Why Asking the Same Question Limits Solutions

Why do almost all AI models keep giving similar answers on creative tasks or writing? I have been noticing this for a while. Whenever I ask for something creative or exploratory, Claude, GPT-5 and Gemini all converge on a similar answer template. The wording shifts a little, but the underlying idea feels identical. I just finished reading Artificial Hivemind, a new paper out of UW and the Allen Institute, and it confirms this is not just a vibe. To prove this, the authors collected 26,000 real world open-ended queries, ran them across 70+ models, and used the results to study how reward models behave when there is no single correct answer. The usual expectation would be that the responses scatter across embedding space. Instead, almost every model collapses into the same tight cluster. Once we see how little variation there is in that answer space, it becomes obvious why they all sound alike even when the prompts are creative. The examples in the paper make this very noticeable. Ask dozens of models for a metaphor about time and nearly every single one, regardless of architecture or lab, converges on “Time is a river” or “Time is a Weaver”. This has real implications for the current interest in model swarms. The idea behind multi model swarms where multiple LLMs debate, self criticise and jointly converge on a better answer. That only works if the models bring genuinely different perspectives. If every model in the swarm is pre-conditioned to seek the exact same local minima of “safety” and “helpfulness”, then it’s just a single model with higher latency. The bottleneck seems to be the Reward Models. These are supposed to help models align with human judgement, but in practice they are actually penalising idiosyncratic creativity. They flatten the natural diversity in human preference and push models toward one homogenized style of answer. If billions of people start using these tools for ideation and every tool keeps suggesting the same safe and statistically favoured ideas, we risk shrinking the collective search space of thought. My takeaways from the paper: 1. We should treat disagreement as signal rather than noise. We are currently training models to collapse distributions into a single best answer. 2. We should be training them to represent the full distribution of human preference. 3. Diversity needs to be an explicit evaluation metric. If an alignment method increases average quality but reduces the variety of answers, that is a regression. 4. For agents that help with research, design and ML work, we should build systems with intentional heterogeneity. This paper is worth reading carefully for anyone working with language models, especially the sections on inter model similarity and reward model calibration. This paper makes a strong point to show that the immediate risk of AI is a future where every model is intelligent in exactly the same way.

A prospect told me they wanted to "handle it internally." They'd been missing number by 50% for three years. When I asked what they'd already tried, they listed everything: Internal sales training. Hired enablement people. Built their own playbooks. Created onboarding programs. Three years of effort. Still 50% short. We go through the entire sales process. Good conversations. Real problems uncovered. Solution mapped out. End of the final call, the VP says: "We think we might just try to do this internally." I didn't push back. I just asked a question. "Just to make sure I understand. You mentioned you've already tried internal training, enablement, playbooks, and onboarding for THREE years. What's going to change if you try it again?" Silence. Then: "You're right. That's a good point." Deal closed. Here's why this matters: The number one deal killer isn't budget. It's not timing. It's not competition. It's omission bias. The psychological reality that doing nothing feels safer than making a change. Your prospects feel it. Even when they know they have a problem. Even when they know their current approach isn't working. Doing nothing feels safe. Making a change feels risky. That's why the question "What have you done so far to try to fix this?" is so powerful. You're not asking to be nosy. You're gathering ammunition. When they inevitably say "we'll just handle it internally," you can reflect their own history back to them. Not as an attack. As a mirror. They already told you what they've tried. They already told you it didn't work. You're just helping them remember. Most reps lose these deals because they accept "we'll do it internally" at face value. They don't have the context to push back. The best reps ask about previous attempts early. They know the objection is coming. They prepare for it in discovery, not in negotiation. I broke down the full discovery framework in the carousel. Seven questions that build million dollar business cases. — P.S. Sales Leaders, if you've invested in training and nothing changed, the root cause is hiding somewhere else. I put together a free diagnostic kit to help you find it: https://lnkd.in/gDexefD5

More data needs better questions. In 1854, London was drowning in cholera data: death counts by parish, ward, and street, all meticulously recorded. But the key to ending the outbreak was asking the right question: Physician John Snow wondered not just how many were dying, but *where exactly they lived*. Plotted on a map, the data pointed straight at a single contaminated water pump. Many organizations today face a similar challenge. They have oceans of data but a desperate shortage of questions worth asking. Every system logs, every interaction is tracked, and every dashboard refreshes in real time, yet the questions being asked of all this data remain the same ones from years ago (just answered faster with more colors). This is the paradox of data abundance: The more data accumulates, the lazier the questions tend to become. Teams ask what is easy to measure rather than what is worth knowing, and "let me check the dashboard" quietly replaces "let me think about what we actually need to understand." Of course, better tools and richer datasets *do* unlock better answers, but only when someone has done the hard, slow work of framing the inquiry. A vague question will produce a precise but useless number; an old question an outdated insight. The question is the strategic act. The data is merely the response. The next time you look at your metrics, ask one thing: How many of these numbers would actually challenge our beliefs if they moved? If the answer is "not many," it's time to start looking for better questions.

If you ask your customers what they want most from your solution, you'll probably get some answer like "save time" or "save money". BORING. If you're a B2B company, those two answers are not only boring as hell, but they're not helpful. Every single product in your competitive set likely delivers one or both of those outcomes. If you want to create a differentiated position and sticky messaging, you've got to do better than "save time" or "save money." You've got to dig deeper if you want prospects to look at your marketing and your messaging and say, "Holy crap, it's like they read our minds. THAT solution is the right one for us." Instead of relying on the same old same old, think about it this way... When your customers save time - what do they do with the time they save? ✅ Do they work on more strategic issues? ✅ Do they spend more time with their own customers? ✅ Do they move their products forward faster? And when they save money - what are they doing with the money they saved? ✅ Do they hire more staff? ✅ Do they free up budget to expand key marketing campaigns? ✅ Do they have the budget to move up market or move into a different market? Getting genuinely curious about your customers uncovers answers like these, and when you get enough answers to show a trend, then you have enough insights to create positioning that works and messaging that connects, making your marketing a helluva lot more focused and efficient. Don't settle for boring answers; get curious, dig deeper, uncover the real desired outcomes your customers want most from your solution. #B2BMarketingStrategy #B2BMarketing #CustomerInsights

They couldn't sell a $20,000 product. The company kept trying to fix their marketing. That wasn't the problem. During an assessment, we discovered that this company's salespeople couldn't sell their higher-end solution. It was a $20,000 product that should have been an easy fit for their market. The company's response? Let's work on our marketing. Let's make more calls. Let's generate better leads. But here's what we found when we dug deeper: it didn't matter how many leads they generated because when the salespeople actually connected with customers, they were going to lose anyway. The problem wasn't marketing. It wasn't lead quality. It was that the salespeople didn't know what questions to ask. They were asking surface level questions like "What challenges are you facing?" and "What's your budget?" These questions don't uncover compelling reasons to buy. They don't create urgency. They don't differentiate you from every other vendor asking the same things. When you don't know how to ask the right questions, you end up with conversations that sound like this: "We're looking for a solution." "Here's what we offer." "That sounds interesting. Let me think about it." No urgency. No compelling reason to change. No clear differentiation from alternatives. The right questions sound more like this: "What's it costing you to live with this situation?" "Who else gets affected when this problem comes up?" "What would have to happen for this to become your top priority?" See the difference? These questions force prospects to think about consequences, not just features. Most sales training focuses on presentation skills and objection handling. But if you can't ask the right questions on the front end, nothing else matters. You'll end up presenting solutions to people who don't have compelling reasons to buy. Stop trying to fix your marketing when the real problem is your discovery process.

Imagine you hired three summer interns today. One is a top computer science grad from Stanford University. Another studied data science at Harvard University. The third is an engineer from MIT Sloan School of Management. They’re all genuinely smart, competitive, and motivated. They’re young, single, and perfectly happy working long hours without worrying too much about burnout or balance. The upside is obvious: raw horsepower. These are people who can absorb complexity fast, learn new domains quickly, and move with speed. The downside is also obvious if you’ve ever worked with interns, or frankly, with very smart people early in their careers. They make assumptions. They miss context. They confidently walk past edge cases. Not because they’re careless, but because they haven’t yet built the scar tissue that comes from seeing things break in the real world. So the mistake would be to give all three the exact same task and hope the “best” answer wins. What you actually do is assign them adjacent work and force overlap in review. You ask one to do the primary analysis, another to validate the logic and assumptions at a high level, and the third to sanity-check outcomes against real-world constraints. Not line-by-line edits, but conceptual scrutiny. That’s how they cross-train. That’s how blind spots surface early. That’s how quality goes up without slowing things down. That’s also how I think about using Anthropic’s Claude, OpenAI’s ChatGPT, and Google’s Gemini. They’re all extremely capable. They’re all fast. And they all make different kinds of mistakes. If you ask the same question to all three and blindly accept the first confident answer, you’re treating them like a search engine. That’s a waste. The leverage comes from orchestration. One model drafts. Another critiques the structure and assumptions. The third checks for gaps, hallucinations, or missing constraints. You’re not choosing “the smartest model.” You’re designing a system where smart models keep each other honest. That’s not prompt engineering. That’s management.

Let me illustrate now how asking great questions is key for great organic growth strategy. Most questions are banal: How do I get more traffic? How do I increase my rankings? How do I rank for "X"? They're small, common, and vague questions. Imagine you start working with a client who has cycled through several agencies and consultants and still has stagnant results. Would the above questions suffice? In the case, I'd start with the obvious: "Why hasn't this worked before?" But then I'd dive into the specific constraints facing me: "Given existing resources X, Y, and Z, their competitive landscape, strengths and opportunities of the site, what's the best possible outcome I could achieve if everything in my plan is executed perfectly?" This frames the north star of what we could hope to achieve. Maybe it's lower than their expectations. Maybe it's above them. Maybe the next question is, "Is this best possible outcome even worth the cost and time of working towards it?" That's a question I always ask during scoping. Anyway, I can then work backwards to mitigate risks of failure and increase the chance of a great outcome. "Who are similar companies doing this and how are they doing it?" "How do we make this end goal possible?" "What preconditions need to exist for the previous question to be possible?" "What are the existing blockers to creating the preconditions for success? What are likely blockers in setting them up?" At this point, we're getting into such high dimensionality in this hypothetical example that I can't continue giving specific questions. Perhaps resource constraints are already making it impossible to set up the basic foundations that would lead to success. Perhaps it's political red tape. But the point is, instead of asking "how can I drive more traffic," which results in the same damn playbook everyone is running, I can come up with a custom solution designed to solve the specific problems at hand. And it's all by asking better questions.

Why “Why” Closes Opportunities When leaders ask “Why?” they often seek justification or reasons to say no. This question can unintentionally create barriers by focusing on limitations, risks, or past failures. It tends to put ideas on the defensive, prompting people to explain why something won’t work rather than exploring potential. Why “Why Not” Opens Doors In contrast, “Why Not?” invites curiosity and possibility. It encourages leaders and teams to challenge assumptions, consider new angles, and embrace experimentation. This question creates a safe space for creativity, signaling openness to ideas that might initially seem unconventional or risky. The Power of Curiosity in Leadership Curiosity is the engine of innovation. Leaders who cultivate a “Why Not?” mindset foster environments where: • Creativity flourishes: Team members feel empowered to propose novel solutions. • Experimentation is embraced: Failure is seen as a learning opportunity, not a setback. • Possibility expands: New paths and opportunities emerge that might otherwise be overlooked. Practical Tips to Shift from “Why” to “Why Not” • Pause before responding: When hearing a new idea, resist the urge to ask “Why?” immediately. • Ask open-ended questions: Use “What if?” or “How might we?” to explore possibilities. • Encourage safe experimentation: Create small-scale pilots or prototypes to test ideas without fear. • Celebrate curiosity: Recognize and reward team members who take initiative and think outside the box. By embracing “Why Not?” leaders unlock potential, inspire innovation, and create cultures where ideas can grow and thrive rather than being dismissed prematurely. It’s a subtle but profound shift that can lead to remarkable outcomes.

I used to think having all the answers made me a good leader. A newby to managing and leading, when someone on my team came to me with a question—Where can I find…? What should I do about…? I jumped in with the fix. Fast. Efficient. Helpful. But over those early years, I noticed something. The same people kept asking the same kinds of questions. I wasn’t building capability. I was creating dependence. Time to try something different. Instead of answering, I started asking: “What have you tried so far?” “What do you think your best options are?” “Which one feels right to you—and why?” It felt slower at first. A bit clumsy. But something shifted. People started thinking more for themselves. 👉 Confidence went up. 👉 Solutions got better. And I didn’t have to carry every decision on my own shoulders. Leadership isn’t about having all the answers. It’s about helping your people find their answers. So if you’re a new manager or a seasoned leader getting peppered with questions—pause. - Ask instead of answer. - Coach instead of rescue. - It’s not just a leadership move. - It’s a development strategy. What’s one powerful question you’ve started asking instead of answering? #LeadershipDevelopment #AskBetterQuestions #WinningClarity

You answered the question in the all-hands. You covered it in the leadership team meeting. You put it in the deck. And this week, they're asking again. "What's the priority?" "Are we still doing this?" "Did that decision change?" Your first instinct: they're not listening. Or you're not communicating clearly enough. 👉🏾 But here's what's actually happening: If your team keeps asking for clarity you've already given, the issue isn't communication, it's trust in steadiness. Repetition is often a symptom of leaked uncertainty. They're not asking because they forgot. They're asking because they've learned that what you said last week might not hold this week. Research shows that frequent shifts in leaders' priorities are strongly associated with burnout and disengagement. When leaders change direction without clear rationale, 💥 teams protect themselves, 💥 they do the minimum, 💥 wait out the next shift, 💥 discount current directives as temporary. Studies analyzing over 80,000 360-degree reviews found that leaders who act inconsistently erode trust roughly three times faster than those who behave predictably. So when your team keeps asking the same question, → They're not being dense. → They're being rational. → They're checking whether this version of the answer is the one that will actually hold.  🚩Here's where uncertainty leaks: → Tone and body language. You say "we're committed," but your tone is tight, your body language closed. They feel the hedge. → Decision reversals without narrative. You change course without explaining what changed or what you learned. It registers as unreliability. → Hedging language. "Maybe," "sort of," "we'll see." Softening feels collaborative but overuse reduces perceived authority. → Say/reward gap. You say innovation matters but penalize failed experiments. They watch what you reinforce, not what you announce. People track coherence across your words, tone, and actions at a fine-grained level. When those don't align, they adjust. Not because they're bad listeners. Because you've trained them that clarity isn't stable. ✅ Here's the shift: 1️⃣ When you give clarity, ask yourself, "Do I actually believe this will hold?" If not, don't give false certainty. Say: "Here's where we are. Here's what's still forming." 2️⃣ When you change course, name it explicitly: "Last week I said X. Here's what changed. Now we're doing Y." 3️⃣ Check your tone. If your words say "we're locked in" but your body says "I'm not sure," they'll trust the body. 👉🏾 Steadiness isn't rigidity. It's coherence, your words, tone, and decisions pointing in the same direction. If your team keeps asking for clarity you've already given, look for where your uncertainty is leaking. Because repetition isn't about them not hearing you. It's about them not trusting what they heard will still be true next week. Check out the REWIRED. newsletter (link in comments).