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3.95 AVERAGE
I was enthralled by the objective of the book; adding to my passable knowledge of AI is something that is a goal of mine. The bulk of the book did that, but towards the end the author wrote about his grave confrontation with the most existentially-illuminating experience: cancer. Here is where I was mildly disappointed. Personally speaking, I do not like to read books with mixed orientations; I prefer for memoirs and scientific reads to be read separately.
Lee wrote that investors and researchers, who developed technological advancements in AI, should take responsibility for their actions by resolving the issues of the workforce reconstruction and fortuitous anxiety. But that is the problem: he is an investor and a researcher who further extended implications of this 'cutting-edge' technology. No singular individual should be held liable for this looming anxiety circulating AI, but placing the blame on VCs is not the answer.
Also, Lee's TED talk summarizes the book well enough to be it's stand in.
Lee wrote that investors and researchers, who developed technological advancements in AI, should take responsibility for their actions by resolving the issues of the workforce reconstruction and fortuitous anxiety. But that is the problem: he is an investor and a researcher who further extended implications of this 'cutting-edge' technology. No singular individual should be held liable for this looming anxiety circulating AI, but placing the blame on VCs is not the answer.
Also, Lee's TED talk summarizes the book well enough to be it's stand in.
A really interesting primer on AI and how its utilization is the new electricity, predicting a disruptive shift in our daily lives sooner than we think.
Kai-Fu compares the Western and Eastern cultures where a distinct difference can be seen in the internet and technology ecosystems. In the West, there is typically one standalone application for products or services. Compared to China however, has more of a gigantic centralised super-application ecosystem that you can do everything in, pay for food, book medical appointments, book ride-sharing, etc all in WeChat.
Because of this centralised integration, China has quickly dominated the forefront of online to offline applications services that focus on convenience, where one hour shopping delivery orders, QR codes and mobile payments are the norm, blurring the lines between digital and reality. The perception of AI use is also more easily adopted in the East due to this blur, where AI is already integrated into certain aspects of their daily lives such as in the supermarkets but the West isn't too far behind.
Kai-Fu compares the Western and Eastern cultures where a distinct difference can be seen in the internet and technology ecosystems. In the West, there is typically one standalone application for products or services. Compared to China however, has more of a gigantic centralised super-application ecosystem that you can do everything in, pay for food, book medical appointments, book ride-sharing, etc all in WeChat.
Because of this centralised integration, China has quickly dominated the forefront of online to offline applications services that focus on convenience, where one hour shopping delivery orders, QR codes and mobile payments are the norm, blurring the lines between digital and reality. The perception of AI use is also more easily adopted in the East due to this blur, where AI is already integrated into certain aspects of their daily lives such as in the supermarkets but the West isn't too far behind.
Recently, I came across a VICE video investigating China’s “996” workers, most of them based in giant tech corporations like Tencent and Baidu. These employees work from 9-9, 6 days a week (sometimes longer) to ensure that their employer becomes or remains China’s number one tech empire.
Lee’ thesis is that at the rate that these tech giants are growing, with the push of the CCP and 996 workers, the Chinese tech market will soon mine enough data to overtake Google, Facebook, and Apple. While U.S. tech culture values breakthroughs in research (Artificial intelligence being the current trendsetter), the Chinese tech market is more adept at applying existing technologies to the lives of common people: app-based payment systems, bike-sharing schemes, online shops, and food-delivery services, these technologies offload the inconveniences that accompany living in crowded cities and are used widely in many Asian countries. So while the U.S. government and Silicon Valley will continue to fuel technological innovation, it is China who’s turning these findings into gold.
Though Lee’s analysis is spot-on (with decades of experience in the field), I found the chapter on his cancer a little cliché: Chinese tech mogul turned venture capitalist is suddenly convinced that technology should be used to love others better… ok. Still, I do appreciate his proposal for a more humane economic policy as the labor market shifts drastically in these next few decades.
Lee’ thesis is that at the rate that these tech giants are growing, with the push of the CCP and 996 workers, the Chinese tech market will soon mine enough data to overtake Google, Facebook, and Apple. While U.S. tech culture values breakthroughs in research (Artificial intelligence being the current trendsetter), the Chinese tech market is more adept at applying existing technologies to the lives of common people: app-based payment systems, bike-sharing schemes, online shops, and food-delivery services, these technologies offload the inconveniences that accompany living in crowded cities and are used widely in many Asian countries. So while the U.S. government and Silicon Valley will continue to fuel technological innovation, it is China who’s turning these findings into gold.
Though Lee’s analysis is spot-on (with decades of experience in the field), I found the chapter on his cancer a little cliché: Chinese tech mogul turned venture capitalist is suddenly convinced that technology should be used to love others better… ok. Still, I do appreciate his proposal for a more humane economic policy as the labor market shifts drastically in these next few decades.
Très bon livre qui se lit rapidement.
Bien évidemment, l'auteur n'est pas neutre dans son avis sur le modèle chinois mais reste quand même réaliste par rapport à celui-ci.
Ce livre m'a permis de comprendre un peu mieux le marché et la concurrence chinoise qui jusque là ne m'avais jamais vraiment intéressé. J'ai été stupéfait par la concurrence et la violence qui règne dans le marché chinois.
Ce livre m'a permis de me rendre compte de l'ascension phénomènal de la Chine en matière d'intelligence informatique, bien loin de l'image que certains se font d'une Chine en retard sur la scène internationale.
Je recommande cet ouvrage.
Bien évidemment, l'auteur n'est pas neutre dans son avis sur le modèle chinois mais reste quand même réaliste par rapport à celui-ci.
Ce livre m'a permis de comprendre un peu mieux le marché et la concurrence chinoise qui jusque là ne m'avais jamais vraiment intéressé. J'ai été stupéfait par la concurrence et la violence qui règne dans le marché chinois.
Ce livre m'a permis de me rendre compte de l'ascension phénomènal de la Chine en matière d'intelligence informatique, bien loin de l'image que certains se font d'une Chine en retard sur la scène internationale.
Je recommande cet ouvrage.
AI Superpowers several short books in one: a fantastic book about China’s recent internet innovation, a very good book about the current and near future of AI, a sometimes moving but mixed personal reflection memoir and a terrible set of predictions about the economics of AI. It also doesn’t ask some of the most fundamental questions one would want in a book like this.
Leading Chinese AI researcher and internet innovator (founder of Microsoft Research China, Google China, and now a leading venture firm Sinovention Ventures) Kai-Fu Lee sets out to compare the prospects of AI development in China and the United States. Many would give the United States the edge because of our greater degrees of creativity and cutting edge research whereas China, to date, has done more to copy, adapt and apply. Lee argues that this misses what is actually going on in AI: “the casual observer—or even expert analyst—would be forgiven for believing that we are consistently breaking fundamentally new ground in artificial intelligence research. I believe this impression is misleading. Many of these new milestones are, rather, merely the application of the past decade’s breakthroughs—primarily deep learning but also complementary technologies like reinforcement learning and transfer learning—to new problems... Much of the difficult but abstract work of AI research has been done, and it’s now time for entrepreneurs to roll up their sleeves and get down to the dirty work of turning algorithms into sustainable businesses.”
Lee argues that the success of this “dirty work” will require four inputs: (1) data, (2) entrepreneurs, (3) AI scientists, and (4) a policy-friendly environment. Taking these four in turn:
DATA: Lee argues that China is collecting much more data and willing to use it, including data that intersects between the real and online world. This seems overwhelmingly true and was not exactly a novel insight. The only open question is how important will data be in the future. My guess is very and Lee’s statement “Given much more data, an algorithm designed by a handful of mid-level AI engineers usually outperforms one designed by a world-class deep-learning researcher.” will remain true. But I don’t think we’re sure, as AlphaGo Zero, for example, was better than the original AlphaGo and used no data. Lee is thinking within the current deep learning paradigm and may be understating the importance of what comes next.
ENTREPRENEURS: This was the most fascinating and novel part of the book. Lee provides a capsule history of the internet in China, the copycat businesses, the failure of American businesses in China, and the emergence of much more innovative firms. At its heart is the argument that recent Chinese entrepreneurship arguing that it is more successful than the American variant because it is hungrier, willing to work harder, less distracted by any purpose other than moneymaking, and more willing to copy instead of innovate. A lot of this seems true but I think devalues the successes of Silicon Valley and underestimates the degree to which Chinese entrepreneurs may become more like that in the future. And perhaps for obvious and forgivable reasons, but some of this discussion misses the role of the Chinese policy—for example the book portrays the exit of Google from China as an unfortunate decision by the company not the result of censorship policies in China.
AI SCIENTISTS: Lee argues there are more and more of them in China, they’re excellent, but not as innovative as Geoffrey Hinton or Yann Le Cunn. But he argues that the innovations are not needed anymore. As discussed above, this is interesting, plausible, but may undervalue the paradigm changing innovations we can’t foresee.
AI POLICY: Lee argues that China has made a concerted policy effort to encourage AI while the United States has effectively ignored it. This is true. The question is how consequential it is. Lee gives the example of China using subsidies to create an AI/innovation cluster outside of Beijing to mimic the dense networks in Silicon Valley. But by analyzing only one success case Lee fails to understand what worked about the policy. Governments around the world have done similar cluster funding with nothing like China’s success, so this hardly seems necessary (Silicon Valley did without) or sufficient. Moreover, I would think future Chinese policy is a big risk to AI if continued clamp downs discourage innovation of certain types in China.
Lee’s analysis leads him to a make predictions about four areas of AI applications and how relative US-China capabilities today will shift over the next five to ten years (e.g., from 50-50 in internet applications to 60-40 in favor of China, 10-90 in autonomous applications today in favor of the US to 50-50, with similar trends in business applications and perception AI).
Lee’s predictions about China’s development in the future and the relative balance of capabilities seems broadly reasonable, subject to the caveats above. But he never addresses the fundamental question: is this zero sum or positive sum? The “Superpowers” of the title would make one think it was zero sum, that there is a first mover advantage and whoever, for example, figures out self-driving cars will own that industry permanently. In reality, in much of AI I think “positive sum” is likely to be the better model as innovations are disseminated, copied or reverse engineered. The entire relative capabilities frame makes much less sense in this positive sum world.
All of the above was the meat of the book and the reason it was much more interesting than even the summaries I read. I would recommend most readers just stop there, about 60% of the way through, and I wish I did too. Instead what follows is an absurd semi-apocalyptic argument that AI will take all our jobs, cause massive inequality, and lead to a huge economic gap between the US and China and the rest of the world.
The AI jobs discussion is economically rudimentary and Lee misses some major points (e.g., he acknowledges AI will create new types of jobs but seems to miss that richer people will want more of old types of jobs, eg will eat out more and thus more restaurant servers). The GDP discussion is similarly rudimentary (I would predict US and Australia GDP closer 50 years from now than US and China GDP, he seems to not understand how these innovations can be used by countries that didn’t make them and the notion of spillovers and convergence). More importantly, there is a huge tension between Lee’s skepticism about the imminence of general AI, his belief that we will not make future paradigm innovations but instead will continue to tinker and apply, and then his claim that existing modeling attempts understate the pace of AI progress.
Finally, Lee has a sometimes moving account of his own battle with cancer, how it changed his priorities towards more balance with life, and why this tells him that future AI policy should center not around UBI but subsidies for volunteering, caring, and other human interaction. This was sometimes moving, a little thought provoking, but also had the usual shortcoming of a highly successful person who can afford to make these types of statements without giving up any of that success.
Overall, I thought I knew the main points of this book from the reviews and that it would not add much. The first 60% of the book did indeed add a lot. Unfortunately, if you paid attention to the last 40% it would also subtract a lot so maybe just skip or skim that part.
Leading Chinese AI researcher and internet innovator (founder of Microsoft Research China, Google China, and now a leading venture firm Sinovention Ventures) Kai-Fu Lee sets out to compare the prospects of AI development in China and the United States. Many would give the United States the edge because of our greater degrees of creativity and cutting edge research whereas China, to date, has done more to copy, adapt and apply. Lee argues that this misses what is actually going on in AI: “the casual observer—or even expert analyst—would be forgiven for believing that we are consistently breaking fundamentally new ground in artificial intelligence research. I believe this impression is misleading. Many of these new milestones are, rather, merely the application of the past decade’s breakthroughs—primarily deep learning but also complementary technologies like reinforcement learning and transfer learning—to new problems... Much of the difficult but abstract work of AI research has been done, and it’s now time for entrepreneurs to roll up their sleeves and get down to the dirty work of turning algorithms into sustainable businesses.”
Lee argues that the success of this “dirty work” will require four inputs: (1) data, (2) entrepreneurs, (3) AI scientists, and (4) a policy-friendly environment. Taking these four in turn:
DATA: Lee argues that China is collecting much more data and willing to use it, including data that intersects between the real and online world. This seems overwhelmingly true and was not exactly a novel insight. The only open question is how important will data be in the future. My guess is very and Lee’s statement “Given much more data, an algorithm designed by a handful of mid-level AI engineers usually outperforms one designed by a world-class deep-learning researcher.” will remain true. But I don’t think we’re sure, as AlphaGo Zero, for example, was better than the original AlphaGo and used no data. Lee is thinking within the current deep learning paradigm and may be understating the importance of what comes next.
ENTREPRENEURS: This was the most fascinating and novel part of the book. Lee provides a capsule history of the internet in China, the copycat businesses, the failure of American businesses in China, and the emergence of much more innovative firms. At its heart is the argument that recent Chinese entrepreneurship arguing that it is more successful than the American variant because it is hungrier, willing to work harder, less distracted by any purpose other than moneymaking, and more willing to copy instead of innovate. A lot of this seems true but I think devalues the successes of Silicon Valley and underestimates the degree to which Chinese entrepreneurs may become more like that in the future. And perhaps for obvious and forgivable reasons, but some of this discussion misses the role of the Chinese policy—for example the book portrays the exit of Google from China as an unfortunate decision by the company not the result of censorship policies in China.
AI SCIENTISTS: Lee argues there are more and more of them in China, they’re excellent, but not as innovative as Geoffrey Hinton or Yann Le Cunn. But he argues that the innovations are not needed anymore. As discussed above, this is interesting, plausible, but may undervalue the paradigm changing innovations we can’t foresee.
AI POLICY: Lee argues that China has made a concerted policy effort to encourage AI while the United States has effectively ignored it. This is true. The question is how consequential it is. Lee gives the example of China using subsidies to create an AI/innovation cluster outside of Beijing to mimic the dense networks in Silicon Valley. But by analyzing only one success case Lee fails to understand what worked about the policy. Governments around the world have done similar cluster funding with nothing like China’s success, so this hardly seems necessary (Silicon Valley did without) or sufficient. Moreover, I would think future Chinese policy is a big risk to AI if continued clamp downs discourage innovation of certain types in China.
Lee’s analysis leads him to a make predictions about four areas of AI applications and how relative US-China capabilities today will shift over the next five to ten years (e.g., from 50-50 in internet applications to 60-40 in favor of China, 10-90 in autonomous applications today in favor of the US to 50-50, with similar trends in business applications and perception AI).
Lee’s predictions about China’s development in the future and the relative balance of capabilities seems broadly reasonable, subject to the caveats above. But he never addresses the fundamental question: is this zero sum or positive sum? The “Superpowers” of the title would make one think it was zero sum, that there is a first mover advantage and whoever, for example, figures out self-driving cars will own that industry permanently. In reality, in much of AI I think “positive sum” is likely to be the better model as innovations are disseminated, copied or reverse engineered. The entire relative capabilities frame makes much less sense in this positive sum world.
All of the above was the meat of the book and the reason it was much more interesting than even the summaries I read. I would recommend most readers just stop there, about 60% of the way through, and I wish I did too. Instead what follows is an absurd semi-apocalyptic argument that AI will take all our jobs, cause massive inequality, and lead to a huge economic gap between the US and China and the rest of the world.
The AI jobs discussion is economically rudimentary and Lee misses some major points (e.g., he acknowledges AI will create new types of jobs but seems to miss that richer people will want more of old types of jobs, eg will eat out more and thus more restaurant servers). The GDP discussion is similarly rudimentary (I would predict US and Australia GDP closer 50 years from now than US and China GDP, he seems to not understand how these innovations can be used by countries that didn’t make them and the notion of spillovers and convergence). More importantly, there is a huge tension between Lee’s skepticism about the imminence of general AI, his belief that we will not make future paradigm innovations but instead will continue to tinker and apply, and then his claim that existing modeling attempts understate the pace of AI progress.
Finally, Lee has a sometimes moving account of his own battle with cancer, how it changed his priorities towards more balance with life, and why this tells him that future AI policy should center not around UBI but subsidies for volunteering, caring, and other human interaction. This was sometimes moving, a little thought provoking, but also had the usual shortcoming of a highly successful person who can afford to make these types of statements without giving up any of that success.
Overall, I thought I knew the main points of this book from the reviews and that it would not add much. The first 60% of the book did indeed add a lot. Unfortunately, if you paid attention to the last 40% it would also subtract a lot so maybe just skip or skim that part.
Lee is clearly an expert on AI and that really shines through in this book. Sometimes when folks talk about technology like AI, they can get really caught up with a pie in the sky vision for the future that seems really out of reach. Lee has a good balance of explaining what’s possible and also grounding those possibilities in reality. His opinions are well thought-out, researched, and informed.
Knocking off one star for three reasons. First, there’s a clear bias towards China that comes through in several different parts of the book. This is understandable given the censorship we have seen in China and the fact that Lee currently resides in China but it does skew the book. Second, I felt like Lee overlooked many of the inequities and biases we see today and will see more of in the future with AI. As an example, he talks briefly about the legal system using AI for sentencing and insurance agencies using it to calculate rates but he does not go into the systematic biases that we’ve seen in these AI use cases. Third, the book takes a really weird turn in the last 50 or so pages when Lee talks in depth about his cancer diagnoses and how it changed his outlook on life. He then ties this into AI by talking about a future where people don’t have traditional careers but instead have careers focused around loving and caring for one another? Very odd given the focus of the rest of the book.
All in all, I recommend it. Highly informative and very interesting.
Knocking off one star for three reasons. First, there’s a clear bias towards China that comes through in several different parts of the book. This is understandable given the censorship we have seen in China and the fact that Lee currently resides in China but it does skew the book. Second, I felt like Lee overlooked many of the inequities and biases we see today and will see more of in the future with AI. As an example, he talks briefly about the legal system using AI for sentencing and insurance agencies using it to calculate rates but he does not go into the systematic biases that we’ve seen in these AI use cases. Third, the book takes a really weird turn in the last 50 or so pages when Lee talks in depth about his cancer diagnoses and how it changed his outlook on life. He then ties this into AI by talking about a future where people don’t have traditional careers but instead have careers focused around loving and caring for one another? Very odd given the focus of the rest of the book.
All in all, I recommend it. Highly informative and very interesting.
Kai-Fu Lee's perspective is eye-opening, after 4 decades of his life dedicated to optimizing technologies and maximizing his impact in development and education, he is brought to the point of true meaning and understanding...
After great detail of how China embarked the AI train and pursued to optimize this with every step taken and a comparison in parallel with the US potential and development, one can draw their own conclusion about each country's trajectory to what we may call success. However, in the final chapters on the book, Kai-Fu Lee beautifully describes what he has understood as success for human life and what we all should seek to achieve. For some it might be surprising, for some it may come as a reassurance, nevertheless we must all learn from his experience and pursue this goal for a better future.
After great detail of how China embarked the AI train and pursued to optimize this with every step taken and a comparison in parallel with the US potential and development, one can draw their own conclusion about each country's trajectory to what we may call success. However, in the final chapters on the book, Kai-Fu Lee beautifully describes what he has understood as success for human life and what we all should seek to achieve. For some it might be surprising, for some it may come as a reassurance, nevertheless we must all learn from his experience and pursue this goal for a better future.
I guess the book is meant for a lay audience so I didn't learn all that much. There was some cultural insight into China (helping me lose a few preconceptions), but not much new about AI. There's a lot of starry-eyed predictions of how AI and data will improve everything, but no details or original examples, just repeating the well-known ones. (Also, he's name-dropping companies a lot, which I can only assume are his investments).
Thankfully, the author is aware of the real issues (societal problems due to automation, not AGI and robot overlords). I kinda liked the summary of various predictions of jobs to be automated soon. His critique of universal basic income as just a patch that the Silicon Valley elite wants to use to keep the masses docile was the most original idea in the book for me. But again, when the author tries to give some ideas on solutions, they're vague (and naive?) ideas: a class of ethical investors should develop who are content with linear returns and government should somehow reward socially-beneficial activities.
Thankfully, the author is aware of the real issues (societal problems due to automation, not AGI and robot overlords). I kinda liked the summary of various predictions of jobs to be automated soon. His critique of universal basic income as just a patch that the Silicon Valley elite wants to use to keep the masses docile was the most original idea in the book for me. But again, when the author tries to give some ideas on solutions, they're vague (and naive?) ideas: a class of ethical investors should develop who are content with linear returns and government should somehow reward socially-beneficial activities.
This is a very good book to learn about the current state of AI. The policy prescriptions to address the hyper-stratified AI economy were a little lacking in my opinion (he advocates for private sector investment and a way to compensate currently uncompensated work, such as caregiving), but I think the book's lack of detailed solutions is reasonable considering his area of expertise.
As philosophical as it is informative. It delivers on its premise and so much more.
1. The evolution of technology in China and the USA as a result of cultural backgrounds.
It was interesting to see the tech-evolution differences between the US and China -- and how these differences reflect each nation's culture. With lofty ideals, the American culture of innovation is a gentleman's game: where rules are followed, copying is looked down upon, and originality is key. Meanwhile, the Chinese "arena" is filled with scrappy entrepreneurs harboring a kill or be killed mindset. It's an environment where domain names are stolen, American websites are shamelessly copied, and police raids are conducted to beat the competition. It's a coliseum. And the stories Kai-Fu tells are crazy.
What makes this so interesting is that the Chinese way of doing things is actually more conducive to innovation. When competition is as cut-throat as it is, the thousands of entrepreneurs are forced to be creative: to find better solutions, to cut costs. With that level of innovation now being applied to AI, who knows what's about to happen?
2. The next industrial revolution
AI will be more like the industrial revolution than we think. And of course, this comes with the slew of jobs, inequality, and environmental concerns. Kai-Fu talks about this concept of the 'fundamental breakthrough' and how it is this turning point in AI's innovation that will resemble the invention of the steam engine. And whoever possesses this first, will have a significant advantage. (Well, unless posted online or something, as online communities for learning are widespread.) All signs point to the scrappy, government-backed, Chinese entrepreneur to do so. Personally, I find privatization as a means to innovation fascinating -- what innovations are made exclusively through the public sector?
3. Coexistence
A blueprint for coexistence relies on our ability to use AI in a way that allows us to retain our human qualities. Let the machines do machine stuff. Let humans be gang. AI will let humans be free to pursue their own projects due to economic abundance.
That being said... it was also interesting to see Kai-Fu's sentiments on UBI: that UBI could be more like a painkiller to numb the inequality gap between billionaires who make it big thanks to AI, and the others. Silicon Valley and Zuck have championed UBI. But could that be because they're preparing for an inequality uprising? (I probably phrased this really poorly)
1. The evolution of technology in China and the USA as a result of cultural backgrounds.
It was interesting to see the tech-evolution differences between the US and China -- and how these differences reflect each nation's culture. With lofty ideals, the American culture of innovation is a gentleman's game: where rules are followed, copying is looked down upon, and originality is key. Meanwhile, the Chinese "arena" is filled with scrappy entrepreneurs harboring a kill or be killed mindset. It's an environment where domain names are stolen, American websites are shamelessly copied, and police raids are conducted to beat the competition. It's a coliseum. And the stories Kai-Fu tells are crazy.
What makes this so interesting is that the Chinese way of doing things is actually more conducive to innovation. When competition is as cut-throat as it is, the thousands of entrepreneurs are forced to be creative: to find better solutions, to cut costs. With that level of innovation now being applied to AI, who knows what's about to happen?
2. The next industrial revolution
AI will be more like the industrial revolution than we think. And of course, this comes with the slew of jobs, inequality, and environmental concerns. Kai-Fu talks about this concept of the 'fundamental breakthrough' and how it is this turning point in AI's innovation that will resemble the invention of the steam engine. And whoever possesses this first, will have a significant advantage. (Well, unless posted online or something, as online communities for learning are widespread.) All signs point to the scrappy, government-backed, Chinese entrepreneur to do so. Personally, I find privatization as a means to innovation fascinating -- what innovations are made exclusively through the public sector?
3. Coexistence
A blueprint for coexistence relies on our ability to use AI in a way that allows us to retain our human qualities. Let the machines do machine stuff. Let humans be gang. AI will let humans be free to pursue their own projects due to economic abundance.
That being said... it was also interesting to see Kai-Fu's sentiments on UBI: that UBI could be more like a painkiller to numb the inequality gap between billionaires who make it big thanks to AI, and the others. Silicon Valley and Zuck have championed UBI. But could that be because they're preparing for an inequality uprising? (I probably phrased this really poorly)