Tech Disruptions

Shrinking the Brain: How MIT’s 'CompreSSM' Could Break the AI Compute Bottleneck

April 10, 202621:21Tech Disruptions

This episode explores the current, inefficient "train big, shrink later" paradigm in AI development, which involves costly and environmentally unsustainable methods like pruning, quantization, and knowledge distillation. It explains why large models are initially necessary despite their size, and introduces a groundbreaking approach from MIT researchers. Listeners will learn how this new method enables AI models to become optimally efficient during training, making AI development more accessible and sustainable for everyone.

Key Takeaways

Detailed Report

The artificial intelligence industry currently faces a significant compute bottleneck, largely due to a prevailing development strategy: training colossal models and then spending months or even years to shrink them into usable forms. This inefficient 'train big, shrink later' paradigm is not only costly but also environmentally taxing, limiting advanced AI development to a few major players.

The Costly Cycle of AI Development

The "Train Big, Shrink Later" Problem

To achieve state-of-the-art performance, AI models like GPT-4 or Llama are initially trained as massive behemoths, requiring thousands of GPUs and consuming vast amounts of energy over months. The larger the model, the more parameters it has, generally leading to better performance. However, these colossal models are too large, slow, and expensive for practical deployment on devices like smartphones or in cars.

Traditional Compression Methods

Once a large model is trained, a secondary, often equally expensive, phase begins to make it smaller and faster. Three common techniques are used:

  • Pruning: This involves identifying and removing the weakest, least important connections within a neural network, then fine-tuning the remaining structure to recover accuracy.
  • Quantization: This technique reduces the precision of the numbers representing a model's parameters (e.g., from 16-bit to 8-bit integers), saving memory but potentially degrading reasoning capability.
  • Knowledge Distillation: Considered the most expensive, this method uses the massive 'teacher' model to train a much smaller 'student' model by mimicking its outputs, effectively doubling the training effort.

Why Small Models Fail from Scratch

A frustrating quirk of machine learning is that a small model, trained from scratch, almost always performs significantly worse than a large model that was trained and then compressed. Large models can explore a vast, complex parameter space to find optimal solutions, while small models tend to get 'stuck' in local optima, unable to achieve the same level of performance.

The Compute Arms Race

The reliance on massive compute has led to an arms race, with companies like Meta investing billions in GPU infrastructure. This 'compute is the new oil' mentality locks smaller startups and academic labs out of frontier AI development, highlighting the urgent need for more efficient solutions.

CompreSSM: A Paradigm Shift in AI Training

MIT researchers from CSAIL have introduced CompreSSM, an algorithm that fundamentally rewrites the AI playbook. Instead of compressing models *after* training, CompreSSM allows AI models to discover their optimal, efficient shape *while they are still learning*. This means models literally grow smaller and faster as they actively train.

Shrinking While Learning

The core idea behind CompreSSM is to integrate compression directly into the learning process, sidestepping the 'train big, shrink later' trade-off entirely. The model effectively self-optimizes and reduces its complexity dynamically.

The Control Theory "Hack": Hankel Singular Values

The breakthrough leverages concepts from control theory, specifically Hankel singular values. These mathematical metrics combine two crucial ideas:

  • Controllability: How easily an internal state of the model can be influenced by input data.
  • Observability: How much that internal state contributes to the model's final output.

By combining these, Hankel singular values provide a mathematically rigorous ranking of the true 'energy' or importance of every dimension within the model, definitively identifying essential components from noise.

Early Stabilization: The 10% Revelation

A surprising discovery by the MIT team is that the relative importance of these model components stabilizes incredibly early in the training process. By the time a model is only about 10 percent of the way through its training, the Hankel singular values lock into place. At this 10% mark, CompreSSM pauses, ranks all dimensions, surgically removes the 'dead weight,' and then allows the remaining 90% of training to proceed on a drastically smaller, faster, and more efficient model.

Turbocharging State-Space Models (SSMs)

CompreSSM is specifically designed for State-Space Models (SSMs), an emerging AI architecture that poses a significant challenge to the currently dominant Transformers.

The Transformer Bottleneck

Transformers, the architecture behind models like ChatGPT, rely on 'Self-Attention,' which requires comparing every word in a sequence to every other word. This results in quadratic time complexity (O(n²)), meaning computational cost quadruples if the sequence length doubles. This leads to massive 'memory walls' and high costs for long documents or continuous data streams.

The Efficiency of SSMs

SSMs, such as the popular Mamba architecture, are rooted in continuous dynamical systems. Instead of pairwise comparisons, they compress the entire history of a sequence into a fixed-size hidden state vector. This approach yields linear time complexity (O(n)), where doubling the sequence length only doubles the compute. This makes SSMs blindingly fast and incredibly memory-efficient, effectively solving the Transformer bottleneck for long sequences.

Unprecedented Performance Gains

The empirical results of CompreSSM applied to the Mamba architecture are compelling:

Speed and Size

  • 4x speedup in training time: A model that previously took a month to train can now be trained in a week.
  • Over 10x reduction in complexity: A model with a 128-dimensional state space was reduced to just 12 dimensions while maintaining competitive performance.

Outperforming "Born Small" Models

On the CIFAR-10 image classification benchmark, a CompreSSM-compressed model, reduced to a quarter of its original size, achieved 85.7% accuracy. In contrast, a model trained from scratch at the exact same small size only managed 81.8%, proving that CompreSSM delivers superior performance even with an initially larger structure.

Benchmark Superiority

CompreSSM also proved to be more than 40 times faster and achieved higher accuracy compared to a competing state-of-the-art spectral compression technique called 'Hankel nuclear norm regularization.'

The Dawn of "Physical AI" and Edge Computing

Making AI smaller and faster has profound real-world implications, primarily by untethering it from centralized cloud servers.

Untethering AI from the Cloud

Currently, many AI applications rely on massive data centers, introducing latency, privacy risks (as data leaves the device), and dependence on internet connectivity. CompreSSM paves the way for 'Physical AI' – intelligence that runs *locally* on the device itself, a concept central to edge computing. This is crucial for applications where real-time decisions are critical, like autonomous vehicles.

Liquid AI: Commercializing Compact Intelligence

Liquid AI, a spin-out co-founded by MIT CSAIL Director Daniela Rus, is actively commercializing these non-Transformer architectures, including 'Liquid Neural Networks' and 'Liquid Foundation Models' (LFMs). Their mission is to enable interpretable, highly adaptive, and compact AI for edge devices such as smartphones, drones, pacemakers, and industrial robotics. With significant funding, including a $250 million Series A round, Liquid AI demonstrates strong market confidence in this approach.

Democratizing AI and Cost Savings

By enabling state-of-the-art SSMs to be trained faster and run locally, CompreSSM democratizes access to advanced AI. This allows non-tech enterprises, such as hospitals, banks, and manufacturers, to deploy private, on-premise AI without the 'hyperscaler tax' of cloud computing. For example, an automotive manufacturer reduced operational costs by 70% by deploying a local model, shifting value away from infrastructure providers and back to enterprises and consumers.

Limitations and the Road Ahead

Current Architectural Focus

The primary limitation of CompreSSM is its current applicability. It is proven on Linear Time-Invariant (LTI) State-Space Models and extensions like Mamba. It is *not* yet applicable to the massive Transformer architectures (GPT-4, Llama 3) that dominate the current enterprise market.

Bridging to Transformers

The research team is actively working to expand CompreSSM's applicability to matrix-valued dynamical systems, which are used in linear attention mechanisms. Success in this area would create a bridge, potentially bringing in-training dynamic compression directly to Transformer architectures, which would be a monumental game-changer for the entire AI landscape.

Broader Implications: Jevons Paradox and Ethical Questions

If AI becomes dramatically cheaper and faster to develop and deploy, the Jevons Paradox suggests that overall demand for compute might increase rather than decrease. While individual models become more efficient, the sheer proliferation of AI applications across every industry and device could lead to a net increase in compute demand, albeit in a more distributed fashion. This shift could mean fewer monolithic data centers but a massive growth in localized AI deployments, fostering a more diverse and resilient AI ecosystem.

This future also raises new ethical considerations. If advanced intelligence becomes embedded in nearly every object around us, what new challenges arise concerning privacy, control, and accountability? Furthermore, even with greener individual models, the Jevons Paradox prompts a critical question: is 'more efficient' always 'more sustainable' in the long run for the global energy footprint?

Show Notes

Works Referenced

Glossary

Sources / References

Full Transcript

HostOkay, so imagine this: the entire AI industry, right now, is basically playing a game where to build anything meaningful, they first have to sculpt a 100-ton block of marble, and then, *after* it's done, spend months chiseling away 90 tons of it to make it actually usable. It's insane.
ExpertAnd it’s not just inefficient; it's crippling. It's why only the biggest players can even get into the game. We're talking about billions in infrastructure, just to get a model to a point where it *might* be deployed.
HostBut what if you didn't have to do that? What if the marble could basically shrink itself as you're pouring it? That's what some MIT researchers just pulled off, and it's not just a tweak; it's a fundamental rewrite of the AI playbook.
ExpertThey've effectively found a way to make AI models discover their optimal, efficient shape *while they're still learning*. We're seeing claims of 4x training speedups, models shrinking by over 90% in terms of their complexity, and still outperforming their "born small" counterparts. This is a game-changer for anyone not named Meta or OpenAI.
HostThat's the part that really got me. This isn't just about making things a little bit faster; it's about making AI accessible and deployable in places we've only dreamed of.
ExpertExactly. And it tackles the dirty secret of modern AI: this "train big, shrink later" paradigm that's both economically and environmentally unsustainable.
HostLet's unpack that, because it really sets the stage. We've talked before about how companies like OpenAI and Meta are throwing insane amounts of compute at their models. You mentioned "train big, shrink later." What exactly does that look like in practice, and why is it such a problem?
ExpertIt's the dominant paradigm, and honestly, it’s mind-bogglingly wasteful. To get a state-of-the-art model today, you basically have to train a behemoth. Think of GPT-4 or the latest Llama models. These things are colossal, requiring thousands of GPUs running for months, consuming massive amounts of energy.
HostRight, because the bigger the model, the more parameters, the better it performs. That's been the mantra.
ExpertPrecisely. But here's the rub: once you have this incredible, intelligent "teacher" model, it's far too large, too slow, and too expensive to run on your phone, in your car, or even on a typical laptop. So, then you have to embark on a whole *second* phase, which is dedicated to making it smaller and faster, just so it can actually be used.
HostSo, you spend millions to train it, and then millions more to *un-train* parts of it? That sounds incredibly inefficient. What are the main ways they try to shrink these giants?
ExpertThere are three common techniques. First, **pruning**. Imagine a neural network as a vast web of connections. Pruning is like going in after the fact and snipping the weakest connections, the ones closest to zero, then fine-tuning to recover any lost accuracy. It's a bit like deleting irrelevant data from a spreadsheet.
HostOkay.
ExpertSecond, **quantization**. This is about reducing the precision of the numbers that represent the model's parameters. Instead of using 16-bit floating-point numbers, you might crunch them down to 8-bit or even 4-bit integers. It saves a ton of memory, but there's a trade-off: you often degrade the model’s reasoning capability slightly. It's like taking a high-resolution image and saving it as a lower-resolution JPEG.
HostMakes sense. And the third?
ExpertThe third is **knowledge distillation**. This one is particularly expensive. You take your massive, highly-trained "teacher" model and use it to train a much smaller "student" model. The student doesn't learn from raw data; it learns by mimicking the outputs of the teacher. The researchers at MIT noted that this effectively "doubles the training effort."
HostDouble the effort just to get a smaller model? That's the part that feels most like this "sculpting a 100-ton block" analogy. My immediate question would be, why not just train a small model from scratch? Save all that money and energy?
ExpertAnd that's the question everyone asks! But here's the frustrating quirk of machine learning: a small model, trained from scratch, almost always performs significantly worse than a large model that was trained and then compressed.
HostReally? Why is that?
ExpertLarge models have this massive, complex parameter space. They can explore and find optimal solutions in ways smaller models just can't. Small models, when trained from the ground up, tend to get "stuck" in local optima; they can't see the forest for the trees. So, paradoxically, to get a *high-performing small model*, you first *have* to pay the astronomical cost of training a massive one.
HostWow. So, it's like you need the huge canvas to even conceive of the masterpiece, even if you eventually want to put it on a postcard. This compute obsession you mentioned earlier, the Meta benchmark—that's all tied into this, right?
ExpertAbsolutely. Mark Zuckerberg's announcement that Meta was hoarding something like 350,000 Nvidia H100 GPUs by the end of 2024? That's an infrastructure investment of over $10 billion on the silicon alone. It’s a clear signal: compute is the new oil. The sheer volume of processing power and token spend required to stay relevant has become *the* benchmark for AI capability. It’s environmentally taxing, and it locks smaller startups and academic labs out of frontier AI development. It screams for a disruptive, elegant engineering solution.
HostWhich brings us to this MIT breakthrough. They call it CompreSSM. So, instead of sculpting a giant and then chipping away, how does this "control theory hack" actually work?
ExpertIt's brilliant. The MIT CSAIL team, led by Makram Chahine, essentially said, "What if we don't wait until the end to figure out what's important?" CompreSSM is an algorithm that completely sidesteps that "train big, shrink later" trade-off. Their analogy is perfect: it's as if the statue discovers its own efficient shape while the marble is still being poured. The model literally grows smaller and faster *while it's actively learning*.
HostThat's a huge shift in philosophy. How do they actually do that? How do you know what's "dead weight" in a neural network *while* it's training?
ExpertThat's where the "control theory" hack comes in. This is where they borrowed from a completely different discipline, typically used in aerospace engineering and robotics to manage dynamic systems. They use a mathematical metric called **Hankel singular values**.
HostHankel singular values. Sounds dense. Can you break that down?
ExpertThink of it like this: to safely compress a model, you need to know which parts are truly pulling their weight and which are just taking up space. Hankel singular values combine two crucial concepts: **controllability** and **observability**.
HostControllability and observability... in an AI model?
ExpertExactly. **Controllability** asks: how easily can a specific internal state of the model be influenced or excited by the input data? And **observability** asks: how much does that internal state actually contribute to the final output of the model? By combining these, Hankel singular values give you a mathematically guaranteed ranking of the true "energy" or importance of every dimension within the model. It tells you, definitively, which parts are essential and which are just noise.
HostSo, it's not just a heuristic guess; it's a mathematically rigorous way to identify what's critical.
ExpertPrecisely. And here's the kicker, the surprising phenomenon they discovered: the relative importance of these components stabilizes incredibly early in the training process. By the time the model is only about **10 percent** of the way through its training, those Hankel singular values lock into place. They don't change much after that.
HostWait, really? So, 10% of the way through training, you can already tell what's essential and what's not? That's wild.
ExpertIt *is* wild. At that 10% mark, CompreSSM pauses, ranks all the dimensions, and then surgically amputates the dead weight. The remaining 90% of the training process then proceeds on a drastically smaller, faster, and more efficient model.
HostSo, instead of training a massive model for months and *then* compressing it, you train a massive model for, say, a few days, compress it, and then let the smaller, more efficient model finish training for the rest of the month? That's a huge time and resource saver.
ExpertDaniela Rus, who's the Director of MIT CSAIL and a senior author on this paper, views this as a fundamental paradigm shift. She argues that compression is no longer an afterthought or some post-processing chore; it's an active, dynamic part of the learning process itself. It's about building technology that's both efficient and interpretable, proving that "progress doesn't have to mean more. It can mean smarter." CompreSSM turns the model into a self-optimizing system.
HostThat's powerful. But this technique isn't universal, right? It's specifically designed for a certain type of AI architecture.
ExpertYou're right to point that out. It targets State-Space Models, or SSMs. This is crucial because while Transformers dominate the AI landscape today – the "T" in ChatGPT – SSMs are emerging as a major challenger.
HostTransformers are everywhere. What makes them so dominant, and why are SSMs considered a threat?
ExpertTransformers rely on what's called "Self-Attention." Think of it like this: to understand a sentence, a Transformer has to compare every single word in that sentence to every other word. Mathematically, this creates what's called **quadratic time complexity**, or O(n²). If you double the length of a conversation, the compute required quadruples.
HostSo, if I'm feeding it a long document or a complex piece of code, the computational cost just explodes?
ExpertExactly. It hits massive "memory walls" and requires enormous Key-Value (KV) caches, making Transformers incredibly expensive for long documents, continuous audio streams, or extensive codebases. That's why models often have context windows – a limit to how much information they can consider at once.
HostOkay, so Transformers are powerful but computationally hungry, especially with long sequences. How do State-Space Models like Mamba solve that?
ExpertSSMs, like the very popular Mamba architecture, are rooted in continuous dynamical systems. Instead of looking back at every previous token individually, they compress the entire history of a sequence into a fixed-size hidden state vector. It's like distilling a whole book down to a single, constantly updating summary paragraph.
HostSo, it's not looking back at *all* the individual words, but rather at a distilled representation of everything that came before?
ExpertPrecisely. Because they don't need to do that pairwise comparison, they operate with **linear time complexity (O(n))**. If you double the length of the sequence, the compute only doubles, not quadruples. This makes them blindingly fast and incredibly memory-efficient. They effectively solve the Transformer bottleneck for long sequences.
HostThat's a huge advantage, especially for real-time applications or devices with limited memory. And CompreSSM is specifically designed to turbocharge these already efficient SSMs. What were the actual results? The hard data you mentioned earlier?
ExpertThe empirical results are incredibly compelling. When they applied CompreSSM to the Mamba architecture, they saw an astonishing **4x speedup in training time**. Think about that: a model that took a month to train now takes a week.
HostA 4x speedup? That's not marginal; that's transformative for R&D cycles.
ExpertAnd it gets better. They achieved mind-blowing dimensionality reduction. The algorithm took a model with a 128-dimensional state space and crushed it down to just 12 dimensions. That's a greater than 10x reduction in complexity, all while maintaining competitive performance.
HostSo, smaller, faster, and just as good. But you also mentioned it beats the "small model curse"?
ExpertYes, this is critical. On a standard image classification benchmark, CIFAR-10, a CompreSSM-compressed model, reduced to a quarter of its original size, achieved 85.7% accuracy. But a model trained from scratch at that exact same small size only managed 81.8%. That 4-point difference is significant in ML, proving you can get superior performance even when starting with a larger initial structure and then compressing it *during* training.
HostSo, it really does solve that fundamental problem: you don't have to train huge and then post-process, but you still get the benefits of having a larger "canvas" initially.
ExpertExactly. And to put it in perspective, they benchmarked CompreSSM against a competing state-of-the-art spectral compression technique called "Hankel nuclear norm regularization." CompreSSM proved to be more than **40 times faster** while also achieving higher accuracy. It's not just better; it's orders of magnitude better.
HostOkay, so this is a genuine technical breakthrough. But how does this translate into real-world business implications? What's the big picture here for industries outside of pure AI research?
ExpertThe ultimate goal of making AI smaller and faster is to untether it from centralized cloud servers. This is huge. Think about it: right now, if you use a voice assistant, or a smart feature in your car, your device is likely pinging a massive data center hundreds or thousands of miles away.
HostThat introduces latency, obviously. But there are other issues too, I imagine.
ExpertOh, absolutely. Latency is just one. You've got privacy risks, because your data is leaving your device and going to a third-party server. And there's total reliance on internet connectivity. As Daniela Rus points out, "If your car is driving 60 miles an hour, you can't wait ten seconds for the cloud to tell you what to do."
HostSo, this is paving the way for AI that runs *locally*? On the device itself?
ExpertPrecisely. This is the realm of "Physical AI" and edge computing. And that brings us to Liquid AI, a spin-out intrinsically linked to this research. It was co-founded by Daniela Rus and other MIT CSAIL alumni. They’re commercializing these non-Transformer architectures, specifically "Liquid Neural Networks" and what they call Liquid Foundation Models or LFMs.
HostLiquid AI. That name sounds like it belongs in a sci-fi movie. What's their vision?
ExpertTheir mission is to realize this "Physical AI" – intelligence that's interpretable, highly adaptive, and, crucially, compact enough to run locally on pretty much any edge device. We're talking smartphones, drones, pacemakers, industrial robotics. Their models are actually inspired by the brain of the *C. elegans* roundworm, which has only 302 neurons but can navigate incredibly complex environments. It's about minimalist intelligence that can still do complex things.
HostAnd they're not just an academic project, either. They've raised some serious capital.
ExpertStaggering capital. After a $37.6 million seed round in late 2023, they closed a massive $250 million Series A led by AMD in late 2024/2025. That rocketed them to an estimated $2 billion valuation. This isn't just theory; this is capital markets saying, "This is real, and it's big."
HostThat kind of funding, paired with this CompreSSM breakthrough, feels like it could really democratize AI.
ExpertIt absolutely does. If techniques like CompreSSM allow state-of-the-art SSMs to be trained 1.5x to 4x faster and then run locally, it fundamentally changes who can leverage advanced AI. Non-tech Fortune 500 companies, for example. Think about an automotive manufacturer Liquid AI recently helped. By deploying a local model, they lowered their operational costs by **70%** simply by reducing their dependency on cloud computing.
HostSeventy percent! That's not just a nice-to-have; that's a massive competitive advantage.
ExpertIt completely shifts the value proposition. It decouples intelligence from centralized data centers. The value moves away from the infrastructure providers – like AWS or those massive Nvidia GPU clusters – and back to the enterprises and the consumers. It paves the way for private, on-premise AI that can be trained natively by hospitals, banks, and manufacturers without paying that "hyperscaler tax." It could truly unlock a new era of bespoke, privacy-preserving AI.
HostThat's incredibly optimistic, and the technical side of it sounds solid. But, you know us, we have to put a "hype check" on everything. What are the limitations here? What's the catch?
ExpertYou're right to ask. The primary limitation right now is architectural. CompreSSM is currently proven on **Linear Time-Invariant (LTI) State-Space Models** and extensions like Mamba. It is *not* currently applicable to the massive Transformer architectures – the GPT-4s, the Geminis, the Llama 3s – that dominate 95% of the current enterprise market.
HostSo, if you're a company heavily invested in the Transformer ecosystem, this doesn't help you today?
ExpertNot directly, no. That's the big caveat. However, the researchers are acutely aware of this. Makram Chahine, the lead PhD student, explicitly stated that applying CompreSSM to standard SSMs "had to be the first step, because this is where the theory is neat and the approach can stay principled." They chose their battleground carefully.
HostWhich implies they're looking to expand its applicability?
ExpertAbsolutely. The team is already looking to push the CompreSSM algorithm further into **matrix-valued dynamical systems**, which are used in linear attention mechanisms. If they succeed there, that would be the bridge, bringing in-training dynamic compression directly to the Transformer architectures that underpin the world's largest AI systems. That would be the ultimate game-changer.
HostSo, it's a huge leap for SSMs, with the potential to then jump to Transformers. That changes the timeline for widespread impact significantly.
ExpertIt does. It means the current success is a proof of concept for a much broader ambition.
HostOkay, so let's zoom out a bit for our final thoughts. If this kind of efficiency becomes widespread, what's the macroeconomic impact? Will CompreSSM actually cool down this massive data center boom, or will it just accelerate something else?
ExpertThat's a fascinating question, and it brings us to something called the **Jevons Paradox**. Historically, when a resource becomes more efficiently used, the overall demand for that resource actually *increases*, rather than decreases.
HostSo, if AI becomes cheaper and faster to train, we don't necessarily use *less* compute; we just find *more* ways to use AI?
ExpertExactly. Think of it this way: if it becomes dramatically cheaper and easier to develop and deploy high-performing, specialized AI models, then every company, every industry, will want to embed AI into everything. Your car, your toaster, your pacemaker, your infrastructure, your medical devices. The total addressable market for AI applications explodes.
HostSo, while individual models become more efficient, the sheer *number* of models and the *breadth* of their deployment could still lead to a net increase in compute demand, albeit in a more distributed fashion.
ExpertThat's my read. It could mean fewer monolithic data centers, but a massive proliferation of smaller, localized AI deployments. The hyperscalers might still grow, but their growth won't be the *only* story. It opens the door for a much more diverse, distributed, and possibly more resilient AI ecosystem. It's a shift from AI as a centralized utility to AI as an embedded, ubiquitous intelligence.
HostThat's a powerful vision. So, a few key takeaways from today: First, the "train big, shrink later" model is fundamentally broken, unsustainable, and locks out innovation.
ExpertSecond, CompreSSM offers a radical alternative, allowing models to self-optimize and shrink during training, thanks to a clever control theory hack.
HostThird, this breakthrough is supercharging State-Space Models like Mamba, making them incredibly fast and efficient, posing a real threat to the Transformer's dominance.
ExpertAnd finally, this paves the way for "Physical AI" – intelligent, compact models running on edge devices, democratizing AI and dramatically reducing operational costs for businesses.
HostThinking about this, if AI becomes so efficient that it can run on almost any device, anywhere, what new ethical considerations arise when every object around us potentially has advanced intelligence embedded within it?
ExpertAnd, if the Jevons Paradox holds true, and demand for AI compute skyrockets due to this efficiency, what does that mean for the global energy footprint, even if individual models are greener? Is "more efficient" always "more sustainable" in the long run?