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.
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.
And post-ChatGPT, very few people want to have to deal with "a (more or less) general purpose chatbot."
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.
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'.
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.
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.
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.
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.
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.
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.
- 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.
This is the fundamental problem that prevents generative AI from becoming a "foundational building block" for most products. Even with rigorous safety measures in place, there are few guarantees about its output. AI is about as solid as sand when it comes to determinism, which is great if you're trying to sell sand, but not so great if you're trying to build a huge structure on top of it.
The reason we can build such deep and complex software system is because each layer can assume the one below it will "just work". If it only worked 99% of the time, we'd all still be interfacing with assembly, because we'd have to be aware of the mistakes that were made and deal with them, otherwise the errors would compound until software was useless.
Until AI achieves the level of determinism we have with other software, it'll have to stay at the surface.
We probably need a lot more work along this dimension of finding use cases where strong automatic verification of AI outputs is possible.
Going further, our predecessors put so much work into getting non-deterministic electronics together providing us with a stable and _correct_ platform, it looks ridiculous how people were trying to squeeze another layer of non-determinism in between to solve the same classes of problems.
>If your AI travel agent books vacations to the correct destination only 90% of the time
that would be using the wrong tool for the job. an AI travel agent would be very useful for making suggestions, either for destinations or giving a list of suggested flights, hotels etc, and then hand off to your standard systems to complete the transaction.
there are also a lot of systems that tolerate "faults" just fine such as image/video/audio gen
But that’s a recommendation engine and we have that already all over the place.
Well, I don't agree. I think there are ways to make this successful, but you have to be honest about the limitations you're working it with and play to your strengths.
How about an AI travel agent that gets your itineraries at a discount with the caveat that you be ready for anything. Like old, cheap standby tickets where you just went wherever there was an empty seat that day.
Or how about an AI Spotify for way less money than current Spotify. It's not competing on quality, it can't. Occasionally you'll hear weird artifacts, but hey it's way cheaper.
That could work, imo
AI is creating a post-scarcity content economy where quality is going to be the only driver of value.
If you are the rights holder of any premium human created media content you are not going to let a 'cheap' AI tool get access to recommend it out to people.
I'm not disagreeing with the "needs to work deterministically" -- there is a need for that, but this is a poor example. "Hey robot, plan a trip to Mexico" might still save me time overall if done right, and that has value.
Call centre workers are often dreadfully inaccurate as well. Same with support engineers.
Heck even for banking, there are enormous teams fixing every screw up made by some other employee.
If you're writing a random number generator, that generates numbers between 0 and 100. How would you test it? Throw your hands up in the air and say nope, can't test it, it's not deterministic! Or maybe you can just run it 1000 times and make sure all the numbers are indeed between 0 and 100. Maybe count up the number frequencies and verify its uniform. There's lots of things you can check for.
So do the same with your LLMs. Test it on your specific use-cases. Do some basic smoke tests. Are you asking it yes or no questions? Is it responding with yes or no? Try some of your prompts on it, get a feel for what it outputs, write some regexes to verify the outputs stay sane when there's a model upgrade.
For "quality" I don't think there's a substitute than humans. Just try it. If the outputs feel good, add your unit tests. If you want to get scientific, do blind tests with different models and have humans rate them.
Abyss: 1 Ambiguous: 3 Cacophony: 3 Crescendo: 3 Ephemeral: 3 Ethereal: 3 Euphoria: 3 Labyrinth: 3 Maverick: 3 Melancholy: 3 Mellifluous: 3 Nostalgia: 3 Oblivion: 3 Paradox: 3 Quixotic: 1 Serendipity: 3 Sublime: 3 Zenith: 3
It's all well and good to say "Make something people want" but for anything that people want usually one of three things is true
1. Someone else is already making it.
2. Nobody knows how to make it.
3. Nobody knows that people want it.
People experimenting with 2 and 3 will have a lot of failures, but the great successes will come from those groups as well.
Sure, every trend in business has a lot of companies going "we should do this because everyone else is" It was a dumb idea for previous trends and it is a dumb idea now. Consider how many companies did that for the internet. There were a lot of poorly thought out forays into having an internet presence. Of those companies still around, they pretty much will have an internet presence now that serves their purposes. They transition from "because everyone else is" as their motivation to "We want specific ability x,y,&z"
Perhaps the best way to get from "everyone else is doing it" to knowing what to build is to play in the pool.
But AI has always been a secondary augmentation to the product itself. It’s a tool, it shouldn’t be the other way around.
I find it useful for:
* throwing ideas at a wall and rubber-ducking my emotional state and feelings.
* creating silly, meme images in strange circumstances, sometimes.
* answering simple "what's the name of that movie / song / whatever" questions
Think of it like the school and the early days of Wikipedia. "Can I use Wikipedia as a source? No. But you can use it to find primary sources and use those in your research paper!"
When I look for answers to specific questions, I either search Wikipedia, or ask ChatGPT. "Searching the Internet" doesn't work anymore with all the ADs, pop-ups and "optimized" content that I have to consume before I get to find the answers.
Its like talking to a intelligent person about a topic you want to learn, but they know it good enough to teach you if you keep asking questions.
a) Write me shell script which does this and that. b) what Linux command with what arguments do I call to do such and such thing. c) Write me a function / class in language A/B/C that does this and that d) write me a SQL query that does this and that e) use it as a reference book for any programming language or whatever other subject.
etc. etc.
The answers sometimes come out wrong and / or does contain non trivial bugs. Being experienced programmer I usually have no problems spotting those just by looking at generated code or running test case created by ChatGPT. Alternatively there are no bugs but the approach is inefficient. In this case point explaining why it is inefficient and what to use instead ChatGPT will fix the approach. Basically it saves me a shit ton of time.
We initially were developing a system that we had hoped could handle everything and eject any workflow issues to a human so the operations team could kick the machine. We were hoping to avoid an interface all together on the customer side.
After a few versions and attempts at building this system, we moved towards a traditional app where we focused on building a product people wanted and automate parts of it over time. But even the parts we automated needed an interface for customers to spot check our work. So we found a great designer.
...Before we knew it, we were building a traditional company, with some AI. The company is doing well and people love what we're building, but it's different than we imagined.
We still believe in the long term vision and promise of the technology, but the article is right, this isn't going to be an overnight process unless some new architecture emerges.
In the mean time, we're focused on helping people get from A to B easily using whatever means necessary, because moving f**ing sucks. If you're moving soon or know anybody who is, we'd be happy to help them. -P
It's okay, I mean even the internet started out as Charlie_Bit_Me.avi and free porn.
I'm betting the adoption curve for AI hits when the first company sells photorealistic porn of anyone you have a picture of. Hell, half ass Photoshop porn is lucrative already.
Charlie Bit Me is of the YouTube generation, so it wasn't passed around as an avi email attachment like some older memes of the previous generation. From that long ago, Exploding Whale comes to mind.