The Medium is Creating the Message

This Week:

• The Medium is Crating the Message (read on)
• Tools to Understand Appropriate Reliance on AI
• Good, Better, Best Generative AI Design
• Worried That AI Might Make You Obsolete? Read This: A Case Study in Radiology

Technology has shaped how we create and access knowledge. From books to telegrams to radio to TV to the internet, the media formed by technology has shaped our experience of knowledge. As media theorist Marshall McLuhan claimed, the media alters the message enough to become the message itself, coining his now famous phrase, “the medium is the message.” The effect is that technology, through media, has shaped the public discourse of our global commons.

In traditional media, the role of technology has been largely to convey, distribute, or modify content created by humans. Books, radio, television, and even the internet have served as conduits or platforms for human expression. Generative AI, however, is different from any previous technology because it is creating the messages itself.

Generative AI introduces a paradigm where the technology itself assumes the role of creator, generating original content that are distilled versions of human thought, filtered through algorithms. This transformation is profound—as AI becomes the lens through which we view the world, our understanding becomes shaped not by the multifaceted dialogue of human creativity but by an algorithmically condensed simulacrum.

For example, imagine generative AI being used widely in educational settings to create textbooks. The AI is capable of digesting vast amounts of data on a subject, say, World History. It then synthesizes this information to generate a comprehensive textbook that highlights significant events, cultural shifts, and key figures. On the surface, this seems incredibly efficient—teachers and students now have access to up-to-date, concise, and well-organized material.

However, the profound shift occurs in the nuance of human thought and historical interpretation that this AI might overlook or simplify due to its algorithmic processing. For example, the nuances of cultural exchanges and the subtleties of socio-political contexts could be condensed into bullet points or simplified narratives. Over time, students educated with AI textbooks might begin to see history not as a complex web of interconnected events, cultures, and perspectives but as a series of clear-cut, straightforward happenings without the full diversity human emotions, conflicts, and resolutions.

This shift from passive transmitter to active creator represents a fundamental change in the nature of the medium itself. If the medium is actively creating the message, then the nature of the message is no longer merely shaped by the constraints or affordances of the medium but is born from it.

Generative AI’s departure from previous media raises questions about authorship, creativity, and the essence of human contribution to cultural and intellectual production. As we increasingly rely on generative AI, how much of human discourse, learning, and shared experience will be lost? Might we seek purely human content or might pure human thought be lost?

The dominance of singular generative AI systems raises questions about the effect of embedded corporate goals. As McLuhan said, “The ‘content’ of a medium is like the juicy piece of meat carried by the burglar to distract the watchdog of the mind.” How might Big Tech employ generative AI to distract us? And how might a small number of dominant generative AI systems distract us all at once?

Covering the internet with Mary Meeker in the late 1990s, we framed the new media opportunity as limited to three players in each category, following the market dominance model of previous media like newspapers, radio, and TV. While that pattern is likely to repeat with generative AI as well, what is fundamentally different is that none of those three will shape human thought. Instead, our next media will contain only machine thought, shaped by the corporate goals of its creators.

This Week from Artificiality:

Tools to Understand Appropriate Reliance on AI

By emphasizing critical engagement, transparency, bias mitigation, deliberate decision-making, user autonomy, and continuous education, Microsoft's research offers valuable guidelines for designing AI systems that promote appropriate reliance and user empowerment.

Good, Better, Best Generative AI Design

IBM's principles for generative AI applications underscore the necessity of a thoughtful, user-centered approach to designing GenAI applications.

Worried That AI Might Make You Obsolete? Read This: A Case Study in Radiology

In 2016, AI experts predicted radiologists would be obsolete within years as machines outperform humans. This did not transpire.