Every single-player tool eventually loses to a multiplayer tool. What's interesting about the current state of applied AI, especially in the enterprise, is that most applications are either: 1. Single-player by design, or 2. Multiplayer by legacy, meaning AI is being plugged into an existing multiplayer system and doing the best it can. But the AI itself usually isn't "multiplayer" in those contexts. It feels like we haven't yet seen the biggest companies or the top players in most verticals, the ones that will truly embrace multiplayer AI. Who's actually building multiplayer AI today?
Adam J. SmithRelated
The biggest friction for AI over the next two years will be at the applied level, not the foundational level. The models and core capabilities are advancing quickly, but enterprises are struggling to actually integrate them into workflows and get real leverage. The gap is mostly on the talent side. There just aren't enough engineers or architects who know how to work with these models and meaningfully embed them into business processes. That's where adoption will lag.
There are basically two dominant stories being told about AI right now, and neither of them is right. One says AI is overhyped, unreliable, and basically vaporware. The other says we're on the verge of post-AGI, that it's the most transformative technology we've ever seen, and everything will change in the next couple of years. I think both of those takes are wrong. There are two other stories that I think better capture where we're at. First, foundational AI research is progressing incredibly fast. We've seen multiple orders of magnitude in performance improvements and cost reductions over just the last few years. Second, applied AI, the part that actually drives productivity, economic growth, and real value, is lagging. One big reason is that the talent needed to translate raw model capabilities into enterprise value is scarce. OpenAI, Anthropic, Google DeepMind, they'll capture some value, of course. But most of the long-term value will come from the businesses that figure out how to build on top of this technology and make it useful. Think of the early internet: right now, we're basically in the pre-2000s phase. The tech is revolutionary, but the applications haven't fully caught up yet. Both things can be true: AI can be disappointing today in many ways, and at the same time, it can be a revolutionary technology whose impact is inevitable once the foundational progress and the applied use cases converge.
Attention is All You Have
Our attention is stolen in pennies—notifications, emails, feeds—until our entire fortune is gone. We need AI not as an assistant, but as a bouncer: keeping the riff raff out so we can focus on what matters.