W. Russell Neuman, Evolutionary Intelligence: How Technology Will Make Us Smarter, Cambridge, MA: The MIT Press, 2023, 256 pp., $26.95 (hardcover), $15.99 (kindle).

 

Reviewed by

Jonathan D. Aronson

University of Southern California

 

OpenAI released the first version of ChatGPT on November 30, 2022. Less than ten months later, on September 26, 2023, Evolutionary Intelligence: How Technology Will Make Us Smarter, a broad-ranging artificial intelligence (AI) overview by the distinguished media technology scholar W. Russell Neuman, now affiliated with both New York University and the University of Michigan, was published.

 

A little more than two years after that, I was asked and agreed to review it. Teaching intervened. Grades were submitted in May 2026, and I finally read the book. Today, almost four years after ChatGPT’s public launch, AI is far more advanced and ubiquitous. So, given the book’s subject and departing from normal review practice, I asked Anthropic’s Claude to review, in hindsight, what Evolutionary Intelligence got right and what it got wrong.

 

I integrate Claude’s review with my own comments, but I identify as much as possible which is which. Claude applauded the focus on cognitive bias and the framing of evolutionary intelligence (EI) as the “evolutionary partnership of human cognition and computational systems” instead of as a threat to humanity. Claude and this reviewer both identified Professor Neuman’s effective use of historical contextualization as a great strength of this volume. In addition, by keeping human agency at the center of his analysis, Professor Neuman was one of the first scholars to distinguish between artificial intelligence as a rival to humans and “augmented intelligence” as a useful tool that could assist people.

 

So, where was the book off the mark or where did it leave issues unresolved? Claude questions whether the subtitle How Technology Will Make Us Smarter should have been tweaked. EI will likely help people more quickly make more informed, less biased decisions, but will it help people make better, smarter, and wiser decisions? It also “thinks” that Professor Neuman is too much of a techno optimist who accepts too uncritically that EI will be “democratically available” to most or all. Further, Claude and I agree that the extended review of the behavioral economics literature, especially Kahneman and Tversky, in chapter 3 will be familiar to many readers and does not push the theory ahead, and that the “brief” case studies in chapter 7 on EI and personalized medicine, online dating, and cyber-finance could be augmented. Claude’s final complaint is accurate, but unfair. It concerns what the author did not emphasize in his book except in passing. Important issues related to policy, governance, and institutions are largely absent from the analysis, but the delay in writing this review meant that much of the political-economic fallout of concentrated AI power, threats to jobs, the importance of large language models, and geopolitical competition emerged after the manuscript was completed.

 

Departing from Claude, my reaction is that the seven substantive chapters, when taken together, form a loosely integrated whole but that they also can be read as stand-alone essays. Professor Neuman’s use of stories, anecdotes, and illustrative details is memorable. I especially applaud chapters 1, 5, and 6, which present the case and provide the historical context.

 

Chapter 1 makes the case that evolutionary intelligence embodies a revolutionary leap in communications, empowering intelligent amplification that adds value, intelligent translation that provides seamless interconnectivity of people, data, and devices, and intelligent transactions that will facilitate negotiation and exchanges.

 

I found chapters 5 and 6 the most satisfying. My students, especially the undergraduates raised in a time of COVID-19 and the emergence of AI, need more historical perspective. Professor Neuman provides a foundation and framework for understanding fundamental human revolutions that preceded the digital revolution and AI explosion.

 

Chapter 5 concisely summarizes four historical revolutions that propelled humanity. First, the emergence of language was the engine that enabled humans to coordinate their activities and transmit culture from one generation to the next. Second, most humans gradually moved from being hunter-gatherers and learned to settle, grow crops, and domesticate animals. Third, the Industrial Revolution that began in Britain in the late 18th century ushered in the age of machines that coincided with a shift from human and animal power to mechanical power. The resulting demographic changes, urbanization, and the creation of both great wealth and inequality transformed society. Fourth, in many countries mass education resulted in mass literacy and the rise of the middle class. The digital revolution then prepared the world for the emergence of evolutionary intelligence.

 

In chapter 6, Professor Neuman lays out five short case studies that show that the seeds of evolutionary intelligence were present decades before 2022: (1) Early fax machines pioneered machine-to-machine communication that preceded seamless interoperability; (2) The Internet reduced the information asymmetry between car buyers and sellers; (3) AuctionWeb, later renamed eBay, developed a reputational feedback system to create trust between strangers wishing to transact business; (4) Intelligent filtering arose to partially combat spam that came with information overabundance; (5) New AI translation devices are significantly improving the ability of people who do not share a common language to communicate more smoothly. In short, Professor Neuman suggests that “a new form of human connectivity” (p. 168) is emerging that will be universal, immediate, networked, and self-correcting.

 

Chapters 2, 3, and 4 are less emphasized in this review, but also merit attention. Chapter 2 provides an understandable, straightforward overview of how human perception works, explaining the neuroscience of sight, sound, and touch. It considers how EI systems could connect with sensory channels. Chapter 3 provides a behavioral economics tutorial largely building on Kahneman and Tversky’s framework of human biases that distort decision-making and suggests how EI can mitigate and even correct systemic human biases by “nudging” human deciders in a more rational direction. Readers familiar with the issues can review or sail through chapters 2 and 3 as they prefer.

 

Chapter 4, entitled “Here Be Dragons,” surveys how the proliferation of EI could threaten privacy, undermine cybersecurity, and introduce algorithmic and other biases. Professor Neuman recognized early that EI inevitably would introduce these risks into the digital economy and suggested minimizing their impact by ensuring market competition, promoting transparency, and identifying and enshrining best practices. Since Professor Neuman’s volume came out, these and other issues have come into sharper focus and are addressed more thoroughly elsewhere.

 

In short, Russell Neuman is a serious scholar who provides a serious, well-written overview of one of the critical innovations shaping the future. Evolutionary intelligence, and the artificial intelligence it is built on, already is emerging as a powerful, transformative force that will be part of our future. Professor Neuman is cautious, but not panicked. EI can improve the lives of the rich, the middle class, and the poorest among us. EI may even become an essential tool that helps humanity address complex, existential threats like climate change. But developments since 2022 show clearly that the spread of AI comes with real risks. Others—scholars, policymakers, experts, and even AI itself—will need to design, negotiate, and implement appropriate policies and regulations to ensure that harms suffered by the many do not overshadow the benefits that might otherwise be widely shared.

 

 

Copyright © 2026 (Jonathan D. Aronson, [email protected]). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at https://ijoc.org.

https://doi.org/10.65476/n4k0mz38