Innovation partnerships can create breakthrough markets—or hand them to competitors through terrible decisions. I know because I lived through both outcomes.

Bill Geiser from Fossil and I had it exactly right. We built the MetaWatch—a smartwatch with week-long battery life, Bluetooth connectivity, and every feature that would later make the Apple Watch successful. We had HP's massive retail reach, Fossil's manufacturing scale, and the technical vision to create an entirely new market.

But our organizations couldn't execute on what we knew was right. Leadership chaos at HP and innovation paralysis at Fossil killed a partnership that should have dominated the smartwatch market—handing Apple a $50 billion opportunity. I've shared the complete behind-the-scenes story of the people, strategies, and decisions that killed our partnership in my Studio Notes post “How HP and Fossil Handed Apple the Smartwatch Market.”

Today I'm applying the DECIDE framework to our partnership failure. If you haven't seen my DECIDE framework yet, grab the free PDF—it's the innovation decision tool I've developed over 30 years of making high-stakes choices. Because here's what this partnership taught me: having the right vision means nothing without the right decision framework.

Let me start by explaining why the HP-Fossil partnership should have worked. This wasn't just another business deal—it was the perfect storm of complementary capabilities.

Bill Geiser, Fossil's VP of Watch Technology, had been working on smartwatches since 2004. The man was practically clairvoyant. In 2011, he told me, “Phil, I wouldn't be shocked if Apple evolved the Nano to take advantage of this space. They'll legitimize it in consumers' minds worldwide.”

Bill understood something most people missed: Apple didn't need to be first to market—they needed to be first to create a platform.

Meanwhile, I was developing HP's connected device strategy. We had the technology foundation, unmatched retail distribution—about 10% of consumer electronics shelf space—and the same retail muscle that helped launch the original iPod.

Together, Bill and I had solved the hard problems. We had the vision, the technology, and the market insight. But we couldn't overcome the organizational machinery that prioritizes short-term comfort over long-term position.

Innovation partnerships aren't just about having the right technology or market vision. They're about having the right decision framework when uncertainty meets organizational reality.

Before I show you how DECIDE could have saved our partnership, let me show you the three traps that derail even the smartest collaboration. Bill and I understood what needed to happen, but our organizations fell into every one of these traps.

This is when you apply the wrong decision framework because you've misidentified what type of innovation you're actually pursuing. It's the most common partnership killer because different innovation types require completely different approaches to risk, timing, and success metrics.

In our case, Bill and I understood that smartwatches represented a platform opportunity—a new ecosystem that would change how people interact with technology. But our organizations treated it as a product extension that wouldn't threaten their existing businesses.

HP's leadership viewed MetaWatch as another device in their portfolio, rather than as the foundation of a connected ecosystem spanning tablets, phones, and laptops. Fossil's leadership saw it as a “development platform” priced at $200—innovation theater that wouldn't cannibalize their traditional watch sales.

Here's the partnership recognition question: Have you correctly identified what type of innovation you're pursuing together? Because applying incremental decision frameworks to breakthrough opportunities, or product frameworks to platform opportunities, kills partnerships before they can succeed.

This combines revenue protection with organizational risk aversion. Both companies wanted to appear innovative without actually risking their core businesses. HP didn't want to cannibalize enterprise focus. Fossil didn't want to threaten traditional watch revenues.

So instead of going all-in on market creation, both organizations positioned MetaWatch as a “safe” innovation—a development platform for engineers, not a consumer product that could disrupt markets.

Bill faced an impossible organizational reality: Fossil's watch sales had tripled to $3.25 billion during the smartphone era. How do you convince leadership to risk that success for an uncertain new category?

The partnership recognition question: Are you innovating to create markets, or are you innovating to appear innovative while protecting existing revenue?

Bill and I found ourselves fighting the same battle on different fronts: convincing leadership that wearables represented a platform shift, not just a product extension. But even when we had the right vision, we couldn't execute at market speed.

Multiple stakeholders created governance complexity without clear decision authority. We'd have month-long approval cycles for changes that startups could implement in days. I remember one meeting where we spent 45 minutes debating some minor specification while Apple was probably finalizing their entire ecosystem strategy.

The fatal blow came from HP's leadership chaos. We cycled through three CEOs in 13 months, each with completely different visions. When Leo Apotheker arrived, he immediately decided HP should become an enterprise software company, not compete in consumer platforms.

Our partnership needed startup decision velocity, but we were trapped at committee speed across two large organizations with conflicting priorities.

Partnership recognition question: Can you move at market speed together, or are you trapped at the slowest organization's committee speed? Because innovation partnerships under uncertainty require aligned decision velocity.

Now let me show you how the DECIDE framework—which you can download free at philmckinney.substack.com—could have saved our partnership. This isn't theoretical. These are the specific decisions Bill and I needed our organizations to make differently.

Our first mistake was never clearly defining what we were actually deciding as partners. HP saw it as extending our mobile ecosystem. Fossil saw it as diversifying its product line. I saw it as platform creation. Bill saw it as market preparation for the inevitable.

But we never aligned on the fundamental question: Were we creating a new product category together, or were we each using the partnership to serve our existing strategies?

The DECIDE framework forces this clarity upfront. What decision are both partners actually making? Until you have alignment on the decision itself, you can't make it well together.

What we should have defined: “We are deciding to jointly create and dominate the smartwatch platform category before Apple legitimizes i Smartwatch Market.”

Traditional partnership evaluations focus on combining existing capabilities. Innovation partnerships need to evaluate market creation potential.

Bill understood this completely. He predicted exactly what Apple would do. But neither organization had a framework for evaluating market creation partnerships versus product extension partnerships.

For market creation partnerships, you can't study competitors or analyze customer segments that don't exist yet. You need different evaluation criteria focused on timing, platform potential, and ecosystem readiness.

What we should have examined: “Are we positioned to create and own a new category, or are we just building another device?”

Here's where partnerships get interesting. Fossil feared cannibalizing traditional watch sales. HP feared distracting from enterprise transformation. But we never addressed how a partnership could protect both companies from external cannibalization.

The framework question isn't whether the partnership will cannibalize existing business—it's whether the partnership creates better protection than going alone.

What we should have challenged: “What happens if Apple creates this category without us? What market position will each company have then?”

We treated our partnership like an incremental product collaboration when it was actually a breakthrough market creation partnership. These require completely different success metrics, timelines, and risk tolerance.

Incremental partnerships: You can require certainty, detailed market research, and predictable ROI.

Breakthrough partnerships: You have to accept uncertainty, optimize for learning speed, and measure different success metrics like market position and ecosystem readiness.

What we should have identified: “This is breakthrough innovation requiring platform thinking, not incremental innovation requiring product optimization.”

Traditional partnership planning tries to predict success. Innovation partnership planning prepares for multiple futures—together.

We should have built scenarios like: “What if Apple legitimizes this category in two years?” “What if consumers aren't ready for smartwatches?” “What if we succeed and create a billion-dollar market?”

But most importantly: “What if we don't partner and pursue this separately? What if we don't pursue this at all?”

What we should have designed: “Multiple scenarios where our combined capabilities create different outcomes than our individual capabilities.”

This is where our partnership completely failed. We optimized for committee consensus instead of rapid market learning. We needed to fail fast or succeed fast—together.

Instead of quarterly partnership reviews, we needed weekly sprint cycles. Instead of seeking approval for perfect plans, we needed permission to test and iterate quickly.

What we should have executed: “Rapid market validation cycles where both partners learn and adapt together, not separate approval processes that slow everything down.”

Want to apply this to your current innovation partnerships? Here's a 30-second audit using the DECIDE framework:

If you can't answer these questions clearly with your partners, you're not ready to make breakthrough innovation decisions together. And that's okay—clarity about partnership uncertainty is better than false confidence in flawed collaboration.

Let me be clear about what DECIDE would and wouldn't have guaranteed for our partnership.

Would this framework have eliminated uncertainty? No. Nothing eliminates uncertainty in breakthrough innovation partnerships.

Would it have guaranteed success? No. Even perfect decision frameworks can't overcome poor market timing or execution failures.

But would it have given the HP-Fossil partnership a fighting chance? Absolutely. It would have aligned our decision-making process with the type of decision we were actually making together.

Most importantly, it would have helped us recognize that we were making a platform partnership decision, not a product partnership decision. That recognition alone might have changed our entire approach to market timing, resource allocation, and success metrics.

Innovation partnerships aren't about having the best technology or the smartest people—they're about using the right decision framework when uncertainty meets organizational reality.

Bill Geiser and I had the vision, the technology, and the market insight. But our organizations couldn't execute on what we knew was right because we didn't have the decision framework to bridge individual company interests with partnership potential.

The DECIDE framework won't eliminate uncertainty—nothing can. But it will help you and your partners make better choices under uncertainty, which is what innovation partnership leadership actually requires.

So here's my challenge for you: What innovation partnership opportunity is your organization considering right now? Apply the DECIDE framework with your potential partners and see what you discover. Are you making the right type of decision together? Are you optimizing for the right outcomes? Are you prepared for multiple scenarios as a partnership?

Because somewhere out there, another brilliant partnership is about to hand their breakthrough innovation to a competitor through bad decision-making. Don't let it be yours.

The framework is free at philmckinney.substack.com. For the complete story of how organizational failures killed our partnership—including the specific people and pivotal moments that changed everything—read “How HP and Fossil Handed Apple the Smartwatch Market” in my Studio Notes. The partnership opportunity is waiting. The choice is yours.

What will you and your partners decide together?

To learn from the innovation partnership autopsy of HP and Fossil’s lost chance at market domination, listen to this week's show: Innovation Partnership Autopsy: HP, Fossil, and the Smartwatch Market.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

You know that moment when you walk into a meeting and immediately sense the mood in the room? Or when a proposal looks perfect on paper, but something feels off? That's your intuition working—and it's more sophisticated than most people realize.

Every leader has experienced this: sensing which team member to approach with a sensitive request before you've consciously analyzed the personalities involved. Knowing a client is about to object even when they haven't voiced concerns. Feeling that a project timeline is unrealistic before you've done the detailed math.

That instinctive awareness isn't luck or mystical insight—it's your brain rapidly processing patterns, experience, and environmental cues. The leaders known for “amazing judgment” haven't been blessed with superior gut feelings. They've learned to systematically enhance this natural capability through practical thinking.

By the end of this post, you'll understand the science behind intuitive judgment, why some people seem to have consistently better instincts, and how to use Practical Thinking Skills to make your own intuition more reliable and actionable.

Intuition is your brain's rapid processing of experiences, patterns, and environmental cues that occur below the level of conscious awareness. When you sense the mood in a room, your mind is instantly analyzing dozens of subtle signals: body language, tone of voice, seating arrangements, who's speaking and who's staying quiet.

This isn't mystical—it's sophisticated pattern recognition. Your brain has stored thousands of similar situations and can quickly compare current circumstances to past experiences, delivering a “gut feeling” about what's likely to happen or what approach will work.

Everyone has this capability. You use it constantly:

The difference between people with “great intuition” and everyone else isn't the quality of their initial gut feelings—it's how systematically they validate, investigate, and act on those insights.

Leaders who are known for excellent judgment have developed what I call practical thinking—the systematic approach to using their knowledge and experience to enhance their intuitive insights.

Here's what they do differently:

They treat gut feelings as valuable data, not emotions to dismiss or blind impulses to follow. When something feels off, they investigate systematically rather than ignoring the signal or acting without validation.

They've learned to distinguish between intuition based on genuine patterns and reactions driven by personal bias, stress, or recent events. They can separate “this timeline feels aggressive because similar projects have failed” from “this timeline feels aggressive because I'm overwhelmed today.”

They apply structured approaches to validate their intuitive insights before making important decisions. They don't just trust their gut—they use their gut as the starting point for systematic investigation.

They understand stakeholder psychology at a deeper level, using their intuitive read of people to design approaches that work with human nature rather than against it.

The leaders with reputations for “brilliant intuition” have simply learned to make their natural pattern recognition more reliable and actionable through systematic frameworks.

Practical thinking is the systematic approach to using your knowledge and experience to validate, investigate, and effectively implement your intuitive insights. It transforms valuable gut feelings into consistently reliable judgment.

Think of intuition as your brain's early detection system, and practical thinking as the methodology for investigating and acting on those signals systematically.

Your intuition signals: “This reorganization plan feels wrong.”

Practical thinking investigates: “What specific elements am I reacting to? Is it the timeline, the stakeholder alignment assumptions, or the communication approach?”

Your intuition warns: “This customer seems hesitant despite saying yes.”

Practical thinking explores: “What might they be worried about that they can't voice directly? How can I address their real concerns?”

Your intuition detects: “This team meeting feels tense.”

Practical thinking examines: “What underlying conflicts or pressures might be driving this dynamic? What does each person need to feel successful?”

When you combine intuitive insight with systematic investigation frameworks, you develop judgment that gets more accurate with experience. This is how great leaders seem to “just know” what will work—they've learned to systematically validate and act on the patterns their intuition detects.

The framework consists of three interconnected skills that transform your natural intuitive insights into reliable decision-making capabilities. Unlike traditional analytical approaches that ignore gut feelings or emotional approaches that follow instincts blindly, practical thinking creates a systematic bridge between your intuitive awareness and effective action. The power comes from combining all three skills—most people excel at one or two but miss the integration that makes intuitive judgment consistently reliable.

What it is: Use your intuitive insights to see situations as they actually exist, not as frameworks or org charts suggest they should be structured.

Why it matters: Your gut feelings often detect mismatches between official reality and actual reality. Most decision failures happen because people address the stated situation rather than the real situation.

How to apply it:

Example in action: Your gut says a reorganization plan “won't work” even though it looks logical on paper. Reality Recognition helps you investigate: your intuition is detecting that the timeline is too aggressive for this culture, key influencers weren't consulted, and the plan ignores current team workload realities. Your gut feeling was accurate—it sensed the gap between the plan and actual organizational dynamics.

Common mistake: Dismissing gut feelings because the official version looks reasonable.

What it is: Use your knowledge of similar situations and these specific stakeholders to adapt your approach, rather than applying generic solutions.

Why it matters: Your intuition draws on your unique experience with these people, this culture, and these types of challenges. That contextual knowledge is more valuable than best practices from other organizations.

How to apply it:

Example in action: Your intuition suggests that a client is hesitant about a proposal despite their positive words. Experience Application helps you investigate: you remember that this client typically asks detailed implementation questions when they're serious, but they haven't asked any. Your experience with them suggests they're worried about execution complexity but don't want to seem unsophisticated. You adapt by proactively addressing implementation support rather than pushing for a decision.

Common mistake: Researching what worked elsewhere instead of applying what you know about this specific context.

What it is: Decode what people really need and fear versus what they say they need, then design approaches that align with their actual psychology.

Why it matters: Most initiatives fail not because of logic or resources, but because they clash with stakeholders' deeper motivations, fears, or unspoken constraints that no one addresses directly.

How to apply it:

Example in action: A department head enthusiastically supports your efficiency initiative in meetings, but your gut says they're not really on board. Psychology Reading reveals they're actually worried that improved efficiency will make their large team look unnecessary, threatening their status and job security. Instead of focusing on efficiency benefits, you reframe the initiative as expanding their team's capabilities and strategic value. This transforms resistance into genuine partnership.

The key insight: While others try to convince stakeholders with better arguments, practical thinkers recognize that people's real decisions are driven by emotions, incentives, and fears that are rarely discussed openly. They design solutions that address these psychological realities, not just the stated requirements.

Common mistake: Focusing on logical persuasion when stakeholders are driven by emotional or political concerns they can't voice directly.

The 30-Day Practical Thinking Challenge

Week 1: Reality Recognition Practice Start documenting your gut feelings about situations and meetings. When something feels off, practice investigating what your intuition might be detecting. What mismatches between official reality and actual reality is your gut picking up on?

Week 2: Experience Application For each significant decision you face, consciously apply your experience with these specific people and situations. Instead of researching best practices, ask: “What do I know from previous similar situations with these stakeholders that should guide my approach?”

Week 3: Psychology Reading Practice reading stakeholder psychology by listening for what people don't say. When someone agrees quickly or shows unexpected enthusiasm, ask yourself: “What might they be worried about that they can't voice directly?” Design small experiments to test your psychological reads.

Week 4: Integrated Framework Combine all three steps for important decisions. Start with your intuitive read (Reality Recognition), apply your contextual experience (Experience Application), then design approaches that address stakeholder psychology (Psychology Reading). Track when this systematic approach improves outcomes.

Practice with others: Share your intuitive insights with trusted colleagues and walk through your practical thinking process. Explaining why you're getting a particular gut feeling often reveals additional insights and helps validate your approach.

Success indicators: You'll know your practical thinking skills are enhancing your intuition when your gut feelings become more accurate, when stakeholders volunteer information they usually keep hidden, and when your solutions work better because they align with how people actually think and behave.

Your intuition is already providing sophisticated insights about stakeholder dynamics, organizational realities, and what will actually work in practice. The challenge isn't developing better gut feelings—it's learning to systematically validate and act on the insights you're already receiving.

Practical thinking doesn't replace your natural judgment—it makes it more reliable, more explainable to others, and more actionable in complex situations. When you can read reality accurately, apply your experience systematically, and understand stakeholder psychology, you develop the type of judgment that improves with every decision.

This is the foundation of Decision Thinking—the approach I've developed for making effective decisions when information is incomplete, stakeholders have conflicting interests, and the stakes are high. Practical thinking helps you leverage your natural capabilities to navigate complexity that would overwhelm purely analytical approaches.

You already have sophisticated pattern recognition and stakeholder awareness. Practical thinking helps you turn those natural capabilities into systematic competitive advantages.

Ready to develop reliable, practical thinking? Join our community of leaders in Substack Chat where we're exploring this question: What's one situation where you had a strong gut feeling that something was off, but you didn't trust it enough to investigate—and later wished you had?

In the next episode, we will examine how the HP-Fossil partnership could have challenged Apple's dominance in the smartwatch market. Fossil’s Bill Geiser's intuition about Apple entering wearables was dead-on accurate. My assessment of the platform shift was equally accurate. But intuition without the decision-making frameworks to act on it just becomes expensive foresight. It will show how two Fortune 500 giants chose comfort over courage.

Share this with someone who has great instincts but struggles to act on them systematically—you'll be helping them turn natural judgment into reliable leadership capability.

To learn how intuitive judgment works, why some leaders seem to have better instincts, and how practical thinking makes gut feelings more reliable in complex situations, listen to this week's show: Why Great Innovators Read Rooms, Not Just Data.

Picture this: You're in a conference room with 23 executives, everyone has perfect PowerPoint presentations, engineering milestones are ahead of schedule, and you're about to sign off on a $25 million bet that feels like a sure thing.

That was the scene at HP when we were developing the Envy 133—the world's first 100% carbon fiber laptop. Everything looked perfect: engineering was ahead of schedule, we projected a $2 billion market opportunity, and the presentations were flawless.

Six weeks after launch, Apple shifted the entire thin-and-light laptop market, and our “sure thing” became a $25 million cautionary tale about decision-making.

Here's what I discovered: Your people aren't lying to you—they're protecting you. Every layer of management unconsciously filters out inconvenient truths. We had two massive blind spots:

Information in organizations goes through more filters than an Instagram photo. Each management layer edits out inconvenient truths—not from malice, but from basic human psychology. People want to be helpful, to be problem-solvers, to avoid being bearers of bad news.

I started treating information like a scientist treats data, using three temperature checks:

Technique 1: Pre-Mortem Confessions

Anonymous submission of biggest fears before major decisions. Read aloud without attribution to remove personal risk and stress-test plans against criticisms.

Technique 2: Messenger Reward System

Formally reward people who bring bad news, not just problem-solvers. Recognition in leadership meetings and promotion consideration. Within six months, intelligence quality improved dramatically.

Technique 3: Devil's Advocate Rotation

Assign someone to formally challenge assumptions in every major presentation. Rotate among team members to institutionalize dissent and make doubt safe to express.

Technique 4: Customer Voice Channel

Spend 25% of time with direct customer contact. This included executive briefings but also weekends in retail stores watching real customer behavior. The gap between what customers wanted and what product teams assumed was staggering.

Technique 5: Failure Story Requirement

Every presentation must include one failure story—not dwelling on failures, but incorporating lessons from setbacks into decision-making.

I developed a six-factor scorecard (1-5 scale) to measure information quality:

Review quarterly—scores below 3 signal information silos are forming.

Building a truth-telling culture isn't about finding better people—it's about creating better systems for handling difficult information. The market will always contain signals that contradict your plans. The question is whether those signals can survive the journey to your desk.

This Week's Challenge: Try one technique—run a pre-mortem confession on your next major decision or assign a devil's advocate to your next presentation. Small changes in how you handle information can prevent million-dollar mistakes.

For the complete Truth-Telling Scorecard and detailed frameworks, visit Phil's Studio Notes on Substack. For the full backstory on the HP Envy 133 project, including all the details, check out the complete article there.

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To learn more about why best employees are sabotaging decisions, listen to this week's show: Why Your Best Employees Are Sabotaging Your Decisions (And How to Fix It).

Every breakthrough innovation starts the same way: everyone thinks it's a terrible idea. Twitter was dismissed as “breakfast updates.” Google looked “too simple.” Facebook seemed limited to “just college kids.”

Yet these “stupid ideas” became some of the biggest winners in tech history. After 30 years making innovation decisions at Fortune 100 companies, I've identified why smart people consistently miss breakthrough opportunities—and how to spot them before everyone else does.

The problem isn't intelligence or experience. It's that we ask the wrong questions when evaluating new innovations. We filter breakthrough ideas through frameworks designed for incremental improvements, not revolutionary changes.

Most innovation decisions fail because of three specific thinking traps that cause us to dismiss ideas with the highest potential for transformation.

The Question That Kills Innovation: “What existing problem does this solve?”

Why It's Wrong: Breakthrough innovations don't solve existing problems—they create entirely new behaviors and meet needs people don't even know they have.

Real-World Example: Airbnb seemed insane when it launched. Staying with strangers? Seeing them in the kitchen? The “problem” it solved—expensive hotels—wasn't what made it revolutionary. It created an entirely new behavior: experiential travel that hotels couldn't provide.

The Better Question: “What new human behavior could this enable?”

The Question That Kills Innovation: “Where are all the features? This looks too basic.”

Why It's Wrong: Simplicity isn't a lack of sophistication—it's the hardest thing to achieve. When something is designed to be insanely simple to use, that signals massive effort and thought behind the design.

Real-World Example: Google was just a white page with a search box while Yahoo crammed everything onto their homepage. Google looked unprofessional and incomplete, but it eliminated complexity everyone thought was necessary.

The Better Question: “What complexity is this eliminating?”

The Question That Kills Innovation: “How big is the addressable market? Why limit yourself so severely?”

Why It's Wrong: Breakthrough innovations don't serve existing markets—they create entirely new markets. The biggest opportunities come from ideas that seem too niche or focused.

Real-World Example: Facebook was just for college students requiring .edu email addresses. Critics said the market was too narrow. But social media users didn't exist before Facebook—the company created the entire market.

The Better Question: “What market could this create?”

When evaluating ideas that seem “stupid” or “too simple,” use this three-question filter:

These questions force you to look beyond surface-level problems and features to identify transformational potential.

For Investors: Stop asking “What problem does this solve?” Start asking “What behavior does this create?”

For Product Teams: Stop adding features. Start eliminating complexity.

For Leaders: Stop looking for big existing markets. Start looking for new market creation potential.

For Innovators: Stop following what everyone else thinks is smart. Start looking for ideas that violate conventional wisdom.

The current AI boom follows the exact same pattern as the dot-com bubble. Every company is racing to add AI to their pitch, just like they added “.com” in 1999.

But the real breakthrough opportunities? They're probably something completely different—ideas that look terrible to everyone following the AI herd.

The companies that will win are those that can recognize breakthrough potential when it violates everything the market thinks is smart.

Recognition is only half the battle. The hardest part is having the courage to act on opportunities when they contradict expert opinion and market consensus.

The biggest question isn't whether you can spot these opportunities—it's whether you'll have the conviction to pursue them when everyone else thinks they're terrible ideas.

Because twenty years from now, someone will be writing about the “stupid idea” they missed in 2025 that became the next trillion-dollar company.

This framework came from some painful (and expensive) lessons about dismissing breakthrough ideas. I share the full story—including how I wrote off the team that created Twitter after Apple destroyed their original business—in this week's Studio Notes.

Listen to the full analysis: Subscribe to the Killer Innovations podcast for deeper dives into innovation decision frameworks.

See the framework in action: Watch my case study of how these exact decision traps led to HP's $1.2 billion WebOS disaster.

To learn more about innovation decision traps that kill breakthrough ideas, listen to this week's show: 3 Innovation Decision Traps That Kill Breakthrough Ideas (And How to Avoid Them).

In 2011, HP killed a $1.2 billion innovation in just 49 days. I was the Chief Technology Officer who recommended buying it. What happened next reveals why smart people consistently destroy breakthrough technology—and the systematic framework you need to avoid making the same mistake.

HP had just spent $1.2 billion acquiring Palm to get WebOS—one of the most advanced mobile operating systems ever created. It had true multitasking when iOS and Android couldn't handle it, an elegant interface design, and breakthrough platform technology. I led the technical due diligence and recommended the acquisition because I believed we were buying the future of mobile computing.

Here's a question that should keep every innovation leader awake at night: How do you destroy breakthrough technology worth over a billion dollars in less than two months?

The answer isn't what you think. It's not about bad technology, poor market timing, or insufficient resources. It's about systematic thinking errors that intelligent people make when evaluating innovation under pressure. And these same patterns are happening in companies everywhere, right now.

I'm going to show you exactly how this happens, why your company is vulnerable to the same mistakes, and give you a proven framework to prevent these disasters before they destroy your next breakthrough innovation.

On my Studio Notes on Substack, I share the personal story of watching this unfold while recovering from surgery. In this episode, I want to focus on the systematic patterns that caused this disaster and the decision framework that can prevent it.

Read Studio Notes on Substack

Here's my promise: by the end of this episode, you'll understand the five thinking errors that consistently destroy innovation value, you'll have a complete decision framework to avoid these traps, and you'll know exactly how to apply this to your current innovation decisions.

Because here's what this disaster taught me: intelligence doesn't predict decision quality. Systematic thinking frameworks do.

Let me start with the fundamental problem that makes these disasters predictable. When the HP Board hired Leo Apotheker as CEO, they created what I call a “cognitive mismatch,” and it reveals why smart people make terrible innovation decisions.

Apotheker came from SAP, where he'd run a $15 billion software company. HP was a $125 billion technology company with breakthrough mobile platform technology. The board put someone whose largest organizational experience was half the size of HP's smallest division in charge of evaluating platform innovations he'd never encountered before.

But here's the crucial insight: the problem wasn't his experience level. The problem was how his professional background created mental blind spots that made him literally unable to see WebOS as an opportunity.

Here's what's dangerous: Apotheker couldn't see WebOS as valuable because his entire career taught him that software companies don't do hardware. His brain was wired to see hardware as a distraction, not an advantage. To him, WebOS represented exactly the kind of hardware business he wanted to eliminate.

Your expertise becomes your blind spot. You literally can't see opportunities outside your professional comfort zone.

And this is the first critical principle: Your job background creates mental filters that determine what opportunities you can even see.

And this pattern is happening in your company right now. Your finance team evaluates platform investments using metrics designed for traditional products. Your marketing team rejects concepts they can't explain with existing frameworks. Your engineers dismiss breakthrough ideas that don't fit current technical roadmaps.

The pattern is always identical: intelligent people using the wrong thinking frameworks to evaluate breakthrough technology. Let me show you exactly how this destroys innovation value.

WebOS died because of five predictable cognitive errors that occur when smart people evaluate breakthrough technology under pressure. These aren't unique to HP—I've seen identical patterns destroy innovation value across multiple industries.

The most dangerous mistake happens before you evaluate any options: framing the wrong decision question.

Apotheker was asking “How do I transform HP into a software company?” when the strategic question was “How do we build competitive advantage in mobile computing platforms?” When you optimize solutions for the wrong problem, you get excellent answers that destroy strategic value.

The Warning Sign: Your team jumps straight to evaluating options without questioning whether you're solving the right challenge.

Your professional background creates systematic blind spots about breakthrough opportunities.

Software executives see software solutions. Hardware leaders focus on hardware opportunities. Financial experts optimize for traditional metrics. This cognitive filtering happens automatically and distorts how you evaluate platform technologies that don't fit conventional categories.

The Warning Sign: Your evaluation team all have similar backgrounds and reach the same conclusions about breakthrough technology.

When executives become obsessed with major initiatives, everything else feels like a distraction.

Apotheker became obsessed with acquiring Autonomy, a software company that fit his transformation vision. This tunnel vision made everything else—including breakthrough mobile technology—feel like a distraction from his primary goal.

The Warning Sign: Leadership dismisses promising innovations because they don't support the current primary initiative.

Platform technologies require different evaluation timeframes than traditional products.

Forty-nine days isn't enough time to build developer ecosystems, establish retail partnerships, or demonstrate platform traction. But pressure to show decisive leadership compressed HP's decision timeline artificially, creating the illusion of strong leadership while increasing the probability of strategic errors.

The Warning Sign: Your team is evaluating breakthrough technology using the same timelines as conventional product launches.

Innovation decisions require fundamentally different success metrics than traditional business evaluation.

HP focused on TouchPad sales numbers instead of developer adoption rates, user engagement patterns, or platform differentiation sustainability. They used product metrics to evaluate platform potential, which guaranteed they would see failure instead of recognizing early-stage ecosystem development.

The Warning Sign: You're applying traditional business metrics to evaluate breakthrough technology investments.

Here's what makes these errors so dangerous: they're invisible to the people making them. Smart teams use these flawed frameworks and feel confident they're making data-driven decisions while systematically destroying innovation value.

But these patterns are preventable. After analyzing hundreds of similar disasters, I developed a systematic framework specifically designed to avoid these thinking traps.

The DECIDE framework addresses each cognitive vulnerability that consistently traps intelligent leaders in innovation contexts. Let me show you exactly how it works and why it would have saved WebOS.

Most innovation failures begin with teams optimizing excellent solutions for poorly defined problems.

The Tool: Reframe your decision question three different ways. If all three point to the same choice, you're probably asking the right question. If they point to different choices, you need to determine which frame captures the real strategic challenge.

Examples of Different Frames:

HP's Application:

What This Reveals: The reframes show TouchPad was one product in a larger platform opportunity that deserved different evaluation criteria entirely.

Your professional background creates invisible filters that can systematically distort how you interpret breakthrough opportunities.

The Tool: If you hired someone with completely different expertise to make this decision, what would they choose? When the gap is huge, you need outside perspectives with different cognitive frameworks.

HP's Gap: Enterprise software CEO versus consumer platform strategy requirements. They needed mobile platform thinking, not enterprise software optimization, but never brought that expertise into the decision process.

The most dangerous assumptions feel like established facts and shape your entire analysis without being examined.

The Tool: What would have to be true for your least favorite option to actually be the right choice? This forces you to consider alternative interpretations of the same evidence.

HP's Assumptions: Platform businesses need immediate profitability, mobile computing won't dominate, differentiated operating systems can't compete with Apple and Google. All of these assumptions were provably false by 2011, but they drove the evaluation process.

Different types of decisions trigger predictable cognitive biases that distort evaluation in systematic ways.

The Tool: Which specific biases is your decision most vulnerable to? Create explicit countermeasures for each identified bias.

Common Innovation Decision Biases:

HP's Specific Traps:

Most innovation failures result from evaluating limited options well rather than evaluating good options poorly.

The Tool: Generate five genuinely different approaches before evaluating any of them. Breakthrough solutions often emerge from non-obvious alternatives.

HP's Missing Options: License WebOS to manufacturers, integrate into PC ecosystem, pivot to enterprise mobile, create hybrid hardware-software strategy. All had genuine potential but were never seriously considered.

Platform technologies require fundamentally different success metrics than traditional product evaluation.

The Tool: What evidence would predict success for this specific type of innovation? Use frameworks appropriate for breakthrough technology, not conventional business metrics.

HP's Error: They used quarterly sales performance and immediate profitability to evaluate platform potential. Platform businesses lose money initially while building network effects that create sustainable advantages later.

Let me show you how to use this framework with your current innovation decisions.

Step One: Identify Your Highest-Stakes Innovation Decision

What breakthrough technology, platform investment, or disruptive opportunity is your team evaluating right now? This framework applies to any decision where traditional business metrics might mislead about innovation potential.

Step Two: Run the Decision Question Test

Before evaluating any options, reframe your decision question three different ways. Are you asking “How do we minimize risk?” or “How do we maximize strategic opportunity?” The frame determines the solutions you'll even consider.

Step Three: Audit Your Evaluation Team

Who's making this decision? What cognitive filters might their backgrounds create? Do you need advisors with different expertise to see opportunities your current team might miss?

Step Four: Challenge Your Obvious Assumptions

What would have to be true for the option you least prefer to actually be right? Those conditions might exist or be emerging faster than you realize.

Step Five: Identify Your Decision Traps

Is your team vulnerable to loss aversion? Anchoring on early data? Tunnel vision around other initiatives? Create specific countermeasures for each identified bias.

Step Six: Generate Multiple Approaches

Push beyond obvious choices. What would someone from a completely different industry do? What creative alternatives combine elements from different options?

Step Seven: Use Appropriate Evidence

Are you evaluating platform potential with product metrics? Breakthrough technology with conventional criteria? Innovation investments with traditional business frameworks? Match your evidence to your innovation type.

Download DECIDE Framework Toolkit

The DECIDE framework works because it addresses the specific cognitive vulnerabilities that consistently trap intelligent people in innovation contexts.

Traditional decision-making assumes you know the right questions to ask, can see opportunities clearly, and will use appropriate evaluation criteria. Innovation decisions violate all these assumptions. Breakthrough technologies don't fit existing categories. Platform investments don't follow traditional timelines. Disruptive opportunities can't be evaluated with conventional metrics.

The companies that consistently succeed at innovation aren't smarter—they use systematic frameworks designed for uncertainty, breakthrough potential, and non-obvious opportunities.

Three Companies Getting This Right:

Amazon evaluates platform investments with different metrics than product launches. They expected Kindle, AWS, and Prime to lose money initially while building long-term competitive advantages.

Google uses systematic frameworks to avoid identity bias in breakthrough technology evaluation. Android didn't fit their search advertising identity, but they evaluated it with platform-appropriate criteria.

Apple applies different decision frameworks to breakthrough products versus incremental improvements. They gave iPhone multiple years to build ecosystem momentum instead of expecting immediate profitability.

These companies avoid the systematic thinking errors that destroyed WebOS because they use decision frameworks designed for innovation uncertainty.

Here's the reality: this challenge isn't going away. Breakthrough technologies will continue emerging faster than traditional business frameworks can evaluate them. The companies that develop systematic innovation decision capabilities will capture enormous value. Those that rely on conventional thinking will consistently destroy breakthrough opportunities.

Your Three Action Steps:

First: Download the DECIDE Framework toolkit and apply it to your current highest-stakes innovation decision before evaluating any options.

Second: Audit your innovation evaluation processes. Are you using traditional business metrics to evaluate breakthrough technology? Conventional timelines for platform investments? Identity-driven thinking for disruptive opportunities?

Third: Build systematic innovation decision capabilities into your organization. Train your team to recognize cognitive biases, use appropriate evidence frameworks, and generate multiple creative alternatives.

Questions to Consider:

But here's the final piece of this story that shows just how costly these thinking errors can be: Leo Apotheker was fired on September 22, 2011—just 35 days after shutting down WebOS and eleven months after taking over as CEO. The board finally recognized the systematic thinking errors that had destroyed billions in value, but it was too late for WebOS.

The human cost of these decisions goes beyond stock prices and quarterly reports. There are real people who believed in breakthrough technology, fought for innovation, and had to watch it get destroyed by preventable thinking errors.

The complete personal story of watching this disaster unfold—including details about the brutal aftermath and why I still believe in HP despite everything—is in this week's Studio Notes over on Substack. 

Read Studio Notes on Substack

Remember: when you have breakthrough technology in your hands, the quality of your decision-making process matters more than the quality of your technology. Intelligence and good intentions aren't enough. You need systematic frameworks for thinking clearly about innovation under uncertainty.

The tools exist to prevent these disasters. The question is whether you'll implement them before your next WebOS moment.

Remember—in a world where billion-dollar innovations can be killed in 49 days, systematic decision frameworks might be your most valuable competitive advantage.

If you found this week's episode valuable, subscribe to the podcast or watch on the YouTube channel.

To learn more about HP's WebOS failure and the innovation decision-making framework, listen to this week's show: The $1.2 Billion Innovation Disaster: 5 Decision Mistakes That Kill Breakthrough Technology (HP WebOS Case Study).

University of Washington researchers discovered something that should concern every parent: children who use AI to create can no longer create without it. And here's the concerning part: most parents have absolutely no idea it's happening.

If you've been following our series on Creative Thinking in the AI Age, you know I've been tracking how artificial intelligence is rewiring human creativity. We've explored the 30% decline in creative thinking among adults, the science of neuroplasticity, and practical exercises to rebuild our creative capabilities.

But today's episode is different. Today, we're talking about your child's developing brain. And I need to be direct with you—the next 30 minutes might be the most important parenting conversation you have this year.

Because while we've been worried about AI taking our jobs, it's already changing our children's minds. Unlike us adults, who developed our creative thinking before AI existed, our kids are growing up with artificial intelligence as their creative co-pilot from the very beginning.

Here's my promise to you: by the end of this episode, you'll know exactly how to tell if your child is developing AI dependency, you'll understand why their developing brain is more vulnerable than yours, and you'll have an assessment tool to evaluate your family's situation—plus immediate strategies you can start using today.

But first, let me show you what's happening in homes just like yours—and why this is both preventable and completely reversible.

A few weeks ago, a mother shared a story that stopped me in my tracks. Her 10-year-old daughter used to spend hours drawing elaborate fantasy worlds, completely absorbed in her creative process. Now, when her mother suggests drawing something, the daughter responds, ‘Can I just use AI to make it look better?' At first, this seemed like smart efficiency—why not use available tools? However, when the mother asked her daughter to draw a simple picture with no digital help, something alarming occurred. The child just stared at the blank paper and started crying, unable to create anything on her own.

This story isn't unique. It's happening everywhere, and parents are missing it because the signs look like success.

Before we go further, let me be clear: this isn't your fault. AI dependency developed gradually, and most parents missed the early signs because they actually looked positive.

Think about your own child for a moment. Has their homework gotten easier? Do they finish writing assignments faster than they used to? Are their projects suddenly more polished? If you answered yes, you might be looking at what I call the “homework mirage.”

Here's what the homework mirage looks like: Your child sits down to write a story for English class. Instead of staring at the blank page like kids have done for generations, they open ChatGPT. They type: “Write me a story about a brave knight.” In thirty seconds, they have three paragraphs that would have taken them an hour to write.

You see the finished assignment. It's well-written, grammatically correct, and creative. You think, “Great! They're learning to use technology efficiently.” But here's what you don't see: your child's brain just missed a crucial workout.

Remember in our first episode when we talked about brain pathways being like muscles? When we don't use them, they weaken. This is happening to children at a speed that concerns researchers worldwide. (Reference: Newman, M. et al., 2024, “I want it to talk like Darth Vader: Helping Children Construct Creative Self-Efficacy with Generative AI,” University of Washington)

Dr. Ying Xu from Harvard put it perfectly when she asked the critical question: “Are they actually engaging in the learning process, or are they bypassing it by getting an easy answer from the AI?”

And here's the concerning part—kids who use AI to complete tasks do produce higher quality work in the short term. But when you take the AI away, their abilities are worse than before they started using it.

But this goes way beyond homework. Children are experiencing what experts call the “Creative Confidence Crisis.” Kids who used to love making art now say, “I'm not good enough” when they see AI-generated images. Children ask AI to help with simple creative tasks, such as making up games or telling stories.

The scale of this problem is significant. Recent research shows that 31% of teenagers are already using AI to create pictures and images. Sixteen percent are using it to make music. And parents? Most have no idea how much their children are depending on these tools.

As one researcher told me, “Parents and teachers are pretty much out of the loop, so young people are using AI platforms with virtually no guidance.”

This brings us to a crucial question: Why are children more vulnerable to this than adults?

In our second episode, we explored neuroplasticity—your brain's ability to reorganize itself throughout your life. But children's brains aren't just plastic; they're in active construction mode.

Think of an adult brain like a well-established city with roads and infrastructure already built. A child's brain is more like a city being built from scratch. The roads they travel most frequently become the highways of their adult thinking.

This is why the creative pathways your child develops now will determine their innovative capabilities for life. While AI can already outperform humans at data analysis, writing, and even coding, it cannot replicate the uniquely human ability to make unexpected connections, challenge assumptions, and imagine what doesn't yet exist. 

The children who develop strong creative thinking skills today will be the ones who thrive in tomorrow's AI-dominated world—they'll be the innovators, entrepreneurs, and problem-solvers who can work with artificial intelligence without being replaced by it.

These future-critical abilities depend on four specific creative thinking systems that are strengthened or weakened based on how children use them. When children become AI-dependent, these four systems are at risk:

Cognitive Flexibility—your child's ability to switch between different thinking modes. This is what allows them to see a cardboard box as a spaceship, then a house, and then a robot costume. When children always ask AI, “What should I make?” instead of experimenting, this flexibility is weakened.

Associative Thinking—connecting seemingly unrelated ideas. This is how kids come up with wild and wonderful ideas, like “What if cars could swim?” When AI provides ready-made connections, children stop making their own unique associations.

Divergent Thinking—generating multiple solutions to open-ended problems. AI excels at convergent thinking—identifying the best answer. But human creativity thrives on divergent thinking—exploring all possible answers.

Constraint Breaking—the ability to identify and overcome hidden assumptions limiting their thinking. This is what lets children question rules like “stories have to make sense” or “art has to look realistic.” When AI always provides solutions within conventional parameters, children stop challenging the boundaries of what's possible.

When these systems weaken, children develop what is called “Creative Bypass Syndrome.” They learn to jump straight to AI whenever they encounter creative challenges. Their brains literally rewire to avoid the hard work of original thinking.

But there's another crucial element that supports all four of these systems: frustration tolerance—your child's ability to persist through difficult problems without immediate relief. This is where the real creative magic happens. Those moments when your child sits with a problem, feels stuck, pushes through the discomfort, and discovers something unexpected. AI eliminates this essential struggle by providing instant solutions.

Think about the last time you watched your child work through a challenging puzzle or try to build something that kept falling down. That frustration they felt? That's their brain building resilience and creative persistence. When children can immediately turn to AI for answers, they miss these crucial mental workouts.

But here's the encouraging news: because children's brains are so adaptable, they can also recover faster than adults. The creative pathways that have weakened can be rebuilt. The confidence that's been lost can be restored.

Now, before we talk solutions, you need to know where your child stands right now.

I've developed a simple test that you can do at home to evaluate your child's creative resilience. You can download the complete assessment tool from our website, but let me walk you through the key elements right now.

Important setup instructions: Set aside 15 uninterrupted minutes for this assessment. Choose a time when your child is relaxed, not rushed or hungry. Find a quiet space—the kitchen table works perfectly. Have other siblings play elsewhere during the test. If your child resists or asks “why,” simply say “I'm curious about something” and keep it light.

The assessment is based on one fundamental principle: creative confidence shows up in how children respond to open-ended challenges with no right answer.

For ages 5 to 8, try what I call the “Magic Box Challenge.” Give your child an empty cardboard box—a shoe box works perfectly. Tell them: “This is a magic box that can become anything you want. Show me what you'd like it to be.”

Then step back and observe. Don't give suggestions. Don't offer help. Don't provide materials unless they specifically ask. Just watch how they respond.

For ages 9 to 12, try the “Problem Inventor Challenge.” Ask your child to invent a problem that needs solving, then solve it. Give them exactly ten minutes. No devices, no external input. Say: “Pretend you're an inventor. What problem would you want to solve, and how would you solve it?”

For teenagers, ages 13 to 17, use the “Original Idea Test.” Ask them to come up with an original, creative project idea in any medium—art, writing, music, video, anything. They need to explain why this idea interests them personally. Give them up to ten minutes and say: “If you could create anything right now—no limits on time or resources—what would you make and why?”

Now, let me show you how to score what you observe. As you watch your child during their assessment, use this scoring guide to identify which traits they exhibit. You can circle or check off the behaviors you notice, then see which zone has the most matches.

Beyond these specific tests, there are daily warning signs every parent should recognize:

Watch for homework that suddenly takes much less time than usual. Notice if their writing voice changes dramatically—AI-generated text often sounds more sophisticated than your child's natural voice, but it lacks their personality. Pay attention to decreased tolerance for boredom. Listen for language changes—are they asking “How do I…” instead of experimenting?

This scoring guide will help you determine if your child is in the Green Zone (creative confidence intact), Yellow Zone (some AI dependence developing), or Red Zone (significant creative confidence impact).

If you discover your child is in the Yellow or Red Zone, take a deep breath. Remember, children's brains are remarkably adaptable—this can absolutely be improved. Let me show you how.

If your assessment revealed concerning signs, here's your “start here” action plan: If you do nothing else this week, try the assessment. If your child is in Yellow or Red Zone, then implement the 24-hour creative pause I'm about to describe.

I want you to try what I call the “24-Hour Creative Independence Check.” This is a gentle but effective way to help your child reconnect with their natural creative abilities.

Step One is the Creative Pause Period. For twenty-four hours, your child takes a break from AI assistance for any creative task. No ChatGPT for writing. No AI image generators for art projects. No “looking up” ideas or solutions online for creative work.

Think of this like giving their creative muscles a chance to stretch and remember what they can do on their own. During this period, your child will likely experience some frustration. This is normal and actually beneficial—it's their brain remembering how to work through challenges independently.

Step Two is building a Support System. Your job isn't to eliminate their frustration but to support them through it. Instead of saying “Let me help you,” try “This seems challenging. What are you thinking?” Instead of “Maybe look it up,” try “What would happen if you just tried something?”

The most important thing you can do is manage your own discomfort with their struggle. When you see your child working through a creative challenge, every parenting instinct tells you to help. But supporting them through creative problem-solving builds the brain resilience they need.

Step Three is celebrating effort over outcome. When your child creates something during this pause period, focus your praise on their thinking and persistence, not the result. Say “I love how you kept trying different approaches” instead of “That's beautiful.”

After the 24-hour pause, establish ongoing creative strength training. Think of these as daily workouts for your child's creative brain:

For younger kids: Try “Daily Wonder Questions”—five minutes of “what if” questions with no right answers. “What if gravity worked backwards on Tuesdays?”

For middle schoolers: Use the “Daily Assumption Challenge”—question one obvious assumption each day. “Why do schools have to happen in buildings?”

For teenagers: Try the “Creative Perspective Shift”—describe any current event from three completely different viewpoints.

For more detailed creative exercises tailored to different ages and situations, check out our Episode 3, “Creative Thinking Exercises: 10-Minute Daily Brain Workout to Boost Innovation.”

Your role as a parent is crucial. You're not trying to become your child's creative manager. Instead, you're becoming what researchers call a “creative support system.”

Model working through challenges yourself. Let your children see you tackling problems, trying new approaches, and persisting when things don't work immediately. Create a family culture that values thinking over knowing. Celebrate questions as much as answers.

The strategies I've shared will help immediately, but developing strong creative independence requires a systematic approach. I recommend a two-phase development plan:

Phase One, weeks 1 to 2, is Assess and Adjust. Begin with the creative independence assessment while gently reducing AI dependence. This is when you implement the 24-hour pause and begin daily creative exercises.

Phase Two, weeks 3 to 6, is Develop and Strengthen. Increase daily creative exercises and build persistence with challenging tasks. This is when you'll start seeing real improvements in your child's creative confidence and independence.

There's also a Phase Three—learning strategic AI use that enhances rather than replaces creativity. However, this assumes you've first mastered the strategic use of AI yourself. If you want to learn these skills, watch our episode “The AI Creativity Multiplier: 5 Steps to Amplify Your Innovative Thinking.“

Let me be realistic about expectations. During the first week or two, you'll likely see some resistance. Your child might complain that creative tasks feel more challenging without AI assistance.

This is normal and temporary. You're asking them to use creative abilities that have been getting less exercise. Signs of improvement include increased creative attempts, less anxiety about “not knowing” the right answer, and willingness to experiment with ideas.

You should see positive changes within two to three weeks of consistent practice. For most families, this systematic approach will not only restore your child's natural creative abilities—it will make them stronger creative thinkers than they were before.

You're at a choice point right now. You can assume this will work itself out as children “figure out” the right balance with AI. Or you can take action today to protect and strengthen your child's creative brain.

Here's the reality: this challenge will only grow as AI tools become more sophisticated and appealing to children. The longer you wait, the deeper these patterns become.

But the encouraging news is that you have everything you need to start making a difference today.

Your specific next step: Download the complete Family Creative Assessment from our website. This includes detailed instructions for the Challenges, plus additional age-appropriate creative challenges, a parent observation checklist, progress tracking sheets, and the complete 24-hour creative pause protocol.

Download The Family Creativity Assessment and Tools

Try one of the assessment challenges with your child this week—start with whichever one matches their age. Then post a comment, and share one specific thing you discovered about your child's creative independence.

Based on your comments on this episode, I'll know whether to create more detailed, age-specific guides for families who want to build their child's creative muscle.

Remember, the children who develop creative resilience today will be the innovators, problem-solvers, and leaders of tomorrow. They'll be the humans who can work with AI without losing what makes them uniquely, authentically human.

Your child's creative brain is waiting for your guidance. The question is: Are you ready to take that first step?

Until next time, I'm Phil McKinney, and remember—in an age of artificial intelligence, your child's creative thinking has never been more important.

Download the Complete Family Creative Assessment: https://open.substack.com/pub/philmckinney/p/download-your-guide-for-protecting

To learn more about AI dependency, listen to this week's show: Human-AI Creative Partnership: How AI Dependency Is Rewiring Your Child's Creative Brain (And What Parents Can Do About It).

The most innovative creators don't use AI as a replacement – they use it as a strategic partner in a carefully choreographed dance of human and machine intelligence.

Welcome to Part 4 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement.

In Part 1, we explored the alarming decline in creative thinking as we've grown dependent on AI. In Part 2, we discovered how neuroplasticity allows us to rebuild and enhance our creative capabilities. And in Part 3, I gave you a practical 10-minute daily workout to strengthen the neural pathways essential for innovative thinking.

Today, we're bringing it all together with something immediately actionable: a framework for creating productive partnerships with AI that enhance rather than diminish your creative capabilities.

This isn't about rejecting AI – it's about using it strategically to amplify your uniquely human abilities. When used properly, AI can handle routine cognitive tasks while freeing your mind for the breakthrough thinking that algorithms simply cannot replicate.

Let me start by clarifying the fundamental difference between human and machine intelligence that drives this partnership:

Convergent thinking is the process of analyzing existing data to find optimal solutions within defined parameters. This is what AI excels at – processing vast amounts of information to identify patterns and generate options based on probability distributions of what has worked before.

Divergent thinking is the ability to generate novel ideas by making unexpected connections, breaking conventional patterns, and imagining what doesn't yet exist. This is where humans uniquely excel – our capacity for intuitive leaps, metaphorical thinking, and insight that transcends existing data.

The most powerful creative partnerships leverage both: AI's computational strength and the human capacity for originality. Let me demonstrate with a simple example.

If I asked an AI to design a chair, it would analyze thousands of existing chair designs and generate variations based on established patterns. The results would be functional but predictable.

But what if I first engaged in divergent thinking by questioning the very concept of sitting? What if I reimagined a chair as something that supports the body in motion rather than at rest? This human insight – this conceptual leap – changes everything about how we might approach the design.

Now when I engage AI, I'm not asking it to “design a chair” but to help explore a completely new approach to supporting the human body. The AI becomes a tool for expanding and refining my original insight rather than a replacement for it.

This is the heart of creative partnership: human divergent thinking provides the spark of originality, while AI convergent thinking helps develop and refine that spark into something practical.

Before we dive into our five-step framework, let's talk about what makes an effective AI prompt for creative work. The way you communicate with AI dramatically impacts the quality and originality of what you receive in return.

Throughout this episode, I've included actual prompts formatted in code blocks that you can copy, edit, and paste directly into your favorite AI tool – whether that's ChatGPT, Claude, or others. These aren't theoretical; they're battle-tested approaches I've used with innovation teams.

The most powerful creative prompts share three key characteristics:

When using the example prompts throughout this episode, customize them to your specific challenge, but maintain these structural elements that encourage exploration rather than premature convergence. The goal is to shape AI responses that serve as thought-provoking material for your own creative thinking, not as final answers.

Here's a quick formula for effective prompts:

Now, let's explore our five-step framework for forming creative partnerships with AI that enhance rather than diminish your creative capabilities.

The most common mistake I see is turning to AI too early in the creative process. This typically happens because facing a blank page is uncomfortable – we're seeking the path of least resistance.

But this short-circuits your brain's ability to make original connections. Instead, I recommend priming your brain before engaging any AI tools.

Here's how:

This priming step activates your associative thinking networks – the neural pathways that connect seemingly unrelated concepts. When you later engage AI, you'll do so with your creative faculties already warmed up and ready to evaluate AI outputs critically.

How you engage with AI fundamentally shapes what you get from it. The key is to position AI as a thought partner exploring a problem space rather than a solution generator.

Instead of asking: “Generate ideas for a new water bottle design”

Try: “What are the unsolved problems in how people stay hydrated throughout the day?”

The first prompt tells AI to generate variations on a water bottle – convergent thinking within established parameters. The second prompt opens a problem space that invites exploration of the underlying challenge.

Similarly, rather than asking AI to “write a marketing campaign,” ask it to “identify emotional tensions between consumers and existing products in this category.”

This framing preserves your role in the most crucial part of creativity – defining the right problem. It positions AI as an explorer rather than a solver, helping you see facets of the challenge you might otherwise miss.

Example Problem-Framing Prompts:

Example 1:

Example 2:

While AI excels at convergent thinking, we can strategically use it to accelerate certain aspects of divergent thinking as well. The key is to use AI to generate raw material that you then transform through your human creativity.

Here's the technique:

For example, if you're designing a new learning app, you might ask AI: “How do master chefs structure the process of teaching complex skills?” or “What principles do video game designers use to maintain engagement during difficult challenges?”

The AI responses become raw material for your own divergent thinking process. You aren't adopting the AI's suggestions directly – you're using them to trigger new neural connections in your own thinking.

This approach leverages AI's knowledge breadth while preserving your uniquely human ability to make unexpected connections across domains.

Example Divergence Acceleration Prompts:

Example 1:

Example 2:

Once you've generated truly original directions through divergent thinking, AI becomes extraordinarily valuable for convergent activities – developing, refining, and optimizing your creative insights.

This is where many people go wrong – they either overuse AI (surrendering the creative process entirely) or underuse it (ignoring its analytical strengths).

Here are specific convergent tasks ideally suited for AI delegation:

The key principle: Use AI for expansion and refinement of ideas that originated from your divergent thinking, not as the source of the original insight itself.

For example, if you've conceptualized a novel approach to remote team collaboration, you might ask AI to:

This leverages AI's analytical power while preserving your role in the creative breakthrough.

Example Convergence Delegation Prompts:

Example 1:

Example 2:

The final step is perhaps the most important: consciously maintaining your creative authority throughout the process.

AI tools are designed to be persuasive – they present information confidently and comprehensively. This creates what psychologists call the “authority bias” – our tendency to accept information from perceived authorities without sufficient scrutiny.

To maintain creative authority:

Remember, the goal isn't to reject AI's contributions but to engage with them critically and creatively. Your unique human perspective – your lived experience, intuition, and values – should always remain the guiding force.

Example Creative Authority Prompts:

Example 1:

Example 2:

Download Your Guide/Prompts for Turning AI Into a Creativity Multiplier

Let me share how this framework transformed the product development process at a consumer electronics company I worked with recently.

Their team had been using AI tools extensively, but primarily as idea generators – essentially asking the AI to design new products directly. The results were predictably mediocre – variations on existing products with marginal improvements.

We implemented the five-step framework, beginning with creative priming exercises before any AI engagement. Then, instead of asking the AI to generate product concepts, we asked it to explore unresolved tensions in how people interact with technology in their homes.

This exploration revealed something fascinating – people were increasingly concerned about technology fragmenting family attention rather than enhancing connection. This human-centered insight came not from the AI directly, but from the team's analysis of the problem space with AI assistance.

This led to a breakthrough concept: a family gaming system designed specifically for collaborative rather than competitive or individual play, with features that actively encouraged rich social interaction rather than isolated immersion.

Once this novel direction was established through human divergent thinking, the team then used AI extensively for convergent tasks – researching existing collaborative technologies, identifying potential technical challenges, and developing implementation variations.

The result was a genuinely innovative product that addressed deeply human needs in ways that AI alone could never have conceptualized. The product has since become one of their most successful launches, precisely because it originated from human insight about social connection rather than algorithmic prediction.

We've now completed our five-step framework for creative partnerships with AI: prime your brain first, frame challenges not solutions, use AI for divergence acceleration, delegate convergence, and maintain creative authority.

Each step is designed to leverage both human and machine intelligence in their respective domains of strength – your divergent thinking and AI's convergent capabilities.

This approach represents a middle path between two extremes. On one side is complete AI dependency – surrendering our creative faculties to algorithms and experiencing the cognitive atrophy we discussed in earlier episodes. On the other side is AI rejection – ignoring powerful tools that could genuinely enhance our creative capabilities when used properly.

The creative partnership I've outlined offers something better: a complementary relationship that amplifies your uniquely human creativity while leveraging AI's computational power.

Remember the key principles we've explored throughout this series:

As we move deeper into the AI age, the ability to form these productive partnerships will increasingly distinguish those who merely execute from those who truly innovate. By understanding the complementary relationship between human and machine intelligence, you can develop creativity that no algorithm can replicate.

Download the FREE guide, with the AI prompts, that will help you use AI as your creativity multiplier.

Download Your Guide for Turning AI Into a Creativity Multiplier

Join me next time for “Your Child's Creative Brain on AI” We'll explore how to assess your creative development and build systems that continuously enhance your innovative thinking. Your child's creative brain is waiting for your guidance. The question is: Are you ready to take that first step?

Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, the most valuable thinking happens at the intersection of human insight and computational power. That intersection exists in only one place: your creatively engaged mind.

Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a paid subscriber on Substack.

Your support helps make this content possible.

To learn more about harnessing AI, listen to this week's show: Human-AI Creative Partnership: How to Harness AI While Preserving Your Innovative Edge.

Humans who committed to four thinking exercises for 10 minutes daily generated 43% more original solutions than the most advanced AI systems.

Welcome to Part 3 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement.

In Part 1, we explored the concerning 30% decline in creative thinking as our use of AI tools has increased. In Part 2, we discovered how neuroplasticity – your brain's lifelong ability to reorganize itself – offers us a pathway to not just recover but enhance our creative abilities.

Today, I'm giving you something concrete and practical: a complete 10-minute creative thinking workout based on cutting-edge neuroplasticity research. This isn't just theory – it's a systematic approach to rebuilding the neural pathways essential for innovative thinking.

What makes today's episode especially valuable is that these exercises directly target the four core domains of creative thinking we identified last time:

These aren't just abstract concepts – they're distinct neural networks in your brain that physically strengthen or weaken based on how you use them. Neuroscience has clearly mapped these networks using fMRI studies. When we frequently outsource creative challenges to AI, these networks get less exercise and gradually atrophy. This atrophy directly affects not just our individual capabilities but our collective ability to solve complex problems as a society.

Think of these four domains as the core muscle groups of creative thinking. Just as a neglected muscle weakens over time, these neural networks diminish when underutilized. And just as physical weakness limits our bodily capabilities, creative atrophy limits our problem-solving potential, career advancement, and ability to address society's most pressing challenges.

The research I shared last time showed that consistent practice leads to measurable changes:

This gives us a clear roadmap for strengthening our creative capacities: commit to eight weeks of practice, with meaningful milestones along the way.

Before we dive in, I want to emphasize something important: consistency matters more than duration. Research shows that 10 minutes daily produces significantly better results than 70 minutes once a week. This aligns with what neuroscientists call “spaced practice” – shorter, regular sessions that allow your brain to consolidate learning between sessions.

Also, approach these exercises with playfulness rather than pressure. Neuroplasticity research shows that stress inhibits the very neural changes we're trying to promote, while curiosity and enjoyment accelerate them.

Ready to begin? Let's start with our first exercise.

Our first exercise targets Cognitive Flexibility – your ability to switch between different thinking modes and see situations from multiple perspectives.

This exercise activates your prefrontal cortex – the brain region responsible for cognitive flexibility. This region weakens with routine AI assistance, as algorithms typically present optimized single perspectives rather than multiple viewpoints.

Here's how the exercise works:

The key is to shift rapidly between perspectives rather than dwelling on any single viewpoint. Each shift creates new neural firing patterns that strengthen cognitive flexibility.

Let me show you some examples with this coffee mug:

Now it's your turn. Find an object near you, pause the video, and spend 2 minutes shifting through different perspectives.

When you're done, take a deep breath. You've just activated neural pathways associated with cognitive flexibility.

What you'll notice with consistent practice is that this ability to shift perspectives begins extending to all areas of your thinking – helping you see multiple angles in business challenges, personal relationships, and creative projects.

Our second exercise targets Associative Thinking – your ability to connect seemingly unrelated concepts to form novel ideas.

This practice activates your brain's default mode network. This network gets less exercise when we regularly use AI for creative solutions, but rebuilds with exercises that create unexpected connections.

Here's the exercise:

Let me demonstrate with my three random words: “mountain,” “keyboard,” and “breakfast.”

I might create a concept for: “Summit Typing Café” – a mountain-top co-working space that offers spectacular views and serves breakfast all day. Digital nomads can work at ergonomic keyboard stations while enjoying high-altitude inspiration and nourishing food.

Or perhaps: A new productivity app called “Peak Breakfast” that uses keyboard shortcuts to help you plan your most important tasks during your morning meal – the idea being that like reaching a mountain summit, completing your most challenging task first thing gives you perspective for the rest of your day.

Now try it yourself. Generate three random words, pause the video, and spend 2 minutes creating a concept that combines them.

The magic of this exercise is that it forces your brain to create connections where none previously existed. Each time you practice, you're physically strengthening the neural pathways involved in associative thinking.

With regular practice, you'll notice your ability to connect disparate ideas improving in all areas of your life – leading to more original solutions and creative insights.

Our third exercise targets Divergent Thinking – your ability to generate multiple solutions to an open-ended problem.

This exercise stimulates your frontal and temporal lobes. These brain regions show increased connectivity after divergent thinking practice but get less activation when we habitually ask AI to generate ideas.

Here's how it works:

Let me demonstrate with a simple rubber band:

Now it's your turn. Choose an object, pause the video, and list as many uses as you can in 2 minutes.

The first few uses typically come from memory – things you've seen before. As you push beyond those obvious answers, different neural pathways activate.

Research shows that the most creative ideas emerge after the obvious ones are exhausted. By generating many options, you train your brain to access deeper, more original ideas more readily.

With consistent practice, you'll notice yourself spontaneously generating more options in everyday situations – whether designing products, solving problems, or making decisions.

Our final exercise targets Constraint Breaking – your ability to identify and overcome hidden assumptions limiting your thinking.

This exercise activates your anterior cingulate cortex – the brain region that detects cognitive conflicts. This area receives less stimulation when we frequently use AI systems that operate within established parameters rather than questioning basic assumptions.

Here's the exercise:

Let me demonstrate with a common product: a refrigerator.

Assumptions about refrigerators:

Now, let's reverse these:

Your turn now. Choose a product or service, pause the video, and spend 2 minutes listing and challenging its assumptions.

This exercise reveals invisible constraints we place on our thinking without realizing it. Each practice session strengthens your ability to identify and question assumptions – essential for breakthrough innovation.

With consistent practice, you'll begin questioning assumptions automatically in various contexts, finding original approaches others miss.

Now that we've explored each exercise individually, let's discuss how to incorporate them into your routine and apply them to specific situations.

Think of these four creative thinking domains like the major muscle groups in your body. Just as physical fitness requires working all muscle groups – not just your favorites – cognitive fitness demands exercising all four creative domains. Without this balance, your creative abilities will develop unevenly.

We've all seen the bodybuilder with massive upper body development but skinny legs – what trainers call “chicken leg syndrome.” The same imbalance happens in creative thinking when we only exercise our preferred domains. You might excel at divergent thinking (generating many options) but struggle with constraint breaking (questioning assumptions).

The most effective approach for building complete creative fitness is to practice all four exercises in sequence, allocating 2 minutes to each, with a brief transition between them. This provides balanced “cross-training” across all four creative thinking neural networks.

I recommend starting your day with this workout, ideally before checking email or social media. Research from the University of California shows that creative thinking is significantly higher in the morning, before our brains become loaded with external inputs.

However, these exercises are also incredibly versatile for specific situations. Consider bookmarking this video to quickly access the exact exercise you need for different challenges:

Each exercise serves as a targeted tool you can deploy in specific professional and personal contexts. The timestamps in the video description make it easy to jump directly to the exercise you need in the moment.

Just as with physical training, these exercises might feel challenging at first – that's normal and actually a good sign. The neural equivalent of “muscle soreness” means you're creating productive disruption that leads to growth. And just as physical training requires progressive challenge to avoid plateaus, you should gradually increase the difficulty of these exercises by setting more ambitious targets or tighter time constraints.

Also like physical training, consistency trumps intensity. A daily 10-minute workout will produce far better results than an occasional hour-long session. Neuroscientists call this “spaced practice” – shorter, frequent sessions that allow your brain to consolidate learning between workouts.

To track your progress, I suggest keeping a simple creativity journal. After each workout, spend 30 seconds noting:

Over time, you'll notice patterns – exercises that initially felt difficult become easier, and your idea generation becomes more fluid and original.

Let me share how one innovation team I worked with integrated these exercises into their process.

This team was developing new healthcare technologies and had hit a creative plateau. They began each day with this 10-minute workout, then immediately applied the activated thinking patterns to their current challenges.

Within three weeks, they reported two significant breakthroughs:

First, the Perspective Shifting exercise helped them reimagine their user interface from the viewpoint of different stakeholders – leading to a design that accommodated both clinical and patient needs in ways their competitors had missed.

Second, the Assumption Reversal exercise helped them question fundamental assumptions about data security – leading to a novel approach that provided better protection while actually improving system performance.

The team leader described it as “mental cross-training” that enhanced their collective intelligence beyond what AI tools alone could have contributed.

You can apply this same process to your challenges:

Over time, you'll develop what neuroscientists call “trained intuition” – generating creative insights without consciously applying techniques.

We've now completed our creative brain workout – four exercises that systematically strengthen the neural networks essential for innovative thinking.

As we discussed in our previous episodes, the increasing integration of AI tools into our daily work has led to measurable changes in how we approach creative challenges. But the science of neuroplasticity offers us a powerful counterbalance – the ability to deliberately strengthen our innovative thinking capabilities throughout our lives.

This research applies to everyone, regardless of age or background. Whether you're a student, professional, entrepreneur, or retiree, these exercises enhance creative capabilities through physical changes in your brain structure.

Remember the key milestones we discussed:

The choice ultimately comes down to being intentional about how we use technology. You can automate creative processes entirely with AI and potentially experience the gradual atrophy of these essential cognitive abilities. Or you can strategically partner with AI while deliberately strengthening your uniquely human capabilities that drive breakthrough innovation.

My hope is that you'll choose the latter path – not just for your individual benefit, but for our collective future. The challenges we face as a society – from climate change to healthcare access to sustainable energy – require precisely the kind of boundary-breaking, assumption-challenging thinking these exercises develop.

Join me next time for “The AI Creativity Multiplier: 5 Steps to Amplify Your Innovative Thinking.” 

Ever wondered how top innovators use AI to amplify their creativity rather than replace it? I'll reveal the surprising “creative handoff points” where AI transforms from a creativity killer to creative rocket fuel. You'll discover how to craft AI prompts that break through creative barriers instead of building new ones, turning your favorite AI tools into innovation accelerators unlike anything you've experienced.

If this episode gave you the exercises to strengthen your creative thinking muscles, the next one will show you how to apply that strength in partnership with AI – creating results neither could achieve alone.

Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, your creative brain remains your most valuable asset. Take 10 minutes to strengthen it today.

Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a paid subscriber on Substack.

Your support helps make this content possible.

To learn more about strengthening creative thinking, listen to this week's show: How to Strengthen Creative Thinking: The 10-Minute Daily Brain Workout Based on Neuroplasticity Research.

Harvard neuroscientists confirm: creative thinking uses neural pathways that AI can't replicate – and never will.

Hello, I'm Phil McKinney, and welcome to my innovation studio.

Welcome to Part 2 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement.

In Part 1, we explored the alarming decline in creative thinking as we've grown dependent on AI. We saw how our ability to solve complex problems without algorithmic assistance has dropped by 30% in just five years, and how this cognitive atrophy affects everyone from students to seasoned professionals.

Today, we're moving from problem to solution – exploring the revolutionary science of neuroplasticity and how we can deliberately rebuild and enhance our creative thinking skills.

What's at stake here goes far beyond individual convenience. If we continue to surrender our creative thinking abilities to AI, we risk a future where innovation slows, where original ideas become increasingly rare, and where our unique human capacity for breakthrough thinking gradually fades. More critically, we may lose the very cognitive tools required to solve society's most pressing challenges – disease, pandemic response, clean energy development, food security – precisely when we need these abilities most.

We're already seeing early evidence of this decline, but the science I'll share today offers a powerful alternative – a path to not just preserve but dramatically enhance the creative abilities that drive human progress.

I've seen this firsthand in my work leading innovation teams. Years ago, I noticed that even brilliant engineers and designers would hit creative walls. When I introduced specific neuroplasticity-based thinking exercises into our daily routines, the transformation was remarkable. Teams that had been spinning their wheels suddenly generated breakthrough concepts. Projects that seemed stuck found fresh momentum. And the most exciting part? The improvements continued long after the initial training.

These transformations aren't magic – they're biology in action. Your brain is changing right now as you watch this video. Every thought you have, every skill you practice, and every challenge you undertake physically reshapes your neural architecture. This isn't metaphorical – it's literal, structural change happening at the cellular level.

This phenomenon – called neuroplasticity – is the brain's ability to reorganize itself by forming new neural connections. And our key to reclaiming and enhancing our creative thinking abilities in the age of AI.

For decades, scientists believed that brain development stopped after childhood. We now know that's completely false. Your brain remains malleable throughout your entire life, capable of dramatic transformation well into your 80s and beyond.

Research has shown that our brains continually remodel themselves based on our experiences and practices. Think of it like a path in a forest – the routes you travel most frequently become wider and clearer, while those rarely used gradually disappear.

Now, I understand some skepticism here.

We've all seen dubious claims about “brain training” games and apps that promise to boost intelligence. Most of these have been rightfully criticized for overpromising and underdelivering.

The difference with creative neuroplasticity training is that it's not about playing generic puzzles – it's about targeted exercises that specifically engage the neural networks involved in creative thinking. And unlike those commercial products, these approaches have substantial peer-reviewed research supporting their effectiveness.

The implications are profound. If our cognitive abilities are declining due to AI dependency, as we discussed in the last episode, we can deliberately reverse this trend through targeted exercises and practice.

Let's be honest – breaking AI dependency isn't easy. Many of us have developed reflexive habits of turning to algorithms before engaging our own thinking. Our brains naturally seek the path of least resistance. But the research is clear: the effort to rebuild these creative pathways is absolutely worth it. And the good news is that even small, consistent practice can yield significant results.

The science behind this is compelling. A landmark study at Harvard Medical School used functional MRI to track brain activity before and after an 8-week creative thinking training program. The results were striking.

Before training, participants showed activity primarily in conventional problem-solving regions when tackling creative challenges. After training, their brains revealed significantly increased activity in regions associated with novel idea generation and reduced activity in regions associated with conventional thinking.

What's even more fascinating is that the neural training correlated with a 43% increase in measured creative output. The participants weren't just thinking differently – they were producing significantly more original ideas.

This is neuroplasticity in action – physical changes in your brain leading to measurable improvements in creative capacity.

But neuroplasticity works both ways. When we outsource our thinking to AI, the neural pathways associated with creative problem-solving literally weaken from disuse. It's a biological principle called “competitive plasticity” – the brain reallocates resources away from underused functions toward frequently used ones.

The good news is that this process is reversible. Even if you've grown dependent on AI for creative tasks, your brain can rebuild these pathways through deliberate practice.

Let me share a personal experience from my own work.

I once coached a senior product designer and their team at a major tech company who were tasked with developing disruptive ideas in an area where three major competitors were already investing heavily. When we started working together, they were stuck, repeatedly generating variations of the same concepts and feeling increasingly frustrated.

Brain science would suggest their neural pathways had become rigid through years of conventional problem-solving. So we implemented a series of targeted creative thinking exercises. Within eight weeks, something remarkable happened.

Not only did their idea generation rate triple, but the quality of their concepts ‌shifted. They developed a breakthrough approach that combined elements no one had previously connected, essentially creating an entirely new product category.

When we brought in AI tools to analyze the solution space, the team's most innovative concepts fell completely outside the AI's prediction patterns. What does this mean? The neural connections they had formed with their training weren't following the statistical patterns the AI model had learned.

The product they launched went on to capture significant market share precisely because it operated from a different conceptual framework than competitors. This wasn't just a professional transformation. It had a personal impact. This senior product designer reported feeling a renewed sense of cognitive confidence that extended into other areas of their life as well.

These transformations aren't random. The science of neuroplasticity has identified four core domains of creative thinking that respond most dramatically to training:

Each of these domains weakens with AI dependency but rebuilds with targeted practice.

What excites me most is that there are practical exercises anyone can use. In my innovation workshops, we've adapted these into simple daily practices that build creative muscle memory:

These aren't complex or time-consuming – they're deliberate mental practices that target the exact neural networks we need to strengthen. And remarkably, participants report greater idea fluency within just days of consistent practice.

Let me demonstrate one of these domains with a quick exercise that you can do right now. We'll focus on cognitive flexibility.

I want you to visualize a circle. Just a simple circle. Now, in your mind, transform this circle into something else by adding just one line. Now add one more line and transform it again. One more time – add another line and see what new object emerges.

I will give you 30 seconds. Imagine a simple circle and transform it three times, adding a line each time.

I will wait. Go!

How did you do?

This exercise activates your prefrontal cortex – the brain region responsible for cognitive flexibility. Most people initially create predictable objects: a face, a sun, or a balloon. But as you practice, your brain begins forming less common connections. Advanced practitioners might see a clock becoming a bomb becoming a planet becoming an eye.

Brain scans reveal increased neural firing in creative regions even during this simple 30-second exercise. You're literally strengthening synaptic connections that enhance your creative thinking.

The timeline of these changes follows a clear and consistent pattern:

This gives us a clear roadmap for reclaiming our creative capacities: commit to eight weeks of practice, with meaningful milestones along the way.

This transformation is remarkably accessible. Just 10 minutes of daily practice can trigger these changes. In our next episode, I'll guide you through a complete workout, but here's a preview of the two core approaches we'll use:

These aren't just theoretical concepts – they're the foundation of the 10-minute daily workout I'll guide you through in our next episode. Each exercise targets explicitly the neural networks involved in the four creative thinking domains we've explored today.

What makes these practices so powerful is the underlying principle we've discussed throughout: our brains physically change based on how we use them. This biological fact puts the choice squarely in our hands. Either we surrender our cognitive processes to algorithms, or we deliberately strengthen these uniquely human abilities.

The stakes are higher than we might realize.

If we do nothing, then we face a future of diminished creativity, which means technological progress that plateaus, businesses that can only optimize rather than reimagine, and education that produces technically proficient but intellectually passive graduates.

This is precisely what Bonhoeffer warned about in writing on “stupidity” – not as a lack of intelligence, but as the voluntary surrender of independent thinking. As we discussed in the first episode, Bonhoeffer observed that people become ‘stupid' not because they lack capacity, but because they willingly abandon critical and creative thought to “others”. This surrender happens gradually, unnoticed, as we choose comfort over challenge.

With AI, we face exactly this choice.

Will we surrender our creative faculties to algorithms, essentially choosing a form of ‘creative stupidity'?

Will we create a society where independent thinking grows rare, not because it's forbidden, but because it's surrendered?

Will we accept a world where ideas are judged by their conformity to algorithmic patterns rather than their originality?

But that's not the future we have to choose.

Join me in the next video in the series for “The Creative Brain Workout,” where I will guide you through 10 minutes of exercises that trigger the neural changes that will help you build stronger, uniquely human creative thinking skills that AI simply cannot replicate.

Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, your mind remains remarkably adaptable. The power to reshape your creative thinking is literally in your hands.

If you found value in today's video, please hit that like button and subscribe so you don't miss the next episode in this series.

Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a paid subscriber on Substack.

Your support helps make this content possible.

Thanks for watching, and I'll see you in the next episode.

To learn more about outthinking AI, listen to this week's show: Train Your Brain to Outthink AI: Boost Creativity 40% (2025).

Our ability to solve complex problems without AI has plummeted 30% in just five years.

That's not just a statistic – it's the sound of your brain cells surrendering.

We are announcing a new series we are calling –  Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement.

Today, we will explore how AI dependency is creating a pandemic of reduced creative thinking and why this matters more than you might realize.

Look around. We've all seen it – colleagues endlessly prompting AI for answers, friends asking their devices the same questions with slight variations, and kids who reach for ChatGPT before trying to solve a problem themselves. It's happening everywhere. We're witnessing a slow, subtle decline in our collective ability to think deeply, creatively, and independently.

This cognitive shift is measurable. Recent research from the University of Toronto found that college students today show a 42% decrease in divergent thinking scores – our ability to generate multiple solutions to problems – compared to students just five years ago. The difference? The widespread adoption of AI tools.

This isn't just happening in schools. Creative professionals show similar patterns. Marketing agencies report that junior staff increasingly struggle to generate original campaign concepts without AI prompting. Engineering teams face growing difficulties when asked to ideate without computational assistance.

But this isn't a rant against technology. AI is here to stay, and it offers tremendous benefits. The real issue is how our relationship with these tools is reshaping our cognitive capabilities.

Remember when calculators became widespread? Many feared we'd lose our ability to do basic math. They weren't entirely wrong, but we adapted. The difference now is that AI doesn't just handle calculations – it's beginning to think for us.

This surrender of our thinking faculties brings us to an uncomfortable but powerful concept from theologian Dietrich Bonhoeffer. Writing from a Nazi prison in 1943, he described a phenomenon he called “stupidity” – not as a lack of intelligence, but as a social contagion where independent thinking is surrendered to external forces.

Bonhoeffer wasn't talking about AI, obviously. But his insight that humans will easily surrender their thinking faculties to external authorities is profoundly relevant today. We're increasingly outsourcing our cognitive heavy lifting to algorithms, and our brains are adapting accordingly.

Let me show you what I mean with a quick demonstration. Take 30 seconds right now to list five uncommon uses for a paperclip. No use of AI. I'll wait.

How'd you do? If you struggled, you're not alone. In tests conducted before widespread AI adoption, the average person could generate 8-12 unique ideas. Today, that number has dropped to 3-5.

This decline in creative thinking ability is not only disappointing – it has neurological implications. When we regularly outsource thinking, the neural pathways associated with creative problem-solving literally weaken. It's cognitive atrophy – it's like any other muscle, use it or lose it. And with AI, you aren’t using it.

The consequences are more serious than you might think. Here's what's happening: AI is great at finding the optimal solution within defined boundaries using “convergent thinking.” Give AI the parameters of a problem, and it'll efficiently identify the best answers within a set of constraints.

But what humans uniquely excel at is “divergent thinking” – our ability to break through boundaries, reimagine the entire problem, and make unexpected connections between seemingly unrelated ideas. This is where breakthroughs happen. Recent research from the University of Bergen shows that while AI can generate more ideas than the average person, the most creative human solutions significantly outperform AI in originality and innovation.

Here's the paradox: the more we rely on AI, the more we get trapped in what psychologists call “AI-reinforced conventional thinking.” Let me demonstrate.

In a creative thinking workshop I ran not long ago, I asked participants to design a new coffee cup. Most drew variants of the same cylindrical container with a handle. When asked why, they couldn't explain – they'd simply imposed an invisible constraint.

But when one participant suggested a coffee cup that could be worn as a ring, the floodgates opened. Suddenly, people were designing coffee cups that doubled as plant holders, that changed color with temperature, and that folded flat for storage.

This mental breakthrough reveals what neuroscientists call the “first insight phenomenon” – that moment when one disruptive idea shatters the invisible walls of conventional thinking and unleashes a cascade of creative possibilities. We're not just limited by what we know, but by what we don't realize we're assuming.

When we look at history's greatest innovations, this ability to think beyond self-imposed constraints becomes even more critical. The transistor. Penicillin. The theory of relativity. The internet itself. None of these came from incremental optimization.

They required creative leaps that defied conventional thinking – precisely the kind of thinking we're at risk of losing in our growing dependency on AI.

But here's the good news – research from cognitive neuroscience and psychology confirms what I've seen firsthand: our thinking skills can be systematically improved. We can rebuild and strengthen these creative pathways with the right techniques.

This is where the concept of neuroplasticity becomes crucial. Like muscles, cognitive abilities respond to consistent, targeted exercise. And just as we've developed scientific approaches to physical fitness, we now have evidence-based methods for improving creative thinking skills.

The research findings are encouraging: In just minutes a day of targeted practice, people show measurable improvements in creative output. And unlike many skills that decline with age, creative thinking can actually improve throughout our lives – if we nurture it.

We stand at a crossroads. One path – cognitive surrender – is seductively easy. The other path requires effort but leads to something extraordinary: a partnership where AI handles the routine while we cultivate our uniquely human capacity to imagine what has never existed before.

Here's what gives me tremendous hope: our brains remain remarkably adaptable throughout our entire lives.

In the next episode, we'll dive into this revolutionary science and learn how to rewire our thinking for an AI-augmented world without losing what makes us human.

Join me for “Creative Neuroplasticity: The Science of Enhanced Creative Thinking.”

Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, authentic human thinking has never been more important.

Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a subscriber on Substack.

Your support helps make this content possible.

To learn more about the decline in creative thinking because of AI, listen to this week's show: Your Brain on AI: The Shocking Decline in Creative Thinking (2025).