When Gen. Keith Alexander became the new director of the National Security Agency in 2005, “his predecessor, Mike Hayden, stepped down, seething with suspicion”– towards Alexander.
As told by Fred Kaplan in his new book Dark Territory, Gen. Hayden and Gen. Alexander had clashed years before in a struggle “for turf and power, leaving Hayden with a bitter taste, a shudder of distrust, about every aspect and activity of the new man in charge.” The feeling was mutual.
The subject (and subtitle) of Kaplan’s book is “the secret history of cyber war.” But the most interesting secrets disclosed here have less to do with any classified missions or technologies than with the internal bureaucratic evolution of the military’s interest in cyber space. Who met with whom, who was appointed to what position, or even (as in the case of Hayden and Alexander) who may have hated whom all turn out to be quite important in the ongoing development of this contested domain.
Kaplan seems to have interviewed almost all of the major players and participants in this history, and he has an engaging story to tell. (Two contrasting reviews of Dark Territory in the New York Times are here and here.)
Meanwhile, the history of cyber war is becoming gradually less secret.
This week, the Department of Defense openly published an updated instruction on Cybersecurity Activities Support to DoD Information Network Operations (DoD Instruction 8530.01, March 7).
It replaces, incorporates and cancels previous directives from 2001 that were for restricted distribution only.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.