Hello and welcome to a Weekend News episode of the Leveraging AI Podcast, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This Isar Metis, your host, and this week is a unique episode. It is unique, first of all, because it is the first time using my own developed application to run this news episode, which I'm very excited about. I'm gonna tell you more about it afterwards.
It also unique because we're going to talk maybe even more than the past few weeks about the big picture and where we are in the world. We have several deep dive topics. The first one is going to be the article by Matt Schumer about something big is happening where he's talking about what you heard me talk about in the past, few weeks. We're also going to talk about new releases from some of the major labs, so several different models that were released, uh, this week.
Significant, important models that were released. This week, we're going to talk about the impact of all of what's happening right now on actual economy, and then we have a lot of interesting rapid fire items to cover. So let's get started. The first thing we're going to cover this week is an article written by Matt Schumer on X, Matt Schumer. Those of you who don't know has been a founder and the CEO of an AI startup for six years now.
He's also an investor in the space, so he is definitely well established in the AI universe.
He understands this field very, very well, and what he's saying is grounded in realities that he have a deep understanding about and the article that he published it's called Something big is happening, and right now when it's just over a week from the moment he published it, there's over 84 million views on that article on X. And I wanna read the beginning of it, and then I'm gonna provide you some other quotes from it. And then we're gonna talk about what it means.
So the article starts with, think back to February, 2020. If you are paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren't paying close attention. The stock market was doing great. Your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone would've told you. They were stocking toilet paper. You would've thought they've been spending too much time on a weird corner of the internet.
Then over the course of about three weeks, the entire world changed. Your office closed, your kids came home. Your life rearranged itself into something you wouldn't have believed if you described it to yourself a month earlier. I think we're in this seems overblown phase of something much, much bigger than COVID.
And what he's talking about, he's talking about the major shift of AI capabilities in his field, so writing code and software companies, and how much he has changed in the past few weeks after accelerating dramatically. Mostly in the second half of 2025. He's talking specifically about Claude Opus 4.6.
And on ChatGPT 5.3 and how it has went in just a few weeks from him giving guidance to the AI and having AI as an assistant, helping him in doing what he's doing to ai, doing his job better than he can and faster than he can do it.
Basically, from him giving guidance and assistance and direction to the ai to just describing the required outcome, walking away and coming back after the AI has developed the whole thing, tested it, and it's ready to be delivered, or for a very final review by him, where in most cases he's saying is just perfect. Or in his words, I describe what I want built in plain English, and it just appears not a raft draft. I need to fix the finished thing.
What he's saying is something I've been saying in the past few weeks in this podcast that the latest versions have done a major shift in these tools, abilities to understand, to reason, to have taste in user interface designs, to make decisions, to test what they're doing and to come up with finished products.
Well, what he's saying is something that I've said on this podcast many times that the fact that it is currently impacting mostly people writing code was a choice the labs have made, and the reason they made that choice is because for them, writing code is the critical path to the next model. If they can write code faster, they can train the next better model faster, which will then write better code and will create a better model and so on. That's why they chose that first.
And then he's describing, and I'm quoting again, my job started changing before yours. Not because they were targeting software engineers. It was just a side effect of what they chose to aim first. They've now done it, and they're moving on to everything else. The experience that tech workers have had over the past year of watching AI go from helpful tool to, does my job better than I do, is the experience. Everyone else is about to have.
Law, finance, medicine, accounting, consulting, writing, design analysis, customer service. Not in 10 years. The people building these system say 1, 2, 5 years, some say less. And given what I've seen in just the last couple of months, I think less is more likely. If you've been a regular listener to this podcast, you know that I've been saying this in the past few weeks in the most profound way that I can.
We are in a completely different universe than we were two months ago with the capabilities of these tools to do really complex, sophisticated tasks without assistance for long periods of time, and coming up with extremely well done outputs without any human supervision. I'll summarize with a final quote from Matt. Nothing that can be done on a computer is safe in the medium term.
If your job happens on a screen, if the core of what you do is reading, writing, analyzing, deciding, communicating through a keyboard, then AI is coming for significant part of it. And I could not agree more. And that actually takes me to what I told you that I'm excited about, that I'm doing this particular news episode on a new application and not just a new application, an entire environment of pipelines and tools that all make my life of creating this episode significantly easier.
And I want to describe to you a little bit on how that process worked, because it is. Completely aligned with what Matt is describing to do this episode, specifically the weekend episode, the news episode that I, that you are listening to every Saturday or whenever you're listening to it.
I would spend multiple hours during the week between me and my team, probably seven to nine hours go into creating this episode because there is reading articles, curating the articles, deciding which articles actually gonna go in, putting them in a specific data set that I can then review, deciding which one is going to go only to the newsletter, which ones are going to be a part of the podcast, what order they're going to be in, how to logically combine them together into something that the
flow would make sense to you and not jump around between five different topics every three minutes. All of that requires a lot of manual work. And then there is summarizing it, highlighting the sections that I really want to talk about. It's multiple hours of myself and the team. Last week, just before recording the news, I decided that I'm done and I wanna do something much, much better.
And I've worked in combination with Claude Cowork and Claude Code to create a completely new pipeline, a new way for me to do this. That pipeline was built with multiple steps and across a long list of agents that do multiple steps in that process, combined with NA 10 workflows that automate a lot of the steps and combined with the final glorious output, that is a completely brand new application web application that allows me to manage, view and run the entire process in a much smoother way.
This was custom built for my specific needs in the way I like to do this. And the really cool thing is that as I was starting to use it this week, I have on another tab, cloud code and cloud cowork still open, and I'm fixing and adapting and making changes and adding features as I am already using the previous version of the application now I don't think I actually ever shared a screen in a weekend news episode, but I am going to do this right now just to show you something.
And those of you, again, who are listening, I will describe what's on the screen. So you don't have to worry about this, but if you are on Spotify, you can look at this, or if you're on YouTube, you can definitely, you're already watching this, but what I'm showing you right now is a flow chart diagram. It has, I don't know, 20 something different components. They're color coded to, different things. You can see that all these things is a very detailed chart of exactly how this process worked.
It has input and triggers. It has NA 10 detection and routing. It has processing and pipeline and article and voice. It has news hub application, which is the application that I developed, and it has external services that it connects to, and every one of these blocks, and there's again 25 of them, if I highlight it, it shows me the links to where it is connected, what it is doing with a short description so I can actually understand how this entire pipeline works.
Now, the reason I'm sharing this with you is to highlight the point that Matt was mentioning and that I feel in the past few weeks. Two months ago, to build something like this would've forced me to hire a team of people.
Those people would've needed to understand my universe, how I work, what kind of news I'm looking at, what is my process, analyze it, write requirements, write a lot of code, develop different processes including databases and workflows and APIs and automations that connect multiple things in my tech stack and in the flow the way I want it. And then write software, test the software, and develop and deploy the software.
This would've cost me a lot of money and would've taken multiple weeks, and I was able to build it alongside other similar projects that I'm building in parallel, alongside doing my day job. This is where we are right now. And again, this is not code writing, this is doing online research, understanding what's important.
Collecting information, creating summaries, organizing them in a logical way, and providing a simple, easy to use and fully controllable user interface that allows me to override everything that the AI does.
All of this was developed in more or less 48 hours, and then since then I've been just using it and fixing it as I'm going along with simple one one line to one paragraph inputs to Claude Code and Claude Cowork to make changes that I wanted to make or that needed to be made because it didn't work properly the first time.
And I actually was in the process of writing an article that I wanted to call The End of the World, as we know it is already here and 99.999% of the population are not aware of it. And then Matt Schumer wrote his article first. So first of all, thank you, Matt, for stealing my thunder. But I actually glad that Matt wrote it because he gotten way bigger explosion than I could have gotten on my article.
I will still write my article because I think I have my sense to add, but the overall concept is very much the same. Every knowledge work. Can currently, with the current AI capabilities be done by ai faster, better, and cheaper than any other human on the planet doing that work. And yes, it requires to be a super geek to do it right now. And yes, it will take time to it to diffuse through the workforce and the economy.
And there's a lot of red tape and there's a lot of data security issues and change management issues and stuff like that. And yes, the stuff that I can do, because I'm a company of a few people, is not the same stuff that large enterprises are big companies can do. So it will take another two years, three years, four years. But the bottom line is the current technology without deploying new AI capabilities, can do most knowledge work right now very successfully. If you just know how.
Now because I believe that this capability should be available to anyone and not just super geeks, I'm actually building two different things. The first thing that I'm building is an entire infrastructure layer that will enable anybody who doesn't know how to create this infrastructure layer to start without knowing literally anything. You don't need to know what is GitHub. You don't need to know any 10.
You don't need to know any of those things, and you'll be able to build the same kind of sophisticated systems which I just showed you, one project that I did on top of it, and I'm planning to make that thing available as a service so anybody who wants access to it will be able to pay a membership fee and get access not just to what I built already, but to everything that I'm going to build as part of that membership and every update that I made and so on will.
Automatically and magically be available to anybody who wants to use it.
The second thing that I'm doing is I'm building a more advanced course than the courses I'm teaching right now, which will teach how to do all these things that I'm doing, how to build agents, how to build entire pipelines, how to connect your tech stack through NA 10 and other services, how to use cloud code in order to complete the components that don't exist right now, just by creating mini applications that do the things that needs to be done and so on.
Those of you who are interested in one, two, or both of these things, I would really appreciate if you just drop me a line on LinkedIn. I just wanna gauge the interest. I already know there's a huge interest because a lot of people who heard about me talking about this already asked me to get access to this yesterday, even though it's not ready yet. but I do wanna know your opinion and I wanna know, uh.
If you're interested and if you are roughly what you're doing and want to use it for so I can custom the course to the needs of actual people. That's it for that. Now let's continue with the deep dive of the news. I told you that this week we had several major releases from the big labs. So the first one I wanna talk about is Anthropic launched Cloud Code 4.6, which if you look at the history of how Anthropic released their models previously, they usually released Sonnet first.
So they have three level of models for every model they released so far. Haiku, which is the smallest one, sonnet, which is the middle one. And then Opus, which is the largest one so far, they always released sonnet first. It's kind of like the best of both worlds. It's not crazy expensive and it's not crazy heavy when it's running. Uh, but it provides good capabilities.
Now this time around, they released Opus First going back to they wanted to wow the world with really advanced capabilities, and now a few weeks later, they released sonnet. And Sonnet is actually as good as Opus or almost as good at Opus at significantly lower cost. So Claude Sonnet 4.6 keeps the pricing of Sonnet 4.5, which is $3 for a million tokens entry, and $15 for a million tokens of output, which is about 20% of what Opus costs right now.
So you can get roughly the same incredibly advanced capabilities that I use to build the stuff that I just showed you. And a lot of other really crazy cool stuff. And not just me, but anybody that is in the bubble of the universe that I live in and every computer developer on the planet that is using Claude. And it now has a million tokens context window as well.
And it is already available and it is the default for the free and Pro plan, as well as the Max plan, and it's obviously available through the API as well. Now to put things in perspective, SONET 4.5, the previous model of Sonet was maybe the biggest darling in code writing history when it comes to writing with ai and in initial testing, developers have preferred the output of Sonet 4.6 over SONET 4.5 70% of the time, so almost three quarters of the time.
Developers on a white label, kind of like blank test, has preferred the new model over the old model citing better code context reading, reduced logic duplications, and so on and so forth. The more interesting fact about these tests is that they found that developers preferred sonnet 4.6, the current model over Opus 4.5, the big previous model, 59% of the time.
So again, almost two thirds of cases, the mid-size model is now better than the bigger model from their previous version, but it is not just for writing code. So with early user tests on sonnet 0.46, they are reporting human level capability in spreadsheet navigation, multi-step web form completion, and day-to-day applications. Pace insurance, which is an insurance company, reported that they achieved 94 accuracy on their insurance benchmark for computer use.
The highest performing model tested for a mission critical flow from their perspective. The president of Repli, Michelle Kaa. Said, and I'm quoting, the performance to cost ratio of Claude Sonnet 4.6 is extraordinary. It's hard to overstate how fast Claude models have been evolving in recent months. Sonnet 4.6 outperforms on our orchestration evals, handles, our most complex agent workflows, and keeps improving the higher you push the effort settings. So what does this mean?
That means that everything I told you in the beginning of this episode of where we are right now is not the end is just the current state, and that state keeps on accelerating and becoming cheaper and more available, and this is going to keep on happening. Also, this week, Google released Gemini 3.1 Pro. Which is its latest model that that delivers significant advancements in core reasoning, achieving a verified score of 77.1% on the ARC agi.
I two benchmark, Just to put things in perspective, ARC AGI I two Highest score so far was not surprisingly, Opus 4.6, the best model from Claude so far, which scored 68.8% saw net 4.6. The model we just talked about scores 58.3%. Gemini three pro. So the flagship model from Gemini until this week scored 31.3% and now the new model, Gemini 3.1, as I mentioned, scored 77.1%.
Now, while I'm not a big believer in benchmark, this particular benchmark is interesting because it's an abstract reasoning on puzzles. It does not count on previous knowledge or experience. It actually just tries to understand and solve situation it has never seen before. And this is why I think this particular benchmark is really interesting to look at.
The model also leads the artificial analysis Intelligence index with a score of 57, which is outperforming 100% of other models on coding, and 96% of other models in Ag agentic capabilities. So a highly capable and powerful new model from Gemini as well. It is accessible through everything. Google, so you can get it in Google. Ai. Studio Gemini, CLI, Google Anti-Gravity, which is their IDE, development platform, Android studio.
It's also obviously available on Vertex if you are on the enterprise level and through Gemini Enterprise and through the consumer versions of Gemini as well, including Notebook, lm, and the applications that you're using. Now the focus of this new model, as you can expect from everything we've been talking about, is complex and long tasks, including, and I'm quoting from their website, synthesizing complex system data into live dashboards, visualization, internal.
Now, the focus of this model, as you can expect, is agentic complex tasks. Examples that they gave is synthesizing complex system data into live dashboards and creating interactive 3D experiences. The model have excelled in initial testing in real live environments, including manipulating spreadsheets, applications, creating dashboards, and it has also 1 million tokens context window.
And it is very good at comprehending and working across multimodal data sets, including text, audio, images, video, PDFs, and entire code repositories. So while I haven't had a chance to test, Gemini 3.1, I really like the previous models of Gemini and hence I assume I'm really going to like this one as well. I'll say something else about Gemini, that the day before they released this model, Gemini actually crashed and was down for a very long time.
And for some users, myself included, I lost my entire chat history, at least on the web. So I can still see my chat history on the application, but when I go on the web, I still did not get it back. So I don't have any chats from before a few days ago on Gemini, which is really annoying, which also shows you the vulnerability of counting on these tools as part of your major workflows.
And that's why, as I said, multiple times in this podcast, if you're developing something that is on the critical path of things in your company, always have a fallback of a different model. Test the fallback as well to make sure it actually works and then you are good to go. As an example in the application that does the news, it always reviews it with both Gemini and Claude.
And yes, I know I'm paying double the tokens, but I don't really care because A, I get better highlights and better views because I'm merging ideas from two different sources. And B, if one of them falls down, I can still continue doing what I need to do. Another big release this week is Grok 4.2. So Grok has been making a major shift in the past few months from a spicy consumer facing model to a highly capable enterprise grade model because everybody understands that that's the right direction.
What? And that's where most of the money is. And so Grok 4.2 is not different than that. It is specifically positioned to do long term, complex tasks across multiple aspects In real life. In a business, they have shown multiple capabilities in engineering and healthcare. As an example, Elon Musk said that you can drop a screenshot of your health results and get a second opinion. That is going to be highly helpful and accurate.
Musk is also claiming that this new model is, and I'm quoting an order of magnitude smarter and faster than Grok four. He's also promising that this is just the first iteration on this and that they've built an infrastructure to allow them to iterate very, very quickly and is promising more significant improvements every single week moving forward. And we also got a new model from China this week.
Alibaba just released Quinn 3.5 39 7 BA 17 B. That's a mouthful of a long name, but it is the new open weight vision language model that they've just released, which is a significant step into the Agentic era, which again is not a surprise compared to what everybody else has been focusing on.
Now, the interesting thing is this model has a massive 397 billion total parameters, but it is actually using 17 billion parameters at any given point, which delivers extremely high performance while reducing computational costs. It is also working significantly faster, so it is 19 times faster on decoding long context tasks compared to the previous model, and 8.6 times faster than standard at standard workflow compared to its predecessor, Quin three Max.
And it's doing all of this while reducing the deployment memory by 60%. Now, on the benchmarks, it ranks around the same rate as GPT 5.2 and Cloud Opus 4.5 and Gemini three. So the previous generation of models, which was deployed a very long time ago, sometime in Q4 of 2025. Like all the latest models, it is native multimodal and it knows how to get inputs and work at the same time with text images, user interface, screenshots, and basically any kind of content you wanna push into it.
It is not as advanced in video and image analysis and so on, and, and voice like Gemini, but I'm sure the next models will close that gap as well. So, quick summary of the two first topics. We are in a very different world than we were at the end of 2025 when it comes to capabilities and it keeps on accelerating and coming faster and faster with better, cheaper, and faster models to be able to achieve the things that are already possible in a much cheaper and faster way.
The third deep dive topic that I want to talk about is the AI summit that happened in India this week. So India hosted the first ever AI action summit in the global south that took place in New Delhi on February 16th to the 21st. It attracted people from more than 100 countries with 20 plus heads of states and every major tech CEO or expert in the AI space. A few interesting outputs from the summit.
First of all, it has produced commitments to over $250 billion in future investments, mostly into India. It has driven partnerships with some giants from India and other countries. So OpenAI signed a partnership agreement with Tata Group to bring AI solutions to Indian businesses. Anthropic partnered with Infosys and announced a new office in Bangalore, LNT. Partnered with Nvidia to build a gigawatt scale AI factory in India as well.
Google DeepMind signed agreements with Indian government and Google partner with karma, Yogi Barat, for civil services AI training. So some global scale agreements and partnership came out of this summit. Another big announcement out of this summit was that over 70 countries signed the Delhi Declaration, which is a non-binding framework for responsible AI development.
Now, notably the US did not sign the declaration, but it still represents the largest international AI governance agreement that was ever signed. You heard me talk about this multiple times. I think this is a path that has to be promoted, include the US and include every other country in the world in order to put things a little bit under control.
It was very obvious in this conference that the safety of humanity in the future is at risk, and many of the leading figures in the AI space has mentioned different aspects of this. As an example, venture capitalist Vinod Kla, which if you don't know him, he's been on the space for a very long time, highly respected, and he's a really interesting individual. I heard him speak, in person several times and listened to a lot of times that he's being interviewed.
He said IT services and BPOs can almost completely disappear within five years because of ai. Now, because these two things are a huge part of the Indian industry, it was important that he said that in that particular context. For them to understand that yes, there's a huge opportunity and huge investments and amazing international partnerships, but there's a huge risk to some core aspects of their economy. Other important people that spoke in this conference.
Almost everybody who spoke in that conference from the leading labs and around it, shared fears about the future. Dario Amide, the CEO and co-founder of Philanthropic, has said things that he said before, but he talked about the exponential that it's been on the last 10 years, and he's not seeing it slowing down. He talked about that within the next one to two years, we're going to have a country of geniuses in the data center. Again, not something new. He's been saying that for a while.
He mentioned specific dangers that include autonomous weapons that are operating without humans in the loop, and that the threat of massive surveillance both internal and external to countries that was not possible before. And he's stating that this kind of surveillance is fairly important for the company, meaning anthropic and for democracy to put a red line of what is acceptable and what is not.
On both these topics, he was also talking about economic disruption and he mentioned, and I'm quoting, we believe that AI will greatly grow the economic pie, but we need to work together to better manage that time of disruption and bring better prosperity smoothly to all. And again, do I think AI can generate huge business value? A hundred percent. Can it grow the overall economy? For sure. Will it be distributed in a way that will be helpful to the vast, to most of humanity?
I sadly think that in the current setup, the answer is a hundred percent no. And this could lead to a complete chaotic situation, and this is what he's talking about, that maybe in the long term future, AI will provide pro prosperity to all and a future of abundance, as you said, many times. But the short to midterm is going to be really problematic and really scary.
And everybody, governments, academia, industry and cross national partnerships have to happen in order for this to evolve in a positive direction versus a negative one, or as he summarized. AI has the potential to cure diseases that have been incurable for thousands of years to radically improve human health and to lift billions out of poverty. But because it is happening so fast, it may lead to a time of disruption, and I am certain in the second half, I hope for the first half.
Sam Altman also talked about the urgency of global regulation on this, and I'm quoting centralization of this technology in one company or country could lead to ruin. This is not to suggest that we won't need any regulation or safeguards. We obviously do urgently. Like we have for other powerful technologies. Brad Smith, the vice chair of Microsoft, also had similar warnings.
And he talked about the fact that the digital divide or the technology divide may grow a lot bigger unless we take specific steps. And he said, and I'm quoting, closing this economic divide, orating it, and making it wider. He stated that the key to enabling people to use this technology is skilling for people, and that we need to focus on, and I'm quoting again what people will use the technology to do for people.
By the way, this point of skilling and training is something that was mentioned in a very clear way by Matt Schumer that actually had a few more than a few paragraphs on what you should actually do, including start using it seriously, not as a search engine, including using the paid version instead of the free version, because there's a huge difference between the two, including going deep instead of wide on topics that you're using AI for, and including learning new skills that are AI infused
in order to increase your chances of actually having a job in the near to medium future. Now we also heard some opposing aspects to this. First of all, Arthur Mensch, who is the co-founder of Misra, which is a French. AI company said these are mostly distraction tactics. In reality, the real risk of artificial intelligence in the near future is of massive influence on how people think and how they vote.
And you heard me say that on this podcast many times, and I agree the nearest term is from the collapse of what is real and what is not, and how can that impact how society works as a whole. I mentioned that multiple times in the past few weeks, but I'll mention it again. If you want to hear my detailed opinion. Go back to, I believe it's episode 10 of the podcast. It was called The Truth is Dead. Just look for that episode and you can hear everything I think about this.
But I agree with him that the nearest immediate risk is that that doesn't take away from the much bigger risks moving forward. Michael Osis, who is the White House technology advisor, said that he does not agree that we need global governance. And he stated, and I'm quoting, we totally reject global governance of ai. AI adoption cannot lead to brighter future if it is subject to bureaucracies and centralized control, I strongly disagree with what he's saying.
I know that this has been the position of the current administration to push forward as fast as possible. Regardless of anything. I think this is a huge mistake and I hope we're not going to pay a very dear price for that mistake. By the way, Emmanuel Macron, the president of France, was actually pushing back a little bit on the US position that always looks at Europe and says, Hey, watch too much regulation is actually doing. And he declared that I'm quoting.
Europe is not blindly focused on regulation. Europe is a space for innovation and investment, but in a safe space. I think the truth is somewhere between them. I have clients in Europe and I have people who are in my community from Europe, and they definitely think Europe is overregulated right now.
I think the US approach is wrong as well, and there's probably a happy medium between them, and I really hope that the entire world, including China, including India, including Russia, including Europe, including everybody, will find a way to find that happy median sooner rather than later.
Now we'll switch to a rapid fire item, but the first two items are directly related to everything we talked about in the deep dive on how AI is impacting the real world, and we are already seeing some very significant signs of how it's impacting real life in real companies and real aspects of the economy and glimpse of where this may lead to. So Accenture. Has announced this week that it is conditioning the promotion of the leadership roles on regular use of AI tools.
Associate directors, and senior managers across most of companies, 780,000 workers must demonstrate regular adoption of AI in order to get a promotion tool. Usage is becoming something that they're tracking. So how many times you're going into AI tools, how frequently using them, and for what aspect is becoming. And I'm quoting a visible input to talent discussions. Their CEO, Julie Sweet stated that all employees would be expected to, and I'm quoting, retain and retool at scale.
And she's also acknowledging that the company would exit employees without, and I'm quoting again, a viable path for skilling to acquire needed capabilities. This is the first major company that I hear speaking very clearly out loud, saying you're either gonna use AI or you are out, and it's starting at the highest level. So they're starting with the managers, and this will obviously then trickle down to lower tiers of the company as well.
And based on everything that I've been seeing in the past few weeks in things that I'm doing and that leading companies that work with me in my consulting hat are doing, they're a hundred percent correct in that approach. They may not fire these people that quickly, but definitely in the long run, people who will not know how to use AI effectively to at least a decent level, will not be able to be productive to an organization.
Another interesting topic that is related to AI adoption and how quickly it can spread in different areas is 11 Lab has just secured an AIUC1 certification and they're becoming the first company to offer insurance to cover for AI voice agents. So let me take a step back and explain what the hell is AIUC1 certification. AIUC stands for Artificial Intelligence Underwriting Company.
It is a new insurance company that ensures the usage of AI in different companies, and they have created a. And certificate program. That is called AIUC-1 e. What that program does is it tests different AI solutions for data privacy, security, safety, reliability, accountability, and society. And you have to pass rigor testing, including 5,835 technical tests across 14 risk categories in order to earn the certification.
But what this means, it means that right now, if you are an enterprise or a smaller company and you want to be able to be insured against claims that come because you are using AI as your sales team or customer service and so on, you can do that, meaning one of the biggest barriers that companies had for ai Voice agent adoption is now removed. For those of you who don't know, 11 Labs is one of the leading, if not the leader in the voice AI space.
And it's 11 agents solution is now being deployed at 75% of Fortune 500 companies, including like companies like Cisco and Square and other Giants. And their ability to now provide insurance is obviously gonna accelerate the adoption to more aspects in these organizations and many more organizations. The way 11 labs were able to do this is by building a three stage protection into its platform. The first part is pre-production, where they have safeguards and red teaming and simulations.
The second one is in conversation enforcement via system prompts and guardrails and real time moderation that can automatically end unsafe conversations and ongoing monitoring with automated evaluation criteria that is applied across all calls. So with these three tiers that we're able to now get the certification, which means anybody who's using 11 lab voice agents can now be ensured against the outputs of what these agents do. From that too.
A few news on shifting capabilities, and we're gonna start with open ai. Sam Altman just told CNBC first of all, that Chinese tech companies are progressing, and I'm quoting remarkably fast. The initial part of this conversation was in which Sam Altman said, and I'm quoting, the progress of Chinese tech companies across the entire stack is remarkable. When he's talking about the entire stack.
He's talking about the fact it's not just the models, but also the underlying infrastructure as well as the. Hardware that is now Chinese built and how they're closing the gap across the board. He also said we are growing at an extremely fast rate right now. I think as long as we can have reasonable unit economics, we should focus on continuing to grow faster and faster and we'll get profitable when we think it makes sense.
So you're probably asking yourself, what do you mean when you think it makes sense? You just decide to be profitable. And the answer is yes. There was a very interesting interview of Dario Ade with Dsh just a couple of weeks ago. By the way, Rakesh, had Elon Musk and Dario back to back in two separate episodes. Both definitely worth a listen. In that interview with Dario, he states that he still thinks that we're gonna get a country of geniuses in a data center within the next two years.
This is basically means that you're gonna have Nobel Prize level in any topic in abundance for anyone. Now, he's saying that this can unlock trillions of dollars in potential revenue, but what is going to slow it down is the rate of diffusion through the economy.
So, as I mentioned earlier, it's going to take time for countries, companies, and individuals to learn how to leverage the technology and how to go through red tape in means of data security integrations and so on in order to actually see the benefits. And this is the reason why he's hedging a little bit his investment in in future compute. He also said that they've been growing 10 X every single year since the launch of the company. They. Reached 10 billion this past year.
He assumes at the current pace and he thinks they're gonna make it reach a hundred billion in revenue in 2026, which means they will make a trillion dollars in revenue in 2027 if the curve continues. He doesn't think it is going to continue, but he's saying that on, based on that, he should be able to invest 5 trillion in compute commitment for the next five years, 1 trillion per year, and he should be able to pay it.
But he's saying that even if he's wrong by 20% and he's not gonna make a trillion dollars in two years, he is gonna make $800 billion in two years, which is still insane amount of money, and it is an even more insane amount of money for the company that is three years old. He will still go bankrupt. So if he commits to the 1 trillion per year and he only makes 800 billion per year, nobody will give him $200 billion to pay for the rest of it in order for the company to survive.
So what he's saying in general terms is that the way all these companies are operating right now is they are trying to project how much demand they're going to get based on that demand. They're going to decide how much compute they need in order to serve the demand, versus how much compute they need in order to continue doing research. And he is saying that if they miscalculate their projection, two things can happen.
If they have more demand, that means they need to do less research and they may stay behind. And if they have a little less demand, they can do more research to build better models, to be more competitive, to get more demand. But if they get significantly less demand, they will go bankrupt because the commitments are so crazy and the gaps between being successful and not successful are so high.
But what it also means, connecting it back to the point from Sam Altman, it means that they can technically decide when they become profitable and he's saying that point will come with the point of diminishing returns on the. Not on the technical scales of the models, but on the business value of the models.
We'll get to the point that the models can grow from a technical perspective, maybe at the same scale they are right now, but it will make very little difference to the economical value that they provide at that point. You don't need to do as much research at the same pace, which means you need less compute for research, which means the extra money that you have doesn't go into buying more compute for research. It actually becomes profit.
By the way, since we talked about Dario in relation to open ai, and we already covered the India AI Summit. Sam Altman and Dario Amide were the only people that, in a very visible way, declined to hold hands in the final image, in the group photo of all the leaders that were on stage at the same time. So if you look it up, it's actually pretty hilarious.
Like kids in the kindergarten, everybody's holding hands and raising their hands together, other than two people that somehow somebody decided to put one next to the other on stage, which is Dario and Sam, and they're avoiding holding hands while everybody else is now back to open ai. OpenAI just acquired Open Claw. So we talked about Open Claw several times in the past few weeks. Open Law is a viral, open source AI agent project that was created by Peter Steinberg.
It has took the geeky AI universe by a storm. It is basically an AI agent that can work 24 7 365 and complete whatever tasks you give it access to. It can connect to every system in your tech stack. It can run locally on your computer and really run every single thing that your computer has access to. And the acquisition was announced on February 17th.
They are presumably gonna keep this as a completely separate unit, meaning it is going to run open claw as a separate department unrelated to the main open AI universe Now this acquisition is interesting from several different reasons. One, it was very obvious somebody's gonna pick it up because of the amount of buzz it created. The technology is not that unique. I mean, once it was obvious how this tool works. Anybody literally, you yourself, can replicate how this is working.
But the buzz in the community that it built around, it is worth owning because all these people are at the far edge, the tip of the spear of AI experimentation, and the people who are experimenting with it are willing to take risks that enterprises won't.
So if you own this technology and you have the ability to put it under a microscope and see exactly how people are using it, what use cases, where is it breaking, where is it safe or is it unsafe and so on, you can then bake these capabilities into the safer, bigger OpenAI technology instead of just keeping it in a box somewhere. I think that is the reason why OpenAI decided to buy open law.
Obviously huge, crazy, insane success for Steinberger, who is now, I dunno, because they didn't close the numbers, but either a multi-millionaire or potentially a billionaire. I'm not sure exactly how much money he got, but I'm sure he got a crazy amount of money for a project he was working on for a very short amount of time.
I'm also curious what was happening behind the scenes and how many other offers he got before he decided to go to OpenAI, if you remember a long time ago when we talked about this two weeks ago when the craze started, I told you that the original name was Claude Bot spelled not like Claude d Ai, but sounds definitely like it.
So it sounded like it was written like Claw, CLAW and yet Anthropic send him a cease and deceased letter to change the name, which had to change twice, but I'm curious if immediate, after the cease and deceased letter, they send him a potential acquisition letter.
I don't know if we're ever gonna learn that, but I assume there were other companies who were trying to buy Open Claw other than OpenAI and even companies outside of the big labs, like I see people as Salesforce being maybe interested in something like this or these kind of companies as well. Another company that came to my mind is meta, but they recently bought Manus, which is their path to potentially increasing their agentic capabilities.
They're not exactly the same thing, but the general direction is the same direction. What Steinberger said, he said, what I want is to change the world, not to build a large company. And teaming and teaming up with OpenAI is the fastest way to bring this to everyone. Now, by the way. One more thought from me. While the technology is really easy to build open AI or any of the other big labs cannot release something like this, they are focusing on enterprise grade solutions.
And this is way, way too risky. And hence, despite the fact that they could build something like this, it wouldn't make sense on the main path. So having a parallel universe in which they can experiment and see what's happening is very, very powerful and gonna be very helpful for open ai. Staying on the OpenAI topic, it seems that the ink is going to dry on their a hundred billion dollars round at an $850 billion valuation in the next few days if it hasn't happened yet.
This is one of the largest, if not the largest single funding round for a non-public company in history. The biggest players in this investment are Amazon, which are likely to put in $50 billion. Soft bank that are looking at $30 billion in Nvidia that are reportedly nearing $30 billion in investment. If you do a quick math, that's 110 billion instead of a hundred billion, uh, which means they might raise more than a hundred billion when this is, when this is all done.
Now OpenAI already committed to reinvesting a lot of that money into Nvidia hardware running on Amazon AWS. So if this is not a circular kind of investment scheme, I don't know what is, but this is where we are right now, by the way, despite these soaring valuations and they're talking about how fast they're growing, they are bleeding crazy amounts of money. And the numbers that have been thrown around is that they're spending more than $700 million a month for compute infrastructure alone.
That's without the crazy salaries that they're paying and running the business and overhead and legal and stuff like that. To make it even more interesting, and we discussed this in the past, they have $1.4 trillion of commitment over the next eight years for new compute, which means this a hundred billion is a, maybe not a drop in the ocean, but a glass of water in a bucket to the amount of money they actually need in order to keep running their company without going bankrupt.
A few interesting new things in the robotics space. Google Gemini three Flash, which is their fast and extremely effective model that they released just a few weeks ago, is now being used in order to train robots in an effective way. So because it knows how to analyze video very well, they're now using it to translate video input into JSON inputs for robots. So the AI is watching a video of a specific process translate, of a specific process.
He watches it multiple times, about 50 to get it perfect, and then once you watched it 50 times, 50 humans doing the process, it will then create A-J-S-O-N set of instructions where the robot can use to execute whatever the process was. What this means is that they don't need to write code or develop specific training environments for robotics.
They can just show them videos of people doing the tasks and the robots can learn how to do it in almost a seamless way using a really cheap and effective model. Like Gemini three, flash. Another robotic related announcement this week came from Unit Tree. Unit Tree has been one of the leaders in robotics in the world for a few years. There are Chinese company who has several different models.
They have manufactured an estimated of 5,500 units in 2025, and they're projecting to either double or quadruple that in 2026. So they announced that their goal is to ship between 10,000 and 20,000 robots this year. Now the robots that are doing crazy things every single time they're been shown, has captured a lot of attention. In the spring festival gala that just took place, they had an entire segment where multiple robots were performing perfect kung fu style action fully autonomously.
They had some of them climbing walls and showing skills are way beyond walking and doing basic daily stuff. Now to make this even more interesting, unit tree has been profitable since 2020. Their revenue has exceeded $140 million this past year. And if they keep on growing in this trajectory, there will be a highly successful player in the robotic space even moving forward.
We've talked about them many times in the past when I was mentioning that their G one robot that is their little robot that they're creating, it's about four feet tall, cost between six to $16,000 depending on the variation that you have. And there's already companies in China that are renting them out for different applications. This is compared to tens of thousands of dollars or hundreds of thousands of dollars for full scale humanoid robots from other robotics competitors.
But I wanna mention an interesting quote from their CMO as part of this announcement. If 2025 was about the race to mass produce, 2026 is about the race to deliver. The focus has moved from factory floor to the commercial channel, where real measure of a company's health is no longer how many robots we can build, but how many it can successfully integrate into real world scenarios.
Meaning in addition to everything we talked about in the beginning of this episode about knowledge work and how AI can practically do basically any knowledge work. Right now we are already seeing robotics company who are saying that their plan for this year is to focus on integrating robots beyond production floors and into day-to-day life.
And the third interesting example, before I tell you what I think about the current status of robotics comes from a company called Weave Robotics, who has launched Isaac Zero, which is a stationary laundry folding robot that is now available for purchase for $8,000 or $450 monthly rental. It knows how to fold most standard size clothing.
It still does not know how to do blankets and bedsheets and something that is really large or inside out clothing because it doesn't know how to flip them around. It looks really weird. It's not a humanoid robot. It's like a laundry folding robot. But apparently folding laundry is not such an easy task figures. Humanoid robot figure oh two achieve the first fully autonomous laundry folding back in August of 2025.
Since then, one or two companies was able to replicate this, but this robot for a really cheap amount of money knows how to do this at scale. Now the Isaac. Zero robot operates in a hybrid model, meaning it completes all the stuff that it knows how to do on its own in an independent way, but when it needs assistant, a human specialist can take over for five to 10 seconds and intervene in order to get the job done and get the folding process done properly.
Now connect this to what I said earlier related to robotics ability to learn from video and examples. And you understand that when the human intervenes and is helping the robot do whatever it is doing, it needs to do it only X number of times, and over time it will learn how to do this on its own. Which basically means that these humans that are helping remotely these robots to do their tasks and they're not the only company who uses this hybrid approach are on a, are basically doing.
What it means is that the job of these people that are helping the robots remotely, and they're not the only company that is doing this, this hybrid approach, these jobs are basically in an hourglass and the sand is falling down through the hourglass. Every time they're helping one of these robots do a specific task because after X number of times they learn how to do it on their own, and then the humans that do the remote job are not necessary anymore.
By the way, my other company that does AI agent base invoice reconciliation works in a similar way where it can learn over time based on the corrections that humans are making to the what the agent's outputs is. So this is not rocket science and this is what we're going to see everywhere, all around agents learning what we do, learning how to replicate it, and then replacing us in doing that work. Opening parenthesis for that for a second.
If you are in a company that does a lot of invoice vouching and it's taking you a lot of time and costing you money of people doing mostly that reach out to me on LinkedIn. We're now accepting new clients to this really remarkable platform that does the work of a team of three people in minutes instead of a full team working on it for days.
Another topic that I think will be relevant and applicable to many of you is that WordPress just launched a built-in AI assistant to create and edit websites. So I told you last week that there's a new MCP access from Claude to be able to manipulate WordPress, but now there's a built-in WordPress AI assistant that can help edit websites and create new pages inside of WordPress, just with simple prompts.
The tool also knows how to edit and generate images with nano banana translate and edit text, recognize site layouts, create new pages, and basically do everything a human could do inside a WordPress website. Why is this a big deal? Because WordPress host most of the world's websites, and so there's a decent likelihood that your website runs on WordPress as well. And so having this kind of capability makes perfect sense for WordPress.
And by the way, for any other software company, adding a AI assistant inside the software, even the software that I'm using right now, the application I told you before that helps me manage the news, has an AI assistant built into it that can help me do more or less everything that I can do in the software as well. Every company on the planet will have something like this. The thing is that over time, this will replace the actual software because you won't need the software.
What you need is the output of the software. And if you can do this with a few words or sentences, why do I need all the user interface that exists today? I don't know. And if that is the case, why do I need the software? I don't actually need it. I can do it with a third party AI that has access to the data. And this is, I think, where the world is going right now. We haven't talked about Apple for a while and for many reasons they haven't done anything interesting in the AI space.
They're far behind and there hasn't been, seems to be a cure on the horizon. But the news this week is that they are going all in on AI wearables. So we shared with you previously that they're working on a pendant that will be like an air tag size pendant with two different cameras and a microphone and a speaker. Well, apparently they're working on a family of products similar to what we heard from OpenAI with their Johnny Ive team.
So apparently they're working on that pendant together with smart glasses and AI enhanced AirPods. Now, I told you before when we were talking about the pendant, that I do not understand why the first step from Apple is not doing an AI enhanced AirPods. It makes perfect sense to me. They already have a huge audience that absolutely love AirPods that swear by them, and that puts them in their ear all the time, 24 7, unless they're charging them.
And this would be the perfect audience to actually provide AI capabilities too. All you need is to add an AI layer on top of that. And even if you want to go crazier, add a small little camera in there that can see what's happening around you and you have a solution that already a huge audience is already using, wearing and doesn't have to get used to. So that is a part of that. And the third component is smart glasses. It's not going to be.
Augmented reality, meaning it's not going to have a display, it's just going to be able to see through cameras and be able to communicate with you through voice so you can speak and hear what it's saying back while it's being aware of the environment. So kind of like the current generation of glasses from meta, but not the next version of meta glasses. All these devices are going to be developed during 2026 and the release is expected either at the end of this year or the beginning of 2027.
I said that multiple times before. I think AI wearables will become a big deal and the more they are in a form factor that we're used to right now, some, not pendants, but glasses and earbuds, I think they're going to see a huge success. And I think in the not too far future, most of these devices, definitely earbuds, will have AI infused into them. That's it for today. There are a lot more news that did not make it into the podcast episode, but they are available on our newsletter.
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