[HN Gopher] Revenge of the GPT Wrappers: Defensibility in a worl...
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Revenge of the GPT Wrappers: Defensibility in a world of
commoditized AI models
Author : kiyanwang
Score : 124 points
Date : 2025-02-07 11:25 UTC (3 days ago)
(HTM) web link (andrewchen.substack.com)
(TXT) w3m dump (andrewchen.substack.com)
| dcdgo wrote:
| Great article.
| EternalFury wrote:
| Business as usual. While electricity is remarkable, no one gets
| extremely rich selling it. End-user value is the only value that
| can be sold at a profit.
| mohsen1 wrote:
| And guess who has a grip of the end user? Operation System
| owners. Now that you might not need an app for most things, OS
| vendors are in even more powerful position. Gone the days of
| "this amazing app can do X", now it's going to be "have you
| noticed you can ask Siri to do X?" They have all of the context
| that app developers are going to miss about the user.
|
| Both Apple and Google are doing a poor job of integrating AI
| capabilities into their Operation Systems today. Maybe there is
| room for a new player to make a real AI-first Operation system.
| oarsinsync wrote:
| > Operation System
|
| OS is generally expanded to Operating System, not Operation
| System, in English
| nicewood wrote:
| I agree that the OS vendors are in a great position to add
| value via broad, general purpose features. But they cannot
| cover it all - it's breadth over depth. So I think the
| innovation for niches and specific business processes will be
| still owned by specialized 'GPT Wrappers'.
| echelon wrote:
| > AI-first Operation system.
|
| An AI-first pane of glass (OS, browser, phone, etc.) with an
| agent that acts in my behalf to nuke ads, rage bait, click
| bait, rude people on the internet, spam, sales calls and
| emails, marketing materials, commercials, and more.
|
| If you want to market to me, you need to pay me directly. If
| you want to waste my time, goodbye.
| koakuma-chan wrote:
| Does anyone actually use Siri?
| raincole wrote:
| No one gets extremely rich selling food, water and electricity
| because these fields attract government intervention all the
| time.
|
| (Not saying it's a bad or good thing, nor saying AI is
| comparable)
| abrichr wrote:
| Food:
|
| - Ray Kroc - Turned McDonald's into a global fast-food
| empire.
|
| - Howard Schultz - Scaled Starbucks into an international
| giant.
|
| - Michele Ferrero - Created Nutella, Kinder, and Ferrero
| Rocher, making his family billionaires.
|
| Water:
|
| - Francois-Henri Pinault - Controlled Evian via Danone.
|
| - Antoine Riboud - Expanded Danone into a bottled water
| empire (Evian, Volvic).
|
| - Peter Brabeck-Letmathe - Former Nestle CEO; Nestle owns
| Perrier, Pure Life, Poland Spring, etc.
|
| Electricity:
|
| - Warren Buffett - Berkshire Hathaway Energy owns multiple
| utilities.
|
| - Li Ka-shing - Built major energy holdings through CK
| Infrastructure.
|
| - David Tepper - Invested heavily in power utilities via
| Appaloosa Management.
| wcrossbow wrote:
| I read this and of course couldn't believe it. Isn't 14.7B
| enough to be considered extremely rich these days[1]? In the
| the Forbes real-time billionaires list is quite easy to find
| _many_ such examples.
|
| [1] https://www.forbes.com/profile/sarath-ratanavadi/?list=rtb/
| kgwgk wrote:
| > While electricity is remarkable, no one gets extremely rich
| selling it.
|
| Enron did!
| esafak wrote:
| I better buy some shares in them!
| ggm wrote:
| If you believe a prompt of the form "hey, GPT A make yourself
| behave like GPT B" can be articulated to be a Chinese room, I put
| it to you the amount of missing information between what informs
| A and what informs B will make this a mountain of work.
|
| Do you think it's less work than just making GPT B and why? What
| quality in the system (inductance aside) is this simply additive?
|
| My strawman reads as "wishing for fairytales" basically. But this
| strawman to me, is the reductive intent inside the article. "Ask
| a GPT to perform like another later different GPT" epitomises
| magical thinking.
|
| Why bother training if the recursive application is that simple?
| Because... it's not that simple.
| returnInfinity wrote:
| A wrapper will do more than this.
|
| Imagine a new UI/UX for a CRM. Completely redesigned from the
| ground up.
|
| Multiple GPT wrappers in a single product. All wrappers working
| together to achieve a single goal.
|
| Also throw in agents.
|
| And distribution matters, if some app goes viral, it has a high
| chance to succeed and beat the current incumbent.
| hobs wrote:
| The better the LLM GPT thing you build the more your arm your
| competition to build better LLM GPT things, there's no moat
| there.
| TeMPOraL wrote:
| There's no moat in any of it, but it's not like it matters
| much if you're not one of the major platforms. This is not
| a weather for sand-castle builders, it's a weather for
| surfers. LLM winds a-blowin, pick a wave to jump on, ride
| down the profits gradient until it's spent, jump to
| another. It's a time for a million products to bloom, each
| promising stars, every one gone in 10 months.
|
| I'd be more worried about platform dependency at this
| point. The ol' adage about building your business on
| someone else's API applies, doubly so in the current
| geopolitical climate. All those hot AI startups are but a
| single executive order away from losing half their market,
| or getting erased from existence altogether.
| imjonse wrote:
| 'In recent years, innovative AI products that didn't build their
| own models were derided as low-tech "GPT wrappers." '
|
| The ones derided were those claiming to be 'open-source XY' while
| being a standard tailwind template over an OpenAI call or those
| claiming revolutionay XY while 90% being the proprietary model
| underneath. I am not sure how many were truly innovative that
| weren't cloneable in a very short time. Using models to empower
| your app is great, having the model be all of your app while you
| pitch it otherwise is to be derided.
| muzani wrote:
| I was mentoring at a hackathon this weekend. Someone asked how
| they could integrate a certain open source pentesting agent
| into their tool.
|
| I asked them, "Well, it's open source. Instead of making a
| bunch of adapters, couldn't you just copy the code you want?"
|
| Turns out the whole agent was 11 files or so. The files were
| about 200 lines. Over half were just different personas to do
| the same thing. They just needed to copy one of the prompts and
| have a mechanism to break the loop.
|
| The funny part with open source is nobody reads the code even
| though it's literally open. The AI pundits don't read what they
| criticize. The grifters just chain it forward. It's left-pad
| all over again.
| t_mann wrote:
| Contrary take: "AI founders will learn the bitter lesson" (263
| comments): https://news.ycombinator.com/item?id=42672790 the
| gist: "Better AI models will enable general purpose AI
| applications. At the same time, the added value of the software
| around the AI model will diminish."
|
| Both essays make convincing points, I guess we'll have to see. I
| like the Uber analogy here, maybe the winners will be some who
| use the tech in innovative ways that only leverage the underlying
| tech.
| bearjaws wrote:
| Not to mention, if you have a good idea, OAI, Anthropic,
| Google, will implement it.
|
| e.g. OAI Operator, Anthropic Computer Use, and Google
| NotebookLM.
| danenania wrote:
| They may implement it, but it's questionable whether they'll
| have the best implementation in any particular category.
| kridsdale3 wrote:
| And they don't have to pay the margin on the API calls. So an
| equal UX on the same model API will be twice as profitable
| when operated by the first-party.
| deepsquirrelnet wrote:
| The differentiator is whether or not your company operates
| with domain specific data and subject matter experts that
| those big companies don't have (which is quite common).
|
| There's plenty of applications to build that won't easily get
| disrupted by big AI. But it's important to think about what
| they are, rather than chase after duplication of the shiny
| objects the big companies are showing off.
| glooglork wrote:
| > _Imagine it becomes truly trivial to copy cat another product
| -- something as simple as, "hey AI, build me an app that does
| what productxyz.com does, and host it at productabc.com!" In the
| past, a new product might have taken a few months to copy, and
| enjoyed a bit of time to build its lead. But soon, perhaps it
| will be fast-followed nearly instantly. How will products hold
| onto their users?_
|
| It's actually not that easy to copy/paste AI agents, prompts take
| quite a lot of tweaking and it's a rather slow and manual process
| because it's not that easy to verify that they're working for all
| the possible inputs. This gets even more complicated when you get
| a number of agents in the same application and they need to
| interact with each other.
| tossandthrow wrote:
| Have you tried using Ai to write your prompts? It is quite
| efficient.
|
| Besides that, you quote "imagine it becomes...", it is a fair
| to assume that these technologies will become better.
| glooglork wrote:
| Yeah, I'm using it and I agree it will probably become a lot
| better, but I don't think we're really close to a point where
| AI itself will be able to just write an app that has 100s of
| prompts that interact with one other. Even if it does, you'll
| probably be able to get it running better by manually
| optimizing a bunch of stuff (when I say manually, I'm also
| including iterating over a prompt in a chat with LLM).
|
| It's capable of creating CRUD apps from scratch more or less
| by itself, and I can see how in this area we soon might get
| to a point where you can get your own clone of a lot of apps
| up and running in 30 minutes.
|
| But I imagine a lot of future value we might see created will
| come from:
|
| 1) specialized prompts - looks simple but I don't think it
| is, especially if you have 100s of them in your application
| and you have complex logic on how they interact between each
| other, you're using different models for different parts of
| your application based on their strengths, etc
|
| 2) access to structured data you can connect your agents to
|
| 3) network effects - app that is mostly used gets better just
| by using the usage data (the article did talk about network
| effects)
|
| I don't think it's really easy to replicate these 3 factors.
| The article is also mentioning some of this, I'm not really
| arguing with that, just pointing out that I don't think it
| will be that simple to c/p full applications.
| satisfice wrote:
| "Have you tried..."
|
| It's not "trying" that matters. What matters is testing. But
| nobody is testing LLMs... Or what they call testing is mostly
| shrugging and smiling and running dubious benchmarks.
| kridsdale3 wrote:
| If the imperative-code based apps that I've been shipping
| my whole career had failure rates on par with the *best*
| LLM prompts (think 10 to 35 percent), I'd not have a
| career.
| deepsquirrelnet wrote:
| Stanford NLPs framework DSPy really encourages a
| traditional ML development process. It's about the only one
| I'd consider to be a true ML framework.
| swyx wrote:
| its truly interesting to see this come around full circle from
| 2023 when i started writing about the role of the AI Engineer,
| and now this https://www.latent.space/p/gpt-wrappers
| delifue wrote:
| Software can take a freeride of hardware improvements. GPT
| wrappers also can take a freeride of foundation model
| improvements.
| kridsdale3 wrote:
| I always roll my eyes when someone makes a "Show HN" post that
| their wrapper app has amazing new capabilities. All they did
| was push a commit where they typed "gpt4o-ultra-fancy-1234" in
| to some array.
| daxfohl wrote:
| The real question is how do they achieve vendor lock in? My bets
| are on Microsoft to figure that out.
| CharlieDigital wrote:
| Having worked with AI and LLMs for quite a bit now as a
| "wrapper", I think the real key is that doing well (fast,
| accurate, relevant) requires a really, really good ETL process in
| front of the actual LLM.
|
| A "wrapper" will always be better than the the foundation models
| so long as it can do the domain-specific pre-generation ETL and
| data aggregation better; that is the true moat for any startup
| delivery solutions using AI.
|
| Your moat as a startup is really how good your domain-specific
| ETL is (ease of use and integration, comprehensiveness, speed,
| etc.)
| iamwil wrote:
| Is this not consensus yet that people in the model layer are
| fighting commoditization and so-called wrappers have all the
| moats? I'd written something similar back in Nov of last year,
| and I thought I was late in writing it down.
|
| https://interjectedfuture.com/the-moats-are-in-the-gpt-wrapp...
| ramesh31 wrote:
| >Is this not consensus yet that people in the model layer are
| fighting commoditization and so-called wrappers have all the
| moats?
|
| Yes. It will become a duopoly, where the leading frontier model
| holds >90% market share, and most useful products will be built
| around it. With the remaining 10% being made up by large
| portions of the other big vendors, and then everyone else for
| niche cases.
|
| The idea of picking and choosing between individual models for
| each specific use case is going away rapidly as the top ones
| pull away from the pack, and inference prices are falling
| exponentially.
| KaoruAoiShiho wrote:
| I predict this article to be embarrassingly wrong. The moat of
| models is compute, wrappers are just software engineering, one of
| the first things to be commoditized by AI in general.
| pchristensen wrote:
| Software engineering followed by product research and product
| market fit. Those are less at risk.
| KaoruAoiShiho wrote:
| Idea guys are a dime in a dozen.
| DebtDeflation wrote:
| Probably worth thinking more about what we mean by "wrapper". A
| year or so ago, it often meant a prompt builder UI. There's no
| moat for that. But if in 2025 a "wrapper" means a proprietary
| data source with a pipeline to deliver it along with some
| proprietary orchestration along with the UI (and the LLM API
| being called), then it likely warrants looking at it differently.
| lacker wrote:
| If everyone has incredibly good AI, then perhaps the unique asset
| will be training data.
|
| Not everyone will have the training data that demonstrates
| precisely the behavior that your customers want. As you grow,
| you'll generate more training data. Others can clone your product
| immediately... but the clone just won't work as well. In your
| internal evals, you'll see why. It misses a lot of stuff. But
| they won't understand, because their evals don't cover this case.
|
| (This is quite similar to why Bing had trouble surpassing Google
| in search quality. Bing had great engineers, but they never had
| the same data, because they never had the same userbase.)
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