Welcome to the podcast, 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, 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.
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,
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The link is in the description if you want to try it out.
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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 20 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 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 100000 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 20 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 for a 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 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.
And 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, you know, six day work weeks and 16 hours a day on Claude code and Claude co work, 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 Lillipod this week.
Basically, it's a beast.
It's about 1000 NVIDIA Blackwell Ultra GPUs and they're delivering over 9000 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 2000 molecular ideas per target year.
And because every experiment requires, you know, physical synthesis and lab testing.
Eli Lillipod removes all of that bottleneck and they're creating what's essentially a computational dry lab.
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 assistants, 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 Lily is promising in terms of kind of accelerating drug development.
Then I think a lot of the downstream impacts on patients is huge.
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.
Actually, I'm really stoked about AI and healthcare.
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.
Um, if it doesn't make them more money, so anyways, they're gonna probably discover some awesome drugs that solve a lot of things and they're gonna make a lot of money from it.
So pessimistically, i don't know it's, i guess, good and 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, 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 AI, they're able to get outputs for 100 times less energy.
And the accuracy is tripled.
And then they're going to, I mean, what's interesting though, is this is basically a lot closer.
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 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 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 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 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 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's a good showing, right?
Meta's sort of in the races still, but I would say they're not really, because when they're like hey, we came up with a new model and it's number four out of, Basically what's happening is every three months 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.
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 uh, 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 open AI platforms. 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.
They're like chat GPT-4 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.
You can't just talk to your phone and any software you could ever imagine gets built and you can go use it instantly.
Thank you so much for watching.
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.
