Comment by neom

> LLMs don't understand. It's mind-boggling to me that large parts of the tech industry think that.

I think you might be tied to a definition of "understanding" that doesn't really apply.

If you prompt a LLM with ambiguous instructions, it requests you to clarify (i.e., extend prompt to provide more context) and once you do the LLM outputs something that exactly meets the goals of the initial prompt, does it count as understanding?

If it walks like a duck and quacks like a duck, it's a duck,or something so close to a duck that we'd be better off calling it that.

I don't believe the user meant "understand" in the classical biological and philosophical sense, or were otherwise attempting to anthropomorphize the systems. They were speaking from the practical experience of "this thing takes a somewhat ambiguous input with unique constraints and implements the ask more-or-less as intended".

They understand. Anything able to reason about any arbitrary request and form a plan tailored to that request understands well enough to qualify for the verb. The mechanism behind it may feel hollow or fake. But if its responses reliably show understanding, the LLM understands - by any ordinary measure.

Nearly every argument like this has the same fatal flaw, and it's generally not the critique of the AI, but the critique reflected back on to humans.

Humans also don't understand and are frequently faking understanding, which for many tasks is good enough. There are fundamental limits to what humans can do.

The AI of a few months ago before OpenAI's sycophancy was quite impressive, less so now which means it is being artificially stunted so more can be charged later. It means privately it is much better than what is public. I can't say it "understands," but I can say it outclasses many many humans. There are already numbers of tasks based around understanding where I would already choose an LLM over a human.

It's worth looking at bloom's taxonomy (https://en.wikipedia.org/wiki/Bloom%27s_taxonomy): In the 2001 revised edition of Bloom's taxonomy, the levels were renamed and reordered: Remember, Understand, Apply, Analyze, Evaluate, and Create. In my opinion it is at least human competitive for everything but create.

I used to be very bearish on AI, but if you haven't had a "wow" moment when using one, then I don't think you've tried to explore what it can do or tested it's limits with your own special expertise/domain knowledge, or if you have then I'm not sure we're using the same LLMs. Then compare that experience to normal people, not your peer groups. Compare an LLM to people into astrology, crystal healing, or homeopathy and ask which has more "understanding."

meh. I feel this is just a linguistic shortcut, similar to how _trained_ biologists can talk about a species or organism evolving some trait. Of course the organism isn't _really_ evolving with any goal in mind, but that's clear to the speaker and audience. Whether or not LLMs understand (very unlikely), it's clear what we mean by an LLM "understanding": has the context + prior training to make reasonable predictions. But no one wants to write that each time.

They understand tho, it's different than how it's done in our brain but they solve task that would be impossible to do without understanding. I would even say that they can now reason through problems thanks to powerful reasoning models like Gemini 2.5 Pro and o3.

How do you know?

Definition of understanding is based on connecting relations. If there is one thing a llm can do its connecting relations. So I am not sure why you say llms are not understanding.

What is the limit my system will reach?

Thats an interesting word to pick on. Understanding still means something here in a relative sense.

Asking a short question but in a serious way: so what?

I would be annoyed along with you if I thought the post was true.

No employees accomplish anything on their own in the real world, all employees are part of a team. That's why I designed a business strategy and analysis layer (over half the system, in fact), with web tools and connections to all of the insights systems (like mix panel). I built the exact same thing I build at digitalocean but instead of humans I defined them with code, digitalocean runs just fine, so does my LLM system. The whole system I build is self learning, insight gathering and refinement. Competition is for losers, the best teams win via the best insights.

The only investor is me. I build it on my own over a weekend, on my own. I just wanted to confirm it can be done therefore will exist, that is all. Personally, I decided not to peruse it because I am old and lazy and don't want to compete against a16z and sequoia funded adderall filled teenagers.

Thanks!!! I decided not to build it, that space is already too busy, there is a startup with $25MM in stealth, who else is in stealth? On top of that, this method will get stale very very quickly, foundation model businesses are just too hard to work around right now, it's a silly way to do business. My magic is I've build a startup from scratch to over 400 people and watched what they do, it won't be long till that isn't worth much.

Goods and services are a byproduct of business, business is primarily concerned with systems and processes that facilitate value exchange, so my tool, can work with a user, to build a business, not a product or a service. If you bake cupcakes, my tool can get you 100 people at your door, it cannot open the door or provide the cakes.

I would love to know how to do it another way if you have any ideas, I'm sadly not experienced or intelligent enough to think of another way to do it.

Humans modeled time, and morning and night are real.