Goldman Sachs says the return on investment for AI might be disappointing

AI will do the same.

That a bunch of companies couldn't figure out how to use the internet is neither here nor there. There were plenty of factories that never figured out how to use electricity. Doesn't mean that steam power is still viable.

I think the hype was there because companies needed to update their data to follow standards and update how that data interacts with other companies. The specifics of XML are less relevant - once things are standardized there can be standardized ways of converting to other formats.

Imagine needing to write custom parsers for every API; not needing that enabled SaaS APIs to be much more practical.

That might happen if the AI has an off switch, knows this, and knows that we'll use that if it goes out of line, and wants to live.

But it isn't necessary.

The point is to construct it to want what we want, in the sense of sharing our values rather than envy, and then there's no conflict in the first place.

We just don't know how to even properly specify what we want.

Every attempt so far to specify what it means to be good has edge cases and loopholes — we don't even all agree on "don't kill" (war, death penalty, suicide assisted or otherwise, abortion, pollution that carries a statistically significant increase in mortality rates where no single death can be definitively proven to have been caused but overall many must have been, meat).

There are examples of where LLMs are useful for information retrieval in that they can take vague queries and produce good summaries, but they're just as likely to produce reasonable-sounding nonsense that needs to be carefully verified.

I've also seen examples where LLMs can produce code that implements well-defined toy problems but when used on a real software system with millions of lines of context they do not produce code that can remain coherent with the system.

From what I've seen LLMs have yet to graduate from "impressive demo" and everyone characterizing them as being a revolution are still describing some potential future state with no real evidence that we'll get there with this technology or any time soon.

Bailey:" AI can replace your entire human labor (the most expensive cost of business) by scraping the content made by laborers on the internet without compensation"

Unfortunately, businesses are being sold the Bailey. And the sad part is that some companies aren't even pretending to hide behind the motte. I'm sure some, many businesses won't fall for the hype, but many others will and it will take a huge toll on the workers as a result.

What an interesting comment. So, your thesis is that a few players that come out ahead will be worth substantially more than the biggest players now and they will all be using LLMs? What's driving the consolidation? High barrier to entry for making a foundation model for finance?

so are you saying that you are okay with LLM hallucinations causing your company to make possible huge losses, even if rarely ?

I should mention that I am an LLM newbie, or rather a zerobie. all I have is a very high level sketchy overview of the field. but I am a dev with many years background in many areas, though not AI at all, except for reading a little bit now and then about it.

feel free to blast me, but preferably with data-backed statements.

People said the same things about blockchain—that it would be the foundational tech that would power a new iteration of the Internet, a web3.

Not that LLMs are in the same category, they certainly seem to have more utility in certain contexts than I ever saw in blockchain, but OP is correct that not every hyped technology turns into the next internet.

For example, AI leveraged SOC is going to be operational within 3-5 years, and there goes 50-70% of jobs in that sector.

Others segments are significantly behind (robo-taxis) and I'm sure there are other segments much further ahead (call center automation), but the change is absolutely coming - not because of LLMs alone, but because the entire ecosystem around ML/AI is a 15 years old now, and there's an entire generation of SWEs that are just as competent as ML PhDs were 15 years ago.

You can make the argument that it’s not “worth it” in terms of cost to our environment, but you have to acknowledge the upside of it simultaneously.

And the dynamics are different in that we’ve reached an efficiency asymptote with cars and are still in the middle (or perhaps at the start) of the S curve with AI systems.

Being consistent, though, I should've included the amortized cost of training and the energy cost of the rest of the data center to the 4o analysis. I don't think that those costs would be more than a factor constant greater.

When we can create disembodied brains that don't have needs for shelter and desires for energy-intensive conveniences, I buy that we can do 10% of the energy accounting.

I’ve got a huge pension lined up so I’m going to go on holiday a lot basically. I get a lump sum early payment of that soon so I can invest that somewhere more productive.

Just think what companies will be doing well 10 years from now and don’t buy at crazy valuations.