Chi Kang Pai
Why Interfaces Matter — and Why “Just a GPT Wrapper” Isn’t So Simple
February 18, 2025 · Tech, Thoughts
Why Interfaces Matter — and Why “Just a GPT Wrapper” Isn’t So Simple
Lately, I’ve heard people dismiss certain startups as “just GPT wrappers,” doomed to vanish when OpenAI adds a competing feature. That’s a narrow view. It ignores what makes a company valuable in the first place: giving people something they need, in a way they can actually use.
In software, an interface can be the layer between user and backend(or in OOD, a contract that defines a set of methods that a class must implement, without specifying how these methods should be implemented…). In business, it’s the layer that connects demand with supply. A lot of so-called “wrappers” around AI aren’t mere fronts; they’re portals that translate raw tech into something users actually want.
The Role of Interfaces
At its core, business is about linking demand to supply. Each big tech shift (or paradigm shift) changes how businesses connect with customers. After the internet took off, physical stores gave way to online platforms. Amazon leveraged this new “interface” to dominate retail: a site where shoppers could find nearly anything. Likewise, UberEats and DoorDash built an interface between hungry people and restaurants looking for delivery revenue.
These platforms aren’t just tech front-ends. They’re solutions to real problems—logistics, trust, convenience. If you control the interface, you end up controlling the customer relationship. And that’s where the value lies.
The Power of Focus
An interface that does one thing really well can own its niche. TSMC stuck to manufacturing semiconductors—nothing else. By focusing all their resources on making chips, they became the go-to supplier for anyone designing them. With that comes expertise, market share, and freedom from worrying about direct competition with their customers.
Amazon started much the same way. First, it was just books. They nailed online book sales, and only after that success did they expand. Focus lets you perfect your interface, dominate that space, and then branch out if you want to.
Interfaces in the Age of AI
We’re now in the AI era. Models like ChatGPT are powerful, but most users don’t talk to the raw model. They talk to a specialized tool—a “wrapper”—that helps them code, write, or learn. Critics say these wrappers have no real value because they depend on someone else’s technology. But that misses the point. People don’t care if you invented the engine; they care if you provide the best ride.
That’s why user-centric AI interfaces matter. They make complex tech accessible. As AI spreads into everything—writing, research, coding, learning—whoever builds the smoothest on-ramp for each domain will win.
GPT Wrappers vs. Big Tech
Big companies like Microsoft and Google have huge resources, but smaller startups can still stand out by focusing on a niche. They collect real-world feedback, iterate fast, and improve. That’s how a good interface matures. Right now, I see two crucial interface types: those that jumped on AI early and built a great product around it, and those whose existing product naturally fits into AI’s growth.
Type One: Early AI Adopters (Cursor)
Take Cursor. ChatGPT can help with code, but Cursor integrates with entire codebases—no need to copy and paste. It’s built specifically for developers, which makes it more convenient. That laser focus keeps users coming back.
Type Two: Deep Product Fit (Heptabase)
I use Heptabase for learning. Right now, I pair it with ChatGPT to gather information quickly. Soon, I may have conversations with an AI that’s smarter than me—learning directly from it. But Heptabase didn’t just slap on AI features. Their goal is clear: help people understand complex topics through a great user experience. They’ve been rolling out features carefully, including collaboration tools, and plan to add AI integration in 2025. That steady approach—staying true to their core mission—makes them a prime example of a great interface.
Even now, AI plus Heptabase feels powerful. I’m excited about using advanced models like Deep Research, DeepSeek, or o3/Grok3 in a more cost-effective way as they become available.
So far, I’ve used AI mostly for coding and learning. And it’s already made me love both more. That’s what a technological shift can do.
Conclusion
Interfaces matter. They decide how easily people tap into real value—whether they’re buying a book or using a powerful AI. When the internet arrived, Amazon nailed online shopping and became a giant. Now that AI is here, the companies that smooth out complexity and give users exactly what they need will shape the future.
Calling these products “just GPT wrappers” misses the point. It’s not who owns the engine—it’s who builds the best dashboard. As self-learning grows more vital, the tools that make AI accessible will matter even more. If you want to thrive, build an interface that truly harnesses AI. Because in the end, the ones who best connect technology and user needs will lead.