The Terrain Beneath Technology

How Possibility Space Shaped Zoom, AI, and Every System We Build

Welcome back to Making Sense of the Chaos, my newsletter exploring the hidden patterns inside the complex systems that shape our world. My aim is to offer tools and insights that support a systems-thinking approach to business, government, and society, especially in times of rapid change.

In previous posts, we’ve explored the concept of the attractor: a coherent pattern of behavior that emerges through the resolution of gradients and is reinforced by feedback. We’ve examined how scaffolds—technological, emotional, and symbolic—provide the infrastructural support that allows attractors to stabilize and grow. And we’ve introduced a framework for grouping human emotional drivers to better understand why people do what they do.

Through case studies of cryptocurrencies, electric vehicles, and even Crocs, we’ve shown how the Emergent Systems Theory (EST) interpretive lens applies across widely different domains. Now, we turn our attention to another foundational concept in EST: possibility space.

In EST terms, possibility space is the range of viable paths a system can explore from its current state. It’s shaped by initial conditions, constraints, emotional gradients, and feedback loops. Crucially, possibility space is not universal, in business for example, two companies in the same market may face radically different terrain depending on where they start, what scaffolds they inherit, and how they navigate.

This concept also underpins how neural networks optimize in AI systems. Just as AI uses gradient descent to navigate high-dimensional terrain in search of optimal solutions, human systems must probe their own possibility space—often blindly—to discover stable attractors. Possibility space can be imagined as varied terrain: mountains, valleys, ridges, and basins. The low points—basins—are the stable attractors we seek. In mathematical terms, they’re local optima or minima.

This terrain is multi-dimensional, beyond our ability to fully visualize or map. In human systems, the gradients in play may be unknown, or if foreseen, not fully understood. So, to find optimal configurations businesses and policymakers must often explore by launching experimental probes, testing paths to discover what holds.

And here’s the twist: the shape of possibility space depends on where you start. The paths available are constrained by what you bring to the search, your scaffolds, history, and assumptions. Even more intriguingly, navigation itself reshapes the terrain. As attractor basins form, they deform the surrounding possibility space, making some paths easier, others harder, and some obsolete altogether.

This may sound abstract, but the point is to make the invisible visible. To ask: what are the affective and infrastructural drivers that must be resolved to create a stable attractor? What ridges must be crossed? What gradients are pulling us forward—or holding us back?

Let’s explore a historical example of possibility space navigation: video conferencing, a technology whose emotional gradient, presence, connection, visibility has been building for decades. My earliest recollection of a video phone came from a story in Weekly Reader, a science-y publication for kids. It featured the Bell System’s demo of the Picturephone at the 1964 World’s Fair. Even then, the symbolic pull was clear: the desire to see each other’s faces while hearing their voices was entering the cultural imagination.

That vision—now over 60 years old—was part of the possibility space, but it sat far off in the distance, separated by technological ridges: connectivity, bandwidth limitations, hardware costs, and hundreds of other hurdles.

Fast forward to my years at General Electric, where I worked after my company, SmartSignal, was acquired. From 2011 to 2017, I was part of a vast, globally distributed enterprise. Teams and leadership were scattered across continents, yet the value of face-to-face communication was deeply understood. GE invested millions in high-end teleconference centers at major business units—custom-built rooms with mirrored layouts, spatial audio, and dedicated networks.

These installations were costly, but they reflected a human emotional truth: presence matters. The technology was still developing, and the possibility space was constrained by infrastructure, cost, and complexity. But the emotional gradient—the longing for connection—was already pulling us forward.

The developmental journey of real-time business collaboration is a textbook case of possibility space navigation. Each phase resolved key technological gradients—lowering ridges, expanding access, and forming attractor basins that now define our daily workflows.

The evolution of real-time collaboration wasn’t just a story of technological progress, it was a dynamic exploration of symbolic terrain. Each company launched from a different starting point, made different trade-offs, and navigated toward different attractors. And while many had a clear vision of what remote collaboration might look like, the details were murky and the ridges unpredictable. The terrain had to be felt, not just forecast.

In the analog era, collaboration was tethered to the phone line. AT&T Teleconferencing and InterCall dominated the landscape, offering dial-in bridges and numeric meeting codes. These services were functional but emotionally sterile—no faces, no gestures, no presence. The possibility space was narrow, hemmed in by the constraints of analog infrastructure and the absence of visual fidelity.

Companies didn’t so much innovate as occupy the available terrain. The emotional gradient for face-to-face connection remained unmet, and no major player attempted to resolve it. The attractor basin was stable but shallow—sufficient for coordination, not for connection.

As broadband expanded, the terrain shifted. Connectivity ridges were being scaled, and new players emerged to explore the widened possibility space. Webex, Skype, and GoToMeeting became early navigators. Webex, backed by Cisco, positioned itself as the enterprise-grade solution; secure, scalable, and hardware-friendly. Skype leaned into consumer and small business use, offering free VoIP and basic video. GoToMeeting carved out a niche in mid-sized organizations, emphasizing ease of use and screen sharing.

The dominant mode was a shared PowerPoint deck paired with a parallel voice call. It worked well for teams with existing rapport, but it was poor at cultivating new relationships. The emotional ridge of symbolic presence remained high—video was available, but unreliable and more trouble than it was worth.

Each company made trade-offs. Webex prioritized stability over simplicity. Skype offered accessibility but struggled with quality. GoToMeeting tried to balance both, but none fully resolved the emotional gradient for visual connection. The attractor basins were forming, but they remained fragmented.

In parallel was the cathedral. Cisco doubled down on immersive fidelity with TelePresence; custom-built rooms with mirrored layouts, spatial audio, and dedicated networks. Polycom followed suit. These systems were expensive and exclusive.

At GE, TelePresence rooms were used to reduce travel costs and reinforce executive rituals like operations reviews. But they were tethered to fixed locations, requiring scheduling, coordination, and access. They tied business centers together, but not desktops—or living rooms.

The attractor basin here was supported but gated. It met the emotional gradient of presence—finally, you could see and hear your colleagues in real time, with high fidelity. But only those with access could participate. The possibility space remained constrained by cost, complexity, and location.

Cisco positioned itself as the architect of TelePresence. It didn’t try to democratize—it tried to elevate. That was a strategic choice, but it left the terrain open for disruption.

As bandwidth expanded, cloud infrastructure matured, and laptops with embedded cameras became ubiquitous, the terrain flattened. Skype, Google Hangouts, and early Zoom versions brought video to the desktop. Skype, absorbed by Microsoft, struggled with clutter and inconsistency. Google Hangouts offered simplicity but lacked polish. Zoom entered quietly, focusing on frictionless UX and scalable architecture.

The attractor basin began to democratize. Video was no longer tethered to rooms, it could live on laptops, tablets, and phones. The emotional gradient was partially met, but the terrain was still uneven. Quality varied, and adoption was slow.

Each company made strategic moves. Microsoft began integrating Skype into its enterprise stack, but the fit was awkward. Google kept Hangouts lightweight, missing the chance to support enterprise needs. Zoom, meanwhile, quietly optimized for ease, clarity, and reliability—positioning itself for the moment when the terrain would shift.

Then came COVID-19. The possibility space tectonically shifted. Remote work and education became essential. The symbolic gradient of presence, once aspirational, became existential. Zoom surged ahead, offering instant onboarding, stable video, and a generous free tier. It became the default choice for millions—teachers, therapists, families, startups.

Microsoft Teams scaled rapidly, especially in enterprise environments already using Office 365. Its integration with Outlook, SharePoint, and Excel made it the logical choice for internal collaboration. Google Meet, rebranded from Hangouts, gained traction in education and small business—especially where Google Workspace was already embedded.

Streaming technology resolved the final ridges. Adaptive bitrate made video viable across devices. Cloud distribution enabled mass participation. Low-latency protocols supported real-time interaction. Device-agnostic access collapsed the need for specialized hardware.

Zoom didn’t just win on features, it won on connection with the need for simplicity, ease of use. It resolved the symbolic gradient of presence with minimal friction. Microsoft Teams reinforced enterprise rituals. Google Meet sustained educational continuity. Cisco Webex remained strong in legacy environments but struggled to adapt its cathedral logic to the new terrain.

By early 2020, Zoom led in education, small business, and social use. Microsoft Teams dominated enterprise. Google Meet held strong in schools and G Suite environments. Cisco Webex persisted in regulated industries and legacy setups.

Each company navigated the possibility space differently. Zoom launched from a clean slate with fewer legacy constraints and scaled fast. Microsoft leveraged its enterprise scaffolds. Google leaned into simplicity and ubiquity. Cisco held its ground but didn’t pivot.

The Bell Picturephone, once speculative, had become infrastructural. A decades-long exploration of possibility, shaped by incremental technological development, but not fully realized as a deep attractor until the massive disruption of a global event.

So what are the takeaways?

• Navigation of possibility space is always incremental.

• Learning and adjusting to the emerging terrain is critical.

• Sometimes your history holds you back. Know your constraints; and be willing to question or abandon them.

• Once scaffold-like attractors begin to deepen, consolidation follows. Human affective drivers seek simplicity and economics favors scale.

• And sometimes, unpredictable external events reshape everything.

So what about AI?

This is a technology fully engaged in the exploration of possibility space. But unlike the video phone, the endpoint isn’t clear—it’s speculative, fluid, and emotionally charged.

Where are we in the hype cycle? But what is hype if not a vision of possibility space, a projection of what might be? It may be true, it may not be, and what evolves could be something entirely different.

We are in a period of wide exploration. Probes are underway everywhere, searching for stable attractors that generate value. Coding, law, customer service, “agentic” AI, search—all experiments to see what captures human affective drivers and also sustains economic logic.

I can’t predict how this will shake out, but I can offer perspective. My advice to anyone exploring AI technologies: understand what they actually do. You don’t need to master the math, but you do need a working model of what happens inside the black box.

Then start with a clear hypothesis. Probes into possibility space are experiments; so know what theory you’re testing. What gradients are you trying to resolve? What ridges stand between you and resolution?

With that in hand, go out, experiment, learn, and repeat. Maybe you’ll reach a stable attractor. Maybe you won’t. Or maybe you’ll discover something better.

That’s what navigating possibility space looks like; in AI, in business, and in all the human systems we build.