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Welcome to the podcast. I'm your host, Jaden Schafer. Today we have an absolutely packed lineup for the show. The biggest thing that I've been super excited about. Yesterday Anthropic unveiled their hosted AI agents platform. It's their Claude platform. I've been playing around with it a ton. There's so much there and I think this is a really big shift, basically upending a lot of what OpenClaw did, but there's some nuances so we're going to get into that. In addition, Meta just dropped their very first model that was built with Alexander Wang. Remember that's formerly the CEO of Scale AI with a kind of acquired him in. We also have a research team at Tufts that figured out how to cut AI energy consumption by a factor of hundreds, which is definitely a big deal if you think about how much power these data centers are burning through. Eli Lilly just flipped the switch on what they're calling the most powerful AI human supercomputer in pharma. Over a thousand Blackwell GPUs which are aimed at cutting drug development timelines in half. OpenAI published a set of policy proposals that include robot taxes and a four day work week, which is probably the most OpenAI thing I've read in a while. And Google released Gemma 4, which is their latest open source model that's getting a lot of attention for what it can do relative to its size. I mean, basically this is an edge model that you can put on devices. So a lot to cover in the show today. Before we get into that, I want to mention AI Box, which is a tool I use every single day at this point, if you haven't checked it out, it gives you access to over 80 AI models in one place. This is my own startup, so. So instead of paying for separate subscriptions to Claude, ChatGPT, Gemini and everything else for, you know, 11 labs for audio or tons of the image models. You get all of that on one platform for 8.99amonth. And we have annual plans give you 20% off that. It honestly pays for itself pretty fast when you're not stacking three or four different AI subscriptions. The link is in the description if you want to try it out. You get access to every single top AI model in the world, basically all the best ones in one place. So you're not juggling tabs and juggling subscriptions. All right, let's get to the first story, which is Google Gemini. I want to talk about their new open source situation with Gemini 4. So earlier this week they released Apache 2.0 license and basically this is their latest family of open models built specifically for reasoning and agentic workflows. What I think is really interesting about Gemini 4 is what Google is calling, you know, the best intelligence per parameter ratio in any open model right now. Basically you're getting the frontier level capabilities of what you'd expect out of something like cloud or chatgpt or without needing a massive hardware setup. You know, something like Llama for Maverick requires that huge hardware setup and so you're basically getting around that. The model already has over 400 million downloads and the community has spun up over 100,000 variants, which I think just kind of tells you how quickly developers are adopting this. I think the significance is that it's less about kind of the benchmarks and it's more about the trend. Right. The gap between open source and closed source models is definitely shrinking and I think that Gemini 4 is just another data point in that direction if we want to get into kind of the licensing on this. The Apache 2.0 license is also really important because it means that companies can actually use this commercially without worrying about any sort of restrictive terms. I remember when Llama first came out from Meta and they were like, look, it's like an open source model and it's like, well, it's not really open source, it's just like, you know, open weight and like you can use it, but if you really want to use it for something commercial, you got to let us know. And there was like all this kind of, I don't know, it was very unclear. And I think Meta is just getting right around this. For anyone that's building agents or doing, you know, reasoning heavy work on their infrastructure, this is probably the most capable option that you can run locally right now. And I think given everything that we're going to talk about with Meta in a minute, the open source AI community really needs models like Gemini 4 to keep delivering. All right, let's talk about OpenAI. They just published a set of policy proposals this week which they are outlining how they think wealth and work should be restructured and what they're calling the quote, unquote, intelligence age. Now, maybe this is a big marketing thing. It feels like, you know, OpenAI and Anthropic, they have like these, these big, huge, I don't know, visions for the future and how AGI is going to take over the world. And sometimes it feels like that's a bit of a, a bit of a marketing stunt. I think what's interesting, OpenAI is essentially saying, you know, AI is going to displace a lot of jobs and here's how we think society should handle it. And they're kind of combining like traditional left leaning ideas like wealth redistribution with a very kind of market driven capitalistic framework. It's pretty deliberate. I think they're kind of attempting to position themselves as a responsible player in all of this. Now, whether any of this is actually going to have any sort of influence on policy, I think that's definitely a different question entirely. Tech companies publishing policy papers doesn't exactly have a strong track record for changing legislation. But I do think it matters that the company is building some of the most capable AI systems and they're at least putting kind of a stake in the ground on the economic consequences. You know, there's a bunch of funny things in there, right? They're like, we should have like a four day work week and it should be powered by AI productivity gains. It's not the worst pitch in the world that I've ever heard. At the end of the day though, I just don't know if it's going to get there. And personally, if I'm being 100% honest from my own experience, and I think a lot of people like myself, Instead of doing four day work weeks, I'm now doing six day work weeks and 16 hours a day on Claude Code and Claude Cowork because I can get so much done. But maybe at some point the hype will die down and I'll return to a more reasonable cadence. I hope. Who knows, right? All right. Eli Lilly has inaugurated what they're calling Lilypod this week. Basically, it's a beast. It's about 1,000 Nvidia Blackwell Ultra GPUs and they're delivering over 9,000 petaflops of AI performance. So it's basically the world's first Nvidia DGX superpod with DGX B300 systems. And it's the most powerful AI factory wholly owned by a pharmaceutical company. Right. So of course there's other AI companies that have more powerful systems right now, but for pharmaceutical companies, this is the most powerful one. I think the numbers that really were kind of shocking to me is that historically, even productive drug research teams can analyze about 2,000 molecular ideas per target year. And because every experiment requires, you know, physical synthesis and lab testing, Eli Lillypod removes all of that bottleneck and they're creating what's essentially a computational dry lab. So, so they're doing this at a huge scale. Scientists can Simulate and evaluate billions of molecular hypotheses in parallel before committing to physical experiments. Right. So the AI is going to sort of simulate what happens when you merge these molecules together. And then it's like, look, these are the ones that look promising to actually try. So the goal is to cut the traditional 10 year drug development timeline about in half. That's what Eli Lilly is trying to do right now. Nvidia is kind of working on this. They've also invested up to a billion dollars over five years in an AI co innovation lab in the Bay Area. To me this is just a really big example of AI delivering really like legitimate tangible value. You know, not just in the tech industry. I think we talk a lot about like chatbots or coding assistance, but using AI to find new medicines faster. That is an application that I think could actually change millions of lives. If Eli Pod delivers even half of what Lilly is promising in terms of kind of accelerating drug development, then I think a lot of the downstream impacts on patients is hu. And of course the profits for Eli Lilly are going to be massive. So they're going to be thrilled. And I'm just going to, I don't know, I don't like to be too pessimistic because actually I'm really stoked about AI in health care and I think there's a lot of benefit. Somehow I have no hope in pharmaceutical companies because I feel like there's a lot of solutions that they don't talk about if it doesn't make them more money. So anyways, they're going to probably discover some awesome drugs that solve a lot of things and they're going to make a lot of money from it. So pessimistically, I don't know, it's I guess good and bad it, whatever it is what it is. Okay, let's talk about neuro symbolic AI cutting energy use by 100x. I think this is from a bunch of researchers at Tufts University. It's one of the biggest stories in this list. I think if you kind of look at this from a long term perspective. The team which is led by Matthias Schutz at the School of Engineering, they essentially they built this neuro symbolic AI system and it achieves a 95% success rate on structured manipulation tasks while using just 1% of the training energy that standard vision language action models require. Okay, I know that sounds like a big mouthful. Let me explain it in the Most simple terms. 100 times less energy and nearly triple the accuracy is what they've been able to achieve. So when they're, when they're training these systems, when they're using these, when AI, they're able to get outputs for 100 times less energy and, and the accuracy is tripled. Basically the approach they took is merging some traditional neural networks with symbolic rule based reasoning. Instead of kind of throwing these massive compute at pattern matching, the system is kind of breaking the problem into a bunch of smaller logical steps and categories and then they're going to. I mean what's interesting though is this is basically a lot closer to I think, how humans actually think through a problem. Right. When you have a big problem you think of, okay, what are the steps to like achieve that? You kind of break it down in your head. I mean that's literally where the term break it down came from. And, and they're just teaching the AI model to do this in a more human way. And surprise, surprise, it saves energy. Obviously our brains are designed to not you, you know, burn too much energy when we're trying to figure stuff out. I think this matters because US data centers and also AI workloads now consume more than 10% of the entire country's total electrical output. Like this is so much electricity and I think that number is projected to double by 2030. So if neuro symbolic approaches can deliver this kind of efficiency gain across more domains, I think it's going to make a really big impact. Not just, you know, some people are like, oh, it's awesome for the environment. Yeah. It's also awesome for economics. Right. If you could do this for 95 or 100 times, you know, more efficient, that saves you a ton of money for these companies. Or I put another way, it makes this AI way cheaper for the user to use, which I think is really awesome right now. It's still a proof of concept. So we're not going to see this into production models today. But the direction I think of kind of where they're going with this is promising. I think kind of the broader industry is going to pay close attention to this because if they're able to achieve this, like I mentioned for, you know, the bottom line on all of these companies, it's going to be amazing. All right, META has just debuted Muse Spark. This is their first AI model that has come out under, they have new leadership. Alexander Wang, who came from Scale AI, when Meta acquired, it came over to Meta. This is the first model that's been put out under him. They had a whole bunch of kind of reorganization as it felt like Meta was falling behind. I mean, still feels like they're falling behind. But they had this huge reorganization. They brought him as the CEO in June last year they spent $14 billion to acquire Scale AI and him, they bought 49%, a non voting stake in the company. So essentially they kind of acquired it. But in any case, in terms of capability, Meta says that Musespark is competitive with the leading models from OpenAI, Anthropic and Google across a bunch of different things. It ranked fourth on the Artificial Analysis Intelligence index with a score of 52. It's really good at figuring out and understanding and medical reasoning, but it does not surpass basically all of the top models across the board. So it's, it's a good showing, right? Meta's like sort of in the races still, but I would say they're not really because you know, when they're like, hey, we came up with a new model and it's number four out of like, like basically what's happening is every three months what the top lab comes out with their newest model and it beats everyone in the benchmarks and they can take a victory lap and say I'm the best, right? And we saw this. It's not just, you know, Anthropic and OpenAI and goog Google, like Grok is also in the mix too. So somewhere between those four models, they're constantly kind of beating each other. And so if Meta comes out and is like, hey look guys, we're number four, like what? Maybe they slightly better than grok. And behind OpenAI, anthropic and meta, it's just like, yes, they could get integrated into all of the Meta kind of, you know, WhatsApp and Instagram and Facebook, they get some users from that, but it's not like any can be anyone's go to model. So yeah, I mean I think they're, they're pushing forward, but it's not that impressive. What I do think is really interesting though is musespark is a closed model. So this is a huge pivot from Meta's kind of Llama strategy strategy which they were really aggressively open source for years. Llama is one of the most important things to happen to the open source AI community. And I think now Meta is going in the opposite direction. So the model's design and code isn't going to be made public. I think it tells us a couple things. Number one, Meta clearly believes that they need a competitive closed model to keep up with OpenAI, anthropic kind of at the frontier because in the past when they were doing all the open Source. They were like, look, our model's not quite as good, but it's open source and, well, sort of open. It's like open weights or whatever they were calling it. And they're like, yeah, you can like run it on your own computer and stuff. And that was cool. That did have a big draw. A lot of people that didn't want to have to pay to run these models were grabbing it or hosting it, and so that was cool. Now that they're going closed source, I think they got to be a lot more. I mean, their model has to get a lot better. The open source strategy was really good for adoption and kind of developer goodwill, but it was not winning the race as far as the best company goes. Now there are other people working on open source and I hope that open source models get, you know, continue to get pushed. Not every task that you need an AI model to do needs like the absolute greatest, you know, frontier model you could. There's plenty of tasks where you're sorting folders or files or searching for things or, you know, rewording something and like, you don't need anything crazy. And so I think a lot of those open source models would be great, would save compute and energy and all that. And so I hope that this still gets pushed forward and I also hope that we continue to make really powerful, really good open source models. But we'll see where this goes. It feels like Meta, right, for now is kind of getting closed source and it's also kind of at a weird place in the industry, right? We have OpenAI who just came out today and was like, hey, we have a model also that's just as good as Anthropic's, you know, doom model that can like hack. You can find vulnerabilities in every software platform. And so, you know, Anthropic is like, we can't give this to the general public, we're giving it to a handful of people. And so OpenAI is like, look, we got one of those two. You also have to think like, if meta was like, yeah, we have one too, and it's open source and anyone can use it, then everyone be like, oh no, you know, China, Iran, Russia, they're going to take over the world with it. So I think at the end of the day their argument is going to be that they got to close source this because the models are getting so big, it's too dangerous to have like an open source version. People were already making that claim years ago. I remember Elon Musk in like what, 2014, 20, 24 or something was, you know, we got to have like a six month break on releasing any new models because they're too good with like chat GPT4 and it's like, wow, how times have changed. Those models seem so archaic now. Anyways, it's, it's an interesting point, an interesting place. That's the argument they're going to make. But I'm sure in two years we're going to look back at the models we had today and be like, oh man, those things were so bad. Like you can't just like talk to your phone and any software you could ever imagine gets built and you could go like, use it instantly. So I'm sure things will change a lot. Guys, thank you so much for tuning into the podcast today. If you enjoyed this episode, I would appreciate it to no end. If you could leave a rating or review any sort of comment you drop on Apple. Really helps the show to get found by more incredible people like yourself. On Spotify, you have to hit the about tab. You could drop some stars. It helps out a lot. And as always, make sure to try out AI Box, my own startup. There's a link in the description. You get access to over 80 models and our automation builder. It's 8.99amonth. Anyways, I'll catch you guys all in the next episode.