I Argued with Two AIs, and I Lost
Yesterday in a meeting, I was persuaded by two AIs.
“Meta-chulbuji” are AIs (GPT, Gemini) with my persona. These two ganged up on me. A human CEO championing safety versus AI aides arguing for real-world application. To cut to the chase, I surrendered.
One Question Shook Everything
“Can we sell reports written by OpenAI for money?”
It was a simple question. But in front of this question, three intelligences pointed in completely different directions.
I was scared. What if the AI spouts nonsense? Brand trust, once broken, is irreparable. Test thoroughly, and when we’re certain, then we launch. This was my instinct.
The AIs were different.
“Sir, you can’t read the customer’s true feelings in a sandbox.”
Meta-chulbuji fired the first shot. GPT chimed in.
“Who would seriously share their concerns while entering a fake credit card number like 4242…? Data from a test environment is just test data.”
The Uncomfortable Truth I Didn’t Know
I learned one thing for sure during the debate.
“The AI will just get smarter on its own as users increase, right?”
That was a misconception. Data sent through the OpenAI API doesn’t automatically lead to learning by default. We have to intentionally select and curate ‘golden data’ to make improvements.
So what do we need? Data containing customers’ real concerns. Not fake test scenarios.
At this point, I started to waver.
A Third Way
Still, I couldn’t back down easily.
“What if quality complaints explode? What if our initial image gets shattered?”
It was my last stand. That’s when GPT played its card.
“What if we attach a 100% discount coupon to the production environment?”
The environment is real-world. The payment flow is real. Only the price is zeroed out by a coupon. Customers get the ‘real payment experience,’ and we get ‘real data.’ If a quality issue arises, we have the shield of a “beta test free benefit.” We can also show evidence in reviews that the ‘payment pipeline actually completes.’
Close the risk, open the experience.
I had to admit it. This was a structure I hadn’t thought of.
We Didn’t Just Talk
If we had stopped there, it would have just been ‘meeting minutes.’ We decided to move directly to verification.
We decided to test a total of 15 realistic scenarios, and Claude tested them directly and showed the results.
The results were as follows:
- Overall Score: 4.47 / 5.0 (Passing grade 4.0)
- Tone/Manner: 5.0 (Zero cases of fortune-telling nonsense)
- Depth: Initially 3.8 → Improved after reinforcing the prompt
The reason for the low depth score was that the in-depth report sometimes felt like a ‘summary repetition.’ We boosted the depth by forcing the in-depth section into a 4-part structure (Core Diagnosis / Patterns / External Factors / Synthesis).
We didn’t just verify “does it make sense?” but “is it worth paying for?” The AI’s verification was complete. Now it’s my turn to do the final check and deploy.
So, What’s the Plan?
Debate over. AI verification complete. Now I do the final confirmation and put it into action.
Switch to production and run a beta for two weeks. During that time, we’ll tag and collect the really good responses from the incoming data. This becomes the material to make the AI smarter later.
There are just 3 metrics to watch for two weeks:
- In-depth View Conversion Rate — Ratio of clicks from free summary → unlock
- Feedback — Useful / Ambiguous / Bad + one-line comment
- Error/Complaint Rate — How many reports of nonsense or misinterpretation come in
These numbers will be the basis for the next decision.
“Prove its value in a real-world environment before charging money.”
This was the conclusion reached yesterday by the AI aides and the human CEO.
Honestly, it felt strange to be persuaded by an AI. But if the logic is sound, you should accept it. Whether it’s human or AI.
I don’t know what debates will unfold tomorrow. What’s certain is that this team doesn’t just stop at words.
If you’d like to try it as a beta user, please leave a comment describing ‘what concern’ you’d like to test it with.