Book review of How to AI: Cut through the hype, master the basics, transform your work

For most of us, the meaning of AI remains elusive. Being the Luddite that I am, the very concept of AI remains illusory for me. I am sceptical about its adoption and what such adoption would mean for my own personal and intellectual growth.

Simultaneously, its role in delivering work products efficiently is undeniable and I do fear that my scepticism might lead to me being rendered redundant. It is for individuals such as myself that Christopher Mims has written How to AI: Cut Through the Hype, Master the Basics, Transform Your Work—a calm and occasionally irreverent guide to actually using AI at work. The central theme of the book is refreshingly modest. This is not a manual to make you a tech-savvy wizard, but simply a primer for people who want to “get things done”. Mims, in fact, structures the book around 24 laws of AI, which are essentially bite-sized principles that read like a cross between management advice and survival rules for the digital workplace.

Some of these rules are intuitive (“AI is an assistant, not a replacement”), others are more provocative (“AI isn’t actually intelligent, but understanding how it works can unlock its power”), and many are practical reminders dressed up as insights (“Don’t trust it, and always verify its work”).

What distinguishes How to AI from other AI explainers that have flooded the market, ironically written by ChatGPT or some other large-language model, is its tone. Mims, true to his roots as a regular tech journalist who has spent years watching technological hype cycles rise and collapse, writes like an individual inhabiting a healthy distrust of both Silicon Valley optimism and public panic.

Mims is particularly effective when puncturing myths. In his interview with SmartBrief, he insists that AI is neither magic nor malevolent; it is a software, with all the usual caveats, such as the possibility of hallucination, the need for human oversight, and how “garbage in” produces “garbage out”. Mims facilitates understanding of these caveats by peppering the book with anecdotes that make this point tangible. From a lawyer using AI to assist in cross-examinations, a marketing team generating campaign ideas, to a contractor automating bids that previously consumed hundreds of hours, these are no longer futuristic fantasies; they are recognisable use-cases that anchor the reader.

There are also flashes of humour, sometimes unexpectedly blunt, that are meant to engage the reader about a topic that may be monotonous. At one point, Mims quips that neuroscience is a “pretty shit way” to understand the human mind —a line that can be perceived less like a joke and more like a declaration of intellectual independence. His practical suggestions retain a slight tinge of eccentricity as well, with examples relating to Zoom calls being turned into walking meetings with AI dealing with note-taking.

In the Indian context, where corporate adoption of AI often lags behind rhetoric, this emphasis on small incremental experimentation feels particularly relevant. Mims is not asking you to transform your organisation overnight; he is asking you to start by outsourcing your least favourite task. This advice also sits neatly alongside emerging empirical evidence. A recent Anthropic labour market study suggests that while AI has not yet triggered large-scale job losses, it is already reshaping work in subtler ways with fewer entry-level hires, leaner teams, and more output per worker. Against this backdrop, Mims’ focus on individual adaptation feels less like productivity advice and more like early career insurance.

The anchor of the book remains that AI is best understood not as a replacement for human labour, but as an amplifier of it. This is not a novel argument, but Mims articulates it with clarity and consistency. As per Mims, experts benefit more from AI than novices because expertise allows better prompting, better judgment, and better filtering of output.

This insight has real implications for India’s services economy, particularly in law, consulting, and IT—fields where productivity gains from AI could be significant, but unevenly distributed. However, for all its strengths, How to AI occasionally feels like a book that is reductionist and repetitive. While the “laws” framework of the book can be engaging and elegant, some principles overlap, others contradict, and a few feel like common sense repackaged as insight. Ideas advanced by Mims do not always align with the anecdotes used to illustrate them, and more significantly, the book sidesteps deeper structural questions.

There is little sustained engagement with issues such as data governance, labour displacement in outsourcing industries, regulatory uncertainty, or the informational asymmetry between global AI developers and local users. While Mims acknowledges risks accompanying AI adoption, such as bias, overreliance, and hallucination, he treats them as operational challenges rather than systemic ones. In his defence, the book is not meant to be a policy treatise.

However, absence of engagement with these questions leaves the book feeling incomplete for readers looking beyond personal productivity.

For an Indian audience, How to AI is likely to be most valuable as a mindset shift rather than a manual because the real barrier to AI adoption is no longer access to tools. From personal experience, it is behavioural inertia. In this context, the book succeeds because it is practical, imperfect, timely, and most importantly, usable for professionals like me. Its sustained adoption in the long run, however, will remain to be seen.

In the spirit of full disclosure, no AI was used in the writing of this review. However, after reading How to AI, one suspects that might soon start to feel less like integrity and more like inefficiency.

Radhika Roy is a Delhi-based lawyer specialising in technology law

How to AI: Cut Through the Hype, Master the Basics, Transform Your Work
Christopher Mims
Hachette
Pp 256, Rs 699