Speaker 1: Welcome to the debate.
Speaker 2: So historically, if a if a systems engineer wanted to
Speaker 2: know if a newly designed airplane wing would, you know,
Speaker 2: snap under extreme aerodynamic pressure, they had to physically build
Speaker 2: the thing.
Speaker 3: Right. Yeah. Like an actual scaled thing. Physical model. Exactly.
Speaker 2: You build it, you stick it in a massive wind tunnel,
Speaker 2: you blast it with air, and you just literally watch
Speaker 2: the smoke flow over the curves.
Speaker 3: To see where the stress points are.
Speaker 2: Yeah, to see exactly where it fails. But today, we're
Speaker 2: standing at the edge of this radically different paradigm, right?
Speaker 2: Where AI is becoming the wind tunnel. But the industry
Speaker 2: is currently just totally fractured over what the fundamental architecture
Speaker 2: of that AI should actually be.
Speaker 1: And we're seeing this massive divergence in the source material today.
Speaker 3: We really are.
Speaker 1: It's a huge split.
Speaker 3: Right.
Speaker 2: On one side, we have this company, Accelerated Understanding. And
Speaker 2: they just unveiled a purely physics-based model. And it's capable
Speaker 2: of processing this staggering 5 trillion pieces of data at once.
Speaker 3: Which is just a crazy number.
Speaker 1: It's wild.
Speaker 2: But then on the other side, we have Moonshot's massive 2.8
Speaker 2: trillion parameter language model, Kemi3. And they're right now negotiating
Speaker 2: these unprecedented revenue-sharing deals with major U.S.
Speaker 1: Cloud giants. So my position on this is pretty clear.
Speaker 2: I argue that AI built on neural operators, which is
Speaker 2: a nature-centric, physics-first view of reality, is the absolute only
Speaker 2: logical path forward for systems engineering and industrial experts.
Speaker 3: Well, and I look at this entirely differently. I mean,
Speaker 3: I argue that massive language-centric architectures, you know, exactly like
Speaker 3: Moonshot's K3... they remain the superior and just the most
Speaker 3: practical baseline.
Speaker 1: Okay, but how?
Speaker 3: Because language models offer this generalized, multi-step reasoning capability. And
Speaker 3: that is simply indispensable for modern enterprise cloud infrastructure, regardless
Speaker 3: of what's happening in the physical lab.
Speaker 2: I mean, let's start right at the architectural foundation then.
Speaker 2: Because to me, trying to use a language model to
Speaker 2: solve a physical engineering problem is a foundational error.
Speaker 3: A foundational error? That's a strong phrase.
Speaker 1: I really think it is.
Speaker 2: It's fundamentally applying the wrong tool to the deepest problems
Speaker 2: we face. I mean, look at the breakthrough from Caltech
Speaker 2: professor Anima Anankumar and Benedict Jenick and Accelerated Understanding.
Speaker 3: Right, their new model.
Speaker 2: Yeah. What they've done is dispense entirely with the Google-invented
Speaker 2: transformer architecture.
Speaker 1: They just tossed it.
Speaker 3: Well, they're not trying to parse text, so... Exactly.
Speaker 2: They aren't parsing text. They are utilizing neural operators to
Speaker 2: model reality natively in space and time. It's a completely
Speaker 2: different approach.
Speaker 3: But I think we really have to clarify what you
Speaker 3: mean by applying the wrong tool. Because language isn't just
Speaker 3: about human conversation or, you know, writing a clever email
Speaker 3: or a poem.
Speaker 2: Sure.
Speaker 3: It is the ultimate abstraction of logic. When you look
Speaker 3: at Moonshot's Kimi K3, you're looking at an open-weight model
Speaker 3: with 2.8 trillion parameters.
Speaker 1: Which is massive, I'll give you that.
Speaker 3: Right. And based on the latest evaluations, it performs on
Speaker 3: par with the absolute elite frontier models. We're talking OpenAI's GPT-5.5
Speaker 3: and Anthropic's Claude Opus 4.8.
Speaker 1: Wait, but reasoning about what exactly?
Speaker 3: About complex logic.
Speaker 1: If you're reasoning about a text document, sure. But if
Speaker 1: you're reasoning about.
Speaker 2: Say, thermodynamics on a microchip, a transformer is ultimately just
Speaker 2: guessing the next most likely word based on.
Speaker 1: Its training data.
Speaker 3: I think that's a bit reductive.
Speaker 2: But let's unpack the mechanism for a second. Think of
Speaker 2: a transformer like a beads on a string model.
Speaker 3: Okay.
Speaker 2: It takes in a sequence of discrete units, right? Words
Speaker 2: or code snippets. And it predicts the next bead in
Speaker 2: the sequence based on statistical probability.
Speaker 3: Yeah, next token prediction. Right.
Speaker 2: It is inherently a human-centric view of intelligence because it's
Speaker 2: built on our language. Now, contrast that with a neural operator.
Speaker 3: Which works differently. Totally differently.
Speaker 1: Totally differently.
Speaker 2: A neural operator doesn't calculate point A, then point B,
Speaker 2: then point C. It maps the entire continuous flow of
Speaker 2: a physical system as a single mathematical object.
Speaker 3: As one continuous thing.
Speaker 2: Yes. They map what we call infinite dimensional function spaces.
Speaker 3: Okay. See, that sounds incredibly elegant in a mathematics paper.
Speaker 3: It does. But let's break down what that actually means
Speaker 3: for an enterprise on the ground.
Speaker 2: Well, it means it understands the entire field of reality simultaneously.
Speaker 2: not step by step.
Speaker 3: But how does an engineer use that?
Speaker 2: Look, expecting a transformer model to optimize next generation semiconductor
Speaker 2: materials is, it's like asking a master poet to build
Speaker 2: a suspension bridge. Right?
Speaker 3: A master poet?
Speaker 2: Yeah, based strictly on descriptions of bridges they've read in
Speaker 2: a library.
Speaker 3: Oh, I see where you're going. Right?
Speaker 1: Like, they might know the word tension.
Speaker 2: They might be able to write a beautiful, you know,
Speaker 2: logically coherent essay on steel. but they don't needively understand
Speaker 2: the underlying mathematical equations of load-bearing structures.
Speaker 3: Okay, but.
Speaker 2: Neural operators do. They don't learn about physics by reading textbooks.
Speaker 2: They learn the actual equations of the physical world directly.
Speaker 2: You just cannot text-predict your way to optimal thermodynamics.
Speaker 3: See, I have to challenge you on that bridge analogy,
Speaker 3: because I actually think it perfectly highlights the blind spot
Speaker 3: in the physics-centric approach.
Speaker 1: How so?
Speaker 3: Let's say your master architect, right, your neural operator, figures
Speaker 3: out the perfect mathematical state for that suspension bridge or
Speaker 3: that semiconductor.
Speaker 1: OK, it's mapped the perfect physical state.
Speaker 3: Right. But how does the enterprise actually use that? A
Speaker 3: bridge isn't built by a single physicist doing math in
Speaker 3: a vacuum.
Speaker 1: Well, of course not.
Speaker 3: It requires a massive team. You're coordinating logistics. You're writing
Speaker 3: software integration tests, configuring manufacturing equipment. Parsing complex diagnostic logs.
Speaker 1: Sure, there's an implementation layer.
Speaker 3: But language is what orchestrates that entire ecosystem. If your
Speaker 3: physics model just outputs raw tensor data, infinite dimensional or not,
Speaker 3: it's essentially an oracle screaming raw numbers into a void.
Speaker 2: I wouldn't call it screaming into a void.
Speaker 3: It kind of is. It's completely useless without the language
Speaker 3: layer to translate that vast physical complexity into actionable, auditable
Speaker 3: steps for the systems engineers.
Speaker 1: I mean, I don't think it's screaming into a void
Speaker 1: at all.
Speaker 2: The output interface is directly with the engineering software.
Speaker 3: But who builds that interface?
Speaker 2: But more importantly, the bottleneck in industrial engineering isn't writing
Speaker 2: the integration test or coordinating the logistics.
Speaker 1: That's the easy part.
Speaker 3: I don't know if the guys on the factory floor
Speaker 3: would call it easy.
Speaker 2: Fair but the real bottleneck is the trial and error
Speaker 2: in the laboratory it's the months sometimes years spent testing
Speaker 2: physical materials that ultimately fail under.
Speaker 3: Stress right the prototyping phase.
Speaker 2: Exactly accelerated understandings AI fundamentally eliminates that trial and error
Speaker 2: because of its context scale and we really need to
Speaker 2: talk about this scale because it's mind-blowing.
Speaker 3: The five trillion data points.
Speaker 2: Yes Their system processes 5 trillion pieces of data in
Speaker 2: a single prompt.
Speaker 3: And we need to clarify what pieces of data means
Speaker 3: in this context, because 5 trillion is just a staggering
Speaker 3: number to wrap your head around.
Speaker 1: It really is.
Speaker 2: To put it into perspective, 5 trillion pieces of data
Speaker 2: is roughly 5 million times what Anthropic or Google's flagship
Speaker 2: models can typically consume in.
Speaker 1: A single query.
Speaker 3: 5 million times? Yes.
Speaker 2: It is the equivalent of reading Tolstoy's War and Peace
Speaker 2: five million times in one sitting.
Speaker 3: That is, yeah, that's heavy.
Speaker 1: But it's not reading text. That's the key.
Speaker 2: It is ingesting the complete state of physical systems. We're
Speaker 2: talking temperature, velocity, pressure, material density, all across time.
Speaker 3: So it's looking at the whole system at once.
Speaker 2: Exactly. You ingest all the physical variables of a system
Speaker 2: at once. NVIDIA CEO Jensen Huang, he recognized this potential
Speaker 2: years ago.
Speaker 3: Oh, right, the weather prediction thing.
Speaker 1: Yeah.
Speaker 2: Back in 2021, when Anand Kankumar's team showed how AI
Speaker 2: could speed up weather prediction with the accuracy of those
Speaker 2: incredibly complex computational models, Huang didn't just praise it as
Speaker 2: a neat trick.
Speaker 3: What did he say?
Speaker 2: He specifically stated he wanted AI to, quote, Eat all
Speaker 2: physics theorists' lunches.
Speaker 3: Right, I remember that quote.
Speaker 2: Because once you can map that many continuous variables, you
Speaker 2: don't need brittle, bespoke math models anymore.
Speaker 1: You just simulate reality instantly.
Speaker 3: It is a phenomenal vision, I'll give you that. But
Speaker 3: this is exactly where the theoretical elegance... crashes into the
Speaker 3: reality of enterprise infrastructure.
Speaker 2: How?
Speaker 1: It solves the court problem.
Speaker 3: Let's look at the practical deployment of these massive context windows.
Speaker 3: You're talking about ingesting 5 trillion data points of continuous
Speaker 3: physical states. What is the infrastructure cost of evaluating that continuously?
Speaker 1: It's high, but.
Speaker 3: The entire cloud ecosystem, which is the backbone of modern enterprise,
Speaker 3: is already built around the language paradigm.
Speaker 2: But the infrastructure will adapt to the value of the output.
Speaker 2: I mean, if a chip manufacturer can skip six months
Speaker 2: of R & D, they will absolutely build the servers.
Speaker 3: It's not just about building servers, though. It's about the
Speaker 3: fundamental business model of compute. We need to talk about
Speaker 3: K3' s 2.8 trillion parameter scale and how it's actually hosted.
Speaker 1: Right, the cloud giants.
Speaker 3: Yeah, K3 requires major cloud infrastructure, Azure, AWS, Google Cloud,
Speaker 3: simply to handle the immense computing costs. And how do
Speaker 3: those cloud giants build the enterprise? By tokens. Exactly. They
Speaker 3: use token-based usage billing.
Speaker 1: Which is a completely arbitrary metric born out of text generation.
Speaker 3: It's not arbitrary at all. It's the foundation of cloud
Speaker 3: economics right now. now. A token is essentially a chunk
Speaker 3: of a word, right? A discrete unit of data.
Speaker 2: Yeah.
Speaker 3: It is a measurable, highly auditable system for enterprises. When
Speaker 3: an engineer uses K3 to run a multi-step diagnostic on
Speaker 3: a factory floor, the cloud provider knows exactly how many
Speaker 3: tokens were processed. Structure doesn't speak continuous topology.
Speaker 1: Well, not yet.
Speaker 3: It speaks in discrete billable units. If I run a
Speaker 3: 5 trillion point continuous physics simulation, how do you audit that?
Speaker 3: You audit the output. But the data access and auditing
Speaker 3: for the continuous physical variables themselves remain completely unresolved. It
Speaker 3: is incredibly costly and opaque compared to the token economy.
Speaker 2: See, I feel like you're prioritizing the cash register over
Speaker 2: the actual technological breakthrough here.
Speaker 3: I'm prioritizing practicality for systems engineers.
Speaker 2: But yes, token billing is convenient for Microsoft right now.
Speaker 2: But if a system can accurately predict extreme weather patterns
Speaker 2: or instantly sift through complex geological data for an offshore
Speaker 2: energy company, the enterprise will figure out how to track
Speaker 2: the compute costs.
Speaker 3: Will they? Of course!
Speaker 2: The sheer value of true physics-centric intelligence is proven by
Speaker 2: the lengths capital will go to try and capture it.
Speaker 3: Capital is certainly flowing everywhere in AI, but where is
Speaker 3: it flowing most aggressively today?
Speaker 2: Well, look at what Anand Kumar and Yannick just walked
Speaker 2: away from. This is crucial context from the source material.
Speaker 3: The Amazon thing.
Speaker 2: Yeah, they were heavily recruited to lead Jeff Bezos' Project Prometheus.
Speaker 2: And the offer they received wasn't just, you know, a
Speaker 2: standard tech recruitment package.
Speaker 1: It was staggering.
Speaker 3: It was a massive number.
Speaker 1: We are talking about a $ 2 billion capital outline for
Speaker 1: committed financing rounds.
Speaker 3: Just to start? Right.
Speaker 2: They were offered a 35% equity stake in the company,
Speaker 2: and their salaries were set to double to $ 2 million
Speaker 2: after just three months.
Speaker 3: And they turned it down.
Speaker 1: They walked away from all of that.
Speaker 2: Just walked away to build their own independent model for
Speaker 2: enterprise AI spanning robotics, energy, and geological data.
Speaker 3: Which is a huge risk.
Speaker 2: But they did it because they knew that a single
Speaker 2: generalized physics AI is the actual holy grail of the
Speaker 2: industrial world. They didn't want to be locked into someone
Speaker 2: else's narrow vision.
Speaker 3: Right, and let's not forget the aftermath here. Once they
Speaker 3: rejected the offer... Prometheus had to go out and raise
Speaker 3: a massive $ 12 billion Series B just to chase that
Speaker 3: exact same goal of automating the manufacturing of complex physical systems.
Speaker 1: That's a lot of capital.
Speaker 3: It proves the underlying thesis. The market knows that whoever
Speaker 3: maps the physical world wins the industrial future.
Speaker 2: Look, I don't dispute that the pursuit of a generalized
Speaker 2: physics model commands mass evaluations. A $ 12 billion Series B
Speaker 2: is undeniable proof of investor appetite.
Speaker 1: Absolutely. Exactly.
Speaker 3: But if we are debating the immediate implications for systems
Speaker 3: engineers and industrial enterprise experts, Moonshot's traction proves the absolute
Speaker 3: dominance of language models today.
Speaker 2: Even with all the massive geopolitical baggage Moonshot carries?
Speaker 3: Especially because of it. Honestly, this is what makes the
Speaker 3: enterprise adoption of K3 so incredibly telling.
Speaker 1: How do you mean?
Speaker 3: Let's look at the immense friction Moonshot is currently facing. U.S.
Speaker 3: Treasury Secretary Scott Besson has publicly threatened to add them
Speaker 3: to a trade blacklist.
Speaker 2: Right. U.S.
Speaker 3: Officials have actively accused the company of stealing from Anthropic's
Speaker 3: Fable model. There are serious accusations about them illegally acquiring
Speaker 3: NVIDIA chips.
Speaker 1: Those are heavy accusations.
Speaker 3: Exactly. These are not minor PR issues. These are massive,
Speaker 3: existential red flags for any enterprise supply chain, which should
Speaker 3: make any systems engineer incredibly wary of integrating K3 into
Speaker 3: their core pipeline.
Speaker 2: You would think so. And yet, despite every single one
Speaker 2: of those red flags, U.S. cloud giants are still clamoring
Speaker 2: to host K3.
Speaker 3: Yeah, it's wild.
Speaker 2: We are seeing Microsoft Azure, AWS, and Google Cloud willing
Speaker 2: to negotiate up to 30% revenue sharing agreements just to
Speaker 2: get K3 onto their platforms.
Speaker 3: But I think that just speaks to a desperation for
Speaker 3: massive parameter counts. not necessarily a superiority of the language
Speaker 3: architecture itself.
Speaker 1: You think they just want the size?
Speaker 3: The cloud providers just want the biggest model to sell compute.
Speaker 3: They want to push as many tokens as possible. Now,
Speaker 3: I really think it speaks to undeniable utility. When major
Speaker 3: industry players, and let's remember, Moonshot is also backed by
Speaker 3: Alibaba and raised $ 2 billion in May. When the industry
Speaker 3: fights this hard for a model, it proves that language-centric
Speaker 3: reasoning is the required foundational layer right now.
Speaker 1: Even with the blacklist threat?
Speaker 3: Microsoft, Amazon, and Google are willing to navigate an absolute
Speaker 3: geopolitical minefield and give away 30% of their revenue. They
Speaker 3: wouldn't do that if enterprise clients weren't actively demanding K3
Speaker 3: to orchestrate their systems.
Speaker 2: I mean.
Speaker 3: Moonshot claims their performance gains stem from original changes to
Speaker 3: their underlying architecture, and the results back it up. K3
Speaker 3: ranks first in Arena.ai's web interface building benchmark. The industrial
Speaker 3: market is voting with its infrastructure dollars and it's voting
Speaker 3: for language.
Speaker 1: But again, we have to ask, what are they actually
Speaker 1: voting for?
Speaker 2: They are voting for the ability to automate administrative workflows, interface.
Speaker 1: Building, and software logic.
Speaker 3: Which is core to engineering.
Speaker 2: Which, I concede, is highly valuable. But when we look
Speaker 2: at the true horizon of systems engineering, the physical world
Speaker 2: is the ultimate constraint.
Speaker 3: Okay, but.
Speaker 2: Think about a next-generation chip design company. They don't just
Speaker 2: need a model that can write a Python script or
Speaker 2: generate a report on thermodynamics. They need a model that
Speaker 2: fundamentally understands heat at the molecular level without running a bespoke, brittle,
Speaker 2: computational fluid dynamic simulation that takes four days to render. Right.
Speaker 1: They need to move past the transformer.
Speaker 3: See, your framing of moving past the transformer implies that
Speaker 3: the transformer is a dead end rather than, you know,
Speaker 3: an evolving, generalized platform that can eventually integrate physical data.
Speaker 2: For native physics, it absolutely is a dead end. Next
Speaker 2: token prediction is... simply does not mathematically map to continuous
Speaker 2: space and time.
Speaker 3: You don't think they'll bridge that gap?
Speaker 2: You can scale a transformer to 10 trillion parameters, and
Speaker 2: it will still just be a highly sophisticated pattern matcher
Speaker 2: of discrete symbols. A physics-centric approach utilizing neural operators represents
Speaker 2: the true frontier.
Speaker 3: It's a frontier, sure.
Speaker 2: When an enterprise wants to push the boundaries of material science,
Speaker 2: extreme weather prediction, or robotics, They require an AI that
Speaker 2: predicts reality itself, not an AI that just generates text
Speaker 2: based on historical patterns of human language describing reality.
Speaker 3: And my position remains that the immense parameter scale and
Speaker 3: complex task capabilities of models like K3 demonstrate that language-centric
Speaker 3: intelligence is the most versatile practical tool available right now.
Speaker 1: Practical for the IT department, maybe.
Speaker 3: Practical for the whole operation. An industrial enterprise is not
Speaker 3: just a physics problem. It is a wildly complex web
Speaker 3: of humans, legacy software, physical interfaces, supply chains, and machines.
Speaker 1: That's true.
Speaker 3: Language is the universal translator across all of those diverse domains.
Speaker 3: The sheer demand from the major cloud giants proves this
Speaker 3: is the indispensable tool in the ecosystem.
Speaker 1: I hear you, but.
Speaker 3: You can have the most perfect physical simulation in the world.
Speaker 3: But if you can't seamlessly interface it with the rest
Speaker 3: of your enterprise stack through generalized reasoning, it stays locked
Speaker 3: in the lab.
Speaker 2: It is fascinating, though, because despite our deep disagreement on
Speaker 2: whether systems engineers should rely on continuous physics architectures or
Speaker 2: discrete language architectures, both of these approaches share a striking
Speaker 2: reality in the source material.
Speaker 3: They really do.
Speaker 2: Whether pursuing accelerated understandings neural operators or moonshots language-centric transformers,
Speaker 2: The future of this scale of AI is strictly enterprise-focused.
Speaker 3: That is a crucial point of convergence here. Neither accelerated
Speaker 3: understanding nor moonshot is prioritizing consumer applications right now.
Speaker 1: Right, no consumer apps.
Speaker 3: We aren't talking about chatbots for retail customers, but computational
Speaker 3: demands are simply too vast. They both require massive external
Speaker 3: computing partnerships just to function. Yeah. K3 obviously need AWS
Speaker 3: and Azure. And even Anand Kumar's startup had to rely
Speaker 3: on undisclosed hardware clusters just to develop their initial AI.
Speaker 2: The era of a startup building a frontier model entirely in-house,
Speaker 2: you know, isolated from the major cloud hardware giants, seems to.
Speaker 1: Be completely over.
Speaker 3: Oh, totally over.
Speaker 2: The staggering computing requirements are entirely dictating the business models
Speaker 2: and the partnerships.
Speaker 3: Absolutely. And it leaves a profound open question regarding innovation.
Speaker 3: how quickly systems engineers will actually adopt these competing paradigms
Speaker 3: on the factory floor. Right. Will they find a way
Speaker 3: to integrate neural operators directly into their workflow, bypassing the
Speaker 3: token economy? Or will they continue to mediate all of
Speaker 3: their complex engineering problems through the vast, generalized reasoning capabilities
Speaker 3: of a language model?
Speaker 2: The material clearly suggests there is much more nuance to explore.
Speaker 2: as these divergent technologies move from theoretical capability into heavy
Speaker 2: industrial application.
Speaker 3: Yeah, it's just the beginning.
Speaker 1: It brings us right back to our opening thought.
Speaker 3: The wind tunnel.
Speaker 1: Exactly.
Speaker 2: The systems engineer of tomorrow is standing in front of
Speaker 2: a digital wind tunnel. The question is no longer whether
Speaker 2: an AI will run the simulation.
Speaker 3: Right.
Speaker 1: The question is.
Speaker 2: Whether that AI learned about the wind by reading every
Speaker 2: textbook ever written, or if the AI natively understands the
Speaker 2: mathematical fabric of the air itself.
Speaker 3: It's a fascinating structural divide, and the implications for the
Speaker 3: enterprise are really just beginning to unfold.
Speaker 2: And one that will completely redefine industrial engineering in the
Speaker 2: coming years. Thank you for joining us on this exploration
Speaker 2: of the material. Keep questioning the architecture behind the answers.