Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Hello, and welcome back. We are looking at something today
Speaker 2: that on the surface feels a little bit like a
Speaker 2: classic David and Goliath's story. But the more I look
Speaker 2: at the research, the more I start to think maybe
Speaker 2: David is actually being secretly funded by another Goliath.
Speaker 3: That is a surprisingly, surprisingly accurate way to put it.
Speaker 3: It's definitely not the fairy tale version people might imagine, right.
Speaker 2: It's way more complicated. Yeah, we're talking about what is
Speaker 2: and I don't think this is an exaggeration arguably the
Speaker 2: defining technological rivalry of our era. It is absolutely talking
Speaker 2: about the battle between open source AI and big tech.
Speaker 3: It's huge, and honestly, the landscape is shifting so fast
Speaker 3: that by the time you're listening to this, the battle
Speaker 3: lines might have already moved again.
Speaker 2: That's how fast this whole thing is going. And I
Speaker 2: think for a lot of people, you know, the narrative
Speaker 2: feels pretty simple It's the scrappy underdogs, right, the hackers,
Speaker 2: the independent researchers, the global open source community.
Speaker 3: Right, the rebels, exactly, the rebels versus the big bad
Speaker 3: corporate giants. But looking at the material we have today,
Speaker 3: the reality is, well, it's a lot messier.
Speaker 2: It's so much messier and frankly, so much more interesting
Speaker 2: because this isn't just about who sells the most software
Speaker 2: or you know, who has the highest stock price. This
Speaker 2: is really about the fundamental structure of power. It's about
Speaker 2: who holds the knowledge, who owns the infrastructure, and ultimately
Speaker 2: who gets to shape the twenty first century.
Speaker 3: That is a heavy way to open the structure of power.
Speaker 2: I think it needs to be, because we have to
Speaker 2: move past this simple binary idea of open versus closed.
Speaker 2: It's not that simple. Both sides are winning in different arenas,
Speaker 2: and you know, both sides are facing these huge, almost
Speaker 2: existential threats they might not even fully see yet.
Speaker 3: Okay, so let's set the board. Then. If we're looking
Speaker 3: at this as a kind of conflict or maybe a game,
Speaker 3: who are the main players. Let's start with the heavy hitters.
Speaker 2: The big tech camp For sure, these are the names
Speaker 2: everyone knows, you have open Ai, which is of course
Speaker 2: heavily heavily backed by Microsoft the tens of billions of
Speaker 2: dollars camp that's a good name for.
Speaker 3: Them, exactly. Then you have Google deep Mind, which is
Speaker 3: the force behind the Gemini models. You've got Anthropic, which
Speaker 3: is a really interesting one because it was founded by
Speaker 3: ex OpenAI people, but now it's backed by both Amazon and.
Speaker 2: Google at a bit of a tangled web.
Speaker 3: There, a very tangled web. And then of course you
Speaker 3: have the quiet giant Apple doing its own thing, mostly
Speaker 3: focused on on device intelligence.
Speaker 2: Okay, so that's the fortress, that's the establishment. And on
Speaker 2: the other side, who are the rebels.
Speaker 3: The open source ecosystem? And this is where it gets much.
Speaker 3: It's more diffuse. It's not one company. It's a global network.
Speaker 3: You have incredible startups like Mistral and Friends. You have
Speaker 3: academic researchers and universities all over the world. You have hobbyists,
Speaker 3: and then you have this massive community coalescing on platforms
Speaker 3: like hugging face.
Speaker 2: Hugging Face I always love that name. It just sounds
Speaker 2: so friendly and innocent for something that is essentially an
Speaker 2: arms depot for AI models.
Speaker 3: It is a charming name. Isn't it. But the scale
Speaker 3: is anything but charming. It's deadly serious. They host hundreds
Speaker 3: of thousands of models. We're talking billions of downloads. It is,
Speaker 3: for all intents and purposes, the GitHub of the AI revolution.
Speaker 2: So we have the giants and we have the swarm,
Speaker 2: and the mission for us today is to figure out, well,
Speaker 2: who's actually winning, because looking through all the source material,
Speaker 2: the answer seems to depend entirely on how you define winning.
Speaker 3: Precisely, Winning the performance benchmark is one thing. Winning the
Speaker 3: enterprise market is another. Winning the hearts minds of developers
Speaker 3: that's a third. They're all different battles.
Speaker 2: So let's start with the giants. Let's talk about why
Speaker 2: big tech is so dominant. What is this fortress they've built?
Speaker 2: What are the walls made of?
Speaker 3: The first, and by far the thickest wall is what's
Speaker 3: called the compute mote.
Speaker 2: The compute mote. Okay, I want to pause on this
Speaker 2: because I think when people hear expensive, they think, okay,
Speaker 2: like a really nice car expensive, or maybe a house expensive.
Speaker 2: But looking at the numbers here, we are talking about
Speaker 2: nation state level spending, aren't we.
Speaker 3: We really really are to even begin to visualize what
Speaker 3: it takes to train a frontier model, the absolute bleeding
Speaker 3: edge systems like GPT four, Gemini Ultra. You have to
Speaker 3: start with the hardware, right, we aren't talking about the
Speaker 3: graphics card in your gaming PC. We're talking about the
Speaker 3: top of the line in Vidia H one hundreds, the
Speaker 3: AI chips.
Speaker 2: The chips that there's a global shortage of the ones
Speaker 2: everyone is fighting.
Speaker 3: Over the very same These chips are, you know, almost
Speaker 3: a strategic national resource at this point. A single one
Speaker 3: one of these costs as much as a luxury car.
Speaker 2: A single chip.
Speaker 3: Yes, now imagine you need to buy it, not one,
Speaker 3: but maybe twenty five thousand of them, twenty five thousand,
Speaker 3: and you have to tie them all together with specialized
Speaker 3: high speed networking cabling that costs more than most people's houses.
Speaker 3: You stick all that in a massive, custom built data
Speaker 3: center and you run it at one hundred percent capacity
Speaker 3: for ninety.
Speaker 2: Days straight, ninety days, NonStop.
Speaker 3: NonStop, twenty four to seven. And this is where the
Speaker 3: moat gets very, very physical. It's not just about buying
Speaker 3: the chips. It's about the physics, the heat.
Speaker 2: I saw a note here about thermal densities.
Speaker 3: That what you mean, that's exactly.
Speaker 2: It.
Speaker 3: If you put that much compute in one room, the
Speaker 3: air doesn't just get hot, it turns into a blast furnace.
Speaker 3: The racks would literally melt. So you need these incredible
Speaker 3: industrial scale liquid cooling systems, pipes of chilled water running
Speaker 3: to every single server rack.
Speaker 2: So it's not just a server room. It's a power
Speaker 2: plant and a plumbing project it is, and you.
Speaker 3: Need access to a power grid that can handle the
Speaker 3: load of a small city. We're talking hundreds of megawatts
Speaker 3: of sustained power. The electricity bill alone for a single
Speaker 3: training run, just the electricity can be tens of millions
Speaker 3: of dollars.
Speaker 2: Get to wrap my head around that. So when we
Speaker 2: say the kid in the garage is locked out of
Speaker 2: this game, it's not because the GID isn't smart enough.
Speaker 2: It's because the garage would literally melt and the entire
Speaker 2: neighborhood grid would blow a transformer.
Speaker 3: The garage would melt, the transformer would blow, and they'd
Speaker 3: get a bill for ten million dollars. So, yes, this
Speaker 3: is why the list of companies that can actually train
Speaker 3: a Frontier model from scratch is so incredibly short. It's
Speaker 3: basically Microsoft, Google, Meta, and Amazon. The barrier to entry
Speaker 3: is a capital expenditure of billions of dollars before you've
Speaker 3: written a single line of useful code.
Speaker 2: You use the analogy of a really talented carpenter in
Speaker 2: their backyard workshop versus a massive industrial factory that spans
Speaker 2: ten city blocks. It feels like that.
Speaker 3: That's a perfect analogy, and it doesn't even stop at
Speaker 3: the hardware. It's also the talent the people. Yes, the
Speaker 3: huge expertise required to orchestrate one of these massive training
Speaker 3: runs is incredibly rare. It's a kind of dark art.
Speaker 2: What do you mean by that.
Speaker 3: Well, when you have twenty thousand GPUs all trying to
Speaker 3: work in perfect parallel, things break all the time. Mysterious
Speaker 3: failures just happen.
Speaker 2: Like a cable comes loose or a network switch fails.
Speaker 3: It can be that simple, or it can be much weirder.
Speaker 3: A few GPUs might just start overheating and produce infinitesimally
Speaker 3: small errors in their calculations bad math. Now, in a
Speaker 3: normal computer program, you might get an error message it crashes.
Speaker 3: In a training run like this, those tiny errors can
Speaker 3: act like a poison. They can slowly destabilize the entire
Speaker 3: mathematical process. You might lose days of progress, or even worse,
Speaker 3: the model might end up subtly brain damaged in a
Speaker 3: way you don't even detect until weeks later when it
Speaker 3: starts giving bizarre answers.
Speaker 2: So the engineers running these things, yeah, they aren't just coders.
Speaker 2: They're more like nuclear reactor operators staring at dials trying
Speaker 2: to prevent a meltdown.
Speaker 3: They are absolutely high stakes problem solvers. They are staring
Speaker 3: at hundreds of graphs looking for tiny, almost imperceptible anomalies
Speaker 3: that suggest that AI's brain is getting sick. That level
Speaker 3: of operational expertise is a huge part of the moat.
Speaker 3: You can't just buy the chips and plug them in.
Speaker 3: You need the priesthood of engineers who know how to
Speaker 3: keep the beasts alive.
Speaker 2: And I'm guessing those people are very, very expensive.
Speaker 3: Extremely and they almost all work for the giants.
Speaker 2: Okay, so that's the hardware in the talent. The first
Speaker 2: wall of the fortress is thick. But there's another piece, right,
Speaker 2: the data.
Speaker 3: The data advantage. And this is where it gets a
Speaker 3: lot more subtle, but maybe even more.
Speaker 2: Powerful, because I think most people's first thought is, well,
Speaker 2: the Internet is open, right, can anyway, just scrape the
Speaker 2: web and get the same data you can.
Speaker 3: And the open source models do rely heavily on public
Speaker 3: data sets like the common crawl, but big tech has
Speaker 3: access to vast oceans of proprietary data that is completely
Speaker 3: invisible to the public web, like what for exams. Okay,
Speaker 3: just think about Google. They have over two decades of
Speaker 3: indexed search queries. They don't just have the text of
Speaker 3: the Internet. They know what billions of people are looking for,
Speaker 3: how they ferze their questions, and critically, what results they
Speaker 3: actually click on.
Speaker 2: Right, they don't just see the words, they see the
Speaker 2: intent behind the words.
Speaker 3: Precisely the intent. Think about Microsoft. They have deep integration
Speaker 3: into enterprise workflows. They'll see how businesses write emails and outlook,
Speaker 3: how they structure documents and word how they build presentations
Speaker 3: in PowerPoint. That's a huge unique data set about professional
Speaker 3: communication and meta the social graph decades of behavioral data,
Speaker 3: how people interact, what they like, what they share, the
Speaker 3: nuances of casual human to human conversation. And then you
Speaker 3: have Apple with behavioral patterns from millions of devices, understanding
Speaker 3: how people use apps.
Speaker 2: It seems like the difference between having a library of
Speaker 2: every book ever written, which is the public web, and
Speaker 2: having that same library plus a secret video recording of
Speaker 2: every single person who ever read a book, yeah, showing
Speaker 2: what pages they lingered on, what they highlighted, what made
Speaker 2: them laugh.
Speaker 3: That is a profound and perfect distinction. It's the quality
Speaker 3: versus quantity aspect. But there's an even bigger data advantage
Speaker 3: that builds on top of that, the feedback loop.
Speaker 2: Explain that, what do you mean by feedback loop?
Speaker 3: Big tech sees how users interact with their finished models
Speaker 3: in real time. Every time you use chat GPT and
Speaker 3: you don't like an answer, so you hit regenerate response.
Speaker 2: Open ai sees that they log that.
Speaker 3: They log it as a failure case every time you
Speaker 3: give a thumbs down to a response from Gemini. Google's
Speaker 3: reinforcement learning systems take note. They have this incredibly tight
Speaker 3: feedback loop with hundreds of millions of users constantly telling
Speaker 3: them what works and what doesn't. They are perpetually refining
Speaker 3: the model based on massive real world.
Speaker 2: Usage, and the open source community doesn't have that same
Speaker 2: direct line, not.
Speaker 3: In the same way. Now, when you download a model
Speaker 3: from hugging face and run it on your own laptop,
Speaker 3: the developers of that model have no idea what you're doing.
Speaker 3: They don't get that data back, because that's the whole.
Speaker 2: Point, right privacy. You run it locally so they can't
Speaker 2: see your data exactly.
Speaker 3: It's a feature, not a bug. But the cost of
Speaker 3: that privacy is that the model creators lose that invaluable telemetry.
Speaker 3: They're flying blind compared to Google or open ai, who
Speaker 3: have a god's eye view of how their creations are
Speaker 3: performing in the wild, so.
Speaker 2: Hearing all of that, the billions in compute, the rare talent,
Speaker 2: the proprietary data, the real time feedback loops, it honestly
Speaker 2: feels like game over. How can open source possibly compete?
Speaker 2: It feels like you're bringing a well crafted knife to
Speaker 2: a thermonuclear war.
Speaker 3: It absolutely looks that way on paper. The structural disadvantages
Speaker 3: are immense. But this is where the rebellion gets really
Speaker 3: really interesting, because despite all of that, open source is
Speaker 3: not only surviving in some very important areas, it's actually thriving.
Speaker 2: Oh how is that even possible? Is it just the
Speaker 2: sheer number of people working on it.
Speaker 3: That's a big part of it. But the main technical driver,
Speaker 3: the core reason they can compete, is a relentless focus
Speaker 3: on efficiency efficiency. The open source community, precisely because they
Speaker 3: don't have unlimited resources, has become world class at doing
Speaker 3: more with less necessity.
Speaker 2: Is the mother of invention, as they say.
Speaker 3: It's the perfect embodiment of that saying, when you don't
Speaker 3: have a billion dollars for your next training run, you
Speaker 3: have to get incredibly clever. And they've developed these amazing
Speaker 3: techniques like quantization.
Speaker 2: Okay, I keep seeing this word in the research quantization.
Speaker 2: It sounds like something out of a sci fi movie,
Speaker 2: but it seems absolutely critical to the open source survival guide.
Speaker 2: What is actually happening there.
Speaker 3: It is absolutely critical. The best way to think of
Speaker 3: it as like image compression. You know how a raw
Speaker 3: photo file from a professional camera can be huge, maybe
Speaker 3: fifty megabytes.
Speaker 2: Sure, it has all the raw sensor data, every bit of.
Speaker 3: Information exactly, But if you convert that photo to a
Speaker 3: JPIG it shrinks down to maybe two or three megabytes.
Speaker 3: You lose a tiny, tiny bit of detail. Maybe if
Speaker 3: you zoom in one thousand percent, the shadows aren't quite
Speaker 3: as perfect, But to the human eye the picture looks
Speaker 3: basically identicle and it.
Speaker 2: Loads way faster. In a website and takes up a
Speaker 2: fraction of the space on your hard drive.
Speaker 3: Precisely, quantization is basically doing that same trick to the
Speaker 3: AI's brain. These huge models are usually stored with incredibly
Speaker 3: high precision numbers. Think of every connection in the neural
Speaker 3: network having a weight that's like sixteen decimal places of accuracy,
Speaker 3: which takes.
Speaker 2: Up a ton of computer memory and processing power massive amounts.
Speaker 3: Quantization is a process that basically says, hey, do we
Speaker 3: really need sixteen decimal places of precision? What if we
Speaker 3: just round it down to four or even two?
Speaker 2: And the model doesn't just get stupid when you do that.
Speaker 3: That's the magic. It turns out for many of these models,
Speaker 3: you can round down those numbers very aggressively and the
Speaker 3: model only loses a tiny fraction of its overall intelligence.
Speaker 3: But suddenly a model that required a forty thousand dollars
Speaker 3: server with a ton of RAM can now run on
Speaker 3: a two thousand dollars gaming laptop.
Speaker 2: So they are literally shrinking the giant's brain so it
Speaker 2: fits inside a regular person's head.
Speaker 3: That is exactly what they're doing, and that's not the
Speaker 3: only trick. Then there's something called distillation, which is think
Speaker 3: of it like a teacher student relationship. You take a huge, smart,
Speaker 3: expensive model like GPT four and you use it to
Speaker 3: teach much smaller, cheaper model.
Speaker 2: How does that actually work in practice?
Speaker 3: You can, for instance, ask the big teacher model to
Speaker 3: generate thousands of perfect answers to questions on a specific
Speaker 3: topics a customer service. Then you take those thousands of
Speaker 3: perfect question answer pairs and you use them as the
Speaker 3: training data for the small student model.
Speaker 2: Ah, so the student learns from the master's work exactly.
Speaker 3: The student model might not know everything the professor knows
Speaker 3: about physics and poetry, but for that one specific task
Speaker 3: of customer service, it can get ninety percent of the
Speaker 3: way there for one percent of the cost to run.
Speaker 2: That's a huge leverage point. It's like you're borrowing the
Speaker 2: intelligence of the giant to train your own litle army.
Speaker 3: It is and because of techniques like this, the performance
Speaker 3: gap between the massive closed models and the nimble open
Speaker 3: models has narrowed again and again. The community is just
Speaker 3: relentless about optimization.
Speaker 2: And this all leads to what one of our sources
Speaker 2: calls the deployment victory. I found this concept fascinating. The
Speaker 2: idea that winning isn't just about having the single smartest
Speaker 2: brain in a lab somewhere, but about being the one
Speaker 2: that actually gets used out in the real world.
Speaker 3: This is such a critical distinction. There's a massive difference
Speaker 3: between the frontier, the absolute smartest, most capable model possible,
Speaker 3: and deployment what a company actually feels safe and comfortable
Speaker 3: putting into their production systems. Let's say you're a healthcare
Speaker 3: company you're handling sensitive patient records, or you're a bank
Speaker 3: and you're handling financial data. Are you really going to
Speaker 3: send all of that incredibly sensitive private data over the
Speaker 3: Internet to a third party API owned by big tech?
Speaker 2: Probably not, if you can avoid it. Your compliance department
Speaker 2: would have a heart attack. You want to keep that
Speaker 2: stuff locked down on your own servers. You definitely don't
Speaker 2: want opening ISIS and IS potentially reading your patient files exactly.
Speaker 3: You want to own the an entire stack. You need privacy,
Speaker 3: You demand security and control.
Speaker 2: I would imagine total control.
Speaker 3: Massive control. If you build your entire product on top
Speaker 3: of a proprietary model from a single company, you are
Speaker 3: taking on an enormous strategic risk. What happens if they
Speaker 3: decide to double the pricing next year?
Speaker 2: You're stuck.
Speaker 3: What if they change their roadmap and deprecate the version
Speaker 3: of the model you rely on. What if they go
Speaker 3: out of business or get acquired.
Speaker 2: You're building your entire house on land that you're just renting.
Speaker 2: They could evict you at any time.
Speaker 3: Precisely, open source offers stability and sovereignty. You download the model,
Speaker 3: weights there are yours. You run it on your own infrastructure,
Speaker 3: behind your own firewall. No one can ever take it
Speaker 3: away from you.
Speaker 2: So for the enterprise, you know, the big serious companies
Speaker 2: that are actually paying to implement this stuff in the
Speaker 2: real world, open source is often the better strategic bet.
Speaker 3: For many regulated industries. It's often the only acceptable option.
Speaker 2: There's another factor here too, which is just the sheer
Speaker 2: speed of innovation, the swarm intelligence. I want to dig
Speaker 2: a little deeper into that. Can you give me a
Speaker 2: concrete example of a time the open source community just
Speaker 2: completely outran the giants? Oh?
Speaker 3: Absolutely. The perfect example is the rise of mixture of
Speaker 3: experts architecture or MOE.
Speaker 2: Sounds fancy, but what is it? In simple terms?
Speaker 3: Okay, So, a traditional model is what we call dense.
Speaker 3: It's one single giant brain. Every time you ask it
Speaker 3: a question, the entire brain has to light up and think.
Speaker 2: About it, which is computationally expensive.
Speaker 3: Very and MOE model is different. Instead of one giant brain,
Speaker 3: the model is made up of many smaller expert submodels.
Speaker 3: Maybe you have eight different experts, and when you ask
Speaker 3: a question, there's a tiny router network at the front
Speaker 3: that decides which one or two experts are best suited
Speaker 3: to answer it.
Speaker 2: Like a receptionist at a big hospital directing calls.
Speaker 3: Exactly like that. Oh you have a coding question, I'll
Speaker 3: send you to the python expert. You have a question
Speaker 3: about ancient Rome, let's talk to the history expert.
Speaker 2: And why is that better?
Speaker 3: It's massively more efficient for any given thought. You're only
Speaker 3: activating a small fraction of the total brain, so it's
Speaker 3: much faster and much much cheaper to run, but you
Speaker 3: still get the benefit of the combined knowledge of all
Speaker 3: the experts.
Speaker 2: Okay, I get it. So how did the swarm outrun
Speaker 2: the giants on this?
Speaker 3: Well? Big tech has been using this technique internally for
Speaker 3: a while, but when the open source company Mistral released
Speaker 3: their first MOEE model, the community just exploded. They dissected it,
Speaker 3: they understood it, and they started building their own improved
Speaker 3: variants instantly. Within weeks, we had people figuring out clever
Speaker 3: hacks to run these incredibly complex architectures on consumer grade hardware, on.
Speaker 2: MacBooks, a laptop, a model with multiple expert brains. How
Speaker 2: is that even possible.
Speaker 3: It's possible because the community became obsessed with optimization. They
Speaker 3: found these brilliant ways to load the different experts in
Speaker 3: and out of your computer's memory so fast that you
Speaker 3: could run a model that on paper should never be
Speaker 3: able to fit on your machine.
Speaker 2: As that classic hacker spirit I love.
Speaker 3: Yeah.
Speaker 2: Big text answer is buy a bigger, more expensive server.
Speaker 2: The open source answer is no, let's rewrite the code
Speaker 2: to make it fit on the server we already have.
Speaker 3: That is the fundamental cultural difference right there.
Speaker 2: And look at the timeline in a big tech company.
Speaker 2: If a researcher has a brilliant new idea like.
Speaker 3: That, it has to be written up in a proposal,
Speaker 3: It has to go through a committee review, it has
Speaker 3: to get prioritized and put into the next quarters product roadmap.
Speaker 3: It can take months or even years to see the
Speaker 3: light of day.
Speaker 2: And the open source world.
Speaker 3: Someone reads a new academic paper on a Tuesday morning.
Speaker 3: By Tuesday night, some brilliant person has a working implementation
Speaker 3: of it on GitHub. By Wednesday, someone else has forked
Speaker 3: it and made it ten percent faster. By Friday, it's
Speaker 3: been integrated into the main community tools and is available
Speaker 3: for everyone in the world to download and use.
Speaker 2: It's like evolution on fast forward, a hyper revolution it is.
Speaker 3: It's a decentralized that iterates and improves immediately in parallel.
Speaker 3: Big tech has deep pockets and amazing researchers, but they
Speaker 3: simply cannot match that sheer, chaotic velocity of global experimentation.
Speaker 2: And then there's the geopolitical angle, which was something I
Speaker 2: hadn't fully considered before digging into this.
Speaker 3: It's huge. It's easy to forget that not every researcher
Speaker 3: or developer is sitting in Siliton Valley or London. If
Speaker 3: you're a brilliant AI researcher in a country that doesn't
Speaker 3: have easy access to US proprietary tech, maybe because of
Speaker 3: sanctions or just because of economics, you can't use the
Speaker 3: closed APIs. OpenAI might be blocked in your country or
Speaker 3: just be too expensive.
Speaker 2: So for a huge portion of the world, open source
Speaker 2: is the only game in town.
Speaker 3: It's a force for democratizing access. It means a student
Speaker 3: in India, a researcher in Brazil, a startup in Nigeria.
Speaker 3: They all get access to the same cutting edge tools
Speaker 3: that were until recently locked away inside a handful of
Speaker 3: American corporations.
Speaker 2: So we have this picture of the big tech fortress
Speaker 2: with all the money and data, and we have the
Speaker 2: open source rebellion with all the speed, the privacy in
Speaker 2: the global reach. But then then we have to talk
Speaker 2: about the meta paradox.
Speaker 3: Ah, Yes, the agent of chaos, the spanner in the works.
Speaker 2: This brings us to the weirdest, most confusing part of
Speaker 2: the whole map. You have meta Mark Zuckerbert's empire, which
Speaker 2: sits squarely, undeniably in the big tech camp. But then
Speaker 2: you look at what they're doing with their Lama models,
Speaker 2: and it looks for all the world like they're the
Speaker 2: primary arms dealer for the rebels.
Speaker 3: It's the single biggest plot twist of the last decade
Speaker 3: in tech. They are spending those billions and billions of
Speaker 3: dollars we just talked about building the data centers, burning
Speaker 3: the megawatts of electricity to build these massive state of
Speaker 3: the art brains and then they just put the weights
Speaker 3: on the internet for free.
Speaker 2: It feels like charity, which I'm going to go out
Speaker 2: on a limb here and assume Meta is not a charity.
Speaker 3: You would be correct. This is not altruism. This is
Speaker 3: one of the most ruthless and brilliant strategic moves in
Speaker 3: the history of the tech industry. It's a concept that
Speaker 3: economists call commoditizing the compliment.
Speaker 2: Commoditizing the compliment. Okay, break that down for us in
Speaker 2: plain English. What does that mean?
Speaker 3: Okay, think about it this way. If your core business
Speaker 3: is selling hot dogs, what do you want to be
Speaker 3: true about hot dog buns?
Speaker 2: I want them to be as cheap and as widely
Speaker 2: available as humanly possible free if I can manage it.
Speaker 2: Why Because the cheaper and easier it is to get buns,
Speaker 2: the more of my hot dogs people will buy. The
Speaker 2: bun is a necessary part of the equation. But it's
Speaker 2: not where I make my money.
Speaker 3: Right, The buns are the compliment to your main product. Now,
Speaker 3: look at Meta. What is their core business? What do
Speaker 3: they actually sell?
Speaker 2: They sell us, They sell our attention. They sell advertisements
Speaker 2: on Instagram and Facebook and WhatsApp.
Speaker 3: Correct, They do not sell cloud computing services. They don't
Speaker 3: sell subscriptions to AI models. For Google and Microsoft, the
Speaker 3: powerful AI model is the hot dog, it's the product.
Speaker 3: They need that model to be expensive and exclusive so
Speaker 3: they can charge you twenty dollars a month for hy
Speaker 3: access to it.
Speaker 2: But for Meta, the AI is just the bun.
Speaker 3: The AI is the bun. It's the complementary good, it's
Speaker 3: the infrastructure. By giving away their state of the art
Speaker 3: LAMA models for free, Mark Zuckerberg is trying to drive
Speaker 3: the market price of raw intelligence down to zero.
Speaker 2: Which completely scorches the earth. For his primary competitors, Google
Speaker 2: and Microsoft.
Speaker 3: It annihilates their business model. If any startup in the
Speaker 3: world can grab Lama three for free and build a
Speaker 3: fantastic business on top of it, why would they ever
Speaker 3: pay open Ai or Google huge licensing fees. Meta is
Speaker 3: strategically trying to make sure that no one else can
Speaker 3: build a gatekeeper position in the AI layer of the
Speaker 3: Internet that could one day threaten their advertising empire.
Speaker 2: That is absolutely rusiless. Okay. Another analogy, imagine if you
Speaker 2: were a company that made all its money selling shaving cream,
Speaker 2: and your main competitor made all their money selling expensive
Speaker 2: proprietary razors. You might start giving away really good razor
Speaker 2: handle for free. Yes, perfect, because if everyone has a
Speaker 2: free razor handle that works great, nobody needs to buy
Speaker 2: the competitors' expensive ones. But everybody still needs to buy
Speaker 2: your shaving crank.
Speaker 3: That is the perfect analogy. Meta is giving away the
Speaker 3: razor handles to protect it shaving cream business. They are
Speaker 3: scorching the earth so that their rivals can't build castles
Speaker 3: that might one day charge Meta a toll.
Speaker 2: And I'm guessing there are other benefits to Meta too, right,
Speaker 2: beyond just kneecapping their competitors.
Speaker 3: Oh, massive benefits. Remember that swarm intelligence we were just.
Speaker 2: Talking about, Yeah, the hyper evolution.
Speaker 3: The second Meta releases a new Lama model, thousands of
Speaker 3: the world's smartest developers immediately start hacking on it. They
Speaker 3: find bugs and they fix them. They figure out new
Speaker 3: ways to make it faster, they discover how to run
Speaker 3: it on cheaper hardware.
Speaker 2: And Meta gets all of that research and development for.
Speaker 3: Free, completely free R and D. They are effectively outsourcing
Speaker 3: a huge chunk of their innovation pipeline to the entire world.
Speaker 3: Plus they build up this incredible reservoir of goodwill with
Speaker 3: the developer community. Suddenly Meta isn't the bad guy from
Speaker 3: the privacy scandals. They're the good guy who is enabling
Speaker 3: open science and empowering the little guy. It's a masterful
Speaker 3: repositioning of their brand. They become the neutral Switzerland like
Speaker 3: party in the AI wars.
Speaker 2: That is some serious forty chess right there.
Speaker 3: It really is, and it completely complicates the whole narrative.
Speaker 3: It's no longer just corporations versus the people. It's corporation
Speaker 3: A versus corporation B, with corporation A using the people
Speaker 3: as a strategic labor against corporation B.
Speaker 2: So with all these complex pieces on the board, the fortress,
Speaker 2: the swarm, the chaotic agent in the middle, what is
Speaker 2: the current score If we were to freeze the game
Speaker 2: right now today, who is actually winning?
Speaker 3: Well, we have to be realistic about it. If we're
Speaker 3: judging purely on the frontier, the absolute cutting edge of
Speaker 3: raw capability, big tech still has the lead.
Speaker 2: The big proprietary models are still smarter.
Speaker 3: On the whole. Yes, if you look at the leaderboard
Speaker 3: for complex reasoning, for advanced coding challenges, for multimodal tasks
Speaker 3: that involve understanding both images and text. The flagship models
Speaker 3: from open Ai, Google and mpropic still consistently beat the
Speaker 3: best open models.
Speaker 2: So the gap is narrowing, but it's still there.
Speaker 3: It hasn't closed yet. And for users who need the
Speaker 3: absolute best in class performance, think of a doctor using
Speaker 3: AI to help diagnose a complex disease or a lawyer
Speaker 3: using it for intricate legal analysis, that small performance gap
Speaker 3: can really matter.
Speaker 2: And what about integration just getting it into the hands
Speaker 2: of normal people.
Speaker 3: Big tech wins there hands down. It's what we call
Speaker 3: the distribution.
Speaker 2: Advantage, which means what exactly it means.
Speaker 3: The AI is showing up almost invisibly inside the tools
Speaker 3: you already use every single day. It's Copilot appearing in
Speaker 3: Microsoft word, it's Gemini helping you write an email in
Speaker 3: Google Docs. It's AI photo editing build directly into your phone.
Speaker 2: Right. I don't have to go find a special website
Speaker 2: or installing it. It's just there. The friction is zero,
Speaker 2: zero friction.
Speaker 3: The open source alternatives might be fantastic, but you still
Speaker 3: have to make a conscious choice to go find them,
Speaker 3: download them, maybe configure them. Most people, most of the time,
Speaker 3: will take the path of least resistance. And big tech
Speaker 3: owns those paths.
Speaker 2: And this is just the simple brand factor.
Speaker 3: Right heask your neighbor, ask your parents to name a AI.
Speaker 3: They will say chat GPT. They won't say mistroll or
Speaker 3: Lama or mixt roll eight by seven B.
Speaker 2: That brand recognition drives usage, which drives.
Speaker 3: Revenue, and that revenue funds the next billion dollar training run.
Speaker 3: It's a powerful self reinforcing cycle.
Speaker 2: But there's some big X factors here, wild cards that
Speaker 2: could still tip the scales, and one of the biggest
Speaker 2: is the government regulation.
Speaker 3: The regulatory dimension. This is where the battle moves from
Speaker 3: the server room to the halls of Congress and Brussels.
Speaker 2: How does regulation play into this? My first assumption would
Speaker 2: be that everyone hates regulation, that it just gets in
Speaker 2: the way.
Speaker 3: Actually, no, and this is one of the most counterintuitive
Speaker 3: parts of the whole story. Big tech often quietly welcomes
Speaker 3: certain kinds of regulation.
Speaker 2: Really, why on earth would they want more rules?
Speaker 3: Because compliance is expensive?
Speaker 2: Wow.
Speaker 3: Of course, if the government passes a law that says,
Speaker 3: to legally release a powerful AI model, you must first
Speaker 3: perform ten million dollars worth of safety testing, formal auditing,
Speaker 3: and red teaming.
Speaker 2: Who can afford to do that, Microsoft, Google, the big guys.
Speaker 3: Exactly, and who can afford.
Speaker 2: To do that the two college students in a garage
Speaker 2: or a small, bootstrapped open source startup.
Speaker 3: Precisely expensive. Regulation, while sounding good on the surface, often
Speaker 3: ends up creating a massive barrier to entry. It protects
Speaker 3: the incumbents from new competition. It's a way for them
Speaker 3: to pull up the ladder behind them.
Speaker 2: So big tech might publicly say, oh, yes, please regulate us,
Speaker 2: this technology is so dangerous, while privately thinking this is fantastic.
Speaker 2: This will crush all the small fry competitors.
Speaker 3: It's a very common dynamic in mature industries. It's a
Speaker 3: form of what's called regulatory capture.
Speaker 2: And what's the argument from the open source side?
Speaker 3: They argue that you simply cannot regulate software in that way.
Speaker 3: Their core argument is essentially math is speech. You can't
Speaker 3: put the genie back in the bottle. You can't make
Speaker 3: it illegal to share a file of numbers.
Speaker 2: If I download it, I can run it.
Speaker 3: And if you regulate the US and European developers too heavily,
Speaker 3: the top talent and the innovation will just move to
Speaker 3: a country with looser rules. You don't stop it, you
Speaker 3: just offshore it.
Speaker 2: So there's a real fear that heavy handed regulation could
Speaker 2: actually hurt the good guys in the open community. Yeah,
Speaker 2: far more than it hurts the giants they're trying to
Speaker 2: rain in.
Speaker 3: That is the central fear. The final shape of regulation
Speaker 3: could end up deciding the winner of this war more
Speaker 3: than any single technological breakthrough.
Speaker 2: There's one more X factor I want to touch on,
Speaker 2: which is synthetic data. We talked earlier about how big
Speaker 2: tech has this huge data mode, but isn't there a
Speaker 2: movement now to just create your own data.
Speaker 3: Yes, this is a major frontier for the open source
Speaker 3: world and a potential moat killer.
Speaker 2: What is it exactly?
Speaker 3: Well, the problem is that you can run out of
Speaker 3: high quality text on the public internet to train your next.
Speaker 2: Model, on which I hear we are actually getting close
Speaker 2: to doing.
Speaker 3: We are getting surprisingly close. The really high quality, well
Speaker 3: written stuff is finite. So the idea is this, what
Speaker 3: if you use a really smart existing model like GBT
Speaker 3: four or claude to write new training data. You ask
Speaker 3: it to generate textbooks or to create thousands of high
Speaker 3: quality examples of Socratic dialogues, and then you use that
Speaker 3: perfectly clean AI generated data to train your next new model.
Speaker 2: So the AI is teaching the next generation of AI.
Speaker 3: Yes, exactly, isn't that.
Speaker 2: A bit incestuous? Don't you risk degrading the quality? It
Speaker 2: feels like making a photocopy of a photocopy of a photocopy.
Speaker 2: Eventually it just turns into a blurry mess.
Speaker 3: That is the number one risk. It's a very real
Speaker 3: phenomenon called model collapse or habsburg AI. If you aren't
Speaker 3: incredibly careful, the models do start learning their own mistakes,
Speaker 3: They start hallucinating more, their knowledge becomes weird and distorted.
Speaker 2: So how do you prevent that from happening?
Speaker 3: Meticulous curation and this is another area where the open
Speaker 3: source community swarm intelligence shines. You have organizations like a
Speaker 3: Luther AI and literally thousands of volunteers who treat building
Speaker 3: these data sets like a massive science project. They are
Speaker 3: manually cleaning data, filtering out the bad stuff, and rating
Speaker 3: the quality of the synthetic examples.
Speaker 2: So it's human verified synthetic data. A hybrid approach.
Speaker 3: Exactly, It's a hybrid and this is a very powerful
Speaker 3: technique for helping to close that data mode. If you
Speaker 3: can generate your own high quality training data. You don't
Speaker 3: need access to Google's private search logs nearly as much
Speaker 3: as you thought you did.
Speaker 2: That is fascinating. It really challenges that old idea that
Speaker 2: data is the new oil and only the giant oil
Speaker 2: barons have access to it. It turns out you can
Speaker 2: kind of synthesize your own oil if you're smart enough, if.
Speaker 3: You are smart enough and extremely careful.
Speaker 2: So let's try to bring this all together. Let's try
Speaker 2: to reach a verdict. We've looked at the advantages of
Speaker 2: stre the paradox as the wild cards. Who wins? Is
Speaker 2: there a clear winner in this battle for the future?
Speaker 3: You know. The honest answer, and it might be a
Speaker 3: little unsatisfying, is that it's not binary. It's not going
Speaker 3: to be one side standing victorious on the ashes of
Speaker 3: the other.
Speaker 2: So it's a split decision.
Speaker 3: I think it's a permanent coexistence. They're going to win
Speaker 3: different layers.
Speaker 2: Of the stack. Okay, break that down for me. Who
Speaker 2: wins what.
Speaker 3: Big tech wins the frontier for the foreseeable future, the
Speaker 3: absolute smartest, most powerful, most capable systems are going to
Speaker 3: come from the groups with the billions of dollars to
Speaker 3: spend on compute and the unique data feedback loops.
Speaker 2: Okay, so they own the top of the mountain.
Speaker 3: They will likely also win the broad consumer layer, the
Speaker 3: apps on your phone, the voice assistant in your car,
Speaker 3: the AI built into your search engine. That will be
Speaker 3: a big.
Speaker 2: Tech because of that convenience and distribution advantage.
Speaker 3: We talked about it, right, But open source is going
Speaker 3: to win the deployment.
Speaker 2: Layer, the infrastructure, the plumbing.
Speaker 3: The plumbing of the world. Yes, enterprise back ends, specialized
Speaker 3: tools for science and medicine, the infrastructure for finance. Most
Speaker 3: of that will eventually run on open models because of
Speaker 3: the critical need for cost effectiveness, control, privacy, and stability.
Speaker 2: So, if I'm hearing you right, big tech pushes the
Speaker 2: absolute ceiling of what's possible higher.
Speaker 3: And higher, and open source raises the floor for everyone
Speaker 3: and spreads that technology everywhere.
Speaker 2: That's a really interesting, almost symbiotic dynamic. They need each
Speaker 2: other in.
Speaker 3: A weird way, they really do. And frankly, I think
Speaker 3: the most important victory for open source might not be
Speaker 3: market share at all.
Speaker 2: What is it?
Speaker 3: Then, It's the establishment of a principle.
Speaker 2: What principles say, The principle.
Speaker 3: That AI capability doesn't have to be concentrated in a
Speaker 3: handful of corporations, that we don't have to live in
Speaker 3: a world where three companies in California hold all the
Speaker 3: keys to the future.
Speaker 2: It's an insurance policy for the world.
Speaker 3: It's exactly that open source ensures that the knowledge, the tools,
Speaker 3: and the town are distributed globally. It means that no
Speaker 3: matter what happens with corporate roadmaps or government regulations, the
Speaker 3: fundamental capability to build and understand these powerful systems belongs
Speaker 3: to everyone, not just to a few boardrooms.
Speaker 2: That's a really powerful thought. It shifts the focus from
Speaker 2: who makes the most money to who gets to participate
Speaker 2: in building the future.
Speaker 3: Exactly, it's about preserving human agency in the age of AI.
Speaker 2: You know, we've talked about winning in these broad strokes,
Speaker 2: but I want to ask you personally, as someone who
Speaker 2: analyzes this space every day, where do you put your
Speaker 2: chips if you had to bet on what the AI
Speaker 2: landscape looks like and say five years.
Speaker 3: Five years is an absolute eternity in AI time.
Speaker 2: Humor me, what's your gut feeling.
Speaker 3: I think we're going to see what I'd call a
Speaker 3: barbell distribution of intelligence. A barbell What do you mean
Speaker 3: On one end of the barbell, you'll have these massive, centralized,
Speaker 3: super intelligent cloud models owned by maybe two or three companies.
Speaker 3: This is what you'll use when you needed to solve
Speaker 3: cancer or design a fusion reactor, or plan a global
Speaker 3: logistics network.
Speaker 2: The really heavy lifting.
Speaker 3: The planetary scale heavy lifting. On the other far end
Speaker 3: of the barbell, you will have billions of tiny, highly efficient,
Speaker 3: highly personalized open source models running locally on our phones,
Speaker 3: our glasses, our laptops, maybe even our home appliances.
Speaker 2: The edge, the personal intelligence.
Speaker 3: Layer, the edge, yes, and the middle. I think the
Speaker 3: middle gets squeezed out. The medium sized general purpose proprietary
Speaker 3: models might just disappear because they won't be as smart
Speaker 3: as the giants or as cheap in private as the
Speaker 3: local models.
Speaker 2: So you're either a god in the cloud or you're
Speaker 2: a trusted personal assistant running in my pocket.
Speaker 3: That is where the economics and the user needs seem
Speaker 3: to be pointing.
Speaker 2: And open source in your view, owns that personal assistant layer.
Speaker 3: I think it has to, because I do not want
Speaker 3: Google or any single corporation owning the AI that runs
Speaker 3: on my glasses and sees everything I see, And here's
Speaker 3: everything I say I want that to be mine, verifiably
Speaker 3: private and under my contry, and open source is the
Speaker 3: only technological path to guarantee that that.
Speaker 2: Makes a ton of sense. It's the privacy and control
Speaker 2: argument again. It always seems to come back to that
Speaker 2: in the end.
Speaker 3: It almost always does.
Speaker 2: So as we wrap this up, what's the one thing
Speaker 2: our listeners should take away from all this when they
Speaker 2: see the next big headline about open AI or Google
Speaker 2: or nu Lama model, what lens should they view it through?
Speaker 3: I would say, try to look past the simple versus narrative.
Speaker 3: Don't just ask who has the higher school on the
Speaker 3: latest benchmark today, ask a different set of questions. Where
Speaker 3: is this technology actually being deployed and who controls that deployment?
Speaker 2: Follow the power and remember the meta paradox. Nothing is
Speaker 2: ever as simple as good guys versus bad guys.
Speaker 3: Never The chaotic strategic middle is where the real story
Speaker 3: is almost always hiding, and.
Speaker 2: I think that's a perfect place to leave it. We
Speaker 2: often think about technology as this linear race to a
Speaker 2: finish line, but maybe it's more about the kind of
Speaker 2: landscape we're building as we go. Who gets to build
Speaker 2: the roads, who owns the land and Are we allowed
Speaker 2: to build our own houses if we want to?
Speaker 3: That is the fundamental question. What kind of world do
Speaker 3: we want this AI to help us build? And who
Speaker 3: gets a real say in the blueprints?
Speaker 2: Something to mull over. Thank you for listening, Stay curious,
Speaker 2: stay skeptical, and we will see you next time.