[ryandrake]

Companies keep going at it the wrong way. Instead of saying "We have AI, let's find products we can make out of AI!" they should be saying, "What products do people want, let's use whatever tools we have (including maybe AI) to make them."

The idea that a company is an AI company should be as ridiculous as a company being a Python company. "We are Python-first, have Python experts, and all of our products are made with Python. Our customers want their apps to have Python in them. We just have to 'productize Python' and find the right killer app for Python and we'll be successful!" Going at it from the wrong direction. Replace Python in that quote with AI, and you probably have something a real company has said in 2024.

[cooperx]

Its the same as all the "we are a blockchain company" startups that popped up looking for a problem to solve with their tech rather than the right way round.

However, a lot of those got a bunch of investment or made some decent money in the short term. Very few are still around. We will see the same pattern here.

[javiramos]

I had already forgotten about the blockchain

[_heimdall]

git still works pretty well, I just wouldn't try to use it as a bank account.

[sham1]

Well, Git's also not a blockchain at least in the way commonly meant by the term. But yeah, it'd be a pretty bad bank account (and an even worse way of doing money transfers.)

[lagniappe]

Git is by the literal definition a blockchain.

[turkey99]

Seems a bit of a stretch. Git is not linear, and there’s no consensus mechanisms

[_heimdall]

No idea why this got down voted.

git is very much a blockchain

- sequential list of changes to a data source (commits)

- single, shared history of changes (main branch)

- users creating potential next change(s) to be added to the history (side branches and forks)

- consensus mechanism for new change blocks (merge requests and code reviews or approvals)

What's missing?

[freemoney99]

git blockchains are famous for being easy to fork (-;

[Log_out_]

You can fork a blockchain by building a ecosystem with a different consensus.

[_heimdall]

Any blockchain is easy to fork, just have a fully copy of the chain and make a new block.

[tux1968]

It was likely the same back when the steam engine was invented. Everyone who could start a steam engine company, started a steam engine company. Because learning how to be a steam engine company was difficult, new, and unique. It would be a while before finding all the products that could be sold to people incorporating that new tech.

[KineticLensman]

Tech was very different then. The first commercial steam Engine appeared in 1712 - big static thing thing that could pump water out of mines. It took about about 100 years until 1804 to get to a steam engine small but powerful enough to pull a train. Mines and factories were pretty much the only users for decades and there were very few people who had a steam engine for the sake of it

[__MatrixMan__]

How was tech different then? We've been working on AI for 70 years now, seems comparable.

[KineticLensman]

We’ve been working on digital computers for 70 years and AI is just one part of that. Computers are everywhere and suddenly AI is being crammed into every product, it seems. In the first 90 years of steam, steam engines did not suddenly appear everywhere. Outside of factories and mines, they essentially did not exist. No one had a steam engine in their home. Innovation happened very slowly, no equivalent of Moore’s law, no continuous year on year improvements to the basic tech

[__MatrixMan__]

The products are different, sure. And there are more people participating now than then. But I'm not convinced that the two are that fundamentally different. Some technologies just take the better part of a century to mature.

[necroforest]

I don't entirely disagree with you, but "what products do people want" is overly conservative. Pre-ChatGPT, very few people wanted a (more or less) general purpose chatbot.

[AdieuToLogic]

> Pre-ChatGPT, very few people wanted a (more or less) general purpose chatbot.

And post-ChatGPT, very few people want to have to deal with "a (more or less) general purpose chatbot."

[dragontamer]

One of my local car dealerships is using an chat system of some kind (probably an LLM?).

It's awful and a complete waste of time. I'm not sure if LLMs are getting good use yet / general chatbots are good or ready for business use.

[lancesells]

But this is like companies forcing self-checkout at retail. Companies get to cheap out on their customer support by paying a monthly fee instead of paying a person to do it. It doesn't matter if it's worse for you, as every business is getting sold on using this technology.

You misery and wasted time is their improved stock price and bonus package.

[AlexandrB]

This works if you have a dominant market position, like Meta or Amazon, but a business like a local car dealership might be very ill served with this model since their customers can just go elsewhere.

[honestjohn]

Some of these are just text matchers with hardcoded options, no ML involved. Essentially phone trees, except worse because they don't tell you your options.

There are some more advanced ones using ChatGPT now. I'm guessing they simply pre-prompt it. Can lead to funny results like a customer making the Chevy bot implement an algo in Python.

[dyauspitr]

Who cares. I literally use ChatGPT 30 times a day. It answers incredibly complex queries along with citations I can verify. Isn’t “this not good enough yet” line getting old? There nothing else that can estimate the number of cinder blocks I need to use for a project and account for the volume of concrete required for it (while taking into consideration the actual available volume available in a cinder block and settling) with a few quick sentences I speak to it. I can think of literally thousands of things I have asked that would have taken hours of googling that I can get an answer for in minutes.

I think the problem is you haven’t shifted your mindset to using AI correctly yet.

Edit: More everyday examples from just the last 3 days

- Use carbide bits to drill into rocks. Googling “best bits for drilling rocks” doesn’t bring up anything obvious about carbide but it was the main thing chatGPT suggested.

- gave it dimensions for a barn I’m building and asked it how many gallons of paint I would need of a particular type. I could probably work that out myself but it’s a bunch of lookups (what’s the total sq footage, how many sq ft per gallon, what type of paint stands up to a lot of scuffing etc.)

- coarse threaded inserts for softwood when I asked it for threaded insert recommendations. I would have probably ended up not caring and fine threaded slips right out of pine.

- lookup ingredients in a face cream and list out any harms (with citations) for any of them.

- speeds and feeds for acrylic cutting for my particular CNC. Don’t use a downcut bit because it might cause a fire, something I didn’t consider.

- an explanation of relevant NEMA outlets. Something that’s very hard to figure out if you’re just dropped into it via googling.

[johnnyanmac]

>Who cares.

clearly anyone trying to buy a car, which is already an ordeal with a human as is.

>I literally use ChatGPT 30 times a day

good for you? I use Google. mos of my queries aren't complex.

>Isn’t “this not good enough yet” line getting old?

as long as companies pretend 2024 AI can replace skilled labor, no. It's getting old how many more snake oil salesmen keep pretending that I can just use ChatGPT to refactor this very hot loop of performance sensitive code. And no ChatGPT, I do not have the time budget (real time) to hook into some distributed load for that function.

I'm sure in a decade it will wow me. But I prefer to for it to stay in its lane and I stay in mine for that decade.

>There nothing else that can estimate the number of cinder blocks I need to use for a project

is Calculus really this insurmontable feat to be defending big tech over? I'm not a great mathmatican, but give them excel/sheets and they can do the same in minutes.

>I can think of literally thousands of things I have asked that would have taken hours of googling that I can get an answer for in minutes.

I'm glad it works out for you. I'm more scrutinous in my searches and I see that about half the time its sources are a bit off at best, and dangerously wrong at worst. 50/50 isn't worth any potential time saved for what I research.

>I think the problem is you haven’t shifted your mindset to using AI correctly yet.

perhaps. But for my line of work that's probably for the best.

[preordained]

"I think the problem is you haven’t shifted your mindset to using AI correctly yet"

There is an indictment of AI "products" if I ever heard one

[dyauspitr]

That’s a glib, low effort dismissal but it makes sense if you consider it.

It’s like people that kept going to the library even with Google around. You’re not playing to the strengths of AI and relying on whatever suboptimal previous method you used to find the answers. It does really, really well with very specific queries with a lot of looks ups and dependencies that nothing else can really answer without a lot of work on your end.

[Fricken]

How come this spoon won't hold my soup? Don't tell me I'm holding it wrong!

[ewoodrich]

I mean if my dentist adds a Helpful Super GenAI Infused Chatbot that can't book appointments or answer any questions about their services no amount of "you're holding it wrong" insistence about LLMs in general will actually make it useful to me.

The point is ChatGPT's wild success doesn't automatically mean consumers want and possibly will never want a chatbot as their primary interface for your specific app or service.

[latexr]

> with citations I can verify.

And do you? Every time someone tried to show me examples of “how amazing ChatGPT is at reasoning”, the answers had glaring mistakes. It would be funny if it weren’t so sad how it shows people turning off their critical thinking when using LLMs, to the point they won’t even verify answers when trying to make a point.

Here’s a small recent example of failure: I asked the “state of the art” ChatGPT model which Monty Python members have been knighted (it wasn’t a trick question, I really wanted to know). It answered Michael Palin and Terry Gilliam, and that they had been knighted for X, Y, and Z (I don’t recall the exact reasons). Then I verified the answer on the BBC, Wikipedia, and a few others, and determined only Michael Palin has been knighted, and those weren’t even the reasons.

Just for kicks, I then said I didn’t think Michael Palin had been knighted. It promptly apologised, told me I was right, and that only Terry Gilliam had been knighted. Worse than useless.

[dyauspitr]

I do. It’s not complex to click on the citation, skim the abstract and results and check the reputation of the publication. It’s built into how I have always searched for information.

I also usually follow most prompts with “look it up I want accurate information”

[latexr]

> I also usually follow most prompts with “look it up I want accurate information”

That didn’t work so hot for two lawyers in the news a while back.

[daveguy]

Please don't take this as a glib, low effort answer, but... I am glad you're not an engineer. Taking advice from an LLM on things like outlets, ingredient safety, and construction measurements seems like a mistake.

[yoyohello13]

Did you do the cinder block project? Was its estimate close? From everything I’ve seen LLMs are not that great at math.

[dyauspitr]

Yes, I finished the footings for the barn. I had to get two extra bags on an estimate of 68 bags. Not bad in my opinion considering the wastage from mixing, spilling etc. Also I would have had to do a bunch of tedious math that I didn’t have to.

I had about 5-10 cinder blocks left over, not bad for an order of ~150

[goatlover]

It works for me, therefore it should work for everyone?

[dyauspitr]

It’s pretty much the vein of the comment of the GP I’m responding to.

[johnnyanmac]

>I'm not sure if LLMs are getting good use yet / general chatbots are good or ready for business use.

They left room for the idea that the technology could evolve to be useful. You're simply dismissing anyone who cannot use he technology as is as "using it wrong".

As someone who did a tad of UX, that's pretty much the worst thing you can say to a tester. it doesn't help them understand your POV, it builds animosity towards you and the tester, and you're ruining the idea of the test because you are not going to be there to say "you're doing it wrong" when the UX releases. There's 0 upsides to making such a response.

[honestjohn]

This isn't what the other comment you're replying to was talking about.

[axitanull]

Not sure if this is a sarcasm or "you are holding it wrong" moment.

[Filligree]

There’s skill involved in using these. Of course there is.

That doesn’t make them useless.

[johnnyanmac]

depends on the tool and purpose. There was skill in navigating a file system, and now the next generation (obbscured from folder systems by mobile) seem to be losing that ability.

You can look at it in two ways, neither are particularly wrong unless your job is in fact to navigate file systems.

[dyauspitr]

If you’re unaware of how something can help you, learning how can only improve your outcome.

[honestjohn]

I don't think so, people have been wanting a general chatbot for a long time. It's useful for plenty of things, just not useful when embedded in random places.

[paretoer]

I kind of remember the Turing Test was a big deal for some 70+ years.

We should have known that once we pass the Turing Test it would almost instantly become as passe as Deep Blue beating Kasparov on the road to general intelligence.

I am taking a break from my LLM subscriptions right now for the first time to gain some perspective and all I miss it for is as a code assistant. I would also miss it for learning another human language. It seems unsurprising that large language models use cases are with automated language. What is really surprising is how very limited the use cases for automated language seems to be.

[honestjohn]

Stanford researchers said that ChatGPT passes the Turing Test. Honestly I don't understand how, since it's pretty easy to tell that you're talking to it, but yeah I don't think it really matters.

Far more useful than simulating a person, OpenAI managed to index so much information and train their models to present it in a compact way, making ChatGPT better than Google search for some purposes. Also, code generation.

[wokwokwok]

To be fair, the overwhelming feedback appears to be that people dont want a general purpose chatbot in every product and website, especially when it's labelled 'AI'.

So... certainly there's a space for new products.

...but perhaps for existing products, it's not as simple as 'slap some random AI on it and hope you ride the wave of AI'.

[nunez]

Chatbots were HUGE in the late 2010s!

[hobs]

And they hugely sucked, and basically were a sign you were dealing with either a fly by night company or a corporation so faceless and shitty you'd never willingly do business with them.

It was literally replacing a hierarchical link tree and that almost always was easier to use.

[tourmalinetaco]

Or you were told this one chatbot was “haunted” by a creepypasta character and was amazed that it would actually reference that story (not knowing it was just a silly program trained on its interactions, leading to a feedback loop).

[johnnyanmac]

>very few people wanted a (more or less) general purpose chatbot.

I mean, I still don't. But from a cynical business point of view, cutting customer servce costs (something virtually every company of scale has) of 99% of customer calls is a very obvious application of a genera purpose chatbot.

expand that to "better search engine" and "better autocomplete" and you already have very efficient, practical, and valuable tools to sell. but of course companies took the angle of "this can replace all labor" instead of offering these as assistive productivity tools.

[slavboj]

A ton of the industrial revolution was actually motivated by that input-driven thinking. You don't decide you want an Eiffel Tower from first principles, you consider "what is the coolest thing I can make out of wrought iron".

[GeneralMayhem]

But the Eiffel Tower is an art project, not something of actual utility...

[ebcode]

It was actually scheduled to be demolished, but then they added an antenna: https://www.toureiffel.paris/en/news/130-years/why-was-eiffe...

Also: https://www.reuters.com/world/europe/eiffel-tower-grows-six-...

Utility may have been an afterthought, but it's still there.

[candiddevmike]

I blogged about our glorious journey Becoming an AI Company (TM)

https://candid.dev/blog/becoming-an-ai-company/

[batch12]

I was unmoved until I hit "Natural Language™".

[mondrian]

I 95% agree, but "what people want" is probably not a strong indicator on the thresholds of paradigm shifts, since people don't know what's possible.

[johnnyanmac]

I'd look at the collorary. "what people DON'T want" is a stronger (but still imperfect) indicator of how far you can push the overton curtain.

If you can't convince people that this is benefiting them, and instead focus on talking to investors about how much you can kill off the working class (aka, your "customers" and nowadays "product audience"), you will make it harder to properly sell your product nor audience. Companies have forgotten who the real customers are, no wonder their products aren't resonating.

[SkyPuncher]

I only partially agree with this. Having spent a lot of time in the “find a problem then the solution” way of working, I’ve found the solutions are often too tame and lack innovation.

When you’re truly bring novel new value to things, sometime you need to say “we can do this cool thing, but don’t know what that means”. Simply knowing that capability opens you up to better sets of solutions.

[8n4vidtmkvmk]

What's wrong with tame and lack of innovation? Sometimes people just need to get things done. There are lots of businesses with basic needs that aren't being met.

[tourmalinetaco]

Especially amongst programmers the feeling of constantly pushing boundaries and grinding to a new unexplored territory is far more interesting than working on practical “tame” solutions. See for instance the amount of people who spend tons of time hacking at and opening up platforms for GNU/Linux that are poor fits for the OS. It would make far more practical sense for say Marcan to spend his time working on the Linux kernel more directly than playing around with Apple devices that are made to burn out in a year and become ewaste. However, he feels more satisfaction working on that “new frontier”, regardless of its practicality.

[mu53]

AI is trending right now. The most important thing for new companies is finding investors, and those people have been throwing cash at any company with AI.

Customers are also more interested in AI products. The tech industry has stagnated for years with incremental improvements on existing products. ChatGPT and generative AI are new capabilities that draw interest, and companies have been doing anything they can to stand out today.

[siruncledrew]

The market is sorting itself out right now, and eventually the wheat will get separated from the chaff.

Every cycle, theres all types of people hop on board whatever the hype train is... it's the same mindset as pioneering for gold in the wild west.

I just hope we can move along more in the "wheat" direction with AI products. There's so much low-effort crap already out there.

[ants_everywhere]

There's still a lot of real work to be done knowing what can be built and operated profitably, because the underlying tech is so new.

So just zooming out, we need people trying to figure out what can be built with this Lego set. We also need people like you're saying to work the other side so everyone can meet in the middle.

[sroussey]

This has been the case for decades. Look at the internet and .com’s. Mobile. Etc.

[slg]

It really is one of the more effective ways to identify a bubble when companies shift to selling themselves on the tools and technology they use rather than the problem they are solving.

[widenrun]

You are forgetting marketing is temporal. Fifteen years ago you could sell your software as the Cloud version of a legacy app. Right now, there's a window that being the AI version will get you a call.

[lallysingh]

That requires they you understand the capabilities and limitations of the tech way better than anyone does. So instead "let's see what we can do with this" is the underlying approach.

[kohbo]

Might be a good philosophy for a hobbyist, not (usually so) for a business.

[lallysingh]

For VC funded startups, this is exactly the business model. Each company is a different lottery ticket for them.

[tim333]

A python company is too specialized but software companies are a thing, Maybe AI will be another tool for software companies.

[_xiaz]

To be fair, astral is the python company and thank god they are. I love ruff and uv

[seydor]

People want a faster horse

[goatlover]

Also one that flies, fueled by atomic power.

[johnnyanmac]

if you're some Python contractor company, the angle makes sense. but of course, very few AI companies are out there trying to help others solve problems.

[xbmcuser]

this is how things evolve everything was .com company when internet started going mainstream then real product and service providers were left standing

[diatone]

Ehhh it’s a spectrum. First you innovate, then you commercialise. Even Google took a few years to successfully monetise and they weren’t the first mover in web search. LLMs have been around for, what, coming up on three years? Probably two to four more years to see results.

[DylanDmitri]

I’m seeing a lot of meh products that take like 4 units of effort to integrate. I think multiple LLMs, deeply integrated into a cohesive product with 100+ effort units, that can be great. An AI that’s familiar with the use of every settings menu on windows would be awesome

[highfrequency]

I'm not so sure. When a technological wave is big enough, it seems reasonable to start by asking: "what business can be built on this exponential wave?" This is contrary to standard YC advice (make something people want right away, don't create a solution in search of a problem) but empirically a lot of big companies started this way:

- Bezos saw the growth rate of the internet, spent a few months mulling over the question: "what business would make sense to start in the context of massive internet adoption" and came up with an online bookstore.

- OpenAI's ChatGPT effort really began when they saw Google's paper on transformers and decided to see how far they could push this technology (it's hard to imagine they forecasted all the chatbot usecases; in reality I'm sure they were just stoked to push the technology forward).

- Intel was founded on the discovery of the integrated circuit, and again I think the dominant motivation was to see how far they could push transistor density with a very hazy vision at best of how the CPUs would eventually be used.

I think the reason this strategy works is that the newness of a truly important technology counteracts much of the adverse selection of starting a new business. If you make a new To-Do iPhone app, it's unlikely that people have overlooked a great idea in that space over the last 10 years. But if lithium ion batteries only just barely started becoming energy dense enough to make a car, there's a much more plausible argument why you could be successful now.

Said another way: "why hasn't this been done before?" (both by resource-rich incumbents as well as new entrants) is a good filter (and often a limiting one) for starting a business. New technological capabilities are one good answer to this question. Therefore if you're trying to come up with an idea for a business, it seems reasonable to look at new technologies that you think are actually important and then reason backward to what new businesses they enable.

Two additional positive factors I can think of:

1. A common dynamic is that a new technology is progressing rapidly but is of course far behind traditional solutions at the outset. Thus it is difficult to find immediate applications, even if large applications are almost guaranteed in 10-20 years. Getting in early - during the borderline phase where most applications are very contrived - is often a big advantage. See Tesla Roadster (who wants a $100k electric sports car with 200mi range and minimal charging network?), early computers (what is the advantage of a slow machine with no GUI over doing work by hand?), and perhaps current LLMs (how valuable is a chatbot that frequently hallucinates and has trouble thinking critically in original ways)? It's the classic Innovator's Dilemma - we overweight the initial warts and don't properly forecast how quickly things are improving.

2. There is probably a helpful motivational force for many people if they get to feel that they are on the cutting edge of technology that interests them and building products that simply weren't possible two years ago.

[lispisok]

You're suggesting boring business way to do things. The tech ecosystem is full of startups doing that ridiculous thing you said chasing the hot new thing and raising huge amounts of money off the hype. This AI hype cycle is really bad and before that we had cryptocurrency.