AI Startups Depend on Costly APIs of Companies Burning Billi…

The AI industry is stuck in a paradox. On one hand, it is hailed as the most important technology shift of our time. On the other, most companies calling themselves AI startups are little more than thin wrappers around APIs built by OpenAI, Anthropic, or Google. 

Strip away the hype and the reality looks fragile.

Alex Issakova, CEO of Huckr AI, said it bluntly. “80% of AI startups depend on APIs from companies burning billions. How long can that last?”

That dependency is the core weakness. When OpenAI raises prices, when Anthropic cuts back credits, or when Google shifts its model tiers, entire businesses get shaken. As Gergely Orosz, best known for his newsletter The Pragmatic Engineer, noted, many AI startups are “boasting about ARR milestones” that don’t add up once you factor in compute bills. 

A company may claim $100 million in revenue, but its costs are just as high. Margins vanish the moment the API provider decides to change the math.

OpenAI spent $9 billion in 2024 and lost $5 billion. Anthropic burned through $5.6 billion. Google is sinking more than $10 billion every year into AI, while still making its real profits from advertising. If the giants are struggling to make money, what chance do the startups built on top of their work have?

Wasn’t Being a Wrapper Good Enough?

“The majority of people can start with a wrapper and then, over a period of time, build the complexity of having their own model,” Prayank Swaroop, partner at Accel, had earlier told AIM. 

Swaroop emphasised that Accel has no inhibition in investing in wrapper-based AI companies, as long as the startup can prove its ability to find customers by building GPT or AI wrappers on other products. However, he said that for a research-led foundational model, it is crucial to stand out, and simply creating a GPT wrapper does not qualify as a new innovation.

But the API layer is where the illusion of innovation lies. Most companies branded as “AI-first” are reselling access to models with a slick interface or a narrow workflow. This is also visible with the latest release of YouTuber Dhruv Rathee, which faced severe backlash from the Indian tech community.

Sam Altman, OpenAI’s CEO, has been warning about this for months. On the recent podcast with Nikhil Kamath, Altman said, “Using AI itself does not create a defensible business. You’ve always got to parlay that advantage that comes from using the new technology into a durable business with real value that gets created.”

Altman compared today’s flood of AI wrappers to the early iPhone App Store. At first, people made money selling gimmicks like flashlight apps. Apple eventually absorbed those into the operating system. But Uber, which used the iPhone as an enabler rather than a crutch, became a lasting business. 

The message is clear: if your startup exists because the model doesn’t yet offer the feature, you’re on borrowed time.

Not Enough

OpenAI’s GPT-5 was hyped for two years as the breakthrough that would push AI closer to general reasoning. Instead, users saw a product that was faster and cheaper but not dramatically smarter. Even Altman admitted, “I think we totally screwed up some things on the rollout.”

That stumble matters because of the sheer weight of expectations. Investors, startups, and enterprises all act as if the next model will fix hallucinations and deliver accuracy levels safe for medicine, law, and government. 

Instead, hallucinations are still in the range of 10–20% depending on context. For domains where mistakes can cost lives, that failure rate makes the technology unusable without heavy guardrails.

The obsession with bigger models and shinier demos hides the real problem of profitability. Microsoft has added trillions in market value since its OpenAI partnership, but the money comes from Azure cloud services, not from selling AI subscriptions.

Google still lives off ads. Oracle is chasing cloud margins but barely moving the needle. Beneath them, hundreds of AI startups are living off cheap capital, venture subsidies, and API credits.

Even for Meta, the latest release of Llama 4, to say the least, was underwhelming. Harneet SN, co-founder of Rabbitt AI, earlier told AIM that while Meta’s Llama 4 appears promising on paper with its Mixture-of-Experts architecture and native multimodality, its real-world performance has left some gaps. 

“Its long-context capabilities don’t quite hit the mark they advertise, and image understanding can sometimes be a bit off, leading to unexpected outputs,” he noted. This reaction is echoed across the industry, with some saying, “it’s a model that shouldn’t have been released”.

What Happens Next?

“The question is not whether AI is powerful. It is whether it’s profitable,” argued Issakova.

That is where the bubble risk emerges. If capital tightens, if investors demand returns, if API prices rise even slightly, most AI startups would collapse. Already, churn spikes when subscription prices go up. Consumer demand is not infinite.

Altman himself acknowledged the race against the clock: “You can definitely build an amazing thing with AI, but then you have to go build a real defensible layer around it.” In other words, you need to own the customer, not just the API call. 

Companies like Cursor, which has grown by deeply embedding itself into developer workflows, show that durability is possible. Most others will disappear the moment the foundation models catch up.

Issakova warns that unless AI “builds models that solve real-world problems at scale” and “develops business models not reliant on cheap capital,” the cycle will end in another freeze.

For now, AI is powerful. It is useful. But it is also propped up by billions in losses and fragile startups pretending to be something more than what they are: renters of someone else’s infrastructure. 

Henning Steier, chief marketing and communications officer at Bluespace Ventures, said, “If your startup pitch deck includes ‘we’ll figure out monetisation later’ and your core tech is someone else’s API… congratulations, you’re basically WeWork with GPUs.”

Nik Kotecha, founder of Earl Global has another analogy: “Most AI startups aren’t building companies, they’re renting margins from Microsoft.”

The longer this continues, the sharper the correction will be.

The post AI Startups Depend on Costly APIs of Companies Burning Billions appeared first on Analytics India Magazine.

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