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Summary
John opens with a hot take that’s on everyone’s mind: is AI as big a deal as everyone says it is? Instead of swapping opinions, Eric proposes a framework: three 2x2 matrices used to evaluate any technology’s real-world impact, then walks through historical examples before applying all three to AI.
Matrix one is breadth versus depth: does a technology affect one area deeply, many areas broadly, or both? Matrix two is rate of improvement versus rate of adoption: how fast does the technology get better, and how quickly can people actually access those improvements? Matrix three is novelty versus precedent: is the technology truly new, and does it feel familiar enough to adopt quickly?
GPS scored high on depth first, then breadth later. The iPhone scored high on precedent and breadth but was barely novel. Most technologies land high on one or two axes but rarely all three.
AI, Eric argues, is high on all three simultaneously and in the first years of its existence, which is historically unusual. The conversation ends with personal examples: a presentation Eric built in two hours that would have taken weeks before, and a best man speech John polished with voice AI coaching he never would have sought otherwise. Their conclusion is quiet but firm: AI will produce an unleashing of human creativity unlike anything we have seen before.
Key takeaways
Breadth plus depth is the bar for technologies that change everything: a technology that only affects one industry or user deeply rarely reshapes society. The ones that go broad and deep, across industries and users, tend to be the transformative ones.
Rate of adoption can lag rate of improvement by decades: fiber internet is the clearest example. The technology is unambiguously superior, but capital cost means most people still don’t have it. AI is nearly the opposite: improvements are immediately available to anyone.
Novelty alone is not enough, and neither is precedent alone: GPS was truly novel and took decades to reach consumers. The iPhone was barely novel but was adopted almost instantly because it wrapped familiar behaviors in a better form. AI is rare in being genuinely high on both axes at once.
The thing that looks like a better search engine is actually something else entirely: many people are using AI as a smarter Google. That framing is not wrong, but it undersells what the technology is capable of by a wide margin.
AI’s novelty goes all the way down to hardware: Andrej Karpathy’s observation that GPUs and TPUs are replacing CPUs as the baseline compute layer illustrates that this is not just a software shift. The infrastructure of computing itself is being redesigned around it.
The most underrated use of AI is learning: amplifying skills you already have gets most of the attention, but using AI to rapidly acquire skills you don’t have is arguably more powerful and less discussed.
AI enables things people simply would not have done before: John’s use of voice AI to rehearse and refine a best man speech is not productivity. It’s a category of effort that just didn’t happen before the tool existed.
Notable mentions and links
GPS is used as the primary historical example for the breadth-versus-depth matrix: it started with extremely deep impact in military and industrial applications, then spread broadly to consumers over decades as consumer devices caught up.
Internal combustion engine is Eric’s example of a technology that scored high on both breadth and depth early: it powered everything from yard tools to cruise ships, and also changed how factories operated.
Fiber internet is John’s example for the rate-of-improvement versus rate-of-adoption matrix: the technology has been clearly superior for years, but infrastructure capital cost keeps adoption far behind the curve.
The iPhone is analyzed as a case where novelty was low and precedent was high: internet access, email, and phone calls already existed, but removing the keyboard collapsed adoption barriers and triggered the mobile revolution.
Andrej Karpathy, co-founder of OpenAI and now at Anthropic, is cited for his observation that AI novelty extends to the hardware layer: GPUs and TPUs are becoming the baseline compute architecture, displacing CPUs from the center of the stack.
The movie Limitless surfaces again as John’s shorthand for the feeling Eric is describing when he calls AI “almost like a drug” for the curious and discerning person.
Vercel is mentioned as the context for Eric’s presentation story, where he built a full AI engine optimization talk in two hours the week of the event instead of weeks in advance.