I’m new to AI and really appreciated this thorough introduction to the subject. Now I have an AI account and starting to experiment.

This book should be required reading for all high school students in the AI Era. Real, practical knowledge!

“Co-Intelligence: Living and Working with AI" by Ethan Mollick is a clear and practical guide on how to integrate AI into our daily lives. Mollick, a professor at Wharton, breaks down the complex world of AI into easy-to-understand principles. He shows how AI can be a helpful partner in work, a tool for learning new skills, a coach for personal growth, and a source of creative inspiration. This book is a must-read for anyone curious about the practical uses of AI and how to navigate the changes it brings.

Great book that lays down both a historical (recent) foundation of where AI has come to potential scenarios of what AI will be. However, what is most magical about this book is the way the author outlines how best to use and interact with AI. Those nuggets alone are worth the read and it is something you can apply immediately. This booked changed how I use and view AI for the positive and I’m exited and energized and maybe just a bit nervous about the future of AI.

Ethan Mollick's *Co-Intelligence: Living and Working with AI* presents a compelling exploration of artificial intelligence and its impact on our lives. Mollick's clear and accessible writing makes complex topics understandable. His fearless approach ensures readers are both informed and engaged. Mollick balances optimism with caution in his assessments of AI. He discusses potential risks and ethical concerns while highlighting AI's transformative potential. His thought-provoking analysis encourages readers to consider how AI can be harnessed responsibly. Mollick introduces the concept of "co-intelligence," where humans and AI collaborate symbiotically. He argues that AI should augment human intelligence rather than replace it, presenting a refreshing and optimistic perspective. Mollick excels in making complex ideas accessible without oversimplifying. His clear and engaging prose appeals to both tech-savvy readers and those new to AI. For those interested in AI, this is a good primer.

The first half is more destined to those who have yet to use generative AI on a regular basis – in all probability a shrinking crowd. Anyone who interacted with ChatGPT 3.5 and then 4 will have similar anecdotes. And while certainly useful, I found Ethan’s four principles to mix rules of thumb with prompting techniques and more general observations. Part two I personally found of much greater interest, as Ethan delves into the implications for workers, demonstrating how they can learn to use AI to great effect, e.g. in creative work. Ethan dives deep into how gen-AI can produce efficiencies and superior results, but also lead to disaster if users get too comfy and “fall asleep at the wheel”. While several studies and research papers exist on these topics, it’s one of the first books that explains these concepts to laymen. It’s also enjoying to see him, rather than trying to dissect office jobs he may not be familiar with, instead humbly take himself as a guinea pig to explain in great minutiae how he wields this revolutionary tool in his work, at times using the very paragraphs we read as examples. I found in several occasions echoes of my own book, e.g. when Ethan insists on how technology cannot be treated in a vat but goes hand in hand with frameworks and trends, thus how the way corporations have work organized is likely to change in serious ways, as it did in previous industrial revolutions. He too does not fall for the fallacy that because certain tasks in a job cannot be automated, the job is immune to disruption. He too notes the risks associated with a greater deployment of metrics and the diktat of data, what others have called “Digital Taylorism”, and alludes to what I termed being “pushed off of the sumo ring of cognition” by an AI that calls the shots. He also takes a lucid and pragmatic approach to how such disruptive technology will be deployed in the workforce, detailing how employees use it covertly out of fear that their managers find out, though less convincing are his recommendations to leaders on how to instill a pro Gen-AI culture in the company – a pity given how this is fast becoming a concern for managers. A few other considerations also went neglected, like how those companies that fail to adapt will quickly fall behind those that do. But I feel this is also because he is primarily addressing employees, and that is nice to see in contrast to all the books guiding managers. My favorite is his insistence that AI is proving most beneficial to juniors, who can boost their performance level to the vicinity of veterans; rather than concluding in the lines that “See, this is a great enhancement tool, not something that will eliminate jobs - so don't be afraid!”, or “it’s not AI that will displace workers, but the workers who master AI that will displace those who don’t” as the majority of so-called experts yell and parrot from every hilltop, he questions what implications this might have for seasoned workers, for those whose expertise becomes somewhat eroded and may no longer justify their wages. Following what I called the commoditization effect, AI could become a great equalizer, but (Duh!!) overall this will drag wages down. He notes how now some companies hire fresh graduates for jobs there where they used to hire people with at least five years of experience, because they can do practically just as well now with the help of AI. Alas he also rightly note that expertise will still play a role, with solid arguments. But he dares not venture say how much of this expertise will still be required and how this will reshape companies - indeed, only time will tell. We could end up with a split of say 80% of juniors – or rather people paid with junior salaries – and only 20% of experts there where we had a more balanced split before, and such junior talent could be outsourced instead of employed, etc. I pass on the final chapters on education and the future of AI, not for lack of interest but to avoid too long a commentary. Also great observations, for instance on how a future AI-mentor would be superior both for providing more constant feedback but also in its ability to take on several different roles (as opposed to the subjectivity of a single human coach, teacher or mentor). And here again there is the risk of overdependence, for instance (with his example of architect) consulting the AI on every single stroke. All in all, a very good read that remains concise and echoes some of my concerns which I believe will only balloon with time as organizations reshape work.

I will definitely go and rethink how I integrate LLMs and other ML tools into my work after reading this book. Helpful and encouraging advice + fun to read

Enjoyed the easy read and helpful ways AI is transforming the world. Appreciated the contributions to education.