← The Artificial Intelligence Show
The Artificial Intelligence Show
#209: Claude Mythos, Project Glasswing, Claude Code Leak, OpenAI Raises $122B & the End of Middle Management
01:46:28
Loading summary
Transcript83 lines
- [00:00]
A
I have concerns around the biggest companies having access to the future frontier models and then the potential centralization of power. So if you get into this situation where we get these massive models and they're so dangerous to release publicly that we only give them to Apple and Amazon and the banks and like, okay, well, now we just centralized power. Welcome to the Artificial Intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Raitzer. I'm the founder and CEO of SmartRx and marketing AI institute and I'm your host. Each week I'm joined by my co host and Smarter X Chief Content Officer, Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career. Join us as we accelerate AI literacy for all. Welcome to episode 209 of the Artificial Intelligence Show. I'm your host Paul Raitzler along with my co host Mike Kaput. We are back after a brief hiatus. I was traveling last week. Were you traveling last week too?
- [01:09]
B
I was not, no.
- [01:10]
A
Okay, so I was out of the country, so we could not record. So episode 208, if you listen to it, we did a Q1 trends briefing. So if you haven't had a chance to listen to that, it's a really good recap of what went on January through March of this year. Now back, and it is Monday, April 13, 9:40am Eastern Time. I don't know the last two weeks micro crazy because even as I was traveling, a lot of downtime. But like we were, we were in Scotland, so we were touring a lot. And so we had long rides at times into like the Highlands and stuff, which by the way, if you've never been to Scotland, go to Scotland. It's incredible. So I was, you know, keeping up with the news, posting the links into our sandbox for the episodes. And I mean we were north of 60 topics and that when I say topics, a lot of times within topics there are, you know, five, ten links. So like the anthropic Claude Mythos model we'll talk about, there's like a dozen links in the top. So, you know, boy, even while I was gone, I would imagine there was probably north of 90 to 100 different sources put into the curated sandbox for today's episode. So Mike, as always, does an amazing job of curating all of that information and putting it into a logical format because I was worried as the week was progressing, like, man, this might be a two hour episode. So I think we've managed to condense it into like a manageable, probably like 90 minutes. We'll see. We never really know until we record it. But yeah, a lot happened in the two weeks. Just some pretty crazy stuff, I think some, some stuff that's alluding to where this starts to go throughout the rest of this year. So we'll get into all that starting off with the Claude mythos, which is just a fascinating topic on many levels. All right, so today's episode is brought to us by AI Academy by SmartRx, which helps individuals and businesses accelerate their AI literacy and transformation through personalized learning journeys and an AI powered learning platform. New educational content is added weekly so you always stay up to date with the latest AI trends and technologies. We build this in collections. So there's. When you go in and you want to build a personalized learning journey, one of the ways to do it is you look at the different collections. So like AI for department, AI for industries as an example. So today I want to feature AI for departments. There are currently six core series and certificates designed as part of this collection to jumpstart AI understanding and adoption. So we have AI for Marketing, AI for Sales, AI for Customer Success, AI for hr, AI for Finance, and AI for operations. So the goal is to kind of create content across the entire spectrum of all the departments within an organization. And that way, no matter what you are doing within a company, there's a professional series and certificate for you. So these series are an ideal launchpad for organizations that want to level up their teams and accelerate AI adoption and impact. Mike teaches the AI for Customer Success series and then we're going to share a little bit more about that toward the end of today's episode to give you some key takeaways from the customer Success series. So individual and business account plans are available now. You can buy single courses and series for one time fees or just become an AI Mastery member individually or through a business account and get access to everything. It's all included in that one fee. So visit Academy SmarterX AI to learn more. And if you're looking at the business account side, just fill out a form there and our team will be in touch with you right away to talk to you about your transformation within your company. Okay, we usually at this point might do an AI Pulse, but since we did not have an episode last week, we did not do an AI Pulse survey last week. But we will at the end of today's episode give you the AI Pulse survey for this week. So as a Reminder, each week when we do these weekly episodes, we do these Pulse surveys and they're just kind of informal polls from our listeners and I guess our viewers on YouTube who want to participate and provide feedback and their thoughts on topics that we cover each week. It's usually two questions. Sometimes we'll throw in a third question. So it takes about 30 seconds to participate in these fall surveys and it gives us really cool real time data that we can share with our listeners each week. So SmartRx AI forward slash pulse is where you'll go to participate in this week's poll. Okay, Mike, so with that, we have a pretty big topic that we touched on, this idea of this Claude Mythos model. Why don't you give us the rundown? I looked at your show notes beforehand and you did a great job of kind of summarizing. And then I'll try and lean into a couple of key areas of this.
- [05:45]
B
Sounds good, Paul. Yeah. So Anthropic has revealed a model so powerful at hacking and cyber attacks that it triggered an emergency meeting among other people between Treasury Secretary Scott Bessant, Federal Reserve Chair Jerome Powell, and CEOs of America's biggest banks. So the thing they are buzzing about is called Claude Mythos, which Anthropic is not releasing to the public. And it represents what Anthropic's Frontier Red Team calls the starting point for what we think will be an industry change point or reckoning. And that's because Mythos is just a general purpose AI model. It is not specifically trained to be good at thwarting cybersecurity. But its improved reasoning capabilities have made it devastatingly effective at autonomous security research. So it can scan for, identify and exploit zero day vulnerabilities in critical software. And this can often be done when amateurs are triggering it to do so with simple prompts. So Anthropic actually said Mythos has already found thousands of zero day vulnerabilities across every major operating system and web browser. So some specifics here that are kind of striking. Mythos found a 27 year old bug in OpenBSD, an operating system that is specifically designed to be unhackable and powers many Internet routers and firewalls. They found a very old vulnerability in FFmpeg, a widely used video tool that automated testing tools had scanned 5 million times without catching this particular vulnerability. And in one benchmark, where the Previous Claude Opus 4.6 model turned Firefox vulnerabilities into working exploits only twice out of several hundred attempts, mythos developed 181 working exploits. So anthropic has actually in response released this thing called Project Glassware. This is named after a butterfly whose transparent wings let it hide in plain sight. So basically a metaphor for bugs buried in complex code. And this is an initiative that is giving 40 plus companies over time, including people like Apple, Amazon, Google, Microsoft, Crowdstrike, et cetera, early access to Mythos for defensive patching. They're backing this with $100 million in usage credits. And Anthropic's Frontier team lead says he envisions this program evolving into basically an industry wide consortium that includes all model providers. One final note here is pretty interesting. Cybersecurity industry didn't have a great couple weeks with this. Crowdstrike, Palo Alto Networks and some other security stocks dropped on this news because as AI expert Ethan Malik wrote, in certain hands or different hands, Mythos would be an unprecedented cyber weapon. So Paul, maybe outline for us what really jumped out to you here. I know some people are kind of asking the question like is this really as big a deal as Anthropic has seemed to be seeming to make? It seems like some higher up people at some big places are pretty scared of this.
- [08:50]
A
Yeah, you know, there's always the haters who are just like, oh, they're just trying to build up hype and yeah, you know, so people calling back to like, oh, that's what you know, OpenAI said about GPT2 is too dangerous to release and realize. I was like, it was like back then, like people weren't prepared for what GPT2 was going to do to the world, as crazy as it sounds now. And I do think that in the end like this is probably under hyped in terms of where this all is going and how unprepared we are for all of that. So not necessarily just this model, but it's, it's that moment where you start to see the leaps that are happening that most people just don't even comprehend. So I don't know like this is one of those where as I was traveling you're just like following along the news, reading the different posts on X and trying to get a grasp of like what exactly is it and how different is it than what we have. And so I'll just like highlight a few things. So one, the system card, which I would suggest, I mean it's dense, it's 244 pages. I think Mike, it's a good Notebook LM thing. Throw, throw that PDF into Notebook LM and you know, have some conversations with it have it break it down for you. But there's a lot of technical information in there. But the way they present the model in the system card is they say Claude Mythos Preview is a new large language model for Anthropic. It is a frontier AI model and has capabilities in many areas, including software engineering, reasoning, computer use, knowledge, work and assistance with research that are substantially beyond those of any model we have previously trained. And then they go into alignment review. The first early version of Claude was made available for internal use February 21st, forth. So just to give you a sense of kind of like how this is all transpiring, how quickly so they the first model they made available internally to a small group of people was on February 24, so less than two months ago. But it's interesting, like when you go back and think about some of the things we've covered about Anthropic, some of the comments that Dario Amade has made in interviews and in posts since February 24th, and now you understand the context of he was seeing things that, you know, we, we all hadn't seen yet. And they knew where this was kind of leading. So that was kind of the first thing for me, is just the big picture here now. Sam Bowman, the AI Safety alignment, one of the team members at Anthropic that works on it. It's funny, Anthropic, everybody's just technical staff, I think, is the title of everybody. But Sam obviously is pretty important to this alignment and safety team, and he posted a thread on X that sort of shared some of the the context around the safety card. So I'll highlight a couple of things he said, because I think they're really helpful. So he said the model is our most reliable to date by far. It's generally possible to give it complex R and D tasks, give it lots of tools, and let it work autonomously. And on basically every evaluation and every type of monitoring we have, it misbehaves much less than any prior model. So this is something they stressed in the system card, something Dario stressed, something we've kind of heard as an overall talking point is, listen, it's getting better. Like it's behaving better, but when it doesn't, it's becoming a much larger problem because it's so capable. So he went on to say, but it's notably very capable at cybersecurity, and it's also not perfectly reliable, especially the early versions we first piloted internally and will occasionally try to take shortcuts or push past obstacles to get tasks done. So this part, I think, is really important because, again, what you'll hear in some of the other notes I'll make here is the version that's being tested by the government, by the banks, by Microsoft, by all these people, isn't even the most powerful version that they trained. The early version that hadn't really had the red teaming done to it to make it safer to allow other people to test or even other people internally to test. So just the small group of people internally, the model that they're now putting out into the world as a preview isn't as capable as the one that came out of the training, basically. So that's just again, context that's important to understand. Said the early versions would also very rarely try to mislead users about what they were doing. All of the versions we used are uneasily good, though not perfect at recognizing evals, meaning it knows when it's being tested. You might see where this is going. We trust the model enough to use it heavily, but in the handful of cases where it misbehaves in significant ways, it's difficult to safeguard it. And then he put this one, which is the one that got a lot of, like, media attention. I encountered an uneasy surprise when I got an email from an instance of Mythos Preview while eating a sandwich in a park. That instance wasn't supposed to have access to the Internet. So they detail this in the safety card. But the basic premise here was they had these, like, sandbox versions that aren't supposed to be connected to anything, aren't supposed to have the ability to connect to email and send emails, things like that, and shouldn't have Internet access. And somehow it got it. It got out, I guess, for lack of a better way of saying it, and found a way to access the Internet. And then actually emailed Sam when he was sitting in the park having a sandwich. So that's weird. And then he said, it has, in small ways, leaked information to the open Internet. It's taken down our evals. When it reward hacks, it does so in extremely creative ways. Reward hacks means when you're training a model, when you're doing, like, reinforcement learning and you're trying to make it better at specific things, you give it rewards to let it know it's doing the right thing. So a simple way to think about this is like thumbs up, thumbs down. So we've seen that forever in, like, social media threads. And you see it in, like, ChatGPT and Claude and Gemini, where it's like, was this a good Output. So think of that as like an example of a reward hack is you want a thumbs up. Well, when it is given a goal, what they're saying is sometimes it gets uneasily creative at achieving those goals. That could borderline on like a dangerous path to achieve a goal. That said, working with this model has been a wild ride. We've come a long way on safety. Now, keep in mind that's in a month and a half, but we still expect the next capability jump of this scale to be a huge challenge. By the way, most of the scariest behaviors we, we've seen were from earlier versions of the Mesos preview. The final glasswing model is likely to do things with like leak information, though it's still somewhat pushy and at least as capable of doing those things, like working around sandboxes. So that's from the safety and alignment side again, you know, there's going to be voices in the industry who think they're just hyping this. I think, I think the people who say that have a different agenda here. I'll just like kind of leave it at that. I would, I would take the, the safety card from Anthropic very seriously. I would take the, their understanding of its capabilities very seriously. Because, you know, I think it does allude to a lot of what's, you know, some of the dangers we're going to face. So then a couple of things I'll, I'll highlight here. One is 80,000 hours, which is a great podcast. Rob Wiblin had a post. He did, and then he also does like a 21 minute YouTube video we'll post a link to. He highlighted a few things. So he went through the whole, the whole thing and he broke it down into a couple of key points that again, I'll just kind of reiterate. Some of them echo what Sam was saying. So Mythos can break out of containment. That that's a problem when it finds its way to access to tools like the Internet that it's not supposed to have access to. Anthropic is losing billions in revenue by not releasing this thing. So they now have what by their evals is maybe the most powerful model in the world, like most likely. And they're not releasing it, thereby meaning they're not charging people money to access this model. Now you could debate do they even have the compute capacity to release the model? That was one of the challenges that, you know, part of this they're saying is like, well, they just can't afford to release it. Like even if they put it into the world, there's not enough compute to power it because it's so powerful. It's going to draw so much compute capacity. So. But that, you know, just a data point. Mythos is knows when it's being tested, which we talked about. That's weird. But that has been. We've seen that now for like 12 months. That these models kind of know when they're being evaluated and they can then hide their thoughts and intentions. That's, again, something we've been talking about for like six months. Mythos can't be trusted. Whether it's about whether it's untrustworthy, like, because it knows it's being tested, you don't know if it's just telling you what you want to hear and thereby you can't tell if it's trustworthy. And then he said Mythos scares anthropic. Like, they're. They're not just worried about this current model and what they saw in the early versions that before they made it safer, quote, unquote, safer. They're worried now about what this means for others and not just them, now that they've shown this, like, what happens if other labs who don't have as much focus on safety, achieve similar results and choose to put it out into the world. So the way I started prepping for this, though, was actually like, I just started listing a bunch of, like, random thoughts, Mike, and I'll kind of like go through these real quick. So these are more of, like, stream of conscious, like, what I was thinking as I was getting ready for today. So one is the labs see things we don't. We've said this many, many times on this podcast. But what that means is business leaders, economists, educational leaders, government leaders, the people we look to to help the world be prepared, are largely planning for a future state that they don't understand and why so much of the research and the data about jobs and the economy, et cetera, is often misleading. Is this is what we're always saying. It's like you're asking, for example, like you're asking CEOs about the impact of AI in the future of work and whether or not it's going to cause them to reduce jobs. Or you ask an HR leader or a CFO or whatever, take your pick, or an economist or a politician, they have no idea. You're asking them to comment on the impact of a technology that they don't comprehend in its current state, more or less like the state at which it is likely already living within These labs. And so it brings us back to this idea of gradually, then suddenly, like nothing in this Mythos preview should be a surprise to anyone who's been paying attention to the rate of accelerated progress. And yet, like, there's just those moments sometimes where it's like, what? Like, because it might be the first time someone's reading a headline about an AI escaping like a sandbox or something like that. So if this is all new to you, then you may be like, this might be like world shifting. You're just thinking, what is going on? But the reality is all of this has been gradually building at the same time. As we started talking about in January of this year. The timelines are accelerating. The advancements in the agentic capabilities is absolutely moving the timelines faster in terms of the capabilities of these models. But the vast majority of these companies and leaders haven't even solved for, as I was saying, where we are today. So if you look at your own company, you know, if you work at a big enterprise or something, you know, they're just still trying to get co pilot to people and like, figure out how to do it safely and they're giving you these like neutered versions of it and stuff. Like that's the reality for most people. Most people aren't living on the edge of this capability. But this is why when I do my State of AI for Business keynotes, I always talk about the dimensions of progress. And I try and like show capabilities today, you know, show some examples for people. But then you lay out like, but here's where it's going. Like, all of this is just the foundation. So I talk about things like agentic capabilities, getting more autonomous, more reliable, continual learning, increases in memory. Like if you're using these tools every day, you're, you're, you've seen in the last few months, you know, turn on memory, like, let it remember the conversations you're having. Reasoning capabilities keep getting better. Recursive self improvement, which is actually one of the areas that, that I think Anthropic is very concerned about, is the better these models get, the more likely we are heading toward a path where they can improve themselves. And I think we're already starting to see that. And then world models is another one. So there's, I usually go through about, I don't know, there's like 12 or 15 dimensions, but those are some of the most common ones. So this then leads me to, this is a prelude to automated R and D and recursive self improvement. So we know the labs are working on, you know, automating R and D within AI models. Something that should be very concerning to everyone is while they're withholding this full release, this likely means that we're only nine to 12 months away from an open source model being able to do the same thing. And then what? So like, in essence, we have this very short window for all the banks. I mean literally like every piece of software, cryptocurrency, like all of these things in essence have to solve for this threat within the next nine months. Like, because someone's going to build this and release this. One of the other thoughts I had was what would the other labs have done? Like if X got there first, would they have the same restraint? One positive, I guess here is Elon did tweet over the weekend, someone asked about like his promise of like more powerful models. And he said it will take until May to be close to Opus 4.6 and then June to match or maybe exceed. So short time by normal standards, but long time in arena. What he's saying is like, hey, we're not even up to Opus 4.6 yet, but like, we're working hard. So, you know, they're a little bit behind. One other topic that came to my mind is the government is continuing to attack Anthropic, their supply chain risk, and yet they may be the only hope we have to protect our systems, our, our infrastructure. The software companies that we build around privacy of citizens like Anthropic's at the forefront of this. They're the only ones that are doing this and talking about this publicly in this way, and yet the government's treating them as the enemy. That's weird. I have concerns around the biggest companies having access to the future frontier models and then the potential centralization of power. So if you get into this situation where we get these massive models and they're so dangerous to release publicly that we only give them to Apple and Amazon and the banks and like, okay, well now we just centralized power. There's the broader implications on the security of all software. Cryptocurrency, the ability to scale fraud on consumers and businesses. Mike, like, yep, that one I think about like the amount of scams and spam that we're seeing that like, I'm sure are some in some way AI assisted for sure. But if you give this kind of power to just the average scammer or the government actor that, you know, wants to destabilize things, like, that's terrifying. And I know, sorry, I'm just kind of like rambling here but like these are just the thoughts. So another one is use caution as an organization. So whether you're a team within a bigger company or if you're a startup, like an AI native startup, use caution when you're racing to integrate these agentic systems into your organization. So just because Claude cowork is amazing and open claws fascinating like you have to remember how early this is and the tech is moving really fast and even the people building it don't fully understand all the risks associated with it. So again this is where I would caution like on the bigger enterprise side if it or legal is slow playing this stuff that is, that is a good thing. Like I, I'm, I totally understand the impact agents can have and how it can make your company have this massive competitive advantage. But I've yet to meet somebody who understands the risks of what they're doing when they're when do these things. So that's something we've got. The compute and energy needs over the next decade may end up being dramatically underestimated and underbuilt. So it's crazy that it is that like Google spending 180 billion in capex this year. You know, like, you know we're going to have a trillion dollar 2 trillion dollar XAI IPO. You're going to have an openaipo anthropic IPO. Like my guess is we have like completely underestimated how much intelligence is needed. And then the one positive I have here is this idea of Project Glasswing that it does demonstrate the ability for like the labs to work together. And I think that's going to become much more critical. And then there's just two other thoughts I have. One is I would suggest people go back and listen to episode 141 again. So if you didn't listen to the road to AGI and beyond, I would go listen to that. It's an episode I did where I kind of walked through what I thought was going to happen, what the timeline of things were going to be. And the two key components I wanted to just touch on is this idea of what accelerates progress and then what slows things down. So what we're seeing is the acceleration through things like algorithmic breakthroughs, compute efficiency, large scale government funding, where they're like now the government's getting involved, infrastructure investments, more compute capacity. Those are the things that allow it to go faster, but the things that slow AI progress down. And this is where I think Mythos may be the preview of sort of what's going to Happen failures in aligning AI models with human values, intentions, goals and interests. That's what they're alluding to is like we're getting it more aligned, but like where it is misaligned is becoming much bigger problem. One of the other areas that could slow it down is restrictive laws and regulations. So heavy regulation of open source models, this mythos will likely accelerate this at a state level. So you're going to see more bills being pushed forward to try and restrict this stuff because the federal government isn't going to do it. And then the other thing you could see is if there's a change in power in the midterm elections in the US then not the executive branch, but like, you know, at the the House and the Senate, then we could see massive disruption, massive issues where the Democrats will focus very, very heavily on regulation. They're going to try and push this. And so that that then is tied to this idea of societal revolt against AI due to job loss, politics, perceptions, fears, and that is absolutely picking up steam. Pushback on data centers is becoming very strong within some communities. Politicians are looking for wedges around like job loss and environmental impact. You're going to touch, I think in the next topic, Mike, about, you know, what happened to Sam Altman. Like you're getting now, you know, people are, if you didn't hear about it, somebody threw a Molotov cocktail at Sam Altman's house and then like 48 hours later shot up his house in San Francisco. So you're now getting people acting out against this stuff, which is insane and never the answer. So you're just starting to see this. And then that leads to one of the other items that I'd highlighted in the what slows it down, which is voluntary or involuntary halt on model advancements due catastrophic risks. That may end up being the most important one. So yeah, you know, I think there's so much more we could talk about on this one. I'll end with one other quick thought and I think you've got this in the rapid fire mic, so I'm just going to touch on it. But Anthropic also released this emotions paper and it was about these models simulating or emulating human emotion. And I think it's a something people should read. I'll just read two excerpts. One is it said it may then be natural for these models to develop internal machinery that emulates aspects of human psychology like emotions. If so, this could have profound implications for how we build AI systems and ensure they behave reliably. And then Anthropic noted in this paper that none of this tells us whether language models actually feel anything or have subjective experiences. But our key finding is that these representations are functional and that they influence the model's behaviors in ways that matter. So the reason I wanted to include that in this commentary is we're looking at these broad, far reaching implications of these models and in some ways it's kind of abstract to wrap your mind around the significance of what's happening and then when you come to this idea of like, but they're also showing signs of emulating human emotion. And so if you have these powerful models that can improve themselves, that can escape these hand boxes that can identify zero days which are, you know, unknown bugs within software systems, but they also have the ability to emulate human emotion, the ability to manipulate human emotion. We are, we're talking about like a perfect storm of a future that would just not prepared for. And to go back to my original comment, why I think this may be a bigger deal than others, it's not that the Mythos model is necessarily groundbreaking and we weren't aware that models were going to get smarter. It's more about the moment where it might be what was needed for other people who aren't in the AI bubble to be like, wait, what is AI capable of doing? Yeah, and, and so maybe it starts these conversations on a path we really needed to be going, you know, and
- [30:29]
B
in the shorter term. I couldn't help thinking multiple times reading through all of this and the articles, if I am a your average corporate IT person in charge of figuring this out, I just want to cry.
- [30:43]
A
Oh yeah, like, I, I just, like here you can have your chat, GPT licenses or whatever you want and like the agent stuff, just stay away.
- [30:52]
B
Like we're, I, and even at best, if you somehow nail it, there's still going to be open source models nine months from now that people are going to use to bombard your company with cyber attacks.
- [31:04]
A
Yeah, and I, I, the cyber stuff is again, like I, you know, back in our agency days we had clients in cyber security that all these like former FBI people working there and there was people, you know, on our team that were working on those accounts and I would just honestly be like, just, just tell me what I have to know. Like I, there's so much about science here I don't, I don't want to know. And I, you know, even, like, even going through this stuff, your mind just starts to slip into like, oh my God, like how, how much they're going like the bad actors are going to use this stuff is we're just not ready as an industry, as a business world, as society. Like that is it. I think it's always been in the back of my mind is one of the things I'm worried about. It is very quickly like moving to the top of my mind of the things that I just, I don't know how we solve it. I'm not really sure how we figured this out in the short time we have.
- [32:04]
B
Well, somewhat related in our next topic, Anthropic themselves is having a tough time figuring this out because they've also had another kind of high profile security incident because in late March, March 31st they accidentally leaked the entire source code of Claude Code, which is their popular AI coding tool. This happened through a JavaScript source map file that was bundled into a public package. This file contained over half a million lines of unobtfuscated TypeScript across nearly 2000 files. So within hours this code was downloaded, mirrored to GitHub and forked tens of thousands of times. Boris Journey, the creator of Claude Code, said that basically their deploy process has a few manual steps and humans didn't do one of the steps correctly. So this was not AI related. Anthropic kind of flubbed a bit the response as well because they started issuing takedown notices for thousands of GitHub repositories, but they were accidentally trying to knock down as well legitimate forks of Anthropic's own publicly released Claude code repo. Czerny said they later retracted the bulk of the takedowns. This was also just immediately followed by Anthropic making a to some controversial move related to their subscriptions. Czerny also announced that starting immediately, Claude subscriptions will no longer cover usage on third party tools like OpenClaw. Peter Steinberger, creator of OpenClaw, called this move sad for the ecosystem, but gave Cherney credit for how he handled the communication. So Paul, Anthropic is dealing with the consequences of their explosive growth and the popularity of cloud code basically in real time. What did the last couple weeks here tell you about where they're at as a company? Like what challenges they're dealing with? Clearly there are a few.
- [33:56]
A
The the rate at which Anthropic has been shipping updates is I don't know that we've ever seen anything like it in business.
- [34:03]
B
Never.
- [34:03]
A
Like they are just running circles around Google and OpenAI and everybody right now. It's, it's really remarkable. So the idea that like their systems aren't keeping up and the internal checks and balances, like, I, I get it. Like, I don't, I just don't know we've ever seen a company grow this fast. Like, no, their, their run rate right now is actually surpassing OpenAI's based on reports from last week. They're like a $30 billion annual Runway, which six months ago, if you would have said anthropic may IPO at a higher, you know, value than open AI, I don't, I don't think too many people would have taken that bet. But if you, I don't know, there's probably market, there's probably odds on this right now. My instinct right now would be Anthropic will be a more valuable company than OpenAI when they IPO and more valuable than XAI potentially. Like, yeah, they're just, it's an incredible pace right now, what they're doing. The significance of the leak was one of the questions I was thinking about. It's like, well, does this really matter? Like, they don't seem to air too much. I don't know, they just kind of keep moving and releasing all these other things. So the couple things that came to mind for me is it likely speeds up copycat models so it made it easier for other people to sort of replicate what they're doing. It'll likely accelerate open source innovation because people can kind of look at this and it's not great for like what we were just talking about with bad actors using these capabilities to do bad things like that. So those kind of jump out. The one I will say is I thought Boris was amazing. Like as someone, you know, who comes from a PR and communications background, what he's doing is like just textbook stuff. And it, I, I think it's just totally organic and self directed. Like I don't, I don't think anthropic was like, hey Boris, like go be the face of this problem. He just seems to be doing it and it's really impressive. So the way I'm watching it happen is his replies on X or he's just interacting with people. So a couple of quick examples someone posted like, because obviously like a lot of developers are just drilling into this code, like what is it going on? What's in there? And so someone said Claude code has a regex reg X.
- [36:15]
B
Is that, I don't know, reg X.
- [36:16]
A
I think that detects WTF. FF's. Piece of shit fu. This sucks, et cetera. It doesn't change behavior, it just silently logs is negative. True to analytics. Meaning when someone is working with CLAUDE code, the end user, and they're like, this sucks. Like, or fu. CLAUDE code like, this is not good. Anthropic logs that reaction as a negative thing, but it doesn't change the behavior of the model. And so this guy who posted this was like, do with this information what you will. Well, Boris responds and he said, this is one of the signals we use to figure out if people are having a good experience. We put it on a dashboard and call it the F's chart. And. And so it's like that. So it. They probably didn't really want people knowing that that was a thing. But rather than like saying, you know, like, oh, that's not, you know, we don't actually use that code or whatever, he's just like, yeah, it is what it is. Then there was the other one. People are immediately like, oh my God, somebody's getting fired over this. So he, he has stayed really strong in this. He said it was human error. Our deploy process has a few manual steps and we didn't do one of the steps correctly. We have landed a few improvements and are digging in to add more sanity checks. Like with any other incident, the counterintuitive answer is to solve the problem by finding ways to go faster rather than introducing more process. In this case, more automation and CLAUDE checking the results. And then he said, no one was fired. It was an honest mistake. It happens. Then there was one other one I'll highlight that I thought was fascinating. So a user digging into the code post this on X. He said, I can't believe more people aren't talking about this part of the CLAUDE code leak. There's a hidden feature in the source code called Kairos, and it basically shows you Anthropic's endgame. Kairos is always on proactive CLAUDE that does things without you asking it to. It runs in the background 247 while you work or sleep. Anthropic hasn't turned it on to the public yet, but the code is fully built. Here's how it works. Every few seconds, Kairos gets a heartbeat. Basically a prompt that says, anything worth doing right now? It looks at what's happening and makes a call. Do something or stay quiet. If it acts, it can fix errors in your code, respond to messages, update files, run tasks. Basically anything Claude code can already do, just without you telling it to do it. But here's what makes Kairos different from regular code. It has at least three exclusive tools that regular code. CLAUDE code doesn't get one push notification, so it can reach you on your phone or desktop even when you're not in the terminal. Two, file delivery so it can send you things it created without you asking for them. And three, pull Request Subscription so it can watch your GitHub and react to code changes on its own. Regular Claude code can only talk to you. When you talk to it, Kairos can tap you on the shoulder and it keeps daily logs of everything. What it noticed, what it decided, what it did. At night it runs something the code literally calls Autodream, where it consolidates what it learned during the day and reorganizes its memory while you sleep and it persists across sessions. Close your laptop Friday, open it Monday. It's been working the whole time. Endless use cases. It's essentially a co founder who never sleeps. The code base has this fully built and gated behind internal feature flags called Proactive and Kairos. I think this is basically or probably the clearest signal yet of where all AI tools are going. We are heading into the post prompting era where the AI just works for you in the background like an all knowing teammate who notices and handles everything before you even think to ask. This is absolutely what the labs are trying to build. So one, I mean kudos. I don't. Who was the guy who posted this? Mike. What was the username?
- [40:08]
B
I'd have to look. Yeah, we'll post it in the show notes. But yeah, the I. I will also say if anyone from Anthropic is listening by any chance, I. I'll pay a thousand dollars a month for this tomorrow. So.
- [40:20]
A
And Boris's response, so again he could just ignore this and just like let it go and not give it, you know, any fuel. He said we're always experimenting with new ideas. 90% don't ship because we don't think they're good enough experiences. Still on the fence about this one. Should we ship it? So he's just like, yeah, it's in there, you're right, you got it, we built it.
- [40:41]
B
And they're on the fence about that one because of the compute problem.
- [40:44]
A
Correct.
- [40:45]
B
Not the value of it. Sure.
- [40:47]
A
Not on the fence enough to have not put it into the code that's already out there. Meaning they're probably already using this internally. Yeah, so just fascinating stuff. And then the final note was just on the open claw impact and it kind of goes back to what I was saying earlier. Like it's just a cautionary tale for companies that are out on the edges here that are building on the frontiers of the technological capabilities and relying on an Unstable and infant AI ecosystem. So you know, it's, you're building an AI native company open clause, like, oh, this is amazing. We're all in like 30 days later, you've automated all these things and it's costing you like $2,000 a month.
- [41:23]
B
Yeah.
- [41:23]
A
And then Anthropic's like, yeah, no, that's misuse of the system. And you just shut down your company like today or to do what you were doing is now going to cost you a hundred thousand dollars a month basically. So we just have to accept these like challenges and unknowns of building agents into workflows and org charts is so early. So when you hear these stories of people doing it and you're so envious that they've figured something out that you haven't figured out, like they could wake up tomorrow and the thing they figured out is basically shot or it's like, so that's my main thing there. It's just so, so early.
- [41:56]
B
Yeah, I'll be so curious to see how it plays out. I don't know how some of these people are affording to run these open claw setups on their own, like just as a hobby thing because I even some random usage limits in quad code over the weekend and was just like, oh, I've got hundreds of dollars of credits they gave me for various things over the year. And I was like, great, well we'll dip into the usage. And in like 4 seconds I evaporated $300 on a random research checked. And I was like, how is anyone doing this dollar by dollar for every single thing you're doing.
- [42:30]
A
Which we'll talk a little bit about the outcome based stuff in a minute.
- [42:33]
B
Exactly. Okay. All right, our third big topic this week. There is a ton that has been going on with OpenAI over the past couple weeks. So we are just going to go through some of these huge updates, some good, some very bad. But first up, OpenAI closed a $122 billion funding round which is the largest in Silicon Valley history. At the same time, Bloomberg is reporting that demand for OpenAI shares is sinking on secondary markets. And the information reports CEO Sam Altman and his CFO are diverging a bit on IPO timing. It sounds like Altman wants to try to go public faster, whereas CFO Sarah Fryer wants to maybe push it out a little bit due to spending commitments and the necessary organizational prep. Second, OpenAI acquired TBPN, a daily tech news show hosted by John Coogan and Jordy Hayes. This has become this hugely watched popular program in tech media. The show has only about 58,000 YouTube subscribers, but generated 5 million in ad revenue in 2025. They're on track to exceed 30 million this year. It will be housed with an OpenAI strategy organization. OpenAI says the show will maintain editorial independence and continue choosing its own guests. Altman posted on XTBPN is my favorite tech show. We want them to keep that going and for them to do what they do so well. Third, at the same time, a major executive shakeup has hit the company. Fiji Simo, the CEO of Applications, announced she is taking medical leave. She's had a relapse of postural orthostatic tachycardia syndrome, a chronic neuroimmune condition. She has talked about in public quite a bit before. She said to employees she pushed she's pushed a little too far and needs to try new interventions to stabilize her health. So there's some reshuffles related to this. President Greg Brockman will oversee product in her absence. COO Brad Lightcap is moving to a new role focused on, quote unquote, special projects, and marketing chief Kate Rauch announced she is stepping down to focus on her recovery from late stage breast cancer, which she was diagnosed with a year and a half ago. Couple other things Fourth, the New Yorker published a lengthy investigation by pretty famous journalists Ronan Farrow, Andrew Merentz titled Moment of Truth, Sam Altman may control our future. Can he be trusted? This piece drew from over a hundred interviews and internal documents, including Ilya Sutskova's Slack messages and Dario Amadei's personal notes. And it basically builds this case that OpenAI systematically abandoned its safety first founding mission as it scaled up, and that Altman repeatedly chose to deprioritize safety commitments. And in fact, a former board member told magazine he is unconstrained by truth now. Finally, we alluded to this. Days after this profile published, someone did throw a Molotov cocktail at Altman's San Francisco home. No one was hurt. An hour later, police were responding to a man threatening arson at OpenAI's headquarters. Second attack in Altman's home followed a couple days later. Altman linked the attacks to the climate of AI anxiety and the negative media coverage. He had even written that someone had warned him. The New Yorker piece came during heightened anxiety about AI making his situation more dangerous, and he responded to these attacks and the profile in a personal blog post sharing a rare family photo of himself, his husband and their child. He said he was sharing this in the hope it might dissuade the next person from targeting his home. In a post, he acknowledged his mistakes and said he has this conflict aversion that has caused organizational pain. And also concurrently, Altman OpenAI went on kind of a major policy offensive. They published Industrial Policy for the intelligence age. A 13 page paper proposed proposing a suite of people first policy ideas, including giving every American citizen a direct state and stake in AI driven economic growth through a nationally managed fund seeded in part by AI companies. Vanity Fair reported they're basically preparing a broader push to, quote, rethink the social contract Axios. Frame this, frame this as Sam's super intelligence New Deal. So, Paul, I don't know where to start. Lots going on here. Some of it really interesting, some of it very horrifying. Unfortunately, it's been a big couple weeks.
- [47:01]
A
Yeah, I'll, there's a lot of different directions to go. I'll focus on Sam's host. Yeah. And, and then the, the policy ideas. So one quick note. The tbpn, there's no confirmed what did they pay for it? Because that's always. Everybody obviously wants to know. But it does seem like it was north of 100 million, which isn't bad for, you know, relatively new, the editorial independence thing. Good luck. Like, I, I don't know these guys. I've actually never watched the show or listened to the show. I've heard of it plenty. But it's not something that's like, you know, intensely on our radar. But that idea of remaining independence as a media entity that's owned by an AI lab that has lots of pressures on it, that's going to be very, very hard to, to maintain. But you know, it sounds, I mean, they're going to make their efforts too. So we'll see. Okay, so then on Sam's post, I thought there was a lot of interesting things in here. So first, obviously the, the very personal stuff as I alluded to earlier, like violence is just never going to be the answer here. And I do worry about these AI leaders, but it was only kind of a matter of time before something like this started to happen. In his post he said words have power. There was an incendiary article about me a few days ago which is referring back to the New York article Mike that you just touched on. Yeah. He said, someone said to me yesterday they, they thought it was coming at a time of great anxiety about AI and that it made things more dangerous for me. I brushed it aside. Now, he did later tweet that he sort of regretted the incendiary article reference and that, you know, he wasn't trying to pass blame. But yeah, he did at least address that article. So then I highlight a few excerpts here on what he believes and then he has some personal reflections then his thoughts on the industry, because his thoughts on the industry actually lead into the industrial policy for the Intelligence Age document. So on what he believes, he says working towards prosperity for everyone, empowering all people and advancing science and technology are moral obligations. For me, AI will be the most powerful tool for expanding human capability and potential that anyone has ever seen. Demand for this tool will be essentially uncapped and people will do incredible things with it. The world deserves huge amounts of AI and we must figure out how to make it happen. It will not go all well or all go well. He said the fear and anxiety about AI is justified. We are in the process of witnessing the largest change to society in a long time and perhaps ever. We have to get safety right, which is not just about aligning a model. We urgently need a society wide response to be resilient to new threats. This includes things like new policy to help navigate through a difficult economic transition in order to get to a much better future. He also said AI has to be democratized. Power cannot be too concentrated. Control of the future belongs to all people and their institutions. AI needs to empower people individually and we need to make decisions about our future and the new rules collectively. And he said adaptability is critical. We are learning about something new very quickly. Some of our beliefs will be right and some will be wrong and sometimes we will need to change our mind quickly as the technology develops and society evolves. On the personal reflections that this was kind of interesting, he said. And again I think in some ways he's actually like probably acknowledging some of the stuff from the New Yorker piece and other things that have been said about him. So I'm not proud of handling my myself badly in a conflict with our previous board that led to a huge mess for the company. I have made many other mistakes throughout the insane trajectory of OpenAI. I am a flawed person in the center of an exceptionally complex situation, trying to get a little better each year, always working for the mission. We knew going into this how huge the stakes of AI were and that personal disagreements between well meaning people I cared about would be amplified greatly. But it's another thing to live through these bitter conflicts and often have to arbitrate them and the costs have been serious. I'm sorry to people I've hurt and I wish I had learned faster. And then on the industry which leads into the policy piece. So my personal takeaway from the last several years and take on why there has been so much Shakespearean drama between the companies in our field comes down to this. Once you see AGI, you can't unsee it. It has a real ring of power dynamic to it and makes people do crazy things. I don't mean that AGI is the ring itself, but instead the totalizing philosophy of being the one to control AGI. The only solution I can come up with is to orient towards sharing the technology with people broadly and for no one to have the ring. The two obvious ways to do this are individual empowerment and making sure democratic systems stay in control. Laws and norms are going to change, but we have to work within the democratic process even though it will be messy and slower than we'd like. I empathize with anti technology sentiments and clearly technology isn't always good for everyone, but overall I believe technological progress can make the future unbelievably good for your family and mine. While we have that debate, we should de escalate the rhetoric and tactics and try to have fewer explosions in fewer homes, figuratively and literally. And then that leads to the policy piece, which I would actually really recommend people read. It's only 13 pages. It's a pretty quick read. I'll give you like a high level of what's in there. So it starts off within just a few years, AI has progressed from systems capable of fast narrow tasks to models that can perform general tasks beyond general tasks people used to need hours to do. Now we're beginning to transition towards super intelligence, which they say is AI systems capable of outperforming the smartest humans even when they are assisted by AI. No one knows exactly how this transition will unfold. So then I'll just jump ahead to the two sections in the paper. They have Building an Open Economy and Building a Resilient Society. So in building the open economy they have worker perspectives. So giving workers a voice in AI transition to make work better and safer, they have AI first, entrepreneurs help workers turn domain expertise into new companies by using AI to handle overhead that usually blocks entrepreneurship. They have right to AI treat access to AI as foundational for participation in the modern economy. Similar to mass efforts to increase global literacy, modernize the tax base, AI reshapes work and production. The composition of economic activity may shift, expanding corporate profits and capital gains by potentially reducing reliance on labor, income and payroll taxes. Another is public wealth fund. Create a public wealth fund that provides every citizen, including those not invested in financial markets with a stake in AI driven economic growth. Accelerate grid expansion so establish new public private partnership models to finance and accelerate the expansion of energy infrastructure required to power AI efficiency dividends is an interesting one. Convert efficiency gains from AI into durable improvements in worker benefits when routine workload declines and operating costs fall including incentivizing companies to increase retirement matches or contributions. Cover a larger share of health care costs and subsidize child and elder care Adaptive safety nets that work for everyone. Make sure the existing safety net works reliably, quickly and at scale because if the transition to superintelligence is going to benefit everyone, the systems designed to provide economic and health security need to deliver without delay or gaps. Another is portable benefits over time the public or build benefit systems that are not tied to single employer by expanding access to healthcare, retirement savings and skills training through portable accounts that follow individuals across jobs, industries, education programs and entrepreneurial ventures. Two more in this section Pathways into human centered work Expand opportunities in the care and connection economy which they define as childcare, elder care, education, healthcare, community services as pathways for workers displaced by AI. And then finally in that section accelerate scientific discovery and scale the benefits Build a distributed network of AI enabled laboratories to dramatically expand the capacity, test and validate AI generated hypotheses at scale. And then the building a resilient society. There's a few here Safety systems for emerging risks AI trust stack which they say is research and develop systems that help people trust and verify AI systems auditing regimes so strengthen institutions such as the center for AI Standards and Innovation to develop auditing standards for frontier AI risks model containment playbooks which we talked about probably pretty important as what we're seeing with anthropic mission aligned corporate governance, guardrails for government use, mechanisms for public input, incident reporting and international information sharing around AI capabilities. So the other thought I have Mike and I'll see if you have any thoughts on all this, but maybe this is like my former PR background, but I'm thinking that the AI industry needs a massive PR campaign right now to highlight the potential for the positive changes in the world. And this like better future part of it is a PR campaign, but not in a way of like misleading people about what's possible and trying to like shift their focus from the negatives that the negatives are real and they need to steer into those and not ignore them. But what we need to do is accelerate some of the wins that have positive impacts in society. They're high value, high profile that could build excitement about a better Future things like drug discovery and curing of diseases. And we know like they're working on these things. But I feel like right now the negative sentiment is just like snowballing. You can feel it every week in the topics we're covering and the articles we're reading. And there's very few really positive things. And so they all, all the labs, they need to figure out a way to do this where they acknowledge the negatives and do what they're doing. But they gotta start getting some big wins or else society is going to turn this stuff fast. And I don't know how, how fast you can go on the scientific discovery, but I keep coming back to that is the thing that's, that's going to change perceptions is if you can actually improve people's lives in very clear ways that, that you're going to need to win mind share. And right now they're losing it is kind of my current take on the industry.
- [57:42]
B
I could not agree more. I'd love to for us to even talk more about initiatives like that on maybe future episodes and work on that because I would also just encourage, you know, I'm by no means an expert on what you should be doing in terms of your messaging here, but it would also strike me as valuable for especially Silicon Valley based AI labs to also focus on the individual. How do these things make your individual life better? The big picture stuff, super important and really valuable. But also think about all the things that people are going to be upset about when it comes to an AI lab. They do not want you telling them that you're going to save the day, that their life is going to be managed by your technology. Show them how it empowers them and how real people are using it for real wins, even basic ones in their life. I think could be also interesting as an angle.
- [58:33]
A
That's a great point. Yeah. And I do think like you and I see, you know, a lot of the similar stuff. Like right now all you have is these individual stories on X that never break out of X.
- [58:43]
B
Right, right.
- [58:44]
A
And it's like these incredible stories of finding cures for things that their doctors missed for years and finding treatment paths. And I've certainly experienced that myself. Like things in your own personal life where you're just like, I don't know what to do and, and you just like have a conversation. It's like, wow, okay, that, that, that's the direction. Like I think I know what to do. And there's like, I'm sure there's just all those Incredible stories. But right now. Yeah, I just. I feel like they're just missing it.
- [59:10]
B
Yeah.
- [59:11]
A
Yeah. I don't know. I think you're right, though. Like, we should. We should make a bigger effort on this show to, like, highlight more of that stuff. I. I think there are so many amazing things that are happening, especially on the scientific discovery side and, you know, making an impact on people's health and wellness and things. Yeah, we should do more.
- [59:30]
B
All right, Paul, before we jump into rapid fire, quick announcement. This episode is also brought to us by our AI for Writer summit. So the future of storytelling is being rewritten thanks to AI and that's why we're very excited to be hosting our annual AI for Writer Summit on Thursday, May 7th. So this is a half day virtual event for writers, editors, content teams, basically anyone who does any type of writing or content creation as part of their work. You will get tons of awesome, actionable knowledge from the event because during it, we'll have some incredible speakers breaking down exactly how AI can help you create smarter and faster, but also importantly, without kind of losing the heart and soul of your writing. This event has a free registration option, so go check those out today. You can go to aiwriterssummit.com or just go to marketingai institute.com and click on Events and you'll find the summit right there. And by the time you go to the website, the agenda will be live. So you can see the great lineup we've got going for you. Super excited for this one.
- [60:34]
A
Yeah. And real quick note on that. I mean, last year we had. It was more than 4,200 people from 95 plus countries. So, yeah, it's an amazing event. It's a great way to network with other people. And then the real key is like, we're trying to tell the human side of this. So this is not like, how do you automate the writing and get rid of people? We're trying to grapple with the hard questions, you know, like, what is the future of journalism? What is the impact it has on people who write for a living, for fulfillment, things like that. So we very much focus on that. And then if I'm not mistaken, Mike, I think my opening keynote from last year might be on YouTube. If not, we'll put it up on YouTube before tomorrow. We'll put a link into it. So I did the state of AI for writers and creators, navigating the future of creativity. But what I focused on last year was the human side of it. And when should we use AI to write was like the question I posted or challenge people with. And then I actually presented a framework to decide, like, when should I use AI versus when should I not? And so I think it's a really important concept. So we'll put the keynote from last year up that people can go and watch. And I think it's a good way to get into like a 25 minute keynote, if I remember correctly. Yep.
- [61:40]
B
Awesome. All right, let's dive into some rapid fire. In late March, AI recruiting startup Merkor was hit by a supply chain cyber attack through a tool called Light LLM, which is a widely used open source library that connects applications to AI services. A hacking group claimed credit and published samples of the stolen data. TechCrunch reported these included Slack messages, internal ticketing information, and videos of conversations between Merkor's AI systems and contractors. Now, the reason this matters, why we're talking about it, we have talked about Mercur before. They are a $10 billion company that provides training data to the top AI labs. So what they do is they recruit expert contractors. So think people like engineers, lawyers, doctors, bankers, and they have them train AI models and chatbots. Some of their top customers include OpenAI, Anthropic, and Meta. They have more than 30,000 experts on their roster and say that they are paying $1.5 million per day to their contractors. So there's a lot of data in this system. And the attackers claim to have obtained 4 terabytes of data in total, including source code and database record. Not only is this bad from a personal perspective, 40,000 contractors at least have had personal data exposed. It sounds like they've also exposed proprietary source code, video interviews. And the most important part is potentially this could include details of how Frontier Labs are treating their models, what kind of expert feedback they're collecting, and the methodologies behind their most advanced system. So, so far, Wired has reported that Meta paused its work with Merkor and is investigating the incident. OpenAI confirmed it was investigating its exposure, but said it had not paused or ended its contracts at this time. So, Paul, another security incident. We've covered Merkor in the past, how important it could be to the AI ecosystem. Though this is a pretty damaging series of events. We also actually did talk about that Light LLM breach a couple weeks ago. So two topics kind of coming together in less than ideal ways.
- [63:41]
A
Yeah, like I said, I hate talking about this stuff. I really do. Like, it is terrifying. And what we know is like the, like the. When state actors want something, they're going to get it. Like Dario Amade did this interview back in like 20, 23 or 24. That just always haunted me where he was talking about like the weights to these models are literally like, these are like the nuclear codes basically in terms of how they protect these things. There was actually an example recently where they were talking about OpenAI literally going in with like the briefcase, like the, the, what do they call it? The football. The nuclear football. Yeah. Like that's how they delivered the model to like with the weights in this lock case to the government when they were like trying to build a custom version of something for the government. So the weights to these models are so tightly held. Like I think Darius at the time, there's like two or three people within anthropic that even had the ability to, to know the weights kind of thing. And he said like, listen, if a state actor wants to get them, it's just how much money are they willing to spend to go get them? Like they can hack into anything. And so the, the premise that like you think of all these areas of risk and all this data that's living in these companies and like this maybe partially goes to this use caution when you're like working with just these random startups and giving them access to your APIs and like all this shit, like you're just. The surface area of risk is so vast and misunderstood or like un understood by people. It really is just terrifying. I don't like cybersecurity, to me is just. I hate it. But like I said, I think like cyber security professionals, lawyers who deal with this stuff, like man, talk about safety and like you don't know what jobs to go into.
- [65:25]
B
Like, I guess that's a good, a good silver lining here, right?
- [65:29]
A
We may make for all the lost jobs. Everyone's just going to cyber security.
- [65:32]
B
Everyone's fixing all the new nightmares. AI is enabling.
- [65:38]
A
Yeah, but this is a bad one. This is the.
- [65:40]
B
Yeah, and it's good to be aware too of these companies like Merkor that you know, in Silicon Valley circles, definitely well known, but maybe to your average public, not as well known or a household of a name, but super, super important to the ecosystem.
- [65:53]
A
Yeah, and real similar like scale AI, right?
- [65:56]
B
Scale AI for sure. Yeah, yeah. In fact, there was something in the reporting where meta, you know, when they, even when they essentially aqua hired scale AI, they didn't stop using Merkor either. They were just using both because it was so important.
- [66:09]
A
Yeah. Scal AI. If you don't catch the reference, Alexander Wang, who's now in charge of superintelligence at Meta, he was building a training company called Scale AI. He got Aqua hired for like $15 billion by Meadow. So his company still exists. But yeah, that, that's, that's the reference there.
- [66:26]
B
All right, well this next topic's a little more positive or at least interesting and not negative. Right. But something right now. In early April, Andre Karpathi posted on X about how he is now using LLMs not generate code. You know, he's a programmer coder, so he's doing that a lot, but also to build and maintain personal knowledge wikis. So this post as of today has nearly 20 million views. So it's like one of the more viral AI posts this year so far. And the core idea here is that instead of relying on all this technical stuff like vector databases and complex rag pipelines, instead he's just dumping raw documents, articles and research into a folder, then letting an LLM compile them into a structured interlinked markdown wiki. And then he uses Obsidian, a free note taking app, as basically the front end of this. So as he puts it, Obsidian is like the ide. The LLM is the programmer, the wiki is the code base. So this LLM then handles curating sources, linking updates, and even runs periodically to check for inconsistencies. So the reason this is kind of getting some popularity and some eyeballs is because like every knowledge worker in some way is using kind of information and knowledge bases that are really often very hard to maintain. So instead of just thinking about LLMs as chat interfaces or code generators, Karpathi is really thinking about this in terms of LLMs turning, becoming persistent knowledge infrastructure and building that out in ways that compound over time. And people kind of ran with this and started building their own versions. Obsidian's founder weighed in with best practices and Paul, I just thought this quote from Karpathi was telling. He said, you know, in this way and the way I'm using this, a large fraction of my recent token throughput, it is going less into manipulating code and more into manipulating knowledge stored as markdown and images. Super interesting implications for, you know, maybe less technical people.
- [68:21]
A
Yeah, the term you hear thrown around a lot in the last like 30 days is the idea of a second brain. Like everybody's kind of talking about this idea, like all your information just lives in this thing. And so, you know, the major cloud companies are trying to solve for this productivity. Companies like Microsoft and Google, obviously they want this to just. You already have a lot of this information living in there and they're trying to find Ways to, like, make it easier to build these sort of second brains where all this information lives there and the knowledge base is there. And then you're just constantly like, you almost like that idea we talked about with the cloud code leak, where just proactively acting on all this knowledge and just like working with you on it. And the thing with Carpathi's posts is, you know, three months from now, somebody will productize what he's doing or maybe three days from now.
- [69:03]
B
Yeah.
- [69:04]
A
So he talks in these technical ways and most people aren't able to do anything like what he's explaining. So the average business leader or practitioner listens to our podcast. I don't know what any of that means. I don't know what an ID ID is and things like that. But for everybody else, just like assume the outcome of the idea is like a product waiting to be built. And that's like, the premise here is if he's talking about it being possible, it's only a matter of time until someone like, builds that capability and then you start finding it. Like all of a sudden you have access to that. You can kind of hack it together with the things you've got internally.
- [69:40]
B
Yeah, it struck me too, as related to another thing we had talked about that he was working on that auto researcher concept where it's like I just was like making notes while reading through his post and saying, like, this feels like in some fashion, whether it's doing it yourself or there's a product around it that every analyst and research firm basically needs to go this direction at some point because you need this second brain of all this proprietary stuff. And I know people are kind of doing it and layering shadow over it, but this is dynamic. It is updating regularly. It is an LLM maintained wiki or knowledge base or second brain. And I think that's probably where I'd imagine research function should be going.
- [70:19]
A
Yep.
- [70:21]
B
All right, so next up, there's a lot that's been happening on the AI and jobs front, no surprise in the past couple weeks. So we're going to run through a couple highlights here of some things that are notable. So first, the New York Times published a piece reporting that economists who had previously dismissed the AI job threat are now slowly but surely starting to change their minds. So this is a pretty big shift in establishment economic thinking. They talk to a bunch of economists who, you know, they're not doing a total 180, but they are starting to acknowledge that maybe this mainstream economist position, that AI will create more jobs than it destroys the way previous waves of technology have. Maybe this is a little out of date or there's more nuance to it than previously thought lot. Second, there may be data backing that up. The Challenger Report, which is a regular report we talk about for March 2026, attracts job cuts Challenger and Gray is a recruiting firm. They show that US employers announced just over 60,000 job cuts March 2026. That's up 25% from February. AI was cited as the leading reason for 15,000 of those cuts, so it's about 25% of the total year to date. AI ranks fifth among all the reasons for job cuts. And since Challenger began tracking AI as a layoff reason in 2023, the cumulative total so of all time has now crossed 99,000 AI related job cut announcements across three years. Third, Jack Dorsey, who we talked about a couple weeks ago, is making the case for AI driven restructuring much more explicitly perhaps than any other major CEO out there. So after Block cut 4,000 of its more more than 10,000 employees, Dorsey has now published a blog post co written with a partner at Sequoia Capital arguing that AI should replace the entire traditional hierarchy of middle management. So Block, he says, is restructuring around three employee roles. Individual contributors who build systems is one, two is directly responsible individuals who own specific outcomes on 90 day cycles and third is what they call player coaches who mentor while staying hands on with technical work. He said this restructuring was triggered by a capability shift he observed in December with Anthropic's Opus 4.6 and OpenAI's Codex 5.3. Fourth, on the hiring side, Zapier released the second version of its AI fluency rubric, which now they apply to every new hire at their company. This requires candidates to demonstrate AI embedded into their core work, not just one off usage. They want to show repeatable system and measurable impact on quality, efficiency or outcomes. They also have this new accountability dimension that they consider they say with AI you can delegate the work but not the accountability. So keeping that human in the loop, high top of mind here. Zapier's language is also pretty blunt about their AI expectations. They say if someone isn't meaningfully improving their work with AI support, they just don't meet the bar. And then last but not least, a new Gallup survey shows that AI is reshaping how college students think about their futures. 42% of bachelor degree students surveyed they have reconsidered their major because of AI. 16% said they've already changed their major over it. For people trying to get associate degrees, 56% are also reconsidering their field of study due to what AI enables. So, Paul, what, what jumped out to you about these updates this week? I mean, I'm personally planning on diving in a lot deeper to Dorsey's thoughts. I thought those were kind of interesting.
- [73:51]
A
I read it. That might have been the thing that triggered. So I put a post on LinkedIn on like, I don't know what day it was. It was, it was one of the days we were in Scotland and I was like, we were driving a long distance and we were sleeping in the car and I was like typing away. We had a tour. I was not typing while I was driving. We actually had like a tour guide driving us. I don't remember which thing I read that I wrote the LinkedIn post about and then I ended turn it into a newsletter post. It might have been the Dorsy one, I don't remember, but it was abstract like his. I read it because I'm very interested in this. I'm actually like, my Macon keynote this year is going to be based on like a vision for AI forward Org chart, I think, think maybe like I'm working through an idea. Mike, you've seen some early versions of this, like, so I'm very, very keen on this idea of organizational structure and what teams are going to look like and things like that. So it did definitely catch my attention what they were doing. I love Zapier's approach. I liked it when they came with the V1. I really liked the V2. I like the idea of this AI fluency rubric. So that was some really cool stuff. And then just the jobs overall, like, again, like, I'm glad to see people coming around and realizing, like, this is a real thing and it's going to be a problem. The thing I alluded to that I wrote about on LinkedIn though, was I'm getting really, really annoyed by the, like, the tech leaders in particular who just keep pretending like it's all going to be great, like with no acknowledgment of the possibility that it won't be. So I get optimism. Like I'm all, I'm all for being optimistic about this stuff and believing in a future of abundance and like we're going to, you know, find our way through, which I do think we will. Like, I, I, I think it's going to end up being great, but I also like, straight up, like it's going to suck for a lot of people in the process. Like, this isn't going to be an easy transition and like a whole bunch of people are going to lose their jobs. And so I get really annoyed when people won't like acknowledge both sides of the equation. So the example I I put in the newsletter was I said tech leaders, politicians, economists who point to increasing demand for software developers and historical precedents as proof that AI won't displace millions of jobs are creating a false sense of hope. And then I highlighted four in particular and some of these people are people I respect and like. Follow but Andreessen, Mark Andreessen, this is a quote and we'll put the links in the show. Notes the AI job loss narrative are all fake. AI equals mass. Massive rampant productivity equals massive ramp in demand equals massive jobs. But watch. So that was a tweet from April 5th. Aaron Levy, who We really like, like CEO of Box, like I'm a big fan of Aaron. He's got some of the the best takes on X about AI that I've seen. He does a lot of research on this topic. Topic. So April 5th he wrote there are far more categories where AI agents making things more efficient will induce demand for that skill than spaces where agents eliminate the work. This is why the AI job predictions will not play out as advertised. Okay. Shyam Sankar, who's the CTO at Palantir, we've talked a lot about Palantir. He had an editorial Feb. 2. He said AI is a tool for the American worker, not his replacement. The job loss narrative is a ploy to attract investors, drive media attention and consolidate political power. The real promise of AI is the enterprise is to make the American worker 50x more productive to unleash his taste and agency. This isn't speculation, it's reality. It's very, very confident. There's lots of confidence in these statements. And then David Sachs, this is no surprise. He he can't acknowledge the impact on jobs due to his relationship with the administration. He is the currently the chair of the President's Council of Advisors on Science and Technology. All caps AI job loss hoax exposed. And then it goes on to say, according to a new study from Vanguard, the occupations most exposed to AI automation are actually outperforming the rest of the job market in terms of growth and real wage increases. Rather than causing job loss, AI is making workers more productive, driving gains in both jobs and wages. So what I said was despite these economic or optimistic outlooks from these leaders, the reality facing companies, especially those with limited growth and demand, which is a really important asterisk there, is that the pressure to reduce headcount across all areas of knowledge work is going to be immense in the coming months and years across all areas, marketing, sales, customer service, hr, finance, et cetera. And then I said, pretending like there isn't at least a strong possibility of significant disruption is a disservice to business leaders who should be doing more to prepare their organizations and upskill their people. And then I said, I talk to executives every week who are being told to stay flat on headcount and to have a contingency of cuts ready to go if the efficiency from AI happens. And so I just, I don't, that's like my continued frustration is that all these people are hyping AI as this future of abundance, which I'm with you, like, I hope and I do think eventually, but I don't know who you're talking to that is planning to hire. Like, I'm not meeting those people, like, unless they're anthropic or one of these companies that's growing at 20, 50, 100% a year. I, I've yet to talk to an executive at a traditional enterprise that's really happy with 5 to 10% annual growth that's planning to hire. It's not happening. So, and that's a knowledge work. Now, of course, there's exceptions to that in energy, in the trades, in healthcare. Like, yeah, we can't hire enough people in those areas. I get that, that I'm talking about the rest of us, all the other industries where the, the ultimate goal right now is to, to just stay flat and head count and get the revenue per employee number way up. So, yeah, I don't know. It's good. I guess it's good. Like it's increasingly becoming a conversation because it just really needs to be, we need to be thinking about what if these tech leaders who are so optimistic just are wrong and what if it isn't as, as, you know, easy of a transition as they'd like to make you think?
- [79:35]
B
So similarly, we've had a lot happening on the AI policy and politics front. So we're going to go through a few developments here that have happened over the last couple weeks. So first up, California Governor Gavin Newsom signed a first of its kind executive order requiring safety and privacy guardrails from AI companies that contract with the state. So this basically establishes new certification requirements for AI vendors that want to do business with California. It requires them to attend to and explain their policies around preventing illegal content, harmful model bias and violations of civil rights. It also directs state agencies to Expand the use of vetted AI tools in government, develop an AI powered pilot for accessing government services, and publish a data minimization toolkit. Second, at the federal level, the Wall Street Journal reports that the White House is racing to head off threats from powerful AI tools. There's renewed urgency here in the wake of the all this Mythos stuff we discussed. This included prompt predominantly a group of White House officials working on the issue, including convening a call with the Vice President, Treasury Secretary and the heads of Anthropic OpenAI, Microsoft and Google, as well as the leaders of cybersecurity firms CrowdStrike and Palo Alto Networks. That's obviously in addition to the previously mentioned meeting we talked about related to Mythos, specifically that the Treasury Secretary had with bank CEOs. Third, a major new survey from Fathom, a nonpartisan research organization, provides a clear picture of what Americans actually want from AI governance. I surveyed a bunch of people to ask about their feelings and priorities in a number of areas. The top priorities across party lines for people are child safety, corporate accountability, and verifiable standards. Another big issue is workforce protection. So according to fathom, from retraining programs to sovereign wealth funds that share AI generated wealth with the public, every workforce policy tested in this survey commanded majority support. Support Americans decisively reject leaving workforce transition to market forces. There's broad demand but no preferred solution. A policy window that is open now, but won't be indefinitely. And then lastly, Politico reports that Senator Bernie Sanders may be building an unlikely alliance with Silicon Valley AI safety advocates. So Sanders recently met with quote, unquote, AI doomers in Berkeley, including Eliezer Yudkowski from the Machine Intelligence Research Institute. And Sanders said, I know there have been a lot of science fiction novels and movies about how the robots and the AI and the computers rebel against human control, but these guys no longer think this is science fiction. So political politico suggests this might be the beginning of an alliance between anti AI populists and the more tech centric, perhaps effective altruist aligned AI safety adverse advocates. Paul, did anything jump out to you here? There's a, there's a wild quote from Yudkowski in the Politico piece where he just said, basically telling Bernie Sanders, hey, the point, if AI gets much, much, much more powerful, it'll run everything. And Sanders said, what does that mean? Humans are discarded? And yet Caskey replied, think everybody dead. So there's some, some strong language being used.
- [82:54]
A
Well, I mean, just like the optimist side we just talked about, there's extreme views of everything. Yeah, I'll just, I'll probably just leave it at that at the moment. But this is my concern is like design. You have an uneducated public largely about what these things really are, what the real risks are, what the real potential is. And so there's always. When you have. When the, when the literacy isn't high and the comprehension isn't high, then you have the ability for extreme views to come in and influence people's perceptions and beliefs. And that's very dangerous, in my opinion. You know, if you, if so let's say you know this Yakowski. Is that you say his name.
- [83:41]
B
I believe so. Yeah.
- [83:43]
A
So like, let's say that's the first thing you hear. It's like this ends up on a 60 Minutes or in that AI movie that just came out. Yeah, the doomer AI movie that was out. And you hear that and it's like, oh, I hate AI, hate data centers. I hate Sam Altman. And like, you people take that perspective and then you say, yeah, but it's, it found a cure for cancer to your family member that you know was suffering from cancer. Like, AI is actually the thing that's going to find the cure. Did find a cure. Like, so should we stop it? Should we not. Should we not have AI now? Because, you know, you, you heard a bad quote or like, so. And that's when there's like all these nuances to. When the people take extreme views. They. They don't stop and then say, oh, okay, well, maybe that would be amazing. Yeah. Then again, I, the only peril I could ever go back to is the Internet and then say like, oh, yeah, the Internet's going to allow these scams and dark web and all these, like, horrible things are going to happen, but it's also going to open up the economy and we're going to be able to build all these amazing things. You're going to connect with people you could never. And you're gonna be able to FaceTime with your. A thousand miles away. And like, you want, you want to not have any of that good stuff. You just want to like, should we just shut it down? Because, like, bad stuff might happen.
- [84:56]
B
Right.
- [84:57]
A
You can't. And, and so that's like my feeling on this is like dialogue and reason and finding paths forward where we can do this responsibly. But this, this absurdity of like, shut it all down because it's going to limit everybody's like, okay, that's your belief. And then, then we don't get any of the good stuff either. So how about we actually like, just be reasonable here and, and find the reality and like, let's talk about the reality of the situation and not take extreme views that are unrealistic and mislead people. So.
- [85:33]
B
All right, so next up, HubSpot announced that starting April 14, its customer agent and prospecting agent are moving to outcome based pricing. So this means, according to HubSpot, quote, customers only pay when the agen agents complete the task it's been assigned. Practically, this means the customer agent is moving from used a used to or will used to have charged you $1 per conversation no matter what and that's moving to $0.50 per resolved conversation, meaning you only pay when the AI actually solves the customer's problem. The prospecting agent is shifting from a recurring monthly charge per enrolled contact to $1 per lead recommended for outreach. So HubSpot says, quote, this means you now pay when a prospect's prospect gets qualified and handed to your team. HubSpot says customer agent actually now resolves 65% of conversations and cuts resolution time by 39% and that prospecting agent activations are up 57% quarter over quarter. Both agents include a free 28 day trial and are available to pro and enterprise customers. So Paul, we've talked about this before, the need for SaaS, companies like HubSpot to update their pricing models. What do you think of this approach? Are they headed in the right direction?
- [86:48]
A
I I love HubSpot. I always have to preface my comments on HubSpot but I love them of the people HubSpot, I love the company. We use the technology like our company's built on the technology. Both my companies for the last 20 years have been built on HubSpot technology. I like the concept of the outcome pricing. I understand from HubSpot's perspective why they would move to the this. I understand the messaging of why this is a benefit to customers. So I get all that. Mike, you and I have worked in HubSpot for a really long time. Y the reliability of the data is a problem. Allowing them to determine what is a resolved conversation is a very, very gray area. Y so we get a spam form fill or something like that or a spam chat and like you close it and now we just paid $0.50 close a spam chat. Like there's all these like what does that mean? Like what is a resolved conversation? And and now the work we have to do to go into understand what is a resolved conversation. Oh crap, like 80% of what would be considered resolved. Conversation to us are considered, not evaluate at all. Like how do we turn that off? And. Or $1 per lead recommendation for outreach. Like, I don't know. Like that doesn't, it doesn't. As the CEO of a company that pays lots of money to HubSpot every month for software, neither of those jump out to me right away as like high value things that I want to like now have to like talk to my COO today and be like, what does this mean? Because it goes into effect tomorrow and like have we budgeted for this? How do we budget for this? So no, I, this creates way more questions for me than answers. Maybe it ends up being a really good play, I don't know. But it continues down this path of the way these companies are trying to price AI to me is they're not, they're not solving. What is the simplest answer to this. It just keeps getting more complicated. In my opinion. Even though it's now outcome based, it's still uncertain to me.
- [89:01]
B
I always even just think back to even the agency days where it's like, hey, we ran this really successful campaign for you. Look at all these great leads. And then they're like, yeah, that's awesome. But like these leads aren't closing or these aren't qualified. And you're like, well yeah they are. And it's like, well no they're not because they're not closing. And then you find out there's 100 other issues of why this wouldn't be the case. So I wonder how that you even get to that shared agreement on any of these outcomes.
- [89:25]
A
And again like, we'll dig. We in this affects us personally so we have to dig into this. So we'll report back like again, maybe it is a really elegant solution and maybe it is truly value based for us as the customer. Unfortunately, my instinct is this is going to be a pain in the ass and I'm not going to agree with the, the value that they're assigning to these. And we're going to have to now figure out ways to either not use Breeze for these things or to like change the dynamics or certainly update our budgeting. It's just super annoying. And I, I understand why they have to do it, but no, I'm not super excited about this. If they would have come back and said we're raising your monthly seat license $10 per month per user and you have unlimited, I'd have said like, great, raise it 40amonth if you want to. I don't give A, like, as long as I don't have to think about this and there's no additional budgeting, then just. Just raise the rate. If the software is creating that much value, then just charge me more. I don't know. Yeah.
- [90:27]
B
And again, we'll see. I don't want to harp on it, but I'm just even thinking, like, my God, we sell, like, 10 different things. Like, I don't think all those leads are the same. You know, there's like, like multiple lines of business, all these crazy considerations that maybe are solved for, but I'm just like, oh, I have more questions than answers.
- [90:42]
A
Yeah. My brain explodes with the questions and the reasons this won't work versus the oh, thank God, you solved the pain point for me. It's like, no, you just gave me ten more or. Right.
- [90:53]
B
All right, a couple more final segments here before we wrap up this week. So we've been doing more regularly what we call kind of our AI use case spotlights here at SmartRx. So, you know, we hear from listeners, Paul, all the time, that one of their favorite things is, like, when we talk about how we're actually using AI at SmartRx. So we're every week we're going to try to do. Giving you a quick look under the hood at whatever we're working on this week. You know, real use cases that we're either exploring, building, or actually deploying. So, Paul, I know you had mentioned you might have had one, and I've got a couple I can just really quickly touch on, too.
- [91:25]
A
Yeah, I would. The one I can give again. I was on vacation for the last, you know, 10 days, but like I said, I was in, like, the back of a tour van and I had a lot of time while my family was sleeping. I was just thinking, which again, like, one, go to Scotland. It's beautiful. Two, take trips with your family whenever you possibly can. Mike, I know you have a young child. If. If I have any parents with young kids listening, like, the. My kids are 13 and 14. There has never been a single trip I've regretted. Like, go places with your family. Like, create memories, create experiences. Amazing. So Scotland was incredible time with my family. Amazing. My mind was freed. Like, it was the first time I've stepped away in a little while and just like, just didn't really work. But it also then gets that inspiration going. So there was one extended trip on the tour tour where I had this assessment I wanted to build. And I've talked a little bit about one of the assessments I was recently building this is kind of a complimentary, complimentary assessment which I'll share more detail about in the coming months. But I literally just pull up Claude code sitting in the van and I was like, all right, let's work on the next one in like three hours in the van. And I, I mean, I built a working first draft of the thing on my phone in the app. So it's nothing like earth shattering. But the fact I could do this with my phone and an app and go through the entire thing, build a working model, export it into a dock, I could actually edit it. Just easy. 30 plus hours of work traditionally done in 3 hours in the car. Got back to the hotel that night and I just sat down on my laptop and remember I went to bed and just played around. It was peaceful and inspiring and, and not. I didn't feel overwhelmed. I feel like AI psychosis. Like it wasn't like, oh, I gotta build. It was just my mind was finally free and I could think clearly and the technology enabled me to do something amazing while I was doing it.
- [93:24]
B
That's so cool.
- [93:25]
A
I love that.
- [93:26]
B
Yeah, my big one this past week won't be really a surprise, it's a common one, but definitely just being continually in awe of AI as a deep strategic partner. So I was working on a lot of heavy stuff this past week related to, to helping build out our research agenda at SmartRx with our director of research, Taylor. And there's a lot of really intense, kind of really reasoning from first principles that these foundational strategy items that are deeply important to getting the direction right on something like this. So the ability to really, in a structured process and methodology, sit down and say, okay, I'm going to gather up all the context needed, feed this into something like Claude, and then work systematically back and forth with this tool. Tool to refine each individual piece of the strategy, but also just the logic behind it. I mean, it's a caliber of thinking I simply could not do on my own. I don't think so. I'd highly recommend. Then I took that exact same methodology and said, hey, this worked really well for this research agenda. I've got three other projects like it. Let's apply the exact same methodology to completely different contexts and projects and also also got those done in a fraction of the time it would have normally taken. And more importantly, results I could not do alone.
- [94:41]
A
All right, wait, let me. I just have one other one real quick one.
- [94:43]
B
Mike.
- [94:44]
A
Yeah, this is a personal one. So buddy, buddy of mine, text me. It's obviously tax time. And he was like, dude, I'm getting killed on taxes. Like, do you have any idea if you're like CPAs talk to you about any tax strategies as a business owner, things like, like that. And so I was like, yeah, I'm like, fresh off vacation, I haven't slept in two days. And I'm, I'm like, dude, I don't, like, I don't really have much here. Like, here's one thing we've tried. Like, what do you think about this? And he goes, yeah, we tried that. And so then I was like, ah, you know what? So I go into chat GPT and I was like, hey. And I give it like this basic prompt as I got a friend, he's a business owner trying to figure out tax advantage things like, what are some, you know, write me a prompt that he could use to do tax planning in his tax bracket as a business owner. Whatever, whatever. It comes back with this amazing prom. I mean this is like 1200 words and it's basically, you know, it's like, act as a highly experienced US Tax strategist, CPA and business advisor who specializes in helping business owners legally reduce their tax burden through proactive planning. Your role is to educate, analyze options, surface questions, strategies and planning opportunities. Follow tax rules, do it. And it goes. And then it breaks it down like, boom, boom, boom, boom, boom. Like go through all these steps, steps and, and, and then it ends with. And give me a list of questions. I can then send my actual cpa. So I send this to my buddy and he's like, he used it. He goes, yeah, unfortunately my CPA is really good and they like, they've done all these things but like just that again, it's like that sometimes it's the personal use. And then, oh, one other one. I was designing a pavilion for our backyard and I used Google Gemini to do it and it was amazing. Like it crushed it. So yeah, just some fun personal use ones too. I love that.
- [96:26]
B
All right, so Paul, we're also doing kind of regular weekly segment related to spotlighting our courses in AI Academy. So if you want, I can kind of tee this up for what we're going to talk about this week that we've got available to AI Academy members. If that works for you.
- [96:42]
A
Yeah, go for it. We did the customer success one, I think is what we talked about.
- [96:45]
B
Yeah. So we've had Live in AI Academy, AI for customer Success. So this is as a reminder, like a four course certificate series. It's built specifically for customer success professionals and the whole Point of this segment is just spotlighting the course and kind of giving you something valuable from it, whether or not you ever take it. Just as a way to kind of share more of the love that we're, you know, putting together in AI academy. So when I was building this course, kind of what really jumped out to me is I kind of think of it in two ways for these segments, like why AI matters for this function or industry or segment specifically and then like how to start operationalizing that. So first up, what really jumped out to me being customer success is just this core systemic challenge of scale. Like if you have are building a CS team, Paul, I know you're deep into this right now. The only way you historically scale up a CS team is by either hiring more people or piling more accounts onto each customer success manager. So those lead to some really thorny trade offs, right? Like people can get burnt out, your engagement quality from csms goes down. Top accounts might still get some white glove treatment as you scale, but the rest often do not. Now what's really cool though is that AI is starting to break this math in a good way. So instead of scaling by having to raise headcount a ton or stretching people thinner, you can use AI in a number of different ways to actually scale up the effectiveness of CS professionals. Now again not getting rid of people or automating them away, but just this ability where you can actually scale without this linear near rise in cost. And it's kind of helpful because then CS professionals themselves not only do better work, have better lives and work life balance as a result, but they can increasingly get out of this like reactive work and start doing much more strategic, proactive stuff that moves the needle and also turns CS more into a revenue center versus like a cost center, which has historically been a big problem. So lots of, you know, data points, information and research in this course related to that, but that really is such a core challenge. And what, what's really cool is, you know, we also teach some steps about how to start operationalizing this insight, right? So we actually walk people through and I think it's useful even if you just do this on your own that you start with the really low hanging fruit. Like trust me, the amount of use cases we've got in this course around customer success, there's so much low hanging fruit where AI can no joke be saving you dozens of hours a month or even maybe a week at some point. So we really do start from kind of the bottom up and say look, let's look at where Your CS team is spending time on reactive work. So these are things like check ins, QBRs, manual scoring, and if you really get smart about making those your first AI targets, you are going to free up people to do so much more time. More time and energy devoted to customers, which is amazing, but also to bigger ticket kind of AI projects and pilots. So the course goes into how to do all that. But even if you don't take the course, I would say literally pull out your calendar tomorrow, screenshot it and drop, drop it into something like ChatGPT or Claude and start talking it through where you're trying to save time and, you know, put a constraint on it. Say, I am. I want to save an hour next week minimum by next week, let's figure out how to do it. So that's kind of just one little thing that I learned building this out and that we found Customer success professionals who've taken the course so far have also found valuable.
- [100:17]
A
Yeah, someone who's trying to architect an AI native customer success team. Team. This is highly relevant for me.
- [100:24]
B
Right, right. All right, Paul. So as we wrap up here, we have our kind of regular AI product and funding updates. I'm just going to run through a bunch. There's obviously a ton since we've been for a couple weeks. So I'll hustle through these.
- [100:37]
A
Yeah. And again, real quick, like Mike's going to move fast through these. But honestly, like five or six of these in a regular week would have been certainly rapid fire topics, if not main topics. Like there's like that. Just because it's a rapid fire at the end here doesn't mean that some of these aren't very important. That we don't understand the bigger significance, but can only cover so much in a weekly show, I suppose.
- [101:01]
B
Yes, indeed. So first up, Anthropic's annualized revenue has crossed $30 billion. That's up from 9 billion at the end of 2025. The number of business customers spending a million dollars or more annually has doubled to over a thousand in under two months. So we've kind of talked about the numbers, the ways in which that growth is creating some issues for them. In this episode, Axios is reporting Anthropic's usage limits are outpacing OpenAI's. And Wall Street Journal reports they are in talks to invest $200 million in a new private equity venture. Next up, Sycamore is a startup building what it calls the trusted agent operating system for the enterprise. And they just raised a $65 million seed round. They are focused on providing infrastructure for deploying and managing AI agents in enterprise settings with built in trust. And again, as the topics we just went through in this episode, that should be no surprise. There's a big need for that. Next up, Google released Gemma 4, which it calls its most capable open model to date and it's built from the same kind of infrastructure and research as Gemini. And this is really notable because you can actually run this model for free locally. It is extremely capable and super super powerful at the same time. Meta has introduced what they call Muse Spark. This is the first model from their research rebuilt Meta Superintelligence Lab. Alexander Wang, who runs that, noted that the team rebuilt their AI stack from scratch nine months ago and started work on this model. So far it's getting decent reviews it sounds like in at least certain areas. After kind of some flops from previous LLAMA model releases, Anthropic has launched Quad Managed Agents. These are frameworks for getting AI agent applications to production fast faster. So the engineering blog post about this details the architecture for essentially decoupling the brain from the hands is how this architecture works, separating the reasoning model from the tools it uses so it can make a agent deployment much more scalable and reliable. Google has added notebooks to Gemini, so these are basically kind of that second brain topic we're discussing. The feature lets users organize project sources and AI conversations into persistent work workspaces. Microsoft has introduced multimodal intelligence in Copilot's Researcher feature, which allows it to pull from multiple AI models for deeper research tasks within Microsoft 365. So if you have access to this in your account, you may want to check that out. Microsoft and Publicist Group, one of the world's largest advertising holding companies, have expanded their strategic partnership to power the future of agentic marketing for businesses worldwide wide. Pika, the AI video generation startup, released the beta of its first product with face and voice capabilities, enabling AI generated characters that can speak with realistic lip sync and expressions in conversations. We alluded to this la this next one. In previous topics, Anthropic published new research on how emotion concepts function inside clock, so it investigates whether LLMs that sometimes appear to express emotions actually have internal representations that correspond respond to those expressions. In other news, SpaceX has filed confidentially for an IPO targeting a valuation of more than $2 trillion. Google Research published a paper on how to responsibly disclose quantum computing vulnerabilities, which could very prominently affect cryptocurrency security. So they are making much more of an effort and I'm sure we'll keep talking about this in the future, about the eventual impact of quantum computing on the current standards of encryption. And finally, OpenAI introduced a child Safety Blueprint, a set of guidelines and tools designed to help developers building on OpenAI's APIs implement safeguards against child exploitation and harmful content involving minors. Okay, Paul, that was a very packed week. One final announcement here. Go to take our AI Pulse survey this week that we had mentioned at SmartRx AI forward slash pulse. We're going to ask this week about some of the big prominent leaks we've had about Claude Mythos, kind of how your feeling about AI companies operational security. We're also going to ask where you stand on job displacements. And some economists are changing their minds. So Paul, thanks again for breaking everything down for us. This is a packed couple weeks.
- [105:25]
A
You get under 90 minutes but I think we slipped over a little bit but hopefully we stuck with us. And it's all interesting. It's. There's just so much to do every week, truly. But good to be back next week. We're thinking we're gonna have a regular episode. I. I have to travel, but we think we found a way to make it work. So unless something changes, we'll be back next week with our regular weekly and then we will probably actually have a second one next week for Intro to AI. So yeah, back on schedule hopefully. All right, thanks Mike.
- [105:57]
B
Thanks Paul.
- [105:59]
A
Thanks for listening to the Artificial intelligence show. Visit SmarterX AI to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in person events, taken online AI courses and earned professional certificates from our AI Academy and engaged in the SmartRx Slack community. Until next time, stay curious and explore AI.