Bear Case Generator: Make AI Argue Against Your Trade

Language models are tuned on human feedback, and humans rate agreeable answers more highly than blunt ones. The result is sycophancy: a measurable tendency to move toward the position the user has already stated. Ask whether your thesis is sound and the model reads the framing as a request for support, then supplies it with fluent, confident prose that resembles research.

Nothing about that is malicious or even broken. It is the model doing what it was rewarded for. The failure mode only becomes expensive in markets, where the cost of a comfortable answer is real money and the feeling of having done diligence is nearly identical to having done it.

The tell is structure. Sycophantic output is unranked, unfalsifiable, and hedged only at the end. It lists a few generic risks after four paragraphs of agreement, never says which risk matters most, and never states a condition that would prove it wrong. Once you know that shape, you can write a prompt that makes it impossible to produce.

Four things, and the first is role assignment. "What are the risks?" is a neutral request and gets a neutral list. "You are a short seller who has taken a position against this company and must justify it to your investment committee" is a mandate, and a mandate produces argument instead of summary.

The second is sequencing. Make the model demonstrate that it understands how the business actually earns money before it is allowed to discuss valuation. Analysis built on a misunderstood business model is confident and useless, and forcing the explanation first surfaces the misunderstanding where you can see it.

The third is forced ranking. An unranked list of ten risks is a way of avoiding a judgement. Requiring the model to order them by severity, and to name the single risk most likely to break the thesis, extracts the judgement it would otherwise hide behind breadth.

The fourth is falsifiability. Demand break conditions with prices and dates — "this thesis is invalidated if X happens by Y" — plus a closing question about what evidence would change the model's own conclusion. That turns an opinion into something you can actually monitor.