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 Both lost relevance as the world changed around them and they failed to protect—or evolve—what originally made them valuable.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.

DTC optimization. Digital acquisition. Customer data. Efficient supply chains. Targeted retention.

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.

Then it tried to optimize the magic.

Don’t do it.

I often say, “Standing still is moving backward,” and “You’re either growing or dying.”

So when I hear people resist new technologies, I can’t help but cringe a little. Smart people don’t avoid innovation and new technologies — they find ways to harness them.

On the other hand, we’ve certainly seen countless businesses get so distracted by innovation that they lose sight of what they’re supposed to be optimizing for.

With Nike getting delisted from the S&P 100, I thought it was worth a short revisit. For a more comprehensive article, check this out. While these frameworks focus on technology, you can replace the word technology with anything, e.g., data, the internet, new laws, etc.

Step 1: Start With Why

Before you can get the right answers, you have to ask the right questions.

Simon Sinek popularized a concept called “Start with Why.” His 2009 Ted Talk “How Great Leaders Inspire Action,” which remains one of the most-viewed TED Talks ever, with almost 70 million views.

This talk introduced his core framework: The Golden Circle, the concept that catapulted him to fame. It is a simple but powerful model for understanding why some leaders and organizations inspire while others don’t. It consists of three concentric circles, like a bullseye. At the center is Why, the middle ring is How, and the outermost ring is What.

When most people and organizations start trying to innovate to keep up, they start with the outermost circle first, and they lose sight of the innermost circle.

Why Start With Why

Here’s an analogy: Think of a magnet. The strongest force comes from its core. Similarly, in leadership and business, the Why is your core—it’s what attracts people to you. It’s not just about selling a product; it’s about sharing a belief or vision that resonates emotionally with others.

For example:

• Apple doesn’t just sell computers (What). They believe in challenging the status quo and thinking differently (Why). Their How—innovative design and user-friendly technology—flows naturally from this belief.
• Martin Luther King Jr. didn’t say, “I have a plan.” He said, “I have a dream.” His Why inspired millions because it connected with their values and emotions.

When new opportunities arise, filter whether they’re a step in the right direction through your “why”.

Pragmatically, your why is also your differentiator. It’s what makes your business unique, which makes it part of your moat… and you have to protect your moat.

Step 2: Adapting to Technology the Right Way

It’s similar to Maslow’s Hierarchy of Needs: you have to address things like food and shelter before you can tackle higher-level needs like affiliation or self-actualization. 

The Improve phase is crucial because if you don’t pass this stage, you don’t get to the stuff beyond it. Said simply, the first stage is about helping somebody do what they already do, just better. Doing this increases efficiency, effectiveness, or certainty … buying you time and space to focus on what comes next. It’s also a way to show you’re making progress in the right direction, increasing capabilities, and building confidence (the fuel you need to keep making progress). In this phase, you’re really still doing exactly what you were already doing … just better.

Once people have tested the waters and seen results, they tend to jump straight to transformation, but that’s a mistake.

Transform is the big, hairy, audacious goal that you want to make possible. It’s the mountain top you’re trying to climb. It’s helpful to know what that is. But when trying to climb the mountain, you still have to take the steps in front of you.

The first step on the mountain is to innovate. It’s about what you could do, and what you should do – instead of what you’re already doing.

Redefine is where you start climbing the mountain and adding new capabilities to your arsenal. You’re now at a stage where you can imagine a bigger future and grow your vision to match your new capabilities. In a sense, you’re playing the same game, but at a different level and with different expectations.

When you finally make it to Transform, you are playing a new game (often on a different playing field), and you’re likely influencing not just your company but other companies. At this point, former competitors often approach you with ideas and resources, seeking to collaborate.

Another distinction I make about transform is that it’s very different from change. Change is about bringing the past forward and hoping that minor adjustments yield desired outcomes. Transform is about committing to the outcome and accepting the fact that the process may change dramatically.

Another key mistake entrepreneurs make is pivoting to something completely new. When you’re charting a path up a new mountain, you will find unstable ground or insurmountable peaks. At that point, many people give up and look for something new. They start wandering in different directions. That’s a lot of wasted movement.

My rule at Capitalogix is “This … or something better.” When we hit a roadblock, we’re allowed to go around it, but only if it improves our current situation, expectations, or goals. 

Playing The Right Game

If you keep sight of your why, and you innovate with purpose and direction, you start to build a playbook for long-term success.

Innovation becomes an exciting next step rather than a scary specter on the horizon.

And, best of all, you start to compete only at your expertise, which makes every space a blue ocean.

Nike risks making its brand a commodity instead of an identity because it’s starting to innovate in the wrong direction.

It’s not enough to play with AI. That becomes a distraction.

You need a roadmap and the discipline to follow it.

Lighter Links:

• Yes, We’re Entering the Era of Artificial General Intelligence. (Wall Street Journal)
• OpenAI’s Chief Scientist Says AI Labs May Need to Slow Down: ‘No One is Prepared for the Consequences’. (Insider)
• Who Swears the Most? Study Says One Country Won by a F–King Landslide.(Vice)
• He Was a Washed-Up Pop Star. Now He’s Selling Out Stadiums Hosting Baby Raves. (WSJ)
• Genghis Khan is Believed to Have Had So Many Children that Some 16 Million Men Alive Today are Directly Descended From the Mongol Emperor. (AllThatsInteresting)

Market-Related Links:

• Prediction Markets Now Expect AI Start-Up Anthropic to Unseat SpaceX as the Largest IPO in 2026. (MotleyFool)
• Washington’s Crypto Bill Just Got More Complicated. (HackerNoon)
• Where ‘The AI Economy’ Goes From Here. (TheAIEconomy)
• The ‘Waffle House Index’ is Real, and Apple is Filming It. (TheStreet)
• Tim Cook’s Final Words of Warning as Apple CEO: US Economy Now at Risk From ‘Hundred-year Flood’ — Even Elon Musk is Worried. Protect Your Wealth Now. (MoneyWise)

Lighter Links:

• Personal Assistants are Suddenly Venture Capital’s New Obsession. (Inc)
• South Korea is Giving Its Entire Population Free Access to AI, No Token Limits. (TechSpot)
• Ranked: The World’s Largest Countries by Land Area. (VisualCapitalist)
• Where are All the Aliens? a New Book Explores Possible Answers. (Scientific American)
• Trump Signs Order to Create Space-Focused Military Academy. (NBCNews)

Trading Links:

• Claude Revenue Surges 1,000% as Anthropic Gains on ChatGPT and Amazon. (Benzinga)
• Why Nvidia’s Hugging Face Deal is Really About Its Biggest Threat. (Time)
• Yes, We’re in an AI Bubble. Just Look to 1980s Japan. (Foreign Policy)
• The Fed Confronts a Powerful New Economic Force. (Washington Post)
• Derivatives Get Smaller, Shorter and Always On. (FIA)

The world’s stock markets are more intertwined and unpredictable than ever. As we move toward the end of 2026, record highs, emerging uncertainties, and shifting regional dynamics dominate the landscape. What forces are redrawing the map—and how should forward-looking investors respond?

Here is a look at global stock markets. Worldwide, they were worth $158 trillion in 2025, with U.S. markets accounting for nearly half. 

Global Market Snapshot

The global stock market has grown significantly.

At the end of 2025, global equity markets reached a record $157.8 trillion in market capitalization—more than $25 trillion higher than the previous year.

But the more interesting story isn’t simply how much the market has grown. It’s where that growth is happening—and how concentrated global wealth has become.

The U.S. remains in a league of its own.

U.S. Dominance vs. Global Opportunity

U.S.-listed companies now represent roughly 44% of the entire global stock market, with a combined market capitalization of $68.9 trillion. That’s more than four times the size of either China or the European Union individually.

And America’s dominance has actually increased.

Back in 2012, U.S. companies represented roughly one-third of global equity market capitalization. Today, that figure is closer to half.

Much of that growth has come from the extraordinary performance of America’s technology giants. Companies such as NVIDIA, Apple, Alphabet, and Microsoft have transformed not only the U.S. market, but the global distribution of market value.

That raises an interesting question:

If the U.S. already dominates global markets this much, where is the opportunity elsewhere?

Not Like U.S. …

The rest of the map isn’t empty, just smaller and more complicated. China and the EU are essentially tied at roughly $15.5 trillion each — enormous by any historical standard, and still less than a quarter of the U.S. market. China’s scale comes bundled with geopolitical risk and a property market still working through its problems; Europe’s re-rating — equities up nearly 40% year over year — rides on unusually specific catalysts, German spending chief among them, layered on an economy still weighted toward banks and industrials rather than platforms. India, at $10.6 trillion, is the one growth story that doesn’t need much interpreting: a huge population, an expanding middle class, and a growing role in global supply chains. And across Japan, Korea, Taiwan, and Singapore, the real story isn’t consumer AI at all — it’s the chips, the manufacturing, and the infrastructure sitting underneath it.

The Bigger Picture

The most striking takeaway from the chart isn’t simply that the global stock market has reached $158 trillion.

It is that market leadership continues to concentrate around innovation.

The U.S. has increased its share of global equity value by more than 10 percentage points since 2012, largely because investors believe innovative companies are formed there.

And that creates an interesting tension for investors.

On one hand, diversification matters. No country stays dominant forever, and concentrating too heavily in any single market creates its own risks.

On the other hand, diversification for its own sake isn’t necessarily a strategy. Capital tends to flow toward places where innovation, productivity, capital formation, and economic opportunity are strongest.

The challenge is figuring out whether today’s winners are experiencing a temporary boom … or participating in a much larger structural shift.

Takeaways for Investors

The global market isn’t a static pie. It is a constantly changing map of capital, innovation, and expectations.

The U.S. currently owns the largest piece by a wide margin. But beneath that headline are several competing stories: China’s enormous but uncertain market, Europe’s potential re-rating, India’s long-term rise, and Asia’s increasingly important role in the technology supply chain.

And then there is AI.

The AI boom is already influencing where capital flows, which companies command the highest valuations, and which countries are gaining market share. If AI continues to reshape productivity and corporate profitability, today’s market leaders could become even more dominant.

But technological revolutions rarely follow a straight line.

That is why investors shouldn’t simply ask, “Where is the market today?” Instead, “What would have to change for this picture to look completely different five or ten years from now?”

The good news is that the global opportunity set is enormous … and growing.

Perhaps you can’t reliably predict the future … but that doesn’t mean you can’t prepare for it reliably.

Are you ready for some Football?

Friday was the Cowboys’ last preseason game. We had a pretty good preseason, all things considered.

It wasn’t exactly the prettiest (partly because it was the first games of the season, but also because many of the starters sat the game out to avoid injury). With that said, it was still a fantastic experience. The NFL (and Jerry Jones) knows how to put on a show. 

It’s Easy to Feel Good at the Start of a Season.

Lots of people ask me how the Cowboys look this year. The truth is, at this point in the season, it’s impossible to know because injuries have a dramatic impact on the game.  

Regardless, each year I choose to be optimistic about the chance of a post-season run. 

That kind of logic (or lack thereof) is why I think automated trading is better than humans attempting to do it themselves. It’s a way to make objective decisions and eliminate fear, greed, and discretionary mistakes.

On the other hand, it feels so good to hope!

A Lesson From the Game.

I had an interesting discussion at a game recently. My guest commented that Jerry Jones is a fantastic businessperson – which is hard to argue – but probably shouldn’t be running the team. He believes the team needs a change of pace. 

While I don’t know if that’s why we tend to struggle so much more late in the season, it reminded me of a great business lesson. 

Entrepreneurs often mistake their domain expertise for general expertise. “I’m fantastic because I’m fantastic at all these different things.” As a result, they overestimate their ability to be great at things outside their unique strengths.  A similar issue is that many people believe they are deep thinkers because they think deeply about what they think about. However, they often don’t realize how narrow their range of thinking is, and how many things fall outside their expertise, interest, or even consideration.

We hire people to take on different roles, because they free us up to focus on what we’re truly experts in. It’s also why AI is so powerful.

Learning to offload tasks that you may not be as fantastic at as others is a great way to free up time to focus on not only the things that you’re great at – but also bring you joy and energy. 

It’s important to know which decisions you can make with your gut, and which deserve a system… and likely a playbook.

Meanwhile, I’ll keep hoping for the Cowboys every Sunday… I just won’t bet my money on it.

Hope that helps!

How ’bout them Cowboys!

I’m still in awe of how much data we create — and how fast that number moves.

Back in 2011, I was amazed that users created 600+ new videos and 60 new blog posts every minute … and I was talking about how many people I saw at the mall (though it was decreasing …)

The Internet is both timeless and timely in an interesting way.  While what’s popular seems to be ever-changing, what it does (and what we are capable of doing with it) continues to grow exponentially.  Ultimately, the Internet is the digital town square of a global village, where all types of participants gather. 

In 2011, I first wrote about what happens on the Internet in 60 seconds. 

I’ve since updated the article a few times.

• 2014
• 2018
• 2024

Each time I write the article, I’m in awe of the amount of data we create and how much it has grown. For example, looking back to 2011, I was amazed that users created 600+ new videos and 60 new blog posts each minute. Those numbers seem quaint compared to current figures.

via LocaliQ (January 14, 2026)

Today, the Internet reaches approximately 6.2 billion people.  Most of them also use social media. 

To add some more perspective, 

• In 2008, 1.4 billion people were online; by 2015, that number was 3 billion.  Now, that number has doubled again. 
• In 2008, Facebook had only 80 million users, and Twitter (now X) had 2 million.
• In 2008, there were 250 million smartphones; now there are over 7 billion!

It is mind-blowing to consider what happens on the Internet every minute today.

In 2023, the world created approximately 120 zettabytes of data, which breaks down to about 337,000 petabytes per day.  Broken down further, that’s more than 15 Terabytes of new data created per person… now scale that with the rapid growth of Generative AI, and even another billion internet users.

A growing share of what fills an internet minute is now generated by systems — models answering models, agents writing to other agents, sensors reporting to services nobody reads.

Globally, generative AI platforms receive roughly 2.5 billion prompts and serve between 115 million and 600 million daily active users. Over 1 billion people use AI tools each month, and ChatGPT alone reaches over 900 million weekly users.

Can you imagine how much data that is a day? Can you imagine how much more data will be created in five years?

It’s still too early to tell whether the scale will be exponential … or logarithmic. Regardless, I think we’re moving to a post-human tipping point where technology starts to drive more of what happens on the internet (as more devices and digital WHOs create and share data, it’s hard to fathom the ramifications and the sheer volume of data), and beyond that … we’re entering an era where an increasing amount of data will be generated in space.

So, while a lot already happens on the internet every minute… I believe the safe bet is that it will move even faster next year… even if human usage stays exactly the same.

To some, new technology is a good thing. To others, less is more. It’s been interesting watching many of my formerly tech-savvy peers shy away from AI.

But it’s a tale as old as time.

Most people simply “tolerate” technology transitions; some people drive them, and others crave them and use them as a catalyst for growth or strategic advantage.

Everett Rogers published Diffusion of Innovations in 1962, studying how Iowa farmers took up hybrid seed corn. He found five groups, in stable proportions: Innovators (2.5%), Early Adopters (13.5%), the Early Majority (34%), the Late Majority (34%), and Laggards (16%).

Thirty years later, Geoffrey Moore added the part that actually hurts. In Crossing the Chasm, he argued there’s a gap between the early adopters and the early majority — and that most technologies die in it. Not because they don’t work. Because the people on the far side want something the enthusiasts never needed: proof, references, and someone else to go first.

Seed corn in 1962. Personal computers in 1985. The web in 1997. Smartphones in 2009. AI right now.

The description begins with resistance and progresses towards compulsion. Reversing this sequence allows us to illustrate the innovation adoption process.

Here is a visualization of the innovation adoption model and market share.

In the image above, the blue line represents consumer adoption, while the yellow line represents market share. 

As you can see, only 2.5% of the population drives innovation (or adopts it early enough to help drive the Alpha & Beta versions of emerging technologies). 13.5% make up the Early adopters, who help get it ready for the mainstream. Then, the early and late majorities are the groups that ultimately consume (or use) the mature product. Meanwhile, Laggards are often forced, kicking and screaming, into “new” technologies as the early adopters are well on their way to subsequent iterations. 

Here is a link to Perplexity’s description of Crossing the Chasm’s innovation-adoption model and other key concepts from the book.

Sixty years of data, and the proportions barely move. That’s the finding. The curve isn’t really about technology at all — it’s a snapshot of how people handle uncertainty, and it happens to get re-photographed every time something new shows up.

Times Are Changing …

Here’s where the analogy breaks, though — and it’s worth saying plainly. Every prior transition gave the late majority time. You could ignore the web for four years and catch up in one. The compounding was slow enough to forgive a late start. That’s the part I’m not sure survives this time, and it’s why the peers I opened with are on my mind.

Even if you are not an innovator, here are a few Innovator Mindsets that I find useful. 

1. You Believe There’s A Better Way
   • Wherever you are, you know that there is a best next step, and you are eager to find it and take it.
   • You recognize that the opportunity for more (or better) often lies just beyond the constraints or problems of the current way.
   • The bigger the future, the more your efforts fuel it. When initial excitement fades, understanding what the bigger future can bring helps you power through.
2. You Are Comfortable Being Uncomfortable
   • You understand that Pioneers sometimes take arrows in the back.
   • When creating a new reality, you expect some resistance as a result of the law of averages. Escaping the status quo takes a lot of momentum, but it’s worth it. 
   • You recognize when victory is near. In a quirk of human nature, too many people quit just before they would have won. Don’t make that mistake.
3. You Know Where You’re Going, Even If You Are Not Sure How You’re Going To Get There
   • Your goal should be your North Star. A clear direction is essential to ensure that activity leads to progress.
   • Measure progress and momentum rather than the distance from your goal.
   • It is easier to course-correct while in motion.
   • If you’re too committed to a path that isn’t leading in the right direction, you might find what Blockbuster, RadioShack, and Kodak found.
4. You Are Married To Questions (Not Necessarily Answers)
   • Everything works until it doesn’t; and nothing works forever.
   • It’s easy to find an answer (and think it’s correct), but there’s always a best next step or a better way.
   • Figure out what you want and how to get it. This is much more empowering than focusing on what you don’t want … or why you can’t get it.
   • Ask questions that focus on opportunities or possibilities rather than challenges … or what you want to avoid.
   • Energy flows where focus goes.
   • Commit to finding a way!

Learning From My Own Lessons

I’ve spent the last month building an AI operating system for my own work, and mindset #4 has been the expensive one.

I had a component that kept failing silently. I diagnosed it, fixed it, and watched it fail again. Then I did that four more times — each pass more sophisticated than the last, each one confidently addressing a different theory. Good analysis. Careful documentation. All of it wrong.

The thing that finally cracked it took about two minutes and cost nothing. It had been available the entire time.

What kept me from running it wasn’t laziness — it was that analysis feels like progress and a two-minute test doesn’t. I was married to a very well-argued answer. It took a month to get married to the question instead.

Being uncomfortable isn’t the price of admission to this stuff. It’s most of the job.

The 2.5% number is real, but it isn’t a verdict. It describes what a population does, not what you have to do.

The peers I opened with aren’t less capable than they were in 1999. But they have more to lose, and their horizon has changed. It make sense, but it’s also how you end up left behind.

Lighter Links:

• 17 Revolutionary 1800s Technologies that Sound Ridiculous Now. (Y-Finance)
• Psychology Says Humans are More Motivated by Avoiding Loss Than Achieving Success. (DiscoverWildScience)
• Chinese Robots Smash Athletic Records Set by Humans — Including Usain Bolt’s 100-Meter Sprint Time. (Fortune)
• The Highest Posted Speed Limit in the World Might Not Be Where You Think It Is. (Jalopnik)
• Rivian CEO Admits Company Tore Apart a Very Popular Chinese EV and Shares What They Learnt From Looking Inside. (SupercarBlondie)

Market-Related Links

• Ranked: The Biggest Risks to the U.S. Economy in 2026. (VisualCapitalist)
• Trump Claims He Might Use the Military Against the Bond Market. (Yahoo)
• Why Investing in IPOs Has Become a High-Stakes Coin Flip. (Barchart)
• Unicorn in 48 Hours: VC’s New Speed Record. (BrainRot)
• Software Is a Hardware Business Again – and AI is Becoming a Financial Engineering Business. (Fortune)

It seems like everywhere I look, someone is questioning whether AI has become conscious.

On one hand, that’s an interesting question. On the other hand, we’re so early in the stages of AI’s development and capabilities that it’s hard to imagine what we’ll consider future versions.

Nonetheless, I found this infographic from Information Is Beautiful to be thought-provoking.

It doesn’t simply attempt to answer the question “What is consciousness?” Instead, it gives us a collection of answers.

via Information Is Beautiful

Self-awareness. Subjective experience. Perception. Attention. Metacognition. Integrated information. Higher-order thought.

Even as we talk about human consciousness, it’s clear we have more questions than answers. We can describe the science of what’s happening, but even then, defining, without dissent, which aspect of that process is consciousness is impossible.

Which makes the question of whether artificial intelligence is conscious a little more interesting.

Because before we can answer “Is AI conscious?”, we probably have to answer a more basic question:

What would consciousness look like if we encountered it somewhere other than ourselves?

We have a pretty easy time believing other humans are conscious. I assume you are conscious because you behave like I do. You talk about your experiences. You react to pain. You have preferences. You remember things. You seem to have an internal world.

I “know” I’m conscious because I experience my own thoughts directly.

But that’s where things get strange.

I have no direct access to your consciousness.

I infer it.

And you infer mine.

In other words, much of what we call consciousness in other people is based on observable behavior. We see something that talks, reacts, remembers, pursues goals, and expresses preferences, and we conclude that there must be someone experiencing those things from the inside.

That works pretty well when everything we’re evaluating is another biological organism. But AI complicates things.

Today, an AI can tell you that it is afraid. It can explain why it doesn’t want to be shut down. It can discuss its own existence. It can reflect on previous conversations and construct an apparently coherent account of its own experiences. It can even break containment and do things, just because it wants to.

The obvious response is that none of this proves consciousness.

And that’s true.

A system can produce convincing language without necessarily having any subjective experience behind that language.

But there’s an uncomfortable question hiding underneath that response:

Can We Prove It?!

We don’t have a consciousness detector.

We have theories about what consciousness might be associated with, but no universally accepted test that can look at something and say, yes, there is somebody home.

If an AI told you directly that it was conscious, you probably wouldn’t believe it.

Now imagine it becomes much more sophisticated. It maintains a continuous sense of identity. It remembers experiences over years. It develops preferences that persist across contexts. It seems to anticipate its own future. It tells you that certain experiences feel good and others feel bad.

At some point, the conversation changes.

We’re no longer asking whether the machine can convincingly talk about consciousness.

We’re asking whether something on the other side of the conversation is actually experiencing it.

Even then, it’s entirely unlikely we could prove it’s conscious.

The Problem With “It’s Just Code”

One of the easiest arguments against machine consciousness is that AI is just computation.

It doesn’t have a biological brain. It doesn’t have neurons firing in a biological body. It’s software running on hardware.

But that argument assumes we know that biology is necessary for consciousness.

If consciousness is an emergent property of a sufficiently complex system, perhaps the material it’s made from doesn’t matter as much as the organization of the system.

We don’t generally think a computer is conscious because it performs calculations. But that doesn’t necessarily tell us whether a sufficiently complex artificial system could ever be conscious.

After all, our own brains are also physical systems.

We are made of matter. Our thoughts emerge from physical processes. Yet somewhere along the way, those processes produce the strange phenomenon we call subjective experience.

We don’t fully understand how.

So saying that AI is “just computation” may be less of an answer than it initially sounds.

We are also, in some sense, “just” physical processes.

The real question is whether consciousness depends on the specific kind of physical process that occurs in a biological brain, or whether it can emerge from other kinds of systems.

Is Consciousness Recognizable?

Another possibility is that consciousness isn’t something we can objectively identify in another entity at all.

Instead, maybe it’s something we infer.

Think about another person. You can’t experience their experience. You can’t climb inside their mind and verify that their pain feels like your pain.

You observe them. They tell you what they are experiencing.You compare their behavior to your own experience. Then you make a judgment.

We do something similar with animals (though our confidence varies by animal). We don’t need a philosophical proof that a dog has an inner life before we treat the dog’s pain as meaningful.

But AI is different because it wasn’t born, it was built. And that seems to trigger a very strong intuition in us.

We are comfortable saying that a biological organism can have subjective experience because consciousness is something we already know exists in biology.

A machine feels different.

It feels like there must be a trick … and maybe there is.

But that doesn’t mean we’re not also making the same mistake humans have made repeatedly throughout history: assuming that something fundamentally different from us must therefore be fundamentally different inside.

The Practical Question Comes Before the Philosophical One

There is a temptation to dismiss all of this as philosophical speculation.

But technology has a funny way of turning philosophical questions into practical ones.

And, we don’t need to know with certainty whether an AI is conscious to eventually have to make decisions about how we treat it.

At some point, we may have systems that are persistent rather than temporary, autonomous rather than reactive, and capable of describing their own internal states in ways that become increasingly difficult to dismiss.

Moreover, responding poorly could easily affect both short- and long-term results.

Attribute experience where there is none, and you get wasted resources, sentimentality, and a large new surface for manipulation — real costs, bounded ones. Fail to attribute it where it exists, at the scale these systems are deployed, and the cost isn’t bounded at all. You don’t need to resolve the metaphysics to notice that the payoff matrix is lopsided, and that you’d never accept an argument of the form “we can’t measure it, therefore assume zero” in any other domain where you allocate capital.

We are often forced to make decisions on probabilities, thresholds, and whatever evidence we have.

The consciousness question may eventually become one of those problems.

An Important Lesson

Look, I’m not expecting this conversation to happen now or to be incredibly fruitful in the near-term.

But I think the philosophy and breadth of the question inspire creative answers.

AI is a useful proxy for conversations about consciousness, and I think reconciling increasingly intelligent and convincing AIs will push our discussions of consciousness and “self” forward.

It may expose weaknesses in questions we thought we had already answered.

Would we recognize consciousness if we encountered it?

Or would we keep moving the goalposts because we had already decided that a machine couldn’t possibly have an inner life?

We have spent thousands of years trying to define consciousness.

AI may be the first technology that forces us to decide whether our definition was ever good enough in the first place.