Your AI Startup Is Not a Company: It Is a Feature of OpenAI or Anthropic

Tue, 05 May 2026 00:00:00 +0000

And you are building inside a crack that may close with you still inside it.

In March 2025, a solo developer celebrated on Twitter that his PDF summarization tool had reached $12,000 in MRR. He used the GPT-4 API, added a polished interface, charged $19 per month, and life was good. Six weeks later, Google launched Gemini 2.5 with native support for processing 1,500-page documents. For free. The solopreneur’s MRR did not decline gradually — it collapsed like a building in an earthquake.

It was not an isolated case. It was geology.

The tectonic plates of AI
#

Think of OpenAI, Google, Anthropic, and Meta as tectonic plates. They are continental masses of capital, talent, data, and compute capacity that move slowly — but when they move, they reconfigure the entire landscape.

Between those plates there are cracks. Temporary gaps where the ground appears stable. Places where an agile entrepreneur can set up shop, plant a flag, and declare that they have found a market. And for a while, they are right. The crack is real, the space exists, customers pay.

The problem is that cracks between tectonic plates are not solid ground. They are zones of friction. And when the plates move — because they always move — the crack closes.

With the entrepreneur inside.

The cemetery of features that thought they were companies
#

The list is long and keeps growing:

Code Interpreter killed code-execution startups. Dozens of companies built products to execute code inside LLM conversations. Then OpenAI added it as a native ChatGPT feature. There was no transition. There was extinction.

GPT-4 Vision wiped out image-description startups. Companies that charged for analyzing images with AI vanished the day vision became a standard capability of the base model.

“Chat with your documents” tools became a commodity. What in 2023 was a differentiated product is, in 2025, a free feature inside Google Drive, Microsoft 365, and Notion. All at once.

Image-generation wrappers that added improved prompts on top of DALL-E or Midjourney watched each new model release make their “enhancement” layer unnecessary.

The pattern is always the same: an entrepreneur identifies a limitation in the foundation model, builds a solution around that limitation, and celebrates having found product-market fit. But what they found was not a market — it was a temporary bug in a giant’s offering.

The solopreneur trap
#

It has never been easier to build an AI product. A single developer, with Claude Code or Cursor, can have a functional MVP in a weekend. And that feels like a superpower.

But that ease is precisely the trap.

If you can build your product in a weekend, what makes you think OpenAI cannot add that functionality in its next release? You are not competing with other solopreneurs — you are competing with organizations that have thousands of engineers, billions in funding, and access to the foundation model your “company” is built on top of.

It is like opening a souvenir shop inside a dormant volcano. Rent is cheap, the view is spectacular, and tourist traffic is incredible. Until the volcano stops being dormant.

The AI solopreneur is not democratizing technology. They are occupying an unstable market space — installed in a crack that the tectonic movement of hyperscalers can close at any time. And the cruelest part is that the better they do, the more visible they become to the plates that will swallow them. Success is the signal that the crack is worth closing.

The difference between a feature and a company
#

Not everything built on top of an LLM is a doomed feature. Some AI startups really are companies. The difference lies in what they have beyond the model:

Proprietary data the model does not have. If your competitive advantage is that you train or fine-tune on data no one else owns — specific industrial data, regulatory history, specialized corpora — the plates can move and your ground still holds. You are not in the crack; you are on your own island.

Network effects that strengthen with use. Every new user makes the product better for everyone else. A marketplace, a community, a collaboration system. A foundation model cannot replicate that by adding a feature.

Deep integration into existing workflows. If your product is embedded in a company’s daily process — connected to its ERP, CRM, or legacy systems — the switching cost is real. It is not an app that gets uninstalled when the base model improves.

Domain expertise the model cannot replicate. Some sectors have regulation, process specificity, or contextual complexity that require knowledge far beyond what a prompt can solve. Healthcare, legal, regulated finance, industrial manufacturing. There, the model is an ingredient, not the dish.

If your startup does not have at least one of these four elements, you do not have a company. You have a feature with temporary revenue.

The crack test
#

Before celebrating your next MRR milestone, ask yourself these questions:

1. Could they add it in a release? If the core functionality of your product can be replicated by OpenAI, Google, or Anthropic adding a feature to their next version, you are in the crack. It does not matter that they have not done it yet. What matters is that they can.

2. Does your advantage survive an improvement in the base model? Every time a more capable model is released, does your product become more valuable or less necessary? If the answer is “less necessary,” you are betting against gravity.

3. Can you explain your moat without mentioning the model? If your pitch begins with “We use GPT-4 to…”, you have already lost. Your moat must exist independently of the model you use underneath. If you switch from OpenAI to Anthropic to Gemini and your value proposition disappears, it was never your value proposition.

4. Are you selling a capability or an outcome? Capabilities commoditize. Always. “Summarize documents” is a capability. “Reduce your bank’s regulatory compliance cycle from six weeks to three days” is an outcome. Outcomes require context, integration, and expertise that are much harder to commoditize.

What to do if you are in the crack
#

Do not panic, but move quickly. The crack may not close tomorrow — but it will close.

First: accept reality. Your wrapper is not a moat. Your beautiful UI is not a moat. Your prompt engineering is not a moat. None of that protects you from a competitor that controls the model your product is built on.

Second: find your own ground. Can you generate proprietary data? Can you create network effects? Can you integrate so deeply into your customer’s workflow that removing you requires a migration project? If the answer to all of those is no, you have a cash extraction business, not a company. Extract the cash, but do not lie to yourself about what you are building.

Third: build the business that remains when you remove the model. If you take GPT-4 out of your product and nothing is left, you do not have a product. If you take GPT-4 out and what remains is a workflow, a database, a community, an integration — then you have something that may survive the next tremor.

The earthquake ahead
#

The hyperscalers are not going to stop moving. On the contrary — they are accelerating. Every release is more capable, every platform absorbs more functionality, every model makes another abstraction layer unnecessary.

I am not saying it is impossible to build a great business on top of AI. It is possible, and it is happening. But the ones that survive are not the ones that found a crack and opened a shop. They are the ones that built on their own rock.

The question is not whether the plates will move. The question is whether, when they do, you will be standing on solid ground or become another solopreneur celebrating an MRR with an expiration date.

Look down. Do you see the crack?

Carles Abarca is VP of Digital Transformation at Tec de Monterrey and former CTO of Banco Sabadell. He writes about the strategic implications of AI at carlesabarca.com.

Startups on Carles Abarca

Your AI Startup Is Not a Company: It Is a Feature of OpenAI or Anthropic

The tectonic plates of AI #

The cemetery of features that thought they were companies #

The solopreneur trap #

The difference between a feature and a company #

The crack test #

What to do if you are in the crack #