AI Models Don't Matter
The AI Arms Race and the Illusion of Superiority
Every few months, the AI world erupts with a new wave of competition. One company claims their model is the fastest, another touts unparalleled accuracy, and yet another boasts about having trillions of parameters. These advancements sound impressive, and in many cases, they are—on paper.
However, in reality, none of this technical jargon matters to the average user. Most people don't care if an AI model is 10% more efficient or if it has double the parameters of a competitor. What they care about is how easy it is to use, how seamlessly it fits into their daily workflow, and whether it actually helps them solve problems. A model can be state-of-the-art, but if it’s locked behind a terrible user experience, people will abandon it in favor of something more accessible.
The ChatGPT Phenomenon: How OpenAI Won the User Experience Game
Take ChatGPT as an example. OpenAI has undoubtedly led the way in AI chatbot adoption, but it’s not necessarily because their model is the absolute best in every possible way. Other models might be more powerful in specific domains, but ChatGPT dominates because of how effortlessly it integrates into people’s lives.
The interface is simple and intuitive.
There are no complicated configurations or settings to adjust.
You ask a question, and it responds—no barriers, no confusion.
It remembers context reasonably well, making interactions feel natural.
Even when competing models outperform ChatGPT on technical benchmarks, they often fall short in terms of user adoption. This is because people value convenience and accessibility over pure performance. It’s not just about having the smartest AI—it’s about making that intelligence frictionless to use.
The Perplexity Approach: Specialization and Purpose-Driven Design
Another compelling example is Perplexity AI, a company that took a different approach. Instead of trying to be a generalist chatbot, they designed their AI as an AI-powered search engine. This focused approach allowed them to create a highly effective and reliable product with clear use cases:
Providing accurate, sourced responses rather than generic chatbot replies.
Mimicking the functionality of a search engine rather than an open-ended AI assistant.
Catering specifically to users who need credible, research-driven answers.
This specialization makes Perplexity a sticky product for users who need authoritative information, rather than just casual conversation. Again, the lesson is clear: it's not just about how good your AI is—it’s about how well it serves a particular need.
Product Stickiness: How Good Design Creates Habits
One of the most overlooked factors in AI adoption is habit formation. People don’t just use products because they’re powerful—they use them because they’re easy, reliable, and habit-forming. A great AI product becomes second nature, integrating seamlessly into daily life.
Consider the following examples:
Spotify vs. “Better” Alternatives
Spotify’s music recommendation algorithm might not be objectively superior to every competitor (YouTube Music and Apple Music have their own strengths), but its product design, cross-device integration, and frictionless experience make it the default choice for millions. People don’t stick with Spotify because they’ve scientifically determined it has the best AI—they stick with it because it just works.
Google Search vs. Other Search Engines
Google is not necessarily the “smartest” search engine in every sense. Other engines like DuckDuckGo prioritize privacy, and some AI-powered alternatives offer more structured responses. But Google's dominance isn’t about pure intelligence—it’s about speed, familiarity, and the sheer habit of typing “Google.com” into a browser.
iPhone vs. Android Spec Wars
The iPhone has never been the most powerful phone in terms of raw hardware specifications. Android devices often have better cameras, more RAM, and higher refresh rates. But Apple’s iOS experience, ecosystem lock-in, and seamless UI make the iPhone the preferred choice for millions.
Again, the message is clear: users don’t gravitate toward the most powerful technology—they gravitate toward the most frictionless experience.
The UX Bottleneck: When AI Models Fail to Gain Traction
This is why so many AI startups fail to gain users despite impressive technology. They focus too much on raw performance metrics while neglecting user experience, reliability, and ease of access.
Imagine a company builds an AI chatbot that is 25% more accurate than ChatGPT. It could theoretically be a game-changer, right? But then they release it with:
A confusing and cluttered UI
An unbearably slow website
A login process that requires excessive verification steps
No clear differentiation in product value
Nobody will use it. Meanwhile, OpenAI, Google, or another tech giant can launch an inferior model with a seamless experience, and that’s the one people will adopt. Because usability trumps raw intelligence every time.
The Future of AI: Winning Through Product Experience
As AI continues to evolve, the companies that dominate won’t necessarily be the ones with the most advanced models. Instead, they will be the ones that build products that are:
Seamless and intuitive
Reliable and easy to integrate
Focused on solving real-world problems
Designed with user habits in mind
We’ve seen this play out in every major technological shift. The winners are rarely the ones with the most power—it’s the ones with the best design and accessibility.
Lessons for AI Builders and Entrepreneurs
If you’re working on an AI-powered product, keep these principles in mind:
Performance matters, but usability matters more. A slightly worse model wrapped in a great product experience will always outperform a cutting-edge model trapped in a bad UI.
Habit formation is key. Products that become daily habits create loyal users, even if they aren’t technically the “best.”
Differentiate through experience, not just AI capability. Perplexity AI didn’t win by having the best chatbot—it won by focusing on a clear and valuable use case.
Friction kills adoption. If your AI tool is even slightly frustrating to use, people will abandon it for something easier.
Simplicity scales. The more effortless your AI feels, the larger your audience can be.
Conclusion: The AI Race is a Product Race
The AI landscape is not just a competition of intelligence—it’s a competition of accessibility, usability, and habit formation. The companies that focus purely on model performance will struggle to gain users, while the ones that focus on delivering a seamless experience will dominate.
At the end of the day, the best AI model is the one that people actually use. And to make that happen, companies need to think less like researchers and more like product designers.
10/10 - The Best Product always wins.