"Whether you’re an investor or entrepreneur, the most important thing to start with is to look for these exponential forces." This foundational insight, articulated by a16z general partner Chris Dixon, anchors a compelling discussion on the a16z podcast about building networks, movements, and AI-native products. Dixon, known for his work in Web3 and network economies, joined Anish Acharya, also a general partner at a16z and an investor in AI-native consumer products, to dissect the enduring power of consumer networks and their future in the age of artificial intelligence.
The conversation between Dixon and Acharya at a16z delves into the underlying "exponential forces" that drive technological breakthroughs and market dominance. Dixon identifies three such forces: Moore's Law, composability (leading to open-source growth), and network effects. Moore's Law, the idea that semiconductor performance doubles every 18-24 months, has driven the evolution of hardware from clunky mobile phones to the sleek smartphones of today. This relentless improvement in computing power enables entirely new product categories and experiences.
Composability, the second exponential force, refers to the open-source nature of software development. Dixon explains that open-source allows developers to build upon existing code like "Lego bricks," harnessing the collective intelligence of the internet rather than relying solely on internal teams. This collaborative model accelerates innovation, as seen in the rise of Linux from a hobby project to a dominant operating system. The third force, network effects, is perhaps the most familiar. A service becomes exponentially more valuable as more people use it. Early internet services like email and the World Wide Web, and later platforms like YouTube, Facebook, and Instagram, all capitalized on this principle. As Dixon notes, "If you were the only one on email, it wouldn't be particularly valuable."
A core insight woven throughout the discussion is the critical role of timing and strategic positioning relative to these exponential curves. Dixon emphasizes that while tactical product decisions are important, these underlying forces will ultimately "overwhelm you, for better or worse." The key for founders and investors is to identify and ride these waves. Apple's insight with the iPhone, for example, wasn't just about creating a good phone, but about seeing the exponential curve of mobile technology and riding it. Similarly, OpenAI's success with ChatGPT stemmed from making a bold bet on neural networks, which were once considered "toys" but improved exponentially. Google, with its entrenched search business, now finds itself in an "awkward position," needing to adapt its incumbent model to a rapidly shifting AI landscape.
Acharya raises a pertinent question about the intentionality of building networks in the AI era, observing a proliferation of tools but a scarcity of robust networks. Dixon responds by referencing his blog post, "Come for the Tools, Stay for the Network." He illustrates this with Instagram, which initially offered cool photo filters (the "tool") but then leveraged existing networks like Twitter for sharing, eventually building its own powerful network. Modern productivity tools like Figma and Notion, while useful in single-player mode, gain essential social features that foster network effects. These emergent networks create defensibility, a crucial aspect in a rapidly evolving tech environment.