As you may have realized, over the past few weeks, I’ve been sharing the unpleasant reality of AI, that side that is rarely—if at all—shared with us through mainstream media but that you should be well aware of.

With a lot of money in the line and AI-led valuations at indecent multiples compared to tangible AI revenues, there’s no interest in sharing anything but an ‘AI is changing the world’ narrative that keeps the party going.

For that, today, I’m bringing a manifold of ‘AI dogmas,’ including the uber-mentioned: “LLMs will lead to AGI” or “LLMs Are All You Need,” with both, in reality, being far from certain, as we’ll see today.

I will also provide potential consequences if these fears materialize so that you can have an unbiased, no-hype, no-bullshit view of the space that reading mainstream media and influencers simply do not provide, giving you an edge over the majority.

“Frontier AI Will Lead Demand”

With success stories like OpenAI, with revenue growth increasing steadily every year since the launch of ChatGPT to a whopping $3.4 billion current run rate, it’s easy to assume that better AI models will lead to the ‘great adoption’ that many incumbents and investors patiently await to justify the insane valuations.

A CAPEX Problem

But as discussed in two previous Leaders segments (here and here), current Generative AI products aren’t as good as some will tell you, which has translated into extremely poor demand.

Nevertheless, if energy constraints are the mid-to-long-term elephant in the room for AI, product demand is unequivocally the main issue in the short term.

But how much demand do we need to avoid a catastrophe?

In a very recent Sequoia update on their “$200 billion question,” their first try at coming up with a number, that value is now closer to $600 billion.

This means that the AI market is heavily overinvested and that Generative AI revenues need to grow into those values to justify the CAPEX investments led by big tech companies and the valuations at which private and public investors have invested.

In other words, unless revenues grow to that number, companies aren’t even breaking even, let alone earning money.

Although we don’t have an exact value, seeing as the hyperscalers don’t reach $20 billion combined for 2024, that number is probably much closer to $30-50 billion for 2024 (a value I still feel is inflated).

This means we need at least a twelve-to-twentyfold revenue increase to break even. Now, please tell me we aren’t in a bubble.

To make matters worse, our savior doesn’t seem to be coming anytime soon. But what do I mean by that?

Edge AI Is Our Only Chance

When we discussed whether AI was in a bubble, I argued that this faltering demand was mainly due to enterprise adoption—or lack thereof—and mainly due to ‘AI inaccuracies’ (incorrectly named ‘hallucinations’), which make the GenAI systems very unreliable for enterprise processes (normally requiring +95% accuracy).

For that, my bet was on the next generation (GPT-5 and others) to close this gap and skyrocket demand, and that we should expect those models by end of year.

Well, now OpenAI talks about late 2025 or early 2026 as the most probable timeline, meaning that we are left with these hallucination machines that can’t be trusted for more than a year from now, unless companies like Lamini really eliminate hallucinations, which is uncertain.

But if we shouldn’t expect huge short-term demand for GenAI products by enterprises, where will the demand we are so desperate for come from?

In my humble opinion, specialized ‘AI on the edge’ products are our only hope to avoid a crash.

I’m referring to things like Apple Intelligence or Microsoft-Phi-3-Silica, which run on your computer and can perform simpler yet unequivocally useful actions on your behalf.

These models are far from frontier AI but very decent models that can perform tasks that don’t require extensive reasoning capabilities. In other words, instead of building AI models for complex cases where their accuracy is just not good enough, we focus on useful tasks with higher accuracy.

Therefore, in my view, the idea that frontier AI models will generate the much-needed demand is misplaced, as good-enough frontier models aren’t coming anytime soon.

Consequences

If this is, in fact, the case, we should expect Apple and Microsoft stocks to soar as they lead the adoption of the technology.

Incumbents like ARM, AMD, and, in particular, Qualcomm, could also see a huge boost if edge devices, like smartphones or laptops, become the main tools for GenAI use.

On the other hand, a shift in the narrative away from frontier AI research could cause that companies at the frontier of AI could suffer.

For instance, NVIDIA’s role in ‘edge AI’ isn’t nearly as prominent as in ‘frontier AI.’ Apple, Microsoft, and Google all design their chips for smartphones and laptops, so their reliance on NVIDIA for edge cases is very small.

But what is this paradigm shift I’m referring to? The truth is that today, being at the frontier adds value to your company.

But if investors lose interest in ‘pushing the veil of ignorance forward’ as Sam Altman would say, and instead focus on companies that deliver revenues, big NVIDIA customers like Microsoft (estimated to be 22% of total revenues in the last quarter) or Meta could downsize their GPU CAPEX investments as investors side-eye their intentions of ‘building AGI.’

Although this scenario shouldn’t create a market crash (as money wouldn’t be leaving the markets but flowing into different companies), it could certainly lead to company crashes (and great winners).

But let me be clear: If edge AI also fails to generate demand (one interesting way to measure this will be whether iPhone users upgrade to iPhone 15 Pro or the upcoming 16 for Apple Intelligence), we are running out of bullets to save the AI market.

On to the next dogma.

“GPUs Are All You Need"

When I saw Jensen Huang, NVIDIA’s CEO, signing a bra, I knew the world was losing its senses over the GPU company.

Nonetheless, everyone seems to be pushing the narrative that “GPUs are all you need.”

This, coupled with another narrative we will address further in the article, “bigger is better,” creates the perfect storm for everyone, both big tech corporations and investors, to go crazy for NVIDIA.

But is this accurate?

Unequivocally, nothing beats NVIDIA's GPUs at scaled LLM training, or Simulation-based Reinforcement Learning (the main approach for physical AI robot training today), probably the two leading areas of research in terms of interest and investment.

However, this dogma quickly loses its force if we consider inference workloads and, above all, edge AI.

For the former, alternatives like Language Processing Units (LPUs) exist, which perform considerably better at scale. Also, recent efficiency techniques reduce AIs' dependency on GPUs by avoiding matrix multiplications, the operator this hardware excels at.

As we mentioned earlier, NVIDIA’s grip on edge AI is very small. The main hardware is neural processing units (NPUs), created to avoid AI workloads eating the device’s battery.

But why? By using hyperspecialized components such as activation function modules, they are much less 'energy-hungry,’ making them better edge device options.

Consequently, while GPUs will continue to play a vital role in LLM training pipelines and simulation training, the percentage of energy allocated to these workloads could decrease dramatically in lieu of inference and edge AI workloads.

Consequences

This dogma is heavily dependent on the previous one in knowing the answer to the big question: What narrative will investors reward in the future?

Right now, it’s clear that what markets value is who is leading the race toward better AI models.

“The company with the best performance and larger models, or the company eating all the revenues are the companies I’m betting in.” is the average Wall Street investor mindset right now.

But the markets aren’t acknowledging that enterprises are simply not buying the hype.

Constant delays in a much-needed improvement in capabilities and accuracy for years to come also mean that, unless demands come from elsewhere, markets will have to sustain inflated valuations for almost two years, which I think will not happen.

Therefore, if “The company with tangible value creation and proven AI revenues is the company I want to invest in“ mentality kicks in, this idea of “GPUs are all you need” will quickly dry up.

In that scenario, as investors no longer reward investing your free cash flow in GPUs, demand for this hardware could falter.

“LLMs Will Lead to AGI.”

With the latest Anthropic release of Claude 3.5 Sonnet, tech enthusiasts have begun, once again, rocking the anthems that “AGI is near” or that “current Large Language Models (LLMs) have sparks of AGI."

However, the truth is that most AI researchers are highly skeptical of LLMs ever reaching AGI status.

But why?

Current LLMs are taught through imitation learning; they learn to imitate us. But while LLMs have indeed learned to imitate human writing, they are far from being able to imitate human reasoning.

Importantly, as the writers of the previous link outlined, they are completely oblivious to the nature of the things they talk about; they don’t care about truth or lies or, to quote the researchers, “They are aimed at being convincing rather than accurate.”

Their inability to perceive the world, a world they see through the proxy of human writing, prevents them from truly understanding what they are talking about.

One reason this may be happening is due to the lack of active inference.

Heavily championed by prominent researchers like Karl Friston or François Chollet, active inference models must be in a constant learning journey.

But that’s not the case today.

Simply put, LLMs have two working modes: training and inference. In other words, current frontier models only learn during training but cannot acquire new skills during inference.

Consequently, as LLMs can’t adapt to changes and find new solutions to a given previously unseen problem, some researchers like François argue that every single ‘act of intelligence’ we see in LLMs today can be reduced to some sort of pattern-matching or memorization, meaning that the model isn’t applying novel reasoning processes but simply fetching the solution from its previous knowledge.

In layman’s terms, what François is trying to tell us, especially if we pay close attention to his conception of what intelligence is from his seminal paper ‘On the measure of intelligence,’ is that he thinks that LLMs are not intelligent.

And the proof this remarkably controversial quote is actually true is strong.

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