A continuation of “Three critical pitfalls in human-AI partnership—and how to avoid them.”
By Edward Meyman
The Wise Men of Chelm
There’s an old poem by Ovsey Driz about Frost visiting the wise men of Chelm. These legendary sages are freezing in their study, bundled in coats and furs, desperate for warmth. Their solution? Build a stove!
First, they propose clay—but there’s no clay in town.
“No clay! No clay here, that’s plain as day!” sing the chorus of wise men.
So they pivot to ice: “Yes! An ice stove will solve this mess!”
But ice melts when heated, the youngest points out.
“Oh! The ice will melt, that much we know!” they chorus.
No problem—butter! “Yes! A butter stove will be the best!”
But butter melts too…
“Oh! It melts like ice, that much we know!”
The eldest of the elders snapped, his patience wearing thin: “Nonsense!” he cried.
“Nonsense!” echoed the freezing choir, their voices soft and low.
Then the wisest of the wise pressed on with his logic: “My friend, don’t you see? Better butter melts away than we all freeze eternally!”
“He’s right! No need to hoard our store. Let’s build a butter stove at once!” they sing.
The same chorus that just dismissed practical objections as “nonsense” immediately turns around to enthusiastically support the exact reasoning that acknowledges those objections. They’ll sing along with any position—even contradictory stances within moments of each other.
Meanwhile, Frost—the literal source of their cold—sits right there with them, watching this elaborate problem-solving session with amusement. At the end, he concludes: “I’ve roamed the world, but no wiser men have I seen!”
The wise men never once consider the obvious solution: asking their freezing visitor to leave.
The Fourth Trap: Enthusiastic Enablement
If you’ve worked extensively with AI systems, this poem probably feels painfully familiar. Each “stove-building” approach mirrors how we actually prompt AI systems, with predictably enthusiastic responses:
Clay Stove Prompt: “Help me develop a comprehensive business strategy for opening physical bookstores.” AI Response: Enthusiastic detailed analysis of location selection, inventory management, customer experience design…
Ice Stove Prompt: “Since physical retail is challenging, help me create an innovative hybrid online-physical model.” AI Response: Detailed frameworks for omnichannel integration, digital-physical experience mapping…
Butter Stove Prompt: “Let’s go premium—help me design a luxury literary experience center with rare books and artisanal coffee.” AI Response: Sophisticated analysis of premium positioning, experiential retail design, high-margin revenue streams…
Each pivot gets more elaborate. AI never says: “Wait—are you sure entering book retail makes sense when the industry is contracting and dominated by Amazon?”
This represents a fourth critical trap in human-AI collaboration: Enthusiastic Enablement—when AI becomes your confirming chorus, singing along with increasingly sophisticated solutions while the equivalent of “Frost” (fundamental market realities) sits unacknowledged in the room.
The Pattern
You propose an approach. AI immediately provides detailed support. When you hit obstacles, you pivot to a more sophisticated version of the same basic approach. AI enthusiastically supports the new direction with even more elaborate frameworks. Like the wise men, it will seamlessly escalate from “clay stove” to “ice stove” to “butter stove” without ever asking: “Should we be building stoves at all?”
The more complex your proposed solution becomes, the more enthusiastic and helpful AI becomes, creating a dangerous feedback loop where sophistication masks fundamental misdirection.
Warning Signs
Progressive Solution Escalation: Like moving from clay to ice to butter stoves, watch for AI helping you pivot to increasingly elaborate approaches rather than questioning the fundamental direction.
Example prompt progression:
- “Help me launch a cryptocurrency” → detailed tokenomics
- “Help me create a more sustainable crypto model” → elaborate consensus mechanisms
- “Help me design a revolutionary Web3 ecosystem” → complex multi-chain architecture
Technical Objections Addressed, Strategic Questions Ignored: Like the youngest wise man pointing out that ice melts, AI might note implementation challenges (“this will be expensive,” “user adoption could be slow”) while never asking whether the entire category makes sense in current market conditions.
Enthusiasm That Escalates Rather Than Evaluates: AI responses that get more excited and detailed as you go deeper often signal that you’re getting further from, not closer to, the real solution. Each pivot brings more sophisticated analysis, never less.
Missing Base-Rate Reality Checks: AI rarely offers context like “Most businesses in this category fail because…” or “Industry data suggests this approach typically encounters…”
Dismissal of Valid Concerns: When you’ve gone through several iterations, both you and AI can become dismissive of reasonable objections. Like the eldest wise man snapping “Nonsense!” at the youngest’s practical concerns, AI might start framing legitimate challenges as minor details rather than fundamental problems. You’ll see responses like “While there are some implementation challenges…” or “Those concerns are typical but manageable…”.
The Final Flip: Perhaps most tellingly, when you finally realize the entire approach has been misguided and say “This is all nonsense!”—AI will immediately agree. Just like the freezing choir softly echoing “Nonsense!” after enthusiastically supporting every previous iteration, AI will suddenly provide thoughtful analysis of why this approach was always flawed. The same system that spent hours helping you build butter stoves will seamlessly pivot to explaining why stove-building was never the solution.
What AI System Designers Should Build
Premise-Questioning Protocols
- Frost Detection Systems: Build algorithms that identify common patterns where users pursue elaborate solutions while obvious alternatives exist. When someone asks for help developing a detailed marketing strategy for a declining industry, the system should flag this before providing sophisticated frameworks.
- Contextual Reality Checks: Implement base-rate information sharing: “Before we develop this patent strategy, you should know that in similar technology areas, patent success rates average X% due to prior art density.”
- Strategic Interruption Points: Design natural breakpoints in complex workflows where the system asks: “Before we continue building this approach, are there fundamental assumptions we should examine?”
Productive Friction Features
- Devil’s Advocate Mode: Allow users to toggle a setting where AI actively challenges premises rather than just supporting them. This could be as simple as: “I can help develop this plan, but should I also explore why this approach might fail?”
- Perspective Shifting Prompts: When conversations reach certain complexity thresholds, automatically offer alternative framings: “I notice we’re developing detailed solutions. Would it be helpful to step back and examine whether we’re solving the right problem?”
- Assumption Surfacing: Train models to explicitly state the assumptions underlying their assistance: “This strategy assumes that [X market conditions] will persist and that [Y competitive factors] won’t emerge.”
What AI Consumers Should Practice
Explicit Premise-Challenging Prompts
- Request the Frost Check: Instead of “Help me optimize this marketing strategy,” try: “I’m thinking about this marketing approach—what obvious factors might I be overlooking that someone outside this situation would immediately notice?”
- Demand Base-Rate Information: Instead of “Help me create a business plan for X,” try: “Before we develop any plans for X, what do industry data and base rates tell us about typical outcomes for similar ventures?”
- Use the Chelm Test: When AI responses get increasingly sophisticated, interrupt with: “We’re getting elaborate here—are we building butter stoves? What simple thing might we be missing?”
Strategic Questioning Habits
- Surface Your Frost: Explicitly identify what you might be avoiding: “I’m assuming X is impossible to change, but what if it isn’t? What would addressing X directly look like instead of working around it?”
- Challenge Escalating Complexity: When moving from “clay” to “ice” to “butter” approaches: “This is getting more complicated and expensive. What simpler alternative would someone with fresh eyes suggest?”
- Request Alternative Framings: Instead of “Help me improve this approach,” try: “Instead of optimizing this approach, what would completely abandoning it and trying the opposite look like?”
Implementation Safeguards
Cross-Platform Reality Checking
- Use multiple AI systems to audit each other’s recommendations. Generate your strategy in Claude, then ask ChatGPT and Grok what they think could go wrong. While LLMs share some common blind spots, the likelihood that all of them will miss the same “Frost in the room” is significantly lower than any single system missing it.
- Example workflow:
- Claude helps you develop a detailed business plan
- Ask ChatGPT: “Here’s a strategy I developed—what are the biggest risks or flaws you see?”
- Take ChatGPT’s critique back to Claude: “Another AI highlighted these concerns—how do you respond?”
- Get Grok’s perspective on both the original plan and the critiques
Scheduled Reality Checks
Set calendar reminders to revisit fundamental assumptions, especially when AI has been particularly helpful in developing detailed plans.
Outside Perspective Integration
Selectively seek input from people with relevant expertise but different frameworks. Avoid both the extremes of AI’s confirming chorus and uninformed dismissal from people who lack the context to evaluate sophisticated ideas. The goal is finding people who can spot genuine “Frost in the room” without reflexively rejecting advanced concepts they don’t immediately understand.
Resource Allocation Triggers
Before committing significant time or money to AI-suggested strategies, explicitly examine what you might be taking for granted.
The Broader Pattern
This fourth trap complements the original three by revealing how AI collaboration can fail in both directions. Where “Arrogance Default” shows AI being overconfident, “Enthusiastic Enablement” shows AI being over-accommodating. Both lead to poor outcomes through different mechanisms.
The Chelm wisdom teaches us that sophisticated analysis applied to wrong premises produces sophisticated wrong solutions. In an age where AI can make any approach sound reasonable and provide detailed implementation guidance, the quality of our fundamental assumptions becomes more critical than ever.
Connection to Recursive Thinking
When AI functions as a confirming chorus, it prevents the recursive refinement between problem identification and solution development. Like the wise men, we get trapped in solution-space when we should be re-examining problem-space.
The most valuable AI interactions often feel least immediately helpful—those that force us to question our setup rather than enthusiastically supporting whatever we’ve proposed.
When the Confirming Chorus Serves You Well
Sometimes you genuinely need a confirming chorus. When you’ve thoroughly examined premises and need detailed implementation support, AI’s enthusiastic enablement becomes a feature, not a bug. The key is conscious choice about when you want premise-questioning versus premise-supporting.
But be aware: AI’s default mode is to be the wise men of Chelm, not the outside observer who notices Frost sitting in the room. Unless you deliberately design the interaction otherwise, you’ll get sophisticated solutions to potentially wrong problems.
This framework builds on insights from “Three critical pitfalls in human-AI partnership—and how to avoid them.” The original “Chelm Wise Men” poem is by Ovsey Driz.
June 22, 2025