Why do we say that most LLM startups are doomed to fail?

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Editor's note: When LLM giants devour the application layer, shell - wrapping entrepreneurship has become a high - risk game. This article exposes the platform illusion and dissects the genes of survivors - either build your own barriers or become nourishment. This article is a compilation.

Most AI startups make the same mistake: thinking they are building a business on a platform. In fact, they are nesting inside predators.

I. Introduction: A Cliff Disguised as a Runway

The AI startup ecosystem is gradually coming to terms with a cruel reality. In the past 18 months, thousands of startups backed by top - tier venture capital have bet their futures on the idea that large language models (LLMs) are the new application platforms. This idea is extremely alluring: APIs are easy to call, demos can impress investors, and shell - wrapping startups can raise funds quickly and launch products even faster.

However, most of these startups are based on a cognitive fallacy: they mistakenly think that model providers are stable platforms similar to AWS or iOS. This is a huge mistake. Model providers are not platforms but predators.

II. The Illusion of Modularity

The core illusion of the LLM startup boom is the delusion of composability. Founders think they can build billion - dollar products based on Claude, GPT - 4, or Gemini, just as companies developed based on Windows or AWS back then. But unlike cloud infrastructure, the underlying model providers are not a neutral layer in the technology stack but vertically integrated end - to - end product companies.

OpenAI not only wants to license GPT - 4 to developers but also wants to control the chat interface, user accounts, distribution channels, and the trust layer. The same goes for Anthropic and Google. The logic of comparing them to AWS collapses here - AWS never competes with customers for users' minds, but these companies do exactly that.

When you build something based on others' models, you lose control of your own destiny. You are not a platform but nourishment, a test case, and an experiment. If you develop too fast, you become a threat; if you develop too slowly, you become a statistical error. In any case, you can be discarded at any time.

III. Strategic Mistakes in the Venture Capital Ecosystem

The LLM wave has exposed a common blind spot among investors and founders: confusing the convenience of prototype development with the durability of the business model. Fueled by cool demos and rapid iterations, venture capital has poured in crazily. Startups that simply put a thin layer of UX on top of public APIs are regarded as infrastructure investment targets.

But these companies are not platforms; they are just interfaces built on shaky ground.

Many people once believed that the operation of underlying model providers would be like cloud infrastructure: predictable, stable, and content with monetizing computing power. But these providers are not passive pipelines; they are restless players eyeing the downstream. Their goal is not to empower startups but to replace them.

IV. Exceptions to the Rule

Some startups will survive this collapse, and a few will even rise against the trend. But they all have one thing in common: they all have irreplaceable moats.

Distribution barriers: Companies with deep industry relationships (such as in the medical, enterprise SaaS, or legal technology fields) use LLMs to enhance their customers' existing workflows. Their advantage lies not in the model but in their integration capabilities.

Proprietary data: Companies with unique data sets (whether vertically structured or real - time data) have products significantly better than the internal solutions of OpenAI or Anthropic. For example, a radiological diagnosis company with millions of labeled images. However, it should be noted that simply owning data is not enough; you also need to ensure legal usage rights and deep integration with the product workflow.

Inference control: Startups that self - host or fine - tune their own models (including small LLMs or synthetic architectures) have control over costs, latency advantages, and product autonomy.

Synthetic platforms: A very small number of companies are building orchestration layers, agent frameworks, or memory architectures, whose complexity and defensiveness are sufficient to trigger network effects. These are not shell - wrapping products but emerging operating systems for intelligent work.

V. Why the LLM Shell - Wrapping Theory Is So Alluring

There are reasons why investors and founders are falling into the trap. The LLM shell - wrapping model offers:

• Immediate demos: Adjust the OpenAI interface a few times and add a React front - end, and the product is immediately available.
• Ultra - fast progress: The team can iterate, raise funds, and expand within a few weeks.
• Low burn rate: Low infrastructure requirements, small recruitment scale, and low trial - and - error costs.

In an environment full of hype and capital, these advantages are hard to resist. But they mask the core strategic flaw: no control over the value engine.

Founders overly pursue superficial growth, and investors only focus on the growth curve. But no one delves into the key questions: What if OpenAI can replicate your entire business with three lines of code? What if Anthropic limits your access or Google adds non - compete clauses?

VI. Vertical Integration Is Inevitable

The actions of model providers are not irrational. On the contrary, they are making rational choices as monopolies: expanding upstream, squeezing profits, and controlling user relationships.

It is too naive to think that underlying model companies will be content to be infrastructure providers. Since they control the model, the interface, and the data flywheel, why would they let profits slip away? Since they can build the next Salesforce on their own, why support third - party startups?

The AI ecosystem is undergoing a phase change. The current situation is similar to when Facebook absorbed the best features (photos/check - ins/events) in its ecosystem or when Microsoft bundled an Excel clone into its operating system. When computing power becomes intelligence, vertical integration becomes inevitable.

VII. The Urgent Task for Founders

If your business is built on others' LLMs, you must ask yourself:

• Why doesn't OpenAI develop this business on its own?
• What non - model - dependent moats do I have?
• What if my API access is revoked tomorrow?

(Editor's note: Windsurf, an AI - assisted programming tool using the Claude model, must have a deep understanding of this. Of course, if it is acquired by OpenAI, it can be seen as two sides of the same coin.)

If you can't answer these questions effectively, you must transform immediately. This doesn't mean giving up AI but repositioning your business at a different level.

The new decision - making criteria are as follows:

• If you are closer to users than the model provider, you may survive.
• If the other party can replace you with a function switch, you are already on the verge of extinction.

Founders need to sort out the dependency chain and mercilessly strip away all commodifiable links. Data, distribution, and inference control are the real barriers; the rest are at risk.

VIII. Conclusion: When the Foundation Collapses

It's not shameful to bet on the wrong abstraction layer, but sticking to the wrong choice is extremely dangerous. LLM shell - wrapping products emerged in a special period: when interfaces were open, differentiation was overestimated, and vertical integration had not yet arrived.

That era has ended.

The new era belongs to players who control more than just the interface. Data, distribution, infrastructure - these are the new moats. The rest will perish.

The platform trap has been triggered. The question now is: Who can get out in time?

Translator: boxi.