Sidu Ponnappa
Mar 21, 2024
I want to debunk two myths that have been floating around the AI world for some time —
Myth 1: "Building a useful ChatGPT wrapper is trivial"
Myth 2: "The only valuable AI innovation will come through training your own models"
I’m sure you must’ve heard people using the term “ChatGPT wrapper” to dunk on a startup.
“Oh it’s just a ChatGPT wrapper” — as if creating a useful wrapper around ChatGPT is something anyone can do.
I'm sorry, but that idea is just plain wrong. If you think slapping together a basic interface and calling an API is all it takes, then you're clueless about what's really involved.
Because remember that it's not about building a wrapper around an AI model; AI is only the means to an end. The end is delivering a business outcome and generating RoI on the AI tool. That's the only way you're going to create and capture value.
And this is not a weekend project. You will need a deep understanding of the specific use case, the ability to design something that's easy to use, and most importantly, the org context needed to integrate the agent seamlessly into existing workflows.
Anyone dismissing the complexity of this process clearly outs themselves as someone who doesn't know what they're talking about.
The second myth I keep hearing is that you absolutely must train your own AI models to build anything worthwhile.
Again, I've got to call BS on that.
Don't get me wrong, having the ability to train your own models is powerful. But let's be real – it's also insanely expensive and time-consuming. Most businesses just don't have the resources to pour into something like that.
So there's no real market for AI as such. You're likely not going to compete with OpenAI and the firepower they have. But there's a hugeee wrapper market that will pay for AI tools that create business outcomes. Lots of potential for automation here.
Find an industry
Focus on a specific department
Target a specific workflow
Automate tasks in that workflow
You'll need to do plenty of market research — even before you get to building something. Specific industry knowledge is going to be invaluable.
The future of AI isn't about who can train the biggest, baddest model. It's about who can take these models, identify excellent UX opportunities around them, and DELIVER WORK.
Don't miss the forest for the trees.