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Reinventing Capitalism In Age Big Data
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First published January 1, 2018
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About the author
Viktor Mayer-Schönberger
24 books105 followersVIKTOR MAYER-SCHÖNBERGER is Professor of Internet Governance and Regulation at the Oxford Internet Institute, Oxford University. A widely recognized authority on big data, he is the author of over a hundred articles and eight books, of which the most recent is Delete: The Virtue of Forgetting in the Digital Age. He is on the advisory boards of corporations and organizations around the world, including Microsoft and the World Economic Forum.
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Displaying 1 - 30 of 34 reviews
December 21, 2020
I am sorry - I started this a few times and then stalled before I finally pushed through. I found this book to be oversold and underargued. The basic point is clear enough — the advent of “big data” will permit information technology to improve upon the current state of the economy by improving the ability to process information effectively and efficiently both in markets and formal organizations and this will help mediate both sides of economic transactions in “data rich” markets such as are likely to improve in the future with continued innovation and the continuation of something resembling “Moore’s Law” in the Technology sector. This involves moving away from price as the fundamental information conveyor of economic transactions and more towards motivating economic transactions by informational richness rather than by price and money -whatever that means. This is all seen as a fundamental change in the nature of capitalism (hence the title), although the nature of that fundamental change is not spelled out and it all seems to be a continuation of trends already well in play - and thus not a fundamental change. Its “all just a little bit of history repeating”, as the song goes.
So what do we have after all of this? The point seems to be that information technology and big data may permit us to do things in managing electronic economic transactions that have not been been possible up until now. The operative word of course is “may” since nothing is really spelled out. Once that is clear, the book becomes another sales pitch for putting information technology to work (and pay some consultants to do it of course). In reading this, I was reminded of an infomercial or a reality show that had everything by a 1-800 number to call now to get a special deal. I am willing to stipulate that information technology and the Internet will further change how we do business. There is no need to buy a book to demonstrate that. Then, the point is made that with increased networked information/communication capabilities, markets can function much more efficiently than they did prior to such capabilities. Again, no argument from me on that, but what’s new about it? As markets become more developed, the costs of transacting go down. Brilliant! I never would have suspected that. But wait! There’s more. With advances in telecommunications and computing capabilities, existing organization structures can become flatter, more dynamic, and more adaptable. Wow! Boeing versus Airbus, right? Ok. In one chapter, the records keeping function in firms is described from “stories” from double entry bookkeeping in the Renaissance to “Scientific Management” and on to the emergence of GM and the Whiz Kids at Ford and then to the Internet. I have to wonder what sort of readers are being assumed for this. I found little that was new anywhere in the book. Even towards the end, when the potential for job losses from automation was considered, there was little discussion of the economic reasons for wage stagnation, such as labor market monopsony following tech mergers.
Business trades are seldom very deep but they more frequently have a rhythm to the story that makes them readable. I wanted to drop this one even in the initial argument and did not find any fun to it as I went deeper into it.
So what do we have after all of this? The point seems to be that information technology and big data may permit us to do things in managing electronic economic transactions that have not been been possible up until now. The operative word of course is “may” since nothing is really spelled out. Once that is clear, the book becomes another sales pitch for putting information technology to work (and pay some consultants to do it of course). In reading this, I was reminded of an infomercial or a reality show that had everything by a 1-800 number to call now to get a special deal. I am willing to stipulate that information technology and the Internet will further change how we do business. There is no need to buy a book to demonstrate that. Then, the point is made that with increased networked information/communication capabilities, markets can function much more efficiently than they did prior to such capabilities. Again, no argument from me on that, but what’s new about it? As markets become more developed, the costs of transacting go down. Brilliant! I never would have suspected that. But wait! There’s more. With advances in telecommunications and computing capabilities, existing organization structures can become flatter, more dynamic, and more adaptable. Wow! Boeing versus Airbus, right? Ok. In one chapter, the records keeping function in firms is described from “stories” from double entry bookkeeping in the Renaissance to “Scientific Management” and on to the emergence of GM and the Whiz Kids at Ford and then to the Internet. I have to wonder what sort of readers are being assumed for this. I found little that was new anywhere in the book. Even towards the end, when the potential for job losses from automation was considered, there was little discussion of the economic reasons for wage stagnation, such as labor market monopsony following tech mergers.
Business trades are seldom very deep but they more frequently have a rhythm to the story that makes them readable. I wanted to drop this one even in the initial argument and did not find any fun to it as I went deeper into it.
January 31, 2022
KARL MARX’S MANIFESTO: BIG DATA EDITION
I think I understand why many people would not like this book as it truly simplifies concepts that are generally thought of as familiar or common knowledge. I for one thoroughly enjoyed it and was genuinely taken back by how much I enjoyed it as I usually and from the bottom of my heart dislike economics, business, finance and politics. The beauty of this book however lies in its thesis, the idea that data will/ SHOULD replace money as a currency in a free market with perfect competition for its multi dimensional informational attribute, which although cannot be described as radical, rather, the way by which the proposal for such a setup was conveyed, was comprehensive, yet, simple, which I loved. The choice of examples, the communication of ideas, the repetition of key messages, the conviction of the authors, all contributed to an enjoyable reading experience of a dull, dry topic.
CH2 - Humans evolved through their abilities to coordinate: this chapter explains the main difference between firms (hierarchal, highly centralised and bureaucratic modes of coordination) and markets (decentralised diffused control that thrives on optimal matching and the all encompassing invisible hand), arguing more vibrantly for the adoption of the latter in data rich markets, but stating that how the information flows and who controls it will determine the mechanism of coordination.
CH3 - SAY GOOD BYE TO THE DOLLA : This is where the book starts to get interesting, the authors make all the arguments necessary to support the fact that money is being displaced and for good reason as although it may served its use in the past as the singular custodian of information, standardising the way by which transactions are conducted where instead of exchanging SALT (YES SALT because of its preservation qualities) and other ‘currencies’, and releasing us from the need to process more information than we have to, PRICE makes VALUE objective; the multi dimensional informational aspects of data make it difficult as we are limited by our brains but recently, the choices we make for any product or service depend on other criteria such as customer ratings, how quickly we can find this item etc. There needs to be a balance between information quality and quantity to avoid informational asymmetry and avoid devastating bubbles. The reductionist quality of money and price and their limitations when it comes to the flow of information is not suitable for the Information Age. Money - “A crude symbol for a service that dispenses with detail and reduces informational richness”
CH4 - Data Rich Markets: this part explains how ONTOLOGY, or categorisation makes or breaks online data rich platforms. How well is it labelled in a system of labels, what are the standard units of preference used, how advanced is the matching algorithm, how can it evolve through the informational flow, capture the data and integrate it back to its meta data. This is what allowed Amazon to be Amazon and the reason books were the choice of product is because they had a standard Dewey System ontology. So it comes down to data ontology, preference matching and adaptive ML algorithms. The data rich market thrives because individual decision making is captured and so the market evolves with every single transaction/ decision/ click. What I found particularly interesting here is that unlike enthusiasts who refer to ALPHAGO and WATSON as the algorithms to watch, the authors suggest that a poker playing algorithm CLAUDICO, since its objective function is to advance strategy and planning in a far more human way than chess and GO, may be the algorithm to watch in the future.
CH5 - INFORMATION? NOT ENOUGH … HELP ME DECIDE ! This chapter discusses the management aspect of the holy grail of the fourth industrial revolution. How to translate all the excessive information flowing through the market into actionable and useful decisions. Behavioural economics, the science of heuristics, and bounded rationality assumptions are all helping in advancing the understanding of human behaviour to eventually let the machines come up with human compatible advice. The chapter puts forwards theories about how the information is going to be controlled: will there be a move towards TAYLORISM where every move in industry is calculated and quantified to ensure a superior centralised mode of information control? Will we take a page from the Medici family and allow information to flow across the board with no centralised control? Could a simple evolving checklist and progressive standards of operation be the key to informed operation best compatible with our cognitive limitations? Should there be a hunger games scenario to filter out people who are NATURALLY more objective and have them as the global pool for management?
CH6 - STRATEGIES OF THE FIRM: two examples are initially provided, one Japanese which is bagging on less humans FOR BIG DECISIONS and is training WATSON in the process and another German that is simply switching to start up mode and relying on higher levels of decentralisation, cross functional flexible setups that foster innovation and empowers employees. The Spotify example and ITS SQUAD STRATEGY was particularly fascinating, made me want to work for Spotify. But essentially he discusses on whether the firm should make way to the market and how much automation is too much automation, suggesting that the T SHAPE should be adopted. Where creative multi functional horizontal decisions should may be remain human and vertical highly specialised decisions should be left to data savvy machines.
CH7 - A REVOLUTION IN FINANCE: here he explains the ever more diminishing role of banks as financial services and the rise of the fintech industry as it thrives in the data age, suggesting that due to the fact that banks harvested and owned all the critical information but were not liable to use it, the data, which was their greatest asset was lost. Financial services like Paypal and Monzo actually put data and analytics at the forefront of finance management, making it the preference for people who want to make the most out of their money. Although he suggests that blockchain might actually revive the centralised control of the financial services but he finds that again, bitcoin may be decentralised but it holds the same limitations as money and price do as it remains a reductionist currency.
CH8 - FEEDBACK, YES PLEASE: In the chapter, all the emphasis is put on the fact that when it comes to data rich markets there are three key factors that will make or break it: NETWORK to expand utilisation, SCALE to expand reachibility and most importantly FEEDBACK to leverage the information, emphasise on the system’s ability to learn and allow for the capitalisation of information. Feedback is where machine learning lies, as through a system’s ability to actually take the data and make changes accordingly, there is little that it can offer. However, he raises concerns of monopoly as he urges loudly for DATA SHARING mandates to allow as many companies to exploit and enhance their feedback mechanisms. Allowing a few companies to collect all the data like Google, Amazon, Alibaba, Apple gives new players and hence innovation, no chance and the reason they will keep on growing is that they keep on collecting data.
CH9 - THE FUTURE OF WORK: BRING ON MARX IN THE DATA AGE ! YES TO UNIVERSAL BASIC INCOME, YES TO WEALTH TAXES, YES TO DATA TAXES, YES TO THE GIG ECONOMY, YES TO FREEDOM, YES TO ACKNOWLEDGING THAT WORK IS NOT JUST ABOUT MONEY, YES TO REDEFINING THE RELATIONSHIP BETWEEN CAPITAL AND LABOUR ! The bundle of regulations suggested in this chapter really summarises how the revolution should start, how to regulate the move into the fourth industrial revolution without harming the little players in the process.
CH10 - “The future of our economy lies in the clever exploitation of our informational surplus, and data rich markets are the mechanisms and the places where we can achieve this. When artificial intelligence and Big Data meet the social reality of human coordination, we can become more sustainable”. With applications ranging from decentralised energy markets, better and cheaper informed health care, more tailored and sustainable retail with premium services and better education opportunities, add to that a self propelling well regulated highly automated market and you have yourself “fully automated luxury communism or Leonid Brezhnev in Gucci loafers”. He concludes eloquently that markets need to be at the centre of the data transition and that money needs to be acknowledged as a thing of the past stating: “We must all appreciate that we cannot dumb down reality to make it conform to our cognitive limitations, that when we limit the possible explanations of how the world works to the simple one, the one that’s easiest to grasp, or the one that we have always believed in, we confine our imagination and we constrict our understanding of the world to the obvious”.
I think I understand why many people would not like this book as it truly simplifies concepts that are generally thought of as familiar or common knowledge. I for one thoroughly enjoyed it and was genuinely taken back by how much I enjoyed it as I usually and from the bottom of my heart dislike economics, business, finance and politics. The beauty of this book however lies in its thesis, the idea that data will/ SHOULD replace money as a currency in a free market with perfect competition for its multi dimensional informational attribute, which although cannot be described as radical, rather, the way by which the proposal for such a setup was conveyed, was comprehensive, yet, simple, which I loved. The choice of examples, the communication of ideas, the repetition of key messages, the conviction of the authors, all contributed to an enjoyable reading experience of a dull, dry topic.
CH2 - Humans evolved through their abilities to coordinate: this chapter explains the main difference between firms (hierarchal, highly centralised and bureaucratic modes of coordination) and markets (decentralised diffused control that thrives on optimal matching and the all encompassing invisible hand), arguing more vibrantly for the adoption of the latter in data rich markets, but stating that how the information flows and who controls it will determine the mechanism of coordination.
CH3 - SAY GOOD BYE TO THE DOLLA : This is where the book starts to get interesting, the authors make all the arguments necessary to support the fact that money is being displaced and for good reason as although it may served its use in the past as the singular custodian of information, standardising the way by which transactions are conducted where instead of exchanging SALT (YES SALT because of its preservation qualities) and other ‘currencies’, and releasing us from the need to process more information than we have to, PRICE makes VALUE objective; the multi dimensional informational aspects of data make it difficult as we are limited by our brains but recently, the choices we make for any product or service depend on other criteria such as customer ratings, how quickly we can find this item etc. There needs to be a balance between information quality and quantity to avoid informational asymmetry and avoid devastating bubbles. The reductionist quality of money and price and their limitations when it comes to the flow of information is not suitable for the Information Age. Money - “A crude symbol for a service that dispenses with detail and reduces informational richness”
CH4 - Data Rich Markets: this part explains how ONTOLOGY, or categorisation makes or breaks online data rich platforms. How well is it labelled in a system of labels, what are the standard units of preference used, how advanced is the matching algorithm, how can it evolve through the informational flow, capture the data and integrate it back to its meta data. This is what allowed Amazon to be Amazon and the reason books were the choice of product is because they had a standard Dewey System ontology. So it comes down to data ontology, preference matching and adaptive ML algorithms. The data rich market thrives because individual decision making is captured and so the market evolves with every single transaction/ decision/ click. What I found particularly interesting here is that unlike enthusiasts who refer to ALPHAGO and WATSON as the algorithms to watch, the authors suggest that a poker playing algorithm CLAUDICO, since its objective function is to advance strategy and planning in a far more human way than chess and GO, may be the algorithm to watch in the future.
CH5 - INFORMATION? NOT ENOUGH … HELP ME DECIDE ! This chapter discusses the management aspect of the holy grail of the fourth industrial revolution. How to translate all the excessive information flowing through the market into actionable and useful decisions. Behavioural economics, the science of heuristics, and bounded rationality assumptions are all helping in advancing the understanding of human behaviour to eventually let the machines come up with human compatible advice. The chapter puts forwards theories about how the information is going to be controlled: will there be a move towards TAYLORISM where every move in industry is calculated and quantified to ensure a superior centralised mode of information control? Will we take a page from the Medici family and allow information to flow across the board with no centralised control? Could a simple evolving checklist and progressive standards of operation be the key to informed operation best compatible with our cognitive limitations? Should there be a hunger games scenario to filter out people who are NATURALLY more objective and have them as the global pool for management?
CH6 - STRATEGIES OF THE FIRM: two examples are initially provided, one Japanese which is bagging on less humans FOR BIG DECISIONS and is training WATSON in the process and another German that is simply switching to start up mode and relying on higher levels of decentralisation, cross functional flexible setups that foster innovation and empowers employees. The Spotify example and ITS SQUAD STRATEGY was particularly fascinating, made me want to work for Spotify. But essentially he discusses on whether the firm should make way to the market and how much automation is too much automation, suggesting that the T SHAPE should be adopted. Where creative multi functional horizontal decisions should may be remain human and vertical highly specialised decisions should be left to data savvy machines.
CH7 - A REVOLUTION IN FINANCE: here he explains the ever more diminishing role of banks as financial services and the rise of the fintech industry as it thrives in the data age, suggesting that due to the fact that banks harvested and owned all the critical information but were not liable to use it, the data, which was their greatest asset was lost. Financial services like Paypal and Monzo actually put data and analytics at the forefront of finance management, making it the preference for people who want to make the most out of their money. Although he suggests that blockchain might actually revive the centralised control of the financial services but he finds that again, bitcoin may be decentralised but it holds the same limitations as money and price do as it remains a reductionist currency.
CH8 - FEEDBACK, YES PLEASE: In the chapter, all the emphasis is put on the fact that when it comes to data rich markets there are three key factors that will make or break it: NETWORK to expand utilisation, SCALE to expand reachibility and most importantly FEEDBACK to leverage the information, emphasise on the system’s ability to learn and allow for the capitalisation of information. Feedback is where machine learning lies, as through a system’s ability to actually take the data and make changes accordingly, there is little that it can offer. However, he raises concerns of monopoly as he urges loudly for DATA SHARING mandates to allow as many companies to exploit and enhance their feedback mechanisms. Allowing a few companies to collect all the data like Google, Amazon, Alibaba, Apple gives new players and hence innovation, no chance and the reason they will keep on growing is that they keep on collecting data.
CH9 - THE FUTURE OF WORK: BRING ON MARX IN THE DATA AGE ! YES TO UNIVERSAL BASIC INCOME, YES TO WEALTH TAXES, YES TO DATA TAXES, YES TO THE GIG ECONOMY, YES TO FREEDOM, YES TO ACKNOWLEDGING THAT WORK IS NOT JUST ABOUT MONEY, YES TO REDEFINING THE RELATIONSHIP BETWEEN CAPITAL AND LABOUR ! The bundle of regulations suggested in this chapter really summarises how the revolution should start, how to regulate the move into the fourth industrial revolution without harming the little players in the process.
CH10 - “The future of our economy lies in the clever exploitation of our informational surplus, and data rich markets are the mechanisms and the places where we can achieve this. When artificial intelligence and Big Data meet the social reality of human coordination, we can become more sustainable”. With applications ranging from decentralised energy markets, better and cheaper informed health care, more tailored and sustainable retail with premium services and better education opportunities, add to that a self propelling well regulated highly automated market and you have yourself “fully automated luxury communism or Leonid Brezhnev in Gucci loafers”. He concludes eloquently that markets need to be at the centre of the data transition and that money needs to be acknowledged as a thing of the past stating: “We must all appreciate that we cannot dumb down reality to make it conform to our cognitive limitations, that when we limit the possible explanations of how the world works to the simple one, the one that’s easiest to grasp, or the one that we have always believed in, we confine our imagination and we constrict our understanding of the world to the obvious”.
December 28, 2018
Many big words overexplaining simple ideas to justify an Oxford sabbatical. I continue to the end hoping for any gem in the verbiage... no avail.
Wherein AI helps imperfect but free people make better decisions, only requiring progressive open sharing of data to avoid controlling firms or govts from monopoly. Can’t wait to hear how they manage privacy and individual control of data, because FB once tried an open developer data ecosystem, which unearned trust made fragile.
The 70+ page review/set up is very readable, but overly simplistic and not especially memorable. The Liberatus story alone was new to me, and I’m mostly surprised that nothing else was. The overall dominance of Firms in the economy is meaningful... both for production and earned income.
Authors summarize their point on p71 where they claim AI will be able to do the work of searching rich-data to the benefit of a personalized shopping experience, which will make the market work more efficiently for everyone. However they stay shallow, escaping with “the actual process is quite technical”, and similar banality. The first sentence of their acknowledgments (P225) feels almost completely ironic: “don’t tell me what I already know”.
Never have I seen as many large words wasted as between page 87 and 132, On the history and rapid change of the Firm. So boring, that even several fresh pages on Spotify couldn’t help.
The entire chapter 7 on capital decline, I found to be wishful thinking. As much as I would like finance to decline, data seems to only strengthen their position. E.g., JP Morgan utilizing Ai, or even the prior chapter’s example of Bridgewater associates. Rich data is as much available to finance as any industry, in fact they can uniquely pay and charge for it. Reduced inequality and weakening of inequality and division of money and power would weaken finance, and none of that is promoted by these trends.
On page 167 the authors finallly introduce their “progressive data-sharing mandate”, and simply assert “ stringently ensuring that privacy is not being unduly compromised.” That facile oversimplification of data sharing with privacy (given that this was published after FB’s Cambridge Analytica fiasco and China’s theft of the entire OMB personnel dataset and Marriot’s complete loss their rewards program drags) reveals the authors don’t understand essential details of information security.
The problem of data monopolies is real. China has found the first and only “solution” of forcing market leaders to share all data with the government, for any and all uses. I don’t think anyone in the “free West” believes that is optimal. Open source calls for open data are interesting, but require massive public demand for openness, which hasn’t appeared. Without a positive viable solution, it is useless to point to a problem that is still only latent.
The review of UBI (p187-193) Is especially banal, and I’m not even sure why it’s in his book. Their simplistic conclusion is just that UBI is not affordable, because it would cost $3T for $12000 to each adult per year. But the entire US economy is $20T, so why is that prima fascia unaffordable? And then they reverse themselves on p205, with a call for a beneficial UBI of $6000/yr, which is affordable because it is only $500/mo and “wouldn’t require raising taxes beyond what is reasonable.”
The citation of Barkai (p194) as proof of the declining share of capital is evidence to me that the authors are equally nefarious as uninformed. With this PhD thesis by an unknown economist they fight Pinketty on whether capital is growing share of control of the economy. Barkai seems to state that since interest rates are low (negative in real terms in some times/places since 1997), capital can’t be gaining. Haha haha. Semantics or torturing data. Did they even read Brynjolfson before citing him?
It’s even more ridiculous to claim that the FAANG companies aren’t “designed to coordinate human activity”... even though they are among the largest employers and constantly hiring. Amazon has 560k employees, second largest Is company.
All of this to “prove” that taxes on capital are a “textbook bad idea” (p197), and argue for tax reform to overall reduce corpora profitability. Excuse me, Did they notice Trump’s tax cut of 2017? And do the notice R&D doubles as a profit tax shielding strategy?
“Investments don’t pay high returns anymore.” (P198). “Governments might consider partial payments of taxes in data rather than money” (p199) Now this is farcical.
These last 30 pages are begging for a one star review. And this is from someone who agrees with some of their conclusions, appreciated their Thiel/Trump bashing.
Wherein AI helps imperfect but free people make better decisions, only requiring progressive open sharing of data to avoid controlling firms or govts from monopoly. Can’t wait to hear how they manage privacy and individual control of data, because FB once tried an open developer data ecosystem, which unearned trust made fragile.
The 70+ page review/set up is very readable, but overly simplistic and not especially memorable. The Liberatus story alone was new to me, and I’m mostly surprised that nothing else was. The overall dominance of Firms in the economy is meaningful... both for production and earned income.
Authors summarize their point on p71 where they claim AI will be able to do the work of searching rich-data to the benefit of a personalized shopping experience, which will make the market work more efficiently for everyone. However they stay shallow, escaping with “the actual process is quite technical”, and similar banality. The first sentence of their acknowledgments (P225) feels almost completely ironic: “don’t tell me what I already know”.
Never have I seen as many large words wasted as between page 87 and 132, On the history and rapid change of the Firm. So boring, that even several fresh pages on Spotify couldn’t help.
The entire chapter 7 on capital decline, I found to be wishful thinking. As much as I would like finance to decline, data seems to only strengthen their position. E.g., JP Morgan utilizing Ai, or even the prior chapter’s example of Bridgewater associates. Rich data is as much available to finance as any industry, in fact they can uniquely pay and charge for it. Reduced inequality and weakening of inequality and division of money and power would weaken finance, and none of that is promoted by these trends.
On page 167 the authors finallly introduce their “progressive data-sharing mandate”, and simply assert “ stringently ensuring that privacy is not being unduly compromised.” That facile oversimplification of data sharing with privacy (given that this was published after FB’s Cambridge Analytica fiasco and China’s theft of the entire OMB personnel dataset and Marriot’s complete loss their rewards program drags) reveals the authors don’t understand essential details of information security.
The problem of data monopolies is real. China has found the first and only “solution” of forcing market leaders to share all data with the government, for any and all uses. I don’t think anyone in the “free West” believes that is optimal. Open source calls for open data are interesting, but require massive public demand for openness, which hasn’t appeared. Without a positive viable solution, it is useless to point to a problem that is still only latent.
The review of UBI (p187-193) Is especially banal, and I’m not even sure why it’s in his book. Their simplistic conclusion is just that UBI is not affordable, because it would cost $3T for $12000 to each adult per year. But the entire US economy is $20T, so why is that prima fascia unaffordable? And then they reverse themselves on p205, with a call for a beneficial UBI of $6000/yr, which is affordable because it is only $500/mo and “wouldn’t require raising taxes beyond what is reasonable.”
The citation of Barkai (p194) as proof of the declining share of capital is evidence to me that the authors are equally nefarious as uninformed. With this PhD thesis by an unknown economist they fight Pinketty on whether capital is growing share of control of the economy. Barkai seems to state that since interest rates are low (negative in real terms in some times/places since 1997), capital can’t be gaining. Haha haha. Semantics or torturing data. Did they even read Brynjolfson before citing him?
It’s even more ridiculous to claim that the FAANG companies aren’t “designed to coordinate human activity”... even though they are among the largest employers and constantly hiring. Amazon has 560k employees, second largest Is company.
All of this to “prove” that taxes on capital are a “textbook bad idea” (p197), and argue for tax reform to overall reduce corpora profitability. Excuse me, Did they notice Trump’s tax cut of 2017? And do the notice R&D doubles as a profit tax shielding strategy?
“Investments don’t pay high returns anymore.” (P198). “Governments might consider partial payments of taxes in data rather than money” (p199) Now this is farcical.
These last 30 pages are begging for a one star review. And this is from someone who agrees with some of their conclusions, appreciated their Thiel/Trump bashing.
June 10, 2019
On page 203 the authors tell what in their view the emerging data age is all about " a move away from money and capital, an appreciation of capturing the richness of reality through the richness of data, an embrace of the market over the firm, and an exceptional opportunity to improve the human ability to coordinate ".
In my view the above summarize their thesis. The problem as some of the critical reviews on goodreads have pointed out - the lack of deeper details or if I may the lack of data-richness of the book is obvious. A chapter on depth of what exactly do they mean data richness and what it will be ahead would have given us the readers more insight - especially those readers who have lived with meta data, taxonomy, machine learning, data science and decision engineering.
The book comes out as if it is rehashing the known into some deep insights which continue to remain superficial to say the least.
There are few points though I liked in this book
1. Delineation of money/price function - value and information. The information function is being unbundled by what the authors call rich data markets.
2. The human economic activty coordination mechanism - market is winning and firms are transforming to markets. However, these need to be data rich markets.
2. The adaptive decision support to take care of unbundled decision making processes - for example reducing the burdensome search or discovery activity and giving it to machine but keeping the enjoyable selection or choice component of the decision process in rich-data markets is interesting though it is not very clearly separated out in the book
3. The danger of control and nudging that someone or few humans can exercise on all of us is highly risky as per the authors.
Their solutions however or prescriptions
1. Taxing rich data players
2..paying part of tax as data
3. Partial universal basic income
Need much more deeper analysis then has come out.
Overall a worthy flowing read for me. I didn't feel bored by the content. If you are already in the data big data or rich data arena the book may give you few inputs but many deja vu moments. However, if you are trying to understand data based evolution of the market - it make sense to invest in this book
4 stars for me !
In my view the above summarize their thesis. The problem as some of the critical reviews on goodreads have pointed out - the lack of deeper details or if I may the lack of data-richness of the book is obvious. A chapter on depth of what exactly do they mean data richness and what it will be ahead would have given us the readers more insight - especially those readers who have lived with meta data, taxonomy, machine learning, data science and decision engineering.
The book comes out as if it is rehashing the known into some deep insights which continue to remain superficial to say the least.
There are few points though I liked in this book
1. Delineation of money/price function - value and information. The information function is being unbundled by what the authors call rich data markets.
2. The human economic activty coordination mechanism - market is winning and firms are transforming to markets. However, these need to be data rich markets.
2. The adaptive decision support to take care of unbundled decision making processes - for example reducing the burdensome search or discovery activity and giving it to machine but keeping the enjoyable selection or choice component of the decision process in rich-data markets is interesting though it is not very clearly separated out in the book
3. The danger of control and nudging that someone or few humans can exercise on all of us is highly risky as per the authors.
Their solutions however or prescriptions
1. Taxing rich data players
2..paying part of tax as data
3. Partial universal basic income
Need much more deeper analysis then has come out.
Overall a worthy flowing read for me. I didn't feel bored by the content. If you are already in the data big data or rich data arena the book may give you few inputs but many deja vu moments. However, if you are trying to understand data based evolution of the market - it make sense to invest in this book
4 stars for me !
February 3, 2021
The authors fail to get me on board with the suggested data sharing mandates or taxes.. for a number of reasons that the book avoids. Providing employers with a new hire tax credit threatens employee loyalty & retention, incentivizing employers to fire & hire for financial gain. For all of the innovative thought provoking ideas explored - the suggested policy responses leave too much power in the hands of the government. I dont think government policy revision is the sole or only answer. Customer Data utilization - sourcing, aggregation, analysis, interpretation, etc is often times a proprietary secret sauce or competitive advantage - which the authors argue should be maintained... Open sourcing data does not solve the socio economic & corporate problems around data, its one small step which i would vote strongly against. This is a speciality people should explore as a career as they will need data expertise to interpret and make decisions based on open source data anyhow. I dont believe in handing out data because it is more often misinterpreted and misused and also not necessarily in the customers best interest. Many companies may not have the legal ability today to share customer level data publicly (or privately). Regulations exist around this for more important reasons focused on consumer protectionism rather than fair corporate capitalism. A better suggestion in my opinion is for the government and private orgs to fund programs to train and teach data utilization & optimization & enable careers - engineering and modeling that provides jobs to the new generation which will be outpaced by capital as the authors desrcibe (and i do agree with). Perhaps open sourcing data would be more problematic than not. Imagine if any company had all of your data points from ALL companies you interact with. Wouldnt that feel like too many companies (and people) knowing too much? How much of your behavior would actually be your own rather than decisions influenced by all of the profiling every company is doing for you...? De centralized data may slow down a Skynet. Food for thought.
This entire review has been hidden because of spoilers.
March 15, 2020
Informative and interesting.
May 15, 2020
God god med godt take på hvordan vi skal forstå data-rige markeder og deres måde at fungere på. Særligt interessant pga forslaget om at store tech-spillere skal dele data med markedet. Men rammer ikke helt rent
Surveillance capitalism kommer ikke bare ud af at Google drengene fandt ud af hvordan data kunne bruges til prediction markets også. Prediction markets er nemlig en ting i sig selv. Det er fremtiden for markedet, siger Viktor Mayer-Schlönberger. Det er et paradigmeskift vi står overfor. Og det er ganske rigtigt Big Data og machinelearning der står bag.
Økonomisk-teoretisk ramme til Surveillance Capitalism
På den måde giver Viktor Mayer-Schönberger med Reinventing Capitalism in the digital age en større økonomisk teori at sætte Surveillance Capitalism ind i. Og i det lys forklarer Viktor Mayer-Schönberger, hvorfor det ikke kun er Google og Amazon og Facebook, der vil drive Surveillance Capitalismen frem. Det er en bevægelse, der vil ramme hele markedet og alle på det.
Datarige markeder - som er dem, der vil komme flere og flere af - er forskellige fra traditionelle markeder. Blandt andet vil de overflødiggøre penge - eller ihvertfald kraftigt reducere deres rolle. Ikke som betaling, men som det signal, den usynlige hånd fungerer på baggrund af.
Kræver ontologier
De datarige markeder kommer ikke til at fungere af sig selv. De kræver skemaer og ontologier, som alle er enige om, og som kan fortælle os alle hvad det er der skal matches med hvad. På vare- og tjenesteydelsesfronten udvikles sådanne ontologier på visse områder allerede i dag af industrien. Det skal der komme mere af, hjulpet af machine learning og dataanalyse. Tænk YouTube videoer. Det samme gælder i forhold til vores præferencer, der virker tættere på vores persondata, og skabes gennem intens surveillance (som Zuboff ville sige det). De vil også blive data-skabte. "Fordi du købte denne.." Og så skal de to kunne matches med hinanden.
Men det er ikke kun signal værdien af penge, der taber til data. Også som egentlig kapital mister penge betydning i forhold til før. Det er selvfølgeligt stadigt nødvendigt med penge, for at en virksomhed skal kunne fungere. Men hvis alle virksomhedens data siger, at virksomheden er sund, vil investeringerne i et datarigt investeringsmiljø komme langt mere automatisk.
(Det her siger han ikke direkte, men det er hvad jeg tænker: Alle de data virksomheden skal bruge for at kunne fungere selv, kan til gengæld blive den knappe ressource. Hvis ikke en virksomhed har adgang til data vil den være på spanden. Det er data, ikke penge, som det bliver svært at skaffe. Data, der kan købes - med penge, eller endnu bedre: tillid!)
Feedback-effekt giver datahøst
En af de ting der præger konkurrence-situationen på dette nye marked er feedback-effekten. Feedback-effekten er en markedskoncentrationsdrivende kraft på linie med de to "traditionelle" kræfter: Economies of scale (stordriftsfordele) og the network effect (produkter og virksomheder der bliver mere værd, jo flere kunder der er ombord). Jo mere data der automatisk gives som feedback, jo bedre for virksomheden. Og dem, der er store, får mere feedback end de andre, og dermed gøres deres magt endnu stærkere.
Man må ikke, skriver han, overse "the gravity of the threat to markets posed by the feedback effect. Services based on machine learning systems fueled by feedback data “buy” innovation at diminishing cost as the user base grows. It feels strangely alchemistic: turning a by-product of usage into the raw material of improvement, like converting lead into gold.” (Kapitel 8, 61% inde i bog)
Det lyder meget som grundmekanismen i Zuboffs Surveillance Capitalism-formel. Forskellen er, at Zuboff fokuserer på videresalget af data i den sekundære "onde" kæde. Mayer-Schonberger skelner ikke her mellem de to kæder - men det er som om, han mest taler om den inderste. Den forståelse bider ham i hælene i hans forslag om datadeling også.
Obligatorisk datadeling
Et af bogens centrale forslag er, at man ved at indføre obligatorisk datadeling kan rette op på disse monopol-ubalancer. Forslaget går ud på, at en tilfældig udtrukket del af virksomheders data skal gives videre:"(W)e suggest what we term a progressive data-sharing mandate. It would kick in once a company’s market share reaches an initial threshold—say, 10 percent. It would then have to share a randomly chosen portion of its feedback data with every other player in the same market that requests it."
Ideen er "to spread data as the source of innovation. Large companies won’t lose the benefits of the feedback data they collect; their products will still improve as they gather more data. But by having to share a portion with others, the value derived from the data gets spread around. This benefits smaller competitors and helps them compete against large players." (Kapitel 8, 62%)
Progressiv deling
Schonberger foreslår også, at datadelingen er progressiv: "How much data it must make available would depend on the market share captured by the company. The closer a company is to domination, the more data it would have to share with its competitors." og "(B)y implementing a progressive lever, data sharing increases whenever concentration increases. It’s a feedback mechanism to counter the feedback effect: the more concentration threatens competition, the more vigorously the data-sharing mandate kicks in. Although large players could also request access to feedback data from smaller companies, the biggies would benefit much less from additional feedback data relative to their smaller brethren. And the mandate to share feedback data with every player in the market avoids creating incentives for players to misstate or” (Kapitel 8, 62%)
"Our plan for progressive data sharing is" siger han "targeted at the growing number of companies that use feedback data and adaptive machine learning to improve their service offerings, from Google, Facebook, Apple, and Microsoft all the way to Tesla. At first this may look like a small slice of the overall economy. Yet product improvements based on data-driven adaptive systems are so impressive that an ever-increasing number of companies will adopt them, thereby expanding the purview of progressive data sharing."
Argumentationen er fin, men han begrænser sig selv for meget, og skyder en anelse skævt. Progressionsdelen er fin nok. Man kan diskutere hvordan præcist den skal virke, om det skal være en glat progression eller bare en stor (skal give), lille (skal ikke give). Men princippet er helt ok. Der hvor det går galt er i to uudtalte antagelser: At data ikke ejes af brugerne selv og at data kun bruges til service-forbedringer, ikke forarbejdning og videresalg.
Dine data er ikke dine egne
Det første handler altså om, at data ses som noget virksomhederne ejer. Det er virksomhedens guld, og den der skal give det videre, hvis nogen andre virksomheder spørger. Det er ikke brugerens data. Selvom størstedelen, hvis ikke nærmest alle, de data der opstår som feedback i brugen, i virkeligheden er data der er afledt af brugernes adfærd. Det er brugerne der frembringer "mest sete", brugerne, hvis klik afslører deres præferencer, og som herefter kan forædles og bruges videre. Og hvis det er brugernes data, er det også brugerne, der må bestemme, hvem der ellers skal kunne bruge dem. Data sharing mandatet skal derfor være et krav om at udstille data på en måde, så brugerne let kan håndtere dem, og let give adgang til at andre også må bruge dem. Og det skal være allesammen, ikke kun en randomnly chosen del.
Kun service-brug af data - ikke den "onde" cirkel
Det andet sted hvor argumentationen ikke folder sig rigtigt ud, er i synet på, hvordan feedback-data bliver brugt. Og her fokuserer Schonberger altså på service-forbedringerne, det som hos Zuboff er den indre, og relativt uproblematiske cirkel, som det helt tydeligt fremgår af citatet ovenfor om, at forslaget er "targeted at the growing number of companies that use feedback data and adaptive machine learning to improve their service offerings, from Google, Facebook, Apple, and Microsoft". Denne blindhed gør ikke sig selv så meget skade på hans forslag - snarere giver datadelings-forslaget endnu mere mening, hvis man tager den anden - extraction, refinement, prediction, selling - cirkel med. Og så giver det endnu mere kraft under "det er brugernes" data argumentet, fordi data jo her handler meget klart om den enkelte forbruger, der kan påvirkes.
Blind for skadelige bivirkninger
På et andet plan har fokuseringen på service-delen af feedback-data en effekt: i synes på, data-brugens skadelige bivirkninger. Schonberger ser skadevirkninger i feedback-effekten selv på konkurrencesituationen. Men ikke meget på mulighederne for politisk misbrug af data a la Cambridge Analytica, og slet ikke på mental wellbeing-diskussionen. Disse aspekter bliver får han derfor heller ikke rigtigt med i sin argumentation.
Surveillance capitalism kommer ikke bare ud af at Google drengene fandt ud af hvordan data kunne bruges til prediction markets også. Prediction markets er nemlig en ting i sig selv. Det er fremtiden for markedet, siger Viktor Mayer-Schlönberger. Det er et paradigmeskift vi står overfor. Og det er ganske rigtigt Big Data og machinelearning der står bag.
Økonomisk-teoretisk ramme til Surveillance Capitalism
På den måde giver Viktor Mayer-Schönberger med Reinventing Capitalism in the digital age en større økonomisk teori at sætte Surveillance Capitalism ind i. Og i det lys forklarer Viktor Mayer-Schönberger, hvorfor det ikke kun er Google og Amazon og Facebook, der vil drive Surveillance Capitalismen frem. Det er en bevægelse, der vil ramme hele markedet og alle på det.
Datarige markeder - som er dem, der vil komme flere og flere af - er forskellige fra traditionelle markeder. Blandt andet vil de overflødiggøre penge - eller ihvertfald kraftigt reducere deres rolle. Ikke som betaling, men som det signal, den usynlige hånd fungerer på baggrund af.
Kræver ontologier
De datarige markeder kommer ikke til at fungere af sig selv. De kræver skemaer og ontologier, som alle er enige om, og som kan fortælle os alle hvad det er der skal matches med hvad. På vare- og tjenesteydelsesfronten udvikles sådanne ontologier på visse områder allerede i dag af industrien. Det skal der komme mere af, hjulpet af machine learning og dataanalyse. Tænk YouTube videoer. Det samme gælder i forhold til vores præferencer, der virker tættere på vores persondata, og skabes gennem intens surveillance (som Zuboff ville sige det). De vil også blive data-skabte. "Fordi du købte denne.." Og så skal de to kunne matches med hinanden.
Men det er ikke kun signal værdien af penge, der taber til data. Også som egentlig kapital mister penge betydning i forhold til før. Det er selvfølgeligt stadigt nødvendigt med penge, for at en virksomhed skal kunne fungere. Men hvis alle virksomhedens data siger, at virksomheden er sund, vil investeringerne i et datarigt investeringsmiljø komme langt mere automatisk.
(Det her siger han ikke direkte, men det er hvad jeg tænker: Alle de data virksomheden skal bruge for at kunne fungere selv, kan til gengæld blive den knappe ressource. Hvis ikke en virksomhed har adgang til data vil den være på spanden. Det er data, ikke penge, som det bliver svært at skaffe. Data, der kan købes - med penge, eller endnu bedre: tillid!)
Feedback-effekt giver datahøst
En af de ting der præger konkurrence-situationen på dette nye marked er feedback-effekten. Feedback-effekten er en markedskoncentrationsdrivende kraft på linie med de to "traditionelle" kræfter: Economies of scale (stordriftsfordele) og the network effect (produkter og virksomheder der bliver mere værd, jo flere kunder der er ombord). Jo mere data der automatisk gives som feedback, jo bedre for virksomheden. Og dem, der er store, får mere feedback end de andre, og dermed gøres deres magt endnu stærkere.
Man må ikke, skriver han, overse "the gravity of the threat to markets posed by the feedback effect. Services based on machine learning systems fueled by feedback data “buy” innovation at diminishing cost as the user base grows. It feels strangely alchemistic: turning a by-product of usage into the raw material of improvement, like converting lead into gold.” (Kapitel 8, 61% inde i bog)
Det lyder meget som grundmekanismen i Zuboffs Surveillance Capitalism-formel. Forskellen er, at Zuboff fokuserer på videresalget af data i den sekundære "onde" kæde. Mayer-Schonberger skelner ikke her mellem de to kæder - men det er som om, han mest taler om den inderste. Den forståelse bider ham i hælene i hans forslag om datadeling også.
Obligatorisk datadeling
Et af bogens centrale forslag er, at man ved at indføre obligatorisk datadeling kan rette op på disse monopol-ubalancer. Forslaget går ud på, at en tilfældig udtrukket del af virksomheders data skal gives videre:"(W)e suggest what we term a progressive data-sharing mandate. It would kick in once a company’s market share reaches an initial threshold—say, 10 percent. It would then have to share a randomly chosen portion of its feedback data with every other player in the same market that requests it."
Ideen er "to spread data as the source of innovation. Large companies won’t lose the benefits of the feedback data they collect; their products will still improve as they gather more data. But by having to share a portion with others, the value derived from the data gets spread around. This benefits smaller competitors and helps them compete against large players." (Kapitel 8, 62%)
Progressiv deling
Schonberger foreslår også, at datadelingen er progressiv: "How much data it must make available would depend on the market share captured by the company. The closer a company is to domination, the more data it would have to share with its competitors." og "(B)y implementing a progressive lever, data sharing increases whenever concentration increases. It’s a feedback mechanism to counter the feedback effect: the more concentration threatens competition, the more vigorously the data-sharing mandate kicks in. Although large players could also request access to feedback data from smaller companies, the biggies would benefit much less from additional feedback data relative to their smaller brethren. And the mandate to share feedback data with every player in the market avoids creating incentives for players to misstate or” (Kapitel 8, 62%)
"Our plan for progressive data sharing is" siger han "targeted at the growing number of companies that use feedback data and adaptive machine learning to improve their service offerings, from Google, Facebook, Apple, and Microsoft all the way to Tesla. At first this may look like a small slice of the overall economy. Yet product improvements based on data-driven adaptive systems are so impressive that an ever-increasing number of companies will adopt them, thereby expanding the purview of progressive data sharing."
Argumentationen er fin, men han begrænser sig selv for meget, og skyder en anelse skævt. Progressionsdelen er fin nok. Man kan diskutere hvordan præcist den skal virke, om det skal være en glat progression eller bare en stor (skal give), lille (skal ikke give). Men princippet er helt ok. Der hvor det går galt er i to uudtalte antagelser: At data ikke ejes af brugerne selv og at data kun bruges til service-forbedringer, ikke forarbejdning og videresalg.
Dine data er ikke dine egne
Det første handler altså om, at data ses som noget virksomhederne ejer. Det er virksomhedens guld, og den der skal give det videre, hvis nogen andre virksomheder spørger. Det er ikke brugerens data. Selvom størstedelen, hvis ikke nærmest alle, de data der opstår som feedback i brugen, i virkeligheden er data der er afledt af brugernes adfærd. Det er brugerne der frembringer "mest sete", brugerne, hvis klik afslører deres præferencer, og som herefter kan forædles og bruges videre. Og hvis det er brugernes data, er det også brugerne, der må bestemme, hvem der ellers skal kunne bruge dem. Data sharing mandatet skal derfor være et krav om at udstille data på en måde, så brugerne let kan håndtere dem, og let give adgang til at andre også må bruge dem. Og det skal være allesammen, ikke kun en randomnly chosen del.
Kun service-brug af data - ikke den "onde" cirkel
Det andet sted hvor argumentationen ikke folder sig rigtigt ud, er i synet på, hvordan feedback-data bliver brugt. Og her fokuserer Schonberger altså på service-forbedringerne, det som hos Zuboff er den indre, og relativt uproblematiske cirkel, som det helt tydeligt fremgår af citatet ovenfor om, at forslaget er "targeted at the growing number of companies that use feedback data and adaptive machine learning to improve their service offerings, from Google, Facebook, Apple, and Microsoft". Denne blindhed gør ikke sig selv så meget skade på hans forslag - snarere giver datadelings-forslaget endnu mere mening, hvis man tager den anden - extraction, refinement, prediction, selling - cirkel med. Og så giver det endnu mere kraft under "det er brugernes" data argumentet, fordi data jo her handler meget klart om den enkelte forbruger, der kan påvirkes.
Blind for skadelige bivirkninger
På et andet plan har fokuseringen på service-delen af feedback-data en effekt: i synes på, data-brugens skadelige bivirkninger. Schonberger ser skadevirkninger i feedback-effekten selv på konkurrencesituationen. Men ikke meget på mulighederne for politisk misbrug af data a la Cambridge Analytica, og slet ikke på mental wellbeing-diskussionen. Disse aspekter bliver får han derfor heller ikke rigtigt med i sin argumentation.
October 24, 2019
A great economist view of Big Data. Being an economist view, the book doesn't actually focus on Big Data and Machine Learning advancements, it focuses on the roles of the firm, markets and labour in the future economy now that the use of data analytic techniques are increasingly used to automate decision processes.
The book is subscriptive in it's ideas for the future economy. It first poses relevant questions about the influence of recent technology on our economy to technology developers and consumers alike, which need to be asked and well thought out. Plus, it proscribes, in a very positive light, how data may be an additional measure of value in our economy similar to the traditional use of money as a notion of value. Viktor does a great job of introducing the historical tensions between firms and the market while entangling together the factors Big Data will play in this economic relationship.
Personally, I did feel inspired by some of the examples provided like Fintechs and Stitch Fix. Though, every time I read a book about Big Data, I feel that the author assumes that the success in a few selected areas translates to successful in every other industry imaginable. The author mentions healthcare, education, pharmaceuticals, air travel and others. I do believe each of them can be influenced and are being influenced, but to very different degrees and rates. Not discussed, because it would have been out of place but is still interesting to consider, was the factors influencing the implementation of these systems in real industries. Further, the sources of data collection, for more traditional industries, are not set up well and will not develop as fast as those industries more easily tackled by San Fran start-ups where the data availability, cleaning methods and analysts are ready to go! A book I would love to read is, one about, "The Data Hype what it is actually Doing and What it Can't or Shouldn't: Investigation by Market Sector". I will stop my complaints about this field and move back to the book...
On another note, examples like Cybersyn make me have serious concerns similar to those voiced originally by Norbert Wiener. The quote from the book which summarizes my sentiments is, "By contrast, government control of adaptive machine learning systems in data-rich markets retains the trappings of decentralized coordination and the appearance of free will, but turns Norbert Wiener’s powerful concept of cybernetics into Big Brother riding data-rich feedback loops. It’s precisely what Wiener was worried about".
Additionally, I appreciated Viktor's discussion on alternative government policy decisions. For example, taxing companies to reveal, not just models, but data used as to give small competitors a chance to remain competitive. However, from discussions about Capital Decline and a universal basic income (UBI), the the author, even though describing the changing role of markets, appears to me to be subscribing a highly government controlled state future if we are to ever going to effectively integrate these technologies.
Altogether, the book provides a much needed discussion on the economic ramifications of Big Data.
The book is subscriptive in it's ideas for the future economy. It first poses relevant questions about the influence of recent technology on our economy to technology developers and consumers alike, which need to be asked and well thought out. Plus, it proscribes, in a very positive light, how data may be an additional measure of value in our economy similar to the traditional use of money as a notion of value. Viktor does a great job of introducing the historical tensions between firms and the market while entangling together the factors Big Data will play in this economic relationship.
Personally, I did feel inspired by some of the examples provided like Fintechs and Stitch Fix. Though, every time I read a book about Big Data, I feel that the author assumes that the success in a few selected areas translates to successful in every other industry imaginable. The author mentions healthcare, education, pharmaceuticals, air travel and others. I do believe each of them can be influenced and are being influenced, but to very different degrees and rates. Not discussed, because it would have been out of place but is still interesting to consider, was the factors influencing the implementation of these systems in real industries. Further, the sources of data collection, for more traditional industries, are not set up well and will not develop as fast as those industries more easily tackled by San Fran start-ups where the data availability, cleaning methods and analysts are ready to go! A book I would love to read is, one about, "The Data Hype what it is actually Doing and What it Can't or Shouldn't: Investigation by Market Sector". I will stop my complaints about this field and move back to the book...
On another note, examples like Cybersyn make me have serious concerns similar to those voiced originally by Norbert Wiener. The quote from the book which summarizes my sentiments is, "By contrast, government control of adaptive machine learning systems in data-rich markets retains the trappings of decentralized coordination and the appearance of free will, but turns Norbert Wiener’s powerful concept of cybernetics into Big Brother riding data-rich feedback loops. It’s precisely what Wiener was worried about".
Additionally, I appreciated Viktor's discussion on alternative government policy decisions. For example, taxing companies to reveal, not just models, but data used as to give small competitors a chance to remain competitive. However, from discussions about Capital Decline and a universal basic income (UBI), the the author, even though describing the changing role of markets, appears to me to be subscribing a highly government controlled state future if we are to ever going to effectively integrate these technologies.
Altogether, the book provides a much needed discussion on the economic ramifications of Big Data.
May 30, 2019
As a perfect complement to “Prediction Machines” Thomas Ramge, a versatile author (The Flicks, Wirtschaft verstehen mit Infografiken) and Technology Correspondent at "brand eins" and Viktor Mayer-Schönberger, professor at the University of Oxford, take us on a bird's eye view on the new capitalism of data driven by AI. In their brilliant book (the German title „Das Digital“ is the perfect analogy to Karl Marx’s classic „Das Kapital“) they explain how the ongoing transformation to data-rich markets changes the role future role of capital and also challenges companies substantially. Both discuss two options: (1) The increase of efficiency driven automatization of routine decisions and (2) the fundamental change of organization and culture in the way like CEO Dieter Zetsche introduced swarm intelligence to the organization of Daimler AG. Proposing the idea of a data-sharing duty and reflecting the idea of robo-tax and also the concept of unconditional basic income (bedingungsloses Grundeinkommen). they give a lot of inspiration and finally a profound reasoned confidence for digital welfare. A clear must-read of every digital citizen.
August 12, 2021
1.5 stars
With this book it is perhaps necessary to separate the ideas and concepts from the actual written content and the style (or lack of) in which the book is written.
The ideas and concepts have a large dose of truth in them, in the age of big data, firstly, decisions will be taken on the basis of 'rich information' rather than reliance on a single proxy measure of price, and secondly, big data will allow for many more decisions to be automated removing the need for complex structures within organisations and endangering the role of some firms when customers are able to have the decisions taken for them without the need for coordinating firm mechanisms. (as other reviewers point out both these trends have been going on for some time, and the hardly merit the grand title of 'reinventing capitalism').
At least that is what I think the book is supposed to about, it is honestly a bit hard to tell because the book is written in an atrocious style that makes it almost unreadable (ref. the many other reviews here of people who gave up on this, even though it is a relatively short book). Most chapters start with some chatty business anecdote, which may or may not have anything to do with what follows, then when it gets to discussing the theory the book slides into verbiage and grandiose terms that obscure exactly what is meant to be said. There is very very little continuity between chapters and it is quite possible to read long passages, and then have very little idea what has actually been said. From an Oxford Prof. and an Economist writer, this is inexcusable, just lazy writing and editing.
Read something, anything else on this important topic, this is not worth your time.
With this book it is perhaps necessary to separate the ideas and concepts from the actual written content and the style (or lack of) in which the book is written.
The ideas and concepts have a large dose of truth in them, in the age of big data, firstly, decisions will be taken on the basis of 'rich information' rather than reliance on a single proxy measure of price, and secondly, big data will allow for many more decisions to be automated removing the need for complex structures within organisations and endangering the role of some firms when customers are able to have the decisions taken for them without the need for coordinating firm mechanisms. (as other reviewers point out both these trends have been going on for some time, and the hardly merit the grand title of 'reinventing capitalism').
At least that is what I think the book is supposed to about, it is honestly a bit hard to tell because the book is written in an atrocious style that makes it almost unreadable (ref. the many other reviews here of people who gave up on this, even though it is a relatively short book). Most chapters start with some chatty business anecdote, which may or may not have anything to do with what follows, then when it gets to discussing the theory the book slides into verbiage and grandiose terms that obscure exactly what is meant to be said. There is very very little continuity between chapters and it is quite possible to read long passages, and then have very little idea what has actually been said. From an Oxford Prof. and an Economist writer, this is inexcusable, just lazy writing and editing.
Read something, anything else on this important topic, this is not worth your time.
December 31, 2018
Some of these other reviews seem quite harsh. I found the book quite thought provoking, particularly with regards to the impact to the banking system, and the investments of those nearing retirement age (when disruptions are expected to escalate given the current state of affairs in AI/data mining). I do believe that the move from having price only as a single metric for value, to a realtime multidimensional method of valuation will have profound effect on markets. The case for how firms will respond made less impact on me - but only because it is more difficult to even fathom how to respond in such a data-rich competitive environment. I fear that the disruption could lead to another great recession, because market liquidity (having enough buyers and sellers) is totally price-centric right now; as we "bleed out" buyers and sellers into alternative markets, we could have am even greater divide between the 1%-ers and the rest of us. So, this book is a VERY good read for those interested in equity and private markets. Probably much less interesting to those more into the "how" big data works.
January 15, 2019
The idea that how data will become the most valuable asset and the inherent value of money will be reduced. How markets will change for better. Marketplaces, products will carry lot more info than only price and will become more efficient. Price will cease to be the only signal, and thus the value of money will reduce.
Future marketplaces will be much more informed, will know the choice and preferences of the buyers and sellers.
Future marketplaces will be much more informed, will know the choice and preferences of the buyers and sellers.
March 7, 2019
Boring, repetitive, and overly long in making its central point that big data provides a totally new form of capitalism.
If you want to read a thorough, well-researched, and interesting book about big data and the influence technology has on capitalism and business models look elsewhere.
My first pointer is the much praised ‘The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power’ by Shoshana Zuboff that was published in early 2019.
If you want to read a thorough, well-researched, and interesting book about big data and the influence technology has on capitalism and business models look elsewhere.
My first pointer is the much praised ‘The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power’ by Shoshana Zuboff that was published in early 2019.
December 27, 2019
This is an important book for anyone interested or involved in research related to Big Data. Prof. Mayer-Schönberger does a great job at giving an overview to the recent emerging technologies – from an economics perspective.
Some reviews here seem to miss the point that this book is addressing impact of Big Data in economics. How companies, markets and labour in the future economy will evolve...
I enjoyed the book, it was also useful for me to reference it in my research work.
Some reviews here seem to miss the point that this book is addressing impact of Big Data in economics. How companies, markets and labour in the future economy will evolve...
I enjoyed the book, it was also useful for me to reference it in my research work.
March 9, 2023
Viktor Mayer-Schönberger is Professor of Internet Governance and Regulation at Oxford University. Thomas Ramge is technology correspondence for Brand eins. This is a widely general look at how the use of data could replace existing price or money based capitalism and in turn change the meaning of the firm as a means of carrying out business that refuses to examine how data is generated and AI created and therefore is of theoretical use only.
May 30, 2018
The authors are strikingly critical of the EU's Second Payment Services Directive. They predict the dominance of traditional banks and an end to intermediary financial services under the directive as it birthes "the sea of information that rewired market unleash." An interesting empirical claim if not a contradiction in the authors' general premises of a prevailing data market.
September 7, 2018
I like the book. It doesn’t give a lot of new information, examples are quite known. I like how he compile argumentation on how we should move forward with this.
He wrote this in a way that it is easy for everyone to read and understand. I don’t think an economist or a data scientist will find this book stimulating though. Or maybe.
He wrote this in a way that it is easy for everyone to read and understand. I don’t think an economist or a data scientist will find this book stimulating though. Or maybe.
January 13, 2020
This book had an interesting key point of how big data will enable a new, better actualized age of capitalism through data-rich economy. The key point is, however, watered down with a bunch of other less or more relevant topics on modern business and economy. The book goes broad where deep whould have been more interesting.
October 2, 2021
A quick, yet, stimulating and forward-thinking read that sheds some light on what the future might hold.
Although the title is, in my opinion, not the best fit, Mayer-Schönberger explores the central idea of data-rich markets in a structured and comprehensible manner.
Eventually, I was lacking a dedicated chapter on the dangers/drawbacks of data-driven markets.
Although the title is, in my opinion, not the best fit, Mayer-Schönberger explores the central idea of data-rich markets in a structured and comprehensible manner.
Eventually, I was lacking a dedicated chapter on the dangers/drawbacks of data-driven markets.
May 5, 2018
can get quite repetitive in each chapter/across chapters but generally rlly insightful on laying out the shift of economy from 'price/money' to 'data', and the implications which firms, markets and governments are just starting to grasp and adapt to
July 26, 2018
Couldn’t finish. Repetitive and simply didn’t capture my interest. Perhaps it’s because I’m not an economist? I love data so was really eager to read the book after hearing an interview with the authors but the book was a huge disappointment.
January 13, 2019
Long-winded puff piece. Money controls the allocation of scarce resources; he ignores the scarce part and somehow imagines that the abundance of data will lead to getting everything for free. The latter half of the book is fantasizing over robots spending your money.
May 31, 2021
This is an Amazing eyes opener book in terms on the changes that our world is currently having and the importance of a new mindset regarding the economy, big data, machine learning and decision making as human been.
June 20, 2018
April 2018
1,5 stars
Didn't finish
1,5 stars
Didn't finish
April 9, 2020
I expected much more insight from someone with the title of 'Chair of internet governance and regulation' at Oxford University.
October 3, 2022
Interesting book, although it sounds a bit too optimistic at this stage
Read
December 31, 2023Dnf
November 5, 2019
Een interessante kijk op de toekomst van datarijke markten en hun economische en maatschappelijke invloed op het bedrijf, de markt en de maatschappij. Veel standpunten in dit boek voelen futuristisch aan maar wordt onderbouwd door praktische voorbeelden die al lang aan de gang zijn. Een nog niet uitgekristalliseerde toekomst waarin automatisering door middel van datarijkheid grote veranderingen op gang brengt heeft mensen nodig met de capaciteit deze ontwikkelingen te schetsen en beoordelen. Mayer-Schönberger en Ramge geven alvast het goede voorbeeld.
January 25, 2021
A book with a big title like this promises a lot. However, I am not sure that it delivered on its promises.
The first 8 chapters were not that information dense, I felt I was reading the same thing over and over. Yes, I get it, data rich markets are the next big thing. Actually, I kinda felt many of the things that were mentioned were already in place for a while, like Spotify, Ebay and Google ads.
However, in chapter 9 and 10, the authors took a turn on a pathway of thought I had not taken before. It went into the governmental and regulatory implications for such a data-rich market world. Most inspiring was their plead for setting up new mechanisms, facilitating the feedback of learnings from data by companies back into the society and open for others to use. This could possibly counter-act the monopolies on data we are seeing nowadays. However, these insights came too little and too late for me to give this book more than 3 stars though.
The first 8 chapters were not that information dense, I felt I was reading the same thing over and over. Yes, I get it, data rich markets are the next big thing. Actually, I kinda felt many of the things that were mentioned were already in place for a while, like Spotify, Ebay and Google ads.
However, in chapter 9 and 10, the authors took a turn on a pathway of thought I had not taken before. It went into the governmental and regulatory implications for such a data-rich market world. Most inspiring was their plead for setting up new mechanisms, facilitating the feedback of learnings from data by companies back into the society and open for others to use. This could possibly counter-act the monopolies on data we are seeing nowadays. However, these insights came too little and too late for me to give this book more than 3 stars though.
August 23, 2020
First half of the book is good intro to the topic of big data but the second half of the book just goes off on a tangent with some leaps of extrapolation and wonky conclusions.
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