Robot-Proof - Vivienne Ming ****

As Vivienne Ming makes apparent, there seem largely to be two views of AI's pros and cons, both of which are almost certainly wrong. It's either doom-saying 'It'll destroy life as we know it' or Pollyanna-ish 'It'll do all the boring work and we can all be wonderfully creative and live lives of leisure.'

Instead, Ming gives us a clear analysis of the likely trajectory for the workplace, particularly for the IT industry. She describes three 'equally flawed, intellectually lazy strategies' to deal with the impact of AI. The first is substitution and deprofessionalisation, using AI to allow cheaper 'AI-augmented technicians' to replace more expensive professionals, producing more low wage jobs and fewer mid-range. This does save money but leaves a company at risk of being easily outcompeted.

The second is what Ming describes as the '"A-Player" Hunger Games', the approach favoured by Silicon Valley. This sees the growing rift between routine and creative work and always searches for the 'supposed "10x" creative geniuses', outsourcing the grunt work. The idea is that you find the absolute best creative programmers and such, who have inherent talent and who will continue to thrive when basic coding is in the hands of AI.

Finally there is what she describes as 'the AI Bait-and-Switch'. This is where AI does all the everyday boring repetitive work so all staff are supposedly freed up to do the fun creative stuff. Ming's modelling suggest that in reality what happens is that 'When AI is deployed merely to make routine work more efficient, it doesn't create more creative time; it creates more routine work.'

This analysis is hugely important, because there is so much misunderstanding of AI's benefits (and flaws). Ming argues that we can indeed provide more opportunities to be creative with the help of AI, but that it's not about automating routine work - it's more about using the AI to enhance the creative process, recognising that we can develop creativity, it isn't a purely inherent ability. She describes how we can potentially build more innovation and creativity into people (with the help of properly used AI), particularly when looking at children. I was, though, slightly unsure about at least one suggested approach she suggests that has raised some academic concerns.

My only other uncertainty regarding Ming's solution is that the book is littered with examples where she has provided tools, for instance, to enable teachers to become more creative and effective which don't get used. I honestly can't see many big businesses actually doing anything with this approach. It's certainly effective for someone like me: as a freelance I find AI can really help in the way she describes. But sadly I think most organisations will go down one of her three doomed routes. (I do slightly differ on the 'A-player' side in that when I ran a team of programmers, there really were some who were a lot more productive than others in a way that could not be retro-fitted. Talent is a real thing.) 

This is, then, an important book. I didn't honestly enjoy Ming's writing style, where she tries too hard to be funny and seems to be somewhat overenthusiastic in sharing stories of how everyone thinks she's wonderful and want to recruit her to senior positions, while effectively noting that a lot of people she meets are stupid. (Shouldn't she be improving their creative viewpoints?) 

Will Robot-Proof change the world? No, because just as Ming has found in real life, most often organisations won't take her advice. But without doubt it's a powerful analysis of how AI could change the workplace for good or evil.

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Review by Brian Clegg - See all Brian's online articles or subscribe to a weekly email free here