An Interview with Asymco’s Horace Dediu About Apple at 50 (Stratechery). Horace Dediu, who has worked closely with Clay Christensen, makes an interesting point in this interview about why, in his view, AI is a sustaining technology for the big incumbents rather than a disruption. Google, Microsoft, Meta, and Amazon are all pouring hundreds of billions into AI instead of being repelled by it and thinking "this isn't for us" or "our customers don't want this". So obviously they all think that AI is a sustaining technology for them.
The interesting exception is Apple, which is the only major tech company that, unlike most others, isn't sprinting to spend as much as possible on AI infrastructure. In Horace Dediu's view, Apple has always positioned itself at the interface between humans and computers, and thinks that the current AI interface (essentially a command line for natural language) isn't where they'd want to compete. Whether Apple is making a smart strategic bet by waiting for the technology to commoditize and then controlling the device and interface layer, or whether they're the one incumbent that actually is being disrupted, is the open question.
When you learn engineering, you learn first science and you learn basic physics and chemistry, you learn mathematics, and you learn that things are axiomatic and things are built on top of each other so that there's consistency all the way up the stack.
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But then when you go to a business school, you realize the way I put it retrospectively is that it's like equivalent of sitting around a campfire telling stories to one another.
— Horace Dediu, From Ben Thompson's interview with Asymco's Horace Dediu on Apple at 50.
The reason why Nvidia can move so fast is because we always have a unifying theory for the company, which is my job [as the CEO of the company]. I need to come up with a unifying theory for what's important and why things connect together and how they connect together and then create an organization, an organism that's really, really good at delivering on that unifying theory.
— Jensen Huang, Stratechery interview with Nvidia CEO Jensen Huang
I think for anybody in any field, if they write about the edge of what's happening in their field, [...] it really hones your thinking, because when you write something down and you do it all the time, there's this inner desire to not be intellectually inconsistent and so you hold yourself actually to understanding things. Going back to that word nuance, you really get into the nuance because you really want it to hold together once you put something down on paper and there are plenty of people outside of ourselves that have studied this writ large, but it's very well understood that writing is a great way to understand things to take it to a higher level.
— Bill Gurley, Stratechery interview about Gurley's book Runnin' Down a Dream
An Interview with Ben Thompson by John Collison on the Cheeky Pint Podcast. John Collison and Ben Thompson sketch out four levels of agentic commerce in this interview. I like that they start from the bottom up instead of jumping to the far end state.
- Reduce friction. Agents that fill out web forms on your behalf. You paste a product URL into ChatGPT and say "buy this for me."
- Contextual search. Natural language queries with real context: "I need a jacket for -10°C in the Alps" instead of guessing keywords.
- Persistent preference profile. A profile the agent builds over time from your pins, browsing history, or style boards.
- Proactive recommendations. Don't wait for the user to search: anticipate what they need and surface it at the right time. Thompson's point is that this already exists at scale. Zuckerberg called Meta's ad platform the most successful agent in the world.
Interesting to think about how these four levels apply to Ren and other AI products. At Ren, we started from the hardest end, level 4, with proactive recommendations.
One could arguably add a level 5: full autonomy. The agent doesn't just recommend, it acts. OpenClaw is the most visible example right now: a local AI agent that browses, buys, books, and executes on your behalf without waiting for approval at each step.
An Interview with Benedict Evans About AI and Software. Benedict Evans articulates a great insight really well here: LLMs might be a real threat to recommender system moats.
The playbook to build a flywheel has been the following: build a platform, measure engagement data, find patterns, and make better recommendations to attract more users. That's the network effect that made Google, Meta, and Amazon so hard to compete with. But LLMs have a compressed representation of that same knowledge from training on vast amounts of the internet, without having to measure engagement of real users.
To overcome the cold-start problem, you don't need years of engagement data anymore. Now you can just give an LLM some context of a user, for example their social media profile, to get high-quality recommendations. It's a fundamentally different entry point to personalization, one that doesn't depend on scale.
[…] I think it’s really two dimensions of improvement. I think of the UI and the UI capability essentially is a force multiplier on the content offering we have. If we can make that 5% better, then all of that spend, all of that investment that we make has that return and that compounds with the ability to invest more in that content space […]
— Greg Peters, Stratechery interview with the Netflix co-CEO
This new screen aims to solve a rather obvious problem with all of the AI apps: what do you use them for? All of the options on this screen are achievable through a chat interface, but you need to know what to ask for, which is actually step 2 of the process: first you have to know what is possible, and most people don't. This screen aims to solve that: there are obvious filters you can use, and ideas for images you might want to create, like a Christmas card. Again, all of these are doable from a text interface, but there is a reason why purely text interfaces are the domain of so-called graybeards: it's not the typing that is the problem, or even knowing what to type: it's knowing what you could type.
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To summarize, one role of product is to show you what you can do; another role is to inspire you to come up with more of your own ideas.
— Ben Thompson, Stratechery: ChatGPT Image 1.5; Apple v. Epic, Continued; Holiday Schedule
A popular strategy for bootstrapping networks is what I like to call "come for the tool, stay for the network." The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.
— Chris Dixon, Cited by Ben Thompson in his piece on Sora and Meta's disruption potential.