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Origen: The End of Silicon Valley as We Know It? \u2013 O\u2019Reilly<\/a><\/em><\/p>\n \u2026coronavirus pandemic, or something worse, had long been predicted,\u00a0but it still caught the world unprepared, a better future not yet invented. Climate change<\/strong> too has been on the radar, not just for decades but for over a century<\/strong>, since Arrhenius\u2019s 1896 paper on the greenhouse effect<\/a>. And it has long been known that inequality and caste<\/a> are corrosive to social stability<\/strong> and predict the fate of nations<\/a>. Yet again and again the crisis finds us unprepared when it comes.<\/p>\n <\/p>\n \u2026the inventions we most urgently need will take us in a very different direction than the consumer internet and social media revolution that is coming to an unsightly end.<\/p>\n We are starting to see the payoff of radically new approaches to biomedical innovation, and in particular, the way that machine learning is turbocharging research.<\/p>\n Why might this mark the end of Silicon Valley as we know it?<\/strong> Second, many of the markets where fortunes will be made are regulated; navigating regulated markets also takes skills that are conspicuously missing in Silicon Valley<\/strong>.<\/p>\n Finally, \u2026it is harder to sustain a hype balloon in a scientific enterprise than in many of the markets where Silicon Valley has prospered. Many Silicon Valley investors have been lucky rather than smart.<\/strong> They may not do so well in a world where capital must be directed toward solving hard problems rather than toward winning a popularity contest.<\/p>\n <\/p>\n The opportunity of machine learning in scientific R&D is profound. But machine learning<\/strong> also challenges our current approach to science, which relies on human theorizing and experiments.<\/strong> A machine learning model may be able to make successful predictions but not to explain them. \u2026\u00a0As Judea Pearl has noted<\/a>, excessive identification of correlations (i.e, \u201ccurve fitting\u201d) makes the definition of authentic causal relationships more challenging<\/strong>. And \u201creal science\u201d needs causal relationships.<\/p>\n \u2026without a better understanding of our machine helpers, we may set them down paths that take us to the edge of a cliff, much as we\u2019ve done with social media and our fractured information landscape.<\/p>\n \u2026internet pioneers expected freedom and the wisdom of crowds<\/strong>, not that we would all be under the thumb of giant corporations profiting from a market in disinformation. What we invented was not what we hoped for.<\/p>\n \u2026The US theory of antitrust has largely been based<\/strong> on the question of consumer harm,<\/strong> which is difficult to prove in marketplaces where services are provided to consumers at zero cost<\/strong> and where the marginal cost of experimenting on those consumers is also close to zero.<\/p>\n The emerging European regulatory effort is properly focused on the role of dominant tech firms as \u201cgatekeepers<\/a>.\u201d\u2026the processes for assessing harms will most likely proceed more slowly than the harms themselves<\/strong>.<\/p>\n Markets are ecosystems<\/strong>, and like other ecosystems, there are hidden dependencies everywhere.<\/strong> The harm of Google abusing its monopoly position will not show up first in harm to consumers<\/strong>, but in depressed profits, decreased R&D investment, and lower wages at the web companies to whom Google once directed traffic. \u2026because the pain is widely distributed and because the platforms are not required to report the information that would make it visible, the problem will not be obvious until much of the damage is irreversible.<\/strong><\/p>\n \u2026\u201csuperstar firms\u201d ruthlessly compete with smaller firms<\/strong> that come up with fresh ideas, not only starving them of talent but often introducing copycat products and services, there is decreased innovation from the market as a whole. Cities are dominated by a new class of highly paid big-company employees driving up housing costs<\/strong> and forcing out lower wage workers; wages and working conditions of workers in less profitable industries are squeezed to drive the growth of the giants<\/strong>. Their very jobs are made contingent and disposable, with inequality baked in from the beginning of their employment<\/strong>. Governments<\/strong> are starved of revenue by giant companies that have mastered the art of tax avoidance<\/strong>. \u2026<\/p>\n In the case of social media platforms, manipulation of users for profit has frayed the fabric of democracy and the respect for truth.<\/strong>\u00a0\u2026<\/p>\n Technology is far from the only offender. It is merely the most visible mirror of our values as a society. The extractive behavior the tech giants exhibit has been the norm for modern capitalism since Milton Friedman set its objective function in 1970: \u201cThe social responsibility of business is to increase its profits.\u201d \u2026generosity of open source software and the World Wide Web, the genius of algorithmically amplified collective intelligence are still there, pointing the way to the Next Economy, but it is an economy we must actively choose<\/strong>, rather than riding the rails of a system that is taking us in the wrong direction.<\/p>\n It\u2019s a sad time for Silicon Valley, because we are seeing not only the death of its youthful idealism but a missed opportunity<\/strong>. Paul Cohen, the former DARPA program manager for AI, made a powerful statement a few years ago<\/strong> at a meeting of the National Academy of Sciences that we both attended: \u201cThe opportunity of AI is to help humans model and manage complex interacting systems.\u201d<\/strong><\/p>\n That statement sums up so much of the potential that is squandered when firms like Google, Amazon, and Facebook fall prey to the Friedman doctrine rather than setting more ambitious goals for their algorithms.<\/strong><\/p>\n I\u2019m not talking about future breakthroughs in AI<\/strong> so much as I\u2019m talking about the fundamental advances in market coordination<\/strong> that the internet gatekeepers have demonstrated. These powers can be used to better model and manage complex interacting systems for the good of all. Too often, though, they have been made subservient to the old extractive paradigm.<\/strong><\/p>\n \u201cThe difference between theory and practice is always greater in practice than it is in theory,\u201d<\/strong><\/p>\n So many of the problems that antitrust actions and other regulations are now gearing up to address<\/strong> are, paradoxically, the result of the prime directive by which our economic and legal system governs its corporations<\/strong>: \u201cThou must maximize profits.\u201d<\/p>\n \u2026some formal experimentation on emotional contagion and reflection on its implications would have been a good idea<\/strong>. Instead, we continue to run global-scale unsupervised experiments on the power of social media to spread negative emotional contagion for profit, while any effort by the platforms to influence their users in positive directions is still considered by many to be inappropriate intervention, or is abandoned because it might reduce user activity and growth. \u2026<\/strong>Facebook engineers reportedly trained a machine learning algorithm to recognize posts that their users would consider \u201cbad for the world,\u201d but the company found that showing fewer of them reduced the number of user sessions and thus, presumably revenue and profits. So they retrained the algorithm to find the point where \u201cbad for the world\u201d posts were reduced but not by so much that they impacted user sessions. \u2026<\/p>\n \u201cShareholder value\u201d is so ingrained in corporate governance that a special class of corporation\u00a0has been defined to protect companies that are managed to take other considerations than profit into account. All \u201cnormal\u201d companies are expected to treat employees, the environment, and society as costs to be minimized, avoided, or eliminated<\/strong>.<\/p>\n Silicon Valley is a mirror of what is wrong with our economy and corporate governance, not the cause of it, or even the worst exemplar.<\/em><\/strong> (Tobacco<\/a>, oil<\/a>, and pharma<\/a> companies vie for the top spot.)<\/p>\n Silicon Valley can still lead in this effort. The big platforms must understand their social responsibility to create more value than they capture<\/a>, focus their algorithmic systems on improving human welfare<\/strong>, find ways to measure and communicate the value that they create, and help our broader society to better \u201cmodel and manage complex interacting systems.\u201d<\/p>\n The danger of regulatory response that simply tries to turn back the clock and doesn\u2019t take into account the ways technology done right could point the way forward is illustrated by the battle over California\u2019s Proposition 22. \u2026Traditional labor protections and benefits assumed a world in which individuals worked for a single employer. \u2026 The gig economy companies have made some small steps toward flexible benefits on their own, but they are a pale shadow of what they might have been if the companies and their gig workers and their customers, not to mention their regulators, had been working together to build systems that would allow benefits to be managed as dynamically as employment.\u00a0<\/strong>\u2026As I argued five years ago in \u201cWorkers in a World of Continuous Partial Employment,\u201d we need a much more robust benefit system that is centered on the worker, not on the company.<\/strong><\/p>\n <\/p>\n The recent news that Elon Musk is one of the world\u2019s richest people is also a harbinger of the biggest opportunity of the 21st century: to avert climate change. Electric vehicles are the tip of the iceberg. Heating and cooling, agriculture, raw materials and manufacturing\u2014all need reinvention. Climate will reshape residential and office construction, insurance, finance, and where and how food is produced.<\/strong> Massive climate migrations have only just begun; tens or hundreds of millions of people will need to be resettled.<\/strong> Will we offer them shantytowns, or will we help them become settlers building a new, better world?<\/p>\n With the exception of Musk, many of the already-minted climate billionaires are outside the US, highlighting the way that other countries already have the lead in these industries of the future.<\/strong> Bloomberg recently named a few<\/a>: China\u2019s Zeng Yuqun, Huang Shilin, Pei Zhenhua, and Li Ping (electric vehicle batteries), Li Zhenguo, Li Chunan, and Li Xiyan (solar panels and films), Lin Jianhua (solar panels and films), and Wang Chuanfu (electric vehicles); Germany\u2019s Aloys Wobben (wind turbines); and Spain\u2019s Jose Manuel Entrecanales (renewable power generation). \u2026<\/em>for the most part, Silicon Valley entrepreneurs and investors are not leaders in this sector.<\/strong><\/p>\n There are five pillars to Rewiring America\u2019s case for electrification<\/strong> as the answer to our urgent need to limit greenhouse gas emissions:<\/p>\n 1. Electrifying everything requires only half as much energy as our current system.<\/p>\n 2. We need to reconceive solar panels, batteries, electric cars, and electric appliances as part of our national energy infrastructure, even when they are on or in people\u2019s homes, rather than thinking of infrastructure as something owned only by utilities or the government.<\/p>\n 3. Markets won\u2019t move fast enough without a World War II-style mobilization of private industry.<\/p>\n 4. Electrifying the US will create jobs\u2014lots of them.<\/p>\n 5. Who gets the financial benefit of this massive investment\u2014utilities, solar installers, or consumers\u2014depends on interest rates.<\/p>\n Utilities already have access to low-cost loans. But consumers don\u2019t, and if you want to create both jobs and cost savings for consumers, low-cost interest rates for home electrification are the best way to do it. Otherwise, the savings all get captured by middlemen, or by utilities, and adoption is much slower.<\/p>\n \n The final<\/strong>, and perhaps most important<\/strong>, reason why Silicon Valley<\/strong> as we know it may be over is that its current incarnation is a product of the extraordinarily cheap capital of the years since the global financial crisis of 2009.<\/strong><\/p>\n There are two economies<\/strong>, often confused: the operating economy, in which companies make and sell products and services, and the betting economy, in which wealthy people bet on which companies will win and which will lose in the beauty contest that stock markets have become.<\/p>\n In the operating economy, the measure of success is<\/strong>, as Nick Hanauer and Eric Beinhocker memorably put it, \u201cthe solution to human problems<\/a>.\u201d<\/strong> Companies compete to solve those problems more effectively and earn a profit thereby<\/strong>. Along the way, they employ people productively, create valuable new goods and services, and contribute<\/strong> to their communities.<\/p>\n1. Consumer internet entrepreneurs lack many of the skills needed for the life sciences revolution.<\/h1>\n
Prediction: The nexus of machine learning and medicine, biology, and materials science will be to the coming decades what Silicon Valley has been to the late 20th and early 21st century.<\/strong><\/h2>\n
\nFirst, the required skills are different. \u2026The hubs where that knowledge can be found are not the special province of Silicon Valley<\/strong>, suggesting that other regions may take the lead.<\/p>\n2. Internet regulation is upon us.<\/h1>\n
Prediction: Because platform businesses have failed to regulate themselves, they will have limits placed on their potential for good as well as harm.<\/strong><\/h2>\n
3. Climate response is capital intensive, and inherently local.<\/h1>\n
Prediction: There will be more climate billionaires created in the next two decades than in the internet boom.<\/strong><\/h2>\n
4. The end of the betting economy.<\/h1>\n