For decades, competition in AI has been all about model development. There have been contests in benchmark improvement, reasoning abilities, context window size, and output quality. The logic was straightforward: the better the model, the more people will be attracted to it.
This logic is beginning to shift.
Nowadays, one of the most promising AI products is not the application itself but its implementation as a feature of a search engine, productivity suite, design program, IDE, customer support platform, or any other business software. In such cases, there is no need to convince users to try out a new application; rather, AI is implemented right where they operate.
From Applications to Features
If we implement AI in products people are already using, adoption becomes significantly easier.
Consider where AI has been implemented:
Search engines
IDEs and code editors
Productivity software
Creative software
Customer support platforms
Marketing and advertising solutions
In most cases, users are not trying to find an application with AI, they are just using a well-known product with AI implemented in it.
Why Distribution Matters
With the continuous advancement of AI models, differences in the performance of competing models may diminish. In this case, distribution will play an important role.
Companies with established ecosystems have such advantages as:
Bigger user base
Identities and accounts
Workflows
Payment systems
Recommendation engines
Developer ecosystems
These aspects create less friction and make adoption of AI models easier.
What It Means for Open Source
The open-source development of AI keeps pushing forward innovations based on transparency, flexibility, and community cooperation. However, having great models is not enough for adoption.
One should also focus on:
Better developer experience
Deployment
Good documentation
Integration
Communities
Onboarding
Distribution is not just marketing but also software discoverability, deployment, and usability.
Looking Ahead
Historical examples show that the best technology is not always the one that wins and becomes dominant. Ecosystem, availability, integrations, and user adoption can be equally important factors.
Perhaps AI has reached the same stage, where success will require more than creating intelligent models – it will require making them available to people at the places where they already work.
This creates a new kind of question for developers, entrepreneurs, and product teams. No longer "which model works the best?", but rather "which AI can fit seamlessly into users' workflows?"
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Which one will be the competitive advantage in the coming years – better AI models or better distribution?
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