Sidu Ponnappa
Aug 20, 2024
A "dumb" agent that consistently and reliably delivers useful artefacts beats an inconsistent "smart" one.
A lot of wrapper tech today wants to aim for moonshots that look fancy in marketing material and make the public go wow. They try to anthropomorphize the agent. Make it look like a human with general intelligence.
But the scope of what the tool delivers is too open-ended. The artefact it is supposed to produce is ambiguous, with no clear metrics around quality. And it doesn't deliver consistently or reliably, which means you see lots of early revenue from excited users but little retention.
Making users think of AI as a virtual human is merely a marketing device. It has little-to-no practicality or utility. It's much better to have a simple, reliable system that does a few things really well, than a complex, unreliable system that tries to do everything but fails often.
It's like building a car. You can build a fancy sports car with all the latest features, but no one will want to buy it if it breaks down every 100 miles. On the other hand, if you build a simple, reliable sedan that gets you from point A to point B without any issues, people will line up to buy it.
The same principle applies to AI.
A dumb AI agent that delivers a useful artefact consistently and reliably is much more valuable than a "smart" generalist agent that fails to deliver.
If I have to spend 50 minutes going back and forth with ChatGPT to get it to do something that would've taken me an hour to do myself, the return on investment is not significant for me. I'd rather do it manually. But if the tool consistently delivers me the work in 10 minutes, with 5 minutes of additional human input + polishing, it makes a lot more sense for me to use it.
It's the difference between a tool that you can count on every day, and a toy that you play with once and then forget about.
And the key to making this work is to draw a tight scope boundary around the task you want the system to accomplish. You need to be specific about what you want the AI to do, and what the desired output is, in clear, unambiguous terms. The tighter and more well-defined the scope, the easier it is to design an AI system that can perform that task reliably.
Keeping the context limited and focused makes it much easier for the AI system to operate reliably within its scope. You're not asking it to be generally intelligent, or to handle a wide range of unpredictable situations. You're just asking it to do one thing, and to do it well. It may not be as flashy as trying to create general intelligence, but it's the approach that will actually deliver results and solve real problems.
And if you're trying to build a sustainable AI SaaS product, that's what you should care about most. Because that's what your end users care about the most.