Despite the unique features of advanced AI systems, a thorough study of historical examples from emergent technologies offers insight into navigation between the risks and benefits.

The rapid advances in the AI development have left the field mostly devoid of large-scale studies of the historical and sociological lessons from earlier similar developments. While AI - both in potential benefits and risks - is in some forms unique, it shares similarities with earlier major technological breakthroughs. While some of these have been studied intensely,^[1] there remains work to do in building a coherent overview of the shifting dynamics new technologies cause.

A new technological breakthrough is a disruption in the fabric of society, originally locally, inevitably later globally. The discussion around AI safety is dominated by professionals of technological perspective. A good researcher, though, might not be a good strategist,^[2] and dismissing the historical lessons is a blind spot we might not afford.

Studying the transitional periods of major technological breakthroughs and their effect on societal dynamics will arguably reveal recognizable patterns. Recognizing and understanding these patterns inevitably makes us better prepared for good governance solutions for the age of artificial intelligence.

I present in this post a research agenda, split into four separate themes of reactive patterns. This text is a map of the territory rather than exploration of each area in depth, providing a backbone for further research. Each of these themes warrants a study on its own. The historical cases of each theme represented should be studied together to form an understanding of the high-level patterns, not as isolated historical cases.

The themes are:^[3]

Regulatory lag and warning shots
Conflict dynamics and emergent technology
Technological disruption’s effect on civil society
International agreements on technological restrictions

1. Regulatory lag and warning shots

Warning shots in the context of technology are situations where a (new) technology creates disvalue through some event(s), but less than it has potential to create. Thus the event moves the Overton window, sparks public discussion and gets policy-makers on the move. While we may not get a warning shot with catastrophically dangerous AI, it is potentially highly important to understand the dynamics and time...

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