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73% of AI startups are just prompt engineering

pub.towardsai.net

131–140 of 212 posts

Re: 73% of AI startups are just prompt engineering

#131
post #92
post #84

But ... what else should they be doing? What's the expectation here? For example, in the 90's, a startup that offered a nice UI for a legacy console based system, would have been a great idea. What's wrong with that?

Actual AI. Not being "AI" users. Being LLM users would be fine but they pretend they do AI.

Everything is a spectrum.

At what point can you claim that you did "it"?

Do you have to use an open source model instead of an API? Do you have to fine tune it? How much do you need to? Do you have to create synthetic data for training? Do you have to gather your own data? Do you need to train from scratch? Do you need to come up with a novel architecture?

10 years ago if you gathered some data and trained a linear model to determine the likelihood your client would default on their loan and used that to decide how much, if any, to loan them- you're absolutely doing "actual AI"

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Any other software you could ask all the same questions but with using a high level language, frameworks, dependencies, hiring consultants / firm, using an LLM, no-code, etc.

At what point does outsourcing some portion of the end product become no longer doing the thing?

Re: 73% of AI startups are just prompt engineering

#132

Prompt is code.

prompt as code is a pipe-dream.

The machine model for natural language doesnt exist - it is too ambiguous to be useful for many applications.

Hence, we limited natural language to create programming languages whose machine model is well defined.

In math, we created formalism to again limit language to a subset that can be reasoned with.

Re: 73% of AI startups are just prompt engineering

#136

> just prompt engineering This dismisses a lot of actual hard work. The scaffolding required to get SOTA performance is non-trivial! Eg how do you build representative evals and measure forward progress? Also, tool calling, caching, etc is beyond what folks normally call “prompt engineering”. If you think it’s trivial though - go build a startup and raise a seed round, the money is easy to come by if you can show res…

> Eg how do you build representative evals and measure forward progress?

This assumes that those companies do evaluations. In my experience, seeing a huge amount of internal AI projects at my company (FAANG), there's not even 5% that have any sort of eval in place.

Re: 73% of AI startups are just prompt engineering

#138

> just prompt engineering This dismisses a lot of actual hard work. The scaffolding required to get SOTA performance is non-trivial! Eg how do you build representative evals and measure forward progress? Also, tool calling, caching, etc is beyond what folks normally call “prompt engineering”. If you think it’s trivial though - go build a startup and raise a seed round, the money is easy to come by if you can show res…

This is like when people say that you should short the market if you think its going to crash. People have different risk premiums.

Re: 73% of AI startups are just prompt engineering

#140
Why is this post published in November 2025 talking about GPT-4?

I'm suspicious of their methodology:

> Open DevTools (F12), go to the Network tab, and interact with their AI feature. If you see: api.openai.com, api.anthropic.com, api.cohere.ai You’re looking at a wrapper. They might have middleware, but the AI isn’t theirs.

But... everyone knows that you shouldn't make requests directly to those hosts from your web frontend because doing so exposes your API key in a way that can be stolen by attackers.

If you have "middleware" that's likely to solve that particular problem - but then how can you investigate by intercepting traffic?

Something doesn't smell right about this investigation.

It does later say:

> I found 12 companies that left API keys in their frontend code.

So that's 12 companies, but what about the rest?

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