99% of AI Startups Will Be Dead by 2026 – Here's Why

In the late ’90s, I was a student at Berkeley watching the dot-com boom unfold like a fever dream.

• Traffic equated to revenue
• Adding .com to the end of something made investors throw money at aspiring entrpepeneurs.
• Startups with no business model bought Super Bowl ads, and many people became papere millionaires over night.

During my internship at Sun Microsystems in ’99, I’d drive down 101 past office buildings wrapped in billboards for AltaVista, Excite, and other names destined for extinction. By 2001, those buildings stood empty.

The following summer, I went to a launch party where a startup dropped what must have been half a million dollars just to announce they were going to start charging for a product they had been giving away for free. The room was filled with VCs — and no one blinked.

By the time I graduated in December 2000, the party was over. From Berkeley, I had the perfect view — just across the bay from the collapse.

And now, 25 years later, we’re back.

The labels have changed, but the logic hasn’t. “AI-powered” is the new “.com.” Startups pitch wrappers. But this time, many don’t even pretend to own the tech they’re built on.

Look closer, and it’s a house of cards:

• Wrappers rely on OpenAI.
• OpenAI relies on Microsoft.
• Microsoft needs NVIDIA.
• NVIDIA owns the chips that power it all.

No one’s in charge. Everyone’s exposed. And no one’s acting like that’s a problem.

Most so-called “AI-powered” tools are just a pretty interface wrapped around OpenAI’s API.

It hit me when I looked under the hood of a podcast post-production tool I’d signed up for. The promise? Upload your transcript, get back social posts, summaries, even a newsletter draft. Clean UX, smooth workflow — $60/month. Then I did the math.

If I dropped the same transcript into a folder and called the OpenAI API directly, I could replicate the entire workflow — in five minutes, for under $4. Even without writing code, I could just ask ChatGPT to walk me through it.

There was no system. No infrastructure. Just markup.

That’s when I realized: these aren’t products. They’re prompt pipelines stapled to a UI.

• Input: a transcript.
• Process: a few hardcoded prompts like “summarize this,” “turn it into a tweet,” “generate a LinkedIn post.”
• Output: formatted text in boxes.

No backend. No IP. Just API calls on rails.

And they’re charging 50–100/month to do what anyone could replicate for pennies. It’s not just overpriced — it’s dishonest. The entire business model relies on the user not knowing how simple it really is.

That’s the heart of the term: LLM wrapper.
It’s not a product. It’s a disguise.

Everyone treats OpenAI as untouchable — the intelligence layer of the entire industry.

Since late 2022, nearly every wrapper, agent, and productivity tool has stood on their shoulders. They built the strongest model. They had the earliest lead. No one has reshaped the market more.

And yet, they’re exposed.

Their dominance depends on distribution — and distribution comes from the very wrappers everyone dismisses. All those SaaS tools built on top of GPT-4? They’re not just passengers. They’re OpenAI’s customer base. And if even a few of them collapse, API revenue goes with them.

This is the hidden risk.

Wrappers burn cash on freemium users running token-heavy workflows. But OpenAI still charges them per request. It doesn’t matter if the user pays — the wrapper eats the cost. Their entire business model hinges on converting fast enough to outpace burn. Some will. Most won’t.

And when they vanish, OpenAI feels it.

That’s the paradox: OpenAI owns the tech, but not the user. Wrappers do. And those wrappers are fragile — thin moats, heavy burn, little lock-in. If they fall, OpenAI doesn’t just lose a customer — it loses the distribution layer that’s propped up non-ChatGPT revenue.

It’s not a one-way dependency. It’s a closed loop:

• OpenAI owns the intelligence.
• Wrappers own the distribution.
• Everyone’s pretending the other isn’t critical.
• But the economics say otherwise.

Every token sent through a wrapper — paid or not — earns OpenAI money. Multiply that by millions of freemium users, and these startups become unpaid distribution arms, subsidizing OpenAI’s growth while bleeding out.

It’s a clever setup. But it’s brittle.

Because if the wrappers go down, OpenAI’s reach shrinks. They can try to convert those users directly — but most of them weren’t signing up for ChatGPT Pro. They showed up for workflow, not raw model access.

• OpenAI has the moat.
• They have the model.
• But they don’t have insulation.

Their reach depends on a fragile ring of wrappers, most of which are loss-making, undifferentiated, and burning investor money to survive. When that money dries up, OpenAI loses more than partners — it loses the scaffolding beneath its revenue.

And you don’t have to squint to see it.

Open Instagram. Scroll through your feed. Dozens of AI tools promise to revolutionize note-taking, health records, podcasting, journaling — all with clean branding, all powered by GPT, and all running the same backend pattern:

• Take your input
• Send it to GPT
• Parse the response
• Drop it into a UI
• Call it a product

That’s it.

And OpenAI gets paid on every call — no matter how thin the differentiation is on the surface.

That’s the real exposure: a brittle network of SaaS shells acting as both customer base and growth engine — all margin-negative, all interchangeable, all one policy change away from failure.

Still think I’m exaggerating?

Here’s what most of these “products” are doing under the hood:

Then they call it from the terminal:

That’s the product.
Everything else is CSS, billing, and a Stripe integration.

Swap the prompt, swap the use case:

• Want tweets from a transcript? Adjust the instruction.
• Want meeting summaries? Change the input.
• Want a smart email assistant? Plug in SendGrid.

There’s no IP. No system. No moat.
Just a well-structured API call, markup, and marketing.

Most of the AI product landscape could be rebuilt by a junior dev in under an hour using ChatGPT, Stripe, and boilerplate frontend.

That’s the engine behind the hype — and the silent vulnerability behind OpenAI’s strength.

The Survivability Math

It’s easy to dunk on LLM wrappers for being fragile. But the truth is more tangled.

These tools don’t own the intelligence they sell — they rent it. Most rely entirely on OpenAI, Anthropic, or Claude. Their “product” is a UI with a few prompts behind it. Every time a user interacts, they pay the model provider.

Unless they’ve built real infrastructure — memory layers, workflow engines, or distribution moats — they’re just middlemen. And middlemen don’t last.

But here’s the twist: OpenAI needs them too.

Wrappers are the API’s growth engine. They bring GPT to verticals, teams, niches. Kill the wrappers, and OpenAI loses reach and revenue. That interdependence matters — but so does leverage.

Survivability comes down to four questions:

• Who owns the margin?
• Who controls pricing?
• Who can switch providers?
• Who can’t be replaced with a nicer prompt?

Let’s break it down:

Jasper

The golden child. Raised 100M+, hit ~90M ARR, then got kneecapped by ChatGPT. They scrambled: pivoted to enterprise, added model routing, tried building light custom models. They’re still alive — but only after valuation cuts and exec turnover. This is what survival looks like when your product rides rented intelligence.

Copy.ai

Smaller scale, same story. 16M raised, 10M ARR, huge freemium base — but zero moat. Core features are GPT with a UI. They’ve started layering workflow tools to add stickiness, but switching costs are low. Their pricing reflects the tension: they need to grow, but can’t afford to give too much away.

Notably

Niche research tool. Likely pre-revenue, fully reliant on OpenAI. Offers summarization, analysis — features that now come bundled in ChatGPT. Their feature set overlaps with native functionality. That’s not competition. That’s extinction risk.

Tome

Viral success story. AI slide decks powered by GPT-4 and Stable Diffusion. Millions of users. But then Microsoft embedded Copilot into PowerPoint — and exposed Tome’s Achilles heel: they don’t own the platform they’re disrupting.

Writesonic

Quiet outlier. Raised little, stayed lean, built their own small models to cut cost. They’re routing dynamically across GPT-4, Claude, and their own engines. Not invincible — but modular. If anyone survives on operational efficiency, it’s them.

Survivability isn’t about who got there first. It’s about who built beyond the wrapper.

Who owns the experience, not just the API calls.
Because when the ground shifts — and it will — the question won’t be who dies.

It’ll be: who has leverage?

Before AI, NVIDIA was a gaming company — GPUs, frame rates, graphics. That’s still how most people think of them. But that version of NVIDIA is long gone.

Today, NVIDIA is the most powerful company in AI — and arguably the least understood. They’re not customer-facing, but their grip on the ecosystem is absolute.

They don’t:

• Build models
• Run apps
• Show up in your ChatGPT tab

But every time you use AI, you’re using NVIDIA.

Almost every major model — GPT-4, Claude, Gemini — is trained and served on NVIDIA hardware. Over 90% of model training runs on their chips. Inference — the process of generating responses — is still 70–80% NVIDIA-powered.

OpenAI runs on NVIDIA clusters inside Azure. Microsoft is racing to secure more GPU supply. AWS, even with its custom silicon, still depends on NVIDIA for key workloads. No one scales without them.

NVIDIA doesn’t just make chips. They control the AI supply chain: from hardware to drivers, software frameworks like CUDA, and the orchestration layer that turns GPUs into deployable infrastructure. They are the quietest, most absolute chokepoint in the industry.

They don’t need a front-end product. They already own the pipeline.

OpenAI may be the brain of the operation, but Microsoft is the nervous system. Every API call, every ChatGPT response, every model fine-tuning runs on Azure. That’s not a footnote — it’s the foundation.

When Microsoft invested billions into OpenAI, they didn’t just buy equity — they bought control. As OpenAI’s exclusive cloud provider, Microsoft now sits underneath every LLM wrapper that uses GPT. Every token is processed on Microsoft’s GPU clusters, owned and orchestrated by Azure, and rented back with margin.

In return, Microsoft got early access to GPT-4 — and embedded it directly into Office, Outlook, and Teams. OpenAI trained the model, but Microsoft controls the distribution. “Copilot” is simply the brand face. The real value sits behind the scenes.

Microsoft doesn’t need to build the best model. They just need to own the infrastructure layer the best models rely on. And if OpenAI ever tries to walk away? Good luck — their entire stack, from orchestration to throughput, is bound to Azure. Microsoft doesn’t just host OpenAI. They own the terrain it runs on.

OpenAI may get the headlines. Microsoft gets the logs.

The real danger isn’t that the wrappers go under. It’s not OpenAI repricing its API. It’s not even Microsoft shifting strategic focus. The threat is lower. Deeper. Structural.

It’s the single-point fragility buried at the bottom of the stack.

If something happens to NVIDIA — a supply chain disruption, a manufacturing delay, a geopolitical sanction, an export ban — the entire AI ecosystem stalls.-

• Training slows.
• Inference bottlenecks.
• Product development halts.

And suddenly, it’s no longer about feature velocity or fundraising. It’s about access to compute — and whether you have any at all.

This isn’t hypothetical. It’s already started.

Export controls on high-end chips have tightened. Demand for NVIDIA’s H100s is outstripping supply. GPU rental costs have surged, in some cases quadrupling when availability drops. These aren’t market blips. They’re warning signals.

Every layer of this ecosystem — from OpenAI’s API, to Microsoft’s Copilot, to the indie wrappers flooding your feed — is built on a supply chain that begins with one company, manufacturing one kind of hardware, in one constrained geography.

That’s not a stack. It’s a fault line.

And when it breaks — because something always does — there’s no soft landing. Only slowdown, rationing, and fallout. Companies will vanish. Markets will correct. The survivors will be the ones who never believed the ground was solid to begin with.

1. The Hardware Choke
A disruption in NVIDIA’s supply chain — whether caused by geopolitical tension, raw material shortages, or manufacturing slowdowns — would halt progress across the stack. No GPUs means no training, no inference, and no scale. Silicon is the oxygen of this ecosystem.

2. The Regulatory Snap
If a major government decides that foundational models are a national security concern or public safety risk, regulation could shut down key parts of the AI pipeline. One ruling, one moratorium, one compliance shift — and the infrastructure goes from open to constrained overnight. The threat isn’t technical. It’s political.

3. The Paradigm Shift
The most destabilizing scenario isn’t collapse — it’s irrelevance. What if someone builds a competitive model without needing GPUs? What if intelligence emerges from signal, not scale? What if a leaner, radically different architecture rewrites the rules? The system wouldn’t crash. It would just get left behind.

Every time a wave like this hits, the same psychology takes over. People don’t just chase opportunity — they chase belonging. They want to be part of it. They want to say they were there early. That they “built with AI.” That they had a launch, a landing page, maybe a TechCrunch mention. It’s less about what’s real and more about what signals.

It doesn’t matter if the product is sustainable or even useful. What matters is the optics: screenshots, traction charts, investor decks showing hockey-stick growth curves stapled to OpenAI’s API. This is the same playbook as every gold rush.

In the 1800s, the miners mostly lost. The ones who got rich sold shovels, lodging, and denim. In the dot-com boom, it was Super Bowl ads and domain grabs. Now? It’s prompt wrappers, fake demos, AI co-founders, and inflated headcount for what amounts to frontend skins on API calls.

Most of these teams aren’t trying to build enduring businesses. They’re trying to look like they already have — just long enough to raise, get acquired, or catch the algorithm. This isn’t innovation. It’s theater. And most of what you see is stagecraft.

That’s why so many “AI tools” look the same. They’re not solving problems. They’re performing proximity to hype.

What’s happening here isn’t just a string of bad bets. It’s game theory.

The AI ecosystem is trapped in a multi-player prisoner’s dilemma — where everyone acts in their own rational interest, but collectively undermines the foundation they all rely on.

• LLM wrappers try to scale fast without owning the model. So they subsidize usage, fake stickiness, and chase top-line metrics while destroying their own margins.
• OpenAI wants API growth. So it feeds wrappers it knows won’t survive — but needs for volume, reach, and use-case surface area.
• Microsoft wants control of the deployment layer. But it depends on OpenAI for capabilities, and on NVIDIA for compute — without owning either.
• And NVIDIA? NVIDIA wins either way.

Every player is acting rationally. None of them are building something stable.

The result is a system of interdependent leverage. OpenAI can’t kill wrappers without losing volume. Wrappers can’t swap models without degrading output. Microsoft can’t dominate without risking OpenAI’s loyalty. And if NVIDIA stumbles, they all take the hit.

This isn’t a pyramid. It’s a loop. And loops don’t have fallback paths.

The fragility isn’t caused by stupidity or malice. It’s just the compound effect of everyone doing the smartest thing they can — until it all breaks at once.

When the wrappers collapse and the funding dries up, only one kind of company survives: the one everything else depends on. The one that can’t be swapped out. The one that doesn’t disappear when the market corrects — because without it, nothing else works.

That’s infrastructure. And almost nobody in AI is building it.

Infrastructure is what others build on, and what no one can afford to lose. It’s AWS. Stripe. Twilio. The invisible layer that only matters when it breaks — and becomes irreplaceable when it does. You don’t choose it because it’s exciting. You choose it because there’s no alternative.

We’ve already seen what happens when nobody builds it.

In the dot-com era, Idealab was the original startup incubator — a pre-Y Combinator factory of ideas. Bill Gross was a generational thinker. He launched over a hundred companies, hired teams, shared resources, and moved fast. But it didn’t survive the crash.

Some Idealab companies went public. Most vanished. Big brands. Strong concepts. Zero leverage. They were interface plays — user-facing, hype-friendly, but system-optional. When the correction came, they disappeared overnight.

Y Combinator took a different path.

It didn’t centralize execution. It distributed risk. It looked for outsiders, bet small, and let the market decide. From that came Stripe, Dropbox, Airbnb — not because YC was more visionary, but because it selected for companies that could endure. Companies that eventually became infrastructure.

Even the second wave of e-commerce proved this point. Companies like Warby Parker and Casper weren’t just branding exercises — they controlled logistics, supply chains, and fulfillment. They looked like DTC brands on the surface, but under the hood, they were systems companies.

AI will follow the same pattern.

Infrastructure companies don’t panic when token prices spike. They don’t crash when NVIDIA misses a shipment or OpenAI changes an endpoint. They don’t compete on interface polish or prompt gimmicks. They define the ground others walk on.

The Questions That Matter Most

Peter Thiel laid out seven in Zero to One — the ones every enduring company should be able to answer:

1. The Engineering Question — Can you create breakthrough technology instead of incremental improvements?
2. The Timing Question — Is now the right time to start your particular business?
3. The Monopoly Question — Are you starting with a big share of a small market?
4. The People Question — Do you have the right team?
5. The Distribution Question — Do you have a way to not just create but deliver your product?
6. The Durability Question — Will your market position be defensible 10 and 20 years into the future?
7. The Secret Question — Have you identified a unique opportunity that others don’t see?

Nobody in the wrapper economy is asking these. Because if they did, the answers would be obvious:

No. No. No. And absolutely not.

The playbook today is simple: slap a UI on GPT, call it specialized, and hope the user doesn’t look behind the curtain. But that’s not infrastructure. That’s camouflage.

The real builders? They’re not just enabling users — they’re making themselves impossible to leave. They’re not chasing the wave. They’re laying rails under it.

Would anyone rebuild your product if it disappeared? Would anything break if it went away? Does anyone depend on it, or is it just riding the moment?

If the answer is no, you’re not infrastructure. You’re noise.

• The gold rush always ends.
• The wrappers always fall.
• The story shifts.
• But the pattern never does.

The survivors are the ones the system can’t delete.

No usage fees. No API tokens. No surveillance. It runs locally, replaces entire workflows, and never sends a prompt to an api endpoint.

Get Early Access