This blog is a book summary/review/some thoughts on the book “AI Superpowers” by Kai-Fu Lee.
One of the most significant things I’ve read over the last few years is China’s AI strategy, which states it’s aspiration to “be the major artificial intelligence innovation center of the world” by 2030 (translation at ). If achieved, the results will be seismic. To get a true sense of why this ground-breaking bit of policy came about, what it means for China and the rest of the world, and what other nations can do to keep-up or simply benefit from this tidal wave of AI investment, I read Kai-Fu’s book.
“major artificial intelligence innovation center of the world”China’s AI strategy end goal, by 2030
I’ll summarise the book below (unless otherwise stated, most thoughts are Kai-Fu’s not mine), broadly based on his chapter order.
AI research has been around since the 50’s – making slow and steady progress, until now – thanks to the combination of two factors:
- Deep Learning has supercharged AI’s ability to learn
- the gigantic computational power nowadays available for low cost (thanks to Moore’s Law)
These two factors means we suddenly are an inflection point where academic achievements can be translated into real-world use-cases, at scale.
These real-world applications are already proving transformational to some industries, leading some (Andrew Ng, a pioneer of deep learning) to compare AI to electricity: A technology applicable to almost all industries, which will revolutionise many of them. Therefore taking a piece of the AI pie will be significant – PricewaterhouseCoopers estimates AI deployment will add $15.7Tn to global GDP by 2030. Later in the summary, we’ll explore why Kai-Fu thinks this is very bad news for global inequality, but first we’ll explore why China is estimated to get about half that sum.
China’s sputnik moment
China’s “sputnik moment” came when AI beat a human in the game Go in March 2016.
Why did this spur a billion-person country into action?
To give a sense of the scale of this achievement consider Chess, a game we would consider intellectual. “Go” is similar in the fact it has two players, but is far more complex than Chess, with around 300 times the number of plays. To give a sense of the popularity of the game, consider that 260 million people tuned in to watch the game between AlphaGo and Lee Sedol, so when Lee lost, it sent shockwaves through the population (in fact the broadcast was censored by the state part-way through…). To the UK reader, it would be perhaps like turning on the TV this weekend and watching robots built by a Chinese manufacturer play Liverpool in the FA cup final and utterly take them apart.
A matter of months later, China released new policy on AI, with clear milestones culminating in being “the major artificial intelligence innovation center of the world” by 2030 (translation at ), accompanied by massive resource allocation at a national and regional level – subsidies for AI start-ups, generous government contracts to accelerate adoption, founding of incubators and special development funds, and significant government money poured into venture capital (VC) with very favourable (to the private sector) rates of return.
This sent ripples through the private sector as, that year (2017), Chinese VC investment made up 48% of global AI venture funding, surpassing the US for the first time.
Why did this policy fall on such fertile ground?
This policy came at the right time, Lee observes, noting two fundamental shifts important to AI:
- The shift from age of discovery to an age of implementation &
- From the age of expertise to the age of data.
What does he mean by these two assertions?
- The age of implementation
The value of single genius in the “Age of Discovery” (when a field is predominantly in the R&D stage) is massive i.e. Fermi was critical in translating nuclear physics to creating the nuclear bomb in the Manhattan project which ended WW2 and established a new nuclear world order.
When thinking about AI, Geoffrey Hinton’s pioneering of Deep Learning might be seen as a (almost) modern-day Fermi-scale effort.
However, Lee argues that now, since powerful deep learning algorithms are freely available in the open-source, and increases in algorithm performance are marginal rather than jaw-dropping, implementation of these algorithms to solve real-world problems is the bigger challenge and the value of a single genius is much less – we’re in the “Age of implementation”.
Implementing AI requires entrepreneurs, and Lee makes the compelling case for China’s entrepreneurs being: (a) abundant and (b) the best-of-the-best at implementation.
Because they are “Gladiators” forged in the “Colosseum”, he states.
He describes Chinese entrepreneurs’ formative days being spent in unimaginably ferocious competition – making copycats of American products with ultra-aggressive pricing, on extreme timescales and with barely any scruples, makes for unbelievably effective entrepreneurs. Their tenacity – working crazy hours (“making silicon valley look sluggish” he describes), rate of iteration to stay ahead of the (fierce) competition, and willingness to “go deep” on a product (that is to get your hands dirty in logistics etc. to make your product more difficult to copy) is unique, and remarkably effective.
This talent is fundamentally better suited to the age of implementation than the West’s near-monopoly on geniuses – Google have got about half of the world’s top 100 AI researchers/engineers and have an inordinate R&D budget… but these ultra-nerds and massive research funding can hardly be referred to as “gladatorial entrepreneurs”.
- The age of data
Aside from having lots of entrepreneurs willing to get their hands dirty and implement AI, China also has an abundance of data – with more internet users than the entirety of the US & Europe combined and a techno-utilitarian culture in which data-sharing is both more acceptable at a policy level (c.f. GDPR) and at an individual level – Chinese citizens are more willing to trade a degree of privacy for convenience.
The quantity of internet data collected isn’t the only important factor – the quality is higher too, as Chinese citizens use a plethora of apps which translate offline actions (i.e. going to the doctors) into online ones – creating data which is simply unavailable in the west.
Take the Chinese app “WeChat” for example – which is widely referred to as the “digital swiss army knife for modern life”. Before reading this book, I thought it was simply the Chinese version of whatsapp… not so! You can pay for groceries with it, book doctors appointments, file taxes, unlock shared bikes… you name it. The data picture collected on you isn’t simply your online activity (i.e. your search history or likes) – it’s your offline activity (cycling, seeing the doctor etc.) too. This far richer data, which captures both online and offline life (unlike, say facebook, which profiles your life indirectly through what you “like”… hardly great data), allows AI algorithms to understand our lives so much better, and opens up opportunities for other applications of AI.
Lee points out that with the easy access to open-source algorithms, an average datascientist with a big dataset can outperform the world’s best datascientist with an average dataset. The balance has shifted to the east.
Having such a significant quantity and quality of data creates a virtuous cycle. More data creates better algorithms, which make better products, which attracts more users, which gathers more data and so the loop self-perpetuates…
AI start-ups in China vs US
To illustrate the above theses, let’s give you a few examples contrasting US start-ups to their Chinese equivalents.
Have you heard of the ride-sharing app Uber? Of course you have. What about “Didi”? The Chinese uber-like start-up.
Didi now offers more rides each day in China than Uber does across the globe… and it’s spreading rapidly into different continents.
Buzzfeed – the news platform? Of course, but what about “Toutiao” – it’s Chinese competitor which is now worth ten-times that of buzzfeed, and has 120million daily users who use the site an average of 74 minutes a day?
Toutiao is a great example of the rapid iteration occuring in AI start-ups in China, and their growing mastery of implementing AI – it’s a news site that no longer requires human editors! It’s algorithms trawl the internet to identify pieces of news, tailors recommendations to each of its users, and filters out fake news on the way. It can even write it’s own news – during the 2016 Summer Olympics, Toutiao created an AI reporter that created short summaries of sports events a matter of seconds after the events finished, covering up to 30 events a day.
Our final example, of Airbnb’s Chinese equivalent “Tujia”, illustrates the fact that Chinese AI start-ups are willing to “go deep” to integrate their products into our everyday lives, and create unique and hard-to-imitate products (a critical skill gained from the copycat era of the Chinese economy). Unlike airbnb which is basically a listing website, when you list your home on Tujia, they offer to take on the hard work – they will install smart locks for you, restock supplies, and carry out the between-stays cleaning. The barriers to signing up are miniscule, turbocharging their growth, and crushing the competition.
The resulting dystopia from AI?
Finally in the book, Lee veers away from the China-US compare and contrast, and touches on AGI (artificial general intelligence – when an AI has human level intellect across the board including empathy etc.) and economics.
Kai-Fu doesn’t personally see AGI as the biggest threat in the near term. His feeling is that it is a considerable distance away in terms of being technologically feasible, and points to widespread technological over-optimism in predicting progress, even calling out his own prediction in the 1980’s (as a world-leading expert on voice recognition at the time), that the software would go mainstream within 5 years… he was twenty years off!
Though he thinks AGI is a while off, he does however think the major problem is that AI will wipe out billions of jobs – his prediction is that 40-50% of US jobs will be technically automatable within the next 10-15yrs. He dispels the common argument that we’ll simply “adapt and find new roles”, just like when modern agriculture or the industrial revolution rolled in, stating that all the stats point to mass unemployment and a “useless class” (as Yuval Harari puts it), who essentially has little/nothing to add to the economy.
He also goes on to paint an even glummer picture, by highlighting that AI/tech favours “winner takes all economics” – around 70% of the gains in the global economy over the next decade (according to PwC consulting) will end up in the hands of a small number of companies in the US & China.
This rising inequality both between countries, and within countries, will have dramatic consequences – both financially, and psychologically, as a large proportion of the world population will lose their sense of purpose, with no meaningful job. Consider that rates of depression triple amongst those unemployed for 6 months.
The jobs left that aren’t in the tech sector will initially be those that require dexterity, though the strawberry picking robots coming online in California and similar will soon put paid to these, and also those that require compassion/human connection.
What’s the problem with this? Well there simply won’t be enough jobs to go around, and they’re not very well paid either. For example “Home healthcare aide” is the fastest-growing profession in America… however it’s one of the lowest paid, with a salary average of $22k/yr.
So what’s the solution
Lee outlines the popular Silicon Valley argument that the super rich corporations will have to be taxed fairly aggressively and this money used for a UBI (universal basic income) to pay this “useless class”.
He points out, however, that he believes this is simply a lazy solution to a complex problem – coming back to the last paragraphs – even if people do have enough cash to live, where will people derive their self-worth from?
Straight up admitting that he was part of the UBI crowd until recently, he spends a whole chapter recounting confronting his own mortality when he was faced with a diagnosis of stage 4 lymphoma. This, he explains, lead him to understand the importance of love, and being loving, in making life worthwhile.
He then outlines a vision which combines the phenomonal ability of AI to “think” with humans’ unique ability to wrap this analysis with “love” and be compassionate. Explaining that;
- Perhaps a stipend will be required, funded by tax on rich corporations, but that enhanced stipend payments will be dependent upon people performing voluntary tasks that benefit the social good; care work, community service and education. i.e. being care assistants, teaching (citing that he believed the number of teachers could go up ten-fold). Rewarding socially beneficial activities in the way we currently reward economically productive activities.
- He expects the landscape of jobs to change – with many jobs powered by AI with a human veneer i.e. the shopping assistant who is aided by a rich AI generated profile of you, who can masterfully up-sell you a special vintage of wine perfectly suited to your wife’s taste for her birthday, or the doctor who benefits from AI’s unique insight into your chances of treatment success for your condition and sensitively discusses the pros and cons of each approach with you – with little time pressure because he doesn’t have to type notes (the AI is taking them) and didn’t need to read back into your history beforehand or analyse a load of test results,.
It was a hell of book! A rollercoaster, in fact.
I hope you enjoyed my short review/summary, and I encourage you to read the book itself – full of fantastic examples which have certainly shaped how I think about geopolitics and AI.
If you have constructive comments, please leave them below, and I’d be delighted to take the conversation further.