Written by Radhika Roy
At a meeting a few weeks ago, the topic of using artificial intelligence (AI) to create prepared documents came up. On paper, it seemed like the best solution. It does the heavy lifting, frees up time for lawyers to consider more complex legal issues, and reduces billable time (although this may actually be a benefit to the client). This proposal received mixed reactions, with the increased use of AI being perceived as either an evangelical force intent on stealing our jobs, or just a tool to draft emails we can’t be bothered to write.
For most of us, the meaning of AI remains elusive. I’m a Luddite, so the very concept of AI remains a fantasy to me. I am skeptical about that adoption and what such an adoption would mean for my own personal and intellectual growth.
At the same time, its role in efficiently delivering work product is undeniable, and I’m concerned that my skeptical attitude will lead to me being fired. Christopher Mims wrote How to AI: Cut Through the Hype, Master the Basics, Transform Your Work, a gentle and sometimes irreverent guide to actually using AI at work for individuals like me. The central theme of this book is refreshing and understated. This isn’t a manual for becoming a tech-savvy wizard, it’s just a primer for people who want to “get things done.” In fact, Mims has structured the book around 24 laws of AI. These are essentially bite-sized principles that are a combination of management advice and digital workplace survival rules.
Some of these rules are intuitive (“AI is an assistant, not a replacement”), some are more provocative (“AI isn’t really intelligent, but understanding how it works can unlock its power”), and many are practical cautions disguised as insights (“Don’t trust an AI, always check how it works”).
What sets How to AI apart from other AI tutorials on the market, ironically written by ChatGPT and other large-scale language models, is its tone. True to his roots as an ordinary technology journalist who has spent years watching the rise and fall of technology hype cycles, Mims writes like an individual who harbors a healthy distrust of both Silicon Valley optimism and public panic.
Mims is especially effective when it comes to busting myths. In an interview with SmartBrief, he claims that AI is neither magical nor malicious. This is software with all the usual caveats, including the possibility of hallucinations, the need for human supervision, and how the “garbage that goes in” creates the “garbage that comes out.” Mims makes these points easier to understand by peppering the book with anecdotes that illustrate this point. From lawyers using AI to assist in cross-examination, to marketing teams generating campaign ideas, to contractors automating bids that previously took hundreds of hours, these are no longer futuristic fantasies. These are recognizable use cases that anchor the reader.
It also includes sometimes unexpectedly candid humor aimed at piques the reader’s interest in what may be a dull topic. At one point, Mims quipped that neuroscience is a “pretty crappy way” to understand the human mind. This line may be taken more like a declaration of intellectual independence than a joke. His practical suggestions still have a bit of a whimsical tinge, turning examples related to Zoom calls into walking meetings with note-taking AI.
In the Indian context, where corporate adoption of AI often lags behind rhetoric, the emphasis on small-scale, incremental experimentation feels particularly pertinent. Mims isn’t looking to transform organizations overnight. He asks you to start by outsourcing the job you hate the most. This advice sits neatly alongside new empirical evidence. Recent human labor market research suggests that while AI has not yet caused large-scale job losses, it is already reshaping work in more subtle ways by reducing entry-level hires, slimming teams, and increasing output per worker. Against this backdrop, Mims’ focus on personal adaptation feels more like early career insurance than productivity advice.
The point of this book is that AI is best understood as an amplification of human labor, rather than a replacement for it. This is not a new argument, but Mims articulates it clearly and consistently. According to Mims, experts benefit more from AI than novices because their expertise allows them to make better prompts, make better decisions, and filter output.
This insight has real implications for India’s services economy, particularly in law, consulting, and IT, areas where productivity gains from AI are significant but unevenly distributed. But despite its strengths, How to AI can sometimes feel like a reductionist and repetitive book. Although the book’s framework of “laws” is attractive and sophisticated, some principles overlap, others contradict, and some principles feel like common sense repackaged as insights. The ideas Mims puts forward don’t always match the anecdotes used to illustrate them, and more importantly, the book avoids deeper structural questions.
There is little continued engagement with issues such as data governance, labor mobility in the outsourcing industry, regulatory uncertainty, or information asymmetries between global AI developers and local users. Mims acknowledges the risks associated with AI deployment, such as bias, over-reliance, and hallucinations, but treats them as operational challenges rather than systemic problems. In his defense, this book is not intended to be a policy paper.
But the lack of engagement with these questions makes the book feel incomplete for readers who are thinking about more than personal productivity.
For Indian audiences, 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’s behavioral inertia. In this context, this book is successful because it is practical, but incomplete, timely, and, most importantly, usable even for professionals like me. However, it remains to be seen whether this adoption will continue in the long term.
In the spirit of full disclosure, no AI was used to write this review. But after reading How to AI, some may wonder if that will soon start to feel like inefficiency rather than integrity.
Radhika Roy is a Delhi-based lawyer specializing in technology law.
How to leverage AI: Beat the hype, master the basics, and transform your work
christopher mims
hachette
256 pages, 699 rupees
