Last week marked the one-year anniversary of ChatGPT, the large language model that introduced Generative AI (GenAI) to the world. ChatGPT’s instant success took most companies by surprise. Even a year later, businesses are still playing catch up as the pace of change continues uninterrupted.

Google now says its own GenAI model, Gemini, will soon have five times GPT-4’s computing power and potentially 20 times the power next year. For companies, GenAI is likely not a one-off technological leap, but the first of a series of rapid advancements that shows no signs of abating. In this new reality, by the time businesses do manage to integrate today’s LLMs, they will already be behind on the next wave of GenAI technologies—and the one after that.

This new state of constant change truly is a permanent revolution, to paraphrase Russian revolutionary Leon Trotsky. It is revolutionary because the change it brings about is often sudden and massive in scale. It is permanent because the rate of AI advancement will continue to exceed the pace of organizational learning, such that companies will fall further and further behind the state-of-the-art technology.

While the PC revolution, by contrast, gave businesses enough time to eventually catch up, the last year shows that catching up is unlikely in the age of AI. This is, in part, because advancements in AI promise to be self-reinforcing, where each breakthrough ripples across systems new and old, refining them and improving performance, scrambling how we live and work, and redefining what we consider possible. There is no end state to the permanent AI revolution—at least not one we can expect in the near future.

The idea that we live in a permanent AI revolution means that companies’ transformation efforts are most likely to succeed when designed with a dual intent: successful adoption of mature technologies and readiness for accelerated experimentation with inchoate ones. Since companies continue to learn at a slower rate than technology advances, success will largely hinge on a business’s relative rate of learning—which, in turn, depends on their ability to become early adopters of the foreseeable technologies on the horizon. Today, for companies working on the adoption of stand-alone LLMs, this challenge takes the form of shaping those LLM-based transformation plans with an eye towards the arrival of what’s coming next—autonomous agents.