Eight years ago, the local news did a story about Capitalogix — not about AI, but about how hard it is to find great tech talent. AI wasn’t the headline back then. It is now.
Finding real talent has only gotten harder since then. Demand for tech talent has risen, success requires stronger mathematical, statistical, and creative problem-solving skills, and AI is changing the playing field in many ways (including taking over parts of many jobs).
And that’s only part of why I’m proud of our team!
The robots aren’t coming for our jobs. We’re creating the robots, the AI, and the automation.
The secret to great AI is that it still has a heartbeat.
Here’s the article. You can watch the video below.
It’s not enough to invest in the right ideas or technologies. You also have to invest in the right people. Here’s what I told them back then:
“Standing still is moving backward … so you don’t only need new technology, you need a new level of data scientists — a new level of professional that can think about what’s possible, rather than how to do what we want to do right now.”
Looking back at that story eight years later is pretty funny … looking at how our tech stack has changed, how our team has evolved, and yet how singular our direction has remained.
That’s probably the part of this story I like most. The specific technologies will keep changing. The names will change. Some of the things we’re excited about today will eventually become obsolete.
That’s okay.
The point was never to bet on one particular technology. It was to build a team and a culture that keeps asking, What’s possible now? And then goes and tries to build it.
Even though we’ve got an incredible edge now, I recognize that edges decay faster than ever. The trick is to stay ahead.
I can predict that the future is bright… and I know the best way to predict the future is to create it.
When you interview someone for a job, what are you really trying to find out?
You can learn a lot from a resume. Experience. Skills. Education. Accomplishments.
Then there are the standard interview questions: What are your strengths? What are your weaknesses? Where do you see yourself in five years?
I’ve never been particularly convinced that those questions tell us much. And at least for those who work directly with me, the interview process is much more interactive. I’m interested in who they are and how they’re likely to respond to the dangers, opportunities, and practical realities of work in the environment they’re likely to do it in.
Most people know what they’re supposed to say.
The more interesting questions are the ones that help you understand how someone actually behaves when the work gets difficult.
Will they keep pushing when something matters? Will they learn when they get something wrong? Can they change their mind when the evidence changes? Can they figure something out when they don’t already know the answer? Can they take the lead when it’s their turn, and step back when someone else has the better idea?
And, perhaps most importantly, do they care?
What you’re really trying to learn
I’ve seen different models for thinking about this. Some focus on culture. Others look at whether someone is organized, competent, and motivated.
Those can all be useful.
But underneath them are some simpler questions:
How long will you keep pushing on something worthwhile before you give up?
How hard is it to get you to change your mind when you’re wrong?
How much do you learn from failure?
How quickly can you learn something new?
How comfortable are you letting someone else take the lead?
How much do you care about the work and the people around you?
Notice that none of these questions is really about a person’s resume.
They’re about behavior.
And you don’t necessarily need to ask them directly. In fact, asking them directly probably isn’t very useful. Most people know what the “right” answer sounds like.
Instead, you listen to the stories.
What happened? What did they do? What did they learn? What did they get wrong? What would they do differently? Who did they give credit to?
You’re looking for evidence of how someone responds when reality doesn’t cooperate.
Because eventually, every job gets hard.
Plans change. Things break. People disagree. Someone makes a mistake. A project takes longer than expected. The original idea turns out to be wrong.
That’s when you find out who you actually hired.
The same questions are becoming relevant to AI.
An interesting parallel is emerging as we start working with smarter, more agentic AIs.
We often evaluate AI by asking whether it can produce the right answer.
That’s important. But it may not be enough.
As AI moves from answering questions to doing work, we’re increasingly giving systems goals rather than instructions. An agent might research a market, analyze a portfolio, write a report, monitor a process, or work through a multi-step problem.
Picture an agent monitoring a portfolio when the market gaps 4% overnight — does it flag the anomaly and ask what changed, or keep executing the plan as if nothing happened?
At that point, the interesting question isn’t only:
“Can it do the task?”
It’s also:
“How does it behave when the task doesn’t go according to plan?”
That sounds a lot like an interview.
How does the agent respond when it can’t find the information it needs? What does it do when two sources disagree? Does it recognize when its original assumption was wrong? Does it ask for help when it reaches the edge of what it knows? Does it adapt when circumstances change? Does it learn from feedback?
And perhaps the AI equivalent of “How much do you care?” is a little different:
Does the system consistently behave in ways that reflect the objective we’re actually trying to accomplish?
That’s a much more interesting question than whether it can generate a good response in a demo.
From prompts to behavior
This is one reason I think the shift from prompts to agents matters.
A prompt is mostly about getting a particular response, but an agent is about getting useful behavior over time.
Those require different kinds of evaluation.
If I’m hiring someone, I don’t expect to give them a 500-page instruction manual for every possible situation they’ll encounter. I want someone who understands the objective, knows the boundaries, can make reasonable decisions, and knows when to ask for help.
The same principle applies to AI systems.
The goal isn’t necessarily to make the instructions longer and longer. It’s to create systems that can operate within clear objectives, constraints, feedback loops, and escalation paths.
And then observe what they actually do.
Maybe that’s the better interview question.
The best interview questions don’t have to be the most clever; they just need to be the ones that reveal something about how a person behaves when the answer isn’t obvious.
As AI systems become more capable, we’ll need to get better at asking them the same kinds of questions.
Not just:
Can it do the job?
But:
How does it behave when the job gets complicated?
That’s a much more useful way to think about intelligence — whether it belongs to a person, an AI agent, or a system that combines the two.
So the next time you’re evaluating either one, what are you really asking?
I love peer groups — groups that double as advisory boards, counselors’ offices, and idea factories. They allow me to see, hear, and discuss things I don’t usually think about, talk about, or even notice. They are also a great source for big ideas from someone not usually in your field of vision or attention.
For example, Genius Network brought in a titan of self-development, Les Brown. Les has been a speaker for over 50 years, has spoken to crowds of 80,000, and has been lauded by too many names to count. His podcast has reached over 2 billion downloads.
Not to mention how powerful his story is. From being adopted, to his school categorizing him as mentally challenged … to being a member of the House of Representatives in Ohio, a radio and talk show host, and more.
It was great to build a relationship with him over those two days.
Toward the end of the event, he graciously recorded a video with me. It’s short, but Les shares powerful quotes and nuggets of wisdom. We talk about staying heart-centered in an increasingly digital and distant world, how to work with A.I. instead of fearing it, and the power of hunger and purpose. Check it out.
Three things stayed with me.
Staying heart-centered isn’t softness — it’s a discipline, and it gets harder exactly as the tools get better.
The people doing best with A.I. right now aren’t the ones who trust it most or fear it most; they’re the ones who stay curious about what it still can’t do.
And then there’s hunger … Les has been doing this for fifty years and still speaks like a man with something to prove. Most people lose that long before they lose the ability.
Fifty years in, he’s still hungry. What are you still hungry for?
You can reverse almost any decision you make in business. A trade, a hire, a product bet, a strategy — if it isn’t working, you exit, you take the loss, and you move on.
You can’t reverse what you said in the moment.
Have you ever said something out of frustration, anger, humor, or even habit that you later regretted — and found the apology didn’t erase it? That asymmetry is the whole problem. We manage our reversible decisions with care, and our irreversible ones on instinct.
We are living in a period of heightened sensitivity to hurt feelings. Whether it’s politics, kids in school, or even in business … it’s clear that emotions and detection sensors are high.
What do you think it means? Has something fundamental changed, or is it just the natural result of stress and high expectations?
Happy people tend to find reasons or ways to be happy. Frustrated people are good at finding what frustrates them. Meanwhile, people naturally notice and avoid things that hurt them.
Of course, a little conflict is normal (or even beneficial). But perhaps the pendulum has swung too far?
Let’s be clear, some people intend to hurt others. However, it doesn’t take “intent” to hurt someone’s feelings (or to have your feelings hurt). Hurt feelings can arise from a simple disagreement, a fixed perspective, a careless remark, or even a look.
Effective strategies can sometimes trigger conflict, too. Why? Because people generally prefer winning to losing. It reminds me of angry kids on a playground. As a result, people often dismiss or excuse minor outbursts with phrases like ‘I was angry,’ ‘I’m only human,’ or ‘There’s no room for emotion in business.’ Nevertheless, we should strive to be better.
Lessons From the Nail In The Fence Parable
To drive the point home further, here is the story of “The Nail in a Fence.” I share it every few years, but as we look to finish the year strong, I think it’s worth revisiting.
Nail In The Fence
There once was a little boy who had a bad temper. His Father gave him a bag of nails and told him that every time he lost his temper, he must hammer a nail into the back of the fence.
The first day the boy had driven 37 nails into the fence. Over the next few weeks, as he learned to control his anger, the number of nails hammered daily gradually dwindled down.
He discovered it was easier to hold his temper than to drive those nails into the fence.
Finally, the day came when the boy didn’t lose his temper at all. He told his father about it; and the father suggested that the boy now pull out one nail for each day that he was able to hold his temper.
The days passed and the young boy was finally able to tell his father that all the nails were gone.
The father took his son by the hand and led him to the fence. He said, “You have done well, my son, but look at the holes in the fence. The fence will never be the same. When you say things in anger, they leave a scar just like this one. You can put a knife in a man and draw it out. It won’t matter how many times you say I’m sorry, the wound is still there.”
A verbal wound is as bad as a physical one.
This story reminds us to be mindful of cause and intent. Hope it helps.
How Technology Can Amplify (Not Replace) Our Humanity
As a technology entrepreneur focused on amplified intelligence (which means making better decisions, taking smarter actions, and continually improving performance), I recognize that we’re not using technology to replace humans. Instead, we’re automating activities that humans used to do – so that humans can focus on things more important and more in line with their unique abilities and tendencies.
On the other hand, one of the main reasons for automation is to avoid certain tendencies that are baked into human nature – like these little outbursts. I say that because, as much as the world has changed in the last several thousand years, human nature has remained stubbornly the same.
Here’s the connection I keep coming back to. A bad decision inside a system leaves a record you can study. A bad word leaves a hole in the fence. One of them compounds into a better process; the other just compounds. That’s why I’d rather hand the repeatable things — the ones where fear, greed, and discretionary mistakes do their damage — to a process that doesn’t have a temper, and save human judgment for the decisions that actually need a human in the loop.
Making Best Practice Common Practice
It’s hard enough to change yourself … so, it’s unrealistic to expect to radically change others. Instead, if you want to increase the likelihood of certain actions, it makes sense to rely on technologies that are simple, reputable, consistent, and scalable to make your best intentions and best practices more common. This is why I say that amplified intelligence has an automatic advantage … because it eliminates the fear, greed, and discretionary mistakes that humans naturally bring to a process.
The Power (And Limits) of Forgiveness
Inflicting pain on others is harmful — but holding on to anger can be just as damaging to ourselves.
Feeling and stoking anger is like taking poison and hoping the other person suffers. It isn’t efficient or practical.
So, what about “Forgiving”? It doesn’t have to be synonymous with forgetting.
Forgiving removes the valence (or charge) from a situation or memory. It serves you even more than it serves the person you’re forgiving.
It only takes a moment to create an emotional trigger (think about how you felt when you saw a high school bully in the hallway). It is simple: evolution and natural selection favored species that remembered and avoided danger. It is in our DNA. But avoidance isn’t always a great strategy … especially when it is blocking the attainment of something beneficial.
Forgiveness is a way to disable or mute the emotional trigger (this is called “collapsing an anchor” in NLP). It’s also a choice to move forward.
Forgiveness is also a release of “claim”. When we are wronged, we expect an apology, retribution, restitution, or recognition. And until we get it, we are stuck, waiting for it. In a sense, forgiveness releases the stuck energy, making it available for something else (hopefully, something better).
Forgiveness changes the route and allows you to move forward.
And I’ve found that good things happen more often when you are in motion.
As you look ahead, ask yourself: Who or what do you need to forgive?
Active Worlds launched in 1995, back when the internet was still something you dialed into. Its original world, AlphaWorld, was an enormous 3D space where people could build houses, cities, monuments, businesses, games—pretty much anything they could imagine. It was one of the earliest attempts to create what we now call a metaverse.
And it is still there. You can log in today and wander around; unfortunately, it’s unlikely you’ll see anyone … though there are still active players.
Across a massive map spanning over 100,000 km, buildings, homes, and other places that once meant something to people still stand. There are memorials. Remnants of communities remain, formed around people who met there decades ago. Some of the people who built these places are still around. Others have died. If you click on an object, you can see when it was placed and by whom.
The world hasn’t necessarily disappeared just because the people did.
There’s something there. Not the emptiness of a video game, A GeoCities page, or an old message board, but closer to walking through an abandoned town.
A Whole New World …
And that distinction is surprisingly powerful, and it makes me think about the metaverse and what other worlds will face similar endings.
We tend to talk about virtual worlds as though they are a new idea. The language changes—metaverse, virtual reality, persistent worlds, immersive internet—but the basic idea has been around for a long time.
Active Worlds came before Second Life. Second Life came before the modern metaverse boom. MMORPGs have been creating persistent societies for decades. Before that, there were MUDs, MOOs, chat rooms, and virtual communities that people treated as real places, even when the graphics were nothing more than text.
The technology keeps getting better. The idea isn’t new.
What may be new is how much of our lives are moving into these spaces.
If the first generation of virtual worlds taught us that people will build communities online, the next generation is teaching us that people will leave things behind online.
A digital memorial for 9/11 was built in Active Worlds shortly after the event. via The Tax Collector Man
That raises an interesting question: What happens when a virtual world outlives the people who made it?
We already preserve physical places because they tell us something about the people who came before us. We maintain houses, museums, churches, battlefields, and cemeteries. We put plaques on buildings. We protect ruins.
But what happens when the ruin is digital?
A Reminder of Time Past …
Maybe a forgotten virtual house becomes the equivalent of an abandoned farmhouse. Maybe an old player-built city becomes a historical district. Maybe the coordinates of an important gathering place become something like an address in a cemetery.
And then there are the memorials.
There is something almost inevitable about people memorializing the dead in the spaces where they actually lived. Active Worlds has memorialized major events and community members for decades. Its own historical archives contain projects dedicated to preserving early AlphaWorld creations.
Why shouldn’t they?
If someone spent ten years of their life in a virtual community, why would the significance of that community disappear simply because the community wasn’t physical?
We may eventually have to get much more comfortable with the idea that a funeral doesn’t necessarily have to take place in a church, cemetery, or funeral home.
It could happen in a virtual world.
You could visit the house someone built. You could walk through a digital recreation of their favorite place. You could encounter an avatar wearing the clothes they always wore. You could leave flowers somewhere that exists only on a server.
And perhaps, eventually, you could talk to them … well, a digital avatar of them.
This is where AI makes the whole thing considerably stranger.
We are already moving toward a world where AI agents can inhabit digital environments, interact with people, remember conversations, and behave with some degree of continuity. Put one of those agents into a persistent virtual world, and suddenly an empty world doesn’t necessarily have to stay empty.
Imagine exploring an abandoned virtual city twenty years from now and encountering a handful of AI characters.
They aren’t necessarily pretending to be dead people. They might simply be inhabitants designed to keep the world alive. They could run shops. Give tours. Tell stories about the history of the place. Maintain buildings. Organize events.
Or perhaps one day you encounter an AI modeled after someone who actually lived there.
That’s where the philosophical questions get uncomfortable.
Digital “Who” Takes On A New Meaning
If an AI can reproduce someone’s mannerisms, remember their stories, speak in their voice and respond to questions using years of material that person left behind, what exactly are you talking to?
And if that question wasn’t hard enough, what happens when the avatars become indistinguishable from the person?
I don’t know.
And I’m not sure we should rush to answer it.
There is something beautiful about the possibility that the internet will eventually become filled with these strange little mausoleums. Not just websites frozen in time, but entire environments that continue to exist long after their original communities have faded.
Some will be preserved intentionally.
Others will simply be forgotten.
Some may become popular tourist destinations for people who weren’t even alive when they were created. Others may sit quietly on servers, visited by a handful of curious people every year.
And some may be populated by machines that make them feel alive again.
That last possibility is especially interesting.
We’ve spent decades trying to make virtual worlds feel more real by improving graphics, physics, and hardware. Maybe the next step isn’t making the world look more realistic.
Maybe it’s making it feel inhabited.
Which raises another strange possibility: perhaps the future of virtual worlds isn’t one enormous metaverse where everyone gathers together.
Maybe it’s millions of little worlds.
Some bustling. Some private. Some commercial.
And every once in a while, someone will stumble across one.
They’ll walk down an empty road, find a house someone built thirty years earlier, read a name on a memorial, and wonder who these people were.
They’ll be exploring the ruins of a civilization that never physically existed.
Except, of course, it did.
It doesn’t have to be tangible to have left a meaningful impact.
There’s a business in that gap. Somebody will build the equivalent of a cemetery trust for digital worlds — perpetual hosting, curated access, maybe even AI caretakers, sold the way we sell burial plots and estate planning today. Somebody else will build the opposite: a service that guarantees your digital footprint disappears on schedule, for people who’d rather not become a tourist attraction someday.
Neither exists yet at scale. Both are investable ideas.
As we reconcile with a rapidly evolving set of technologies, we’ll need markets and products for digital legacies — not just philosophy.
For most Americans, it’s easy. That moment – and its ripples – are imprinted on our minds. I was at my desk, watching CNBC while trading. For my son, Zach, it was in his 3rd-grade class. They wheeled in a TV on a cart for the students to watch. One of his classmates’ uncles was killed in the attack.
Nearly 3000 people were killed during the attacks.
A colleague of mine was supposed to be in the tower that day but rescheduled a meeting for the following day – narrowly missing it.
And now 25 years have passed. So much has changed. So much has stayed the same. We’ve analyzed the events of that morning a thousand ways from different vantage points … and it’s still impossible to fully grasp the weight of the event.
It’s crazy to imagine that there are now full-fledged adults who have no memory of it.
And with that, Visual Capitalist put together a great chronology of the event. As we honor those who gave their lives – or had them taken from them – it’s a powerful reminder. Click the image to enlarge.
As we remember September 11, it’s also a time of year when the Jewish calendar calls us to do something similar: pause, remember, and reflect. Rosh Hashanah, the Jewish New Year, begins a period of looking back at the year that has passed, taking stock of where we are, and thinking about the kind of people we want to be in the year ahead.
There’s something fitting about that connection. We can’t change what happened on September 11, 2001. But we can choose what we remember, what we carry forward, and how we respond to the world around us. Maybe that’s the real work of remembrance — not looking backward, but letting what we remember quietly shape how we move forward.
P.S. The physical scars have mostly healed — the Pentagon was rebuilt, and a new World Trade Center rises in Lower Manhattan. But for the first responders who ran toward it that day, the wounds haven’t. Here’s a powerful video of Jon Stewart, still fighting for them.
For decades, Nike’s slogan was one of the world’s most recognizable: Just Do It.
In November of 2021, it was worth a staggering $280B. Today, it’s down to $57B (a 78% decline) and is set to be removed from the S&P 100 on September 21. Not the end of the world for the market behemoth, but certainly an ominous bellwether.
After being the face of sports, and certainly basketball, for decades, Nike decided to try something new …
It pulled back from retailers.
It pushed consumers toward direct-to-consumer channels. It became obsessed with digital data, memberships, and measurable marketing. And in the process, it made a classic mistake: confusing efficiency with relevance.
More importantly, it forgot why people bought it in the first place.
Don’t Lose Sight Of The Prize
Companies often forget a simple business principle when things are going well: keep the main thing the main thing. The world is always full of flashy new strategies, technologies, and trends promising to reinvent how business is done. But you can’t get so focused on chasing the new thing that you lose sight of what you’re really chasing.
Kodak didn’t lose because photography disappeared. RadioShack didn’t lose because people stopped buying electronics. Both lost relevance as the world changed around them and they failed to protect—or evolve—what originally made them valuable. Kodak was in the business of making memories, not selling film. Radioshack was in the business of being an expert source, not selling niche tech.
Nike faces a similar risk. Data, DTC, and digital optimization are useful tools, but they were never supposed to become the product. The main thing was always their brand identity … not just making people want to wear Nike, but making them want to wear it while performing. Customers wanted to feel like high-performance athletes, like they could be the next Michael Jordan or Tiger Woods.
When companies forget what made them great in the first place, they can spend years optimizing themselves right into irrelevance.
Nike didn’t disappear overnight (and it’s not dead yet)… but it’s certainly much easier to miss.
Just Do … Something Else
When Nike pulled products from stores, competitors happily filled the shelves. When Nike shifted its focus toward data and retention, smaller brands captured attention and culture. On, Hoka, and others didn’t just gain distribution—they gained an opportunity to become part of people’s identities.
For decades, Nike was more than a shoe company. The swoosh meant something. It represented athletes, ambition, rebellion, and culture. But as Nike focused inward—optimizing its own channels and selling more efficiently to people already in its ecosystem—it left more room for consumers to discover and identify with something else.
And Nike made an even more fundamental strategic mistake, that every sports franchise knows not to make.
Don’t get caught up in your opponent’s pace. Play your game.
Nike had spent decades winning a game almost no one else could play: building one of the world’s most powerful brands through culture, athletes, storytelling, and ubiquity, and pouring ridiculous money into the intangibles because everyone already knew the name.
Then it stepped onto the field with its competitors and started playing their game.
The problem is that this is exactly the kind of game smaller, faster, more lightweight companies are built to play. They can move faster. Experiment faster. Pivot faster. While Nike was trying to keep up, its competitors were doing what competitors are supposed to do: finding openings and taking advantage of them.
Choose A Winning Game
The lesson isn’t that DTC is bad. It’s that distribution isn’t just about sales. Retailers create visibility. Marketing creates cultural relevance. And sometimes the hardest-to-measure things are the ones that matter most.
Nike built one of the greatest brands in history by being everywhere athletes were—and inspiring people who weren’t athletes yet.
