The Anti-LLM Revolution Begins

FUTURE

If you lift your head over the media funnel of AI outlets and influencers that simply echo Sam Altman’s thoughts every time he speaks, you will realize that, despite the recent emergence of OpenAI's New o1 Models, the sentiment against Large Language Models (LLMs) is at all-time highs.

The reason?

Despite the alleged increase in ‘intelligence’ that o1 models represent, they still suffer from the same issues previous generations had. In crucial aspects, we have made no progress in the last six years, despite all the hype.

With all AI money flowing directly into LLMs, much of it is staked on these models being the ‘path to AGI.’ Hence, the implications of this not being true would be disastrous, especially for markets.

Today, based on weeks of gathering the most compelling evidence possible, I present to you the most extensive review on LLM limitations backed up by fresh lapidary research by Apple, Meta, Google, and Princeton University, among many others, with all making the same claim: we are being deceived.

Today, I will open your eyes and provide you with an alternative, facts-based reality of what’s considered the golden ticket to AGI, LLMs, which might not look that golden to you in a few minutes.

Particularly, I’ll provide you with tough-to-find insights, such as:

Compelling evidence that LLM intelligence is primitive at best (I guarantee some of the results are going to make you feel disgusted about all the hype around LLMs).

Proof you are framing LLM intelligence incorrectly and that a new evaluation framework for frontier AI, called the task familiarity/complexity conundrum, or as I like to call it, the ‘bullshitometer,’ is the way to go and illustrates how incumbents are tricking you into believing LLMs are smarter than they really are

Playing devil’s advocate, I’ll give you the other side of the coin too, illustrating how LLMs could still play a significant role in AGI,

And we will end up with a reflection on how ‘fucked up’ this industry’s judgment is and how you should approach AI news from now on.

Let’s dive in.

What is the Real Frontier?

LLMs’ biggest praise and harshest critique come from the same realization: they are great pattern matchers… and that’s it. In other words, they can extract patterns in data (all domestic cat breeds have slit-shaped eyes) and apply them to new data (I see a new feline with slit-shaped eyes, I can infer it’s a domestic cat with a very high likelihood), but nothing beyond that.

But how superficial or deep are these patterns? Do they really understand what they ‘read’ or ‘see’?

While most people, upon seeing the LLM making a reasoned response, assume ‘LLMs do reason,’ plenty of researchers have risen to claim otherwise, and the proof is as surprising as it is overwhelming.

Problem 1: LLMs Are Really Bad Planners

Planning is an essential component of reasoning. It allows us to break a complex problem into simpler tasks that, combined, take us to the goal.

As…

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