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Comment by andrei_says_ | Hacker News Reader

> the models are in an important way indifferent to the truth of their outputs

Everyone who spends a bit of time with LLMs is aware of this.

My question - and I hope someone can address it in the discussion - how is it possible that we see such incredible investments in LLMs?

What are the c-suite execs and specifically CTOs seeing that is worth adoption and investment? These are very smart people.

I, as a laymen on the periphery of this phenomenon, am acutely aware that LLMs are not useful in applications requiring accuracy. I would not invest my company’s dollars or resources into such a technology.

But they do. What am I missing?

Is it the promise that LLMs will replace employees? How would that be believable for anyone willing to investigate the technology and its limitations, even a little bit?

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> how is it possible that we see such incredible investments in LLMs?

Because somehow LLMs became the next "big thing" that nobody can explain but everyone wants to be involved in. It's like audio streaming, the most ineffective form of audio distribution, but for a while it seemed like a good idea.

> What are the c-suite execs and specifically CTOs seeing that is worth adoption and investment? These are very smart people.

The are idiots following the herd. They climb the greasy ladder of corporate career and fear getting fired. Their smarts are in corporate politics not in tech. And even if they were smart enough to tell that LLMs are an example of a research project that has received way too much funding, they need to appease the gods of Wall St who will judge them and punish them with a downgrade of the stock price if they cannot answer one question "What is your AI strategy?" It doesn't matter that the Wall St guys need help getting out of a strip bar, they have the power to ruin stockholders. This is why Apple, Google, and Salesforce are spending money of AI projects as a form of defence, not as a way to build new products or services. It's to show that they are relevant and can innovate.

Voice assistants were the same thing, so were chatbots, and audio and video streaming services (remember Groove Music Pass), touchscreens, pocket audio players (remember Zune?), etc. Fortunately for all involved, investors are already wondering if an AI correction is coming? I think we may see a wipeout of a lot of investments in AI soon.

> Because somehow LLMs became the next "big thing" that nobody can explain but everyone wants to be involved in. It's like audio streaming, the most ineffective form of audio distribution, but for a while it seemed like a good idea.

I know it's slightly off topic, but could you elaborate on the "audio streaming" part?

Sure. Compared to traditional broadcast over airwaves or even a download to a device that can play audio without having to stream it from the server, audio streaming is the least efficient and the least convenient form of audio delivery. When you have a transmitter, people adding a thousand new radio receivers in the area does not increase your cost as a broadcaster, adding a thousand clients requires more bandwidth, and more infrastructure. I never understood why audio streaming became as popular as it did when better forms of distribution were available.

thank you, that makes sense in terms of efficiency! I am not familiar with the subject, but aren't the amount of radio frequencies available rather limited?

Yes, that's a different type of limitation imposed on the broadcaster. My point was that AI has not got a business model except capturing whatever value remains after it destroys existing models, just like audio streaming. Recent backlash against Daniel Ek's musing on X and the general behaviour of Spotify towards artists shows signs of reaching the end of the line for this idea. Same fate awaits AI. If you see how little of substance can be found in answers given by AI luminaries in their numerous interviews you can see for yourself that those guys have no idea and no plan. The best antidote for this bullshit is to display the transcripts on YT videos and read what they are saying. It's pure nothing.

I understand where you are coming from, but I’m not sure I completely agree.

Streaming music was popular even before it became legal, so I believe it's largely driven by people's desire to listen to whatever they want, whenever they want.

Regarding AI, it’s true that the current results might not always be the highest quality. However, there is no theoretical limit to AI's capabilities as far as I know. With this many people working to improve it, I would personally be surprised if significant advancements aren’t made. Maybe not just next year though...

Ultimately, I guess we will have to wait and see. Who knows, maybe you will be right in the end :)

The number of people working on improving AI is relatively small compared to the number of people reselling OpenAI APIs so I don't expect huge improvements in the next 5-10 years. The real value of AI is in ML, but with human supervision and interpretation of results. GenAI is the worst thing that happened to AI, because its coming rejection will reflect badly on the rest of the advances broadly labelled as AI.

> Streaming music was popular even before it became legal

Downloading music was popular. You download once, you listen many times. On Napster, it could take hours until a song was downloaded. No way you'd delete it and download again next time you want to listen to it.

But how many people really stream the same content at the exact same time anymore these days?

Large sports events are probably the only remaining application where broadcast would still see much advantage over individual streams.

In the case of a few simultaneous viewers, unicast can sometimes still use bandwidth more efficiently in case of a(since the transmitter precisely knows the receiver’s SNR and doesn’t need to waste any energy transmitting at the wrong coding rate or the wrong direction/place). That’s how modern 802.11 (Wi-Fi) often does reliable multicast, these days.

A seamless transition from unicast to multicast once the critical ratio of listeners per base station is reached would be very cool, but require pretty deep levels of integration between broadcasters and ISPs that are probably not worth it.

They mean "audio streaming" ala "video streaming" not like audio downloading to your device streaming. The stream means live download not just download through the net.

i.e. realtime audio podcasts, etc.

I assume*

I figured, and I mean that too!

FYI, I work for a big sports streaming company and we are doing "mABR" or multicast adaptive bitrate with some ISPs because it really is worth it. You get a significant improvement in playback stats and the ISP gets massive traffic offload from their network. We started with one big ISP in Italy, then other ISPs saw what we were doing and asked to join in.

That being said, it really is a pain in the arse to build. It's also largely proprietary solutions (yes it's multicast but getting the player to find the content isn't a standard) and it's hard for the ISP to update ALL their routers to support it (even when they want it).

> It's like audio streaming, the most ineffective form of audio distribution, but for a while it seemed like a good idea.

You dismissed it a bit too fast, didn't you? People are listening to streaming music everywhere - it is the new default. Even traditional radios stream over the internet. Is it efficient? Hell no. Is it popular? Extremely, even though a part of the population (including me!) prefers alternatives.

You are ignoring one important factor, streaming services can only stream what they are allowed to and if the artists and their labels decide to use a different distribution channel, streaming will become a thing of the past. Spotify has reached the point where they no longer know what to do and will soon be replaced by a competitor, not necessarily one that's streaming audio.

http://radio.garden/

I agree, but im curious what you mean by it not being efficient? Is it not entirely dependent on how we interact with audio? Like you said its extremely popular, but its popular because its the most efficient way to listen to audio in the way people want too... discovering stuff, playing that track you heard at a party but don't know what its called... etc

I worked for a project that was a kind of mix of Netflix and Audible, just a few orders of magnitude smaller. Earlier, we were selling these multimedia products as digital downloads. That was simple for both sides, but with increased numbers of products and their weights customers started to complain their iPads etc. don't have enough storage to keep them (basically they would delete them and then request a new download link...).

But the amount of things you need to deal with in the subscription model is huge. Everybody expects Netflix-like experience, instantaneous playback and searching etc. We can do that for N simultaneous streams; for more we need to add a slight delay - and that is noticeable. In any case, the same file is streamed multiple times, sometimes dozens of times a day for the same client. This is what I mean terribly inefficient. And although you can probably cache a few audio files, it doesn't work well for videos. From my point of view someone just wastes the bandwidth for not having to store a file locally. Yes I know this is how it is supposed to work, but still.

It took me 10 seconds reading your comments to find you recommending an AI app (topaz AI).

> in applications requiring accuracy

You have it right there. There is a large set of applications that does not require accuracy. These applications will benefit hugely from LLMs. Examples include recommender systems, knowledge extraction, transformation, etc. all in domains where accuracy does not matter too much.

In particular, instruct aligned LLMs makes it very easy to integrate ad hoc machine learning into an application.

I assume that's what c-level people have in their mind:

Nothing is accurate and fail-safe 100%. You just have to reach a level where it is good enough most of the times to make money with it.

Also, human brains are neural networks too, so that's where we know what the minimum is which is (theoretically) possible with this kind of technology.

>My question - and I hope someone can address it in the discussion - how is it possible that we see such incredible investments in LLMs?

Opportunities like this happens once in 5-10 years when “the next big thing” are in the radar. Idea behind this investments is to bullshit it’s way to the Series B or IPO where original investors can exit. It is not about usefulness but about using momentum of the situation to extract money.

I think you’ve really captured the essence of this phenomena in a remarkably succinct way. I do hope that business leaders eventually develop their own capabilities for independent creative thought, rather than being a bunch of mindless also-rans.

> What are the c-suite execs and specifically CTOs seeing

So I can speak to this since I am trying to sell to them.

CEO is being told from the markets, board, consultancies, Gartner etc that you need to have a Generative AI strategy. What that means in practice is irrelevant. You just need to throw it all against a wall and see what sticks. If you don't then the risk is you get left behind.

CEO is telling the CIO that the need to figure this all out with the obvious pilot programs being in Customer Support and Search which the CFO has been tasked to find budget for. The problem is that the data in these companies is poor and getting poorer as the number of SaaS apps explode and they are forced to just dump everything in a messy data lake. Which is why for the CDO, AI is like their #6 concern and is going to make their lives 100x harder.

The logical conclusion to all of this is that 95% of these projects will fail: (a) data is bad with no easy way to fix it, (b) LLMs are inaccurate/biased making them a risk, (c) ROI is poor, (d) data privacy/sovereignty is a mess.

> CEO is being told from the markets, board, consultancies, Gartner etc that you need to have a Generative AI strategy. What that means in practice is irrelevant. You just need to throw it all against a wall and see what sticks. If you don't then the risk is you get left behind.

I can confirm this is the default view at many companies, just the usual FOMO - the irony being this happens even in the companies that are valued for not delivering computer-generated content. Using AI they actually take a greater risk than not doing that - but well, the CEOs somehow are convinced they have no other choice.

Nailed it! I want to buy whatever it is you’re building.

When you have a slack conversation with a coworker they are often not 100% accurate.

Working with an LLM is much like working with a coworker that has a knowledge base as wide as the entire internet.

It is exceptionally useful. There are a wide range of applications that may even require 100% accuracy in final output but do not require 100% accuracy in every conversation that leads to that output.

For the last 6 months I have built things that would have taken me 4x as long in programming languages that I’ve never used before.

I am a team of 1 that has the output of a team of 4 without the overhead of meetings

Could you pls elaborate a bit on what you are developing?

> how is it possible that we see such incredible investments in LLMs?

Profitability doesn’t require accuracy, generally speaking. Bullshitting often is a successful strategy, as politics and marketing amply demonstrate.

> My question - and I hope someone can address it in the discussion - how is it possible that we see such incredible investments in LLMs?

The same reason we did for blockchain. It's a sexy new technology, with amazing demos and hype. People who want to be entrepreneurs but have no ideas of their own race to apply it everywhere because they think hitching their wagon to this horse could be their shot. Boring old companies wanting to seem hot. Lots of FOMO.

What were the amazing demos for crypto? Hype- sure.

Bitcoin itself.

> What are the c-suite execs and specifically CTOs seeing that is worth adoption and investment? These are very smart people.

> I, as a laymen on the periphery of this phenomenon, am acutely aware that LLMs are not useful in applications requiring accuracy.

They are not useful in isolation. They can be very useful as part of a larger process, especially if they particular way that you're using them has a low error rate. (And given a particular task you can in fact measure the error rate, or how often the generated bullshit happens by chance to (not) be true.)

Most processes that involve steps done by humans have built-in pieces to catch and correct errors, because humans also make errors. If the error rate of the LLM component is lower than the error rate of the human it's replacing, you're fine. If it's higher but the cost of catching and fixing the extra errors is lower than the cost of the replaced human, you're still fine. (The person who you just sent the way of the buggy whip manufactures might be less fine, but that's Somebody Else's Problem.)

If you're only looking at English generation, and focusing on the limitations, you're missing the bigger picture. The AI revolution is about the fact that we have now have the ability to produce ChatGPT level of thing, but for all of the kinds of data that companies have, not just English text. The idea isn't that it will replace the employees, but that the employees will be augmented by AI and be able to be way more productive. What executive hasn't wished they had 10x the people on a project at a fraction of the cost. The allure for a CTO isn't about LLMs, it's about resource management.

staring at the hallucination problem is missing the forest for the trees. yes it's a problem, but holy shit, the thing generates essays as fast as the GPU can dot-product and matrix multiply. CTOs are buying into the idea that we've invented cars, when before we were just looking at a faster horse.

> My question - and I hope someone can address it in the discussion - how is it possible that we see such incredible investments in LLMs?

LLMs are going to revolutionize the way the government, your employer and advertisers will spy on you.

That's a pretty high value right there

"Is it the promise that LLMs will replace employees?"

If we accept the conclusions of the paper, perhaps LLMs can replace employees who are bullshitters.

For example, Microsoft has announced layoffs claiming "AI wave" as the cause. Perhaps so-called "tech" companies like Microsoft employ bullshitters and "AI" can replace them.

Employees who act as if they are right 100% of the time when in truth they are wrong at least 20% of the time.

Why hire people who can only be relied on 80% of the time, and who will never do what it takes to reach 100%, if this "work" can be done by a computer.

My view is that there is extrapolation going on. They look at the progression of GPT 2-3-4 over a relatively short number of years and are extrapolating to a near future where these things are much more capable. FOMO. But the history of AI is huge leaps from a technological innovation, followed by years of crawl, and all this extrapolation may be wrong.

> What are the c-suite execs and specifically CTOs seeing that is worth adoption and investment? These are very smart people.

It's a whoa moment for the general population. Look, the computer can understand and respond in natural language. I don't think there is other value in LLMs. Other applications of AI are very good, though.

They are also very good at translating between natural and structured/"computer" language.

At a much higher rate than anything I've seen before in any algorithm, they "get" irony, sarcasm etc., when until a few years ago, I (as a non-linguist), assumed that any such solution to the "aboutness" problem would require solving AGI.

That alone is worth a lot in a world which spends considerable resources on people that effectively work as translators between human and structured language.

Besides that, I suspect that their existence is seen as a strong and remarkable hint that intelligence really might just be a quantitative phenomenon emergent from throwing a lot of compute at a lot of data, which at some point might become self-reinforcing.

Whether that's true or not, and for better or worse, that's what people now seem to be set on doing.

LLMs don’t get irony. That is not how they work. They simulate getting it.

chatgpt and code completion tools have pretty much doubled my productivity... "write me this tedious function in c# for unity" could save me up to a couple minutes, "what are 5 makes and models of PoE switches used in industrial applications?" could save me up to an hour

Because if you utter AI in your earnings call your stock moons. Thats the drive right now. The executive thinks in the short term and keeping up with the joneses so to speak and jumping off the AI cliff with the rest of the lemmings is what their investors are expecting.

> I, as a laymen on the periphery of this phenomenon, am acutely aware that LLMs are not useful in applications requiring accuracy.

How accurate do you think humans are? 100%? 80%? 20%?

If LLMs can be 20% as accurate as humans, the use cases are infinite. And it can only go up.

> the models are in an important way indifferent to the truth of their outputs

They're indifferent to the truth of their inputs, too. That's also a problem.

FOMO, probably. Just like any other tech bubble, really.

> What am I missing?

I think it can be summarised as "FOMO".

That the LLMs are wrong sometimes is no more relevant to their value than that humans are wrong sometimes.

It's how LLMs are wrong that is the problem.

Companies revolve around risk management and whilst people can be wrong, biased, discriminatory etc there are measures that can be taken to mitigate them. There are no comparable mitigations for LLMs.

And so if your LLM continually tells people to eat rocks or glue and you can't do anything about it then that very much impacts their value.

You can tell the LLM it's wrong like you can with people.

There is a market for bullshit and LLMs bring the cost of bullshit generation down.

The market for bullshit is infinite.

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