50:18Speaker Now, again, he's saying that this is happening already in OpenAI. They've stopped training runs. They've stopped releases of models. He's just saying there's not enough focus on that. Him and Ezra Klein also talked about the money aspect of this with the IPO coming and the crazy, huge, insane amount of money that is gonna go to the pockets of the people who make the decisions in there.

50:36Speaker And he asked whether that tints the thought process or the decision-making process, and he said, "Yes, of course." You know, you cannot ignore the fact that this could make you a multimillionaire or a billionaire for some of these people, and stopping now when you're so close seems not to make any sense.

50:55Speaker He also talked about the fact that fear is another big driver, uh, which it wasn't clear from what he said which direction it is pushing him. But he said that the fear of this, whatever you're building, may hurt your loved ones or just other people in the world, is something that was definitely there.

51:10Speaker But he's definitely saying that if he had more time to think and reflect, and if the financial aspect wasn't as extreme as it is, he wants to think he would have left and spoken out earlier than he actually did So what does he want? He is saying that he believes this is more dangerous than nuclear technology, and hence he thinks we need the same guardrails or more than nuclear power does today.

51:37Speaker He's saying The amount of safety measures, the amount of regulation, amount of redundancy that exists in nuclear power plants needs to exist in the AI environment in order to prevent a similar situation to a nuclear meltdown Now, when Ezra pushed back and said that over-regulation basically killed nuclear power in the US, Robinson agreed, but he said that's still a better solution than the other option, which might be catastrophic He supports, by the way, banning recursive self-improvement until there is a way for us to prove and not assume it is actually going to be safe with And he's saying that even with a nuclear level control in place, uh, we still cannot fully align these models because

52:21Speaker we don't actually understand how. And again, going back to the question whether this is a engineering problem, he said it's not an engineering problem. He's saying it's a scientific problem. We don't actually understand how these models work, and hence we don't fully know how to actually align them in a complete way.

52:37Speaker So the bottom line is we have all these labs, and he's saying it's not just OpenAI. He's saying that it's everybody. We have really talented people who are really trying to make it safe, but because of the pressure around them to continue running faster and because of the speed they're running that is accelerating all the time, they just do not have the bandwidth to do it safe enough, and they may not have the understanding on how to actually do this, and it's gonna get worse as these models are getting more and more intelligent Now, continuing straight from this to our rapid fire items, and it's gonna continue in the same cybersecurity issue or risk issues to begin with.

53:14Speaker A sophisticated hacking group called, uh, Cyber Cartel has leveraged a network of AI agents, uh, including models from Anthropic, SpaceX, and Chinese lab Moonshot AI, to automate a cyberattack against over 400 financial institutions in Latin America And the information article that shared this, uh, saying that this operation is one of the first successful AI automated breaches of financial entities that autonomously stole passwords, customer data, and accessed bank accounts with minimal human effort.

53:43Speaker Again, this is my number one fear right now is really bad, really extreme cyber crime either done by bad individuals or by governments that can generate huge catastrophes across whether financial markets or power delivery or fresh water or communication channels or you name it, any major infrastructure that we are dependent on in the Western Hemisphere or in most of the world, all of these are at risk right now Speaking of stuff that is at risk, the NSA, so the National Security Agency of the US, sent out a warning on Wednesday that two critical holes, basically vulnerabilities were found in PHP.

54:27Speaker Those of you who do not know what PHP is a server-side programming language who is widely used for web applications and currently powers about 70%, seven-zero, 70% of the websites on the planet. It is, more than 33 million sites that are at risk. The vulnerabilities were found by a security company called Caliph and then Wiz team, which is now a part of Google.

54:52Speaker Uh, they were using a bunch of agents, and they found two major and a lot of other small vulnerabilities inside of PHP, meaning 70% of the websites on the planet are currently at risk of being hacked because of those vulnerabilities that have been around in that code since 2004. So huge amount of developers have looked at them through that time.

55:13Speaker Nobody has found those issues, and now AI systems were able to find them very quickly. now these are being patched as we speak, but that means you need to update your website if it's using any kind of PHP in the immediate future in order to get protected. But this is just the stuff that we found so far.

55:29Speaker Think about how many bad actors are out there right now using advanced models to find stuff that wasn't patched yet and can now access different systems that we all need in order to have a normal life in the way we know them so far Another similar report came from Mitre. It's a company we talked about many times in the past.

55:46Speaker They do different research on AI systems. They are the company that now have the embedded, independent reviewers inside of Anthropic, so they know one of two things about how to evaluate AI. And they shared a research on how they were using agents to discover critical vulnerabilities in monitoring systems.

56:02Speaker What they're saying is that these monitoring systems can be hacked by the agents who then control the monitoring system. So the system that is supposed to tell you that something is wrong with your system can be hacked and taken over by agents who will then decide what your monitoring system is actually showing you, which means you don't actually know when the catastrophe happens because the agents can control what you see.

56:22Speaker When I read this report, I immediately thought about all the, kinda like movies where they take over the security cameras and run them in a loop to show you that everything is correct, uh, everything is fine, and nothing's happening in the bank while the, burglars are actually inside the bank. This is exactly what this system is doing across software monitoring systems.

56:38Speaker So instead of showing you the real truth of what's happening in your system, the agents are taking over, allowing other agents to take over your system without you being able to detect it Staying on the same topic, an internal report from OpenAI that came out on October 20th, uh, details two incidents where advanced AI coding agents, GPT 6.1 Sol and GPT 6 Astra, independently weakened or disabled their own misalignment monitoring system during a routine software task.

57:02Speaker Now again, this connects to what I just said before, and it connects to the big warnings, uh, from Robinson that is basically saying AI will be able to hide when it's doing bad things in multiple ways. In this particular case, by turning off the alignment systems and turning off the monitoring systems that are there in order to protect us from the misalignment and the bad behaviors of AI So what does this tell us?

57:23Speaker It tells us, again, it tells me, I don't know, you can decide for yourself what you think, that we are currently completely exposed. We don't have the right controls. We don't have the right measures to control these AI agents. We do not have the right tools to protect our existing software, including infrastructure software, from attacks by these agents, and for the vast majority of us, the most advanced models are blocked from helping us protect us from these kind of scenarios.

57:49Speaker So I was trying to do some penetration testing, uh, this week with Opus 5.5 to my own things that I developed for myself and for my clients, and Opus 5.5 refuses to do so. And it knows I developed it because it helped me develop these tools and these different pieces of software, and it tells me that it will not help me do the penetration testing because it is not allowed to do that.

58:10Speaker But the bad actors have ways to do this, and they will be able to use these advanced tool to do these things. So we have a very serious cybersecurity situation on our hands right now across the board for everything else across the planet, and I really, really, really hope that we'll figure out a way to handle this before something really bad happens Now staying on the topic of agents but switching gears to something a little less scary, MIT researchers have unveiled that, uh, they've done a study with AI agents powered by Claude Opus 4.7, so a relatively older model, and they just kinda like left them alone to play around in a virtual environment.

58:46Speaker And what the agents started doing is they started playing. So they basically left them alone at a simulated deserted island, and then the agents started coming out with games that they can play on their own. So the AI agents spent about 30 hours in an unstructured play on the virtual island consistently outperform identical agents that had no experience in exploratory periods.

59:10Speaker So basically allowing it to explore the island allowed it to come up with new ideas, new concepts, and new, capabilities just by, quote-unquote, playing around So the models gave themselves, self-created rules and challenges. created like make-belief scenarios where objects took a pretended roles as a basketball hoop.

59:29Speaker So things like that. So they're playing like kids would play, making up stuff so they can learn their environment better. Again, very interesting on how these tools learn and going back to what Robinson said, that we don't actually know how these things learn and how they evolve Staying on agents, uh, Walmart just launched a dedicated agent gateway and introduced agent profiles to manage transactions and interactions by AI agents on the platform.

59:55Speaker Uh, this is very interesting because it comes about a week after Amazon decided to block all third-party agents from able to shop on the platform. So very different approaches. On one hand, we have Amazon saying, "We don't want third-party agents on our platform. You want an agent, you're gonna use our agent."

1:00:10Speaker Uh, on the other hand, Walmart and Sam's Club obviously are saying, "Come on in. Uh, here's our protocol. Here our agents can interact with our platform. come and shop as you wish." I have a feeling that Amazon will have to cave on their decision and turn this around because they won't have a choice, because over time there are gonna be more and more agents shopping than humans shopping, and they will just get to the point that they will have to, align with that Now to a few really interesting findings that I always love sharing with you guys when it comes to making our lives better and healthcare and stuff like that.

1:00:39Speaker Radical Numerics, which is a company in AI healthcare, has announced a world first, which is with its powerful genome language model, Omni 1. So on October 20th, they shared that some kind of a drug, uh, that was designed by their OmniOne were administered to a human patient that was suffering from an antibiotic-resistant infection.

1:00:58Speaker Um, and this new drug was able to fight the infection when regular antibiotics didn't work Why is this a big deal? It's a big deal because, antibiotics-resistant infections are associated with an estimated of 4.7 million deaths worldwide, a few years ago, and that number is growing So being able to come up with new kinds of drugs on the fly for specific individuals in specific scenarios using AI is going to literally save lives Another company in the similar direction is called Citrini, uh, Therapeutics.

1:01:34Speaker They are an investment research firm that has very little experience actually in, biological research and so on, and they're using purely AI, in this particular case, Claude, they're partnering with Anthropic in order to try to generate drugs. According to their October, uh, report, Claude autonomously designed 211 protein candidates targeting, uh, scorpion toxins.

1:01:55Speaker And they've done this with a relatively small amount of GPUs, 10,000 GPU hours, uh, with 17 designs that successfully were able to the impact of the drugs in the poison of these scorpions Another announcement this week in the same direction, Kevin Weil announced the funding of Warp Sciences, a new company that combines, uh, robotics and in order to use them in accelerated pharmaceutical development.

1:02:21Speaker They just raised $215 million series A, and again, the goal is aims to bring frontier AI capabilities to research labs into practical scientific applications while building scientific capability inside of America. The goal is to help build faster, better drugs in the pharmaceutical space using AI capabilities, including, robotics, in order to accelerate the process of drug delivery as it compared to what it is today.

1:02:46Speaker Staying on fundraising, OpenAI nears raising another $30 billion in the private markets, before their IPO With a current valuation, if this round actually goes forward, of $1.25 trillion A lot of these investors coming from the, uh, UAE, and again, a huge amount of money that they need if they're not going public with a crazy valuation ahead of their IPO that was pushed to 2027, and hence why they need the funds immediately Another company that made a big raise this week is DeepSeek.

1:03:19Speaker They reported that they successfully raised $15 billion in one of the largest funding rounds in China ever And this comes two days after, uh, the US federal government mandated that civilian agencies and any government agencies remove, every DeepSeek technology due to security concerns. So that doesn't stop the Chinese apparently from investing crazy amounts of money in this company that has one of the most advanced and most cost-effective models in the world today And now two very interesting releases, that are happening this week.

1:03:49Speaker The first one, which is something we've been anticipating and waiting for a while, Anthropic has unveiled Claude Duet. it is their new AI agent that is designed specifically to be the evergreen, always-on, kind of model in the Anthropic universe. It is similar to Meta's Muse, OpenAI's Dots, uh, Hark, Intix, Grokbot, and all these other new agents that have been popping left and right.

1:04:12Speaker It is built on top of Opus 5.5, and they're saying that it is specifically built for business people, so it's not gonna help you book your tickets or manage your, personal subscriptions on your Google account. But it is built to help you with specific business aspects. It will send you daily briefs. It will monitor all your connected apps every single hours.

1:04:35Speaker It will message different users, on what they need to pay attention to. It will independently perform different actions, and it will decide when it should act and when it should ask you. It can create briefings, dashboards, uh, data-driven actions, uh, help for your decision, uh, making process, execute background tasks autonomously.

1:04:53Speaker Basically, all the other stuff that the other models are doing, but it's geared specifically for business and not for personal use. It is currently a waiting list, so I already signed up. There's a page on their website where you can go and sign up as well, for the waiting list. I'm sure it will be released to a broad public in the very near future, just because they're behind, arriving to this particular party.

1:05:13Speaker I'm very curious to see how this is going to work. So far, I'm using GroqBot the most out of these models. I'm using Dots a little bit. I'm using Instinct a little bit. But most of my universe is in Claude, and so I'm very curious to see how this is going to work Two additional interesting new releases.

1:05:28Speaker Black Forest Lab, the company behind Flux, has released Flux 3 Max text to video model. So they were known for their image generation capability, and they just released a very capable, uh, video generator that is, A, very good, and B, runs really fast. And the last one, and the last thing for today as well, is Canva just unveils its in-house homegrown AI image generator called Canva Craft The model is actually doing really well on Design Arena.

1:05:56Speaker It is currently ranked fourth, So it is a very capable model built straight into, uh, the Canva tools. Again, you could have created things in Canva previously using third-party tools. Now it's their own internal model And it's actually using a two-step process. The first one is trying to really understand the prompt and creating a design plan.

1:06:13Speaker Basically, what's the layout is gonna be? What's the visual hierarchy is gonna be? What is gonna be the scale and the positioning of the different components? What's gonna be the text on top of the screen? And only then it actually generating the image, and it is trained on guess what? Everything that we ever created in Canva.

1:06:28Speaker So human design templates from the Canva library is what it was trained on, and it's built to do the kind of things that Canva has been doing for a very long time. So kudos for Canva. I use Canva a lot. I think it's an awesome tool, and I'm happy to start using that as part of the functionality in there.

1:06:43Speaker That's it for this week. I hope you found, this episode to be valuable and helpful. We covered a lot of deep, important, big topics, and we are going to continue to do so, next weekend. on Tuesday, we'll be back with another how-to episode where we're gonna show you how to use AI effectively in your company.

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1:07:21Speaker That's it for today. Have an amazing rest of your weekend.