Quick Take
- Narration: Walter Dixon delivers a clean, measured performance that suits the book’s journalistic register without drawing attention to itself.
- Themes: algorithmic takeover, labor displacement, the gap between human and machine judgment
- Mood: Curious and mildly unsettling, with the accessibility of good science journalism
- Verdict: A well-constructed 2012 snapshot of algorithmic proliferation that holds up better as history than as prophecy, but earns its place on any tech-literacy shelf.
I first came across Automate This in a used bookshop in 2015, when the algorithm conversation was still the province of finance people and computer scientists. I remember thinking it felt slightly ahead of its moment. Listening to it now, that feeling has inverted: Christopher Steiner was writing at the exact moment when the questions were becoming urgent, but before most of us understood that. Returning to it as an audiobook, I found myself in a peculiar double-exposure, hearing the world as it was being described by someone watching it change in real time, while sitting inside the world that change produced.
The book opens with Thomas Peterffy, the billionaire architect of algorithmic trading, and the Wall Street Flash Crash of May 2010, when the Dow Jones dropped nearly 1,000 points in minutes, then recovered, all without meaningful human intervention. It is a crisp, genuinely alarming beginning that earns Steiner the listener’s trust. The rest of the book tries to sustain that energy across domains, from medical diagnosis to music composition to legal document review, and the results are uneven but consistently interesting.
The Wall Street Opening That Sets the Standard
The first chapter on algorithmic trading is the best in the book, and the reviewers who flagged it specifically were right to. Peterffy’s story, how he went from Hungarian immigrant to options trading pioneer to billionaire by building systems that thought faster than humans, is told with the pacing of a financial thriller. Steiner is at his best when he has a specific person carrying a specific idea through a specific set of events. The Peterffy chapter works because it never loses sight of the human being behind the automation. The algorithm is the mechanism; the ambition, the ingenuity, and the consequences are entirely human. That tension is the real subject of the book, even when later chapters lose track of it.
Where the Accessible Gets Too Accessible
The book’s central limitation, and the technical reviewers who dinged it for oversimplification were not wrong, is that Steiner is writing for a general audience and knows it. He explains what an algorithm is with genuine care, in a way that one reviewer found valuable and another found insufficient. Both responses are accurate. If you have a technical background in any of the fields he covers, finance, medicine, natural language processing, you will frequently want more depth than he provides. The journalism is smooth and the storytelling is clear, but the machinery behind the stories is described from the outside. This is a book about the social consequences of algorithms, not about how they work. That framing serves some readers exactly and others not at all.
The Flash Crash aside, the most memorable sections involve bots that write sports journalism and music composition algorithms that produce pieces mistaken for Bach. These are chosen for their surprise value, and they deliver it. But Steiner is more interested in the strangeness of the fact than in the technical or philosophical depth beneath it. Why does the Bach-alike algorithm work? What does it mean that we cannot distinguish it? The book raises these questions without quite settling into them. That is a deliberate editorial choice, and it is defensible, but it does leave a certain intellectual hunger unaddressed.
The Prophecy Problem
Any book written in 2012 about the coming algorithmic revolution has to contend with the fact that the revolution has now, at least partially, arrived. Some of Steiner’s predictions have aged well, the displacement of routine legal and medical tasks by pattern-recognition software is exactly what happened. Others, the haiku-writing bots and the social-sensing astronaut crew selectors, feel more like novelty than prophecy. The book also predates both deep learning as a mainstream technology and the current moment of large language models, which means it describes a world of algorithmic automation that is real but incomplete. Listening now, you are effectively getting the first chapter of a much longer story.
Walter Dixon’s narration is exactly right for this kind of material. He is the journalistic audiobook narrator in his element: authoritative without being academic, clear without being flat, paced for comprehension rather than performance. He does not add texture that the prose does not already have, but he does not subtract any either. For a book that lives or dies on its accessibility, that steadiness is a genuine asset.
Who Should Listen / Who Should Skip
If you are new to the idea of algorithmic decision-making and want a readable, narrative-driven introduction that will give you enough context to follow contemporary AI debates, this remains a solid starting point. If you work in finance, technology, or any of the other industries Steiner covers, you will likely find it too thin. It is best understood as an artifact of a specific cultural moment, the years when the algorithm became a household word, and as such it is worth listening to even for its historical dimension. Those expecting the technical depth of more recent books on AI and automation should adjust their expectations before pressing play.
Frequently Asked Questions
How has Automate This aged since its 2012 publication, given how much has changed in AI and automation?
Better than you might expect on the social questions, worse on the technical ones. Steiner’s core argument about labor displacement and the expansion of algorithmic judgment into domains once reserved for humans has proven accurate. But the specific technologies he describes, narrow rule-based algorithms, predate deep learning and large language models, so the book reads as the early chapters of a story that has since moved well beyond what he was describing.
Is the book more useful as a technology primer or as a cultural history of a specific moment in automation?
Increasingly the latter. The technology has moved on significantly, but the cultural anxiety Steiner was capturing, the moment when ordinary people started wondering whether their jobs were next, is well preserved here. It works as a time capsule as much as a guide.
Does Walter Dixon’s narration suit the material, or does a book this dense in financial and technical content need a different kind of voice?
Dixon is genuinely well matched. He has a journalistic clarity that keeps the accessible passages moving and prevents the technical sections from becoming impenetrable. The book is not trying to be a textbook, and Dixon does not narrate it like one.
The synopsis mentions bots writing music indistinguishable from Bach. Is that section as compelling in the audiobook format as it sounds?
It is one of the book’s better moments, precisely because the strangeness of the concept benefits from being heard rather than read. Steiner does not over-explain, and Dixon’s delivery lets the weirdness land. The book is, appropriately, better at surprise than at depth on this particular topic.
