Solve Open Problems. Leave the Closed Problems to the Robots.

Soon (I don't know when) humans will not be needed to solve simple problems. You probably won't even be aware of simple, repetitive tasks being done.

So, your time will be spent on two things:

• Supervising/checking on the results of hard problems done by artificial intelligence

• Solving super-hard problems (with AI assistance)

This is both bad news and good news.

To get into the business of solving super-hard problems, we need to first define them and then figure out how to solve them.

Here we go…

The Great Sorting: Open versus Closed Problems

Every problem we encounter falls into one of two categories, though most people haven't thought carefully about the distinction. The difference determines which problems humans will still be solving in ten years, and which will belong to the machines.

Closed problems arrive with their solutions tucked neatly in their pockets. They have established methods, predictable outcomes, and right answers. These problems resist creativity. They reward methodology.

Examples:

• Calculating project timelines using CPM

• What is the critical path for this project?

• Calculating the area of a triangle

• Determining headcount needs based on workload metrics

• Following established protocols for quality control

Open problems are creatures of entirely different breeding. They arrive without credentials, refuse to declare their intentions, and have the audacity to suggest that perhaps we've been asking the wrong questions entirely.

Examples:

• What is the best way to prevent climate change?

• How do we build a resilient organisational culture during rapid growth?

• Can a cross-functional team stay aligned and motivated on long-term projects?

• How can I optimise my career path to achieve multiple goals?

• How do I build a culture of trust and accountability?

The difference isn't merely academic. Open problems are where human judgment, creativity, and leadership create the most value. They're what keep you awake at night. Once solved, they create massive change.

Why Open Problems Resist Automation

Open problems resist automation for a profound reason: they require judgment about things that cannot be measured.

Consider hiring. You can quantify test scores, years of experience, coding performance. But the question "Will this person thrive here?" involves variables that exist in the space between data points. Company culture, team dynamics, how someone responds to ambiguity. These things matter enormously, but they're not in any database.

Or take product strategy. You can A/B test features, measure retention, track behavior. But deciding what product to build requires intuition about human desires that don't yet exist. It's the difference between optimizing a known solution and divining an unknown possibility.

This is why the most valuable work increasingly happens in the realm of the unmeasurable. It's where human judgment, creativity, and wisdom create irreplaceable value; even with AI assistance.

Why This Shift Matters Now

The sorting is accelerating faster than most people realize. Every month, another category of closed problems gets automated. The economic implications are stark: while supply increases for one type of work, demand intensifies for another.

The Career Reality: Professionals who remain focused on closed problems will find their roles automated away. Those who develop expertise in open problems will become increasingly valuable. Not just employable, but indispensable partners to AI systems.

The Innovation Reality: Breakthrough innovations don't emerge from optimizing existing processes. They come from reimagining fundamental assumptions, exploring uncharted territories, and connecting seemingly unrelated possibilities. AI can help execute these visions, but humans must conceive them.

The Competitive Reality: Organizations that master open problem-solving will capture disproportionate value. While competitors struggle with commoditized solutions, these companies will be creating entirely new markets and building unassailable advantages.

The window for adapting to this new reality is shrinking fast. You need to be ready to lead it.

The Seven Principles of Open Problem-Solving

Through careful observation of those who thrive in the realm of the unsolvable, certain patterns emerge. I call these the seven essential principles:

1. The Principle of Reframing 🔍

The Art: Most people accept problems as initially stated. Masters recognize that every problem is actually a deeper problem in disguise, and the most interesting one is usually the one nobody thought to ask.

You need to ask. You need to dig.

What Not to Do: Don't accept the first problem statement you hear. Surface-level symptoms often mask deeper, more valuable challenges.

The Practice: Use the "5 Levels Deep" technique. Each level reveals a different problem hiding beneath the surface.

Each time you hear a problem, ask yourself what lies deeper. What is the problem that led to this problem.

The initial question "How do we increase sales?" might eventually become "What prevents our ideal customers from discovering our solution when they need it most?"

The Paradox: The solution often lies not in answering the original question, but in discovering the question that should have been asked.

2. The Principle of Embracing Paradox 🧠

The Art: The most profound truths are usually contradictory, and the best solutions often involve doing two opposite things simultaneously.

What Not to Do: Don't force premature closure on complex issues. Avoid "either/or" thinking that limits possibilities.

The Practice: Replace binary choices with "both/and" exploration. Instead of choosing between team training OR new technology, explore how strategic technology investments might actually enhance team capabilities.

The Paradox: Comfort with contradiction becomes a competitive advantage in a world that demands nuanced solutions.

3. The Principle of Orchestrating Voices 🗺

The Art: One brilliant mind is inferior to three conflicting perspectives. Harmony is less useful than productive discord.

What Not to Do: Don't just gather opinions; actively seek people who disagree with your assumptions or approach problems differently.

The Practice: Use the "Perspective Compass" to actively seek voices from different directions. North: customers. South: front-line employees. East: industry outsiders. West: friendly competitors.

The Paradox: The most valuable insights often come from the most uncomfortable conversations.

4. The Principle of Elegant Experimentation 🧪

The Art: Treat solutions as hypotheses to be tested, not truths to be implemented. Fail quickly and beautifully.

What Not to Do: Don't bet everything on your first solution or fall into the "perfect plan" trap that delays action indefinitely.

The Practice: Build "Learning Ladders". Start with the cheapest test, then escalate validation only after each level proves successful. Test assumptions before committing resources.

The Paradox: Moving fast requires slowing down to learn; being right requires being wrong repeatedly.

5. The Principle of Intuitive Analysis 📊

The Art: Data informs your head. But don’t let that stop your intuition. Measure what matters without forgetting that everything that matters most can rarely be fully measured.

What Not to Do: Don't wait for perfect data before acting, but don't ignore data that contradicts your assumptions either.

The Practice: Balance the "Data-Story" equation. Numbers tell you what happened, stories reveal why it matters, patterns suggest what might happen next, and intuition guides what to try.

The Paradox: The most rigorous analysis often requires abandoning rigid analytical frameworks.

6. The Principle of Principled Flexibility 🧭

The Art: Hold convictions lightly while remaining utterly committed to core values. Change course without losing direction.

What Not to Do: Don't make decisions based solely on short-term metrics or competitor actions.

The Practice: Use the "Purpose Filter". When facing impossible choices, align decisions with long-term vision and fundamental values rather than immediate pressures.

The Paradox: The strongest positions often require the most flexible strategies.

7. The Principle of Reflective Momentum 🔄

The Art: Pause for wisdom without stopping for fear. Think deeply while moving quickly.

What Not to Do: Don't set a course and ignore new information. Avoid the "sunk cost" trap that keeps you committed to outdated approaches.

The Practice: Build "Pivot Point Processes". Regular checkpoints to assess what you've learned, what assumptions need updating, and whether your goals remain valid.

The Paradox: Going fast requires knowing when to slow down; staying committed requires knowing when to change course.

The Choice Ahead of Us

We must choose between two futures: one in which we compete with machines at being mechanical, and another in which we collaborate with them by being magnificently, mysteriously human.

The first path leads to irrelevance. An endless race to optimize processes that machines will soon handle better than we ever could.

The second path leads to renaissance. Where we become architects of possibilities that exist only in the space between what is and what might be.

Start small: identify one closed problem you're currently working on and ask yourself, "What open problem is this really pointing toward?" Then choose to solve that instead; with AI as your capable assistant, not your replacement.

The machines can have their closed problems. We shall keep the open ones, for they are the only problems worthy of human souls, and the only ones that create lasting value in an automated world.

The future doesn't belong to those who have the right answers. It belongs to those who ask the beautiful questions.