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.

Over the next couple of years, 2026 and 2027, we're going to see the rise of more verticalized, hyperfocused AI and intelligence systems. I'm talking about small language models, fine-tuned architectures, and federated model systems. It feels similar to what happened after the cloud transition. When software "ate the world," we saw a Cambrian explosion of SaaS, every conceivable type of software for every use case and vertical. That kind of verticalization hasn't really happened in AI yet. So far, we've mostly seen legacy SaaS vendors bolting AI onto their products, rather than true ground-up vertical AI systems. Part of the reason is that foundational AI research is moving faster than enterprises can apply it in real time. And the talent to build these specialized systems just isn't widespread yet. But I think the next two years will mark the early stages of that wave. That's basically what ZoomInfo is doing right now, building a federated intelligence system specifically for GTM.