I don't think it's necessary to keep up with every detail of general AI developments. Depending on your field, it's more effective to focus selectively—for example, I pay closer attention to natural language processing and LLM applications because my work is in language technology.

You don't need to dive deep into what every new service, model, or solution is doing. Instead, I keep an eye on them with peripheral awareness, so to speak. Just because a new model boasts impressive benchmarks doesn’t mean it’s practically useful in real-world contexts. Likewise, trends like agents and “vibe coding” might be valuable in certain scenarios—and that’s great when they are—but they don’t magically solve every business problem.

To be clear, I do explore and make use of new technologies. But my main focus is on what’s actually useful for me. Otherwise, it’s easy to get swept up in the hype—a sea of performative glorification that everyone (and their cats) seems to be chasing. In fact, sometimes there’s value in being a strategic laggard. The latest shiny thing often comes with a hefty price tag, and being aware—without being dazzled—goes a long way.