Chapter 1: What is the main topic discussed in this episode?
Welcome to the podcast. I'm your host, Jaden Schaefer. 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 for
basically upending a lot of what open claw 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 what they 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 you
Thank you for watching. 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 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 $8.99 a month, and we have annual plans to 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 into 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 without needing a massive hardware setup. You know, something like Lama 4 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. The, like, 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.
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Chapter 2: What is the main focus of Meta's Gemini 4?
I mean, that's literally where the term break it down came from. 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 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. Alright, Meta has just debuted MuseSpark.
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.
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Chapter 3: How does Gemini 4 compare to other AI models?
They brought him on 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 OpenAnthropic 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 a good showing, right? Meta's 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 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 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 Lama strategy, which they were really aggressively open source for years. Lama 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 and 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.
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Chapter 4: What are the implications of Gemini 4's licensing for businesses?
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 Anthropix, you know, Doom model that can like hack, you can find vulnerabilities in every software platform.
And so, you know, anthropics like we can't give this to the general public. We're giving it to a handful of people. And so opening eyes like, look, we got one of those two. You also have to think like if Meadow 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 sources 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, 2024 or something was like, you know, we got to have like a six month break on releasing any new models because they're too good. And it's like, wow, how times have changed. Those models seem so archaic now. Anyways, it's an interesting point and 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 can 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 can 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 $8.99 a month. Anyways, I'll catch you guys all in the next episode.
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