Key Takeaways

  • Brett Adcock believes AI will be “a hundred times bigger than the internet,” predicting a future where every human has a digital AI pairing, much like an omnipresent “Jarvis from Iron Man.”
  • His new AI lab, HARK, is focused on creating digital AI-human symbiosis, enabling AI to learn and use general-purpose computers and systems autonomously, bypassing traditional API limitations through reinforcement learning.
  • Adcock sees current hardware, including phones and laptops, as "rubbish for AI" and asserts his team is designing entirely new "AI computers and phones" that will radically rethink form factors and replace existing devices.
  • HARK's initial hardware prototypes are unlike anything Adcock has seen, suggesting a bold departure from current mobile and computing paradigms.
  • Founders can apply Brett Adcock's "10x Better" AI Product Design Framework to envision and build disruptive AI solutions that fundamentally shift user experience.

The Brett Adcock's "10x Better" AI Product Design Framework

Brett Adcock argues that incremental improvements won't cut it when designing with disruptive technologies like AI. Instead, you need to aim for a 10x improvement, starting with the core capabilities of the new technology and unconstrained imagination. Here's how he approaches it at HARK:

  • Step 1: Get to the Substrate Level: first order what what has changed what's changed is we have like a new type of computer which is like I think of AI as a new type of computer new type of automation that's here
  • Step 2: Identify 10x Improvements Deep Learning Brings: when we're designing this we want to design around like key principles that could be like 10x better if it's like one or two times better your phone or computer you're not going to use it it's like literally 10x better what are things now that a deep learning brings that are like 10x better. There's a few of them like one is AI can like basically now like think and use computers and systems for you just like a human can. It can like talk to you. It can like see has like visual understanding. It has a real-time speech to speech. It can uh it can use computers and systems for you as f close to as fast or around as fast as a human can... It also can like have memory, meaning you can put memory into it. It like won't it won't it won't forget anything. I mean, near perfect over time.
  • Step 3: Envision the Magical “Little Human on Your Shoulder” (Unconstrained): So your first step with your team is like just like let's just get rid of like any constraint ever. What would be the coolest magical thing if we had like a little guy on our shoulder that was AI all knowing and could see and hear everything we see and hear and then give advice to us
  • Step 4: Design Around the System's Competitive Advantages: And then from there like we got to like we got to design around that system. The competitive advantages here are that it uh is humanlike and capabilities and it has almost near perfect memory can go back and reference over time.
  • Step 5: Rapidly Prototype: Start start there. And then from there, you got to rapidly prototype. So when you come over, like we have like we've we've designed everything you could possibly think of. We 3D printed it.

When This Works (and When It Doesn't)

This framework shines when you're tackling truly novel problems with fundamentally disruptive technologies. Adcock notes it's for designing products that get to the core of what's changed with deep learning, aiming for a 10x improvement over existing solutions. It's for those moments when you believe the underlying technology represents a “new type of computer” – not just an iteration. The framework pushes for unconstrained imagination, forcing you to think beyond current form factors and interaction models.

However, this approach falters if the perceived "10x improvement" is actually only marginal, or if your understanding of the new technology's core capabilities is shallow. It's not suited for incremental product enhancements or for markets where current solutions are already highly optimized and user needs are met. If you're building a slightly better to-do list app, this isn't your framework. It also demands a willingness to discard current paradigms, which can be a difficult leap for teams entrenched in existing product thinking.

What to Do With This

If you're a founder aiming to build an AI product that genuinely replaces a current device or workflow, apply Adcock's framework this week. Let's say you want to replace how busy professionals manage their inbound communications and scheduling. Here's a quick run-through:

  • Step 1: Get to the Substrate Level: AI can now autonomously act across diverse software environments, understand context, and converse naturally. It's not just a chatbot; it's an agent.
  • Step 2: Identify 10x Improvements Deep Learning Brings: A professional AI agent could, with near-perfect memory, sift through all my emails, texts, Slack messages, and calendar invites, prioritize them, draft contextually appropriate responses, decline irrelevant meetings, schedule others, and even conduct basic research for me—all without me explicitly clicking or typing.
  • Step 3: Envision the Magical "Little Human on Your Shoulder": Imagine a constantly attentive AI assistant, perhaps residing in an unobtrusive earbud or glasses, that proactively manages my day. It listens to my calls, reads my documents, knows my preferences, and acts on my behalf, surfacing only critical decisions or insights. It's a true digital co-pilot that offloads cognitive burden.
  • Step 4: Design Around the System's Competitive Advantages: Focus on perfect, context-aware memory across all digital touchpoints, autonomous decision-making (with guardrails), and natural multimodal interaction (voice, gesture, passive monitoring). The device needs to be always-on, always-listening, and seamlessly integrated into my life.
  • Step 5: Rapidly Prototype: Build a browser extension today that can read emails and draft replies using LLMs. Then, add calendar integration. Later, integrate voice. You're iterating on the core idea of an autonomous agent that deeply understands and acts on your behalf, not just a smarter inbox.