What if the future of design isn’t about interfaces, screens, or even journeys — but about intents?
\n\n\n\nNot tasks.
Not steps.
Not features.
Just: What does the user want to happen?
\n\n\n\nLately I’ve been noticing a pattern across AI, product design, crypto, and even my own daily life. Everything seems to be collapsing into a single primitive: intent. Declare the outcome, and let the system handle the rest.
\n\n\n\nI’m drawing on several frameworks to explore this:
\n\n\n\nThe insight comes from combining these: in technological transitions, the winning approach is often the one that strips away the most unnecessary complexity.
\n\n\n\nFor decades, technology has forced us to think in procedures:
\n\n\n\nBut something big is shifting. Tools like ChatGPT, Gemini, and Midjourney have flipped the model: you tell them what you want, not how to get there.
\n\n\n\nEven in crypto, Uniswap’s ERC-7683 intent standard for cross-chain trading lets you specify the end state — “swap X for Y at the best price” — and a network of solvers competes to fulfill it. Commerce is moving the same way: “Tell me when size 10s drop under €100,” or “Find me a present for a nephew who loves dinosaurs.”
\n\n\n\nThe user provides intent. The system figures out the rest.
\n\n\n\nThis shift sounds simple, but it changes everything.
\n\n\n\nHere’s the part that makes this moment feel urgent:
\n\n\n\nIt won’t just be humans expressing intents — it’ll be AI agents acting for them.
\n\n\n\nWhen millions of agents start firing intents at the world simultaneously, we might see market dynamics humans have never experienced: machine-speed commerce, negotiation, planning, logistics, content flows.
\n\n\n\nPlatforms that aren’t designed to receive, interpret, and fulfill intents could feel suddenly… dated.
\n\n\n\nOnce you see it, you can’t unsee it:
\n\n\n\nEvery interface is just a clumsy way of getting you to reveal your intent.
\n\n\n\nPrompt engineering is intent engineering. It’s learning to express a future state (“Generate a logo…”, “Rewrite this as…”, “Find flights…”) so the system can navigate there.
\n\n\n\nThis mirrors Jeff Hawkins‘ Thousand Brains theory: intelligence emerges from maps, destinations, and recursive sub-goals. Intent is the connecting tissue — the relationship between where I am and where I want to be.
\n\n\n\nOur technology is starting to look the same: nested intents decomposed until they hit executable actions.
\n\n\n\nIt really is intents all the way down.
\n\n\n\nThis isn’t only about tech. Think of your own life:
\n\n\n\nThese are real intents — vague, high-level, meaningful. If technology understood them, it could support us in ways that feel almost human:
\n\n\n\nThe line between “personal growth” and “product design” gets thinner when both are fundamentally about understanding intent.
\n\n\n\nThis connects to another pattern I’ve been exploring: the intent of consumption matters as much as the content itself.
\n\n\n\nWhen you’re browsing for fun, you want serendipity, discovery, rabbit holes. When you’re trying to accomplish something, you want precision, speed, and signal.
\n\n\n\nThe same piece of content serves different intents depending on context. A cooking video might be entertainment while you eat lunch, or critical reference while you’re mid-recipe.
\n\n\n\nUnderstanding user intent transforms how we design, organize, and surface content.
\n\n\n\nIf intent becomes the new interface, the architecture underneath must change:
\n\n\n\nSystems need to understand what the user means, not just what they say.
\n\n\n\nThis isn’t just NLP—it’s inferring context, learning patterns, understanding the difference between “I want a gift” (exploration) and “I need a gift by Friday” (urgency).
\n\n\n\nMerchants, apps, APIs, and agents must advertise what intents they can fulfill.
\n\n\n\nImagine a world where a store’s competitive advantage is:
“We fulfill urgent intents better than anyone else.”
Intents become routable objects — split, delegated, negotiated, or bid on.
\n\n\n\nYour “I need dinner” intent might decompose into:
\n\n\n\nAll handled, not by you clicking through six apps, but by the system understanding and executing your intent.
\n\n\n\nThis is where it gets interesting:
\n\n\n\nWe’ll need rule systems that respect meta-intents: the intents governing our other intents.
\n\n\n\nHere’s a related idea: what if AI’s real value isn’t giving us answers, but helping us understand our own intents?
\n\n\n\nLike the Hitchhiker’s Guide: the answer to life, the universe, and everything is “42” — but the real insight is that we never properly understood the question.
\n\n\n\nAI that coaches us toward clarity (“What are you actually trying to achieve here?”) might be more valuable than AI that just executes blindly.
\n\n\n\nThere’s a balance:
\n\n\n\nThe best intent-driven systems will know when to get out of the way, and when to slow you down to make sure you actually want what you’re asking for.
\n\n\n\nThis reminds me of Bret Victor’s “Inventing on Principle” talk, where he demonstrates systems that respond immediately to your actions, letting you see consequences in real-time.
\n\n\n\nMaybe there’s a new principle here:
\n\n\n\n“Help users understand and achieve their intent.”
\n\n\n\nNot:
\n\n\n\nBut:
\n\n\n\nThis shifts the entire design question from “How do we expose our capabilities?” to “How do we understand their intentions?”
\n\n\n\nThis feels like more than just a UX trend—it might be a paradigm shift:
\n\n\n\nWhat we design next needs to assume:
\n\n\n\nThe user will express intent directly. The system must do the rest.
\n\n\n\nThis piece is the start of an exploration. Over the next few articles I want to dig into:
\n\n\n\nIf the last era of the web was about content, and the current one is about context, the next one appears to be about intent — the cleanest expression of what a human wants.
\n\n\n\nJeff Hawkins’ Thousand Brains theory, developed at Numenta and detailed in his book “A Thousand Brains: A New Theory of Intelligence,” proposes that intelligence emerges from thousands of cortical columns, each building models of the world through reference frames—essentially maps with locations and movements.
\n\n\n\nThe key insight: Intelligence is about knowing where you are and where you want to be, then navigating between them.
\n\n\n\nSound familiar?
\n\n\n\nThat’s intent.
\n\n\n\nOur brains work by:
\n\n\n\nThis recursive process happens at every level—from moving your hand to planning your career. Each level deals in intents that decompose into sub-intents until you hit executable actions (motor commands, API calls, purchases).
\n\n\n\nOur technology is evolving to mirror this architecture.
\n\n\n\nThis analysis assumes a few things that could turn out to be incorrect:
\n\n\n\nI’m assuming that intent recognition will become reliable enough to trust. If the technology for understanding what users actually want remains clumsy or error-prone, we might be stuck with explicit interfaces longer than I’m projecting. Privacy concerns could also limit how much context systems can use to understand intent.
\n\n\n\nI’m assuming users will want to cede control to intent-driven systems. But there’s real value in procedural interfaces—they create understanding of how systems work. If intent-driven design reduces user agency or creates new forms of manipulation, adoption could be much slower than I’m modeling.
\n\n\n\nI’m assuming that the displacement follows Christensen’s disruption playbook—starting simple and moving upmarket. But procedural interfaces might persist in domains where explainability matters more than convenience (healthcare, finance, legal).
\n\n\n\nAn alternative view would emphasize the trade-offs: intent-driven systems might reduce serendipity, create filter bubbles, or eliminate the productive struggle of figuring things out yourself. The best outcome might not be pure intent-driven design, but a hybrid that knows when to get out of the way and when to engage users in the process.
\n\n\n\nI could be wrong about the timeline, the adoption curve, or whether this is truly a paradigm shift versus just another interface pattern. But if the pattern is real, the implications for product design are significant.
\n\n\n\nWhen you zoom out far enough, it becomes obvious:
\n\n\n\nIt’s intents all the way down.
\n\n\n\nFrom the moment you wake up (“My intent is to feel rested”) to the apps you open (“My intent is to connect”) to the purchases you make (“My intent is to solve this problem”) — everything is intent.
\n\n\n\nThe interfaces we’ve built—buttons, forms, menus, flows—are just scaffolding around this fundamental truth. As AI gets better at understanding what we want, that scaffolding becomes unnecessary.
\n\n\n\nThe future of technology isn’t about better buttons, it’s about better understanding.
\n\n\n\nAnd when the system truly understands your intent—when it can decompose it, route it, fulfill it, and adapt based on feedback—the interface disappears entirely.
\n\n\n\nWhat remains is pure expression of will. The work? Understanding what your intents are.
\n\n\n\nIntent → Outcome.
\n\n\n\nNothing in between.
\n\n\n\nPublished: December 9, 2024
\n\n\n\nRelated Content:
\n\n\n\nReferenced Concepts:
\n\n\n\nWhat if the future of design isn’t about interfaces, screens, or even journeys — but about intents? Not tasks.Not steps.Not features. Just: What does the user want to happen? Lately I’ve been noticing a pattern across AI, product design, crypto, and even my own daily life. Everything seems to be collapsing into a single primitive: intent. Declare the outcome, and let the system handle the rest. The Mental Models Behind This I’m drawing on several frameworks to explore this: The insight comes from combining these: in technological transitions, the winning approach is often the one that strips away the most […]
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