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Dear avid readers,

I started writing this while I was on a short trip to Spain earlier this week, and being there got me thinking on Spain's utter failure at this World Cup. Even more than that, it seems that all A.I.-models failed to predict the winner: France. For this win, the French team will receive $38m from FIFA, the international governing body of football, which itself will make c. $3.2bn in profits over $5.2bn revenues from this world cup, as per this tournament economics analysis from Tanay. FIFA will also give c. $100m to the host country (Russia) to help advance football in a way that provides societal benefit through development programs (sic).  

But while we were all watching the game, there was another competition between countries (this time, European ones only) that was critically important. The vote on the copyright law in the EU Parliament, which, had it passed, it would have redefined the freedom of the internet as we know it (n.b. the little freedom it has left). Here are the winners and losers of this competition. Yes, you wouldn't expect to see France on the losing side, this time.


Now let's digest together what I've read since 29th of June.

But before we do that, I remembered that there were readers that asked me why did I choose these main topics to follow in this digest (business strategy in the digital age, tech's relationship with wages, policy and regulations, entrepreneurial ecosystems).

Well, as we're experiencing a shift from the industrial age to the new techno-economic paradigm, we are observing that there is increasing inequality between nation-states, between cities within same state, between companies and between classes of workers. And while it is easy to simply blame these problems on the ruthless greed of corporations and their executives, I believe the blame, but even more, the solution, lies at the intersection between corporations collaborating with ever-growing startups, as well as with governments and policymakers for a new safety net that unlocks the prosperity that seems to be, in the past 30 years or so, everywhere in the numbers but in our households' coffers.

The importance of healthy entrepreneurial ecosystems is paramount, as it will both influence (and force) existing incumbent companies to modernize and stop the economic rent-seeking, but it will also incentivize policymakers, even at the local level, to build safety nets which are needed in this fast changing environment. 
Interesting comparison between Silicon Valley and Seattle to see how cities’ policymakers can have completely different attitudes towards contributing to building a social safety net for their inhabitants.

And hence, unlike what most business schools still teach the business leaders of the future, I believe that, in business strategy, we're moving away from the "maximizing shareholder value" dogma. Especially since, as the late professor Lynn Dunn wrote, we might be misunderstanding what the "shareholders actually value". As it is increasingly accepted that the homo economicus model of purely selfish behaviour doesn’t necessarily always apply, then how do we know that shareholders don't value the interests of the employees, customers, or the community as much as their own?

In the same time, tech entrepreneurs are already finding out that simply deploying tech by itself won't create the New Deal of the digital age, and hence it requires more complex networks so that this multi-player game can evolve. Ultimately, forward-looking, global nation-states that will win and expand their tech companies globally will shape the new safety net and unlock further prosperity. The only danger to this, is that some safety nets won't be to our like and according to our values (especially here in Europe).

To articulate it in a much better way, allow me to use this quote from Bill Janeway's book, Doing Capitalism in the Innovation Economy, which I just finished last week:
"Digitizing the economy of atoms is subject to impediments that are literally mundane, the frictions of the economic, political and cultural world that exists and has existed and will exist. [...] Unfortunately for those who believe we have entered a libertarian golden age, freed by digital technology from traditional constraints on market behaviour, firms successful in disrupting the old physical economy will need to have as a core competency the ability to manage the political and cultural elements of the ecosystems in which they operate, as well as the purely economic ones."

Strategy and Business Models in the Digital Age

The value of one's time 
 This study ran over the course of 12 years, presented by Michael Porter and Nitin Nohria, delves into the how 27 CEOs (two women and 25 men) at various large firms spend their time (analyzes one full quarter for each, basically coded data on nearly 60,000 CEO hours). CEOs are the internal and external face of the organization, and how and where they spend their time and focus mobilizes an entire organization and creates the right narrative for the market. The study found that: CEOs worked 9.7 hours per weekday, on average, but also work circa 4 hours on 79% of weekend days and 2.4 hours on 70% of vacation days. They exercise for 45 minutes a day, on average. Circa 3 hours per days are spent with the family, while 2.1 hours per day for downtime (i.e. hobbies, entertainment). 47% of CEOs work is conducted from the headquarters (while the rest from other offices, meetings, home etc), with 61% of the work being conducted face-to-face (in fact, who CEOs spend face-to-face time with is often of the most important signal that the rest of the company follows). Most CEOs are explicitly agenda-driven, and their time reflects that, with only 36% of their time invested in unexpected developments which require reaction. 11% of the CEOs time is spent on routine activities (operating review meetings, board meetings, earnings calls, and investor days). While most of their time within the internal organization is spent with direct reports, CEOs must also find time to meet the rank and file organization, so that they won't operate in a bubble. CEOs spend 21% of their work time on Strategy, 25% of their time on functional and business unit reviews, 25% on developing people and relationships, 16% on matching organisational structure and culture with the needs of the business, 4% on M&A. The CEO’s single most powerful lever for his or her future time is ensuring that every unit—and the company as a whole—has a clear, well-defined strategy (otherwise they get drawn into too many tactical decisions).  

I guess, for CEOs like Jack Dorsey, who manage two companies (Twitter and Square), it gets twice as complicated. Twitter's stock has suffered for a long time, but in the past two years, it seems that Jack has found its focus and freedom to reorganize the company from a much stronger position

👩‍💼 👨‍💼 One problem with meetings, however, is that most workers despise them, as per this article in The Economist, since in more or less 80% of meetings, any decisions taken will be in line with the HIPPO's opinion (highest-paid person). In other words, meetings are inefficient, or 80% of the time of 80% of the people in meetings is wasted, if you like. The paradox with meetings, however, is that everyone hates them, but nobody wants to miss them (feeling excluded is worse than having your time wasted).  

👍 In this respect, I recommend Shane Parrish' twitter thread on the heuristic process on deciding if to attend to meetings or not. Quite good and simple.

👨‍💼 👩‍💼 Jen McClure argues that, in a way, it is critical for CEOs to focus more on how employees are managed, since only a small proportion of employees feel like their leadership can communicate with them effectively or that they are managed in a way that motivates them, hence they are increasingly feeling disengaged at work. It is true that how and where a CEO spends his time is creating the narrative with external stakeholders, but ultimately good treatment of employees is the most important way that businesses can communicate effectively, since employees are the most trusted source of information about their companies, far more trusted than the CEO, far more trusted than the brand itself. Employees, enabled by social media and review platforms, are now determining the best companies in the world.

On business models, particularly in SaaS
🏷️ Sadhana Balaji writes about the importance of linking the pricing for products (especially SaaS ones) with the value the customers receive from it, rather than tying it with metrics that mean nothing for them - this is also known as value-based pricing as a competitive advantage. She also explores a few case studies, such as when Michelin, who despite producing better quality tires than the cheaper lower-quality tires from Chinese and South Korean companies, was struggling to translate this product-superiority into pricing, decided to change its revenue model by stopping charging  truck fleets for the number of tires they bought and instead setting the pricing based on the mileage driven per month on those tires (removing therefore upfront one-time cost for transportation companies and the risk of replacing damaging tires). This implied that Michelin took up the responsibility for the supply, maintenance, and free replacement of tires, which in turn helped shifting the narrative to a service aimed at improving transportation and mobility (of which better quality tires were a part of the value chain).
     Picking the right value metric is crucial for a revenue model’s success, and also extremely hard to identify. A successful metric should: 1/ be easy for the customer to understand; 2/ align with the value the customer receives ; 3/ grow with the customer’s usage, meaning it should serve as a benchmark of the customer's growth, but most importantly, 4/ it shouldn’t act as a deterrent to the customers from creating more of the value/growth (counter-examples being Podcast hosting platforms pricing by storage or upload time/duration instead of number of podcast downloads; or Project Management software pricing based on number of user-seats or storage space or number of projects which restrict customers from scaling the promised business value). In Sadhana's own case working at a billing software company, the wrong pricing-strategy was pricing based on number of invoices processed by the customer, instead of the revenue they generate.

👍 Getting this right can increase customer retention. As per Alex Clayton, a SaaS company could be growing ARR (annual recurring revenue) over 100% each year, but net dollar retention is one of the most important factors when evaluating a SaaS company (for example, if a business has low net dollar retention <75%, they should probably spend less on acquiring new customers and assess why their current customers are churning and/or spending less).

🔝 Eric Feng writes on why he believes that, notwithstanding the incredible current success of ad-driven business models, on the long-term, this model has flaws, relative to subscription-based ones. In an ad-based business model, there is a critical limitation in that they treat all customers the same, and therefore do not maximize the potential value that the best users could bring. Whether they're an avid, passionate user of a service or an infrequent, casual user, the ad neither knows nor does it care. Therefore, companies that rely on advertising simply cannot earn significantly more revenue from any particular group of users regardless of their usage of your product or service, because ads simply don’t allow companies to maximize the appropriate value from their best users. Hence, a much better business model is based on the strategy of shared-value transactions: a business model that could maximize revenue from the best customers, and then share that value across all the customers, while not annoying users in the process (like ads do).

🖧 I very much enjoyed this read from Steven Sinofsky that explores how building a product that connects to multiple third-party products (let's call it a horizontal system of intelligence) is harder than might seem, since both the systems of record and systems of engagement in the stack have a natural tendency of willing to go vertical. In the SaaS world today, unlike past on-premise solutions, applications have pretty rich and accessible APIs to the data which leads to the impression that companies are open to access to data. In practice, this isn’t really the case. APIs are basically “reports” or “flattened” views of data, and many firms attempt to cache large amounts of data in a middle tier in order to rebuild/recreate the structure to enable reporting, analysis, joins, etc. Steven argues that for a whole host of reasons (security, privacy, etc.), this is not a good strategy and the value is hard to generate. Also, the initial main focus (which is probably the most overlooked) is how to convince the products in the stack you are connecting to (let's say, an ERP) that you will make their products better and generate more value for them, so that they won't block your opportunity.

🤝 This reminded me of an article Martin Casado of a16z wrote a few months ago, about how technical partnerships (where a tech startup partners with a tech company, be it startup or more established, to accelerate its credibility, sales, and MVP) rarely work if the startup is in an early or “pre-chasm” market (this does not mean all of them are bad ideas!). The partnerships can also require a tremendous amount of resources, give-away secrets, damage the customer relationship, and commit the startup to onerous terms that may poison future strategic relationships. For example, tech startups partner between themselves for increasing their total addressable market, but this also amplifies the risk, because startups pivot, entering adjacent markets, kill products, change go-to-market models very often in search for a business model. While partnerships between tech startups and large tech companies end up most of the times with the latter using their leverage to take advantage of the former (i.e. competing against the startups, breaking connection with the customer).  

Platform moats

🏰 Julien Genestoux writes about platform moats, which he starts with a short history about how, in France, the apparent success of Minitel (a French service which predated the Internet) blinded the French government in 1994 into dismissing the Internet as an insecure, incomplete, chaotic and economically insignifiant technology. He argues that indeed, HTTP and HTML protocols were fragile and limited, but that the developers' ecosystems created a sort of "layer 2" tech on top of it, as a way to hack their limitation (i.e. cookies, JavaScript). What this means is that, when analysing new tech platforms, the limits of the protocols shape the types of applications designed on top of them. The relationship between a platform and the apps that are built on it is symbiotic: they need each other more than one needs the other. The network effects will eventually create the "layer 2 hacks" that will enable its true potential. It is also why app developers eventually end up all on one platform, despite the platforms' limitations, while newer platforms (new "layer 1s") aimed at solving technological limitations of the platform struggle to scale.
This argument is what Julien uses to predict that Ethereum is the platform to stay when it comes to smart contracts. Or, a platforms's best moat is it's users (huge numbers of them). A great illustration on how these layers develop (and how quickly) from Kyle Samani in this great post exploring the Web3 stack.

I am thinking of IBM, who in its rush to market, used Microsoft's MS/DOS (later Windows) as the software to control its IBM PC. IBM then developed its own proprietary alternative, OS2, which had more robust features, but Microsoft's hold on the market with Dos/Windows was never shaken, because it wad already rooted in an ecosystem of applications and user-implemented extensions ("hacking layer"), which acted as a moat strong enough that it was only disrupted by the shift of the hardware platform underneath (PC to mobile).

🗺️ Apple recently announced a new take on re-building Apple Maps almost from scratch, this time using own-data from the millions of devices people carry in the offline world, instead of relying on third-party data.
     But this reminded me of an absolutely great visual-essay from Justin O'Beirne that analyses Google Maps' competitive moat over others maps-platforms. It's a data alchemy between places-data (a byproduct from its StreetView) and buildings-data (a byproduct from its satellite-view) that powers an algorithmic process that is able to accurately identify areas of interest across any city. Google is creating data out of data and by doing so, is able to identify areas of interest at scale. Apple needed a reset.

📱 In light of the recent €4.3 billion fine that the European Commission landed on Google over its Android dominance, it is interesting to understand the exact steps Google took to build its competitive moat in the mobile-OS space. Ben Thompson, stellar again, finds that Google Play Store was the initial wall that Google had to create to enable the moat, followed by Google Play Services (initially an easily updatable API layer and then a development platform distributed via the Play Store) which eventually made all the apps (both Google's and third parties) be rewritten on Google's closed-source Play Services, rather than Android, rendering therefore any Android fork pointless, as no third-party app would work without additional effort from developers (more than just putting the app in a new AppStore on a new Android fork). It is why, Ben argues, the Commission doesn’t seem to grasp exactly how Android has developed, the choices Google made, and why.  

On vertical integration
↕️ Staying with Ben Thompson, as he also finds a great angle on Intel's main problem - its obsession with integration strategy which ensured its domination and high profitabilty: Intel’s x86 integration with DOS/Windows and its integration between designing and manufacturing of the microchips as a competitive advantage over AMD. But when the value chain got broken, with Taiwan Semiconductor Manufacturing Company TSMC picking up the manufacturing-at-scale business as its own core, this in turn created a plethora of fabless chip companies on top, designing competing chips with Intel's, including specialized chips (like GPUs). This in turn created more demand and more revenues for TSMC which caught up with Intel on the quality of manufacturing. And this focus on the integrated approach (as a more profitable avenue) made Intel miss the shift to mobile and the shift to dedicated chips. As per Clayton Christensen, in the chase for higher margins, Intel's disruption was almost impossible to avoid.


Now Intel is trying to acquire its way dedicated chips, as it just announced it acquired eASIC, a fabless semiconductor company that makes customisable chips for use in wireless and cloud environments.

Interesting in the context of Intel having itself disrupted the microprocessors market in the 70s, owned mainly by Zilog Semiconductors. During their switch from 8-bits to 16-bits microprocessors, Zilog downplayed compatibility over technological superiority, and so Intel was able to offer technically inferior 16-bit microprocessors, but compatible with all the applications and devices Zilog was previously supporting (I read this in Bill Janeway's book I mention at the beginning of this digest).

↕️ Chris Berg, Sinclair Davidson and Jason Potts write that the application of distributed ledger technology to supply chains are creating a new form of organisational structure, besides the traditional ones: the V-form network as a new corporate form (=a number of fully independent companies that effectively operate as one vertically integrated company through blockchain, coordinated and supplied by a third party). This solves the main problems that affected the two options available for controlling supply chains: vertical integration (which hits diseconomies of scale and misses on the specialisation) and regulation (which works best within single nations but multilateral expansion is difficult and expensive). They argue that blockchains can coordinate supply chains without the need for either (traditional) vertical integration or regulation, since the vertical integration is basically outsourced to a distributed ledger and generates benefits from networks rather than hierarchies, as cooperative behaviour is incentivized. This way, the blockchain economy will have more, smaller firms linked together by protocols, and the question is how many protocols?
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Policy, Techies and Misdemeanours

beware this juice: it's made from concentrate

⚠️The OECD, earlier this month, directly attributed the poor wage growth over the last years to the ‘superstar tech’ firms. The international body warned that technological advances could strengthen a “winner takes most” scenario where worker wages could completely decouple from productivity gains.

🍹 In response to the rising concentration in digital industries (such as search traffic, mobile operating systems, wireless services etc.), commentators and politicians from all sides of the political spectrum have expressed alarm and an increased anti-trust focus (for example European Commission’s chief economist Tommasso Valletti on the need to break Google up), especially since rising market concentration means there is less competition, which could have a variety of economic consequences, from higher prices to lower productivity and wage growth, to reduced innovation and economic dynamism, writes Tim Sablik. However, the consequences are not crystal clear. Some evidence suggests that rising concentration levels are tied to weakening competition, which is likely to have negative effects on consumer welfare and economic productivity. Other work suggests that efficiency is driving firm consolidation, especially into “tech superstar firms”, which is beneficial for consumers. To complicate matters further, both forces could be happening at the same time depending on the industry, making it difficult to disentangle effects in the aggregate economy, writes Tim.



💡 Jason Douglas, Jon Sindreu and Georgi Kantchev are also tackling the issue with the biggest companies sucking up all the gains from innovation, in an excellent essay in WSJ. A reversal of the innovation diffusion (between companies, between industries) is contributing to stagnating productivity growth. More specifically, as leading global giants are becoming more productive, as they’re able to spread their high upfront investment costs in automation over a larger number of customers, the rest of companies cannot compete and are stagnating in terms of productivity growth. Scale makes it possible to explore experimentation with advanced technologies, which in turn leads to more protection from patents (only 25 companies accounted for half of all tech-related patents filed with the European Patents Office between 2011 and 2016). This global scale, along with automation, has led to a shift in employment toward lower-productivity jobs such as gig-deliveries or cleaning.


One proof of this is this diffusion is represented by the rise of the pseudo-A.I. companies: in a race to keep up with the automation and A.I. of the big tech giants, smaller companies “fake” their automation and algorithmic capacities by hiring human workers to do routine tasks, mainly via gig-platforms like Amazon Mechanical Turk. Guess what this does to the productivity.

💸One proposal on how to tackle the declining productivity growth comes from Roger Farmer, who implies that capital gains should be taxed at the same rate with the income tax. This is to tackle the fact that sluggish productivity is only reflected in decreasing standards of living for the workers at the bottom of the value chain, while top-of-the-chain service workers (like financiers) or wealthier persons whose income is mainly derived from stock gains (think of tech employees) saw their living standards increasing. And this is related with the tax gap between income vs. capital gains (e.g. in the UK, capital gains vs. income gains are at 28% vs. a max of 45% respectively; in the US, it’s 20% vs. 39% respectively). Narrowing the gap is another way societies can tax the digital businesses operating in the low-tax environment of the intangible economies (by basically taxing their wealthy owners).

👴 Adrian Adermon, Mikael Lindahl and Daniel Waldenström published a groundbreaking study analysing how wealth is transmitted across generations, in Sweden. Inheritances account for half of the “inter-generational wealth persistence”, a much more important role than the link of education and income between parents and children. So wealth mobility is much lower than income mobility, hence future generations belonging to wealthier parents (whose wealth probably doesn’t come from own income), will end up accumulating more wealth via gifts, in a inter-generational inequality flywheel.


Why and how should we value the "invisible labour"

🛠️ I very much enjoyed this long read from Yonatan Zunger, from which I will quote at large. He is analysing three basic critiques to the Universal Basic Income (UBI) concept that is proposed by some economists as a solution to transfer wealth back to the society. One critique is around the fact that people will stop doing the undesirable jobs which are needed by the society. These jobs are currently low-paid because the only people taking them are people who have no choice, because, in economic terms, they are in a in very weak bargaining position and so have to accept a long-term loss to survive in the short term, so unless the wages will increase dramatically for those particular jobs, nobody will do them, because UBI removes that position of desperation.      So the UBI is basically transforming a “terrible job” in a “supply-limited” job where not many people are willing to do them, argues Yonatan, hence wages in those areas will go up, which should make the overall societal wealth to go up. The struggle, he writes, will be in the fact that wages for jobs which had previously been very low might suddenly become very high, and so there’s going to be a reshuffling of job value and this shift must be politically managed - which is hard.
    The second critique is around not being able to pay for UBI, but Yonatan argues that there is a tremendous amount of wealth gain going on, and we only need to recognize that our decision to allocate it via corporate mechanisms which prioritize shareholder revenue — followed by a tax structure which penalizes income significantly more than capital gains or held assets — is a decision, not a law of nature. The wealth for UBI exists in the system, but shifting that money implies incredible societal shock, as it implies taking away from people what most think it is rightfully theirs.
     The third critique is that people will lose their dignity by taking an UBI instead of working, making them society parasites. One of the answers to this third critique is that maybe instead of UBI, people can be given job guarantees (ensures that everyone has some kind of work, which could provide them with dignity). But this will mean that all the “terrible jobs” will be made “guaranteed jobs”, and without the UBI, the most desperate people will need to take them at lowest wages. So a job guarantee “solves” the problem by maintaining precisely the imbalances which exist in our society today, writes Yonatan. He concludes that both a basic income and job guarantees are inadequate solutions to tackling this problem, and instead we should finds means of paying people for activities that are currently not considered to be jobs, meaning people who contribute actively to our communities in ways we don’t get paid for: people who raise families, mothers, who help other people resolve their differences, who organize events, who listen to each other and provide emotional support, who pay attention to each other’s needs. Today, society only values labour in terms of how much it is paid, and suddenly, raising children or making food has become not just devalued, but not even thought of as labour.
       These forms of “invisible labour” are afforded neither the compensation nor the dignity that we give to “visible” kinds of labour paid an hourly wage or a salary. Maybe this is what we need to change. How we measure the value. Excellent read!

👷 On job guarantees programs, Lawrence Summers underlines the same critiques, especially around the fact that these jobs which would be arguably guaranteed by the state, will be most probably low paid and with limited benefits, and will not do enough to help people in, say manufacturing, that are being laid off or people who want to escape poverty lines. On the other side, if they pay at a premium, they can be an attractive alternative for a quarter or more of the workforce, raising questions of cost and economic disruption. Lawrence argues that job guarantees, like UBI, are a distraction from finding a new safety net that combines wage subsidies, targeted government spending, support for workers with dependents, increased training and job-matching programs etc.

📕On this topic, I am currently reading through Nicolas Colin's book, called HEDGE: A greater safety net for the entrepreneurial age. He explores at large exactly the topics above, I strongly recommend you order it now.

📐 Measuring societal value is one of the topics Mariana Mazzucato has recently published a book about, and in this excerpt article she explores the need to redefine the data-relationship between the consumers and internet giants, to unlock further societal value, since most of today’s business models are built on the commodification of personal data. And a key argument on why this relationship can be renegotiated is because these giants’ technologies (Google’s search algorithm, touch-screen displays, GSP, Siri) were originally created with taxpayer money. We should therefore ask how the value of these companies has been created, how that value has been measured, and who benefits from it. That’s because the way we currently ascribe value to what the internet giants produce is completely confusing, and it’s generating a paradoxical result: their advertising activities are counted as a net contribution to national income, while the more valuable services they provide to users are not. Once we do that, we will be able to get them to use big data and AI to improve the services provided by the welfare state—from health care to social housing, for example, but this new model requires thinking about digital platforms as collective creations.

📏 A critique of Mariana Mazzucato’s approach towards measuring value comes from Dietrich Vollrath, but he ultimately agrees that there is a coherent argument to make about how we could or should value certain economic activities more, in the sense of supporting or encouraging them, even if they do not lead to higher measured GDP, but because they involve the use of some inherently valuable inputs - valuing things based on the nature of the input (e.g. full time work for adults) rather than on the value of output).

💻 The Economist writes about how workers were not properly compensated for labour for most of human history. So it is normal for companies to not expect to pay users for the data they generate. Data is not seen as labour! But there are now two startups that are trying to create a business model of paying users for their data: Datawallet and Wibson (the latter's CEO has even quantified the current value of a person's data for digital ads being at about $240 a year).

💻 Tim Berners-Lee, the creator of the World Wide Web, is busy working on a new platform, called Solid, to reclaim the Web from corporations and return it to its democratic roots. The idea is simple: re-decentralize the Web and radically change the power dynamics of the Web, by giving individuals control of their data, rather than centralized companies. Think of it as Pied Piper going after Hooli in Silicon Valley tv-series :)

🏞️ Mike Maples believes that the crypto, decentralized space can be a solution towards a wealth of the commons. We are used with today’s large-corporation model because it is so prevalent, but in reality the current organization with a CEO, board of directors etc. is an emergent development of trial and error), but as long as we discover new ways to create value, new types of businesses will emerge. As the techno-economic paradigm shifts and pessimism kicks in, we often underestimate what a miracle the last 200 years have been for our standard of living and the prosperity brought about by the large corporations. But now, it is the decentralized world enabled by blockchains and crypto businesses that can drive the standard of living forward even faster, by creating a new type of abundance centered around the wealth of the Commons. A commons can create value more efficiently than free-market ownership or government-enforced regulation (with open source software a compelling recent example). We just have to think bigger, beyond traditional corporate structures, argues Mike, as what crypto’s biggest value proposition is scalable governance of the commons (by leveraging mass computation and connectivity to create “governance markets”, which allow the commons to scale and create abundance in the same way that the stock markets enabled corporations to scale).
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Healthy Entrepreneurial Ecosystems

On Silicon Valley, the U.S.
🇺🇸 It's holiday season soon, so Laurène Tren has put together an amazing list of books to read if you want to understand the history and culture of Silicon Valley. I also very much enjoyed Jessica Livingston's (co-founder at Y Combinator) re-collection of how they started the "first accelerator". Of this, of paramount importance (but often ignored) is the motherly touch and the community organising skills that are critical aspects of a healthy entrepreneurial ecosystem.

🇺🇸 John Doerr writes about Silicon Valley's best kept secret: the people, knit together in networks. In our open-sourced, hyperconnected world, behaviour defines a company and it’s the one thing that can’t be copied or commoditized. But building start-ups in San Francisco has become more and more expensive, Andrew Chen delves into details.


Copy that, China. Roger that, U.S.
🇨🇳 But lately, China is increasingly challenging American dominance of science, write Ben Guarino, Emily Rauhala and William Wan. And now, emerging economies such as China's can do more than just copy innovation, since innovation in emerging markets is following a three-stage curve, as per Tanay Jaipuria.

🇨🇳 Even more, there is a new narrative, that the "copy to China" strategy is over. Now, Western countries, even U.S., are "copying from China" (think of scooters mania). Now, of course, take this narrative with a pinch of salt, since an ex-Apple engineer was just arrested on board of a plane, trying to steal sensitive information regarding Apple for its future Chinese employer. Or in another case of corporate espionage, how just recently, a Taiwanese chip maker company was producing microchips for China, using corporate secrets (chip designs) from U.S. company Micron Technology.

🇨🇳 But still, this stunning essay from Xiaowei R. Wang argues that Chinese tech isn’t an imitation of its American counterpart. It’s a completely different universe. Must read.


The other Asian giant: India
🇮🇳 Nicolas Colin puts it right. Speaking so much about China, we ignore India as simply being “the other country with 1 billion people”. But India has has many assets to utilise in playing a prominent role in the digital economy, but it must refocus its technological efforts, as specializing in outsourcing can be a deadly problem for local entrepreneurship, argues Nicolas. I agree with this last idea, and I think the same about Romania's problems (hopefully I'll bring myself to write about it).

🇮🇳 Now, the The National Institution for Transforming India has just recently published a discussion paper about the country's national A.I. strategy, as the country understands that it is well positioned to stand at the global leaders' table in the A.I. game. And, as Varun Aggarwal mentions, it is exactly the country's mess of complexity that will offer it exactly what it needs for its A.I. ambitions.

Arab states are losing the race for technological development

🛢️ A new ranking of innovation in 126 countries highlights a striking exception to this trend: oil-rich Arab states states are far less innovative than their prosperity would suggest. The three most wealthy Arab countries (Kuwait, UAE, Qatar) lag far behind comparably wealthy countries on measures of the strength of government institutions, human capital, business sophistication and modern technological output.

What does it take to build European Tech Giants?
🇪🇺 Good question from Jean de La Rochebrochard. He argues that we need more ambitious founders, more diversity and more capital. Also, an increased capacity of investors to help founders go from one country to another (the right ones), connect to the best people in the network, and hire the best talents from abroad. And stop building unicorns, but empires. In A.I., London is still the growth capital of Europe.

Share your thoughts

I would love to hear from you on Twitter, @DanColceriu.
Thank you for reading. By the time I finished writing this, I returned back home, so cheers from Switzerland! 

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An essay for the long commute

💻 A decade before the Internet went mainstream, French citizens were interacting via Minitel, a computer network open to anyone with a telephone. In other words, Minitel was the online world France built before the Web. Fascinating read, by Julien Mailland and Kevin Driscoll.
 Top 10 articles I recommend which were featured in this digest:
1. Lynn Dunn - How the dominant business paradigm turns nice people into psychopaths - July 2018
2. Eric Feng - Who Has The Best Business Model (And It’s Not Google Or Facebook) - July 2018
3. Ben Thompson - Intel and the danger of integration - June 2018
4. Julien Genestoux - Platform Moats - June 2018
5. Jason Douglas, Jon Sindreu and Georgi Kantchev - The Problem With Innovation: The Biggest Companies Are Hogging All the Gains - July 2018
6. Adrian Adermon, Mikael Lindahl and Daniel Waldenström - How wealth is transmitted across generations - July 2018
7. Yonatan Zunger Why we should value "invisible labour"
 - July 2018
8. Mike Maples Crypto Commons - June 2018
9. Jessica Livingston - Grow the puzzle around you - July 2016
10. Xiaowei R. Wang - Letter from Shenzhen - July 2018
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     My name is Dan Colceriu and I hope this reading was rewarding. Any opinions expressed here do not represent financial or investment advice. Also, they represent my personal view, and not my employer's, which is in no way associated with this email. 
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Copyright © 2018 a p e r t u r e, All rights reserved.

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