"In tech, often an expert is someone that know one or two things more than everyone else. When things are new, sometimes that's all it takes."
It's no surprise it's just prompt engineering. Every new tech goes that way - mainly because innovation is often adding one or two things more the the existing stack.
Prompt engineering isn't as simple as writing prompts in english. It's still engineering data flow, when data is relevant, systems that the AI can access and search, tools that the AI can use, etc.
Prompt engineering and using an expensive general model in order to prove your market, and then putting in the resources to develop a smaller(cheaper) specialized model seems like a good idea?
When people are desperate to invest, they often don't care what someone actually can do but more about what they claim they can do. Getting investors these days is about how much bullshit you can shovel as opposed to how much real shit you shoveled before.
One of the biggest problems frontier models will face going forward is how many tasks require expertise that cannot be achieved through Internet-scale pre-training.
Any reasonably informed person realizes that most AI start-ups looking to solve this are not trying to create their own pre-trained models from scratch (they will almost always lose to the hyperscale models).
A pragmatic person realizes that they're not fine-tuning/RL'ing existing models (that path has many technical dead ends).
So, a reasonably informed and pragmatic VC looks at the landscape, realizes they can't just put all their money into the hyperscale models (LP's don t want that) and they look for start-ups that take existing hyperscale models and expose them to data that wasn't in their pre-Training set, hopefully in a way that's useful to some users somewhere.
To a certain extent, this study is like saying that Internet start-ups in the 90's relied on HTML and weren't building their own custom browsers.
I'm not saying that this current generation of start-ups will be successful as Amazon and Google, but I just don't know what the counterfactual scenario is.
It is beyond annoying that the article is totally generated by AI. I appreciate the author (hopefully) spending effort in trying to figure out the AI systems, but the obviously-LLM non-edited content makes me not trust the article.
Where is this guy sitting that he is able to collect all of this data? And why is he able to release it all in a blog post? (my company wouldn't allow me to collect and release customer data like this.)
The thing that drives me nuts is that most "AI Applications" are just adding crappy chat to a web app. A true AI application should have AI driven workflows that automate boring or repetitive tasks without user intervention, and simplify the UI surface of the application.
This makes no sense to me? I don't understand why a company, even if it is using GPT or Claude as their true backend, is going to leave API calls in Javascript that anyone can find. Sure maybe a couple would, but 73% of those tested?
Surely your browser is going to talk to their webserver, and yup sure it'll then go off and use Claude etc then return the answer to you, but surely they're not all going to just skin an easily-discoverable website over the big models?
I don't believe any of this. Why aren't we questioning the source of how the author is apparently able to figure out some sites are using REDIS etc etc?
The reason is because VC needs to show that their flagship investments have "traction" so they manufacture ecosystem interest by funding and encouraging ecosystem product usage. It's a small price to pay. If someone builds a wrapper that gets 100 business users then token use on the foundation layer gets that passed down. Big scheme.
That is lower than I expected. There are just a handful of companies that create llms. They are all more ir less similar. So all automation is in using them, which is prompt engineering if you see that way.
The bigger question is, this is the same story with apps on mobile phones. Apple and google could easily replicate your app if they wanted to and they did too. That danger is much higher with these ai startups. The llms are already there in terms of functionality, all the creators figured out the value is in vertical integration and all of them are doing it. From that sense all these startups are just showing them what to build. Even perplexity and cursor are in danger.
I decided to flag this article because it has to be fake.
The author never explains how he is able to intercept these API calls to OpenAI, etc. I definitely believe tons of these companies are just wrappers, but they'd be doing the "wrapping" in their backend, with only a couple (dumb) companies doing the calls directly to OpenAI from the front end where they could be traced.
This article is BS. My guess is it was probably AI generated because it doesn't make any sense.
65 comments
[ 1.9 ms ] story [ 47.9 ms ] thread"In tech, often an expert is someone that know one or two things more than everyone else. When things are new, sometimes that's all it takes."
It's no surprise it's just prompt engineering. Every new tech goes that way - mainly because innovation is often adding one or two things more the the existing stack.
https://github.com/zou-group/textgrad
and bonus, my rant about this circa 2023 in the context of Stable Diffusion models: https://gist.github.com/Hellisotherpeople/45c619ee22aac6865c...
Thus has it always been. Thus will it always be.
Any reasonably informed person realizes that most AI start-ups looking to solve this are not trying to create their own pre-trained models from scratch (they will almost always lose to the hyperscale models).
A pragmatic person realizes that they're not fine-tuning/RL'ing existing models (that path has many technical dead ends).
So, a reasonably informed and pragmatic VC looks at the landscape, realizes they can't just put all their money into the hyperscale models (LP's don t want that) and they look for start-ups that take existing hyperscale models and expose them to data that wasn't in their pre-Training set, hopefully in a way that's useful to some users somewhere.
To a certain extent, this study is like saying that Internet start-ups in the 90's relied on HTML and weren't building their own custom browsers.
I'm not saying that this current generation of start-ups will be successful as Amazon and Google, but I just don't know what the counterfactual scenario is.
Either you have a smash-and-grab strategy or you are awful at risk analysis.
I don't believe any of this. Why aren't we questioning the source of how the author is apparently able to figure out some sites are using REDIS etc etc?
It's still early in the paradigm and most startups will fail but those that succeed will embed themselves in workflows.
The bigger question is, this is the same story with apps on mobile phones. Apple and google could easily replicate your app if they wanted to and they did too. That danger is much higher with these ai startups. The llms are already there in terms of functionality, all the creators figured out the value is in vertical integration and all of them are doing it. From that sense all these startups are just showing them what to build. Even perplexity and cursor are in danger.
The author never explains how he is able to intercept these API calls to OpenAI, etc. I definitely believe tons of these companies are just wrappers, but they'd be doing the "wrapping" in their backend, with only a couple (dumb) companies doing the calls directly to OpenAI from the front end where they could be traced.
This article is BS. My guess is it was probably AI generated because it doesn't make any sense.
Like I don't think 73% of companies are foolish enough to be calling their LLM provider from the front-end with their API key.
Either they are doing something that they aren't mentioning, or this is just complete bogus.