The Best AI Use: Invalidating Your Idea

I wanted to write an article about humanoid robots. Robots are getting impressive. They can walk, run, jump, do acrobatics, move everything around, dance, and sometimes look like they are one software update away from replacing half of humanity. But I thought there was one underrated blocker: hands.

Human hands are ridiculous pieces of biological engineering. We do not just grab things. We feel them. Pressure, texture, slip, vibration, temperature, pain, softness, sharp edges, tiny surface changes. Blind people can read Braille with their fingertips. Our fingertips have one of the highest densities of sensory receptors on the body.

So my original thesis was: Humanoid robots will not become universal workers soon, because I thought we could not replicate the sensor density of human hands - 2,500/cm². You cannot take a robot like KUKA, built for manufacturing, change the software, and ask it to make an omelette. It will break the eggs for sure, but not in the pan.

Then I did what I often do now. I asked AI to argue with me. This is one of the most useful ways to use AI. Not to agree with you and write for you. But to attack your idea before you embarrass yourself with it in public.

And in this case, it worked very well. AI pulled examples of modern tactile sensors.

Meta and GelSight’s Digit 360 artificial fingertip has around 8.3 million taxels, responds to omnidirectional touch, captures multimodal signals, and uses on-device AI to process the data in real time.  

Then there is F-TAC Hand, published in Nature Machine Intelligence, which reports 0.1 mm spatial resolution across 70% of the robotic hand surface.

Wow. Yes, I was wrong. Modern technology is already way beyond density of sensors in human hands. What is missing is packaging the whole thing, including microcontrollers and servomotors, into something cheap, durable, fast, waterproof, and power-efficient. We've done it with smartphones, and then with flexible touchscreens. Human-like robot hands are next.

The real lesson was this: AI helped me invalidate my own idea before I invested too much ego into it. AI is very useful as an argument opponent. A good idea should survive being attacked. A weak idea should die early, preferably in private, before you share it with people who may laugh at you.

Here are five ways to use AI for validating ideas:

1. Ask AI to attack the idea

Use a reasoning model. Tell it to disprove the idea, not improve it. Ask for hidden assumptions, weak logic, missing evidence, counterexamples, and what would make the idea false. This works for science, legal arguments, essays, books, strategy, or technical design. OpenAI recommends reasoning models for complex multi-step tasks (source).

2. Check current facts, not model memory

For any idea depending on facts, law, science, technology, history, or market reality, use web search or deep research with citations. Ask AI to separate facts from interpretation. OpenAI’s web search and deep research tools are designed for up-to-date, sourced research, including legal and scientific research (source).  

3. Ask AI to find stronger opposing arguments

Do not only ask “is this right?” Ask: “What would the smartest critic say?” For legal arguments, ask for the other side’s best case. For science, ask for alternative explanations. For a book, ask why readers may reject the premise. Claude’s docs recommend clear role/task framing and structured prompts (source).

4. Test the structure, not just the conclusion

Ask AI to turn the idea into claims, assumptions, evidence, and gaps. Then test each link. This is useful for essays, theories, legal reasoning, research proposals, architecture decisions, and books. Use XML sections or clear headings for context, claim, evidence, objections, and verdict. Anthropic recommends structured prompting and XML-style separation (source).

5. Use a scoring rubric

Make AI judge the idea against fixed criteria: clarity, evidence, falsifiability, novelty, internal consistency, external contradiction, practical consequences, and weakest point. Structured outputs are especially useful if comparing many ideas, because they force consistent fields instead of free-form vibes. (source).