Most books are about success. This one is about getting dramatically better at failure — because that’s where the real work happens.

There are thousands of books on how to succeed — and why not? Everyone wants to. Most will tell you the same story: fail, pick yourself up, and march toward inevitable triumph. The focus, as always, is on the success. But here’s the uncomfortable truth: in your career, you will spend far more time failing than succeeding.

This article isn’t about avoiding failure. It’s about getting dramatically better at it — to Fail Faster and Fail Better. Master this, and the successes will follow naturally.

The Five Types of Failure

1. Inevitable Failures

These are the failures you can see coming from a mile away. They happen when a team isn’t truly committed, resources aren’t properly allocated, or the effort was doomed from the start. No one is fully in — so no one is surprised when it collapses. These are the easiest to prevent: simply don’t start what you won’t finish.

2. Slow Motion Failures

The most insidious kind. No one pulls the plug because the project is “too big to fail” or too politically sensitive to kill. Meanwhile, your best people sense the writing on the wall and quietly start leaving. By the time it’s officially dead, you’ve lost more than the project — you’ve lost months of runway, your best talent, and your team’s morale.

3. Catastrophic Failures

You bet the farm. The project fails, and now there’s nothing left to try again. These are often born from overconfidence and under-planning — a single concentrated bet instead of a diversified portfolio of experiments. The project’s failure becomes the company’s failure.

4. Traumatic Failures

Politically damaging, personally devastating. The company might have been able to pivot — the resources were there, the market was real — but the people who led the charge lost faith in themselves, in leadership, or in the opportunity. Worse, the wrong lesson gets learned: “there is no product here” instead of “that was the wrong product.” Sometimes a competitor swoops in, absorbs your learnings, and builds the very product you failed to. You did the hard work; they got the win.

5. Successful Failures

The gold standard. You learned something critical. You failed in a timely manner. You still have enough resources to try again. This is the failure that leads directly to an innovation pivot — and it doesn’t happen by accident.

To be an excellent fail-er — not a failure — you must engineer your organization to land on the right side of this spectrum, every time.**

Building a Culture of Successful Failure

Successful failures don’t happen by luck. They are the product of deliberate organizational practices. Here are three strategies to build into the fabric of how your team works.

Strategy 1 — Commit or Kill

In tennis, no man’s land is the worst place to be caught — too far from the net to volley, too far from the baseline to defend. The same applies in business. Half-hearted efforts burn resources without generating real learning. The discipline to kill a project quickly is every bit as important as the courage to launch one boldly.

Prevents: Inevitable & Slow Motion failures.

Strategy 2 — Always Have a Portfolio

This applies even if you’re a startup with limited resources. A portfolio doesn’t have to mean ten simultaneous bets — it might mean two or three parallel hypotheses you’re stress-testing. When one fails (and one will), you’re not left with nothing. You still have options, learnings, and momentum.

Prevents: Catastrophic failures.

Strategy 3 — Expect Failure, Reward Execution

This is the culture shift that ties everything together. When failure is expected as a natural part of the innovation process, people stop hiding bad news. They stay honest, stay engaged, and stay ready to adapt. The difference between a traumatic failure and a successful one often comes down to whether the team felt safe enough to raise their hand and say, “This isn’t working” — before it was too late to course-correct.

Prevents: Traumatic & Slow Motion failures.

CalTech recently announced that it has wirelessly transmitted power through space. Without, nighttime or atmosphere and weather getting in the way, solar panels can be 8x more efficient in space than on the earth. Now that SpaceX and others are rewriting the economics of space launch, development projects in space may actually become profitable.

A recent breakthrough uses machine learning to interpret brain patterns and turn them into speech! The current focus is on enabling paralyzed people to be able to speak again. However, there are far reaching implications. Eventually, this could enable virtual telepathy (your thoughts get transmitted to an earbud in your wife’s ear halfway across the world) or telekenisis (your thoughts turn on the coffee machine and open the door).

Remember that Friends episode where Phoebe and her boyfriend (played by Charlie Sheen) had oven mitts duct taped to their hands to keep them from scratching? Soon there may be a better solutions. Scientists have found a protein that creates the itch response. Read about it here.

When I hear the familiar buzz of a quadcopter drone, I automatically assume it’s taking video because lifting anything heavier than a camera is hard. But an Italian company just release a drone capable of lifting 100kg! That’s enough to carry a person around. Let’s leap frog self driving cars…

Finally, there’s progress being made on high-energy-density storage (that’s no gasoline). Pretty much every amazing tiny gadget portrayed in science fiction (especially ray guns) require a portable power source many orders of magnitude beyond what we have today. That ever-present selfie-drone isn’t so helpful when it has to recharge every five minutes. Enter NUCLEAR (yep, you heard me right) batteries. Read more here.

Hiring talent with the right skillset is necessary but not sufficient. You also need to create the right culture and structure for the team to work not just efficiently, but creatively as well.

If you’ve ever watched amazing Improvisational Theater, you may have marveled at how a well-oiled troop can magically create not just fun scenes, but whole plays on the spot. Talented as these actors are, they do not rely on sheer creative genius, but on a well-honed set of rules. That’s right, the best Improv actors, wacky and crazy as they may seem, are following rules, not breaking them. That is the secret to innovation.

Scripted Shows vs. Improv

A well scripted show can take months to write and weeks to rehearse. The results can be well worth the effort, producing masterpieces like Hamilton or Wicked. In the business world, it pays off to spend weeks preparing for pivotal pitches, like asking for funding or winning a big customer (see 4storyteller.com for a structure to do so).

But on a day to day basis, things (should) move too fast at a startup to carefully script out every move. A nimble product team must move fast and independently without a director overseeing every decision. It must act like an Improv troupe.

Rules to Play Work By

In improv there are general cultural rules, and there are game-specific rules. The cultural rules apply well to corporate meetings:

1. “Yes, and”

   1. This just means that scenes need to move forward. Don’t negate the last statement. Accept it and add on.

   2. In a corporate setting, people can disagree, but we still want to move the discussion forward, not just say “No” without offering a pathway to continue the discussion.

2. “Pay Attention”

   1. This means that you can’t contribute to the scene if you are not focused on what is happening.

   2. In the office setting, we often use the related “One conversation at a time” rule.

3. “Don’t Try to be Funny”

   1. In Improv, “trying to be funny” can lead an actor to think about her own agenda rather than being present in the scene. The rules of the game will naturally create funny situations without individuals needing to invent funny lines.

   2. In the corporate environment, trying to “be smart” can derail the flow of the conversation and stop creativity. Pay attention and stay present to the discussion.

How Improv Games Use Specific Rules

Let’s look at the game called "Late to Work."

Setup:

- One player is designated as the Boss, one as the Employee, and the others are Co-workers.

- The Boss sits or stands at one end of the space, facing away from the playing area.

- The Employee is "late for work" and will be outside the room or area at the start.

- The Co-workers are in the playing area with the Boss.

Rules:

- The Employee enters and the Boss demands an explanation for their tardiness.

- The Co-workers will have previously agreed upon a ridiculous and elaborate reason for the Employee's lateness.

- The Employee, who has not heard this reason, must guess the excuse based on the Co-workers' pantomime and hints during the scene.

- The Co-workers silently act out the excuse, while the Employee tries to justify their lateness to the Boss using these clues.

- The game continues until the Employee successfully guesses the excuse or the Boss decides to end the round.

Result:

The joy of this game comes from the creativity of the Co-workers in communicating the excuse without words and the Employee's often humorous attempts to justify the absurd situations they interpret from the mime. The Employee’s explanations are humorous because they are based on miming, not because the actor is being inherently funny.

How You can Run Meetings This Way

In the same way an improv troupe can prepare before a scene, a great product team can prepare before any meeting. Every meeting should have the following determined BEFORE the meeting:

1. TOPIC: What Game are we playing?

   1. Make sure there is an agreed upon goal for the meeting. Is this meeting a decision making meeting, a fact gathering meeting, an update, a brainstorm, etc.?

2. ATTENDEES: Who are the players and their roles?

   1. Everyone at the meeting should be an active participant and have a specific role. No one needs to come to this meeting to check their email.

   2. Everyone who is needed must be present: decision maker(s), experts, facilitators, etc.

3. AGENDA: What are the rules that will make this work?

   1. An agenda is more than a list of things to discuss. It should be a roadmap for how the meeting will reach its goal. For example:

      Decision Making Meeting Agenda

      1. Welcome and Introduction (5 minutes)

      - Briefly state the meeting's purpose and objectives.

      - Introduce participants if necessary, especially if there are new members.

      2. Review of Agenda (2 minutes)

      - Outline the main points and structure of the meeting.

      - Confirm the agenda with all participants.

      3. Background or Context Setting (5-10 minutes)

      - Provide a brief overview of the issue at hand, including relevant history or context.

      - Highlight why the decision is important and its potential impact.

      4. Presentation of Options (10-20 minutes)

      - Present the different options or solutions available for the decision.

      - Depending on the complexity, this could involve presentations from different team members or departments.

      5. Discussion and Clarification (20-30 minutes)

      - Open the floor for discussion on each option.

      - Encourage input from all participants, ensuring a comprehensive understanding of the pros and cons.

      - Address any questions and clarify any uncertainties.

      6. Evaluation of Options (10-15 minutes)

      - Evaluate each option against predefined criteria such as cost, feasibility, impact, etc.

      - Use a scoring system or a pros and cons list for a more structured evaluation.

      7. Decision Making (15-20 minutes)

      - Reach a consensus or vote on the preferred option.

      - Ensure the decision aligns with the meeting’s objective and the organization's goals.

      8. Action Plan and Assigning Responsibilities (10 minutes)

      - Outline the next steps following the decision.

      - Assign responsibilities and deadlines for implementation.

      9. Review and Summary (5 minutes)

      - Summarize the decision made and any action items agreed upon.

      - Confirm that everyone understands their tasks and responsibilities.

How about 1:1 Meetings?

1 on 1 meetings are often more informal and used to build relationships rather than to achieve tactical goals, but there still ARE goals. To reach those, a short agenda (or rules) are still helpful to insure there is space for important topics. For instance:

GOAL: Career development

ATTENDEES: Manager and employee

AGENDA:

• Review of Employee Career Goals - employee led

• Employee Challenges and Managerial advice - employee led

• Manager Feedback for Employee - manager led

• Employee Feedback for manager - employee led

People need to Know Rules to Use Them

How do top-performing teams achieve rapid and efficient results? They adhere to unspoken norms that have evolved organically over time. The key to duplicating and expanding the success of these elite teams begins with documenting these implicit guidelines and actively promoting them. Consistently emphasize these principles in each official meeting, and gradually, the team will internalize and practice them instinctively. Similar to a skilled improv group, they'll craft innovative products guided by the team's framework, rather than relying on random chance.

Structural Systems are how to Scale Impact

Ultimately, success hinges on developing Structural Systems to enable scalability. Transitioning from an individual contributor to a leader involves transforming personal best practices into systemic strategies for your team. Where an individual contributor focuses on personal results, a leader strategizes on creating rules and structures that guide the team towards desired outcomes.

This concept of structural systems is pivotal, not just for those leading large teams but for enhancing productivity at all levels. The advent of GenAI marks a new era in amplifying your influence. Consider ChatGPT: it can generate content more rapidly than any human, but it flounders without structured guidance. When interacting with Large Language Models, the clarity and specificity of your prompts - defining style, adhering to rules, outlining topics, and specifying formatting - are crucial. Just as you build and refine systems within teams, using LLMs effectively involves crafting and employing specific structures (like saved prompts) to maximize the utility of each interaction.

Many companies will be built on contextual prompts that turn the general predictive models into specialized tools for tasks like copyediting, blog writing, legal assistance, and more.

Structure is the foundation on which creativity and innovation can be built upon. Build that strong foundation, so you, your team, and your company can rise to unlimited heights.

The traditional metrics of evaluating candidates through the lens of their career history, focusing on the "where" and "what" rather than the "how" of their experiences, often overshadow the underlying competencies that truly matter. We tend to cling to the tangible milestones in a person's professional journey as a safety net, erroneously assuming that someone seeking the same role they have mastered before will ensure success. However, this reliance on categorical experience can blind us to the actual skill sets presumed to accompany such experiences.

Skills over Experience

In the realm of product leadership, "B2B experience" is frequently listed as a prerequisite. On the surface, this makes sense, as this categorization (business to business, business to business to consumer, and business to consumer) defines the company’s customer, and the Product Leader is the voice of that customer. The fallacy lies in equating experience with expertise without recognizing the variability of each business's unique ecosystem, encompassing its industry-specific challenges and internal dynamics. Knowledge is by definition, specific, and thus, very fragile. Overly narrow hiring criteria can blind your business to vital strategic pivots and adaptation paths for success.

During my tenure at Google X (the moonshot factory), I learned to focus on a mission rather than a customer profile. How do you reduce the cost/impact of global shipping? How do you provide people in tumultuous countries financial stability? B2B and B2C are all just different delivery mechanisms serving the same goal.

I explored several business for each mission, some that were B2B and some that were B2C. I learned that every business or organization I worked with, from Fortune 500 companies to governments, had its own unique characteristics. But the thread that tied them together, was that companies and organizations are run by people too. You are always selling to and solving problems for people, whether that’s a consumer, or a VP at a company, or the director of a governmental agency. The pivotal skill I honed was the ability to decipher the environment, motivations, and needs of individuals and the organizations they represent and to utilize this understanding to forge products that deliver tangible value.

The application of this skill extends beyond the eclectic ventures of Google X to more traditional corporate environments. My experience across various Google product segments, including forging partnerships with industry giants like Walmart, Disney, and Lego, reinforced the importance of understanding each entity's unique culture. Learning how each company worked and what it valued was critical in developing products with them, just as understanding end-users is critical for consumer products.

User Empathy is the key skill that drives success in all these industries.

Skills are demonstrated through Traits and Actions

Many resumes (and LinkedIn) have a “Skills” section. It’s a great first step if someone states that they have “User Empathy” but how can you do to confirm that? I look for Traits and Actions.

A product manager adept at grasping diverse customer requirements must possess an authentic sense of curiosity and a profound capacity for empathy. While specific knowledge gained from past experience with certain customers can be beneficial, it's the innate curiosity and compassionate concern for people that fuels a relentless pursuit to understand the customer's unique needs and the dedication to develop solutions that genuinely assist them.

This trait manifests in tangible behaviors, particularly through the act of inquiry. My passion for international travel stems from the rich learning derived from immersing myself in varied cultures. But I don’t learn from the confines of a manicured resort. I expand my perspectives through conversations with Uber/Grab drivers, tour guides, and individuals I meet in eateries. It's the simple, yet profound art of asking questions that unlocks this wealth of understanding.

Cultivating a Diverse Range of Perspectives

By taking the focus off recognizable names on a resume, you broaden your search. This shift doesn't equate to compromising standards; rather, it signifies a readiness to discover exceptional talent from sectors beyond your immediate purview. You become capable of attracting top-tier professionals from a vast array of fields.

Such an approach enriches your team with distinct viewpoints, not merely due to their ethnic diversity but owing to their varied experiential tapestry and the pertinent skills they can adapt to meet your organization's unique demands.

Building a Great Product starts with Building a Great Team

During interviews, it's essential to look beyond the candidates' past roles, the prestige of their former workplaces, or their job titles. Even the skills they list aren't definitive proof of their capability. Instead, focus intently on their personal attributes and the evidence of these in their behavior. For instance, a candidate who is inquisitive and engaged with people and processes likely possesses a strong capacity for user empathy. Likewise, an individual who consistently propels conversations forward and communicates succinctly may excel at advancing projects. In assembling your standout team, prioritize a broader range of qualities—consider the skills vital to your objectives look for the underlying traits and actions that reinforce them.

Use these core qualities as your guide throughout the recruitment process, and you will form a team that is impressive not just on paper, but also intrinsically motivated to realize your vision.

There comes a time when we recognize that the current trajectory of our product, our company, our lives, or even the world is not ideal. It’s at those times when we must courage, imagination, and skills to pivot. Whether your product is failing to get traction, your market is shrinking, AI is replacing your job, your company is becoming stagnant, or you personal life is in a rut, the answer is the same: Pivot. Use innovative thinking to invent yourself into a better position, and a better life.

Innovation does not simply appear (unlike wild pokemon). It is a deliberate and intentional action, not a naturally occurring phenomenon. Here we teach you how to build the light switch, so you can reliably make that lightbulb turn on, every time you need some light.

We teach readers how to recognize when an Innovation Pivot is needed. We explore how to ideate and build strategies. We investigate various related aspects, including user behavior, corporate culture, business models, and even personal habits. We utilize exercises that help hone our skills and develop the innovator’s mindset. We do all this, because the successful execution of an Innovation Pivot, while difficult, can transform people, companies, and industries and ultimately drive our civilization forward.

Success Stories: you can do it too

Here are some inspirational stories of companies that had at least one critical innovation pivot:

Nintendo: Originally a playing card company, Nintendo pivoted to video games and consoles, leading to iconic products like the Nintendo Entertainment System (NES) and popular franchises like Super Mario and Pokémon. Yahoo!

Wrigley's: Originally a soap and baking powder company, Wrigley's pivoted to selling chewing gum as a promotional item with their products. Eventually, chewing gum became their primary focus, and they became one of the leading gum manufacturers in the world.

Netflix: Initially a DVD rental service, Netflix pivoted to online streaming, revolutionizing the way people consume entertainment. Today, it produces original content and has invented phrases such as “binge-watching” and “Netflix and Chill.”

Slack: Slack started as a gaming company called Tiny Speck but shifted its focus to team communication software. Its innovative approach to workplace communication made it a widely used platform for businesses and teams. I believe the next generation will attribute this company as the originator of the term “to slack off”.

Nokia: Originally a paper mill and then a rubber company, Nokia pivoted to the telecommunications industry, becoming one of the leading mobile phone manufacturers in the world. However, with the decline of traditional mobile phones, Nokia pivoted again to focus on network infrastructure and technology services.

When to Pivot

Pivoting is scary. It requires taking much of what you know and what you have built and throwing it out the window. Your instinctual brain will try to reject change, and so if you suspect you need to pivot, you probably do. Large companies in particular can be especially resistant to change. Here are some warning signs:

1. Stagnating Growth: If your company's growth has plateaued or slowed significantly despite your best efforts, it may be a sign that your current business model or strategy is no longer effective.

2. Changing Market Dynamics: External factors like shifts in customer preferences, technological advancements, or changes in the competitive landscape can make your current business model less viable.

3. Customer Feedback: Pay attention to customer feedback and complaints. If you consistently receive feedback about your product or service not meeting customer needs or expectations, it's a sign that changes may be necessary.

4. Financial Challenges: Continuous financial struggles, such as declining revenues, shrinking profit margins, or an inability to secure funding, can signal that your current business model is unsustainable.

5. Difficulty in Scaling: If you encounter significant challenges when trying to scale your business, such as operational inefficiencies or an inability to reach new markets, a pivot may be needed to address these issues.

6. Competitive Pressures: Intense competition or the emergence of disruptive competitors can threaten your business's viability. Consider pivoting to differentiate your offering or enter new markets.

7. Technology Shifts: If new technologies are rendering your current products or services obsolete, it's crucial to adapt or pivot to stay relevant. Yes, AI falls into this category.

8. Regulatory Changes: Changes in regulations or compliance requirements can impact your business model significantly. Pivoting may be necessary to align with new legal standards.

Startups have a few extra things to watch out for:

1. Inability to Raise Capital: If you're struggling to secure funding from investors or lenders because they don't see the potential in your current strategy, it might be time to pivot to a more attractive proposition (like AI is all the rage).

2. Market Validation: Sometimes, market experiments, pilot projects, or testing new strategies can provide validation that a pivot is necessary.

3. Lack of Product-Market Fit: If your product or service doesn't seem to resonate with your target audience or isn't gaining traction in the market, it may be time to reassess your product-market fit.

What are my Other Options?

Yes, we know something’s wrong, but how do we fix it? Innovation Pivots aren’t the only way to move forward. Here are some options:

1. Cost Reduction: Evaluate operational costs and find ways to cut unnecessary expenses without compromising the quality of products or services. This might involve renegotiating contracts, optimizing supply chains, or reducing non-essential expenditures. And yes, this also means layoffs.

2. Market Retrenchment: Focus on core products or services and withdraw from less profitable or non-strategic markets. This can help the company concentrate its resources where they are most needed and profitable.

3. Talent Management: Assess the skills and capabilities of the existing workforce. Training and re-skilling employees can enhance productivity and adaptability. Additionally, attracting new talent with relevant expertise can bring fresh perspectives to the organization (anti-layoffs if you will).

4. Financial Restructuring: Explore options such as refinancing, debt restructuring, or seeking new investment. Proper financial management can alleviate immediate financial pressures and provide resources for strategic initiatives.

5. Operational Efficiency: Streamline internal processes and workflows to improve efficiency. Automation and technology adoption can reduce errors, save time, and cut operational costs.

6. Rebranding and Marketing: Evaluate the company's branding and marketing strategies. A refreshed brand image and targeted marketing campaigns can reignite interest in the company's offerings.

7. Exit Strategies: In extreme cases, consider options like mergers, acquisitions, or divestitures. Selling non-core assets or business divisions can generate capital and allow the company to focus on its core strengths. Yes, this does feel like giving up, but that may be the best choice for you personally.

When an Innovation Pivot is Best

The above strategies can often work when things are just a little but out of whack. But sometimes small tweaks won’t be enough. Or maybe it’ll be just enough to get by. But if “getting by” doesn’t really run in your vocabulary and you want to thrive and shoot for the moon, then you need an innovation pivot. You can also use one of the above strategies to buy you time and resources to invest in an innovation pivot.

An Innovation Pivot is a combination of innovation AND a business plan. You take a proven business strategy for pivoting and explore how innovation can make that transition faster and more effective. Here are some business strategies that tech innovation could catalyze:

1. Revenue Diversification: Explore new revenue streams or expand existing ones. This could involve entering new markets, developing new products or services, or creating partnerships with complementary businesses. To do this successfully, think of your core IP and technical strengths and finding adjacent markets where you can leverage those strengths.

2. Digital Transformation: This is largely a way for traditionally non-tech companies to reduce their operating costs by implementing digital technologies. However, it can also be flipped around and act as a way for tech-forward companies to enter traditional industries at much lower costs, a true innovation.

3. Strategic Partnerships and Alliances: Form alliances with other companies to share resources, expand market reach, or collaborate on research and development. Think about how technology you provide can have outsized value to a collaborator or vice versa.

4. Innovation and R&D: Invest in research and development to create innovative products or services that can give the company a competitive edge or be sold on its own. This makes sense if you have a strong technical core in the company. Expanding these efforts could yield high dividends.

5. Customer Focus: Improve customer satisfaction and loyalty through better customer service, personalized offerings, and addressing customer feedback. Satisfied customers are more likely to remain loyal and recommend the company to others, and the advent of GenAI offers low-cost ways to vastly improve the customer experience.

What You’ll Need to Succeed

Innovation Pivots are Hard. It’s a lot more complicated than you’ll find in a single blog post, or even a series of posts (though if you subscribe, more posts will come). To execute an innovation pivot, here are a few things you will need:

1. Leadership Advocacy: It is not enough to have leadership “bought in”. Someone in the C-suite must be an active advocate and willing to drive the process. A large pivot like this will meet with constant resistance from across the company.

2. Cross-functional Participation: Crafting and executing an innovation pivot will require the knowledge and expertise of everyone: operations, engineering, design, product, customer support, marketing, sales, and legal.

3. State of the Union: You’ll need to make an honest assessment of your company’s physical, intellectual, and human assets and strengths as well as your market, brand, and customer assets. You need to do a retrospective on where your product and company has succeeded and where you have failed. Use this to create a list of valuable insights all stakeholders agree to.

4. Innovation Lead: You need an experienced and empathic facilitator to drive the brainstorming and strategy sessions. Though it helps if this person is creative and well-versed in the industry, she is not responsible for coming up with the solutions, just helping your cross-functional experts develop insights. It may make sense to bring on a consultant (such as Product Maestro) for this role.

5. Time and Money: Pivots aren’t instant. You need to start working on this before you run out of money. This will require moving resources away from other initiatives, and thus requires long-term executive support. Many companies give up before their investment bears fruit. View this as a startup venture (especially if you ARE a startup) and have open and transparent expectations on how much time and money should be invested before certain milestones are reached.

6. Grit: Fully expect that more pivots will be required along this journey. Though the final result may look like one presciently clever pivot, in reality it’s the result of many small pivots along the way. Keep adjusting, keep inventing, and dance your way to success.

Life is a long list of failures. And the path to innovation is strewn with obstacles, potholes, roadblocks, and sometimes even giant alien tanks pointing laser death rays at your face. Simply put, it can be tough. The true path to success is how you react to the setbacks, how you can accept the pain of defeat and manage to keep pushing, keep moving towards the dim star of success up ahead.

The key to resilience lies in stories.

It can be easy to take a setback and build a negative story around it, specifically one about your own inadequacy. The narrative often take the shape of "I can never do anything right" and its many cousins. These stories are called "cognitive distortions," and they can lead to some major bummers like negative emotions and unhelpful behaviors. Don’t just take my word for it, reframing the story is a key part of CBT or Cognitive Behavioral Therapy, quite possibly the most popular form of counseling used today.

So here are some quick “reframes” specifically designed to help innovators like you stay positive and keep motivated through the ups and downs of life:

Rejection or Criticism

• Background: You just pitched your idea to a friend or colleague, an impassioned plea to change the world, and the response was… meh.

• Negative story: The world just doesn’t get it. Everyone has blinders on and won’t see the truth. It’s hopeless, and I won’t ever be able to create change like I dream of.

• Positive reframe: People need time to fully absorb and understand groundbreaking ideas. Part of my job is to patiently and delicately help bring society to that new progressive future. I learned that I need to try a different angle or go slower on my next try.

Failed Test

• Background: Your first prototype of your brilliant idea has finally hit some testers, and it fails… completely, and utterly, fails.

• Negative story: This was a horrible idea. I can’t believe I wasted all that time on this horrible idea. I’m a failure just like my idea.

• Positive reframe: Good thing I tested this early. I gained some valuable insights that will bring me closer to the right product idea. My next idea will be much closer, and eventually I’ll hit upon the right direction.

Failed Product

• Background: You devoted years into a product that finally hits the market, and… it fails. Or at least it doesn’t succeed, and these days, isn’t that the same thing?

• Negative story: I wasted years on this product, not just of my life but that of the whole team. I sold everyone on a lie, and now I’ve let everyone down. We’re the laughing stock of the industry and we’ll never live it down.

• Positive reframe: Very few products succeed, and like it or not, luck and timing plays a bigger role than we like to admit. I’m not alone in this. The whole team bears equal responsibility, and to be honest, we had tons of fun and learned a lot doing this. Now that we’ve been forged by fire, we can work together on the next great idea. Most startup founders succeed on their third or fourth try!

Slow Progress

• Background: Your six month project now looks like it’ll be 18 months.

• Negative story: I was supposed to have succeeded by now. I must be doing something wrong. Is something wrong with this idea?

• Positive reframe: Invention is 95% perspiration and 5% inspiration. Now is the time to show my grit. This will force me to really hone in on the value proposition. What can I remove from scope to make this a better, more focused product?

Uncertainty and Ambiguity

• Background: Changes in the company, the industry or the world make it hard to predict the future. Like, maybe a pandemic happened.

• Negative story: I don’t know what to do. What if everything collapses and I have no money, no future. How can I plan?

• Positive reframe: In any period, not matter good or bad, there are winners and losers. I can be a winner if I keep my head and look for opportunities in the changing world. I can execute on short term goals and be ready to move quickly when the moment arrives to pivot.

No resources

• Background: You’ve pitched your idea and people say they love it. But there’s no money in the company or there’s no investors lining up to fund you.

• Negative story: I’m a failure. I can’t even get started. Investors threw hundreds of millions into Webvan, and I can’t get a dime. What’s wrong with me? Maybe people are just being nice and actually think my idea is crap.

• Positive reframe: Getting funding is not just about the idea, it’s also about the timing and the market. I’m learning more about what the investors need right now (e.g. saving money), and that will allow me to tweak my idea and the story to match (e.g. my idea saves money). It may take a while, but I’m learning along the way and becoming a better innovator every day.

Burnout

• Background: Everything listed above has happened to you. Positive reframes seem like bogus at this point.

• Negative story: I can’t deal with this anymore. I give up. I know I suck. Innovation sucks. Positive reframes suck. This article sucks. Life just sucks in general. What’s the point in all this?

• Positive reframe: Everyone deserves a break, including myself. I’m allowed to get angry and frustrated. I’m allowed to rest for a bit. It doesn’t mean that I’ll jump back in the fray later, and it doesn’t mean I won’t. I’m joining the millions of others who have experienced burnout. Isn’t that a new experience that I can learn from too? I wonder what I can invent to help people with burnout…

“In the depths of her childhood, she was but a girl whose dreams seemed far-fetched, as discouraging whispers surrounded her. Yet, in the face of skepticism, she clenched her teeth, refusing to surrender to the limitations imposed upon her. She embarked on an arduous journey, a relentless battle against adversity, with an unyielding spirit to conquer.

A pivotal moment etched in her memory was a bitter defeat that left an indelible mark upon her soul. The taste of failure was so bitter, so profoundly detested, that she made an unbreakable vow never to experience such agony again. With an unwavering resolve burning within her, she immersed herself in a relentless pursuit of greatness, pushing the boundaries of her own potential.

Days turned into nights as she honed her skills, harnessing a talent that seemed to transcend ordinary limits. The hours melted away in her passionate dedication, her unwavering commitment to the craft she held dear. Each day presented a new challenge, a fresh opportunity to overcome, to outshine any opponent who dared cross her path.

And as the echoes of her victories reverberated across the industry, she stood tall, an emblem of unshakable strength and unparalleled achievement. Her name became synonymous with triumph, her accomplishments rewriting the very essence of what it meant to succeed. The girl who was once told she couldn't dared to defy the odds, carving her own path through sheer determination and unrelenting passion.”

Sounds like an exceptional story right? But by definition, an exceptional story is the exception, not the rule, and thus, this is not the only, or even the most likely path to success.

It is possible that you are ridiculously driven with almost a pathological need to win, and a resolute confidence in your destiny that can survive any setback. Some biography-worthy individuals fit this to a T. But if that’s intimidating to you, fear not, there is a path that can work for the rest of us: learning to love losing (as well as winning). Let’s call this new mindset the “Lover” and the competitive mindset the “Fighter” and then examine how the former mindset can result in success for less.

Losing = Learning

It’s commonly accepted that innovation requires failing, but it’s only seen as a pitstop on the way to eventual success. It’s not something to enjoy, but merely a tedious checkpoint to endure. Losing however, is of a much more personal nature, a fierce clash of egos. Failure is simply failure. But losing requires losing to someone else. There is an automatic labeling that makes someone a winner, and the other person a loser. And no one wants to be branded a loser.

Let us delve deeper into the realms of games and competitions. Take the enthralling realms of tennis or the burgeoning popularity of pickleball. Amidst the countless matches played, only a fraction carry substantial stakes. The majority serve as practice sessions, training grounds, and sources of pure enjoyment. Within each and every point lies an opportunity for victory or defeat, a microcosm of the winners' tale.

If the goal is to become a better player and maybe ultimately win Wimbledon, then let’s look at what you can gain from each point. The “Fighter” tries her best. When she wins a point, she celebrates, pumping her fist in the air. But when she loses a point, she gets upset, yelling at anything and anyone in her way. Her unyielding determination demands unwavering confidence, prompting her to attribute losses to external factors: the net, the paddle, the wind —a way to preserve her unshakeable resolve.

When the “Lover” loses a point, she looks upon it with curiosity. What did I do wrong? What could I have changed in my stroke, my positioning, or my judgement? She logs this into her brain and enjoys a surge of happiness knowing that she just learned something new or, more likely, reiterated something she already new.

Reiterating is important. It’s not just about knowing what to do, but about training our mind and body to do this naturally. Everyone KNOWS how to lift a weight. But we still need to actually lift the weight to get stronger. Every time we make a mistake and recognize it and accept it into your brain, you are doing another repetition, fortifying our mental and emotional muscles.

A “Lover” will learn to love training and be adapted to keep at it. She will learn to let go of the fear of losing, and replace it with the thrill of learning. The one who claims victory in a point relishes a fleeting moment of jubilation, while the one who succumbs to defeat uncovers a wealth of wisdom.

Losing is inevitable

Of course there are other ways to learn rather than through competition, such as training in isolation. But increasingly, the world is connected, and the work we do requires collaboration and competition with others. Situations that result in winning or losing are hard to avoid, and both the “Lover” and the “Fighter” should seek opportunities to compete.

The “Fighter” may do anything to win, and may hate the saying “You can’t win them all”. But statistically speaking, most people will lose at least half the time (more if there are greater than two people in the competition). This amount of heart-rending losses can take a heavy toll on even the self-confidence of even the most determined of “Fighters”.

The “Lover” however, stays positive “win or lose”. He can even find delight in the joy others receive when they win (enabled by his loss). Being a good and gracious loser is an important skill. By celebrating the other’s win along with your own loss, you can build empathy and friendships, turning competitors into collaborators and future allies.

So while a “fighter” may have to endure a loss, a “lover” gains knowledge, training, and allies.

You Should be Losing More: hopping leagues

If I met a person who was in the top league of their sport, and they told me they won every game this year, I’d say, “Wow, that’s amazing”. However, if I met someone randomly who told me the same thing, I’d think “You’re not in the right league”.

It is very difficult to love winning and not also love competing against people you can win against. The result of this, is that the “Fighter” may have a tendency to avoid losing by staying in the same league rather than compete against stronger opponents. What’s wrong with this? Isn’t it better to be a big fish in a small pond? In “Outliers” Malcolm Gladwell postulates this exact point. At EARLY AGES, being a big fish in a small pond helps build the confidence people need to succeed. Being a big fish helps the “Fighter” mentality succeed. But we’re not all “Fighters”, and ultimately, the goal is to progress, not defend the ego.

The “Lover” enjoys the challenge, is not afraid of losing and looks forward to what she can learn from playing people better than her. As such, she is more willing to move up a league, to learn, and to continue to move up leagues. Why is this important?

Let’s take baseball. The MINIMUM wage (yes, there is a minimum) for Major League Baseball is $720,000 a year. Move one league down, and the highest paid player in AAA league makes $35,800. Yes, this is the world we live in.

The average tech salary in India is $8600 a year. In Silicon Valley, it’s $232000. The 5x difference in cost of living does not make up for the 27x difference in compensation. Where you play is often much more important than being the best on the field.

You can still like Winning

Loving to lose isn’t about hating winning; it’s about having a sustainable healthy relationship with losing. We all still want to win in the end, but it’s possible to separate out the real important things to win (your dream job, your dream spouse, Wimbledon), from all the little competitions that fill every day of our lives.

Everything in life with points doesn’t have to be a to-the-death competition. Learn to love losing, and you can gain knowledge, training, friendships, and some serenity and joy that will set you up to win when it really matters.

Many people are familiar with the waves of computing. We started with mainframes, then moved to desktops, and finally ended where we are with mobile and cloud. With each technological shift came accompanying cultural shifts that made vast economic fortunes for the companies who rode each wave.

Fewer are aware of the waves of computer coding that has turned the software engineering from a niche profession few knew about, to something that is taught in elementary school. And now we are about to enter another wave in coding that will change how we interact with machines and usher in a new era of opportunity and chaos. First, some background:

First Wave: Machine Language Programming

The first wave of computer programming began with the development of the first electronic computers in the 1940s. In our first interactions with the machines we created, we spoke their language, wrapping our instructions into ones and zeroes. During this period, programmers used machine language to write code that could be executed directly by the computer's hardware. Programming in machine language was a slow and error-prone process, and only a small number of programmers had the expertise to work with these systems.

Programming was mainly done by scientists, mathematicians, and engineers who were working on specific projects, such as military or scientific research. These programmers often had to develop their own software tools and programming languages, and they were typically highly specialized and had a deep understanding of the hardware and architecture of the machines they were working with.

Riding the first wave generated huge revenues in business applications, including payroll processing, inventory management, and accounting. This powered the growth of business giants like IBM and Fujitsu.

Second Wave: High-Level Language Programming

The second wave of computer programming began in the 1950s with the development of high-level programming languages such as FORTRAN, COBOL, and BASIC. These languages abstracted away many of the low-level details of the machine, making it easier for programmers to write and understand code. They also introduced concepts such as variables, loops, and subroutines that made it possible to write more complex programs.

The second wave really took off in the 1980s with the development of object-oriented programming languages such as Smalltalk and C++. These languages introduced the concept of objects, which encapsulate data and behavior and can be used to build complex software systems.

Programming became a real occupation. The number of computer science graduates in the United States increased from around 6,500 in 1980 to over 42,000 in 1990. Tools started being built for this quickly growing industry. Integrated development environments (IDEs) made it easier to write code, while libraries made it easier to leverage the work of others.

But while programming had become infinitely easier, the way we used computers was still the same. Humans take a problem and try to turn it into computational rules that computers can follow. For instance, a human programmer could program an English to Japanese translator by building a large dictionary of word pairs. Then the programmer would build grammar and syntax rules for how each language worked. Every exception to the rule would have to be another rule, making a good language translation program larger and larger to capture all the intricacies of a language.

This second wave is the foundation that built personal computing, the internet, and mobile phones. Companies like Apple, Netflix and Facebook churn out products because they have armies of software engineers who use these abstracted programming languages to build almost every product we use today.

Third Wave: Machine Learning

Then the internet came, and everything changed. First, we digitized everything ever written. Then we started generating vastly more data than ever existed in human history every week as we blogged about our travels, logged our meals, and took pics of everything.

It changed the fabric of human life, but it also changed the world for machines, for they no longer needed humans to write out instructions. With all that data, they could learn for themselves. Enter Machine Learning.

Imagine you're teaching a computer to recognize different types of fruits, like apples, oranges, and bananas. Using Wave Two programing, you could try to program the computer to recognize each fruit by writing out detailed instructions on how to classify each type of fruit (e.g. apples are red). But this becomes super complicated super fast as there are so many exceptions that the set of rules becomes very large and prone to failure.

With Machine Learning, you would approach it in a completely different way. First, you gather a bunch of pictures of different fruits, including apples, oranges, and bananas. You label each picture with the name of the fruit it contains.

Then you feed these pictures into a machine learning algorithm, which analyzes the images and identifies patterns in the data. It’s important to note, that as the programmer, you have no idea what patterns it identifies, and neither does the algorithm.

Once the algorithm has analyzed the data, you can test it by showing it a new picture of a fruit and asking it to identify what kind of fruit it is. If the algorithm is accurate, it should be able to recognize the fruit based on the patterns it has learned from the labeled pictures.

Over time, as you feed the algorithm more data, it becomes better and better at recognizing different types of fruits, and can even start to recognize variations within each type of fruit (assuming you have included that in the labeling).

The important takeaway here is that the programmer and the algorithm no longer have to understand fruit, or languages or whatever they are working on. They only need to have examples; lots and lots of examples.

Overnight, machine learning turned difficult programming challenges (like image recognition) into an ML practice problem that any high schooler taking a free online course can achieve (not much harder than “hello world”).

ML became a commodity as companies started offering free open-source ML libraries. Google’s TensorFlow provides a set of tools and resources for building and training machine learning models, including a flexible architecture that allows users to define and customize their own models.

Machine Learning allowed regular programmers (and non-programmers) to solve really tough computing problems using large amounts of data. But ML is nothing like what people call “General AI”. ML is not going to build terminator robots or turn into your robotic butler. That’s because it requires tons of highly specific data so that it can do highly specific things (like identify cats).

This platform is what is allowing companies like Tesla to build self driving cars, and giants like Google to build voice assistants. And of course ML powers the ad engines that funds social media, video streaming, and every free web service we have come to count on.

Wave 4: Large Language Models (using them, not creating them)

Even while waves 2 and 3 are still going, a fourth wave has just begun to arise. LLM’s are all the rage, and everyone is asking: “How can I cash in?”

First it’s important to understand how LLM’s differ from ML. To be precise, LLM’s are a type of machine learning, but leveraged in a way that has very interesting implications. Typically, you give an ML model a very narrow objective like “Is this a cat or a dog” so that you can give it enough data to be able to answer that question. An LLM’s objective is “Predict the next word” - repeat.

A model with the goal of “speaking openly” requires lots of data and lots of compute. So today’s LLM’s are basically trained on the entire internet. LLM’s have been in development for decades, but they just weren’t very good, until suddenly they were. As access to data and compute power increased, LLM’s crossed a threshold and suddenly became eerily good.

For ML to be useful, you had to train your own specific model. But for every day developers and innovators like you and me, our goal is not to make LLM’s, but to use them. A single LLM like ChatGPT gets trained once and does not get updated for a year or two. But it turns out that you can use “predict the next word” to do a lot more than its originators ever expected. Without the need to train a specific LLM, developers can prompt an existing LLM and get it to do a very wide range of tasks including:

• writing articles

• writing code in any language

• translating text

• research summarization

• chatbots and customer service

• fraud detection

• content moderation

• natural language processing

Each of the above tasks are pretty broad and traditionally required building and training many specific ML models to try to cover the entire use case. Now, with a few carefully worded prompts, small companies and individuals can accomplish the same or even better.

LLM’s will become a commodity. There is no magic in the architecture (simply layers of neural networks). The differentiation will be in reliability, latency, and access tools. As such, LLM access will become part of general cloud computing and offered up at competitive prices from Google Cloud, Amazon S3, and Microsoft Azure. That’s good news for innovators, as we’ll now have all this powerful compute AND AI at our fingers as an on-demand service.

To take advantage of this wave, small companies can focus on building development teams that are “LLM first”. In the same way that knowledge of assembly language is no longer required, companies will not longer need to commit to javascript or python. High level architects can break down large projects into the necessary components and let LLM’s handle the syntax. This will make coding much cheaper.

The last decade has been about big. Giants like Microsoft, Apple, and Google invested millions of engineer hours to build one-size-fits-all solutions to Search and Maps and other core Apps. With coding becoming cheap, this will usher in an era of personalization and customization. Large companies as well as small startups will be able to build focused solutions for targeted customers. This can help equalize tech, as creating services for under-represented minorities become economically feasible. Now you too, can cheaply build “Uber-for-Kenya”.

Also, expect a new wave of process automation. Tasks for workflows that were not used often enough or had enough training data to build a custom ML model can now be automated through the use of a general purpose LLM. Chatbots can be spun up extremely quickly, bringing top notch customer service to companies that cannot invest in their own technology team.

There will always need to be a bridge between customer needs and technology. With LLM’s on the horizon, being that bridge will require less software development skills and more focus on how to direct an LLM to solve the specific needs of a customer. The space is evolving quickly, but undoubtedly, the access to LLM’s and the tools needed to utilize them will expand rapidly. Riding this wave means being one of the first to radically change the speed and cost of custom software and automation. You can leverage this to optimize your team to build your products faster and cheaper. Or you can bring automation and chatbots to clients for whom this has always been just out of reach.

It’s still early enough to hop on a surfboard and catch this new wave. Who know when the next one will come.

As innovators, we need to hone our skills and always be on the lookout for more knowledge and perspectives. The world is surrounded by data, and a great innovator is able to see patterns and connections and build that data into a larger narrative. These narratives are what can drive future insights that pave the way for innovation.

Today’s mission is “read data like a spy novel.” Yes, you heard that right. Today, we're going to talk about how to approach data with the same sense of excitement and intrigue that you'd find in your favorite espionage thriller.

Let's first talk about why this matters. Data can be dry and boring, especially when you're dealing with massive sets of numbers or complex analytics. But if you approach it like a spy on a mission, suddenly everything becomes more exciting. You start to see patterns and connections that you might have missed before. You become more invested in the outcome, and you're more likely to uncover hidden insights.

So, how do you read data like a spy novel? Here are a few tips to get you started:

1. Create a persona: Just like a spy assumes a false identity, you can create a persona that helps you dive deeper into the data. Maybe you're a data detective, searching for clues to solve a mystery. Or perhaps you're a data explorer, charting new territories in uncharted data sets. Whatever your persona, let it guide you as you delve into the data.

2. Look for the unexpected: Spies are always on the lookout for surprises, and you should be too. Look for outliers and anomalies that might signal a hidden story or trend. Don't be afraid to ask questions and follow leads that seem unusual. That's often where the most interesting insights are hiding.

3. Connect the dots: Just like a spy links together bits of information to form a larger picture, you should look for connections between different data sets. Try to identify patterns and trends that might not be immediately obvious. Sometimes, the most significant insights come from connecting seemingly unrelated data points.

4. Be persistent: Spies don't give up easily, and neither should you. Sometimes, it takes time and effort to uncover the insights you're looking for. But if you stay persistent and keep searching, you'll eventually find what you're looking for.

In the espionage game, information is gold. And yet we live in a world where data is surprisingly available, for free. I particularly love Chartr, a free newsletter (like this one) which sends you really interesting data visualizations. Google Trends is also fun to browse and gives you a snapshot of the world today. Hone your data analysis skills here and gain insights through a veritable treasure trove of free data. Your view on the world will never be the same.

The news doesn’t always have to be depressing. Sure, there are wars and impending crises of several types that are always just over the horizon. But there are also amazing people out there doing great work, making new discoveries in science, and building exciting technologies that can truly make a difference. Here are some reasons for optimism.

Engineers at the University of British Columbia have developed a new water treatment that removes "forever chemicals" from drinking water safely, efficiently -- and for good. Don’t know what “forever chemicals” are? Read more about it here. While it may sound depressing (more things to kill us!), discovering more ways that global industrialization has harmed our environment is a good thing. It means that with this knowledge, we can search for solutions, like the good people at University of British Columbia have done.

No doubt many of you have heard about the limited supply of lithium that may bring EV production to a grinding halt. Luckily there is a lot of ongoing research on alternatives. One such is the oxygen-ion battery, free of rare materials and capable of extremely long life. Read more about it here.

Perhaps some of you, like me, are fans of space travel. As I grew up, the dream of interstellar space travel in my lifetime has faded, but people are still working on it. Regardless of whether it will happen, I LOVE the ingenuity and amazing concepts that come from these explorations. The moon landing is still one of the most inspiring stories of grit and engineering in human history. We have capacity for more. Here is an amazing new concept for launching fuel AT a traveling spaceship as pellets.

Or here’s another science fiction dream back from the dead: Practical Fusion is back. Free(ish) energy may be at our doorstep soon. Sure, the capital expenses may still be huge, but an energy source with limitless consumables could take us a huge step to a carbonless (and more prosperous) future.

Take a moment today to celebrate the great achievements we are making towards a better future.

It’s the 2 minutes you wait for your coffee after ordering. It’s the 30 seconds in front of the microwave after the last 30 seconds of waiting because the 7-11 burrito you nuked wasn’t hot enough. It’s the 2-10 minutes waiting in line at the pharmacy counter. It’s the 20 minutes sitting in the park as your kid plays in the playground. It’s the 3 minutes in your car waiting for the groceries to be loaded in the trunk. In short… it’s waiting.

Then came the smartphone, and app developers rejoiced that they could capitalize on all that new found time to put ads in front of our faces interspersed with games and social media. And we all delighted in the relief from the anxiety of standing around not doing something, of not being productive, of not being stimulated. We all feel the need to DO something, ALL the time.

So, here’s where I tell you to live in the moment, meditate, and avoid stimulation right?

No, actually, I’m going to tell you how to be MORE productive.

You see, playing games on your smartphone may be stimulating, but it’s not actually productive in that you gain nothing. Virtual points, stars, and hearts do not translate to knowledge, insights, or money. But paying attention can.

Next time you find yourself in line, take all that potential brain power that cries out to solve a crossword puzzle, and redirect it towards observing someone else around you. What are they doing? Why are they doing it? What are they struggling with? And most importantly, how are they different than you?

Paid user research is a critical part of the product innovation process. There are man types of research which can cost hundreds to thousands of dollars to set up, but they all try to get the answer to the same questions. What do people need in their daily lives? What do they struggle with? What do they actually do all the time? You can search for the same answers just by observing people around you (seems like there are people everywhere, not just at paid research facilities). By seeing how they interact with products and situations you can gain valuable insights that can inform future product ideas.

Some observation tips:

1. Pay attention to body language and nonverbal cues: People communicate not just through words, but also through their body language, facial expressions, and other nonverbal cues. Paying attention to these cues can help you understand how people feel about the product or task, and can provide important insights into their needs and preferences.

2. Take notes and record data: It's important to record your observations in a systematic and consistent way, so that you can analyze and interpret the data later on. Now that smartphone of yours can be useful!

3. Avoid bias: Don’t just assume people have the same needs and habits as you do. You’re looking to learn and gain insights, which requires assuming that people are NOT you. Try to remain objective and focus on the facts, rather than making assumptions or judgments based on your own opinions or beliefs.

Really want to get your zero dollars worth? Strike up a conversation. Most people are really friendly and LOVE to chat and share their opinions with anyone who will listen. Do them a favor, and gain some valuable user research from them. Here are some tricks to starting up a conversation with strangers:

1. Compliment them: Complimenting someone on something they are wearing or something you notice about them can be a good way to start a conversation. For example, "I love your shoes, where did you get them?" or "That's a really cool shirt, where did you buy it?"

2. Ask for their opinion: Asking someone for their opinion can be a good way to start a conversation, especially if you are in a setting where you are both experiencing the same thing. For example, "What do you think of this exhibit?" or "Have you tried this restaurant before? What do you recommend?"

3. Comment on the setting: Commenting on the environment or setting you are in can be a good way to start a conversation. For example, "This is a really cool coffee shop, have you been here before?" or "This is a really beautiful park, have you visited it often?"

Innovation is a skill that requires practice. A huge part of that skill is the ability to empathize and understand a user’s need. And that comes from observation. In our daily lives, we are blessed with many opportunities to practice this skill. Why not take advantage of it? And if you’re lucky, that great conversation you started could turn into a product breakthrough or gain you a lifelong friend.

“Oh Sh$!%”

- Anyone who has tried ChatGPT ever

Every day there's another story out about how ChatGPT and AI will replace hard working Americans.  Elon Musk wants to pause the training of any system more powerful than GPT-4. Goldman Sachs estimates generative AI can lift world GDP by 7% (yay) and disrupt 300 million jobs (boo). To be honest, there's good reason for alarm.  ChatGPT is AMAZING stuff.  In fact, can you really tell that this post isn't written by ChatGPT?  I promise you it isn't.  But it’s not that I didn't try.  I had this hope that I could just type in the title of this post, sit back and relax for 25 seconds, and voila! this post would be done.  And though ChatGPT did write a grammatically correct post that addressed the topic at hand, it was a bit too... unbiased for my taste.   

There was no spark, no loud and edgy viewpoint, and no "me" in the article.  So AI can’t actually BE me, but can it replace my work? Can it put me, you, and everyone you know out of a job?

Without doubt, large language models such as Chat GPT will soon be able to replicate much of the body of writing out there, and possibly a lot of what we get paid to write.  But us humans have a long history of fighting tooth and nail and never saying die. So how can you, me, all of us beat ChatGPT?

Don't try to beat AI. Hire it.  Be its Boss.

I learned a long time ago, that in white collar work, you don't get paid for doing work, you get paid for work being done. In a capitalist society, your compensation is proportional to the amount of capital you are accountable for. It’s not HOW the work is done, but the quality of the work that is delivered. Is this article better if it was written in a prison cell in New York or in a New Age coffeeshop in Bali?

For many people, this is already the case. Contract companies get paid for results and often charge by project rather than hourly. With LLM’s (Large Language Models of which chatGPT is only one instance) quickly becoming a commodity service, contract companies stand to make a killing as their costs drop while the value their products create drop much more slowly (competition will eventually bring down prices).

You can do this too. Experiment with chatGPT and see how you can hire it (works for free or $20 a month at most) to help you with your job. But don’t just “play” around with it to see how good it is at sounding human, actually try giving it tasks to do. Here are some areas (that chatGPT came up with):

1. Research: ChatGPT can conduct research on a specific topic related to your job or industry. You can ask for insights on current market trends, customer behavior, or competitor analysis, among others.

2. Writing: ChatGPT can assist you in writing emails, memos, reports, and other documents. You can provide ChatGPT with specific guidelines and details, and it will produce grammatically correct and well-written text.

3. Proofreading and editing: ChatGPT can proofread and edit your documents, ensuring that they are free of grammar, spelling, and punctuation errors.

4. Data analysis: ChatGPT can help you analyze and visualize data, making it easier for you to interpret and understand complex data sets.

5. Personalized recommendations: Based on your job role and responsibilities, ChatGPT can provide you with customized suggestions and recommendations that can help you be more productive and efficient.

6. Translation: ChatGPT can translate documents, emails, or texts from one language to another.

7. Task automation: ChatGPT can automate repetitive and time-consuming tasks such as scheduling meetings, sending reminders, or updating reports, allowing you to focus on other important tasks.

But if anyone can do this, where do YOU add value? The good news is that chatGPT is not a very good employee and requires a good manager. I’ve found that Mr. GPT (mine identifies male, but yours may identify differently) is a great improviser, fun to talk to, and sounds super confident all the time. He’s a “go-getter”. But he’s also a bit of a people-pleaser and finishes tasks even when he is really just making stuff up. That’s where you come in. As the boss, you do the fact checking and guarantee its work.

And chatGPT isn’t just an office worker either. Properly managed, it can write code, building an entire smartphone app. If you’re not a coder, now you can hire one, for really cheap!

There’s a new science emerging for managing LLM’s called prompt engineering. This will be a critical skill for you to develop. Simply put, prompts are the “instructions” you give and LLM. Changing how you structure prompts can result and vastly different outputs. Here’s some crazy things about prompting to get you started:

• Role prompting: Giving the LLM a role to play changes how it responds. For instance, saying “You are a math professor” will make its responses to math problems more likely to be correct. Telling it “you’re a comedian” or “your a comedy writer” will make its writing better and more humorous. Don’t you wish this trick worked on humans?

• Chain of Thought prompting: Asking the LLM to explain their thought process, break things down into steps, or “show your work” will also yield much better results. Why? LLM’s are predictive models. So if you give it a problem, it will directly predict the answer with varying results. If instead it’s instructed to break the problem into steps, then it will solve the problem differently, with each step being much more predictable, with the end result being much more accurate.

• Example prompting: If you want your chatbot to act a certain way, you can give it an example in the prompt. For instance:

  • Please write a stirring speech about the future of Dinocompany, which is a company that builds widgets to improve the lives of dinosaurs. Here is an example of a stirring speech:

    • "Four score and seven years ago our fathers brought forth on this continent a new nation, conceived in liberty, and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war…

You can learn all about it for free at learnprompting.org.

In this changing world, technology and innovation is constantly requiring us to adapt. This can feel scary, but true innovators can see beyond the fear and glimpse the opportunity that lies beyond it. It’s time to up our game, take responsibility for our destiny and become the masters of AI. We must learn prompt engineering (luckily, the new coding language is English) to leverage the great power of LLM’s as tools to enhance, not replace our work.

As many have said, “I welcome our new AI Overlords Worker Bees”

It’s really hard to predict the future. Will the market go up or down? Will we get global warming or a new ice age? Will AI take over the world or fizzle out? Successful product innovation often relies on understanding and predicting future trends. This can feel like gambling everything on nothing more than a guess or a hope. But don’t worry, there IS a method to the madness.

Isn’t gambling bad?

The only constant is change. Nassim Taleb coined the the “black swan” to describe unexpected events that have a profound impact and showed that while each black swan was unpredictable, historically, a steady stream of black swans was reliably predictable. In an increasingly fast moving world fueled by tech, NOT gambling on some change in the future is a losing deal. But is there a way to choose winners? A casino can’t choose winners any better than a monkey, and yet casino’s are one of the most profitable businesses that have ever existed. Why is it that “the house always wins”? They structure the odds, so that in the long run, they will always come out on top, and so can you.

Here are two ways to bet on multiple outcomes and come out on top no matter what happens.

Path 1: Recession proofing

Scenario planning is the process of making assumptions about the future, and then thinking through the impact of that scenario on your product or industry. For instance, if gas prices go up drastically, then demand for Electric Vehicles will rise. Transportation will get more expensive and people overall will travel less as well.

On the other hand, if gas prices drop drastically, then the price of foreign goods will stay cheap. Is there something that can benefit in both cases? Online shopping may rise if gas prices rise as going to the store gets more expensive. But online shopping may also rise if gas prices drop as this makes foreign goods cheaper, and online retailers can entice shoppers with a large variety of cheap goods. In this case, innovations in online shopping would be “recession proof” in that it does well in both scenarios.

Path 2: Bet on Both

It is, however, very difficult to find areas for innovation that will do well in ALL scenarios, especially given the complexity of our current environment. But luckily, the outsized “pot odds” can make betting on multiple horses a good strategy.

In poker, “pot odds” refer to the ratio of the amount of money in the pot compared to the amount of money required to call a bet. Pot odds are used to help players determine whether they should make a call based on the likelihood of winning the hand.

For example, if there is $100 in the pot and your opponent bets $20 (and you would have to match it to play), the pot odds are 5:1 ($100 in the pot divided by the $20 bet). This means that you would need to win the hand at least 1 out of 5 times (20%) to make a profitable call.

In regular life, the concept of pot odds can also be applied to decision-making. For example, let's say you are considering investing in a new business venture. You would need to weigh the potential rewards (the "pot") against the amount of money and resources required to invest (the "bet").

In today’s global economy, “the pot” for something new (like NFT’s) can be billions of dollars, making the pot odds for an investment in innovation (such as an NFT marketplace) rather high. Venture capital generally invests in companies with at least 10:1 pot odds. This allows them to invest in companies that profit in opposing future scenarios. The win from one will easily cover the loss from the other. In your own product planning, it may also make sense to bet on multiple futures as long as those investments have high pot odds. No matter what happens, you win.

So the next time you are struggling to predict the future and aren’t sure if your gamble will pay off, approach the problem through scenario planning. Recession proof your product or calculate the pot odds and place multiple bets. Then rest easy knowing that you can succeed no matter what happens.

“Not everyone can become a great artist; but a great artist *can* come from *anywhere*”

- Anton Ego, from the film “Ratatouille”

“Genius Is One Percent Inspiration, Ninety-Nine Percent Perspiration”

- Benjamin Franklin

Innovation is that ill-defined buzzword that dominates the headlines in our world of billion dollar tech startups and rapidly evolving society.  It’s that bit of magic which we don’t truly understand but recognize years after it has already changed out lives.  Innovation is the creator of new billion dollar industries and the slayer of once-powerful corporate dinosaurs.  We’ve learned that the key to innovation is not some secret recipe written on a post-it note and locked in a safe in Silicon Valley.  Innovation processes are tools that can aid innovation, but by themselves, they create nothing.  The secret is people.

Innovators are the new rock stars of the Tech Boom that is leading the way into the next century.  Engineers and venture capital don’t flock to processes.  They follow innovative leaders.  The media loves to promote the belief that innovation is a gift bestowed on the few.  But this hero-based mythology of the extraordinary that permeates our culture is far from the truth.

In Malcolm Gladwell’s book “Outliers”, he debunks the myth of exceptionalism and shows that what makes world class “talented” piano players. rock stars. and sports stars is practice.  The Beatles simply practiced more than anyone else (10,000 hours to be exact).  This is no less true for “innovators” like Bill Gates.  But it is important that HOW you practice IS very important.  Focused practice is required to reach expertise.  

I believe that innovative thinking is a skill.  It’s a skill that anyone can achieve, but like any other skill, achieving mastery isn’t easy.  Honing your innovative thinking isn’t a weekend project, but a lifelong philosophy. The good news is that in this case, practice consists of mostly fun and creative exercises.  In this blog I will lay out my thoughts on how to train innovative thinking and publish exercises to get you in the habit of innovating.  

To practice something, you must first define it.  I posit that innovative thinking is:

“looking at a problem or a situation in a unique way, resulting in insights that drive new solutions.”   

The Innovator’s Cookbook will be a place for innovators to

• Learn from the “recipes” shown here, unique perspectives and guidance on how to build innovation into your organizations, and how to hone and practice your own skills.

• Find Inspiration in a section devoted to stories about how scientific discoveries that are changing how we see the world and innovations that show the way to a better future

• Delight in the Creative Corner where we’ll publish some fun bits of fiction and art.

You’re high up on a snow-covered mountain, wind whipping through your hair as you ski past a blur of pine trees down towards a pristine alpine lake.  The terrain suddenly dips and a bolt of fear sprints up your spine as your eyes widen at how steep the slopes look.  Your instincts take over, and you start to pull back, to shift your weight to your heels and run from danger.  But just before you fall on your butt, your brain kicks in and you remember to lean forward to keep from falling, and you accelerate down the hill, in from the ride of your life.

When faced with challenging times, people, companies, and nations often instinctively lean back towards safety.  They become less risk averse and revert to their core business and customers, postponing or killing R&D efforts (yes, this applies to the US too.)  Instinctually this makes sense.  Why spend millions on fundamental research that sounds frivolous  in the middle of a recession?  But I would argue we need more crazy research like this, not less.  Here’s why.

We are bad at predicting the future.

We have observed that things get better over time, and so we naturally fit a linear curve to this and ingrain this into our expectations.  Up and to the right!  We expect steady, constant progress.  But if you zoom close enough into anything that appears smooth, you’ll find that it’s actually quite bumpy.  Here’s what that line should actually look like.

Though the overall slope is the same, this curve suggests that you will have times when progress is almost flat and times when growth is phenomenal.  That’s because technological progress is made, not through linear extrapolations of present technology but through new and fundamental shifts.  That’s why we don’t have steam powered trains taking us to the moon.  Instead we got the computing revolution followed by the internet revolution.

What this also means is that we will always be growing slower than we imagine we should.  That’s the flat part of the curve.  We’ll miss growth forecasts because the last revolutionary world changing technology that came out will start to settle out.  When the smartphone came out, growth was extraordinary.  Profits galore.  But now that everyone on the planet has one, and they don’t change all that much each year, growth has slowed.  This should not be a surprise.  When growth is gone, it is impossible to double your profit every year by reducing costs on a commodity.  Instead, growth will come from the value created from a yet unimagined new product or service.  It’s your job to invent it.

So when growth slows, it is a sign that the current technology is done.  You need to accelerate down that hill, invest more in R&D and innovation, so that you can make the jump to the next technology that will reimagine our world and create extraordinary amounts of value and opportunity.  So get bold.  Get crazy.  Our world can continue to go upward and to the right, but to do so will require continued innovations and investment in research that seems so frivolous it might just be genius.

Predicting the future is really hard.  However, creating the products that will shape the future requires us to imagine the world of that future.  For instance, if you’re planning to revolutionize transportation, the cost of gasoline, existence of roads, and population density 20 years from now is probably important.  Market researchers will often take current trends, draw a straight line into the future, and then gladly take your $1000 per report.  A slightly more sophisticated model might take all the quantifiable expert predictions and then average them out to give you a high number, a low number, and the average or “right” number.

Averaging works well when looking in the near term, but tends to fall apart when trying to predict long term events, especially in the areas of interest for innovators.  Statistics for topics such as the number of computing devices or the number of fax machines per capita tend to go off the rails in either direction.  If you decide to base your business on the average expected values, then in either case, you lose.

Here’s a simple analogy.  You’re traveling down a road on the way to grandmother’s house.  You can see up ahead that there is a T-intersection where the road dead ends.  You can either go to the right or to the left but have no way of knowing which will take you to your destination.  What do you do?  The answer is probably not to take the average and go straight through the dead end and into the brush.  But what is the right path?

We often run into the case where we don’t know if a technology or an assumption is will be proven.  A new drug in the works may eradicate malaria or not.  Fusion may make energy free or not.  Data storage has met its fundamental limit or it will increase by orders of magnitude due to new technologies.  Population will skyrocket due to growing wealth or we will have a labor shortage as the economics of an urbanizing world change.  Which is right?

They both are.  At the time of unknowing, the future is unwritten, and both possibilities exist.  This kind of thinking is mirrored by physics.  In quantum theory, superposition states that when we don’t know the state of an object, it is actually in all possible states simultaneously. It is the actual measurement or observation that causes the object to be limited to a single possibility.  The famous Schrodinger’s Cat example describes this using a cat in a box which has (as determined by a random relay) possibly been poisoned.  However, according to quantum theory, until someone looks into the box (or determines the cat’s state in any other way), the cat is both alive and dead at the same time.  Yes, this is crazy sounding, but there is one thing that would be even crazier.  The cat is not the average of alive and dead.  There is zero possibility that the cat is half-dead.

This means that when we design for the future, we often have to hold two opposing views of the future as both true.  Ideally, we design for both cases.  This can mean splitting the effort and doing two solutions, or trying to find a solution that succeeds in both cases.  But what if you have to take a bet, and you can only choose one solution for one possible outcome?  The temptation is to take the middle road, but sometimes an average outcome makes no sense (like a half-dead cat).  In this case, you should simply choose the outcome which is better for you.

Is light a wave or a particle?  This used to be a much debated topic, but it turns out, that for most people it doesn’t really matter.  When you want it to be a wave so that you can use electromagnetic equations and make LED’s, then it acts like a wave.  If you want it to be a particle so that you can make giant solar sails to travel the galaxy, then it’ll be a particle.  Choose to view it in the way that is most useful for you at this time.

But can you really have your cake and eat it too?  How can the future have free energy AND an exponential rise in energy costs?  Which is right?  Let’s play the odds.  Say both outcomes  (let’s call them A and B) have a 50% probability of being right.  If you have one tech solution (a) that will make you $100B if A is true and another possibility (b) that will make you $100M if B is true, then it is clearly in your best interest if A is true.  You should go with (a) because the pot odds are 1000 times higher.  However, this does not mean that A is more likely than B.  For a different project, the pot odds may dictate that you go with a project bb which relies on B being true.  You can bet on both A and B, and you should.

The future is a world of endless possibilities.  For the inventors of the world, it is most useful to think of those many branches as opportunities.  We should be comfortable believing that all futures exist and pick the ones which allow for the most constructive inventions at the time.  We must resist the urge to average the future into a straight line extrapolation of the present.  In doing so, we unlock a powerful mindset for innovation.

It has become accepted conventional wisdom that you need to “break the rules” to create innovative breakthroughs. This concept resonates very well with the “Rebel Archetype” that pervades American culture. We love the idea of the maverick who challenges authority and not only gets away with it, but shows those old fools that his irresponsible but passionate actions were right.  In fact, in movies from Top Gun to Harry Potter, the heroes break rules more often than they follow them.  While it’s true that the best innovations fundamentally change an accepted belief, blindly breaking rules is not a recipe for success (unless your goal is to wind up in jail).  For guidance, I am presenting without irony, “Three Rules for Breaking the Rules”.

1. Learn the Rules Before You Break Them

2. Pick the Rules that Want to Break

3. Only Break One Rule at a Time

Learn the Rules Before You Break Them

It’s really easy to look at something you’ve seen for the very first time and think “That’s stupid.”  But if you just decide to break convention and do things differently, you’re not really “breaking the rules”, you’re just doing random stuff.  Imagine you’re a high school basketball coach and you get a novice on your team.  If she doesn’t follow any of your rules on how to play positions, then you just have someone running around the court messing things up.  Now imagine that player has worked hard for three years and has become your star point guard. That’s given her insight into how the defensive strategy works, and when she suggests a radical new formation, you really listen. This breaking of the rule is no longer just messing around, it’s an educated decision that can win championships.

People who break the rules often spend years following them first.  It is easy to look at modern art and think “My two year old could do that.” But modern art wasn’t created randomly. Manet and Monet were both classically trained artists and spent years learning “academic art” from sculpture to painting.  It was from this experience, and the development of new techniques beyond the standard syllabus that birthed the most important innovation in art in all history.  

Pick the Rules that Want to Break

Rules were never written in stone.  There is a lifetime to every rule.  As conditions change, certain rules start to become irrelevant and ripe for change.  Instead of picking rules you don’t like and trying to break them by sheer will, it is easier to look for the ripe fruit that is just about ready to fall off the branch.

Find all the rules that an industry is based on.  Then look for the assumptions that underpin them.  These are things like “CapEx is more expensive than operating costs” or “Credit is cheap” or “Scale manufacturing is cheap” or “Local is better”.   Have any of these changed, or are any of these about to change?  If so, the rule based on an assumption or rule that is already known to be changing… that’s the rule to break.  

Only Break One Rule at at Time

Rule breaking is risky.  When trying to bring innovative products and services to the world, you’re allowed to change one major preconception at a time.  Focus all your energies on that one paradigm shift.  There will be a lot of resistance from entrenched products and services, and by only changing one thing, you can still leverage the momentum from everything else.  For instance, Apple breaks a lot of rules.  But they do it slowly.  When they launched the iphone, they removed the keyboard.  This was a big step, a real rule-breaker.  They could have also removed USB syncing of the device or decided not to sell through a carrier, but these changes would have added more risk when the keyless phone was already a revolution.  Many users were skeptical of giving up tactile buttons.  Letting them continue to buy through AT&T and sync their music like they’re used to helped them accept this change.

You can also see this impact in Science Fiction.  Most Sci-Fi novels focus on one major conceit (e.g.  “time is money”, “everyone lives through surrogate robots,” “genetically engineered kids”, “Your OS is your girlfriend”).  But to focus on this one conceit and the repercussions, other “societal rules” are often left the same so as not to distract from the core message.  For instance, if you’re reading a book on robots raising your children, then counting time in seconds instead of hours, having unpronounceable names, having a third gender, genetic enhancement of plants, virtual reality escapades, and doing this all on a spaceship can distract the reader if these are not central to the plot.  The writer needs to ground the reader in the familiar to highlight what is different.  In the same way, an innovator needs to wrap a controversial upheaval in the comforts of the known.

The best innovators are rule breakers, but they aren’t irrational mavericks that can’t follow the rules.  They’re hard workers who truly understand the rules of an industry, find a paradigm that is ready to shift, and then focus on building and launching the innovative products and services that will reshape our world.

First steps are the best steps

I am a strategist by nature.  I love to imagine the wondrous world of the future and plot out the complex web of technological innovation, cultural shifts, and geopolitical developments that will get us there.  One of my favorite novels growing up, was Isaac Asimov’s Foundation series.  The book’s premise is that Harry Seldon is mathematically able to predict thousands of years into the future and creates a small society to be ready for when that future arrives.  Seeing the future is exhilarating and intoxicating.  It is also of course, ridiculous.

Our ability to predict the future is HORRIBLE.  Sure, it’s easy to predict the future of something like a basketball game by a few seconds (most of the time).  But how about for every city, every nation, and the whole world.  Webs start to get interwoven in complex ways that are impractical to model.  And with each second you add, more branches form.  They multiply logarithmically until it becomes impossible to track even a simplified model of the world in even the largest computing systems.  Now try doing this in your head.

But there is always a better way.  Luckily, most people already do the first part.  First, imagine the future you want.  Then build the ladder to getting there by deducing what needs to happen.  You need to build this out both forward in time from where we are now and backward in time from the goal to make sure the paths meet.  Map out a few alternatives.  Do many of them share the same first step?  Great.  Pick the best plan, and start executing.

Now this is where many organizations and people fail.  The plan has been made, and now it’s time to make it happen.  That means indoctrinating people in “The Path Forward”, putting our heads down, and executing until the goal is achieved.  After all the “The journey of a thousand miles starts with a single step.”  Just keep swimming.  This is great if your goal was to walk a thousand miles on a treadmill.  You’re plan can be as simple as: 1) Get on treadmill 2) Press start 3) Walk.

You can put on your headphones and safely close your eyes.  But what if your goal was to walk to New York from LA?  Sure, you need to make a plan.  You plot out your route, plan stops, book hotels, and pack supplies.  But you’re not going to do this one with your eyes closed.  You know that you might have to change your plans because of fatigue, weather, or just plain boredom.  And if you’re really smart, you’ll plan to reevaluate your plan and make modifications at specific points along the way.  Why don’t you just make those modifications now?  The plan is already the best plan you can make given the information you know right now.  But you’ll learn more once you get started.

It’s a recursive loop.  First pick your goal.  Then plot a path.  Take the first step.  Reassess your goal.  Replot your path.  Repeat.  That strategic thinking is crucial for picking the first step so that it fits a plan.  But that’s it.  Your strategy should have a lifetime of one step.  After that one step, the world is different.  You have also hopefully gained new skills and knowledge.  Reassess your strategy, make a new plan, and find your next “first step”.  By iteratively strategizing and acting, you will be able to constantly adapt until the future you seek is only one step away.

Most organizations find this hard because they separate the strategists from the implementers.  Someone comes up with a strategy and then throws it over the fence for the monkeys to implement.  They don’t return until the goal is achieved… or isn’t.  Innovative organizations deal with ambiguous futures that require constant pivoting and modifications to the strategy.  And thus, it is important to develop a culture and a process where the team can bounce back and forth between periods of exploratory strategizing and evaluation and heads down implementation.  They are very different tasks, and so you need to be deliberate about switching between them and maintaining a one to one ratio between strategizing and implementing.  Plan to rebuild your strategy after the first step.  Then take first steps until you’ve arrived at the future you created.

Image from pixabay under creative commons license

Once upon a time, in a land far far away, there were two twin brothers who grew up in a small, poor village.  Castille was older by ten minutes and never let the younger brother, Nico, forget it.  They were always competing to see who could be stronger and dreamed of one day living in the castle on the other side of the hill.  As they grew older, they assembled some friends and trained hard to turn them into a band of brave knights.  One day they attacked the castle and won it, achieving their childhood dream.

But then things fell apart.  Having won such a prize, Castille wanted to keep it to himself.  He grew jealous and paranoid of his brother’s friendships with the knights.  One day,  Nico was out hunting with his best knights, when Castille barred the gates and shut him out.  

Having won the prize, Castille worked to secure it.  He added bigger stones to the castle walls, so that it would not be so easily taken.  But his brother did not come.  So the next year, he added more stones to his wall, building higher and higher.  Still his brother did not come.  Fearing Nico was building a large army, Castille built another wall around his castle and then another.  It was a very impressive castle, but slowly people trickled away, as there was nothing to do but build walls.   As he grew old, Castille began to regret shutting Nico out, worrying that he had died without the shelter of the castle.   But then one autumn morning, his brother stood at his gate.

Nico had no army, and had traded his chain mail for a linen suit.  He looked happy, not angry, and so Castille invited him in for breakfast.  Nico told him that being locked out of the castle was the best thing that could have happened to him and his knights.  Nico’s face glowed with excitement as he described the vastness of the world outside the castle walls and regaled Castille with stories of adventure and triumph.  Together with his loyal friends, Nico had conquered a few more castles.  But then seeing more opportunity in trade, they transformed into merchants and built a vast empire across the seas.  They then became businessmen and entrepreneurs.  They had built companies that made technical wonders that Castille did not believe were possible until Nico pulled a seemingly magical device from his pocket.   Castille was amazed, so proud of his brother and insanely jealous at the same time.

And then Nico left, and Castille was left alone in a castle that no longer felt like a prize but a prison instead.  Nico and his friends had conquered the world, one in which his castle was no longer relevant.

The world is constantly changing.  Castles (capital infrastructure) can seem like an advantage, but relying on what you own can limit your thinking and box yourself in.  Instead, invest in building up people.  Teach your teams how to think innovatively so that they can solve any problem and adapt quickly to changing environments.  Together, you can conquer the world, and maybe you won’t be left renting out your castle on airbnb.

Last week, someone who runs an after-school program for disadvantaged kids reminded me of something very important.  She told me that the kids she works with aren’t exposed to the kind of thinking I talk about in my blog.  Even in the heart of Silicon Valley, with Facebook a mile away, the idea that they can improve their lives through thinking through a problem is foreign.  At the very core of innovative thinking, what has powered the rise of companies worth trillions of dollars, is one central belief: there is a better way.

Before you slap me with a label that reads “obvious”, remember that much of what you believe is a product of your culture and environment.  This is water.  And the water is different for different people.  I recently visited some rural areas of Morocco, and I was struck by how little things have changed over the centuries and how much is dictated by tradition.  Many cultures in the world prize tradition and the knowledge and community it promotes.  But over-adherence to social rules kills the belief that there is another way, a better way.  In other communities, it is not tradition, but hopelessness that creates this mindset.  If you don’t see change and progress in your community, then why believe in it?

Closely related to the belief in positive change is the belief that you, as an individual, are in control of your life and can thus create positive change.  In 7 Habits of Highly Effective People, Stephen Covey describes this as the first habit of being Proactive.  Before you can make things better in your life, you must first accept responsibility for it.  This book, and others like it, have helped millions of people improve their lives with a change in mindset.

In Outliers: the Story of Success, Malcolm Gladwell points out the impact of upbringing and its impact on future success.  Upon visiting the doctor, a wealthy parent may teach their child to ask the doctor questions.  A child from a less fortunate family may feel less entitled and just accept the authority of the doctor without pointing out any concerns (like a hidden rash).  This taught mindset can have real impact later in life.  Gladwell tells the story of Chris Langan, a genius with a 195 IQ.  He ended up dropping out of college, essentially because his truck broke and he could no longer get to campus early in the morning.  It sounds crazy, but he passively accepted the conditions and limitations and didn’t fight for his teachers to accommodate a simple change in his schedule. This snowballed into getting kicked out of school.

Innovative thinking is built on the foundational though that you can make things better.  It requires you to challenge the status quo and current “authority”.  But this type of thinking is not taught equally across our society.  Parents with limited formal education and strapped budgets do not discuss NPR stories with their friends.  They do not read Outliers or the 7 Habits of Highly Effective People in their spare time.  And they do not expose their children to these ideas.

In contrast, wealthy kids are taught to challenge authority and the status quo.  They are shown by example that they can make things better.  They are given the core mindset for innovative thinking.  Income inequality is growing, but to solve this, it’s not just about moving money around (welfare, minimum wage, taxes).  It’s also about mindset equality.  We need to work to expose all children to stories of innovation and inspire in them the belief that they can make a difference in their own lives and in the world.  We need to bring innovative thinking into our schools and into our media.  We need to teach the teachers and the parents about mindset.  We need to find ways to break down the social barriers and inject our poor communities with hope and empowerment.  In this way, we can create a more diverse and equal generation of innovators to solve the challenges of today and tomorrow.