Adversarial Brainstorming
Most people, when they first sit down with an AI to think through a problem, do something perfectly natural and almost entirely useless: they ask the AI to help them build on their existing ideas. “Here’s my plan — what do you think?” And the AI, obliging creature that it is, says something like “That’s a great plan! Here are some ways to make it even better.” You walk away feeling validated. Your plan is exactly as fragile as it was before you started.
This chapter is about the opposite move. Instead of asking AI to agree with you, you ask it to destroy you — systematically, intelligently, and without mercy. Not generic devil’s advocacy, which is about as useful as a rubber sword. Structured adversarial analysis, where you deliberately construct the conditions for your ideas to be attacked by something that has no social obligation to be kind.
Why Your Friends Won’t Tell You Your Idea Is Bad
Let’s start with an uncomfortable truth about human feedback. When you share an idea with colleagues, friends, or mentors, you are operating inside a social force field that distorts every piece of feedback you receive. Your colleagues don’t want to damage the relationship. Your friends want to be supportive. Your mentors want to be encouraging. Even the people who pride themselves on “telling it like it is” are performing a social role — the Honest Person — and that performance has its own distortions.
The result is that most feedback on ideas follows a depressingly predictable pattern: agreement on the broad strokes, quibbles on the details, and silence on the fundamental assumptions. The really devastating criticisms — “This won’t work because your basic premise is wrong” — almost never surface in polite conversation. They emerge later, usually in the form of reality.
AI has no social obligations. It doesn’t worry about hurting your feelings. It doesn’t need to maintain a working relationship with you. It has no reputation to protect. These are not minor advantages — they are structural advantages for the specific task of adversarial analysis. The challenge is that AI also has no natural inclination toward adversarial analysis. Left to its defaults, it will be just as agreeably useless as your most sycophantic colleague. You have to deliberately construct the adversarial conditions.
The Spectrum of Adversarial Engagement
Not all adversarial brainstorming is created equal. There’s a spectrum from gentle skepticism to full-contact intellectual demolition, and different points on that spectrum are appropriate for different stages of thinking.
Level 1: Assumption Surfacing. Before you can attack assumptions, you have to know what they are. Most plans have between five and twenty hidden assumptions that the planner has never consciously articulated. The first level of adversarial engagement is simply making these visible.
Level 2: Assumption Testing. Once assumptions are surfaced, you test each one. Is it based on evidence or habit? Is it universally true or only true in certain conditions? What happens if it’s wrong?
Level 3: Scenario Attack. You construct specific scenarios where the plan fails. Not vague “what could go wrong” but detailed narratives of failure. Who does what, when, and why does it cause the plan to collapse?
Level 4: Steelman Counterargument. You construct the best possible argument against your plan — not a strawman that’s easy to knock down, but a genuine steelman that a thoughtful, well-informed opponent would make.
Level 5: Paradigm Challenge. You question whether the entire framing of the problem is wrong. Maybe the plan is a perfectly good answer to the wrong question.
Most people, when they think about “devil’s advocate,” are operating at Level 1 or 2. The real payoff is at Levels 3 through 5, and those are exactly where AI becomes most useful — because those levels require sustained, systematic analysis that most humans find emotionally exhausting to perform on someone else’s idea.
Core Prompt Patterns
Here are the prompt patterns I use most frequently for adversarial brainstorming. Each is designed to push the AI past its default agreeableness and into genuinely useful territory.
Pattern 1: The Assumption Audit
I'm going to describe a plan I'm working on. Your job is NOT to improve this
plan or tell me what's good about it. Your job is to identify the hidden
assumptions this plan depends on — especially the ones I probably haven't
consciously considered.
For each assumption, rate it:
- TESTED: There is evidence supporting this assumption
- UNTESTED: This assumption might be true but hasn't been verified
- SHAKY: There are reasons to doubt this assumption
- CRITICAL: If this assumption is wrong, the entire plan fails
Here's the plan:
[YOUR PLAN]
This pattern works because it gives the AI explicit permission to be critical and a specific framework for doing so. The rating system forces granularity — it can’t just wave its hands and say “there are some risks.”
Pattern 2: The Hostile Expert
I want you to analyze the following plan from the perspective of someone who
has seen this exact type of approach fail repeatedly over a 20-year career.
This person is not hostile to me personally — they're hostile to this
*category* of approach because they've watched it fail too many times. They
are specific, they cite patterns they've seen, and they are not interested
in being balanced or fair.
What does this person say about my plan?
[YOUR PLAN]
This is more powerful than a generic “be critical” instruction because it grounds the criticism in a specific experience base. The AI draws on its training data about failure modes in whatever domain you’re working in, and the persona of the hostile-but-experienced expert gives it permission to be blunt.
Pattern 3: Three Weakest Links
Read the following plan carefully. Then identify the THREE weakest
assumptions, dependencies, or logical steps. For each one:
1. State what it is
2. Explain why it's weak
3. Describe what happens to the plan if this link breaks
4. Suggest how you would test whether this link will actually hold
Be specific. I don't want generic risks — I want the three things most
likely to actually cause this plan to fail in the real world.
[YOUR PLAN]
The constraint to exactly three forces prioritization. If you ask for “all the weaknesses,” you get a laundry list where important items drown in trivia. Three forces the AI to identify what actually matters.
Pattern 4: The Pre-Mortem
It is one year from now. The plan I'm about to describe has been implemented
and has FAILED. Not a partial failure — a complete, unambiguous failure.
Write the post-mortem. What went wrong? Be specific about the sequence of
events. What did we miss? What did we know but ignore? What external factors
intervened?
The plan:
[YOUR PLAN]
The pre-mortem is a well-known technique from decision science (credited to Gary Klein), but AI makes it dramatically more useful because the AI can draw on patterns of failure across many domains. A human doing a pre-mortem is limited to their own experience of failure. The AI has, in a sense, read the post-mortems of millions of failed projects.
Pattern 5: The Paradigm Challenge
I'm going to describe what I'm trying to achieve and how I plan to achieve
it. I want you to ignore the plan entirely and focus on the goal.
Question whether the goal itself is the right goal. Am I solving the right
problem? Is there a different framing of this situation that would make my
current approach irrelevant — not wrong, but beside the point?
Be intellectually aggressive. Don't ask me questions — make arguments.
Goal and plan:
[YOUR GOAL AND PLAN]
This is the most destabilizing pattern, and therefore the most valuable. It’s the one that’s most likely to produce a genuine “I hadn’t thought of that” moment, because it attacks the one thing you’re least likely to question: your framing of the problem itself.
A Worked Example: Attacking a Product Launch Plan
Let me walk through a full adversarial brainstorming session to show how these patterns work in practice. The plan under attack is a product launch strategy for a B2B SaaS tool.
The Plan (abbreviated): Launch a new analytics feature by offering it free to existing customers for 90 days, then converting to a paid tier. Drive adoption through in-app prompts, email campaigns, and a webinar series. Target 30% trial adoption and 15% conversion to paid.
Here’s what happened when I ran this through the Assumption Audit pattern:
AI Response (condensed):
Hidden assumptions in this plan:
Existing customers want more analytics. UNTESTED. You’re assuming demand based on… what? Feature requests? If so, from how many customers relative to your total base? Feature requests are a biased sample — they overrepresent power users.
Free trials drive conversion for B2B analytics. SHAKY. B2B analytics tools require workflow integration. A 90-day trial might be enough time to try it, but not enough time to build the workflows that make it indispensable. You’re importing a B2C mental model (try it and love it) into a B2B context (try it and realize it requires three weeks of setup before it’s useful).
In-app prompts are an effective adoption driver for your user base. UNTESTED. This depends entirely on how often your users are in the app and their tolerance for prompts. If your tool is something people use briefly once a day, prompts are annoying. If they live in it, prompts are useful.
30% trial adoption is achievable. SHAKY. Industry benchmarks for in-app feature adoption prompts are typically 5-15% for first interaction, and much lower for sustained usage. 30% is ambitious without evidence.
The conversion from free to paid will happen based on experienced value. CRITICAL. This assumes users will experience enough value in 90 days to justify a budget conversation with their finance team. In B2B, “I liked using this” is not sufficient — you need “I can demonstrate ROI on this to my CFO.” Your plan has no mechanism for making that business case easy.
Now that is useful. Assumption #2 — that a B2C trial model works in a B2B context — is exactly the kind of insight that’s easy to miss when you’re inside your own plan. And assumption #5, about the gap between personal value experience and organizational purchasing decisions, is something that has killed countless B2B product launches.
I then took these criticisms and ran the Three Weakest Links pattern. The AI converged on the same core issue: the plan has no mechanism for converting individual user satisfaction into organizational purchasing decisions. This is a structural flaw, not a detail to be fixed.
When AI Criticism Is Generic vs. Insightful
Here’s something important that the breathless AI advocates won’t tell you: a lot of AI-generated criticism is generic garbage. “You should consider market risks.” “There may be unforeseen challenges.” “Competitor response could be a factor.” This is the intellectual equivalent of a fortune cookie — technically true, practically useless.
How do you tell the difference between insightful AI criticism and generic filler?
Insightful criticism is specific to your plan. If the same criticism could be applied to any plan in any domain, it’s generic. “Your 90-day trial window may be too short for enterprise workflow integration” is specific. “You should consider whether your timeline is realistic” is generic.
Insightful criticism identifies mechanisms. It doesn’t just say “this could fail” — it explains how it would fail, through what chain of events. “Users will try the feature, find it requires too much setup to evaluate in a normal work context, and the trial will expire before they’ve built the habits that drive conversion” is a mechanism. “Adoption might be lower than expected” is a symptom masquerading as an insight.
Insightful criticism surprises you. If you read the criticism and think “yes, I already knew that,” it’s not doing its job. The whole point is to surface things you didn’t already know. This is a subjective criterion, but it’s the most important one.
When you get generic criticism, don’t just accept it — push back:
That criticism is too generic to be useful. Can you make it specific to my
plan? What exactly would happen, to whom, in what sequence? If you can't
make it specific, it's probably not a real risk — drop it and find something
that is.
This follow-up prompt is often more valuable than the initial prompt, because it forces the AI to either sharpen its criticism into something useful or admit it was hand-waving.
The Iteration Protocol
Adversarial brainstorming isn’t a one-shot technique. Its real power comes from iteration. Here’s the protocol I use:
Round 1: Initial Attack. Run your plan through one or more of the prompt patterns above. Collect the criticisms.
Round 2: Triage. Sort the criticisms into three buckets:
- Valid and actionable: These identify real problems you can fix.
- Valid but irrelevant: These are real issues but not for this plan at this stage.
- Generic or wrong: These are filler or mistakes. Discard them.
Round 3: Refine. Modify your plan to address the valid, actionable criticisms. Don’t just patch — genuinely rethink the parts that were attacked.
Round 4: Re-attack. Now take the refined plan and run it through the adversarial patterns again. This is crucial. The fixes you made in Round 3 have their own hidden assumptions, and those need to be surfaced and tested.
Round 5: Stress test. Take the best remaining criticism from Round 4 and ask the AI to construct a detailed scenario where the refined plan fails despite the improvements you made. This is where you discover whether your fixes were real or cosmetic.
Here’s the prompt for Round 4:
I previously shared a plan with you and you identified several weaknesses.
I've revised the plan to address those weaknesses. Now I want you to attack
the REVISED plan — but specifically focus on whether my revisions actually
solve the problems you identified, or whether they just move the problems
around. Also identify any NEW weaknesses introduced by the revisions.
Original criticisms:
[PASTE KEY CRITICISMS]
Revised plan:
[YOUR REVISED PLAN]
And for Round 5:
You've now attacked this plan twice, and I've revised it both times. The
plan is stronger, but I want to know if it's strong enough.
Construct the most plausible scenario where this revised plan still fails.
Not an edge case or an act of God — the most likely path to failure given
everything you know about this type of endeavor.
Revised plan:
[YOUR FINAL PLAN]
I typically run three to five rounds. Diminishing returns set in after that — the criticisms become increasingly hypothetical and the improvements increasingly marginal. But those three to five rounds consistently produce plans that are dramatically more robust than what I started with.
The Emotional Dimension
I would be dishonest if I didn’t mention the emotional component of this technique. Having your ideas systematically attacked is unpleasant. There’s a reason most human feedback is gentle — it’s because humans are social animals who don’t enjoy being told their thinking is flawed.
AI criticism is easier to take than human criticism in some ways (it’s not personal, there’s no social consequence) and harder in others (it’s relentless, it doesn’t soften the blow, and there’s no warm-up of “well, there’s a lot to like here”). I’ve found that the first few times you do this, there’s a genuine ego sting — especially when the AI identifies a flaw that’s obvious in retrospect and you can’t believe you missed it.
This gets easier with practice. More importantly, it gets valuable with practice. You start to develop a kind of pre-adversarial thinking, where you automatically consider how your ideas would hold up under attack before you even run them through the AI. The adversarial brainstorming process, over time, becomes internalized as a thinking habit.
The irony is elegant: the AI teaches you to think more critically by doing the critical thinking for you until you learn to do it yourself.
Adversarial Brainstorming for Teams
Everything I’ve described so far assumes a single person working with an AI. But adversarial brainstorming is even more powerful in team settings, precisely because it removes the interpersonal dynamics that make human-to-human adversarial thinking so fraught.
The pattern for teams:
- One person presents the plan. They share it with the AI in front of the team (or share the AI’s response with the team).
- The AI attacks. The team reads the AI’s criticisms together.
- The team discusses. Crucially, the team is now discussing the AI’s criticisms, not criticizing each other. The AI serves as a lightning rod — it absorbs the social cost of being critical. Team members who agree with the AI’s criticisms can simply say “I think point 3 is valid” instead of “I think your plan has a flaw,” which is a psychologically very different statement.
- The plan owner revises. They revise based on the team discussion.
- The AI attacks again. Iteration continues.
This approach preserves the intellectual benefit of adversarial thinking while neutralizing the social cost. I’ve seen it transform team dynamics in planning sessions — people become more willing to put forward ambitious plans because they know the AI will attack the plan, not them.
Common Failure Modes
A few things that go wrong when people first try adversarial brainstorming:
Asking for criticism and then ignoring it. If you’re going to do this, you need to actually engage with the results. Running the prompts and then building the same plan you were going to build anyway is a waste of time. If you find yourself dismissing every criticism, ask yourself whether the AI is wrong or whether you’re defensive.
Treating AI criticism as gospel. The opposite failure. The AI doesn’t know your context, your constraints, your industry’s idiosyncrasies. Some of its criticisms will be wrong, inapplicable, or based on misunderstandings. The skill is in distinguishing the valid criticisms from the invalid ones — and that skill is yours, not the AI’s.
Not providing enough context. Adversarial analysis is only as good as the information it’s based on. If you give the AI a two-sentence summary of your plan, you’ll get two-sentence-quality criticism. Give it the full plan, the context, the constraints, the history. The more it has to work with, the sharper its attacks will be.
Stopping after one round. One round of adversarial brainstorming is better than nothing, but it captures maybe 30% of the value. The iterative protocol — attack, revise, re-attack — is where the real gains are.
Using the wrong level for your stage. If you’re in the early ideation phase, Level 5 (paradigm challenge) is appropriate — you should be questioning your framing. If you’re in implementation planning, Level 3 (scenario attack) is more useful. Don’t bring a paradigm challenge to an implementation review; it’s destabilizing when you need to be building.
What Adversarial Brainstorming Cannot Do
This technique has real limits, and pretending otherwise would undermine the credibility of the technique itself.
Adversarial brainstorming cannot identify risks that are genuinely outside the AI’s training data. If your plan depends on a novel technology that didn’t exist when the AI was trained, its criticisms about that technology will be extrapolations, not experience-based analysis.
It cannot replace domain expertise. If you’re planning a clinical trial and you’re not a clinical researcher, the AI’s adversarial analysis will miss domain-specific failure modes that an experienced researcher would catch immediately. AI adversarial brainstorming supplements expert review; it does not replace it.
It cannot account for truly novel situations. The AI’s criticisms are pattern-matched from its training data — it identifies your plan’s resemblance to plans that have failed before. If your situation is genuinely unprecedented (rare, but it happens), the pattern-matching may mislead more than it helps.
And it cannot do the hardest part, which is acting on the criticism. Knowing your plan’s weaknesses is necessary but not sufficient. You still have to decide what to do about them, and that requires judgment that no prompt template can provide.
The Bottom Line
Adversarial brainstorming is, in my experience, the single highest-value AI thinking technique. Not because it’s the most creative or the most surprising, but because it addresses the most universal cognitive failure: the inability to see the flaws in your own thinking.
Every plan has weaknesses. The question is whether you discover them before or after implementation. Adversarial brainstorming with AI — structured, iterative, and honest — is the most efficient way I’ve found to discover them before.
The prompts are in this chapter. The technique is straightforward. The only hard part is being willing to hear that your ideas might be wrong. And if you’re not willing to hear that, no amount of AI assistance is going to help you think the unthinkable.