Why Perplexity, Cursor, and the “Wrappers” Are Winning the AI Game And What It Means for the Future

Introduction

In the early days of the AI boom which, let’s be real, was like 5 minutes ago in internet years there was a pecking order.

At the top: the model builders. OpenAI. Google. Meta. Anthropic. These were the gods forging Large Language Models (LLMs) in the GPU furnaces of Mount Cloud.
At the bottom: the “wrappers.”

If you launched an app like Perplexity, Cursor, Sesame, or Abridge, you’d get the side-eye.
“You’re just a wrapper,” the tech elite would mutter, like you had shown up to a mech battle with a cardboard box and a Sharpie.

The insult was simple: Wrappers were just pretty front-ends slapped onto someone else’s brains. No real tech innovation, just UI hacks over OpenAI’s API. Silicon Valley’s version of calling someone a poser.

But then something weird happened.

While the model builders kept pushing bigger and brainier models, the so-called wrappers did something much sneakier:
They figured out what real people actually needed.

They weren’t just building flashy demos; they were solving boring, painful, messy real-world problems.
And suddenly, the power dynamic started to shift.

Today?
Apps like Perplexity are eating Google’s lunch.
Cursor is turning non-coders into indie hackers overnight.
Abridge is quietly rewiring how healthcare conversations are recorded and understood.

The wrappers aren’t the sidekicks anymore.
They’re the main characters.
And if you think this shift is temporary, you’re about to get left behind faster than someone still training a GPT-2 model in 2025.

Section 2: The Turning Point When Models Became Commoditized

For a while, the Big Model Builders had it good.
They’d show off a shiny new model smarter, faster, trained on enough data to recreate all of Reddit’s worst takes and the world would lose its mind.

But by late 2023 and into 2024, a strange thing started happening:
All the models started feeling… kinda the same.

Sure, you could nitpick. This one handles math better, that one sounds more “human,” another one hallucinates slightly less often when you ask about obscure Pokémon.
But the massive, jaw-dropping gaps between OpenAI, Google, Anthropic, and Meta?
They shrank.

It was like smartphones after the iPhone moment:
Every new model was a little faster, a little smarter, a little less likely to freak out but there wasn’t another earth-shaking leap.
Models had become commoditized.

You could now pick an LLM like you pick a web hosting provider.

  • “Do you need cheap and fast?”
  • “Do you need reliable and safe?”
  • “Do you need it bilingual in 40 languages with a side of emotional support?”

The secret sauce wasn’t the model itself anymore.
It was what you built on top of it.

And this is where the so-called “wrappers” turned into real players.
While the model builders were busy flexing token counts and trillion-parameter scaling, the app builders went straight to users and asked:

  • “What’s still frustrating?”
  • “Where does AI still suck?”
  • “What tiny pain point can we wipe out so cleanly that people will actually pay for it?”

Instead of worshipping model IQ points, they optimized for user experience, utility, and speed.

Perplexity didn’t care about winning the “smartest chatbot” race it focused on making search not suck.
Cursor didn’t try to build a perfect AGI co-founder it focused on making writing and debugging code insanely easy.

In a world where everyone had access to the same base-level intelligence, execution beat pure horsepower.

And just like that, the narrative started flipping:
Maybe being a wrapper wasn’t so bad after all.
Maybe, just maybe, it was the point.

Section 3: The Rise of “Vibe Coding” and New App Development

While the model labs polished their giant LLMs like they were tuning a Bugatti, something unexpected was happening down in the indie dev trenches:
People stopped caring about the specs.
They just wanted stuff that worked and felt good to use.

Enter: Vibe Coding.
An unofficial but very real movement where the goal wasn’t perfect code, or theoretical robustness, or a 40-page model card explaining biases.
It was simple:
Build something that people love using.

If it vibed, it shipped.
If it didn’t? Kill it, pivot, try again.

One of the loudest success stories? Cursor.
Cursor was born from a single sharp insight:
👉 Most developers don’t want an “AI co-pilot” that tries to be a philosophy major.
👉 They want an actual assistant who helps them code faster, fix bugs, autocomplete functions without needing a therapy session about the meaning of recursion.

Cursor nailed the vibe.
It didn’t overload you with a 17-option menu.
It didn’t pretend to be your co-founder and your therapist.
It just… helped. Instantly. Seamlessly.

And boom Cursor took off like a cheat code.
No fancy university degree required.
No 3-year CS program.
If you had an idea, Cursor helped you build it.
If you got stuck, Cursor debugged it.
If you needed to deploy, Cursor didn’t ask you to file a Jira ticket and pray.

This was vibe coding in its purest form:

  • Fast builds
  • Intuitive UX
  • Real problems solved
  • No gatekeeping

And developers from hobbyists to startup founders loved it.
In the vibe-coding world, you didn’t have to worship the model.
You just made something awesome out of it.

Section 4: Wrappers Are Now Better Businesses Than Model Builders

Here’s the plot twist Silicon Valley didn’t see coming:
The wrappers are winning not just in vibes, but in dollars.

Model builders spent billions training foundation models, hoping that sheer intelligence would be the moat.
But it turns out, users don’t buy raw intelligence.
They buy solutions to their problems.
They buy experiences that feel magical.
They buy apps.

And apps the so-called “wrappers” have something the model labs can only dream about:
Direct user relationships.

When you use Perplexity for search, you don’t say, “Wow, thanks, OpenAI!”
You say, “Perplexity is awesome.”
When you use Cursor to build your side project in a weekend, you’re not thanking Anthropic.
You’re telling your friends, “Dude, Cursor made it so easy.”

Control the user experience, and you control the brand.

Even OpenAI realized this, which is why they didn’t just build an API for GPT they built ChatGPT as a standalone app.
Why Google didn’t just release Gemini behind an API wall — they launched mobile apps, integrations, and branded experiences.

The deeper reality?

  • Models are infrastructure.
  • Apps are the business.

Cursor is reportedly on a rocketship growth curve.
Perplexity is reportedly valued in the billions — and making search interesting again (something Bing and Google spent a decade struggling with).
Abridge in healthcare isn’t trying to impress techies — it’s quietly eating away at a huge real-world vertical where every second saved literally saves lives.

Meanwhile, pure model labs are stuck in an arms race:

  • Bigger models.
  • Smaller profits.
  • Sky-high GPU bills.

And every time a wrapper builds a better front-end for an open model (like Mistral or Mixtral), it pulls more users out of the model labs’ hands.

The age of the app is just getting started.

Section 5: Challenges Ahead for Wrappers

Okay, let’s not kid ourselves.
It’s not all rainbows, billion-dollar valuations, and vibe-coded unicorns.
Wrappers have real risks ahead.

1. Platform Risk:

Most wrapper apps today still depend on someone else’s model.

  • Cursor leans heavily on OpenAI and Anthropic.
  • Abridge relies on stable access to language models.
  • Perplexity uses multiple models under the hood, including OpenAI and Mistral.

If tomorrow OpenAI decides, “Hey, let’s launch our own Cursor competitor and cut API access by 50%,” it’s game over for some startups.
Same if API pricing goes insane or licensing terms change.
(Reminder: OpenAI already hiked prices once, Anthropic will probably follow.)

When you’re standing on someone else’s shoulders, you better hope they don’t shrug.

2. Margins Squeeze:

Even if you control the user, your backend cost is tied to tokens.
More users → More API usage → Bigger bills.
At some point, wrapper companies must either:

  • Build their own models (expensive),
  • Partner deeper with model labs (risky),
  • Or figure out ultra-efficient ways to fine-tune smaller open models (smart).

Otherwise, margins start looking uglier than a Python 2 codebase in 2025.

3. The Megacaps Strike Back:

Google, Microsoft, Meta they’re not asleep.
They see that wrappers are eating parts of the value chain they used to own.
Big tech can:

  • Bundle AI apps into their platforms (ex: Copilot in Microsoft 365)
  • Offer massive discounts if you use their models and their cloud.
  • Out-spend and out-hire smaller wrappers overnight.

It’s like playing StarCraft and realizing the other guy has been building three command centers while you were vibe-coding one Zergling.

4. User Trust and Switching Costs:

Today, users love these apps.
Tomorrow?
If a better, cheaper, faster wrapper comes along, switching takes about 2 minutes.
Wrappers need to build real moats:

  • Amazing UX.
  • Deep integrations.
  • Community lock-in (people talking about your app = free marketing).

Otherwise, they risk becoming just another tab in someone’s overcrowded browser.

In short:
Wrappers have momentum but they’ll need to evolve fast, stay scrappy, and maybe even grow some sharp elbows if they want to stay kings of the hill.

Section 6: What’s Next? The New Era of AI Apps

If 2023 was the year of AI models flexing, and 2024 was the year of “wrappers” proving they weren’t jokes then 2025 and beyond will be the era of real AI applications.
Not experiments. Not side projects. Not gimmicks.
Real, durable, world-changing products.

Here’s where it’s going:

1. Specialized, Not Generalized

Forget the dream of building a single app that “does everything.”
The winners will be the ones that:

  • Go deep into one use case.
  • Solve it better than anyone else.
  • Obsess over real users, not just showing off model IQ.

Cursor? Code writing and debugging.
Abridge? Healthcare documentation.
Perplexity? Search with real answers, not SEO spam.

The future belongs to specialists, not generalists.

2. Personalized AI Experiences

Users won’t settle for vanilla interactions anymore.
They’ll want apps that know:

  • Their preferences.
  • Their goals.
  • Their quirks.

And here’s the kicker: wrappers are way better positioned to do this than the big model labs, because they own the user touchpoint.
Imagine a Perplexity that knows your favorite news sources.
Or a Cursor that adapts to your coding style like a pair of perfectly worn-in sneakers.

3. Faster Iteration Cycles

Big labs move slow.
Compliance reviews. Research bottlenecks. PR disasters waiting to happen.
Meanwhile, apps can:

  • Ship updates weekly.
  • A/B test like maniacs.
  • Pivot without needing a 400-slide deck approval from legal.

Speed will crush perfection every time.

4. Open Source + Wrappers = 🔥

Here’s the hidden weapon:
With the explosion of open models like Mistral, Mixtral, Llama 3, and beyond, wrappers can start swapping models like Lego blocks.
No more vendor lock-in.
No more praying OpenAI doesn’t raise API costs again.

Smart wrappers will stitch together:

  • The best models,
  • The cheapest inference,
  • The most magical UX.

A modular future where the user experience matters 10x more than whatever dusty model is under the hood.

Bottom line:
The next generation of AI giants will not be companies that just build the biggest brains.
It will be the ones that teach those brains to dance — solving problems, delighting users, and making products that feel like magic.

The apps have arrived.
And they’re just getting warmed up.

Conclusion: Wrappers Aren’t Insults Anymore They’re the Future

Once upon a time, being called a “wrapper” in Silicon Valley was an insult.
It meant you were a middleman.
An unnecessary layer.
A poser riding the coattails of the real model builders.

But history didn’t play out the way the critics thought.
Instead of fading away, the wrappers evolved.
They became the bridge between raw AI power and actual human needs.

They didn’t just slap a UI on top of ChatGPT.
They:

  • Built faster, smarter, friendlier apps.
  • Created real businesses.
  • Solved real-world problems.
  • Earned user loyalty (and sometimes massive ARR) faster than anyone expected.

Today, apps like Perplexity, Cursor, and Abridge aren’t jokes.
They’re case studies in how to win the AI wars — not by building the biggest brain, but by making it useful.

The lesson?
In the new world of AI, execution beats invention.
Real-world impact beats theoretical capability.
And wrappers aren’t just surviving they’re thriving.

The next time someone sneers and says, “You’re just a wrapper,”
Smile.
Because in 2025, the wrappers are the ones changing the game.

Helpful Resources

Here are some real resources to dive deeper if you want to see how the AI wrapper revolution is unfolding: