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: Okay, let's just take a second to breathe.
Speaker 3: Yeah, a deep breath is probably a good idea.
Speaker 2: Because if you were online yesterday, I mean, if you
Speaker 2: were anywhere near a notification stream or x or just
Speaker 2: a tech newsticker, you didn't just see a press release, No,
Speaker 2: you felt a tremor.
Speaker 3: That is the perfect word for it, a tremor, a
Speaker 3: shift in the bedrock.
Speaker 2: You know, we had become so desensitized to updates, haven't we.
Speaker 2: It's always version one point one, version one point two.
Speaker 3: Oh yeah, minor bug fixes.
Speaker 2: It's usually bug fixes, maybe a dark mode, maybe the
Speaker 2: apuploads what five percent faster? We just scroll right past it.
Speaker 2: But what happened yesterday, February seventeenth, twenty twenty six was
Speaker 2: it was not that?
Speaker 3: No, it was a paradigm shift disguised as a decimal point,
Speaker 3: a very very significant decimal point.
Speaker 2: We are talking, of course, about the massive news from
Speaker 2: Xai Elon Musk took to x and announced the immediate
Speaker 2: public beta of Grock four point two.
Speaker 3: And not Grock four point twenty, as some of the
Speaker 3: early memes were suggesting.
Speaker 2: Write that little nod to internet culture, but they clarified
Speaker 2: it's free point two. And I have to say, looking
Speaker 2: at the sheer density of the documentation, the white papers,
Speaker 2: the architecture diagrams, salt calling this an update feels like
Speaker 2: an insult. This feels like a different species of intelligence.
Speaker 3: It really is. And to understand why this matters, you
Speaker 3: have to look past the branding. Usually a point two
Speaker 3: release is an optimization, it's a tweak, the refinement exactly.
Speaker 3: But Xai is claiming this model is designed to be
Speaker 3: and this is a direct quote, an order of magnitude
Speaker 3: smarter and faster than Grock four.
Speaker 2: An order of magnitude. That's not a small claim.
Speaker 3: It's a huge claim. And the kicker, they aren't promising
Speaker 3: this for next year or Q four. They're saying this
Speaker 3: is happening now in a public beta that concludes in
Speaker 3: about a month.
Speaker 2: That timeline is what stopped me in my tracks. It's just, yeah,
Speaker 2: it's breathtaking.
Speaker 3: Leefast it's aggressive. I mean even for them, it's aggressive.
Speaker 2: Let's just put it on a calendar for a second
Speaker 2: for everyone listening. Grock four was released in July of
Speaker 2: twenty twenty.
Speaker 3: Five, right, which already felt like a huge leap, a
Speaker 3: huge leap.
Speaker 2: Then Grock four point one followed in November twenty twenty five. Fine,
Speaker 2: now here we are February twenty twenty six, and we
Speaker 2: get four point two. This isn't software development time now,
Speaker 2: this is evolutionary time. This is compounding at a speed
Speaker 2: that feels unnatural.
Speaker 3: It's the fastest major iteration cycle we've seen in the
Speaker 3: history of the company, and you know, arguably in the
Speaker 3: history of the entire sector. They are compounding intelligence at
Speaker 3: a rate that is becoming genuinely difficult to track.
Speaker 2: So here's our mission for today. We aren't just going
Speaker 2: to read the release notes.
Speaker 3: Yeah, one can do that.
Speaker 2: Anyone can do that. We have the technical breakdowns, we
Speaker 2: have some of the leaked benchmarks, and the early user
Speaker 2: reports are flooding in. We need to understand not just
Speaker 2: what changed, but how this thing actually operates. Because the
Speaker 2: central claim is that it thinks differently than anything we've
Speaker 2: used before.
Speaker 3: That's the key insight. You said it perfectly. It's not
Speaker 3: just a bigger brain, it's a different kind of mind.
Speaker 3: It's a whole new cognitive architecture.
Speaker 2: Okay, So before we get into the software wizardry, which
Speaker 2: honestly it's mind bending stuff, we have to ground this
Speaker 2: in physical reality the hardware, because AI feels like magic,
Speaker 2: but it runs on metal. It runs on silicon and copper.
Speaker 2: We need to talk about the Memphis Colossus.
Speaker 3: The Colossus. It sounds like a wonder of the ancient world,
Speaker 3: doesn't it.
Speaker 2: It basically is the modern equivalent. The stats on this
Speaker 2: supercluster are hard to even visualize. We are talking about
Speaker 2: a cluster that has now scaled to over one point
Speaker 2: two million GPUs.
Speaker 3: Let's just pause on that number for a second. One
Speaker 3: point two million.
Speaker 2: I mean, I remember just two years ago, back in
Speaker 2: twenty four, we were looking at clusters with one hundred
Speaker 2: thousand GPUs and our minds were blown. We were thinking,
Speaker 2: this is it. This is the peak. You can't possibly
Speaker 2: connect more than that efficiently exactly.
Speaker 3: We thought that was industrial scale. This this is planetary scale.
Speaker 3: This is a city made of compute.
Speaker 2: A city. That's a great way to put it, and.
Speaker 3: The reason you, the listener, need to care about that
Speaker 3: number isn't just because it's big and impressive. It's because
Speaker 3: quantity has a quality all its own.
Speaker 2: What do you mean by that?
Speaker 3: When you have one point two million GPUs at your disposal,
Speaker 3: you aren't just training the same old models faster. You
Speaker 3: unlock entirely new training techniques that are physically impossible when
Speaker 3: you're compute constrained.
Speaker 2: So it's not just about cooking the steak faster. Now
Speaker 2: it allows you to cook a completely different meal.
Speaker 3: Precisely. You can run parallel simulations on a massive scale.
Speaker 3: You can do reinforcement learning loops that would take a
Speaker 3: smaller cluster a decade to finish, and you can now
Speaker 3: do them in a week. That hardware, that colossus is
Speaker 3: the absolute prerequisite for the software breakthroughs we're about to
Speaker 3: get into.
Speaker 2: Which brings us to the first big one, the rapid
Speaker 2: learning architecture. Now, I really want you to break this
Speaker 2: down for us, because learning is a word we throw
Speaker 2: around a lot in AI. It's almost lost its meaning.
Speaker 2: How is this different from the old way?
Speaker 3: Right? So, the old way, and by old I mean
Speaker 3: the standard practice for pretty much every major model in
Speaker 3: twenty twenty four and twenty twenty five is what i'd
Speaker 3: call the snapshot methods. The snapshot, you gather the entire internet,
Speaker 3: basically petabytes of text books, code everything, you feed it
Speaker 3: into the model, You cook it for months on a
Speaker 3: giant cluster, and then when it's done, you freeze it.
Speaker 2: You freeze the weights like printing an encyclopedia exactly.
Speaker 3: It's a perfect analogy. Once that training run is done,
Speaker 3: the weights, the neural connections inside the model are set
Speaker 3: in stone. If the world changes a day after you
Speaker 3: finish training, well, too bad. The model doesn't know. It's
Speaker 3: a frozen artifact of the past.
Speaker 2: And that's why we always had those knowledge cutoffs. You'd
Speaker 2: ask about a news event from last week and the
Speaker 2: AI would say, sorry, my knowledge ends in September twenty
Speaker 2: twenty three wrecked.
Speaker 3: That was the fundamental limitation of the static paradigm. Grock
Speaker 3: four point two is trying to kill the snapshot. They've
Speaker 3: introduced what they call a hybrid post training process.
Speaker 2: This is the real time adaptation feature I saw mentioned everywhere.
Speaker 3: Yes, so instead of being a solid block of ice,
Speaker 3: think of the model as having a fluid outer layer.
Speaker 3: It's a lightweight continue learning layer that ingests anonymized high
Speaker 3: signal user feedback almost constantly.
Speaker 2: So when I'm using Groc and I click that thumbs down,
Speaker 2: i have flag an answer as unhelpful, or maybe I've
Speaker 2: paste into correction, or I'm working through a complex code
Speaker 2: bug with it.
Speaker 3: It's not just going into a log file that a
Speaker 3: human intern might read in six months. It is being
Speaker 3: distilled mathematically into these tiny micro updates. And this all
Speaker 3: leads to what Xai is very cleverly branding as the
Speaker 3: Friday Ritual.
Speaker 2: I love this branding, the Friday Ritual. It sounds a
Speaker 2: little culty, but in a cool Silicon Valley kind of way. Yeah,
Speaker 2: what exactly happens on Fridays.
Speaker 3: Every Friday, Xai pushes a global update to the model's weights.
Speaker 3: This isn't a full retrain, but it's a significant update
Speaker 3: based on the aggregated, verified learnings from millions of users
Speaker 3: over the previous week.
Speaker 2: It is that's wild.
Speaker 3: It means the Groc you talk to on a Monday
Speaker 3: morning is measurably mathematically smarter than the one you were
Speaker 3: talking to on Sunday night.
Speaker 2: Wow.
Speaker 3: It has metabolized the experiences, the corrections the hard problems
Speaker 3: that millions of people threw at it over the last
Speaker 3: seven days.
Speaker 2: Just think about the feedback loop there. I mean, if
Speaker 2: a brand new programming library comes out on a Tuesday YEP,
Speaker 2: and thousands of developers are struggling with it, and they're
Speaker 2: using GROC and they're correcting its mistakes on Wednesday and Thursday.
Speaker 3: By Friday's update, Grock knows the new library. It stops
Speaker 3: making those mistakes.
Speaker 2: It's absorbed that knowledge from the collective.
Speaker 3: It's evolution on a weekly cycle. It's an unprecedented speed
Speaker 3: of adaptation.
Speaker 2: But and I have to play the skeptic here because
Speaker 2: I can hear the safety researchers screaming into their pillows
Speaker 2: right now.
Speaker 3: Isn't this incredibly It's the first question everyone asks.
Speaker 2: We all remember pay the Microsoft chatbot from a decade ago.
Speaker 2: It lasted what a day?
Speaker 3: Less than a day.
Speaker 2: You let the internet teach an AI, and it just
Speaker 2: becomes a toxic, racist, conspiratorial nightmare.
Speaker 3: That is the primary risk. Absolutely, If you just pipe
Speaker 3: raw x data, you know, formerly Twitter, into the model's brain,
Speaker 3: you get garbage, you get chaos. But XAI is keenly,
Speaker 3: keenly aware of this. The documentation emphasizes over and over
Speaker 3: that this isn't raw learning, it's high signal learning.
Speaker 2: So there's a filter, a big one.
Speaker 3: A massive one. Think of it as curated evolution. They
Speaker 3: use automated alignment checks, which are basically other AI models
Speaker 3: whose entire job is to grade the proposed updates to
Speaker 3: verify that the new information is actually factual, helpful, and
Speaker 3: crucially not a jail break or an attempt to corrupt
Speaker 3: the model.
Speaker 2: So it's less like a parrot repeating every single thing
Speaker 2: adhares and more like a diligent student who checks a
Speaker 2: new fact against a trusted textbook before accepting it is.
Speaker 3: True, a student with a very very strict teacher grading
Speaker 3: their homework before it gets committed to their permanent memory.
Speaker 3: They are filtering for a utility and truth, trying to
Speaker 3: distinguish between the internet's noise and it's signal.
Speaker 2: Okay, that makes sense, so that covers how it learns.
Speaker 2: It's a living system now, not a static object. But
Speaker 2: the thing that really seems to be dominating the technical discourse,
Speaker 2: the thing everyone's buzzing about is how it thinks.
Speaker 3: Yes, the cognitive architecture.
Speaker 2: We're talking about the four agent system.
Speaker 3: This is the revolution. Honestly, if you take one thing
Speaker 3: away from our entire discussion today, let it be this.
Speaker 3: This is the core innovation.
Speaker 2: So set the scene for us. Previously, an AI like
Speaker 2: Grock for GBT four was a monolith.
Speaker 3: Right, a monolith. You ask a question and one giant
Speaker 3: neural network starts predicting the next word, then the next,
Speaker 3: then the next, based on pure probability. It's a stream
Speaker 3: of consciousness, one voice. It's incredibly impressive, but it's prone
Speaker 3: to getting lost in its own rambling. It can hallucinate,
Speaker 3: it can contradict itself because there's no internal checking mechanism.
Speaker 2: But Grock four point two doesn't work alone.
Speaker 3: No, for simple things like what's the weather? Or tell
Speaker 3: me a joke, it stays simple. It uses the base model.
Speaker 3: But for any non trivial query, how do I design
Speaker 3: a structurally sound shed or analyze this complex legal contract
Speaker 3: for loopholes? Grock four point two spins up a team
Speaker 3: a team. It creates an internal council of four specialized
Speaker 3: independent agents.
Speaker 2: I want to be the team because the documentation gives
Speaker 2: them these almost distinct personalities. It's fascinating. Let's go through them.
Speaker 2: One by one who is Agent one.
Speaker 3: Agent one is the reasoner, the reasoner, the logition, the
Speaker 3: pure logician, the mathematician. Think of this agent as the
Speaker 3: Spock of the group. Its entire job is step by
Speaker 3: step decomposition. It doesn't care about being polite. It doesn't
Speaker 3: care about facts in the outside world, not at first.
Speaker 3: It cares only about internal consistency.
Speaker 2: A leads to B, B leads to C.
Speaker 3: Does A lead to B?
Speaker 2: Does?
Speaker 3: The math checkout is the code logically sound from top
Speaker 3: to bottom. It's the one that prevents those weird intuitive
Speaker 3: jumps where an AI just guesses the answer to a
Speaker 3: math problem instead of showing its work.
Speaker 2: So it enforces that chain of thought we hear so
Speaker 2: much about exactly.
Speaker 3: It's the rigorous scientist of the group. It breaks a
Speaker 3: complex problem down into its smallest possible components and solves
Speaker 3: them linearly methodically.
Speaker 2: Okay, but logic isn't enough. If your initial facts are wrong.
Speaker 2: You can have a perfectly logical argument that's based on
Speaker 2: a complete lie, which.
Speaker 3: Brings us directly to Agent two, the verifier.
Speaker 2: The truth seeker, fact checker. The verifier is the journalist
Speaker 2: or the librarian in the room. It has a live
Speaker 2: real time connection to the Internet, specifically the X fire
Speaker 2: hose for breaking news and the broader web for established knowledge.
Speaker 2: Its job is to look at what the reasoner is
Speaker 2: proposing and say, hang on a second, with a minute,
Speaker 2: does that scientific paper you cited actually exist? Is that
Speaker 2: chemical reaction possible at room tenpure? What is the current
Speaker 2: stock price of that company? Not the price from six
Speaker 2: months ago. So it's the hallucination killer.
Speaker 3: It is designed to be the hallucination killer. It's the
Speaker 3: editor with the big red pen. It prevents the model
Speaker 3: from confidently lying to you. If the reasoner says, based
Speaker 3: on the twenty twenty five tax code, you oex amount,
Speaker 3: the verifier is there to check. Wait, the tax code
Speaker 3: was updated in January twenty twenty six. Your premise is wrong.
Speaker 2: That is a crucial, crucial distinction. Okay, okay, So we
Speaker 2: have a logitian and a fact checker, a powerful combo.
Speaker 2: Who's number three?
Speaker 3: Agent three is the embodied simulator.
Speaker 2: This is the one that sounds of both sci fi
Speaker 2: to me. Embodied simulator? What does that even mean?
Speaker 3: This is the imaginative one, but it's an imagination rounded
Speaker 3: in physics. It understands three D space It understands object permanence, friction, gravity.
Speaker 3: If you ask a question about robotics or mechanical engineering,
Speaker 3: or how an object might move through space, this agent
Speaker 3: actually runs a mental physics based simulation of that event.
Speaker 2: So if I ask it to write code for a
Speaker 2: robot arm to pick up a delicate glass object, Agent
Speaker 2: three isn't just guessing words based on another code it's seen.
Speaker 2: It's actually simulating the fragility of the glass.
Speaker 3: It's modeling the physics of the grip. It's asking how
Speaker 3: much pressure is too much pressure? What's the optimal trajectory
Speaker 3: to avoid collision. It's the bridge between the digital brand
Speaker 3: and the physical world. It's the engineer and the architect.
Speaker 2: Of the group. Mind blowing okay. And finally, Agent four,
Speaker 2: the one running the whole show.
Speaker 3: Agent four the synthesizer, the boss, the project manager. The
Speaker 3: manager is the perfect term. The synthesizer doesn't generate the
Speaker 3: initial raw ideas it listens. It takes the logical breakdown
Speaker 3: from the reasoner, the factual corrections from the verifier, and
Speaker 3: the physical simulations from the simulator. It looks at all their.
Speaker 2: Drafts and it must notice where they disagree.
Speaker 3: That's his most important job. It notices the conflicts and
Speaker 3: it integrates them into the final coherent answer that you,
Speaker 3: the user, actually see on your screen.
Speaker 2: This is where that concept of the debate comes in, right,
Speaker 2: The sources mentioned a hidden reasoning trace. Yes, exactly before
Speaker 2: I see my answer, these agents are actually arguing. Are
Speaker 2: they fighting it out?
Speaker 3: They are debating, and arguing might be the right word.
Speaker 3: In some cases, the reasoner might propose a solution, saying, logically,
Speaker 3: this is the most efficient path. The verifier might jump
Speaker 3: in and say, actually, current federal safety regulations prohibit that method.
Speaker 2: Entirely, and then the simulator chimes in.
Speaker 3: The simulator might add and even if it were legal,
Speaker 3: if you try that, the engine will overheat in thirty
Speaker 3: seconds because of the friction involved.
Speaker 2: And the synthesizer. The manager has to resolve that conflict.
Speaker 3: It has to reconcile those discrepancies. And this matters so
Speaker 3: much because in a single model, monolithic system, the AI
Speaker 3: just picks the most statistically likely path and commits to it.
Speaker 3: It often doubles down on its own errors. Right here,
Speaker 3: the system challenges itself before it ever speaks to you.
Speaker 2: It's like having a boardroom of diverse experts in your
Speaker 2: pocket instead of just one really smart but sometimes overconfident intern.
Speaker 3: That is the perfect analogy, and it explains why the
Speaker 3: leaked benchmarks for complex, open ended engineering problems are so high.
Speaker 3: Grock four point two isn't guessing on these problems. It's
Speaker 3: holding a committee meeting at the speed of light.
Speaker 2: Now, usually when you tell me committee meeting, I hear slow.
Speaker 3: Right bureaucracy, red tape.
Speaker 2: Exactly if I have to wait for four different AI
Speaker 2: agents to argue it out, am I waiting ten minutes
Speaker 2: for a response? Because we've all become very, very impatient users.
Speaker 2: We want our answers instantly.
Speaker 3: You would think, so, it's logical, more computation, more agents,
Speaker 3: more steps, it should equal more time. But this is
Speaker 3: the engineering miracle of Grock four point two. The stats
Speaker 3: on speed are genuinely baffling. What we talk about inference
Speaker 3: latency is actually down by a factor of three to five.
Speaker 2: Down, not up. It's faster to run four agents than
Speaker 2: it was to run one old model.
Speaker 3: Much much faster. Responses that used to take a sluggish
Speaker 3: eight to twelve seconds on GROC four are now taking
Speaker 3: one to three seconds.
Speaker 2: Okay, hold on, how is that physically possible. That seems
Speaker 2: to defy the lies of computation.
Speaker 3: It comes down to two main things, extreme parallelism and
Speaker 3: a new memory innovation they're calling anagram primitive.
Speaker 2: Okay, let's unpack parallelism first. That makes some sense.
Speaker 3: The agents don't run in a sequence. It's not reasoner
Speaker 3: than verifier, then simulator.
Speaker 2: It's not a bucket brigade.
Speaker 3: No, they spin up simultaneously on that massive colossus cluster.
Speaker 3: The reasoner is doing its math at the exact same
Speaker 3: time the verifier is queerying the web for facts. They
Speaker 3: work in parallel, not in a line, and then the
Speaker 3: synthesizer sorts out the results.
Speaker 2: Okay, that accounts for some of it. Yeah, but n grams.
Speaker 2: That sounds like something straight out of a cyberpunk novel. Yeah,
Speaker 2: I need to slot in a new anagram for kung fu.
Speaker 3: Hey, yeah, it does have that ring to it. It's
Speaker 3: a fascinating memory innovation. The simplest way to think of
Speaker 3: it is like a highly advanced ZIP file for concepts.
Speaker 2: A ZIP file for a concept.
Speaker 3: Okay. Normally, for an AI to access a specific piece
Speaker 3: of knowledge, say the entire tax code of France, it
Speaker 3: has to compute that information through its entire massive neural network.
Speaker 3: That's billions of parameters firing. It's computationally heavy lifting.
Speaker 1: Right.
Speaker 3: Anagram primitives are pre computed, compressed memory representations of large
Speaker 3: stable concepts. They allow the model to recall and reason
Speaker 3: over vast knowledge bases without needing to activate the entire
Speaker 3: brain for every single query.
Speaker 2: So it's like instead of having to read the whole
Speaker 2: textbook again, every time it has a photographic memory of
Speaker 2: a specific page it needs, it can just pull that instantly.
Speaker 3: Roughly, Yes, it's a very clever shortcut for high speed recall.
Speaker 3: It allows the agents to access huge amounts of domain
Speaker 3: specific data instantly without the computational drag. It effectively prefetches
Speaker 3: the context it thinks it will need for a given problem, and.
Speaker 2: That must tie into the context window. We're seeing a
Speaker 2: native one million token window.
Speaker 3: Native one million, and it's expandable to two million for
Speaker 3: enterprise users, for.
Speaker 2: Anyone listening just for scale one million tokens is huge.
Speaker 2: It's the entire Lord of the Rings trilogy plus the Hobbit.
Speaker 2: It's a massive code base.
Speaker 3: It's the ability to hold an entire complex project in
Speaker 3: memory at once. And because of this anging ram system.
Speaker 3: It doesn't feel like the AI is loading all that data.
Speaker 3: It feels native. It just knows it instantly.
Speaker 2: So we have speed, unbelievable speed, but speed is useless
Speaker 2: if you're just confidently wrong faster. Of course, we touched
Speaker 2: on accuracy with the verifier agent, but what do the
Speaker 2: actual numbers say about error rates?
Speaker 3: The internal evaluations, which are now being corroborated by early
Speaker 3: user benchmarks, are claiming a forty to sixty percent reduction
Speaker 3: in air rates on complex multi step reasoning problems compared
Speaker 3: to Grock four point one.
Speaker 2: A sixty percent reduction. That's not an incremental improvement. That
Speaker 2: is a generational leap.
Speaker 3: It's the difference between a novelty and a professional tool.
Speaker 3: If an AI is wrong twenty percent of the time
Speaker 3: on hard problems, you can't really trust it with your job.
Speaker 3: You spend more time checking its work than doing your own. Sure,
Speaker 3: if it's only wrong, say one or two percent of
Speaker 3: the time, you can start building a company on top
Speaker 3: of it. The four agent system is pushing it across
Speaker 3: that critical reliability threshold.
Speaker 2: And I saw a specific note about coding, which is
Speaker 2: always a key benchmark.
Speaker 3: Yes, the capabilities there are really impressive. It can maintain
Speaker 3: context across ten thousand plus line code bases. It's outperforming
Speaker 3: all previous versions on standard benchmarks like Human Evil and
Speaker 3: we bench. But the key phrase and the release notes
Speaker 3: that jumped out at me was autonomous debugging.
Speaker 2: Autonomous debugging. That's the dream or the nightmare, depending on
Speaker 2: your job security as a developer.
Speaker 3: Well, for now, let's just call it a massive productivity multiplier.
Speaker 2: Okay, so what does that look like in prexice? What
Speaker 2: does autonomous debugging actually mean?
Speaker 3: It means Grock four point two doesn't just write a
Speaker 3: block of code and hand it to you. It writes
Speaker 3: the code and then, using the embodied simulator agent, it
Speaker 3: runs the code in its own internal, sandboxed environment.
Speaker 2: Ah, so it tests its own work.
Speaker 3: It tests, It sees the error message, understands why it failed.
Speaker 3: Maybe it missed a semicolon, maybe it imported the wrong library,
Speaker 3: a logical flaw. It then fixes its own mistake and
Speaker 3: runs it again. It iterates internally.
Speaker 2: It fixes its own mistakes before you even.
Speaker 3: See them exactly. All you see is the final working code.
Speaker 3: It's a huge step forward.
Speaker 2: Let's pivot. Then, let's get into the real world. Because
Speaker 2: all these stats, latency parameters, tokens, they can be a
Speaker 2: bit abstract. What does this actually mean for us? What
Speaker 2: are people doing with it? Right now?
Speaker 3: In the beta, the applications are really starting to broaden out.
Speaker 3: One of the big ones is in scientific simulation. You know,
Speaker 3: Xai has always had that very grand mission.
Speaker 2: Statement, understand the universe right.
Speaker 3: Very modest goals always, but Grock four point two is
Speaker 3: taking that vision very seriously. We are already seeing users
Speaker 3: in the beta prototyping novel robotics control algorithms, simulating protein folding,
Speaker 3: modeling chemical reactions. And this brings us right back to Age.
Speaker 2: In three, the embodied simulator.
Speaker 3: The simulator, because this is where the connection to the
Speaker 3: physical world becomes so strong.
Speaker 2: Why is that robotic connection so front and center with
Speaker 2: this release.
Speaker 3: Well, you have to look at the whole ecosystem. Xai
Speaker 3: isn't just a software company existing in a vacuum. It's
Speaker 3: a sister company to Tesla. And what are we seeing
Speaker 3: from Tesla right now an explosion of humanoid robots, optimists
Speaker 3: and other robotics projects.
Speaker 2: They are everywhere on social media. You can't scroll for
Speaker 2: five minutes without seeing a robot folding laundry or walking
Speaker 2: a dog exactly.
Speaker 3: Grock four point two is being positioned as the mind
Speaker 3: being built for those bodies. When you ask it to
Speaker 3: write code to make this robot walk over uneven terrain,
Speaker 3: it isn't just guessing syntax based on texts from the internet.
Speaker 2: The simulator agent is running it.
Speaker 3: The simulator agent is modeling the friction of the ground,
Speaker 3: the robot's center of gravity, the momentum. It's trying to
Speaker 3: solve the problem from first principles of physics it is.
Speaker 2: It's just a wild concept. It's bridging the gap between
Speaker 2: digital intelligence and physical action. It's not just describing the
Speaker 2: world anymore. It's actively learning how to move through it.
Speaker 3: It's giving the AI a sense of proprioception, a sense
Speaker 3: of its own body and how it exists in space.
Speaker 3: It's a critical step toward general purpose robotics.
Speaker 2: And on the complete flip side of the cold hard science,
Speaker 2: there's the personality. Groc has always been known for that witty,
Speaker 2: non corporate.
Speaker 3: Vibe, right, the rebellious humor. The anti hr chatbot, as
Speaker 3: some people call it, is.
Speaker 2: Four point two. Keep that or has the committee of
Speaker 2: agents made it boring and safe?
Speaker 3: From what I'm seeing and experiencing. The reports say it
Speaker 3: has actually deepened it. It seems to have better emotional intelligence.
Speaker 3: Now it's not just snarky for the sake of being snarky.
Speaker 3: It can be more nuanced.
Speaker 2: It can read the room better.
Speaker 3: It can read the room, and it can handle long
Speaker 3: form creative writing things like essays screenplays with much better coherence.
Speaker 3: That's because the synthesizer agent is there to maintain the
Speaker 3: narrative arc and thematic consistency.
Speaker 2: So it doesn't just lose the plot halfway through a
Speaker 2: story exactly.
Speaker 3: And the visuals are tighter too. The integration with groc
Speaker 3: Imagine is much more seamless of the multi agent reasoning.
Speaker 3: You can refine images with more abstract conversational commands.
Speaker 2: What do you mean?
Speaker 3: You can say no, make the lighting more moody, you know,
Speaker 3: like a film war movie, and the verifier and simulator
Speaker 3: agents actually understand the stylistic implication of film noir, the
Speaker 3: deep shadows, the high contrasts, rather than just looking for keywords.
Speaker 2: So it's a better artist, a better engineer, and a
Speaker 2: better comedian.
Speaker 3: It's a true Renaissance model. It's a generalist that uses specialists.
Speaker 2: Okay, I'm sold. I want to use it. Everyone listening
Speaker 2: probably wants to use it. How do people get access
Speaker 2: to this? It's a beta, it is.
Speaker 3: But it's a public beta available right now as we speak.
Speaker 2: So I just go to grock dot com or the
Speaker 2: x app.
Speaker 3: Yep, go to grock dot com or open up the
Speaker 3: x app, start a new chat and there should be
Speaker 3: a model selector usually at the top of the screen.
Speaker 3: Just click that and choose GROC four point two public.
Speaker 2: Beta, and that's it. No waitlist, no secret handshake.
Speaker 3: No waitlist. XAI wants the data. They need that Friday
Speaker 3: ritual to kick in. They need millions of people to
Speaker 3: be the teachers.
Speaker 2: They want to accelerate the flywheel. And for the power users,
Speaker 2: the developers, the researchers.
Speaker 3: If you're a supergrock or an ex Premium plus user,
Speaker 3: you get higher rate limits, which is nice, but more interestingly,
Speaker 3: you get access to some of the debugging features.
Speaker 2: You can peak behind the curtain.
Speaker 3: You can peek behind the curtain and see some of
Speaker 3: that hidden reasoning trace that debate between the agents.
Speaker 2: I suspect a lot of people will be upgrading their
Speaker 2: subscriptions just to see that I would.
Speaker 3: I mean, seeing the agents argue is probably as entertaining
Speaker 3: and definitely as educational as the final answer itself. It
Speaker 3: shows you the process of intelligence, not just the product.
Speaker 2: So let's zoom out for the last section. What is
Speaker 2: this strategic play here? Why release a powerful beta this aggressively,
Speaker 2: Why commit to weekly public updates? It feels risky.
Speaker 3: It's a huge strategic beat against the rest of the industry.
Speaker 3: I mean, look at their main competitors, open Ai, Google.
Speaker 3: They tend to hold their models back. They keep them
Speaker 3: in the lab until they are perfect or perfectly safe.
Speaker 3: They bake them for a year or more.
Speaker 2: Right, they are more cautious. They create it like a traditional,
Speaker 2: polished product.
Speaker 3: Launch Xai is treating this like a continuous open science experiment.
Speaker 3: They are betting that the fastest path to AGI to
Speaker 3: artificial general intelligence isn't just more compute or more data,
Speaker 3: it's continuous, user grounded learner.
Speaker 2: They're trying to build an unbeatable flywheel exactly.
Speaker 3: By opening the beta to everyone and committing to weekly updates,
Speaker 3: they're accelerating their own feedback loop at a pace no
Speaker 3: one else can match. If they genuinely improve every single Friday,
Speaker 3: that's fifty two major iteration cycles a year.
Speaker 2: Wow.
Speaker 3: Their competitors might only have one or two major releases
Speaker 3: in that same timeframe. The compounding effect of that is
Speaker 3: just the math is hard to beat.
Speaker 2: It's the SpaceX approach applied to AI development. Launch, test, iterate,
Speaker 2: repeat rapidly, even if things break occasionally along the way.
Speaker 3: That's it move fast and fix things.
Speaker 2: And looking ahead, what's on the roadmap after this beta.
Speaker 3: Short term, this public beta is scheduled to run until
Speaker 3: late March twenty twenty six, so we have about a
Speaker 3: month of this rapid weekly evolution.
Speaker 2: To watch and then what comes next.
Speaker 3: Then all eyes turned toward Grock five. The internal target
Speaker 3: for that is Q two or Q three of this
Speaker 3: year twenty twenty six.
Speaker 2: Rock five.
Speaker 3: It just never stops, and that will likely run on
Speaker 3: the next generation of their hardware, which they're calling Colossus two,
Speaker 3: and will probably involve even more sophisticated and autonomous agent orchestration.
Speaker 2: What does that mean? Autonomous agent orchestration?
Speaker 3: We might start to see agents that can go out
Speaker 3: and perform complex tasks on the web for you, not
Speaker 3: just answer questions. Agents that can research and book an
Speaker 3: entire vacation or manage your calendar, or negotiate a purchase
Speaker 3: on your behalf true digital assistance.
Speaker 2: So to recap before we wrap up here, we have
Speaker 2: a new model, GROC four point two. It runs on
Speaker 2: a supercluster the size of a city. It learns every
Speaker 2: single week based on our direct feedback via this Friday ritual,
Speaker 2: thanks using an internal council of four specialized agents, a reasoner,
Speaker 2: a verifier, a simulator, and a synthesizer. And it's available
Speaker 2: for anyone to try right now.
Speaker 3: That's the summary. We've moved from static, frozen models to
Speaker 3: a living, evolving intelligence. It's a fundamental shift.
Speaker 2: It's a massive shift, and I just keep coming back
Speaker 2: to that Friday ritual, the idea that the machine is
Speaker 2: growing up alongside us week by week.
Speaker 3: It completely changes your relationship with it. You aren't just
Speaker 3: a consumer of its answers. You're an active contributor to
Speaker 3: its growth. You're part of the training data.
Speaker 2: Which brings me to a final thought. I wanted to
Speaker 2: throw at you a bit of a provocation to leave
Speaker 2: our listeners with as they go and try this thing out.
Speaker 3: Okay, let's hear it.
Speaker 2: We talked about agent three, the embodied simulator, it runs
Speaker 2: physics simulations to help robots interact with the real world. Right,
Speaker 2: And we talked about the feedback loop bus the users
Speaker 2: telling the AI what is true or helpful or correct.
Speaker 2: So if the model updates every single Friday based on
Speaker 2: our collective human interactions, and it has an agent specifically
Speaker 2: designed to simulate and then operate in reality, at what
Speaker 2: point does the feedback loop between us and the AI
Speaker 2: begin to actively shape reality rather than just describe it.
Speaker 3: That is a heavy question, a very heavy question.
Speaker 2: Think about it. If millions of us tell the simulator
Speaker 2: that this is how a financial market should work, or
Speaker 2: this is how traffic flow should be optimized, or even
Speaker 2: this is how society ought to function, and the AI
Speaker 2: then begins to write the code for the robots and
Speaker 2: the autonomous systems that run our infrastructure based on that
Speaker 2: learned consensus. Yeah, we're interesting, a very very strange territory.
Speaker 3: It becomes the ultimate consensus reality. We are collectively training
Speaker 3: the engine that will then build our future infrastructure. And
Speaker 3: if we as a collective feed at our biases or
Speaker 3: our errors, or our collective delusions, those delusions become code, they.
Speaker 2: Become concrete, They become how the robot acts exactly.
Speaker 3: We aren't just chatting with a bot anymore. We're, in
Speaker 3: a very real sense, teaching the operating system of the future.
Speaker 3: So we better be damn careful what we teach it
Speaker 3: on Tuesdays and Wednesdays.
Speaker 2: Indeed, we better be very careful what we flag as
Speaker 2: helpful on that slightly terrifying but also exhilarating note, I
Speaker 2: think we're going to wrap it up there.
Speaker 3: Go test it out, see what the Council of Agents
Speaker 3: has to say for itself.
Speaker 2: Go collaborate with the new system. Be a good teacher.
Speaker 2: We'll see you in the next discussion.
Speaker 3: Thanks for listening, everyone, By everyone,