AI Startups Are Sick — But Not in a Good Way

In today’s tech industry, everyone seems to be talking about “AI.”

From modern frontend developers to legacy Java engineers, and even founders who lack technical depth but excel at fundraising, all appear to share a blind faith that:

“AI will unlock the future.”

• “AI will differentiate our product.”
• “Just add AI to the UX and it’ll become exponentially more compelling.”

Such vague optimism has led to a flood of products that are little more than wrappers around generative AI APIs.

But let us pause and ask:

Why are so many products — essentially watered-down versions of ChatGPT — being hailed as innovation?

Everyone’s using the same APIs, applying similar UIs, and returning outputs that only look intelligent.

Now that AI has become ubiquitous, how much value do users really derive from these “AI-like” experiences?

What we’re seeing now strongly resembles the dot-com bubble of two decades ago.

And just like back then, many AI startups today face similar structural pitfalls.

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Part 1: The “Triple Trap” of API-Based AI Products

Most AI startups today rely on APIs provided by large model providers like OpenAI, Anthropic, and Google.

While this allows for rapid prototyping, it introduces fatal challenges when trying to scale into a viable business.

1. No Technical Differentiation

If every product uses the same API (e.g., GPT-4), output quality becomes nearly identical.

The user experience differences are reduced to UI tweaks and prompt engineering.

Functional differentiation is extremely difficult to achieve.

2. Razor-Thin Margins

API usage is expensive.

As monthly active users grow, so does the cost — leading to a paradox where scale equals deeper losses.

Even with subscription models, it’s hard to build a profitable cost structure.

3. Overdependence on External Providers

APIs can change pricing, usage limits, or functionality at any time.

If your core product relies entirely on a third-party API, you're effectively at the mercy of OpenAI’s business decisions.

Should they release the same functionality natively?

Your competitive edge evaporates overnight.

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Part 2: Going “Fully In-House” Comes with Its Own Hell

Some founders attempt to escape these risks by building their own LLM infrastructure.

But this often opens the door to a different kind of nightmare.

1. Not Enough GPUs or Talent

Fine-tuning and serving open models like LLaMA or Mistral requires:

• Substantial GPU resources
• A highly skilled MLOps team

Initial setup can cost tens of millions of yen, with ongoing bills in the millions monthly.

2. MLOps Failure = Product Death

Deploying LLMs reliably in production is a non-trivial task.

From:

• Version control
• Tokenizer management
• Inference latency optimization
• Caching strategies

Merely “getting the model to run” is only the beginning.

3. Users May Not Even Notice the Difference

Even after all the effort, users often perceive the outputs as only slightly different.

The massive investment may yield little to no noticeable improvement — a poor return on risk and cost.

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Part 3: The Dot-Com Bubble Parallels

Today’s landscape mirrors the early 2000s.

Back then, people genuinely believed that:

“Building a website means building the future.”

“Having .com in your name boosts your stock.”

But most companies lacked any concrete monetization strategy — they were simply surfing the hype.

Now, substitute “website” with “AI,” and the same structural delusion repeats:

• Blind faith in the underlying technology
• Scaling without understanding how to make money
• Business models overly reliant on tech giants

These patterns make it painfully clear:

Today’s AI startup boom is a new bubble in disguise.

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Conclusion: AI Is a Tool, Business Is the Core

AI is a powerful tool — but it’s still just that: a tool, not a goal.

What matters in SaaS is not the tech stack, but having a clear answer to these three questions:

1. Who is paying?
2. Why are they paying?
3. Will they keep paying over time?

Whether you’re using an external API or running your own model in-house is secondary.

What truly matters is whether your product:

Replaces a human task in a way that customers are willing to pay for — sustainably.

The hype will fade.

What remains is the cold, hard reality of monetization.

Comments

[A.J. Null]

Agree? Disagree? Either way, your comments are welcome — I’m curious to know how others see it.