Why Your New App Won't Be Seen
The "Gold Rush" of AI applications has officially ended, and in its place, we are left with a massive, overcrowded, and increasingly noisy digital landscape. If you are building an AI app today, you aren't just fighting for attention; you are fighting against AI fatigue.
The "Wrapper" Problem
For the past two years, the barrier to entry for building an AI app has been effectively zero. Anyone with an API key and a basic understanding of prompting could launch a "specialized" AI tool. The result? A market saturated with thin "wrappers".
The "Wow" Factor is Dead
We are no longer impressed that a machine can generate text or an image. We are impressed when it can reliably execute a complex, multi-step task without hallucinating or requiring constant babysitting.
The market is currently undergoing a painful but necessary correction.
Let´s see what will come up in year or two.
I think the distinction isn't really "AI app" vs "wrapper" anymore.
It's whether the product removes work or just relocates it.
If users still have to prompt, organize context, switch tabs, and decide what to do next, you've mostly repackaged the problem.
The products that win from here probably won't be the ones with the smartest models. They'll be the ones that quietly turn multi-step workflows into a single action and become part of someone's routine.
Curious, are there any AI products you've used recently that you think crossed that line from "interesting demo" to "I genuinely rely on this now"?
"AI" is a red flag
I believe AI is generating an overwhelming amount of content, and audiences, myself included, are simply not willing to wade through it all.
The sheer volume makes it too easy to disengage.
Totally feeling this—just shipping an “AI wrapper” isn’t enough anymore, users want something that actually solves their problem fast and reliably.
From what I’ve seen, the only apps that still get traction are the ones that feel like a real tool, not just a demo.
"Fascinating read! The focus on using RAG to move beyond generic 'wrapper' apps and provide reliable, data-grounded travel suggestions is exactly what the market needs.
I'm particularly interested in the technical architecture. You mentioned building a specialized RAG database in JSON format to store curated location data. Could you share more details on:
How you handle the retrieval step? Are you using vector embeddings for semantic search, or a different method to query your JSON database?
What was the reasoning behind choosing a JSON-based structure for the knowledge base over other formats?
How do you ensure the data remains up-to-date and accurate?
The 'zero-friction,' no-login approach is also a brilliant differentiator. Thanks for building this!"
Thanks for liking the App :) That´s what I want to hear.
The flow is: user inputs preferences → embeddings are generated → cosine similarity search against the JSON-derived vector store → top matching destinations are retrieved → passed to the LLM to generate the final recommendation with context.
JSON is chosen just because I know that format for a while and there are a lot of good recommendations for JSON. So, actually not very scientific approach :) indeed.
I do manual curation with periodic reviews. Destinations don't change character frequently so I am not afraid that JSON databases will become obsolete.
This hits hard. The wrapper problem is real — and I think the ones that survive will be the ones that committed to a specific human experience, not just a use case. We built Mystic Sage around Eastern philosophy-based counseling, and the differentiation isn't the feature set, it's entirely in how it talks to people. That's the moat now — conversation quality, not capability.
I think, the only who will survive will be as always big players who constantly repeat and advertise their tools, so nobody will be immune on that. :)
thanks!
While the market entered the panic phase, in around 2023, I had taken some time off my job to build something I wanted for ages. Took me 1 year, no AI, this was before ChatGPT.
The next 1 year went in so much panic, when I went back looking for a job. AI news was everywhere. I had started questioning reality.
Should I continue doing this thing, or will AI make my app redundant soon?
Should I shift to python, leave typescript, and lose the type safety like in Rust?
I just kept going, coz I need that app. But fear never left my side.
Now, after so much research and sleepless nights, I have the finished product, at least the way I had imagined it. And AI is no where close to giving me those simple abilities.
The core feature is simple - combine words to recall things at will. Predict results before any click, without AI guess works. I really hope I made the right choice going forward with it.
Thanks for commenting.
My understanding is that effort that you put in developing your app/product will give you advantage in AI era because judgment and deeper insight would always be appreciated.
The "wrapper" frame is doing a lot of work here. The real axis isn't "wraps an LLM vs doesn't" — it's "compresses a workflow from N steps to 1" vs "exposes an LLM with a different theme." The first survives because the LLM becomes interchangeable underneath. The second dies the moment ChatGPT ships the feature. Curious where you'd draw that line on apps you've used recently.
It could be there are excellent "wrappers" but audience does not have time and will to see that. Thanks for commenting.