🌌 Reshaped #3
🌌 Reshaped #3
Data economy, the fight against global poverty, fintech M&As, business transformations and much more.
Welcome to a new issue of Reshaped, a newsletter for those who do not want to miss a thing about the huge transformations of our time.
This week I will introduce a new recurring topic: data economy. In the next issues, I will explore how the new economic system based on data will reshape business, politics, and technology.
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News on tech innovation
🔎 Amazon may soon join the list of Big Tech companies under investigation from U.S. antitrust authorities, as a coalition of labor unions formally presented a petition to the Federal Trade Commission to examine the company for anticompetitive practices (The Verge).
💳 Intuit, a leader in the financial software sector with a market cap of more than $67 billion, has agreed on a $7.1 billion deal to acquire Credit Karma, a credit score provider (Financial Times). The company has committed to maintaining the service free for the user, with revenue streams coming from bank fees. Since Credit Karma counts more than 100 million users, the deal shows how fintech giants are battling for the control of users’ data. Antitrust enforcers should carefully evaluate the risks of creating a new Facebook for financial services (Wired).
🔪 SoftBank’s Vision Fund chief Rajeev Misra has recently been in the spotlight for having run campaigns of sabotage against former colleagues Nikesh Arora and Alok Sama in order to become the head of the $100 billion tech fund (The Wall Street Journal). These accusations happen in the middle of a deep crisis for the fund, weakened by the bad performances of funded startups like WeWork.
✉️ Google, Facebook, and Twitter wrote a letter to Pakistan’s Prime Minister Imran Khan to complain against new severe rules on online content. The fight is basically about who - the government or Big Tech - controls what gets published online. Due to a larger internal movement against this censorship, the government has committed to reviewing the regulation (The New York Times).
📹 The facial recognition startup Clearview AI has been hacked and its client list has been stolen (CNN). The startup has recently been blamed by Big Tech for privacy issues.
🌆 The Waterfall Toronto’s digital strategy advisory panel has requested Google’s Sidewalk Labs to provide clarifications on how it intends to use the huge amount of data collected by its ambitious smart city project in Toronto (BBC). Google is applying various tech solutions in the city, but many people in Canada and abroad question the real need for this mass digitalization.
⛪️ The Vatican has joined the quest for ethical artificial intelligence by releasing guidelines on human-centered A.I. The document has been signed by IBM and Microsoft (Politico).
📊 Data economy
In the last issue of The Economist, Ludwig Siegele has thoroughly analyzed our new data economy under four lenses: economics, infrastructure, business, and geopolitics. I will briefly dive into some of the main topics, which I will deepen in the next issue.
The author distinguishes between two different views of data. One considers data as oil, which is a natural resource that can be owned and traded. This is the typical U.S. approach: those who control data own them. The other defines data as sunlight, a natural resource that can be exploited by everyone without apparent limits. This approach is typical of the many open data movements around the world, which ask for the democratization of data ownership and use. To some extent, China can be seen as a promoter of sunlight data. Both approaches have huge drawbacks. Data as private goods are hard to trade and to be defined in terms of property rights (the value of data will be the topic of the next issue). On the other hand, data as public goods fail to cope with privacy both at the personal and corporate level. Being non-rivalrous and excludable, data can be considered as club goods, fitting perfectly in the goods definition matrix. In Europe, especially, the definition of data as infrastructure is widely diffused; E.U. policies seem to go into that direction, too.
Whatever the approach to data, the author points the finger at the current wealth generated by data and how it is redistributed. A straightforward consequence of data economies is increased production in the form of automation. However, it is not clear how the make the wealth generated by data and artificial intelligence a diffused benefit for the many. At the moment, the data economy is extremely concentrated and a small number of giant platforms dominate the market (see chart below).
This corporate inequality is largely the result of network effects - economic forces that mean size begets size. A firm that can collect a lot of data, for instance, can make better use of artificial intelligence and attract more users, who in turn supply more data. Such firms can also recruit the best data scientists and have the cash to buy the best AI startups. […] Big platforms are not just monopolies, but monopsonies, meaning that they have the power to hold down wages for data labour.
I will explore how this loop can be stopped by antitrust enforcers and policymakers (included the data cooperative options) in future issues.
Alternative perspectives
⚖️ In the March issue of The New Internationalist, Dinyar Godrej argues that poverty - not just extreme poverty - is strictly tied with broad inequalities caused by the dominant socio-economic system. The global fight against poverty should rebalance those inequalities by promoting structural change and social justice.
The benevolent view of capitalism is that enlightened self-interest and the logic of the market create wealth for all. Unfortunately, that isn’t how things have turned out: inequalities of wealth that lead to excesses of accumulation and deprivation are the curse of our age. […] The struggle for greater equality would require a shift in perspective from the wretched notion of social mobility – the weak slugging it out in the hope of making it instead of fighting the forces that exploit them – to the transformative one of social justice.
Among the causes of global poverty, the author mentions corporate rule and concentration, which have caused wage erosion and work precarity also in rich countries. For a deeper analysis of the meaning of wealth, I recommend the new post by Branko Milanovic (author of Capitalism, Alone) on ProMarket.
📈 From this background, in the same magazine, Jason Hickel analyzes how to solve global inequality starting from the assumption that growth is no longer a viable solution.
Faced with the scale of this problem, the orthodox response is to double down on the call for growth. Indeed, the goal of ending poverty has come to be the single greatest defence for growth-at-all-cost thinking, with economists insisting that we have a moral imperative to do everything we can to clear the way for capital.
The author suggests some options, such as creating a global minimum wage, preventing transnational corporations from illicit financial flows and lang grabs, democratizing global economic governance institutions, and canceling the debt of poor countries.
Other readings
🔋 Peter Fairley explores how excess solar and wind energy could be used to convert water into hydrogen, which can be stored in tanks to be transformed into energy when needed (Scientific American). Truly recommended long read.
🗼 Alex Lazarow argues that startups can achieve success also when far from Silicon Valley.
Frontier start-ups take a more balanced approach to growth in which they charge for the value they create from the get-go, build resiliency into their models, focus on growth and profitability, and take a long-term outlook. In emerging markets, they are more likely to tackle fundamental societal challenges and to invest in their workforce.
📹 Adi Robertson tries to imagine a world without YouTube (The Verge).
☁️ New research shows that our estimations of the energy consumption of data centers may be overestimated, due to the positive impact of new technologies (Science).
🎲 In the last issue of the Harvard Business Review, the best business transformations of the last decade are ranked following three criteria: the creation of new offerings and business models, effective repositioning of the core business, and robust financials.
Thanks for reading.
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Have a good weekend!
Federico