I’ll start this post with a small admission: I use AI extensively in my writing. I’ll save the details for another time, but while working through a line of thought recently, the chatbot I use surfaced a term that stopped me in my tracks because it captured exactly what I was trying to express.

That term is the constraint simplex. It sounds a bit jargony, but it neatly wraps up a straightforward idea: as any complex technology evolves, it eventually runs into hard constraints. As the system reaches constraints, pushing further in one direction forces trade‑offs in another, or further progress becomes physically or economically impossible within the current architecture.

A simplex is a geometric term for the simplest flat‑sided shape that can enclose a space. In two dimensions, it’s a triangle. In three, a tetrahedron. And because geometry isn’t limited to three dimensions, you can keep going, four‑dimensional simplexes, five‑dimensional simplexes, and so on.

I hadn’t encountered the word before, but because my mind tends to work in geometric terms, the AI picked up that pattern and suggested it. It fit immediately; conceptually clean, visually intuitive, and with a nice ring to it.

So, the constraint simplex is simply the multi‑dimensional possibility space a technology can evolve within until it runs into the “faces” where further progress becomes constrained or forces steep trade‑offs.

This pattern shows up everywhere; in passenger and freight vehicles, solar panels, EV batteries, and countless other technologies.

And once you start looking at the physical, economic, and practical constraints that define those boundaries, you suddenly have a powerful lens for cutting through hype and seeing what’s actually possible.

The Bicycle

To illustrate the idea, let’s go back to one of my favorite examples from Emergent Systems Theory: the bicycle. I’ve argued that the basic form of the modern bike; two wheels, the rider centered between them, reciprocating pedals, a steerable front wheel with handlebars is a kind of attractor. It’s a persistent pattern that has endured because it evolved into the best form for allowing humans to move under their own power with speed, stability, and efficiency.

Although there are variations, the classic “double‑diamond” frame has been the standard for nearly 150 years. It represents the best set of trade‑offs across safety, comfort, speed, stability, and cost. These are basically the dimensions of the constraint simplex for bikes. In the vast possibility space of what a bicycle could look like, this shape has proven to be the one that best satisfies all those constraints.

If we take it a step further and look at performance, there’s an old adage in the cycling world: strong, light, cheap, pick two. If you want something strong and light, it will be expensive. If you want strength at low cost, it will be heavy. If you want light and cheap, it will be weak. These are all trade‑offs rooted in material properties and manufacturing realities. Nature doesn’t hand out free lunches. These three dimensions define the performance constraint simplex for the bicycle.

This principle shows up in every technological system. When something is first invented, it may take time to discover where the boundaries of its simplex lie, but eventually, they always reveal themselves.

Aviation Example

Let’s take another example: the development of commercial aviation. Early practical aircraft could manage maybe 100 mph and stay aloft for an hour or so. As we learned the basic physics of flight, refined aerodynamic configurations, and improved engines and propulsion, speeds climbed. By WWII, transport aircraft were cruising at 250–300 mph with ranges of several hours. The main limitation at that stage was power. Large piston engines were heavy, complex, and not especially reliable.

The invention of turbine power, the jet engine, created a genuine step change. With far more power available, aircraft could fly faster, grow larger, and carry more fuel. By the 1960s, typical commercial speeds had reached about 550 mph. And interestingly, they haven’t increased much since.

Engines have continued to improve, though at a slower pace, enabling larger aircraft and longer ranges. But other constraints have taken over.

On the speed front, the sound barrier is the big one. You can fly faster than sound, but frictional heating rises sharply, creating materials challenges, and fuel consumption becomes exponential. Those physics have economic consequences. The Concorde, developed in the 1970s, proved the point. It worked technologically, but the cost to save a few hours on a transcontinental trip wasn’t worth it for most travelers. As a result, commercial aircraft speeds have been essentially flat for 60 years.

There have been attempts to revisit these hard limits in the last decade, but none have cracked the underlying physics. Boom Supersonic’s recent pivot to ground‑power units looks, to me, like a sign they’ve run into the same wall.

On other fronts, more efficient engines have enabled bigger aircraft and longer ranges, but those dimensions have their own boundaries. People don’t enjoy being confined in a small space for 17+ hours. And the route logistics for something like the A380 with over 500 passengers only work in a handful of places. So we’ve likely hit practical limits on both range and size.

Commercial aviation has reached the boundaries of the system it operates within; speed, load, cost, range. It functions beautifully inside that space, but when you try to push past those boundaries, the trade‑offs become steep very quickly.

What is Moore’ Law, Really?

Let’s now shift to a much smaller domain: semiconductor manufacturing.

Moore’s Law, the famous observation that semiconductor capability doubles roughly every 18 months is often treated as if it were a natural law, something inevitable and unbounded. It isn’t that. What it really reflects is an industry‑wide learning curve. The idea of the learning curve originally came from studies of labor improvements in aircraft manufacturing, but the principle is simple: when people repeat a complex task over and over, they get better at it, and they invent new ways to keep improving.

That’s essentially what has been happening in semiconductor fabrication for the last 50 years. Each generation exposed new bottlenecks, engineers solved them, and those solutions set up the next generation. It wasn’t a law of nature, it was a feedback loop of learning, investment, and economic incentive. It kept going because there was money to be made in keeping it going.

But now we’re down to the 3‑nanometer scale. At that size, we’re running into real physical constraints with current materials. Quantum effects increase leakage. Power densities rise to the point where heat removal becomes a major challenge. And many of the hardest limits now show up not in the silicon itself but in the packaging; how you connect, cool, and integrate these tiny structures.

In other words, we’re starting to hit the physics face of the constraint simplex for semiconductors. Can these problems be overcome? Maybe. But likely at very high cost, or with trade‑offs somewhere else.

When systems hit these kinds of boundaries, they often begin exploring new architectures. In computing, that shift was already underway for other reasons with the rise of parallelism and graphics processing units (GPUs). Over the next few years, we may see less emphasis on shrinking transistor dimensions and more emphasis on high‑bandwidth memory, interconnects, and packaging which now appear to be the dominant constraints, especially for LLM‑based AI.

Applying the Constraint Simplex to Humanoid Robots

At the beginning of this piece, I said we could use the constraint simplex to sort out which technological visions are achievable and which are mostly hype. So let’s take humanoid robots as a test case.

Evolution has had roughly 600 million years to shape the human form. It may be blind, but it isn’t dumb, and the human body is remarkably good at doing the things humans need to do. But that doesn’t mean the tasks humans perform need to be done by machines that look like humans. You can probably see where this is going…

Most of the current discourse around humanoid robots focuses on using transformer architectures and reinforcement learning to train robots to navigate, maneuver, and manipulate in complex spaces. That work is important, and much of it will transfer to other robot forms. But the real faces of the constraint simplex show up in physics.

A big one is energy. Biological systems are astonishingly good at storing and converting chemical energy into motion and activity. Batteries, by comparison, are heavy and bulky. And bipedal motion driven by motors, gears, and actuators is inherently inefficient. The stop‑start nature of walking is exactly what electric motors dislike. Compared to wheels, bipedal locomotion is only about ten percent as efficient. That’s why most practical robots are wheeled or tracked unless they absolutely must traverse irregular terrain.

Battery storage is also hitting its own simplex faces. As a result, most humanoid robots today run for only a few hours under load. You either need a fleet that can rotate through charging cycles or swappable battery packs, each with its own trade‑offs.

So what’s the practical approach? Use wheels or tracks and modify the environment so robots can operate efficiently. That’s what factories and warehouses already do. It dramatically increases runtime and load capacity. Bipedalism only makes sense in environments that can’t be modified and even then, quadrupeds are usually better. Most land animals figured that out a long time ago.

Hands present another set of constraints. Human hands are extraordinarily capable manipulators, with integrated sensing; touch, temperature, texture, force feedback and a huge number of degrees of freedom. They’re also incredibly strong for their size. Muscle can generate more tension per square inch of cross section than almost any human‑made actuator. That’s why robotic “effectors” tend to be claws, grippers, or suction devices. Fine dexterity is still out of reach, both physically and from a control standpoint.

So, engineers do what engineers always do: specialize and modify the environment. Look at Japan and Korea; they’ve developed a wide range of specialized robots for food service, cleaning, and logistics.

Can a robot clean a toilet? Probably not. But you can design a self‑cleaning toilet. That’s the direction those countries are heading as their populations age and shrink.

As you explore the simplex, humanoid robots only make sense in environments that can’t be adapted for other approaches. And the capabilities that would make them most useful in those environments, especially dexterous hands, aren’t close. That means tasks like elder care, loading your dishwasher, or picking up after your kids will remain human activities. And maybe that’s the way it should be.

Every technology lives inside a possibility space shaped by physics, materials, energy, economics, and human needs. The constraint simplex is simply a way of making those faces visible; the places where progress slows and the edges where new architectures start to emerge.

Humanoid robots sit right at those edges. And when you look at the constraints, the picture of where they actually fit becomes much clearer.

So what do you think? Does the constraint simplex help dig a level deeper and separate what’s real from what’s hype? Or am I wrong and we’ll see fleets of humanoid robots in factories in the near future? I’d love to hear your thoughts, please comment below.

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Welcome back to Inside the Chaos, a weekly newsletter for business leaders, product managers, founders, and policy makers who want to understand how Emergent Systems Theory (EST) can help interpret and shape the messy interactions that define our world.

Each week, I highlight EST principles through real-world examples. Sometimes I reach backward to decode why something unfolded the way it did (or didn’t). Other times, I look forward to explore what could be, what new attractor basins might emerge if we follow the gradients and feedback loops far enough.

This week, I’m looking forward. Specifically, at self-driving cars, a topic that’s captured my attention by Tesla’s unveiling of its control-less Robotaxi. My instincts immediately kicked in: Is this really the next big thing? Could driverless vehicles justify Tesla’s extreme valuations? Or are we witnessing something more symbolic than a real business opportunity?

Let’s dive into how robotaxis might reshape the possibility space of personal transportation, not just as a feature, but as a potential reframing of what cars mean to us; mobility, adventure, and identity.

The Dream of Driverless

I haven’t researched the full history, but I suspect the dream of a driverless vehicle is as old as the automobile itself. Maybe even older. Perhaps someone once imagined a self-guided horse and buggy, an autonomous carriage that knew the way home.

But cars have drivers for a reason. The technology required to self-pilot a vehicle is technically hard, even if it’s cognitively easy for humans. Think about it; serious efforts in autonomous driving have been underway for over a decade. Companies like Waymo have made impressive progress, and yet, a modern teenager can become a competent driver with just 20–40 hours of training.

So yes, self-driving is hard, and the prospect of full autonomy both tantalizing and fraught. In a number of cities it’s now normal, but the question remains: Are we ready for control-less wheeled transportation appliances to become the norm? And more provocatively: Do we want them to be?

Why Move: Affective Drivers and Energy Gradients

Let’s start by looking at the affective emotional drivers behind auto ownership and use (to avoid the potential for confusion between the “driver” of a car and an “affective driver” of emotion, I’m going to use “emotional driver” in this post though that’s not my standard EST terminology). At the most basic level, cars exist to reduce human time and energy gradients. We need to move our bodies from one location to another, for work, entertainment, social interaction, adventure. And as energy-conserving beings, we don’t want to walk everywhere.

So we invented cars (and other variations of vehicles, but I’ll just say “car” from now on for personal transportation, even if it might be an SUV or light truck). And they do more than convey our bodies, they also carry our stuff and other people.

Most trips are solo (about 60% according to the 2022 National Household Travel Survey and data from the Center for Sustainable Systems). Which implies the occupant is fulfilling two roles:

• The “passenger”, focused on mobility and time dilation.

• And the “pilot”, potentially engaged in control, mastery, and sometimes thrill.

Two Poles: Convenience vs. Exploration

For the passenger dimension, time in the car is often a substitution opportunity. Less time is better or time that can be repurposed. Scrolling, contemplation, relaxation. Or maybe it’s a rare pocket of quiet, free from intrusion. Either way, it’s a space of temporal negotiation.

For the “pilot”, the experience is different. I’ll speak for myself, but I like to drive. There’s a sense of control, flow, and sometimes adventure. But there’s also frustration, traffic, delays, interruptions. So we have a dynamic of affective emotional drivers:

• Exploratory drivers, who seek engagement and mastery.

• Convenience and comfort drivers, who seek efficiency.

Self-driving fills the convenience pole beautifully. But it’s contradictory to the exploratory one.

Symbolic Signaling and the Social Gradient

Now let’s shift to the social layer. In modern culture, cars are also symbolic signaling devices. A Porsche 911 says something very different than a Toyota Prius. Even if both could be equipped with autonomous features, the presence or absence of controls changes the signal.

Imagine rolling up to a red-carpet event in a control-less, opulent Rolls-Royce. It’s a statement. But it’s not the same as being chauffeured by a human. There’s a layer of social signaling in being served by another person, status, intimacy, ritual.

Will that gradient shift? Will cars become soul-less transportation appliances like your fridge or dishwasher? Possibly. But it won’t be purely technical. It will require a symbolic reframing of what it means to be a passenger, a pilot, and a participant in our layered mobility and social systems.

Scaling Ridges and Symbolic Friction

Let’s also not forget time and energy savings aren’t just logistical, they’re freedom drivers. The ability to go anywhere, anytime, in your own car is a deeply affective experience. It’s autonomy, spontaneity, and control wrapped into one.

This is why cars are wildly underutilized. Most vehicles sit idle for 22+ hours a day. From a systems efficiency standpoint, that’s absurd. But from a symbolic standpoint, it makes perfect sense. The car isn’t just a tool, it’s a latent freedom reservoir. It’s there just in case. It’s the promise of movement, even when unused.

Efforts to improve utilization, through sharing, pooling, or automation, introduce friction. Not necessarily much, but enough to influence the freedom gradient. Waiting for a ride, coordinating availability, surrendering control. These are micro-costs that accumulate in the symbolic metabolism of freedom.

Affective Drivers and Possibility Ridges

We’ve mapped an extensive constellation of affective drivers, convenience, mastery, signaling, solitude. Some are well-served by control-less vehicles. Others are not.

Now let’s look at the ridges in the possibility space, the terrain that shapes where and how autonomous vehicles can scale.

The technology is tough. It’s being incrementally developed. I was just in Los Angeles for a couple of weeks, and Waymos were everywhere. They handled LA’s complex (and often aggravating) traffic surprisingly well. But I didn’t see them on the freeways. That use case must still be emerging.

And LA has good weather. All of Waymo’s current deployments are in Southern U.S. cities, Phoenix, Austin, San Diego, Atlanta. Bad weather, snow, ice, fog is a higher ridge. I’m sure it’s solvable, but it will take time and more technology development. But general self-driving will need to work anywhere, under the same conditions we expect current cars to operate.

Urban Density and Infrastructure

Waymo’s and the other explorers of this possibility space are also concentrating in dense urban environments. That makes sense. Also electric vehicles pair well with autonomy, control integration is simpler, and range limitations are less constraining. A city cab may make dozens of short trips, but not rack up huge mileage. Overnight charging fits the use pattern.

Urban environments also benefit from support infrastructure; charging stations, maintenance hubs, data overlays. Scale and concentration lower the friction. Navigation problems are easier to solve when patterns are consistent and passenger density is high. Compare Boise where I live to LA and the difference is stark.

Boise’s population density per square mile is one third of LA’s. So learning the streets in LA has a much bigger potential payoff in future rides, even though the map to learn may be much bigger. Go to McCall, ID, population 3200, it’s an entirely different scaling story. And the Boise/McCall kind of relationship is all over the US.

The Cab as an Emergent Attractor

So, let’s zoom to a different perspective on urban density. The for-hire taxicab is an emergent attractor solution, a way to buy transportation on demand. It’s probably been around as long as they’ve been cars in cities. Uber expanded the pool of for-hire, transportation-on-demand by distributing capital costs across private vehicles and incremental demand management. But availability, delays, and rising costs for Uber still remain as friction points to the freedom-of-mobility gradients.

Currently, Waymo fills the same attractor as conventional taxicabs and Uber/Lyft, but eliminates the driver. And over the long haul, I suspect it’s not the cost of the human that’s the bottleneck. There’s a lot of hardware that has to be added and deep technology costs to recover. I think it’ll ultimately be sourcing, control, and reliability of the humans to do the driving. It’s hard to find, manage, and retain large groups of people who want to do this kind of work.

That’s why you see so many immigrants in some cities driving cabs. It’s not a passion, it’s a stepping stone. A way to enter the system, support a family, and build toward something better. Automation isn’t just about lowering costs by replacing labor, it reconfigures opportunity gradients. Perhaps a topic for another time.

Regional Symbolic Logic: Boise vs. LA

And what about here in Idaho? The four-door sedan is nearly extinct. It’s all pickup trucks and SUVs. People choose to live here because they are attracted to the outdoor lifestyle. They need flexible vehicles to reach remote places, dirt roads, winding river valleys, hauling camper trailers, bike racks, kayaks. During the week, they unload the truck and drive to work.

In Chicago or LA, maybe you commute 45 minutes from one suburb to another. You do it because your previous job was nearby, and you don’t want to uproot your life. So, personal transportation patterns can shift. Or you’re a shift worker getting off at 2AM, and you just want to get home, not wait for something to come get you.

Where I’m going with this is simple: personal transportation in the U.S. is wildly diverse. The car has become a giant attractor because it resolves a wide range of affective drivers, freedom, control, convenience, signaling.

So much so that its supporting scaffolds, roads, zoning, infrastructure have reshaped the country’s land use patterns. The car isn’t just a vehicle. It’s a symbolic metabolism that has reorganized space, time, and identity.

Control-less Vehicles and the Limits of Scale

The attractor basin of the personal automobile and its supporting scaffolds is deep. Self-driving as a feature fits easily within that basin. It doesn’t displace the exploratory emotional drivers that many humans enjoy, and it allows for manual override when technical limits are reached. It may also open up time for other affective drivers; reflection, relaxation, and distraction.

But control-less cars? That’s a different story.

In narrow cases, dense urban environments where personal mobility is desired but private traffic is restricted, control-less vehicles could (and are where Waymo is deployed) displace traditional cabs. But as a hardware category, it doesn’t yet feel like a big market. Could it become one? Possibly. But that would require reshaping major scaffolds and attractor basins; infrastructure, cultural signaling, emotional logic.

Imagine a future where cities become so dense that private vehicles are banned from surface streets, yet people still crave personal mobility. In that world, the control-less robotaxi becomes a viable niche. But we’re not there yet.

Ecosystem View: Niches and Adaptive Solutions

Let’s zoom out and take an ecosystem view. The possibility space of personal transportation is vast, with many ridges and valleys. Different niches have or may evolve to different optimized solutions:

• Control-less robo cabs for ultra-dense urban cores

• Large pickups for flexible work and hauling

• 4WD SUVs for weekend adventurers and rural lifestyles

My conclusions. Self-driving as a feature? Almost certainly, and it’s very close. Self-driving as sole control? Probably, but only in niche situations.

This is not a binary shift, it’s adaptation. Each niche metabolizes autonomy differently, based on its symbolic and functional logic.

So far, we’ve focused on personal transportation. But what about trucks and logistics? That’s a different attractor basin entirely, one with fewer symbolic constraints and a stronger business case for full autonomy. Also worth a deeper dive sometime.

Tesla’s Future: Not Just Cars

So back to Tesla. Is the robotaxi Tesla’s future? I don’t think so. And I suspect Tesla sees that too, which is why Elon Musk is increasingly talking about AI and robotics. Would I invest in Tesla as a car company at current evaluations? No.

Would I invest in Tesla as an AI and robotics company? Hmmm…….I’ll leave that to you readers to decide for yourselves.

EST Recap: Mapping the Possibility Space

In this post, we’ve used the lens of EST to explore:

• How human affective emotional drivers shape markets and mobility

• How scaffolds and technology ridges influence system evolution

• What the possibility space of autonomous personal transportation looks like today

I can’t say exactly what will happen with self-driving and fully autonomous personal transportation. But using the tools of EST and systems thinking, I think we can gain a better understanding of what the possibility space might look like.

Thanks for staying to the end. If you have other views, or want me to explore another case through the lens of EST, please leave a comment. I enjoy probing into topics where I have a personal understanding and interest, but also want apply these concepts to other domains. Let’s Explore the Chaos together.

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This post continues our series exploring the basics of Emergent Systems Theory (EST). In previous posts, we introduced the concept of attractors—patterns that emerge due to gradients that are resolved to some “optimal” state. We explored market abstraction and affective drivers through the lens of cryptocurrency, and then zoomed in on how gradients shape attractor formation using the electric vehicle (EV) market as a case study.

In this post we’re going take a different perspective and go a level deeper. Instead of analyzing a category-level attractor like crypto or EVs, we’re looking at a specific product as an attractor: Crocs. Yes, the foam clogs. Their unlikely comeback offers a vivid example of how affective drivers and social media feedback loops can re-energize a brand, deepening a products’ attractor basin.

Now in EST we aren’t inventing entirely new ideas—we’re reframing existing ones and applying concepts across domains. By naming and modeling concepts like affective drivers, gradients, and attractor basins, borrowed from complexity theory and psychology, we gain clarity and strategic insight with other complex systems, like business.

In business and product strategy, this is useful. After all, what is a product if not an attractor? Instead of gravitational forces or heat and humidity, you’re trying to capture psychological forces to draw people in, resolve emotional gradients, and get them to “select” your product by buying it. If your product becomes a strong attractor in the market of people who buy things, you win.

Reframing customer needs as behavioral gradients—emotional tensions seeking resolution—we can shift how we think about product design, marketing, and brand evolution. It’s not just about solving problems; it’s about resonating with emotional logic.

So on to our next core EST concept. Attractor basins grow and stabilize through feedback. In systems terms:

• Positive feedback expands a system—more attention leads to more adoption, which leads to more visibility.

• Negative feedback stabilizes or contracts a system—saturation, backlash, or fatigue can flatten the basin.

In this post, we’ll focus on positive feedback, especially as it plays out in the modern era through social media. Platforms like TikTok and Instagram don’t just reflect culture—they amplify affective drivers at scale. When a product hits the right emotional notes at the right time, feedback loops can deepen the attractor basin rapidly.

The Crocs Comeback: A Case Study

Crocs began as a niche boating shoe in 2002. Made from proprietary Croslite foam, they were lightweight, slip-resistant, and easy to clean. Early adopters included boaters, chefs, and healthcare workers—people who valued function over form.

In 2002-2006 Crocs exploded into popularity, selling millions of pairs and going public. Their comfort-first design resonated widely, and ironic celebrity endorsements helped fuel the hype. However, with the financial crisis of 2007, over expansion, failed product diversification, and fading novelty led to steep losses. By 2011, Crocs was near bankruptcy with mounting debt and declining relevance.

In 2012-2016 Crocs downsized, refocused on its core product, and began investing in digital marketing. The brand stabilized but hadn’t yet regained cultural traction. In 2017-2025 Crocs embraced its “ugly” image, launched high-profile collaborations, and tapped into social media’s emotional logic.

While the financial crisis of 2007 was an external force that triggered Crocs near extinction, the pandemic of 2020 led to its comeback. During pandemic shutdowns comfort became king. Crocs had always been popular with medical workers and first responders because of their comfort, so their “ugly” reputation because accepted and even a badge of honor. Crocs (the company) took advantage of this, embracing their ugly brand image and through TikTok campaigns, influencer partnerships, and customization tools like Jibbitz turned Crocs into a cultural attractor once again.

What Drove the Comeback?

Within EST, we model human behavior from the perspective that, in aggregate, people respond more to emotional factors and not conscious ones. And since we’re most interested in what people actually do, we use the idea of Directionally Affective Drivers (DAD). Basically, below-conscious feelings that shape how we behave.

With DAD’s I’m borrowing ideas from psychology and evolutionary psychology. In psychology there’s emotions, feelings and affect. Since feelings and emotions hard to pull apart and are subject to debate, I’m use “affect” which I’m positioning as the “felt-sense” that subconsciously drives us to do things, as from an EST perspective, I’m interested in what people actually do, and not so much about what they think about.

A lot of the time people can’t really tell you why they like something or don’t like something. There are reasons they have those feelings, they just don’t know why. Sometimes they do know; they like Crocs because they are comfortable to stand in all day, if you have a job that requires you to stand all day. That’s a comfort gradient that is the result of the affective driver to avoid pain we all have.

For reasons that I’ll get into in future posts, I group DAD’s into three general categories. Protective, Exploratory and Social. This is not an exhaustive list and is certainly subject to debate, but we’re not trying to be scientifically rigorous. Being useful is good enough for me. Here’s how DAD’s group:

Protective

Focus: Safety, control, boundary defense, emotional regulation

• Core drivers: Fear, vigilance, withdrawal, dominance, justice sensitivity, pain avoidance

• Functions: Threat detection, risk aversion, self-preservation

Exploratory

Focus: Novelty, agency, curiosity, thrill

• Core drivers: seek, play, autonomy, mastery, aesthetic stimulation

• Functions: Learning, innovation, boundary testing, resource acquisition

Social

Focus: Belonging, recognition, reciprocity, care

• Core drivers: Attachment, visibility, conformity, nurturance, signaling

• Functions: Group cohesion, identity scaffolding, emotional resonance

These can certainly overlap, but idea is to seed the concepts you can use to understand how a lot of human behavior in aggregate is shaped.

Let’s look at the some of the DAD’s that re-energized the Crocs attractor basin:

• Comfort
  Crocs resolve physical discomfort. During the pandemic, comfort became a top emotional priority.

• Boundary Testing (rebellion/non-conformity)
  Crocs embraced their “ugly” image. Wearing them became a way to reject fashion norms—“I don’t care what you think.”

• Playfulness
  Jibbitz charms (small decorative pins) turned Crocs into a canvas for self-expression. Users decorated, remixed, and personalized their clogs.

• Visibility / Social Signaling
  Social media amplified Crocs’ resurgence. Viral posts, influencer collabs, and limited-edition drops created buzz and status-flex moments.

• Belonging
  Crocs became a symbol of inclusion in a quirky, self-aware community. Wearing them signaled authenticity and humor.

These emotional characteristics resonated deeply with buyers. And it’s interesting to note the irony of being non-conformist eventually becomes a signal of conformity, but that’s not uncommon when you look at the life cycle of fashion driven social phenomena. Another topic for a future post.

But back to Crocs. While Crocs did hit an emotional resonance during and post pandemic, that wasn’t enough. Without feedback amplification, the attractor basin would have remained shallow.

Crocs recognized early that TikTok wasn’t just a platform—it was a cultural engine, especially for Gen Z. With over 60% of TikTok users under 24, the brand leaned into the platform’s emotional logic: humor, authenticity, and participation.

The “Flicker, Flash, Flare” Strategy

Crocs jumped into TikTok in a big way and adopted a three-tiered content model:

• Flicker: Reactive content that joined trending memes and challenges (e.g., doctors dancing in Crocs during the “OH NA NA NA” challenge).

• Flash: Planned, episodic content showcasing product drops and collaborations—like the Justin Bieber x Crocs launch.

• Flare: Large-scale campaigns designed to spark user-generated content. A standout was the #StrapBack Challenge, promoting Crocs’ partnership with Afterpay and earning over 7.2 billion views.

TikTok allowed Crocs to tap into affective drivers like playfulness, recognition, and belonging. Users weren’t just buying Crocs—they were remixing them, flaunting them, and bonding over them.

Crocs partnered with pop culture icons like Post Malone, Bad Bunny, and Justin Bieber. This gave the brand broad reach to potential customers and they weren’t just product placements—they were emotional signals that Crocs had re-entered the cultural conversation.

The feedback provided by social media tapped into the DADs of more people and sales exploded. Initial, probably paid product placements, became viral and people started to make their own content, exploiting social DAD’s for further reach.

While the Crocs team did a fantastic job reviving the brand, no one can predict ahead of time what will take off and what won’t. That’s the nature of complex systems. Because of the non-linearity of interactions and unknowns about what’s really going on in the vast social and emotional head-space of people, we just can’t say. So, while I have no insight into the thinking of the Crocs team, the strategy likely began as a series of experiments. That’s how you navigate terrain like this. But they were watching closely and likely adapted and modified as feedback emerged. That’s the nature of attractor evolution—emergent and recursive.

Croc’s key challenge going forward? Sustaining novelty and emotional connection. In fashion, novelty is hard to maintain. People get used to things and then move on. If Crocs can continue resolving emotional gradients—comfort, expression, visibility—they’ll keep the attractor basin deep. If not, feedback loops may weaken, and the basin may flatten. The attractor could collapse again, not from failure, but from emotional fatigue. My sense is if they understand and maintain their niche in the footwear ecosystem and don’t overreach again, they can become a sustainable, profitable brand. But that’s also fodder for a future post.

Final Thoughts

Crocs is more than a comeback story—it’s a vivid illustration of how affective drivers and feedback, shaped product success. They created a new product niche which with insight and careful management may be sustainable. EST gives us the language and tools to model these dynamics even within what seems to be chaos.

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This post builds on our previous post, Perception of Perception: Mapping Crypto Through Emergent Systems Theory—where we began mapping how human behavior, symbolic drivers, and feedback loops shape economic systems. We’ll explain some additional foundational concepts of Emergent Systems Theory (EST) by going into the key elements of attractors and gradients. But first, let’s step back and talk about emergence itself.

What is Emergence?

Emergence is what happens when a persistent pattern arises—often unexpectedly—and holds together for some period of time. It’s all around us, woven into the fabric of daily life, yet we rarely notice unless we’ve learned to see it.

Think about flocks of birds or schools of fish moving in coordinated patterns without a single leader. Or the bare dirt path that appears in a park where people consistently take a shortcut. Or the swirling structure of a tornado. Even the layout of your keyboard is an emergent artifact—shaped by historical constraints, technological gradients, and human adaptation.

These patterns emerge from interacting forces. Sometimes those forces are purely natural—like wind shear and humidity. Other times, they’re shaped by human thought, emotion, and intention. EST is designed to help us decode these forces, not to predict the future, but to guide it more wisely.

My thesis is simple: when we understand the principles that shape the systems we live in; economic, social, political, technological we make better decisions. We navigate complexity more efficiently. We align our actions with the deeper currents.

Let’s start with the most central idea in EST: the Attractor

I’m borrowing this term from complex systems theory, and I’ll be taking some liberties in how I define and use it. In EST, an attractor is a coherent pattern that arises due to interacting forces and persists for a period of time. These forces, whether physical, psychological, or symbolic, we call gradients. We use the term gradient because it implies motion, directionality, and influence.

Gradients can be tangible (like gravity or friction) or conceptual (like emotional drivers or social incentives). In the previous post, I introduced Directionally Affective Drivers—an abstraction that help explain why humans behave the way they do in aggregate.

Let’s ground this with a simple example. Imagine a grassy park. Over time, a bare dirt path appears—cutting diagonally across the lawn. No one planned it. No one marked it. But people keep walking the same route, tamping down the grass until it no longer grows.

That path is emergent. It’s a visible attractor. Why do people take the same route? Because they’re conserving energy and time—two psychological gradients. The path connects two places people want to go, and the shortest, lowest-effort way to get there is a straight line. So they follow it. Over and over, regardless if it’s paved or not. Even if you never see anyone walking it, the path tells you something about their behavior.

Now let’s take a more abstract example: the wheel. We think of the wheel as obvious, but it had to be invented. It’s a concept; a solution to a problem. The gradients here are friction and energy. Wheels convert sliding friction into rolling friction, reducing the energy required to move objects by orders of magnitude. Because the wheel resolves these gradients so effectively, it persists. Across cultures, centuries, and technologies. It’s a very deep attractor—a pattern so efficient and resonant that it replicates itself in countless forms.

Attractors resolve the interaction of gradients in ways that optimize

The visible pattern—the shortcut, the wheel, the tornado—is the result of that optimization. We may not fully understand the exact combination of forces that led to it and what exactly it’s optimizing, but we can observe its effects.

Take the tornado. It’s chaotic in detail, but coherent in form. It arises when temperature gradients, humidity, and wind shear interact in just the right way. It resolves energy flow. And when those gradients dissipate, the tornado dissolves.

Now that we’ve introduced the concept of an attractor, I hope you’re beginning to see them in all kinds of phenomena. That’s the heart of Emergent Systems Theory: decoding what forms attractors, how they persist, and what gradients they resolve. Once you start looking through this lens, patterns begin to reveal themselves—sometimes in places you’ve looked at a thousand times.

A Deep Attractor: The Automobile

Let’s take a familiar example that blends business, technology, and human behavior: the electric vehicle. But before we get to EVs, let’s start with the automobile itself. As a concept, the car is a long-standing attractor. Its basic configuration—a box on four wheels, seats for 2–6 passengers, steerable front wheels, a control interface, and a propulsion system—has remained remarkably stable for over a century. It doesn’t matter what brand, or where in the world, the basic form of the automobile is the same.

Why? Because it resolves a set of persistent human gradients.

At the core, people want mobility. They want to move themselves, others, and their belongings efficiently. That’s the functional gradient. The configuration of four wheels, the steering mechanism, suspension, power plant, all resolve a range of technical issues. But there are also affective drivers—performance, safety, identity and status signaling. These attributes are what a “brand” really is. A brand is basically the perceptual bundle of affective drivers that different car models represent (perhaps more on that in a future post).

The car, in its many variations, is a stable attractor because it resolves these gradients well. It’s been optimized over the course of over 100 years for the needs and desires of its users.

EVs: Shifting Gradients

Electric vehicles share most of the same gradients and use cases as internal combustion engine (ICE) cars. People still want mobility, comfort, and performance. But EVs introduce new gradients—especially affective ones.

Some people want to reduce their carbon footprint. Others are drawn to the smooth acceleration, quiet operation, or futuristic design. There’s also the early adopter gradient: the desire to engage with new technology, to be part of the next wave.

So while the EV resolves many of the same gradients as the ICE vehicle, it introduces new ones—and that changes the attractor dynamics.

Mature Scaffolds Supporting Transportation

Now let’s introduce another key concept in EST: the scaffold. A scaffold is the underlying structure that allows an attractor to form and persist. For automobiles, scaffolding includes roads, highways, gas stations, and the vast network of convenience stores that make refueling easy and ubiquitous.

This scaffold is mature. It’s distributed, robust, and optimized over decades. Gasoline has high energy density and can be replenished quickly. The time-gradient—our desire to minimize waiting—is well resolved. We don’t think much about refueling because the scaffold makes it seamless.

EVs, by contrast, face scaffold tension. Batteries have lower energy density and are heavier. Rapid charging requires high-voltage infrastructure and heavy wiring. The current scaffold for EV energy replenishment is patchy, slow, and underdeveloped.

To reach parity with ICE vehicles, we’ll need massive investment in infrastructure and technology—faster charging, better batteries, and a more distributed energy network. Until then, the EV attractor remains less stable, more contingent on scaffold evolution.

Basins of Attraction

Let’s add one more concept: the attractor basin. If an attractor resolves gradients to an optimum, it’s useful to imagine that resolution as a bowl. Water flows in and stays there because of the shape. The deeper the bowl, the more stable the attractor. The wider the bowl, the more phenomena it can capture.

The wheel, for example, sits in a deep and wide basin. It resolves energy and friction so effectively that we haven’t found anything better. It persists across cultures and centuries. It’s a deep attractor. EVs, by contrast, are still shaping their basin. The gradients are shifting, the scaffold is evolving, and the attractor hasn’t fully stabilized. But the dynamics are visible—and that’s what EST helps us see.

You can think of the EV as a smaller attractor nested within the larger basin of the automobile. It’s smaller because the scaffold it depends on, charging infrastructure, is less developed. And the replenishment gradient (charging) is slower and more constrained.

Let’s look more closely at that scaffold. Urban areas already have robust electrical infrastructure. For commuters who drive modest distances and can charge overnight at home, the EV fits well. There’s no time-loss gradient. The system resolves smoothly.

But long trips are a different story. They require planning, access to fast chargers, and patience. Even with rapid charging, the time gradient reasserts itself. The experience is less seamless, less optimized.

This is why when Tesla launched the first electric cars that truly worked well as everyday cars, they sold primarily in urban areas to progressive, wealthier buyers. These users either took other modes for long trips or had a second vehicle. I’m in that camp myself—I enjoy my EV, but I use it for daily driving. For longer trips, I rely on another car.

If you live in an apartment, a rural town, or drive long distances daily, the EV may not resolve your gradient profile well. The attractor basin is shallower. The scaffold isn’t yet sufficient.

You don’t need EST to conclude that EV growth has limits. But EST systems thinking helps us see those limits in a consistent way. It gives us a framework to ask: What gradients are being resolved? What scaffolds are required? How deep is the basin?

Makers Ahead of the Market?

And it raises a question: Has this kind of analysis been done for EV’s? From the outside, it seems some U.S. automakers may have over-invested in EVs without fully accounting for scaffold constraints. I’m not privy to the internal economics of EV development and manufacturing, but the Japanese—Honda, Toyota appear to have taken a more measured, incremental approach. It’s as if the US auto makers didn’t really fully understand how the scaffolding limitations would impact and shape the real pool of potential buyers.

Europe presents a different dynamic. Climate concerns are taken more seriously, and the geography is more compact. The scaffold problem is reduced. China, meanwhile, has strategic reasons to move away from transport fuels and is investing heavily in EV infrastructure. They are far ahead in EV development because they’ve made it a key government policy to reduce dependence on imported fossil fuels and are investing accordingly.

So where do EVs go from here? Long-term, EVs make sense as part of the human transport mix. Even if you’re skeptical about climate change, fossil fuels are finite. We’ll need alternatives. But in the short term, especially under the current administration, there may be a pause.

Charging technology will likely advance faster in Europe and China. The U.S. may benefit downstream. But here where I live in Idaho, I suspect it’ll be a while before we see EV pickup trucks hauling camper rigs into the woods on weekends.

In Conclusion

In this post, we introduced several foundational concepts from Emergent Systems Theory: attractors as persistent patterns shaped by interacting gradients; gradients as directional forces—physical and emotional—that drive behavior; scaffolds as the underlying structures that support attractor formation; and attractor basins as metaphors for the stability and scope of emergent patterns.

Using the EV as a case study, we explored how these dynamics play out in real-world systems—revealing how infrastructure, emotional logic, and time gradients shape adoption and persistence. In future posts, we’ll explore additional EST concepts like feedback loops, nested attractor hierarchies and social architecture using case studies from business, technology, social media, and culture.

If you have ideas for other everyday situations that would make compelling EST case studies, I’d love to hear them—drop your thoughts in the comments. Let’s decode the patterns together.

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