This post was originally published on the main website on Apr 10
2026.
I am reposting it here for SEO reasons and enabling humble bumble discussions with the DEV community.
Feel free to engage with this post and i am available to respond during weekends.
Sorry about the spam posting all the blogs in one day.
I forgor about my dev account <3!
Hey everyone 👋, In my previous post, Language is Limited.
ASI is Impossible., I spent a long time explaining why language is not the same thing as thought, why words are not the same thing as understanding, and why a machine built on text alone will never cross the wall into true superintelligence.
I still believe all of that, and I will not take any of it back, because the argument was honest and the logic was solid.
But today I want to go further.
I want to talk about something that has been sitting in my head for years, growing louder every day, and I need to get it out before it eats me alive.
I want to talk about why large language models are still genuinely useful, despite their limits, and why large mathematical models, as introduced in this whitepaper draft, are something far more serious, something that could actually begin to crack the surface of reality itself.
I know that sounds extreme, and I know some people will read that sentence and roll their eyes, but I am asking you to stay with me, because the argument I am about to make is not based on hype or fantasy.
It is based on what I have seen, what I have built, and what I understand about the difference between describing the world and actually modeling the world.
That difference is the whole point of this post, and once you see it clearly, everything else falls into place.
I have been thinking about this ever since I wrote An Empty Life Filled With Constant Suffering, where I talked about how words cannot fully capture my thoughts, and how language always falls short of the real thing inside our heads.
That frustration is what led me here, because if language is limited, then we need to ask what comes after language, and the answer is not more language.
The answer is structure, equations, simulation, and direct modeling of the physical world.
That is what large mathematical models point toward, not because they are perfect today, but because they represent a direction that goes beyond text and into something much deeper.
I wrote in It is always the Russians that the Russians took God's skin and wrapped it around a machine, and I stand by that image, because that is exactly what happens when you take human knowledge, strip it of its soul, and feed it into a system that only knows patterns.
But what if the next generation of models does not stop at patterns in text?
What if it starts learning patterns in the physical world itself?
That is the question I cannot stop thinking about, and that is the question this post is built around.
LLMs Are Useful And I Am Not Going to Pretend Otherwise Let me start with something that might surprise people who read my earlier posts.
I think large language models are useful.
I am not going to pretend otherwise, because pretending otherwise would be dishonest, and I have spent too much of my life being honest about hard things to start lying about easy ones.
I wrote in As Engineers, LLMs should pay us for tokens usage about how the system exploits engineers, and I still believe that, but that does not mean the tool itself is worthless.
A hammer can be used to build a house or to crush a hand, and the fact that someone charges you too much for the hammer does not mean the hammer cannot drive a nail.
LLMs can summarize documents faster than I can read them, they can generate boilerplate code faster than I can type it, they can help me think through problems by acting as a sounding board, and they can translate between languages in ways that used to require expensive human translators.
These are real capabilities, not illusions, and anyone who denies them is not being serious.
I have used them my