AI: Finding Its Place Between Innovation and Media Hype

The Dynamics of Signal and Noise in AI

In the world of artificial intelligence, many feel a struggle to keep pace with the rapid developments. Platforms like X or LinkedIn give the impression that changes occur every week. However, the real advancements in terms of capabilities happen only a few times a year. What is constantly evolving is the level of noise. The pertinent question is not how to keep up with AI, but rather where to position oneself on the signal-to-noise curve.

This signal-to-noise curve is a simple model for understanding AI adoption. At the beginning of a technological cycle, everyone experiments. Most of these experiments do not last, generating a lot of noise. Over time, only the experiments that provide real value persist, forming the signal.

Defining Your Persona: Innovator or Early Adopter?

To determine where to position yourself on this curve, it is essential to ask whether you want to be an innovator or an early adopter. These two profiles are distinctly different on the product adoption curve. During the emergence of AI, some innovators delved into complex concepts like transformers and next-token prediction. This is reminiscent of learning TCP/IP in the early days of the Internet. Although I have built many successful Internet products, I still cannot succinctly explain TCP/IP. It’s a level of detail too deep for the leverage I seek to create. This made me realize that I am an early adopter, and that is perfectly acceptable. If you are an early adopter, simply adjust your information consumption habits.

The Importance of Your Professional Role

The necessity of being an innovator or an early adopter largely depends on your professional role. AI remains a tool, and it is crucial to focus on what this tool can bring to your work or your company's strategy. If your responsibility is to sell AI tools, being an innovator is likely essential. You need to be able to answer all the questions your prospects may have, or risk damaging your brand.

In my case, as the founder of a consumer startup with a product-focused rather than technology-focused role, I had to be an early adopter. This meant setting up tools like Cursor and starting to make changes to our product. At the same time, although innovation in AI-generated images and videos is booming, I did not consider it essential for my work. Therefore, I chose to wait until these technologies became a hundred times easier to use.

Strategies for Positioning on the Curve

Intelligently answering the question of whether you should be an innovator, an early adopter, or neither allows you to navigate the signal-to-noise curve of AI effectively. If your role requires in-depth knowledge of AI, be prepared to face a lot of noise and experiment with many technologies that may not prove valuable.

For those who need to be early adopters, the goal is to find a balance between signal and noise, positioning yourself in the right place. If AI is not relevant to your work, waiting for technologies to become more accessible is often the best strategy. The key rule is not to remain stuck in the same place on the curve for everything.

The same person can be an innovator in one area, an early adopter in another, and belong to the early majority elsewhere. For example, as a startup founder, I need to be an innovator to define the vision of an AI product, an early adopter for its construction, and part of the early majority for the rest.

By observing the curve with a few recent major innovations, we see that Claude Code has recently crossed into the early adopter zone for product leaders. AI meeting notes and image generation have already surpassed this threshold. New tools like OpenClaw and Claude Cowork are still in the experimentation phase by innovators.

The signal-to-noise curve allows us to map major innovations from the past two years. For instance, Claude Code has crossed into the early adopter zone for product leaders in the last six months. AI meeting notes and image generation have long surpassed this threshold. New tools like OpenClaw and Claude Cowork are not there yet, but innovators are experimenting with them.