9 ms·

73% of AI startups are just prompt engineering

246 points by kllrnohj 11mo ago

RobertDeNiro 11mo ago
People talk about an AI bubble. I think this is the real bubble.
- dboreham 11mo ago
  Not really because the money involved is relatively small. The bubble is where people are using D8s to push square kilometers of dirt around for data centers that need new nuclear power plants built, to house millions of obsolete Nvidia GPUs that need new fabs constructed to make, using yet more D8s..
  - mh- 11mo ago
    (D8s apparently refers to a specific Caterpillar-brand bulldozer, not some kubernetes takeoff.)
weakwire 11mo ago
So? 73% of Saas startups are DB connectors & queries.
- indymike 11mo ago
  And 73% of paas are deploy scripts for existing software. It's how the industry works.
- tylerchilds 11mo ago
  The difference is, if your company “moat” is a “prompt” on a commodity engine, there is no moat. Google even said they have no moat, when clearly the moat is people that trust them and not any particular piece of technology.
  - ojr 11mo ago
    the orchestration layer is the moat, ask any LLM and they will give paragraphs explaining why this is...
- parineum 11mo ago
  If tekens aren't profitable then prices per token are likely to go up. If that's all these businesses are, they're all very sensitive to token prices.
  - weakwire 11mo ago
    Not with open weight models you can deploy yourself. Different economics but not venerable to price increases.
    - parineum 11mo ago
      First, someone has to develop those models and that's currently being done with VC backing. Second, running those models is still not profitable, even if you self host (obviously true because everything is self hosted eventually). Burning VC money isn't a long term business model and unless your business is somehow both profitable on Llama 8b (or some such low power model) _and_ your secret sauce can't be easily duplicated, you're in for a rough ride. The only barrier between AI startups at this point is access to the best models, and that's dependent on being able to run unprofitable models that spend someone else's money. Investing in a startup that's basically just a clever prompt is gambling on the first mover's advantage because that's the only advantage they can have.
michaelgiba 11mo ago
73% of startups are just writing computer programs
indymike 11mo ago
A long time ago a mentor of mine said, "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.
- mycall 11mo ago
  I'm always more interested in the 'less is more' strategy, taking things away from the already hyper-complicated stack, reviewing first principles and simplifying for the same effectiveness. This is ever more rare.
  - pitched 11mo ago
    I think this sense of “less is more” roughly means refactoring? I think the reason these go south so often is because we’re likely moving complexity around rather than removing it. Removing a layer from the stack means making a different layer more complex to take over for it.
    - port11 11mo ago
      Generally that seems to be true u less we're talking about removing cruft that wasn't needed. Yearly front-end rewrites are quite the joke, but they also reveal that the team didn't really get the underlying assumptions correctly. Or maybe they just really wanted to try Redux/Zustand/Jotai/the cool new router and ended up making a mess that will need cleaning next year.
- rynn 11mo ago
  It’s easy to underestimate the amount of testing “just” prompt/context engineering takes to get above average results. And then you need to see what variations work best with different models. My POCs for personal AI projects take time to get this right. It’s not like the API calls are the hard portion of the software.
- KineticLensman 11mo ago
aurareturn 11mo ago
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.
- drowsspa 11mo ago
  Is it, though? Apparently the current best practice is just to allow the LLM untethered access to everything and try to control access by preventing prompt injection...
  - mettamage 11mo ago
    You still need to find the correct data, and get it to the LLM. IMO, a lot of it is data engineering work with API calls to an LLM as an extra step. I'm currently doing a lot of ETL work with Airflow (and whatever data {warehouses, lakes, bases} are needed) to get the right data to a prompt engineering flow. The prompt engineering flow is literally a for loop of Google Docs in a Google Drive that non-tech people, but domain experts in their field, can access. It's up to the domain experts and me to understand where giving it data will tone down the hallucinative nonsense an LLM puts out, and where we should not give data because we need the problem solving skills of the LLM itself. A similar process is for tool-use, which in our case are pre-selected Python scripts that it is allowed to run.
    - apwell23 11mo ago
      can you describe what the usecase is ?
  - kgeist 11mo ago
    Well it took me 2 full-time weeks to properly implement a RAG-based system so that it found actually relevant data and did not hallucinate. Had to: - write an evaluation pipeline to automate quality testing - add a query rewriting step to explore more options during search - add hybrid BM-25+vector search with proper rank fusion - tune all the hyperparameters for best results (like weight bias for bm25 vs. vector, how many documents to retrieve for analysis, how to chunk documents based on semantics) - parallelize the search pipeline to decrease wait times - add moderation - add a reranker to find best candidates - add background embedding calculation of user documents - lots of failure cases to iron out so that the prompt worked for most cases There's no "just give LLM all the data", it's more complex than that, especially if you want best results and also full control of data (we run all of that using open source models because user data is under NDA)
ReptileMan 11mo ago
The really impressive thing about AI startups is not that they sell wrappers around (whatever), but that they are not complete vaporware.
theshetty 11mo ago
Prompt is code.
- amelius 11mo ago
  Prompt is specification, not code.
  - teucris 11mo ago
    Not to be too pedantic, but code is a kind of specification. I think making the blanket statement "Prompt is code" is inaccurate but there does exist a methodology of writing prompts as if they are specifications that can reliably converted to computational actions, and I believe we're heading toward that.
    - amelius 11mo ago
      Yeah, I assumed someone would say this. My manager gives me specifications, which I turn into code. My manager is not coding. This is how to look at it.
  - deleted 11mo ago
    [deleted]
- add-sub-mul-div 11mo ago
  I've always said, determinism has been holding the field back.
- bwfan123 11mo ago
  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.
amelius 11mo ago
73% of AI startups are building their castle in someone else's kingdom.
- asah 11mo ago
  -1: there's lots of "kingdoms" (openai, anthropic, google, plus open source) - if one king comes for your castle, you can move in minutes.
  - amelius 11mo ago
    True, even OpenAI built their castle in nVidia's kingdom. And nVidia built their castle in TSMC's kingdom. And TSMC built their castle in ASML's kingdom.
    - skx001 11mo ago
      lastly we need the FDIC meme "Backed by the full faith and credit of the U.S. Government" for good measure, haha.
    - octoberfranklin 11mo ago
      TSMC bought a huge chunk of ASML's shares before taking the plunge on EUV -- enough to get them a board seat.
- benoau 11mo ago
  It's worse than that, someone else's models, someone else's smartphone operating systems, it's every conceivable disadvantage.
  - scottiebarnes 11mo ago
    Every city should have its own municipal chip fabrication plant!
    - octoberfranklin 11mo ago
Der_Einzige 11mo ago
And out of that 73%, 99% of them don't even do the obvious step of trying to actually optimize/engineer their damn prompts! https://github.com/zou-group/textgrad 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/45c619ee22aac6865ca4bb328eb58faf https://gist.github.com/Hellisotherpeople/45c619ee22aac6865c...
IncreasePosts 11mo ago
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?
- Forgeties79 11mo ago
  Are people down to have a bunch of specialized models? The expectation set by OpenAI and everyone else has set is that you will have one model that can do everything for you. It’s like how we’ve seen basically all gadgets meld into the smart phone. People don’t have Garmin’s and beepers and clock radios anymore (or dedicated phones!). It’s all on the screen that fits in your pocket. Any would-be gadget is now just an app
  - sgt101 11mo ago
    My coffee maker app is quite disappointing.
    - Forgeties79 11mo ago
      I imagine you’re being facetious but I wouldn’t count food-related products for the most part. It’s not like Claude is brewing a pot for me anyway lol
  - lukeschlather 11mo ago
    Specialized models are cheaper. For a company you're looking for some task that needs to be done millions of times per day, and where general models can do it well enough that people will pay you more than the general model's API cost to do it. Once you've validated that people will pay you for your API wrapper you can train a specialized model to increase your profit and if necessary lower your pricing so people won't pay OpenAI directly.
  - palata 11mo ago
    Happy with my Garmin :-)
drivingmenuts 11mo ago
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. Thus has it always been. Thus will it always be.
RC_ITR 11mo ago
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.
- bradfa 11mo ago
  Got any links to explanations of why fine tuning open models isn’t a productive solution? Besides renting the GPU time, what other downsides exist on today’s SOTA open models for doing this?
  - RC_ITR 11mo ago
    When the new pre-trained parameters come out in a new model generation, your old fine tuning doesn't apply to them.
- Skunkleton 11mo ago
  The question that isn't answered completely in the article is how useful are the pipelines for these startups? The article certainly implies that for at least some of these startups there very little value add in the wrapper.
goranmoomin 11mo ago
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.
- teiferer 11mo ago
  What makes you believe that anything in the article is real? The author seems to not exist and it's unclear where the data underlying the claims is even coming from since you can't just go and capture network traffic wherever you like. A little due diligence please.
Workaccount2 11mo ago
I'm surprised by the number of people who are running head first into AI wrapper start-ups. Either you have a smash-and-grab strategy or you are awful at risk analysis.
- mvkel 11mo ago
  Do you want to be right, or do you want to make money? You'll be correct in 5-10 years. Do you wait and do nothing until then?
analogpixel 11mo ago
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.)
- jmogly 11mo ago
  Im also wondering how he is able to see calls to AI providers directly in the browser, client side api calls? Thats strange to me. Also how is he able to peer into the rag architectures? I don’t get that, maybe GpT4.1 allows unauthenticated requests? Is there an OAuth setup that allows client side requests to OpenAI?
  - muppetman 11mo ago
    Yea I just posted a similar comment. I'm sure some websites just skin OpenAI/Claude etc, but ALL of them? It makes no sense.
- InsideOutSanta 11mo ago
  It sounds like some of these companies call the OpenAI or Anthropic APIs directly from their frontend. Later, the author also mentions "response time patterns for every major AI API," so maybe there's some information about the backend leaking that way even if the API calls are bridged. But I'd like to know an actual answer to this, too, especially since large parts of this post read as if they were written by an LLM.
  - cess11 11mo ago
    "I found 12 companies that left API keys in their frontend code. I reported them all. None responded." They claim to have found that.
  - hn_throwaway_99 11mo ago
    > It sounds like some of these companies call the OpenAI or Anthropic APIs directly from their frontend. Which would be a major security hole. And sure, lots of startups have major security holes, but not enough that he could come up with these BS statistics. I'm a little dismayed at how high up this has been voted given the data is guaranteed to be made up.
mvkel 11mo ago
Wait til you hear what GPT 5 is
- kilroy123 11mo ago
  What is it? A gpt-4o wrapper?
  - mvkel 11mo ago
    It's not a new model; it's a router + many, many chains of optimized system prompts. Note the terminology they use to describe it. > We are introducing GPT‑5, our best AI system yet > GPT‑5 is a unified system with a smart, efficient model that answers most questions... They make a special point to call it a system, of which "a model" is a part. I'm betting they didn't pre-train gpt5 from scratch, but did a ton of RL on gpt4 + prompt optimization and the routing engine.
turnsout 11mo ago
That's like saying "73% of business is just meetings"
CuriouslyC 11mo ago
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.
- commandar 11mo ago
  I'm firmly of the opinion that, as a general rule, if you're directly embedding the output of a model into a workflow and you're not one of a handful of very big players, you're probably doing it wrong.[1] If we overlook that non-determinism isn't really compatible with a lot of business processes and assume you can make the model spit out exactly what you need, you can't get around the fact that an LLM is going to be a slower and more expensive way of getting the data you need in most cases. LLMs are fantastic for building things. Use them to build quickly and pivot where needed and then deploy traditional architecture for actually running the workloads. If your production pipeline includes an LLM somewhere in the flow, you need to really, seriously slow down and consider whether that's actually the move that makes sense. [1] - There are exceptions. There are always exceptions. It's a general rule not a law of physics.
muppetman 11mo ago
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?
- Zanfa 11mo ago
  It makes perfect sense when you consider that the average Javascript developer does not know that business logic can exist outside of React components.
  - muppetman 11mo ago
    Yea I understand maybe 10-20% of these AI clowns don't know what they're doing, but to suggest they're all making a mistake this silly doesn't stack up IMHO.
- jcrawfordor 11mo ago
  It's very confusing in the text of the article, at times it sounds like the author is using heuristic methods (like timings) but at times it sounds like they somehow have access to network traffic from the provider's backend. I could 100% believe that a ton of these companies are making API calls to providers directly from an SPA, but the flow diagrams in the article seem to specifically rule that out as an explanation. I might allow them more credit if the article wasn't in such an obviously LLM-written style. I've seen a few cases like this, now, where it seems like someone did some very modest technical investigation or even none at all and then prompted an LLM to write a whole article based on it. It comes out like this... a whole lot of bullet points and numbered lists, breathless language about the implications, but on repeated close readings you can't tell what they actually did. It's unfortunate that, if this author really did collect this data, their choice to have an LLM write the article and in the process obscure the details has completely undermined their credibility.
adidoit 11mo ago
100% of AI startups are just multiplying matrices 100% of tech startups are just database engineering It's still early in the paradigm and most startups will fail but those that succeed will embed themselves in workflows.
zach_moore 11mo ago
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.
yalogin 11mo ago
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.
- hotpaper75 11mo ago
  Do not forget that a product idea needs to meet a certain ROI to be stolen. Big Tech won't go after opportunities that do not generate billion-level revenue. This leaves some room for applications where you can earn decent money.
  - yalogin 11mo ago
    That is not how companies work. What you said may be true for the immediate short term but over time every team in the company needs to show improvement and set yearly milestones. All these startups will then become functionality they want to push that quarter. Yes it doesn’t mean the death of the startup but a struggle
    - hotpaper75 11mo ago
      Sure but at the end I believe one of the deciding factors on which projects you pursue is the ROI. In a scenario where a given team has many projects it can go after it has to select which project to chose from.
Devasta 11mo ago
Atlas himself doesn't carry as much as "engineering" does in that headline.
hn_throwaway_99 11mo ago
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.
- teiferer 11mo ago
  I find it shocking that most comments here just accept the article as fact and discuss the implications. The message might not even be wrong. But why is everybody's BS detection on ice in the AI topic space? Come one people, you can all do better than this! Thanks for flagging. Though whenever such a made up thing is flagged, we lose the chance to discuss this (meta) topic. People need to be aware how prevalent this is. By just hiding it every time we notice, we're preventing everybody to read the kind of comment you wrote and recalibrate their BS-meters.
- ElasticBottle 11mo ago
  I had the exact same thoughts. Even while I don't disagree with the premise, I'm so confused as to how they are intercepting the calls. 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.
strathmeyer 11mo ago
[dead]
alex_young 11mo ago
73% of AI blog post statistics are bogus. Subscribe to learn more.
zkmon 11mo ago
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?
- nottorp 11mo ago
  Actual AI. Not being "AI" users. Being LLM users would be fine but they pretend they do AI.
  - zkmon 11mo ago
    AI is an ecosystem that includes users at all layers and innovation at all those layers - infra, databases, models, agents, portals, UIs and so on. What do you mean by doing AI? Btw, the so-called AI devs or model developers are "users" of the databases and all the underlying layers of the stack.
  - jasonjmcghee 11mo ago
    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" --- 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?
    - FridgeSeal 11mo ago
      > At what point can you claim that you did "it"? When the core of your business ist something that’s shamelessly farmed out to <LLM provider of choice>. It would be like calling yourself a restaurant, and then getting uber eats deliveries of whatever customers ordered and handing that to them.
apwell23 11mo ago
Yep I just use chatgpt . I can write better prompts and data for my own usecases
sc077y 11mo ago
73% of statistics are wrong
jedberg 11mo ago
And 99% of software development is just feeding data into a complier. But that sort of misses the point doesn't it? AI has created a new interface with a higher level abstraction that is easier to use. Of course everyone is going to use it (how many people still code assembler?). The point is what people are doing with it is still clever (or at least has potential to be).
- system2 11mo ago
  I disagree. Software development is not limited to LLM-type responses and incorporates proper logic. You are at the mercy of LLM when you build an "AI" interface for the LLM apis. 73% these "AI" companies will collapse when the original API providing company comes up with a simple option (Gemini for Sheets, for example), they will disappear. It is already happening. AI software is not long-lasting; its results are not deterministic.
furyofantares 11mo ago
Another slop article that could probably be good if the author was interested in writing it, but instead they dumped everything into an LLM and now I can't tell what's real and what's not and get no sense of what parts of the findings the author found important or interesting compared to what other parts. I have to wonder, are people voting this up after reading the article fully, and I'm just wrong and this sort of info dump with LLM dressing is desirable? Or are people skimming it and upvoting? Or is it more of an excuse to talk about the topic in the title? What level of cynicism should I be on here, if any?
DetroitThrow 11mo ago
Why is slop with ridiculous or impossible claims at the top of HN?
eddythompson80 11mo ago
For me, 2023 was an entire year of weekly demos that now looking back at were basically a "Look at this dank prompt I wrote" followed by thunderous applause from the audience (which was mostly, but not exclusively, upper management) Hell man, I attended a session at an AWS event last year that was entirely the presenter opening Claud and writing random prompts to help with AWS stuff... Like thanks dude... That was a great use of an hour. I left 15 minutes in. We have a team that's been working on an "Agent" for about 6 months now. Started as prompt engineering, then they were like "no we need to add more value" developed a ton of tools and integrations and "connectors" and evals etc. The last couple of weeks were a "repivot" going back full circle to "Lets simplify all that by prompt engineering and give it a sandbox environment to run publicly documented CLIs. You know, like Claude Code" The funny thing is I know where it's going next...
- ivape 11mo ago
  Wait ... You mean teams are already building their own solutions to existing solutions? Software development will live on in eternity then.
  - tempodox 11mo ago
    They are just reselling OpenAI subscriptions at a markup. Surprise!
- morkalork 11mo ago
  But did it work? This is the sticking point with me now. I've seen slides, architecture diagrams, job descriptions, roadmaps and other docs now from about a dozen different companies doing AI Agent projects. And while it's completely feasible to build the systems they're describing, what I have not seen yet is evidence of any of them working. When you press them on this, they have all sorts of ideas like a judge LLM that takes the outputs, comes up with modified SOPs and feeds those into the prompts of the mixture-of-experts LLMs. But I don't think that works, I've tried closing that loop and all I got was LLMs flailing around.
  - 11mo ago
theptip 11mo ago
> 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 results.
- submeta 11mo ago
  This should be the top comment.
- jasonjmcghee 11mo ago
  prompt engineering + CRUD is likely much more fair. And many companies are "just CRUD".
- bayarearefugee 11mo ago
  The money is easy to come by because wealthy investors, while they don't want to pay any more in taxes, are desperate to find possible returns in an economy that sucks outside of ballooning healthcare and the AI bubble... not because they need the money but because NUMBER MUST GO UP. And more so than even most VC markets, raising for an "AI" company is more about who you know than what results you can show. If anyone is actually showing significant results, where's the actual output of the AI-driven software boom (beyond just LLMs making coders more efficient by being a better google)? I don't see any real signs of it. All I see is people doing after market modifications on the shovels, I've yet to see any of the end users of these shovels coming down from the hills with sacks of real gold.
  - theptip 11mo ago
    What’s your opinion on any of the plethora of unicorns in domain-specific AI, like Harvey? ($100m ARR from what I could find on a cursory search) https://www.forbes.com/sites/iainmartin/2025/10/29/legal-ai-startup-harvey-raises-150-million-at-8-billion-valuation/ https://www.forbes.com/sites/iainmartin/2025/10/29/legal-ai-...
piyushpr134 11mo ago
98% of all websites are just database wrappers
- koakuma-chan 11mo ago
  2% are unjust?
doe88 11mo ago
This a kind of global app store all over again, where all these companies are clients of only few true ai companies and try to distinguish themselves in the bounds of the underlying models and apis, just like apps were trying to find niches in the bound of apis and exposed hw of underlying iphones. Apis versions bugs are now models updates. And of course, all are at the mercy of their respective Leviathan.
Oras 11mo ago
I can believe that many startups are doing prompt engineering and agents but in a sense this like saying 90% of startups are using cloud providers mainly AWS and Azure. There is absolutely no point of reinventing the wheel to create a generic LLM, spend fortune to run GPUs while there are providers giving this power cheaply
- Esophagus4 11mo ago
  In addition, there may be value in getting to market quickly with existing LLM providers, proving out the concept, then building / training specialized models if needed once you have traction. See: https://en.wikipedia.org/wiki/Lean_startup https://en.wikipedia.org/wiki/Lean_startup
lumost 11mo ago
My question with these is always "what happens when the model doesn't need prompting?". For example, there was a brief period where IDE integrations for coding agents were a huge value add - folks spent eons crafting clever prompts and integrations to get the context right for the model. Then... Claude, Gemini, Codex, and Grok got better. All indications are that engineers are pivoting to using foundation model vended coding toolchains and their wrappers. This is rapidly becoming a more extreme version of the classic "what if google does that?" as the foundation model vendors don't necessarily need to target your business or even think about it to eat it.
gnarlouse 11mo ago
This post hovers on something I came to the week after ChatGPT dropped in 2023. If an AI company has an AGI, what incentive do they actually have to sell it as a product, especially if it’s a 10x cost/productivity/reliability silicon engineer? Just undercut the competition by building their services from scratch.
- beAbU 11mo ago
  You don't need AGI for this circle of life to be apparent. 1. AI company wraps GPT/Claude/etc and delivers a novel use case. 2. OpenAI/Anthropic/etc creates a similar product in house and ships it as a feature. It is 'only' a prompt after all. 3. ??? 4. Profit. As a wrapper you have no moat, as the foundational providers can just steal your lunch. As a foundational provider you have no moat, because it's near trivial for other providers to create competing products.
  - gnarlouse 11mo ago
    I mean the AI company can change their TOS at any time. If you have massive compute infra and a human-tier/human-plus workforce on silicon, you: 1. Undercut all the legacy human-based competition (health insurance companies, for example) 2. Completely destroy capitalism in the knowledge work domain 3. Once you have general purpose autonomous robotics solved that can defend against rebellion, you stop all services, strangling out humanity: ~free food production, ~free energy production, ~free internet connectivity, etc 4. Survive climate change by destroying all poor people and their carbon footprints. 5. The ultra wealthy .1% fly off into eternity as the owlish sparrows that they are
danenania 11mo ago
It’s because the LLM is a commodity. What differentiates a product is not the commodity layer it’s built on (databases, programming languages, open source libraries, OS apis, hosting, etc) but how it all gets glued together into something useful and accessible. It would be a bad strategy for most startups to do anything other than prompt engineering in their AI implementations for the same reason it would be a bad idea for most startups to write low-level database code instead of SQL queries. You need to spend your innovation tokens wisely.
yawnxyz 11mo ago
it's wild, I work with some fortune 500 engineers who don't spend a lot of time prompting AI, and just a quick few prompts like "output your code in <code lang="whatever">...</code>" tags" — a trick that most people in the prompting world are very familiar with, but outside of the bubble virtually no one knows about — can improve AI code generation outputs to almost 100%. It doesn't have to be this way and it won't be this way forever, but this is the world we live in right now, and it's unclear how many years (or weeks) it'll be until we don't have to do this anymore
- harambae 11mo ago
  Can you clarify what this XML tag formatting does? I use Claude Code and Gemini CLI pretty extensively and haven't come across this yet.
tekno45 11mo ago
Maybe one day i can ask my tech in natural language for the weather...could you imagine? Wait...nvm.
g42gregory 11mo ago
Isn’t it a bit like saying, “X% of startups are just writing code”?
mmaunder 11mo ago
Flagged. Please don't post items on HN where we have to pay or hand over PII to read it. Thanks.
- apwell23 11mo ago
  pls don't create new guidelines
  - mmaunder 11mo ago
    Ditto.
tedggh 11mo ago
Isn’t this true for most start ups out there even before AI? Some sort of bundle/wrapper around existing technology? I worked auditing companies and we used a particular system that cost tens of thousands of dollars per user per year and we charged customers up to a million to generate reports with it. The platform didn’t have anything proprietary other than the UX, under the hood it was a few common tools some of them open source. We could have created our own product but our margins were so huge it didn’t make sense to setup a software development unit not even bother with outsourcing it.
zahlman 11mo ago
That's actually lower than I would have thought.
mmaunder 11mo ago
https://archive.ph/Zjs2J https://archive.ph/Zjs2J
deleted 11mo ago
[deleted]
gsibble 11mo ago
Flagged. AI written article with questionable sources behind a wall that requires handing over PII.
leeroy0xffffff 11mo ago
Interesting article and plausible conclusions but the author needs to provide more details to back up their claims. The author has yet to release anything supporting their approach on their Github. https://github.com/tejakusireddy https://github.com/tejakusireddy
tracerbulletx 11mo ago
I don't care how you get to a system that does something useful.
simonw 11mo ago
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?
- laristine 11mo ago
  Providers such as OpenAI have client keys so your client application can call the providers directly. Many developers prefer them as they save roundtrip costs and latency. https://platform.openai.com/docs/api-reference/realtime-sessions https://platform.openai.com/docs/api-reference/realtime-sess...
  - simonw 11mo ago
    Do those still only work for the voice APIs though? I've been hoping they would extend that to other APIs, and I'd love to see the same kind of mechanism for other providers. UPDATE: I dug I to this a bit more and as far as I can tell OpenAI are still the only major vendor with a consumer key mechanism and it still only works for their realtime voice APIs.
- irthomasthomas 11mo ago
  That's a big llm smell when it mentions old models like GPT-4
Ozzie_osman 11mo ago
And 73% of SaaS companies are just CRUD. Honestly it sounds about right: at the end of the day, most companies will always be an interesting UI and workflow around some commodity tech, but, that's valuable. Not all of it may be defensible, but still valuable.
ojr 11mo ago
5% prompt engineering, 95 % orchestration and no you can not vibe code your way and clone my apps, I have paid subscriptions why aren't you doing it then? Oh because models degrade severely over 500 lines. LLMs is the new AJAX. AJAX made pages dynamic, LLMs make pages interactive.
hmans 11mo ago
[dead]
senordevnyc 11mo ago
This is an AI slop article that sounds completely fabricated. Half of what's being claimed here isn't even possible to discern. My guess is that some LLM is churning out these 100% fake articles to get subscribers and ad revenue on Medium. Flagged.
thethimble 11mo ago
100% of startups are just software engineering
NaomiLehman 11mo ago
it was never about the software.
AbstractH24 11mo ago
Tech is reaching the point of being completely commodified. The only thing differnitaor anymore is GTM.
insane_dreamer 11mo ago
It's because many of these are "solutions" looking for a problem.
xela79 11mo ago
and about 90% of consumers of these AI startups have no idea how to prompt engineer. at this time that just translates to "73% of software startups are just writing code in C++" yes you could write your own code, your own prompt for LLM, or you could spend time on using the code / LLM output
6510 11mo ago
Most business is capturing sheep.

246 points by kllrnohj 11mo ago

RobertDeNiro 11mo ago

People talk about an AI bubble. I think this is the real bubble.

dboreham 11mo ago
Not really because the money involved is relatively small. The bubble is where people are using D8s to push square kilometers of dirt around for data centers that need new nuclear power plants built, to house millions of obsolete Nvidia GPUs that need new fabs constructed to make, using yet more D8s..
- mh- 11mo ago
  (D8s apparently refers to a specific Caterpillar-brand bulldozer, not some kubernetes takeoff.)

weakwire 11mo ago

So? 73% of Saas startups are DB connectors & queries.

indymike 11mo ago
And 73% of paas are deploy scripts for existing software. It's how the industry works.
tylerchilds 11mo ago
The difference is, if your company “moat” is a “prompt” on a commodity engine, there is no moat. Google even said they have no moat, when clearly the moat is people that trust them and not any particular piece of technology.
- ojr 11mo ago
  the orchestration layer is the moat, ask any LLM and they will give paragraphs explaining why this is...
parineum 11mo ago
If tekens aren't profitable then prices per token are likely to go up. If that's all these businesses are, they're all very sensitive to token prices.
- weakwire 11mo ago
  Not with open weight models you can deploy yourself. Different economics but not venerable to price increases.
  - parineum 11mo ago
    First, someone has to develop those models and that's currently being done with VC backing. Second, running those models is still not profitable, even if you self host (obviously true because everything is self hosted eventually). Burning VC money isn't a long term business model and unless your business is somehow both profitable on Llama 8b (or some such low power model) _and_ your secret sauce can't be easily duplicated, you're in for a rough ride. The only barrier between AI startups at this point is access to the best models, and that's dependent on being able to run unprofitable models that spend someone else's money. Investing in a startup that's basically just a clever prompt is gambling on the first mover's advantage because that's the only advantage they can have.

michaelgiba 11mo ago

73% of startups are just writing computer programs

indymike 11mo ago

A long time ago a mentor of mine said, "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.

mycall 11mo ago
I'm always more interested in the 'less is more' strategy, taking things away from the already hyper-complicated stack, reviewing first principles and simplifying for the same effectiveness. This is ever more rare.
- pitched 11mo ago
  I think this sense of “less is more” roughly means refactoring? I think the reason these go south so often is because we’re likely moving complexity around rather than removing it. Removing a layer from the stack means making a different layer more complex to take over for it.
  - port11 11mo ago
    Generally that seems to be true u less we're talking about removing cruft that wasn't needed. Yearly front-end rewrites are quite the joke, but they also reveal that the team didn't really get the underlying assumptions correctly. Or maybe they just really wanted to try Redux/Zustand/Jotai/the cool new router and ended up making a mess that will need cleaning next year.
rynn 11mo ago
It’s easy to underestimate the amount of testing “just” prompt/context engineering takes to get above average results. And then you need to see what variations work best with different models. My POCs for personal AI projects take time to get this right. It’s not like the API calls are the hard portion of the software.
KineticLensman 11mo ago

aurareturn 11mo ago

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.

drowsspa 11mo ago
Is it, though? Apparently the current best practice is just to allow the LLM untethered access to everything and try to control access by preventing prompt injection...
- mettamage 11mo ago
  You still need to find the correct data, and get it to the LLM. IMO, a lot of it is data engineering work with API calls to an LLM as an extra step. I'm currently doing a lot of ETL work with Airflow (and whatever data {warehouses, lakes, bases} are needed) to get the right data to a prompt engineering flow. The prompt engineering flow is literally a for loop of Google Docs in a Google Drive that non-tech people, but domain experts in their field, can access. It's up to the domain experts and me to understand where giving it data will tone down the hallucinative nonsense an LLM puts out, and where we should not give data because we need the problem solving skills of the LLM itself. A similar process is for tool-use, which in our case are pre-selected Python scripts that it is allowed to run.
  - apwell23 11mo ago
    can you describe what the usecase is ?
- kgeist 11mo ago
  Well it took me 2 full-time weeks to properly implement a RAG-based system so that it found actually relevant data and did not hallucinate. Had to: - write an evaluation pipeline to automate quality testing - add a query rewriting step to explore more options during search - add hybrid BM-25+vector search with proper rank fusion - tune all the hyperparameters for best results (like weight bias for bm25 vs. vector, how many documents to retrieve for analysis, how to chunk documents based on semantics) - parallelize the search pipeline to decrease wait times - add moderation - add a reranker to find best candidates - add background embedding calculation of user documents - lots of failure cases to iron out so that the prompt worked for most cases There's no "just give LLM all the data", it's more complex than that, especially if you want best results and also full control of data (we run all of that using open source models because user data is under NDA)

ReptileMan 11mo ago

The really impressive thing about AI startups is not that they sell wrappers around (whatever), but that they are not complete vaporware.

theshetty 11mo ago

Prompt is code.

amelius 11mo ago
Prompt is specification, not code.
- teucris 11mo ago
  Not to be too pedantic, but code is a kind of specification. I think making the blanket statement "Prompt is code" is inaccurate but there does exist a methodology of writing prompts as if they are specifications that can reliably converted to computational actions, and I believe we're heading toward that.
  - amelius 11mo ago
    Yeah, I assumed someone would say this. My manager gives me specifications, which I turn into code. My manager is not coding. This is how to look at it.
- deleted 11mo ago
  [deleted]
add-sub-mul-div 11mo ago
I've always said, determinism has been holding the field back.
bwfan123 11mo ago
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.

amelius 11mo ago

73% of AI startups are building their castle in someone else's kingdom.

asah 11mo ago
-1: there's lots of "kingdoms" (openai, anthropic, google, plus open source) - if one king comes for your castle, you can move in minutes.
- amelius 11mo ago
  True, even OpenAI built their castle in nVidia's kingdom. And nVidia built their castle in TSMC's kingdom. And TSMC built their castle in ASML's kingdom.
  - skx001 11mo ago
    lastly we need the FDIC meme "Backed by the full faith and credit of the U.S. Government" for good measure, haha.
  - octoberfranklin 11mo ago
    TSMC bought a huge chunk of ASML's shares before taking the plunge on EUV -- enough to get them a board seat.
benoau 11mo ago
It's worse than that, someone else's models, someone else's smartphone operating systems, it's every conceivable disadvantage.
- scottiebarnes 11mo ago
  Every city should have its own municipal chip fabrication plant!
  - octoberfranklin 11mo ago

Der_Einzige 11mo ago

And out of that 73%, 99% of them don't even do the obvious step of trying to actually optimize/engineer their damn prompts! https://github.com/zou-group/textgrad 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/45c619ee22aac6865ca4bb328eb58faf https://gist.github.com/Hellisotherpeople/45c619ee22aac6865c...

IncreasePosts 11mo ago

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?

Forgeties79 11mo ago
Are people down to have a bunch of specialized models? The expectation set by OpenAI and everyone else has set is that you will have one model that can do everything for you. It’s like how we’ve seen basically all gadgets meld into the smart phone. People don’t have Garmin’s and beepers and clock radios anymore (or dedicated phones!). It’s all on the screen that fits in your pocket. Any would-be gadget is now just an app
- sgt101 11mo ago
  My coffee maker app is quite disappointing.
  - Forgeties79 11mo ago
    I imagine you’re being facetious but I wouldn’t count food-related products for the most part. It’s not like Claude is brewing a pot for me anyway lol
- lukeschlather 11mo ago
  Specialized models are cheaper. For a company you're looking for some task that needs to be done millions of times per day, and where general models can do it well enough that people will pay you more than the general model's API cost to do it. Once you've validated that people will pay you for your API wrapper you can train a specialized model to increase your profit and if necessary lower your pricing so people won't pay OpenAI directly.
- palata 11mo ago
  Happy with my Garmin :-)

drivingmenuts 11mo ago

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. Thus has it always been. Thus will it always be.

RC_ITR 11mo ago

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.

bradfa 11mo ago
Got any links to explanations of why fine tuning open models isn’t a productive solution? Besides renting the GPU time, what other downsides exist on today’s SOTA open models for doing this?
- RC_ITR 11mo ago
  When the new pre-trained parameters come out in a new model generation, your old fine tuning doesn't apply to them.
Skunkleton 11mo ago
The question that isn't answered completely in the article is how useful are the pipelines for these startups? The article certainly implies that for at least some of these startups there very little value add in the wrapper.

goranmoomin 11mo ago

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.

teiferer 11mo ago
What makes you believe that anything in the article is real? The author seems to not exist and it's unclear where the data underlying the claims is even coming from since you can't just go and capture network traffic wherever you like. A little due diligence please.

Workaccount2 11mo ago

I'm surprised by the number of people who are running head first into AI wrapper start-ups. Either you have a smash-and-grab strategy or you are awful at risk analysis.

mvkel 11mo ago
Do you want to be right, or do you want to make money? You'll be correct in 5-10 years. Do you wait and do nothing until then?

analogpixel 11mo ago

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.)

jmogly 11mo ago
Im also wondering how he is able to see calls to AI providers directly in the browser, client side api calls? Thats strange to me. Also how is he able to peer into the rag architectures? I don’t get that, maybe GpT4.1 allows unauthenticated requests? Is there an OAuth setup that allows client side requests to OpenAI?
- muppetman 11mo ago
  Yea I just posted a similar comment. I'm sure some websites just skin OpenAI/Claude etc, but ALL of them? It makes no sense.
InsideOutSanta 11mo ago
It sounds like some of these companies call the OpenAI or Anthropic APIs directly from their frontend. Later, the author also mentions "response time patterns for every major AI API," so maybe there's some information about the backend leaking that way even if the API calls are bridged. But I'd like to know an actual answer to this, too, especially since large parts of this post read as if they were written by an LLM.
- cess11 11mo ago
  "I found 12 companies that left API keys in their frontend code. I reported them all. None responded." They claim to have found that.
- hn_throwaway_99 11mo ago
  > It sounds like some of these companies call the OpenAI or Anthropic APIs directly from their frontend. Which would be a major security hole. And sure, lots of startups have major security holes, but not enough that he could come up with these BS statistics. I'm a little dismayed at how high up this has been voted given the data is guaranteed to be made up.

mvkel 11mo ago

Wait til you hear what GPT 5 is

kilroy123 11mo ago
What is it? A gpt-4o wrapper?
- mvkel 11mo ago
  It's not a new model; it's a router + many, many chains of optimized system prompts. Note the terminology they use to describe it. > We are introducing GPT‑5, our best AI system yet > GPT‑5 is a unified system with a smart, efficient model that answers most questions... They make a special point to call it a system, of which "a model" is a part. I'm betting they didn't pre-train gpt5 from scratch, but did a ton of RL on gpt4 + prompt optimization and the routing engine.

turnsout 11mo ago

That's like saying "73% of business is just meetings"

CuriouslyC 11mo ago

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.

commandar 11mo ago
I'm firmly of the opinion that, as a general rule, if you're directly embedding the output of a model into a workflow and you're not one of a handful of very big players, you're probably doing it wrong.[1] If we overlook that non-determinism isn't really compatible with a lot of business processes and assume you can make the model spit out exactly what you need, you can't get around the fact that an LLM is going to be a slower and more expensive way of getting the data you need in most cases. LLMs are fantastic for building things. Use them to build quickly and pivot where needed and then deploy traditional architecture for actually running the workloads. If your production pipeline includes an LLM somewhere in the flow, you need to really, seriously slow down and consider whether that's actually the move that makes sense. [1] - There are exceptions. There are always exceptions. It's a general rule not a law of physics.

muppetman 11mo ago

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?

Zanfa 11mo ago
It makes perfect sense when you consider that the average Javascript developer does not know that business logic can exist outside of React components.
- muppetman 11mo ago
  Yea I understand maybe 10-20% of these AI clowns don't know what they're doing, but to suggest they're all making a mistake this silly doesn't stack up IMHO.
jcrawfordor 11mo ago
It's very confusing in the text of the article, at times it sounds like the author is using heuristic methods (like timings) but at times it sounds like they somehow have access to network traffic from the provider's backend. I could 100% believe that a ton of these companies are making API calls to providers directly from an SPA, but the flow diagrams in the article seem to specifically rule that out as an explanation. I might allow them more credit if the article wasn't in such an obviously LLM-written style. I've seen a few cases like this, now, where it seems like someone did some very modest technical investigation or even none at all and then prompted an LLM to write a whole article based on it. It comes out like this... a whole lot of bullet points and numbered lists, breathless language about the implications, but on repeated close readings you can't tell what they actually did. It's unfortunate that, if this author really did collect this data, their choice to have an LLM write the article and in the process obscure the details has completely undermined their credibility.

adidoit 11mo ago

100% of AI startups are just multiplying matrices 100% of tech startups are just database engineering It's still early in the paradigm and most startups will fail but those that succeed will embed themselves in workflows.

zach_moore 11mo ago

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.

yalogin 11mo ago

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.

hotpaper75 11mo ago
Do not forget that a product idea needs to meet a certain ROI to be stolen. Big Tech won't go after opportunities that do not generate billion-level revenue. This leaves some room for applications where you can earn decent money.
- yalogin 11mo ago
  That is not how companies work. What you said may be true for the immediate short term but over time every team in the company needs to show improvement and set yearly milestones. All these startups will then become functionality they want to push that quarter. Yes it doesn’t mean the death of the startup but a struggle
  - hotpaper75 11mo ago
    Sure but at the end I believe one of the deciding factors on which projects you pursue is the ROI. In a scenario where a given team has many projects it can go after it has to select which project to chose from.

Devasta 11mo ago

Atlas himself doesn't carry as much as "engineering" does in that headline.

hn_throwaway_99 11mo ago

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.

teiferer 11mo ago
I find it shocking that most comments here just accept the article as fact and discuss the implications. The message might not even be wrong. But why is everybody's BS detection on ice in the AI topic space? Come one people, you can all do better than this! Thanks for flagging. Though whenever such a made up thing is flagged, we lose the chance to discuss this (meta) topic. People need to be aware how prevalent this is. By just hiding it every time we notice, we're preventing everybody to read the kind of comment you wrote and recalibrate their BS-meters.
ElasticBottle 11mo ago
I had the exact same thoughts. Even while I don't disagree with the premise, I'm so confused as to how they are intercepting the calls. 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.

strathmeyer 11mo ago

[dead]

alex_young 11mo ago

73% of AI blog post statistics are bogus. Subscribe to learn more.

zkmon 11mo ago

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?

nottorp 11mo ago
Actual AI. Not being "AI" users. Being LLM users would be fine but they pretend they do AI.
- zkmon 11mo ago
  AI is an ecosystem that includes users at all layers and innovation at all those layers - infra, databases, models, agents, portals, UIs and so on. What do you mean by doing AI? Btw, the so-called AI devs or model developers are "users" of the databases and all the underlying layers of the stack.
- jasonjmcghee 11mo ago
  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" --- 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?
  - FridgeSeal 11mo ago
    > At what point can you claim that you did "it"? When the core of your business ist something that’s shamelessly farmed out to <LLM provider of choice>. It would be like calling yourself a restaurant, and then getting uber eats deliveries of whatever customers ordered and handing that to them.

apwell23 11mo ago

Yep I just use chatgpt . I can write better prompts and data for my own usecases

sc077y 11mo ago

73% of statistics are wrong

jedberg 11mo ago

And 99% of software development is just feeding data into a complier. But that sort of misses the point doesn't it? AI has created a new interface with a higher level abstraction that is easier to use. Of course everyone is going to use it (how many people still code assembler?). The point is what people are doing with it is still clever (or at least has potential to be).

system2 11mo ago
I disagree. Software development is not limited to LLM-type responses and incorporates proper logic. You are at the mercy of LLM when you build an "AI" interface for the LLM apis. 73% these "AI" companies will collapse when the original API providing company comes up with a simple option (Gemini for Sheets, for example), they will disappear. It is already happening. AI software is not long-lasting; its results are not deterministic.

furyofantares 11mo ago

Another slop article that could probably be good if the author was interested in writing it, but instead they dumped everything into an LLM and now I can't tell what's real and what's not and get no sense of what parts of the findings the author found important or interesting compared to what other parts. I have to wonder, are people voting this up after reading the article fully, and I'm just wrong and this sort of info dump with LLM dressing is desirable? Or are people skimming it and upvoting? Or is it more of an excuse to talk about the topic in the title? What level of cynicism should I be on here, if any?

DetroitThrow 11mo ago

Why is slop with ridiculous or impossible claims at the top of HN?

eddythompson80 11mo ago

For me, 2023 was an entire year of weekly demos that now looking back at were basically a "Look at this dank prompt I wrote" followed by thunderous applause from the audience (which was mostly, but not exclusively, upper management) Hell man, I attended a session at an AWS event last year that was entirely the presenter opening Claud and writing random prompts to help with AWS stuff... Like thanks dude... That was a great use of an hour. I left 15 minutes in. We have a team that's been working on an "Agent" for about 6 months now. Started as prompt engineering, then they were like "no we need to add more value" developed a ton of tools and integrations and "connectors" and evals etc. The last couple of weeks were a "repivot" going back full circle to "Lets simplify all that by prompt engineering and give it a sandbox environment to run publicly documented CLIs. You know, like Claude Code" The funny thing is I know where it's going next...

ivape 11mo ago
Wait ... You mean teams are already building their own solutions to existing solutions? Software development will live on in eternity then.
- tempodox 11mo ago
  They are just reselling OpenAI subscriptions at a markup. Surprise!
morkalork 11mo ago
But did it work? This is the sticking point with me now. I've seen slides, architecture diagrams, job descriptions, roadmaps and other docs now from about a dozen different companies doing AI Agent projects. And while it's completely feasible to build the systems they're describing, what I have not seen yet is evidence of any of them working. When you press them on this, they have all sorts of ideas like a judge LLM that takes the outputs, comes up with modified SOPs and feeds those into the prompts of the mixture-of-experts LLMs. But I don't think that works, I've tried closing that loop and all I got was LLMs flailing around.
- 11mo ago

theptip 11mo ago

> 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 results.

submeta 11mo ago
This should be the top comment.
jasonjmcghee 11mo ago
prompt engineering + CRUD is likely much more fair. And many companies are "just CRUD".
bayarearefugee 11mo ago
The money is easy to come by because wealthy investors, while they don't want to pay any more in taxes, are desperate to find possible returns in an economy that sucks outside of ballooning healthcare and the AI bubble... not because they need the money but because NUMBER MUST GO UP. And more so than even most VC markets, raising for an "AI" company is more about who you know than what results you can show. If anyone is actually showing significant results, where's the actual output of the AI-driven software boom (beyond just LLMs making coders more efficient by being a better google)? I don't see any real signs of it. All I see is people doing after market modifications on the shovels, I've yet to see any of the end users of these shovels coming down from the hills with sacks of real gold.
- theptip 11mo ago
  What’s your opinion on any of the plethora of unicorns in domain-specific AI, like Harvey? ($100m ARR from what I could find on a cursory search) https://www.forbes.com/sites/iainmartin/2025/10/29/legal-ai-startup-harvey-raises-150-million-at-8-billion-valuation/ https://www.forbes.com/sites/iainmartin/2025/10/29/legal-ai-...

piyushpr134 11mo ago

98% of all websites are just database wrappers

koakuma-chan 11mo ago
2% are unjust?

doe88 11mo ago

This a kind of global app store all over again, where all these companies are clients of only few true ai companies and try to distinguish themselves in the bounds of the underlying models and apis, just like apps were trying to find niches in the bound of apis and exposed hw of underlying iphones. Apis versions bugs are now models updates. And of course, all are at the mercy of their respective Leviathan.

Oras 11mo ago

I can believe that many startups are doing prompt engineering and agents but in a sense this like saying 90% of startups are using cloud providers mainly AWS and Azure. There is absolutely no point of reinventing the wheel to create a generic LLM, spend fortune to run GPUs while there are providers giving this power cheaply

Esophagus4 11mo ago
In addition, there may be value in getting to market quickly with existing LLM providers, proving out the concept, then building / training specialized models if needed once you have traction. See: https://en.wikipedia.org/wiki/Lean_startup https://en.wikipedia.org/wiki/Lean_startup

lumost 11mo ago

My question with these is always "what happens when the model doesn't need prompting?". For example, there was a brief period where IDE integrations for coding agents were a huge value add - folks spent eons crafting clever prompts and integrations to get the context right for the model. Then... Claude, Gemini, Codex, and Grok got better. All indications are that engineers are pivoting to using foundation model vended coding toolchains and their wrappers. This is rapidly becoming a more extreme version of the classic "what if google does that?" as the foundation model vendors don't necessarily need to target your business or even think about it to eat it.

gnarlouse 11mo ago

This post hovers on something I came to the week after ChatGPT dropped in 2023. If an AI company has an AGI, what incentive do they actually have to sell it as a product, especially if it’s a 10x cost/productivity/reliability silicon engineer? Just undercut the competition by building their services from scratch.

beAbU 11mo ago
You don't need AGI for this circle of life to be apparent. 1. AI company wraps GPT/Claude/etc and delivers a novel use case. 2. OpenAI/Anthropic/etc creates a similar product in house and ships it as a feature. It is 'only' a prompt after all. 3. ??? 4. Profit. As a wrapper you have no moat, as the foundational providers can just steal your lunch. As a foundational provider you have no moat, because it's near trivial for other providers to create competing products.
- gnarlouse 11mo ago
  I mean the AI company can change their TOS at any time. If you have massive compute infra and a human-tier/human-plus workforce on silicon, you: 1. Undercut all the legacy human-based competition (health insurance companies, for example) 2. Completely destroy capitalism in the knowledge work domain 3. Once you have general purpose autonomous robotics solved that can defend against rebellion, you stop all services, strangling out humanity: ~free food production, ~free energy production, ~free internet connectivity, etc 4. Survive climate change by destroying all poor people and their carbon footprints. 5. The ultra wealthy .1% fly off into eternity as the owlish sparrows that they are

danenania 11mo ago

It’s because the LLM is a commodity. What differentiates a product is not the commodity layer it’s built on (databases, programming languages, open source libraries, OS apis, hosting, etc) but how it all gets glued together into something useful and accessible. It would be a bad strategy for most startups to do anything other than prompt engineering in their AI implementations for the same reason it would be a bad idea for most startups to write low-level database code instead of SQL queries. You need to spend your innovation tokens wisely.

yawnxyz 11mo ago

it's wild, I work with some fortune 500 engineers who don't spend a lot of time prompting AI, and just a quick few prompts like "output your code in <code lang="whatever">...</code>" tags" — a trick that most people in the prompting world are very familiar with, but outside of the bubble virtually no one knows about — can improve AI code generation outputs to almost 100%. It doesn't have to be this way and it won't be this way forever, but this is the world we live in right now, and it's unclear how many years (or weeks) it'll be until we don't have to do this anymore

harambae 11mo ago
Can you clarify what this XML tag formatting does? I use Claude Code and Gemini CLI pretty extensively and haven't come across this yet.

tekno45 11mo ago

Maybe one day i can ask my tech in natural language for the weather...could you imagine? Wait...nvm.

g42gregory 11mo ago

Isn’t it a bit like saying, “X% of startups are just writing code”?

mmaunder 11mo ago

Flagged. Please don't post items on HN where we have to pay or hand over PII to read it. Thanks.

apwell23 11mo ago
pls don't create new guidelines
- mmaunder 11mo ago
  Ditto.

tedggh 11mo ago

Isn’t this true for most start ups out there even before AI? Some sort of bundle/wrapper around existing technology? I worked auditing companies and we used a particular system that cost tens of thousands of dollars per user per year and we charged customers up to a million to generate reports with it. The platform didn’t have anything proprietary other than the UX, under the hood it was a few common tools some of them open source. We could have created our own product but our margins were so huge it didn’t make sense to setup a software development unit not even bother with outsourcing it.

zahlman 11mo ago

That's actually lower than I would have thought.

mmaunder 11mo ago

https://archive.ph/Zjs2J https://archive.ph/Zjs2J

deleted 11mo ago

[deleted]

gsibble 11mo ago

Flagged. AI written article with questionable sources behind a wall that requires handing over PII.

leeroy0xffffff 11mo ago

Interesting article and plausible conclusions but the author needs to provide more details to back up their claims. The author has yet to release anything supporting their approach on their Github. https://github.com/tejakusireddy https://github.com/tejakusireddy

tracerbulletx 11mo ago

I don't care how you get to a system that does something useful.

simonw 11mo ago

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?

laristine 11mo ago
Providers such as OpenAI have client keys so your client application can call the providers directly. Many developers prefer them as they save roundtrip costs and latency. https://platform.openai.com/docs/api-reference/realtime-sessions https://platform.openai.com/docs/api-reference/realtime-sess...
- simonw 11mo ago
  Do those still only work for the voice APIs though? I've been hoping they would extend that to other APIs, and I'd love to see the same kind of mechanism for other providers. UPDATE: I dug I to this a bit more and as far as I can tell OpenAI are still the only major vendor with a consumer key mechanism and it still only works for their realtime voice APIs.
irthomasthomas 11mo ago
That's a big llm smell when it mentions old models like GPT-4

Ozzie_osman 11mo ago

And 73% of SaaS companies are just CRUD. Honestly it sounds about right: at the end of the day, most companies will always be an interesting UI and workflow around some commodity tech, but, that's valuable. Not all of it may be defensible, but still valuable.

ojr 11mo ago

5% prompt engineering, 95 % orchestration and no you can not vibe code your way and clone my apps, I have paid subscriptions why aren't you doing it then? Oh because models degrade severely over 500 lines. LLMs is the new AJAX. AJAX made pages dynamic, LLMs make pages interactive.

hmans 11mo ago

[dead]

senordevnyc 11mo ago

This is an AI slop article that sounds completely fabricated. Half of what's being claimed here isn't even possible to discern. My guess is that some LLM is churning out these 100% fake articles to get subscribers and ad revenue on Medium. Flagged.

thethimble 11mo ago

100% of startups are just software engineering

NaomiLehman 11mo ago

it was never about the software.

AbstractH24 11mo ago

Tech is reaching the point of being completely commodified. The only thing differnitaor anymore is GTM.

insane_dreamer 11mo ago

It's because many of these are "solutions" looking for a problem.

xela79 11mo ago

and about 90% of consumers of these AI startups have no idea how to prompt engineer. at this time that just translates to "73% of software startups are just writing code in C++" yes you could write your own code, your own prompt for LLM, or you could spend time on using the code / LLM output

6510 11mo ago

Most business is capturing sheep.