I came into the comments to see how the discourse is shaping up in light of the author’s claims, and I’m seeing the same old boosterism trying to dismiss it wholesale without providing conclusive evidence.

So without further ado:

* If LLMs can indeed produce wholly novel research independently without any external sources, then prove it. Cite sources, unlike the chatbot that told you it can do that thing. Show us actual results from said research or products that were made from it. We keep hearing these things exponentially increase the speed of research and development but nobody seemingly has said proof of this that’s uniquely specific to LLMs and didn’t rely on older, proven ML techniques or concepts.

* If generative AI really can output Disney quality at a fraction of the cost, prove it with clips. Show me AI output that can animate on 2s, 4s, and 1s in a single video and knows when to use any of the above for specific effects. Show me output that’s as immaculate as old Disney animation, or heck, even modern ToonBoom-like animation. Show me the tweens.

* Prove your arguments. Stop regurgitating hypeslop from CEBros, actually cite sources, share examples, demonstrate its value relative to humanity.

All people like us (myself and the author) have been politely asking for since this hype bubble inflated was for boosters to show actual evidence of their claims. Instead, we just get carefully curated sizzle reels and dense research papers making claims instead of actual, tangible evidence that we can then attempt to recreate for ourselves to validate the claims in question.

Stop insulting us and show some f*king proof, or go back to playing with LLMs until you can make them do the things you claim they can do.

Completely nailed it. The comparison to Adderall and other drugs was exactly how I've been feeling about it all--i watch my boss excitedly post another LLM documentation PR, raving about how he never used to have time for these things, yet the actual output is unclear, ambiguous, and almost entirely useless. But it made him feel good to "get it done," and that alone has largely blinded him to how bad the output actually is, and how long it took him to get.

"If a machine is consuming and transforming incalculable amounts of training data produced by humans, discussed by humans, and explained by humans. Why would the output not look identical to human reasoning? If I were to photocopy this article, nobody would argue that my photocopier wrote it and therefore can think. But add enough convolutedness to the process, and it looks a lot like maybe it did and can."

But its not copying it. That is the entire point. Its using the training data to adjust floating point numbers. If you train on a single data piece over and over again, then yes it can replicate it, just like you can memorize lines of a school play, but its still not copied/compressed in the traditional, deterministic sense.

You can't argue "we don't know how they work, or our own brains work with any certainty" and then over-trivialize what they do on the next argument.

People suffer brain damage and come out the other side with radically different personalities. What happened to "qualia", or "sense of self", where is their "soul". Its just a mechanistic emergent property of their biological neural network.

Who is to say our brains aren't just very high parameterized biological floating point machines? That is the true Occam's Razor here, as uncomfortable as that might make people.

> While Apple is still a major player in the AI space, they typically lean towards a preference for traditional AI. They’re almost certainly still doing LLM research in the background, but they’ve decided against dropping everything to go full throttle on the hype train. Ironically, this has started to make their investors, who have bought into the hype, quite upset. People are starting to question “why isn’t apply doing things with AI”.

It may very well be the case that Apple too finds themselves pressured into going all out on LLM

Besides my stance that LLMs can serve specific tasks very well and are likely going to take a place similar to spreadsheets and databases over the coming years, hasn’t Apple already? Rarely has Apple tried to appear so unified on one goal across their product stack as they did with Apple Intelligence, the vast majority of which is heavily LLM focused.

The Author appears to fully skip over their attempt and subsequent failure, which made the entire point the piece is trying to further rather unsubstantiated and made me check whether this wasn’t posted in 2022, even more for someone like myself who also is very confident that there is a large chasm between LLMs and whatever AGI may end up being.

This post is perfect. It summarizes my position on today's variant of 'AI' exactly. I'm sending this article to everyone that asks me how I feel about it. Thanks for writing it.

One mistake I think many make is to look at technology X and ascribe outcomes primarily to technology X.

AI is one in a long long long line of new technologies. It is generating a lot of investment, new corporate processes and directives, declarations like "new era" and "civilizational milestone," etc.

If someone thinks any of the above are wrong or misguided, it's a mistake to "blame" or look to AI as the primary cause.

The primary cause is our system: humans are actors in the US economic system and when a new technology is rolling out, usually the response is the same and differs only in magnitude.

Don't hate the player, hate the game.

"Yet, every time I tried to get LLMs to perform novel research, they fail because they don’t have access to existing literature on the topic. Whereas, humans, on the other hand, discovered everything humanity knows."

Just because the author was unable to wrangle LLM to do novel research doesn't mean that it's impossible. We already have examples of LLMs either doing or aiding significantly with novel research.

I'm on the image and video side of AI.

I see the claims being levied against LLMs, but in the generative media world these models are nothing short of revolutionary.

In addition to being an engineer, I'm also a filmmaker. This tech has so many orders of magnitude changes to the production cycle:

- Films can be made 5,000x cheaper (a $100M Disney film will be matched by small studios on budgets of $20,000.)

- Films can be made 5x faster (end-to-end, not accounting for human labor hour savings. A 15 month production could feasibly be done in 3 months.)

- Films can be made with 100x fewer people. (Studios of the future will be 1-20 people.)

Disney and Netflix are going to be facing a ton of disruptive pressure. It'll be interesting to see how they navigate.

Advertising and marketing? We've already seen ads on TV that were made over a weekend [1] for a few thousand dollars. I've talked to customers that are bidding $30k for pharmaceutical ad spots they used to bid $300k for. And the cost reductions are just beginning.

[1] https://www.npr.org/2025/06/23/nx-s1-5432712/ai-video-ad-kal...

> People are starting to question “why isn’t Apple doing things with AI”.

Because AI can inadvertently say nasty things. This could potentially damage the company image.

> Who is to say our brains aren't just very high parameterized biological floating point machines? That is the true Occam's Razor here, as uncomfortable as that might make people.

I believe it's quite possible that what is happening during training is in certain ways similar to what is happening to a child learning the world, although there are many practical differences (and I don't even mean the difference between human neurons and the ones in a neural network).

Is there anything to feel uncomfortable about? It's been a long time since people started discussing the concept of "a self doesn't exist, we're just X" where X was the newest concept popular during that time. I'm 100% sure LLMs are not the last one.

(BTW as for LLMs themselves, there are still two big engineering problems to solve: quite small context windows and hallucinations. The first requires a lot of money to solve, the second needs special approaches and a lot of trial and error to solve, and even then the last 1% might be almost impossible to get working reliably.)

This comment would be more useful if you actually provided those examples.

I'm also a researcher and agree wholeheartedly with the article. LLMs can maybe help you sift through existing literature or help with creative writing, at most they can be used or background research in hypothesis generation by finding pairs of related terms in the literature which can be put together into a network of relationships. They can help with a few tasks suitable for an undergrad research assistant.

Well hold on now, are the LLMs doing novel research or not? "Aiding significantly" (as in, humans doing novel research are using LLMs to aid their process) is not remotely the same -- can you show us examples of LLMs doing novel research?

Researchers using GPT to summarize papers may be helping humans create novel research, but it certainly isn't GPT doing any such thing itself.

It's a really weird claim too, because we don't magically communicate the research between minds either. You need a person to find, read, process the new research and the same applies to the LLM - either rag it or provide the whole relevant thing in context.

While in theory you're right - AI tools can "democratize" filmmaking - what will make them unique? There was another anecdote, someone saying "this AI can generate 30.000 screenplays in a day!". Which is cool but... who is going to read them?

In theory (idk it probably exists already) you can generate a script and feed it into an AI that generates a film. Novelty aside, who is going to watch it? And what if you generate a hundred films a day? A thousand?

This probably isn't a hypothetical scenario, as low-effort / generated content is already a thing, both writing, video and music. It's an enormous long tail on e.g. youtube, amazon, etc, relying on people passively consuming content without paying too much attention to it. The background muzak of everything.

As someone smarter than me summarized, AI generated stuff is content, not art. AI generated films will be content, not art. There may be something compelling in there, but ultimately, it'll flood the market, become ubiquitous, and disappear into the background as AI generated background noise that only few people will seek out or watch intentionally.

> customers that are bidding $30k for pharmaceutical ad spots they used to bid $300k for

How does this work? If the quality ads are easier to produce, wouldn't there be more competition for the same spot with more leftover money for bidding? Why would this situation reduce the cost of a spot?

Can you point to a film that has used this technology? Can you point to anything that substantiates those numbers? Genuinely asking, my mind is open to the possibility but I don't want to take it on faith. (I watched that Kalshi ad to be clear.)

I agree the impact on generative media is huge. But I also do not think anyone is making a $100m-equivalent film for $20k anytime soon. Disprove me by doing it!

there is a very strong implicit claim in what you're saying: that AI video output is usefully comparable to human output

>a $100M Disney film will be matched by small studios on budgets of $20,000

I do not believe this is true.

Why would I prefer a film that was generated by you pressing a button over a film that was generated by me pressing the same button?

$20,000 is a budget for what, one animator for three months? What does "a small studio with a budget of $20,000" even look like?

5000x is hilarious (as is 5x and 100x). When you make outlandish claims, at least provide a reference or analysis.