Clarity as a responsibility. The future of science communication

For more than a decade, the Nieman Journalism Lab —an observatory of Harvard’s Nieman Foundation —has published an annual end-of-year review that has become a reference point in the global debate on information: Predictions for Journalism. These are not speculations about the future, but a collection of reflections that shed light on changes already underway in contemporary journalism.  In this sense, the forecasts function as indicators of the tensions shaping the information landscape and guiding its evolution.

The 2026 edition brings together more than 200 contributions from journalists, editors, and media scholars. Given their number, selecting only a few is inevitably arbitrary.  Still, we have chosen to focus on two, using this rich collection as a guide for reflection throughout the year ahead. We selected them for their relevance to science communication and artificial intelligence, and because they address structural issues that affect how scientific knowledge is presented in the public sphere. For these reasons, they are of particular interest to FBK Magazine.

The first contribution, by journalist Davey Alba, focuses on the role of AI in content production. Alba, who has long covered the intersection of artificial intelligence, digital technologies, and information, argues that in the near future AI will produce more text than humans do. In an information ecosystem already overwhelmed by volume, this trend affects not only low-quality content but the entire communication space, including journalism. As a result, the value of journalism shifts from production to selection, from writing to the ability to evaluate and interpret. For science communication, the implications are clear. Scientific research already generates a growing volume of studies, data, and preliminary findings. The challenge is not to produce even more text, but to determine which information deserves attention, how it should be framed, and how cautiously it should be presented.  Automated writing risks amplifying an existing problem: the spread of explanations that are formally correct but lack interpretive hierarchy and clear context.

The second prediction, by Garry Pierre-Pierre—Pulitzer Prize-winning journalist and founder of The Haitian Times—reflects on a shift in audience needs. A growing share of the public, he argues, is not primarily looking for traditional articles, but for tools that help them orient themselves. In a saturated information environment, the article is no longer necessarily the most effective way to support understanding. This insight speaks directly to the core mission of science communication, which aims in part to reduce complexity. Science advances through hypotheses, tests, revisions, and uncertainty. Communicating it requires mediation—guiding the public through processes that are often complex and non-linear. In this context, clarity does not mean simplification. Rather, it means making the provisional nature of knowledge, and its limits, understandable.

Taken together, these two contributions highlight a defining tension of our time: the rapid acceleration of content production alongside a growing demand for understanding. As AI makes writing ever easier, the task of journalism—and science communication in particular—shifts toward mediation, judgment, and responsibility. Informing is not just about making data available, but about helping people interpret it in the right context and at the right time. This is where science communication can find a renewed role: not as a simple conduit for research results, but as a cultural practice that helps the public engage with complex, uncertain, and constantly evolving knowledge. In a world where texts multiply relentlessly, clarity becomes an increasingly precious resource— and for those working in science communication, a form of public responsibility.