The internet reflects us. But new technologies must explore the world beyond. We’ve spent more than two generations and trillions of dollars building the internet. It is, arguably, humanity’s most ambitious technological project. And yet, for all its power to process and reflect information, the internet cannot tell us a single new thing about the physical world around us. The internet cannot detect an approaching asteroid, discover a new species in the deep ocean, or detect changes in the Earth’s magnetic field. It cannot discover a new material or invent something new from it. The internet is, fundamentally, an introspective technology; it is a mirror, showing us only what we’ve already put into it. What we desperately need are more extrospective technologies — windows into the unknown. Consider what the internet actually does: it processes and transmits information that humans have already discovered, documented, and digitized. Even the most sophisticated applications it enables — like artificial intelligence — can only recombine and reinterpret existing information about ourselves and our own creations. They cannot generate genuinely new knowledge about the physical world. (Let’s debate whether pattern recognition truly fits the bill in another pieces.) When we marvel at AI’s capabilities, we’re really just admiring an increasingly sophisticated form of introspection. So what is an example of an extrospective technology? The James Webb Space Telescope is a good one. It offers a striking contrast to the internet’s introspective limitations. While we spend billions optimizing ways to look at ourselves, Webb actually shows us something new about our universe — it shows us more of it that we’ve ever seen before. Every image it sends back is a discovery — whether it’s the atmospheric composition of distant planets, the formation of early galaxies, or phenomena we hadn’t even thought to look for. Unlike the internet, which can only reflect what we already know about ourselves, Webb is quite literally a window for looking outward. It extends our vision not just beyond our natural capabilities, but beyond what any human has ever seen before. The imbalance between introspective and extrospective technologies in our society is striking. The entire James Webb Space Telescope project cost about $10 billion — roughly what Meta spends on its metaverse project in a single year. We have more engineers working on optimizing our digital reflections than on all of our outward-looking scientific instruments combined. The most talented minds of our generation are focused on perfecting the mirror rather than opening new windows to the universe. Of course, this isn’t just about how we spend our money — it’s about our relationship with discovery itself. The internet has made us incredibly efficient at examining and reprocessing our own knowledge, but it may have dulled our appetite for looking outward. We’ve become so accustomed to the immediate gratification of accessing existing information that we’ve forgotten the importance of the long, patient work of discovery. The technologies that can actually tell us something new about our world often operate on different timescales than introspective ones. A deep ocean sensor might need years to reveal meaningful patterns. A space telescope might require decades of operation before making a groundbreaking discovery. These extrospective technologies demand patience and sustained investment — qualities that our internet-shaped attention spans increasingly struggle with. Yet these are exactly the technologies we need most urgently. As we face unprecedented environmental challenges and explore new frontiers in space, we need more windows into the physical world, not better mirrors of our digital one. We need technologies that can warn us of environmental changes, reveal new resources, and help us understand our place in the universe. As enthusiastic as I am about things like the James Webb Telescope, I’m also fascinated by how much we still don’t know about this planet. Only 26% of the ocean floor has been mapped using high resolution sonar technology. Only 5% of the ocean has actually been physically explored. It has been estimated that the ocean’s ecosystem is home to somewhere between 700,000 and 1 million unique species. The vast majority of them — greater than 60% — remain undiscovered. Isn’t it astonishing how little we know about this thing that covers 70% of our planet’s surface? What could we create to learn more about the ocean? It has to be something truly novel — something other than a better submarine. The ocean is just one place to point an extrospective technology. Think of the greatest unknowns. How many of them will be illuminated by the internet? Will the internet expand our understanding of physics, or will it document what we find by some other means? Will the internet ever be more than a catalog of discoveries? Will the internet explain the nature of time, or will it just continue to draw from our finite supply of it? The internet will remain vital infrastructure, but it’s time to rebalance our technological investment. We need to direct more resources and talent toward extrospective technologies — tools that can tell us something new about the world beyond our screens. Only then can we move beyond endless introspection and toward genuine discovery of the universe that exists whether we look at it or not.

Here’s why: I think we need a safe, private place to talk about what AI means for design, and more broadly, the future of work. AI looms as a double-layered threat. We are right to wonder, will AI take my job? But also, we worry that talking openly about our concerns and questions will haste the day — that we’ll look like stubborn luddites, unreliable leaders and teammates, or weak links in the chain. AI, and especially the conversation about it, is moving so quickly that simply keeping up with it takes more energy than we can even imagine putting toward starting to explore it. I’m hearing how stressful it feels to be expected to discover, learn, and use every new tool that becomes available and demonstrate ROI on that effort immediately. I think we’re all scrambling and worrying and wondering how long we can keep this up. And yet I remain optimistic. I’ve kept up with and explored AI exhaustively and I’ve felt every feeling and secretly thought every thought I’ve listed above. And as I believe about everything, it’s better to bring it into the light. I want to do that with you, and I think I might even be able to help. I think there’s a future for thinkers, designers, and firms of all kinds. There’s opportunity in discovery. How it Will Work Here’s the thing. Lots of people do this. In fact, several people whom I admire greatly have set up office hours just like this. But I never make use of them. Here are the reasons why — perhaps you can relate: Intimidation (even though they’ve offered, will they think I’m lame for taking them up on it?) Impostor Syndrome (they’ll realize I know nothing) Introversion (wait I have to talk to someone? Like right at their actual face?) So, a few notes: Ask me anything. I guarantee I have had the same question or worry at some point. Hey, wait, who says I know everything? I have something to learn from you, too. Our meeting will be private. Our secret. 100% confidential. We don’t have to go on video if you don’t want to. Nothing will be recorded. Slots on Wednesdays from 9am-10am and 1-2pm EST and Fridays 9am-10am EST. You can book a time right now, starting as early as tomorrow. 👇 I just took this picture — this is how I look and what it will be like to chat. Friendly is what I’m going for :)

This by no means a definitive list, so don’t @ me! AI is an inescapable subject. There’s obviously an incredible headwind behind the computing progress of the last handful of years — not to mention the usual avarice — but there has also been nearly a century of thought put toward artificial intelligence. If you want to have a more robust understanding of what is at work beneath, say, the OpenAI chat box, pick any one of these texts. Each one would be worth a read — even a skim (this is by no means light reading). At the very least, familiarizing yourself with the intellectual path leading to now will help you navigate the funhouse of overblown marketing bullshit filling the internet right now, especially as it pertains to AGI. Read what the heavyweights had to say about it and you’ll see how many semantic games are being played while also moving the goalposts. Steps to an Ecology of Mind (1972) — Gregory Bateson. Through imagined dialogues with his daughter, Bateson explores how minds emerge from systems of information and communication, providing crucial insights for understanding artificial intelligence. The Sciences of the Artificial (1969) — Herbert Simon examines how artificial systems, including AI, differ from natural ones and introduces key concepts about bounded rationality. The Emperor’s New Mind (1989) — Roger Penrose. While arguing against strong AI, provides valuable insights into consciousness and computation that remain relevant to current AI discussions. Gödel, Escher, Bach: An Eternal Golden Braid (1979) — Douglas Hofstadter weaves together mathematics, art, and music to explore consciousness, self-reference, and emergent intelligence. Though not explicitly about AI, it provides fundamental insights into how complex cognition might emerge from simple rules and patterns. Perceptrons (1969) — Marvin Minsky & Seymour Papert. This controversial critique of neural networks temporarily halted research in the field but ultimately helped establish its theoretical foundations. Minsky and Papert’s mathematical analysis revealed both the limitations and potential of early neural networks. The Society of Mind (1986) — Marvin Minsky proposes that intelligence emerges from the interaction of simple agents working together, rather than from a single unified system. This theoretical framework remains relevant to understanding both human cognition and artificial intelligence. Computers and Thought (1963) — Edward Feigenbaum & Julian Feldman (editors) This is the first collection of articles about artificial intelligence, featuring contributions from pioneers like Herbert Simon and Allen Newell. It captures the foundational ideas and optimism of early AI research. Artificial Intelligence: A Modern Approach (1995) — Stuart Russell & Peter Norvig. This comprehensive textbook defined how AI would be taught for decades. It presents AI as rational agent design rather than human intelligence simulation, a framework that still influences the field. Computing Machinery and Intelligence (1950) — Alan Turing’s paper introduces the Turing Test and addresses fundamental questions about machine intelligence that we’re still grappling with today. It’s remarkable how many current AI debates were anticipated in this work. Cybernetics: Or Control and Communication in the Animal and the Machine (1948) — Norbert Wiener established the theoretical groundwork for understanding control systems in both machines and living things. His insights about feedback loops and communication remain crucial to understanding AI systems.

The Evolution of Digital Space The metaphors we use to describe digital spaces shape how we design them. When we moved from “pages” to “screens,” we were acknowledging a shift from static information to dynamic display. But even “screen” feels increasingly inadequate for describing what we’re actually creating. Modern digital experiences are more like scenes in a play — dynamic spaces where multiple elements interact based on context, user state, and system conditions. A user’s dashboard isn’t just a screen displaying information; it’s a scene where data, notifications, and interface elements play their roles according to complex choreography. As much as it may sound that way, this isn’t just semantic drift. When we design for “pages,” we think in terms of layout and arrangement. When we design for “screens,” we think in terms of display and responsiveness. But when we design for “scenes,” we think in terms of relationships and conditions — how elements interact, how states change, how context affects behavior. Consider a typical social media feed. It’s not really a screen of content — it’s a scene where various actors (posts, advertisements, notifications, user actions) interact according to multiple variables (time, engagement, user preferences, algorithmic decisions). Each element has its own behavioral logic, its own relationship to other elements, its own way of responding to user interaction. What’s more, these actors don’t just behave differently based on context — they can look radically different too. A data visualization widget might expand in size when it detects important changes in its data stream, or adopt animated behaviors when it interacts with related elements. A notification might shift its visual treatment entirely based on urgency or relationship to other active elements. Even something as simple as a status indicator might transform its appearance, motion, and sound based on complex conditions involving multiple scene elements. This layered complexity — where both behavior and appearance shift based on intricate interplays between scene elements — means we’re no longer just choreographing interactions. We’re directing a performance where every actor can transform both its role and its costume based on the unfolding drama. The evolution from page to screen to scene reflects a deeper shift in interaction design. We are no longer adapting static information for digital display. We’re choreographing complex interactions between dynamic elements, each responding to its own set of conditions and rules. This new metaphor demands different questions from designers. Instead of asking “How should this look?” we need to ask “How should this behave?” Instead of “Where should this go?” we need to ask “What role does this play?” The scene becomes our new unit of design — a space where interface elements aren’t just arranged, but directed.