Reflections on AI at the End of 2025

Chain of thought, now including "reasoning", are basically a work around for the simplistic nature of the Transformer neural network architecture that all LLMs are based on.

The two main limitations of the Transformer that it helps with are:

1) A Transformer is just a fixed-size stack of layers, with a one-way flow of data through the layers from input to output. The fixed number of layers equates to how many "thought" steps the LLM can put into generating each word of output, but good responses to harder questions may require many more steps and iterative thinking...

The idea of "think step by step", aka chain of thought, is to have the model break it's response down into a sequence of steps, each building on what came before, so that the scope of each step is withing the capability of the fixed number of layers of the transformer.

2) A Transformer has extremely limited internal memory from one generated word to the next, so telling the model to go one step at a time, feeding its own output back in as input, in effect makes the model's output a kind of memory that makes up for this.

So, chain of thought prompting ultimately give the model more thinking steps (more words generated), together with memory of what it is thinking, in order to be able to generate a better response.

I'm impressed that such a short post can be so categorically incorrect.

> For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots

> In 2025 finally almost everybody stopped saying so.

There is still no evidence that LLMs are anything beyond "stochastic parrots". There is no proof of any "understanding". This is seeing faces in clouds.

> I believe improvements to RL applied to LLMs will be the next big thing in AI.

With what proof or evidence? Gut feeling?

> Programmers resistance to AI assisted programming has lowered considerably.

Evidence is the opposite, most developers do not trust it. https://survey.stackoverflow.co/2025/ai#2-accuracy-of-ai-too...

> It is likely that AGI can be reached independently with many radically different architectures.

There continues to be no evidence beyond "hope" that AGI is even possible, yet alone that Transformer models are the path there.

> The fundamental challenge in AI for the next 20 years is avoiding extinction.

Again, nothing more than a gut feeling. Much like all the other AI hype posts this is nothing more than "well LLMs sure are impressive, people say they're not, but I think they're wrong and we will make a machine god any day now".

> * The fundamental challenge in AI for the next 20 years is avoiding extinction.

sorry, I say it's folding the laundry. with an aging population, that's the most, if not only, useful thing.

Skeptic here: I do think LLMs are a fad for software development. They're an interesting phenomen that people have convinced themselves MUST BE USEFUL in the context of software development, either through ignorance or a sense of desperation. I do not believe LLMs will be used long term for any kind of serious software development use cases, as the maintenance cost of the code they produce will run development teams into bankruptcy.

I also believe the current generations of LLMs (transformers) are technical dead ends on the path to real AGI, and the more time we spend hyping them, the less research/money gets spent on discovering new/better paths beyond transformers.

I wish we could go back to complaining about Kubernetes, focusing on scaling distributed systems, and solving more interesting problems that comparing winnings on a stochastic slot machine. I wish our industry was held to higher standards than jockeying bug-ridden MVP code as quickly as possible.

> They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their first bad output instead of trying to work with the LLM to iterate like everyone who gets value out of them.

"Ah-hah you stopped when this tool blew your whole leg off. If you'd stuck with it like the rest of us you could learn to only take off a few toes every now and again, but I'm confident that in time it will hardly ever do that."