Three years is not long. It is long enough. When Mustafa Suleyman and Michael Bhaskar published *The Coming Wave* in 2023, the book read as an urgent warning from a man with his hand on the lever. By 2025, Suleyman was CEO of Microsoft AI, one of the most powerful positions in the industry he had spent 300 pages arguing needed containment. The book has not changed. Its author's coordinates have. This is the kind of irony the book itself might have predicted—it spends considerable energy explaining why the incentive structures driving technological development are essentially irresistible—but it never quite reckons with the possibility that its own author would become a primary vector of the wave he named. The ten steps toward containment outlined in Chapter 14 now read less like a policy blueprint and more like a letter of intent filed before a merger. Some of the steps have been attempted. None have been achieved at the scale the book demands. The EU AI Act, which Suleyman critiqued as too narrow, has stumbled into enforcement with roughly the limitations he anticipated. China's regulatory apparatus, which the book credited with surprising proactivity, has oscillated between crackdown and acceleration depending on the quarter. The geopolitical AI race the book described has intensified beyond even its own projections—not because the analysis was wrong, but because the timeline was generous.

What the book got right is substantial and specific. The prediction that large language models would become ubiquitous general-purpose tools, that synthetic biology would accelerate past regulatory capacity, that autonomous drones would reshape asymmetric warfare—all of this has materialized with uncomfortable precision. The Ukraine chapter, which used the Russian invasion as a case study for technology-enabled asymmetric resistance, now looks like a modest preview of what drone warfare has become across multiple theaters. The discussion of deepfakes and AI-driven disinformation anticipated the 2024 election cycles in the US, India, and elsewhere with depressing accuracy. The concept of "fragility amplifiers"—technologies that don't cause crises but make existing systems more brittle—has proved to be one of the book's most durable contributions. Every major cybersecurity incident since publication has essentially illustrated this chapter. Where the book stumbles is in its treatment of AI capabilities themselves. Written in the GPT-3.5/GPT-4 transition period, it hedges carefully on timelines for artificial general intelligence while simultaneously arguing that the rate of improvement is "well beyond exponential." The hedging was wise. The framing was not. What has actually happened is messier: capabilities have surged in some domains and plateaued in others, the scaling laws have shown diminishing returns in certain architectures, and the definition of "general intelligence" has become even more contested. The book's tendency to treat AI progress as a single ascending curve now looks like the bias of someone who spent a decade inside the most successful AI lab in history.