Nvidia is one of the greatest tech companies ever built. They created the ecosystem that made modern AI scalable. Most AI startups would not exist without CUDA and Nvidia GPUs.
In my previous post, I covered how AI breaks the SaaS margin model, but there’s a second trap. The platform that makes you powerful can become a liability if you build too tightly around it.
This isn’t anti-Nvidia. It’s anti-dependency. It’s about architectural debt.
Technical debt is like a credit card: you run a balance and pay interest. It’s manageable.
Architectural debt is a predatory mortgage. It looks manageable at first, but eventually, it forecloses on your business model.
The "Success" Paradox
In year one, your job is to build fast. You optimize for speed, not margins. You use the tools that ship fastest (APIs, CUDA). That is rational. But the bill eventually comes due.
When you hit product-market fit, the math changes overnight. At roughly 100M tokens/day, paying a Model Provider’s 70% gross margin becomes lethal. Your P&L will force you to leave the API and host your own models.
This is the trap. You used Nvidia to get to scale, but you can’t afford Nvidia at scale. If you haven't maintained architectural optionality from Day one, you won't be able to pivot when it matters most.
The "Private Cloud" Fallacy
That pivot is what founders rarely price in. Skeptics will argue, "You can't beat Microsoft's hardware cost, so why self-host?" They are fighting the last 2012 cloud war.
You aren't fighting their chip cost; you are fighting their margin and R&D costs.
The only way to win that math is to run on hardware that offers the best Price/Performance ratio, whether that is AWS Inferentia, TPUs, or Groq.
If you commit fully to CUDA, moving off isn't a refactor. It’s a rewrite of core systems; kernels, memory models, pipelines. That rewrite rarely happens until the pain is existential. By then, your margins are already compromised.
The CEO’s Test
Here’s a simple test every CEO should run:
"If we had to move inference tomorrow, could we?"
Not hypothetically. Not with a six-month rewrite. Tomorrow.
Could we run on Inferentia, TPUs, or AMD without rewriting the stack?
If the answer is “no,” you carry architectural debt.
Smart Money
Hyperscalers (Meta, Google, Amazon and Microsoft ) are spending billions to reduce GPU lock-in. They know that owning the stack restores control over economics. Control is the difference between pricing power and margin compression.
Startups don’t have hyperscaler budgets to brute-force a fix later. That’s why Year 1 decisions matter.
The Bottom Line:
Use Nvidia. Ship fast. Win customers. But don’t build assuming CUDA is forever.
Date Nvidia. Don’t marry them.
In my previous post, I covered how AI breaks the SaaS margin model, but there’s a second trap. The platform that makes you powerful can become a liability if you build too tightly around it.
This isn’t anti-Nvidia. It’s anti-dependency. It’s about architectural debt.
Technical debt is like a credit card: you run a balance and pay interest. It’s manageable.
Architectural debt is a predatory mortgage. It looks manageable at first, but eventually, it forecloses on your business model.
The "Success" Paradox
In year one, your job is to build fast. You optimize for speed, not margins. You use the tools that ship fastest (APIs, CUDA). That is rational. But the bill eventually comes due.
When you hit product-market fit, the math changes overnight. At roughly 100M tokens/day, paying a Model Provider’s 70% gross margin becomes lethal. Your P&L will force you to leave the API and host your own models.
This is the trap. You used Nvidia to get to scale, but you can’t afford Nvidia at scale. If you haven't maintained architectural optionality from Day one, you won't be able to pivot when it matters most.
The "Private Cloud" Fallacy
That pivot is what founders rarely price in. Skeptics will argue, "You can't beat Microsoft's hardware cost, so why self-host?" They are fighting the last 2012 cloud war.
You aren't fighting their chip cost; you are fighting their margin and R&D costs.
The only way to win that math is to run on hardware that offers the best Price/Performance ratio, whether that is AWS Inferentia, TPUs, or Groq.
If you commit fully to CUDA, moving off isn't a refactor. It’s a rewrite of core systems; kernels, memory models, pipelines. That rewrite rarely happens until the pain is existential. By then, your margins are already compromised.
The CEO’s Test
Here’s a simple test every CEO should run:
"If we had to move inference tomorrow, could we?"
Not hypothetically. Not with a six-month rewrite. Tomorrow.
Could we run on Inferentia, TPUs, or AMD without rewriting the stack?
If the answer is “no,” you carry architectural debt.
Smart Money
Hyperscalers (Meta, Google, Amazon and Microsoft ) are spending billions to reduce GPU lock-in. They know that owning the stack restores control over economics. Control is the difference between pricing power and margin compression.
Startups don’t have hyperscaler budgets to brute-force a fix later. That’s why Year 1 decisions matter.
The Bottom Line:
Use Nvidia. Ship fast. Win customers. But don’t build assuming CUDA is forever.
Date Nvidia. Don’t marry them.