AI Startups: The Best Model Is Not Enough

Imagine a founder who spent two years building a model that beats its rivals on a standard test, and then a larger competitor releases a close alternative at a lower price. Were the two years wasted? Not necessarily, but the advantage he took for a fortress may not be one. That question separates a company that dazzles and then fades from one that lasts.

A technology nearing commodity status

A summary of Stanford's AI Index 2025 report, citing the Chatbot Arena platform, says the gap between the best closed model and the best open-weight model narrowed from about 8 percentage points in January 2024 to about 2% in February 2025. Elsewhere the report noted that the cost of querying a GPT-3.5-level model fell from 20 dollars per million tokens in November 2022 to 0.07 dollars in October 2024.

A caution is due: Chatbot Arena relies on user preferences, so it is one measure and does not mean the two models are equal in every task. But the direction is clear: what was superiority becomes the baseline.

What do we know about survival?

Less than is claimed. We found no documented figures on the failure of AI startups specifically, so we settle for a broader picture:

• US Bureau of Labor Statistics data show that 77.9% of private establishments opened in the year ending March 2024 were still operating in March 2025, that 51.4% of those opened in the year ending March 2020 remained after five years, and 34.7% of those opened in the year ending March 2015 remained after ten. These figures cover all private sectors, not AI alone.
• In a 2014 analysis by CB Insights of 101 essays written by founders about their failure, 42% said the first cause was the absence of a market need. It is a limited, old sample, but it meets the logic of the question this article asks.

What is hard to copy?

What follows is an analytical framework from the editorial team, not a measured result:

Data the product itself generates. If every use improves the product and attracts more users, the gap with the competitor widens. Data that is bought, or available to everyone, protects no one.

Distribution. Whoever owns the relationship with the customer can add the smart feature to an existing product. A startup can lose its advantage when a large player integrates it into its platform, however good it is.

Depth of the problem. Projects that begin with "what can the model do?" risk becoming a solution looking for a problem. Those that begin with "what real pain deserves a solution?" are more likely to be paid for by the customer.

Timing. A sound idea before the market matures or costs fall may die like a wrong one, and may be harvested by whoever comes after. This is learned by experience, not by calculation.

And the Arab context?

The Arabic language and its dialects, and local sectors not yet served, may offer a real opportunity to those who know the field from within, since the newcomer finds their details hard to grasp. But it is a hypothesis, not a guarantee, and we addressed the position of Arabic in this field in the article "Arabic Artificial Intelligence: Do We Make or Consume? Sovereignty and Arabic Language Models".

A founder's questions before polishing the model

1. What will protect me when what I own becomes available to everyone?
2. Am I designing the product to learn from every use?
3. How will my product reach the customer?
4. Is my problem painful enough that its owner will pay for the solution?
5. And if I fail, have I recorded and analysed what I learned?

Finally, this is knowledge, not investment advice. Whoever wishes to put money into a startup should consult a licensed specialist and observe the Sharia rules. Trust in God does not excuse us from counting the means; whoever takes them and listens well to the market is closer to success.