Justin Tan

Your AI Model Is Not Your Friend Last week I wrote about why the design process isn't dead, it just needs to evolve. Here's a piece of what that looks like. A common criticism of the traditional design process is that it pushes designers to overweight desirability and underweight viability and feasibility. AI gives us an easy way to correct for this early: take a problem statement or job to be done and run it past your favorite LLM for a viability and feasibility gut check before anyone starts solutioning. With a good model, this works. You get pushback on potential technical complexity, market timing, data scarcity, unit economics. Real friction in the right places. But on a lesser model (like whatever is on the free tier of ChatGPT), you get a cheerleader. "That's a great idea! Super viable!" Not helpful. The problem is sycophancy, and it's worth calling it what it is: a form of bias. We talk about AI bias in terms of data and representation, but sycophancy is bias too. A systematic distortion toward positivity. The model isn't giving you an honest assessment, it's giving you one skewed towards what it was trained that you want to hear. And in a context where someone is learning to evaluate ideas (I'm testing this in a class I teach), biased feedback is worse than no feedback. Here's a few ways to prompt past it: Ask for the negative. Instead of "evaluate the viability," try "tell me everything wrong with this" or "why would this be hard to build?" Give it a mean persona. "You are a hyper-critical CTO" or "a risk-averse investor who's been burned in the past." Ask what would need to be true. "What would need to be true for this to succeed?" Forces the model to surface assumptions instead of giving a thumbs up. Use a pre-mortem. "This project launched and failed. What went wrong?" Works on AI for the same reason it works on humans. Request a structured teardown. Have the model rate feasibility, viability, and desirability separately on a scale. It's harder to be vaguely positive when you have to commit to a number. Maybe someday, none of this will be necessary. The best models are already pushing back on their own (Opus 4.6 does a pretty good job of this without all the prompt gymnastics), but on free tiers, you have to prompt for criticism the same way you'd seek out the most skeptical person in a design review. Sycophancy is dangerous because it feeds biases that already exist anywhere people evaluate ideas: confirmation bias, groupthink, the HiPPO effect. AI is just giving us a new, very agreeable source of false validation. This is the kind of thing I'm referring to when I talk about judgment over process. No step-by-step framework is going to tell you when the model is blowing sunshine up your... assumptions. You have to learn to feel it.

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