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Comment by crazygringo | Hacker News Reader

> We don't really know what this is good for

Oh come on. Think how long of a gap there was between the first microcomputer and VisiCalc. Or between the start of the internet and social networking.

First of all, it's going to take us 10 years to figure out how to use LLM's to their full productive potential.

And second of all, it's going to take us collectively a long time to also figure out how much accuracy is necessary to pay for in which different applications. Putting out a higher-accuracy, higher-cost model for the market to try is an important part of figuring that out.

With new disruptive technologies, companies aren't supposed to be able to look into a crystal ball and see the future. They're supposed to try new things and see what the market finds useful.

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ChatGPT had its initial public release November 30th, 2022. That's 820 days to today. The Apple II was first sold June 10, 1977, and Visicalc was first sold October 17, 1979, which is 859 days. So we're right about the same distance in time- the exact equal duration will be April 7th of this year.

Going back to the very first commercially available microcomputer, the Altair 8800 (which is not a great match, since that was sold as a kit with binary stitches, 1 byte at a time, for input, much more primitive than ChatGPT's UX), that's four years and nine months to Visicalc release. This isn't a decade long process of figuring things out, it actually tends to move real fast.

So it’s barely been 2 years. And we’ve already seen pretty crazy progress in that time. Let’s see what a few more years brings.

what crazy progress? how much do you spend on tokens every month to witness the crazy progress that I'm not seeing? I feel like I'm taking crazy pills. The progress is linear at best

Large parts of my coding are now done by Claude/Cursor. I give it high level tasks and it just does it. It is honestly incredible, and if I would have see this 2 years ago I wouldn't have believed it.

What kind of coding do you do? How much of it is formulaic?

Web app with a VueJS, Typescript frontend and a Rust backend, some Postgres functions and some reasonably complicated algorithms for parsing git history.

That started long before ChatGPT though, so you need to set an earlier date then. ChatGPT came about 3 years after GPT-3, the coding assistants came much earlier than ChatGPT.

But most of the coding assistants were glorified autocomplete. What agentic IDEs/aider/etc. can now do is definitely new.

For the sake of perspective: there are about ten times more paying OpenAI subscribers today than VisiCalc licenses ever sold.

Is that because anyone is finding real use for it, or is it that more and more people and companies are using it which is speeding up the rat race, and if "I" don't use it, then can't keep up with the rat race. Many companies are implementing it because it's trendy and cool and helps their valuation

I use LMMs all the time. At a bare minimum they vastly outperform standard web search. Claude is awesome at helping me think through complex text and research problems. Not even serious errors on references to major work in medical research. I still check but FDR is reasonably low—-under 0.2.

> Visicalc was first sold October 17, 1979, which is 859 days.

And it still can't answer simple English-language questions.

it could do math reliably!

From Wikipedia: When Lotus 1-2-3 was launched in 1983,..., VisiCalc sales declined so rapidly that the company was soon insolvent.

I generally agree with the idea of building things, iterating, and experimenting before knowing their full potential, but I do see why there's negative sentiment around this:

1. The first microcomputer predates VisiCalc, yes, but it doesn't predate the realization of what it could be useful for. The Micral was released in 1973. Douglas Engelbart gave "The Mother of All Demos" in 1968 [2]. It included things that wouldn't be commonplace for decades, like a collaborative real-time editor or video-conferencing.

I wasn't yet born back then, but reading about the timeline of things, it sounds like the industry had a much more concrete and concise idea of what this technology would bring to everyone.

"We look forward to learning more about its strengths, capabilities, and potential applications in real-world settings." doesn't inspire that sentiment for something that's already being marketed as "the beginning of a new era" and valued so exorbitantly.

2. I think as AI becomes more generally available, and "good enough" people (understandably) will be more skeptical of closed-source improvements that stem from spending big. Commoditizing AI is more clearly "useful", in the same way commoditizing computing was more clearly useful than just pushing numbers up.

Again, I wasn't yet born back then, but I can imagine the announcement of Apple Macintosh with its 6MHz CPU and 128KB RAM was more exciting and had a bigger impact than the announcement of the Cray-2 with its 1.9GHz and +1GB memory.

[1] https://en.wikipedia.org/wiki/Micral

[2] https://en.wikipedia.org/wiki/The_Mother_of_All_Demos

The Internet had plenty of very productive use cases before social networking, even from its most nascent origins. Spending billions building something on the assumption that someone else will figure out what it's good for, is not good business.

And LLM's already have tons of productive uses. The biggest ones are probably still waiting, though.

But this is about one particular price/performance ratio.

You need to build things before you can see how the market responds. You say it's "not good business" but that's entirely wrong. It's excellent business. It's the only way to go about it, in fact.

Finding product-market fit is a process. Companies aren't omniscient.

> And LLM's already have tons of productive uses.

I disagree strongly with that. Right now they are fun toys to play with, but not useful tools, because they are not reliable. If and when that gets fixed, maybe they will have productive uses. But for right now, not so much.

"it only needs to be good enough" there are tons of productive uses for them. Reliable, much less. But productive? Tons

Hello? Do you have a pulse? LLMs accomplish like 90% of everything I do now so I don’t have to do it…

Explain what this code syntax means…

Explain what this function does…

Write a function to do X…

Respond to my teammates in a Jira ticket explaining why it’s a bad idea to create a repo for every dockerfile…

My teammate responded with X write a rebuttal…

… and the list goes on … like forever

It’s not that the LLM is doing something productive, it’s that you were doing things that were unproductive in the first place, and it’s sad that we live in a society where such things are considered productive (because of course they create monetary value).

As an aside, I sincerely hope our “human” conversations don’t devolve into agents talking to each other. It’s just an insult to humanity.

Exactly what management wants to hear so they can lay off hundreds and push salaries down.

Who do you speak for? Other people have gotten value from them. Maybe you meant to say “in my experience” or something like that. To me, your comment reads as you making a definitive judgment on their usefulness for everyone.

I use it most days when coding. Not all the time, but I’ve gotten a lot of value out of them.

And yes I'm quite aware of their pitfalls.

They are pretty useful tools. Do yourself a favor and get a $100 free trial for Claude, hook it up to Aider, and give it a shot.

It makes mistakes, it gets things wrong, and it still saves a bunch of time. A 10 minute refactoring turns into 30 seconds of making a request, 15 seconds of waiting, and a minute of reviewing and fixing up the output. It can give you decent insights into potential problems and error messages. The more precise your instructions, the better they perform.

Being unreliable isn't being useless. It's like a very fast, very cheap intern. If you are good at code review and know exactly what change you want to make ahead of time, that can save you a ton of time without needing to be perfect.

> a $100 free trial

What?

A free trial of an amount of credits that would otherwise cost $100, I'm assuming.

Could be. Does such a thing exist?

Not outwardly/visibly/readily from a quick scan of their site and a short list of search results.

I misremembered, because I was checking out all the various trials available. I think I was thinking of Google Cloud's $300 in credits, since I'm using Claude through their VertexAI.

Can't speak for the parent commentator ofc, but I suspect he meant "broadly useful"

Programmers and the like are a large portion of LLM users and boosters; very few will deny usefulness in that/those domains at this point.

Ironically enough, I'll bet the broadest exposure to LLMs the masses have is something like MIcrosoft shoehorning copilot-branded stuff into otherwise usable products and users clicking around it or groaning when they're accosted by a pop-up for it.

> A 10 minute refactoring

That's when you learn Vim, Emacs, and/or grep, because I'm assuming that's mostly variable renaming and a few function signature changes. I can't see anything more complicated, that I'd trust an LLM with.

I'm a Helix user, and used Vim for over 10 years beforehand. I'm no stranger to macros, multiple cursors, codebase-wide sed, etc. I still use those when possible, because they're easier, cheaper, and faster. Some refactors are simply faster and easier with an LLM, though, because the LSP doesn't have a function for it, and it's a pattern that the LLM can handle but doesn't exactly match in each invocation. And you shouldn't ever trust the LLM. You have to review all its changes each time.

OP should really save their money. Cursor has a pretty generous free trail and is far from the holy grail.

I recently (in the last month) gave it a shot. I would say once in the maybe 30 or 40 times I used it did it save me any time. The one time it did I had each line filled in with pseudo code describing exactly what it should do… I just didn’t want to look up the APIs

I am glad it is saving you time but it’s far from a given. For some people and some projects, intern level work is unacceptable. For some people, managing is a waste of time.

You’re basically introducing the mythical man month on steroids as soon as you start using these

> I am glad it is saving you time but it’s far from a given.

This is no less true of statements made to the contrary. Yet they are stated strongly as if they are fact and apply to anyone beyond the user making them.

Usefulness is subjective.

Ah to clarify I was not saying one shouldn’t try it at all — I was saying the free trail is plenty enough to see if it would be worth it to you.

I read the original comment as “pay $100 and just go for it!” which didn’t seem like the right way to do it. Other comments seem to indicate there are $100 dollars worth of credits that are claimable perhaps

One can evaluate LLMs sufficiently with the free trails that abound :) and indeed one may find them worth it to themselves. I don’t disparage anyone who signs up for the plans

Ah, my apologies. That makes perfect sense. You are entirely correct, there is no reason to commit to such a spend for evaluation.

This is a classic fallacy - you can't find a productive use for it, therefore nobody can find a productive use for it. That's not how the world works.

I use LLMs everyday to proofread and edit my emails. They’re incredible at it, as good as anyone I’ve ever met. Tasks that involve language and not facts tend to be done well by LLMs.

> I use LLMs everyday to proofread and edit my emails.

This right here. I used to spend tons of time making sure my emails were perfect. Is it professional enough, am I being too terse, etc…

The first profitable AI product I ever heard about (2 years ago) was an exec using a product to draft emails for them, for exactly the reasons you mention.

You go into this process with a perspective, you do not build a solution and then start looking for the problem. Otherwise, you cannot estimate your TAM with any reasonable degree of accuracy, and thus cannot know how much to reasonably expect as return to expect on your investment. In the case of AI, which has had the benefit of a lot of hype until now, these expectations have been very much overblown, and this is being used to justify massive investments in infrastructure that the market is not actually demanding at such scale.

Of course, this benefits the likes of Sam Altman, Satya Nadella et al, but has not produced the value promised, and does not appear poised to.

And here you have one of the supposed bleeding edge companies in this space, who very recently was shown up by a much smaller and less capitalized rival, asking their own customers to tell them what their product is good for.

Not a great look for them!

wdym by this ?? "you do not build a solution and then start looking for the problem"

their endgame goal was to replace Human entirely, Robotic and AI is perfect match to replace all human together

They don't need to find problem because problem is full automatons from start to end

> Robotic and AI is perfect match to replace all human together

A FTL spaceship is all we need to make space travel viable between solar systems. This is the solution to depletion of resources on earth...

I heard this exact argument about blockchains.

Or has that been a success with tons of productive uses in your opinion?

At some point, I'd like to hear more than 'trust me bro, it'll be great' when we use up non-trivial amounts of finite resources to try these 'things'.

It's incredibly good and lucrative business. You are confusing scientifically sound and well-planned out and conservative risk tolerance with good business

The TRS-80, Apple ][, and PET all came out in 1977, VisiCalc was released in 1979.

Usenet, Bitnet, IRC, BBSs all predated the commercial internet, which are all forms of Online social networks.

Perhaps parent is starting the clock with the KIM-1 in 1975?

Arguably social networking is older than the internet proper; USENET predates TCP/IP (though not ARPANet).

Fair enough. I took the phrasing to mean social networking as it exists today in the form of prominent, commercial social media. That may not have been the intent.

They keep saying this about crypto too and yet there's still no legitimate use in sight.

> First of all, it's going to take us 10 years to figure out how to use LLM's to their full productive potential.

LLMs will be gone in 10 years. At least in form we know with direct access. Everything moves so fast that there is no reason to think nothing better is coming.

BTW, what we've learned so far about LLMs will be outdated as well. Just me thinking. Like with 'thinking' models prev generation can be used to create dataset for the next one. It could be that we can find a way to convert trained LLM into something more efficient and flexible. Some sort of a graph probably. Which can be embedded into mobile robot's brain. Another way is 'just' to upgrade the hardware. But that is slow and has its limits.

> to their full productive potential

You're assuming that point is somewhere above the current hype peak. I'm guessing it won't be, it will be quite a bit below the current expectations of "solving global warming", "curing cancer" and "making work obsolete".

> First of all, it's going to take us 10 years to figure out how to use LLM's to their full productive potential.

Then another 30 to finally stop using them in dumb and insecure ways. :p

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