Power Dvd 22 Review

[Miina Hunker]

Thosechoices are determined by the relations of power in a society, and in recent decades our choices have been steered in a negative direction by the power of tech company founders and venture capitalists.

The type of technologies that society invents can be chosen so as to distribute benefits more widely, by avoiding technologies that replace workers and inventing technologies that complement workers.

At the end of the nineteenth century, German chemist Fritz Haber developed artificial fertilizers that boosted agricultural yields. Subsequently, Haber and other scientists used the same ideas to design chemical weapons that killed and maimed hundreds of thousands on World War I battlefields.

[Belief in the power of productivity] suggests that as technology advanced rapidly during the early phases of the Industrial Revolution, wages should have risen. Instead, real incomes of the majority stagnated.

We find no discernible relationship between AI exposure and employment or wage growth at the occupation or industry level, however, implying that AI is currently substituting for humans in a subset of tasks but it is not yet having detectable aggregate labor market consequences.

But Acemoglu and Johnson also spend a lot of time arguing that persuasion is also a form of power. They cite instances in which techno-optimists and businesspeople in 18th century England and 21st century America persuaded the public to enact pro-business policies, through articles, speeches, conversations, and so on. Their explanation for why inequality has increased since the 1970s is, in effect, that silver-tongued technologists managed to persuade American society to weaken pro-worker institutions, and to allow the technologists to invent technologies that replaced human labor instead of complementing it.

[R]eal wages grew more slowly than real GDP per worker during the industrial revolution. However, the discrepancy was much less than has been claimed such that in 1820 the former had risen by about 12 per cent since 1770 and the latter by about 16 per cent. Second, labour productivity grew quite slowly prior to 1830 averaging a little below 0.4 per cent per year in the 60 years after 1770. Nevertheless, in the context of demographic pressure this was a very good outcome by pre-industrial standards. Third, as relative prices changed and exportable manufactures became cheaper, over the long run real product wages grew somewhat faster than real consumption earnings. Fourth, the share of profits in GDP rose over time from 17.2 per cent in 1770 to 31.3 per cent in 1860 but this was associated with a decline in the share of land rents and the share of labour was little changed. Fifth, looked at through the lens of growth accounting the evidence is of total factor productivity (TFP) growth accelerating only gradually to 0.6-0.8 per cent per year during 1830 to 1860 with the steam age only materializing after 1830.

In sum, this looks more like a story of paradoxically slow productivity growth than of pro-rich growth. The story of the industrial revolution is definitely not one of a new general-purpose technology boosting productivity growth at the expense of a big shift in the distribution of income which is the current fear about AI.

In fact, for unions [in the 1960s] the central issue was worker training. They insisted on training provisions to ensure that workers could be brought up to the necessary skill level to operate the new machinery and benefit from it.

This is very different from affecting the direction of innovation! This is a case of workers collectively pushing companies to invest in human capital, so that worker skills can catch up to the direction in which innovation was already going.

As far as I can tell, this book does not contain even one single example of when a union or government supposedly pushed an entrepreneur or company to choose a different path of technology in order to benefit workers. As far as I can tell, it does not even contain one single example of when an engineer, entrepreneur, company or investor chose to create a technology in order to benefit workers more.

In particular, these solutions seem inferior to something far simpler: policies to increase labor share ex post. Labor market institutions like co-determination and sectoral bargaining, and direct interventions like wage subsidies funded by taxes on capital income, can push up the labor share without requiring panels of experts to predict the unpredictable. And if entrepreneurs really do have any degree of foresight about whether their innovations will tend to push the labor share up or down, these policies will act like a Pigouvian tax on the kind of cost-cutting that Acemoglu and Johnson decry. With a wage subsidy, for example, the higher the market rate you can afford to pay your workers, the more of a rebate you can get from the government. So if there are technologies that augment your workers and let you hire new workers, a wage subsidy gives you an incentive to create them.

Meanwhile, real wages have been rising strongly for years now, interrupted only by the post-pandemic inflation. And wages for production and nonsupervisory workers have risen more robustly than those for managerial workers:

All this has happened in exactly the time frame during which AI has exploded. Predictive AI burst onto the national scene in 2012 with the ImageNet paper, the basic technology for generative AI was created in 2017 with the transformer paper, and generative AI became really widespread in 2022-23 with LLMs and AI art programs. To reiterate: essentially all of the commercialization and implementation of artificial intelligence has happened during a time in which wages have been rising, inequality has been flat or falling, and employment has been high.

so i'm really glad to read this review: virtually every point seems to me clearly-stated, accurate, & relevant. taken altogether, usefully deflates a set of views that might otherwise be taken over-seriously.

Another strike against this book is its terrible scholarship: it frequently gets basic facts incorrect because the authors haven't bothered to actually research the topic. Example: it claims at several points that Eli Whitney was responsible for the development of interchangeable parts, a claim that has been widely and thoroughly debunked.

It\u2019s hardly surprising that Power and Progress made it onto practically every list of the most important business books of 2023. First, there\u2019s the unrivaled pedigree of the authors themselves. To call Daron Acemoglu a powerhouse in the world of economics would be a ludicrous understatement:

Acemoglu is also the main proponent of the institutional explanation for national development, through his famous book Why Nations Fail and its sequel, The Narrow Corridor (both with James Robinson). If you hear me talk about \u201Cinclusive institutions\u201D and \u201Cextractive institutions\u201D, I\u2019m channeling Acemoglu.

Simon Johnson, meanwhile, is the author of some of my favorite popular books about economic policy, especially Jump-Starting America (with Jonathan Gruber) and 13 Bankers (with James Kwak). When I write more about the need to spend more on science and to restrain the excesses of the finance industry, I\u2019m channeling Johnson.

The second reason this book was destined to garner attention is that it brings together two extremely timely strains of thought: 1) the widespread distrust of tech companies that has grown in American society over the last few years, and 2) the wave of anxiety over AI-driven automation. Power and Progress weaves those two anxieties into a more-or-less coherent whole \u2014 a sum of all technological fears, if you will. And it seems to have been spectacularly well-timed, since its release coincided closely with the coming of ChatGPT and other generative AI.

But given all of those powerful tailwinds, I have to say I\u2019m kind of surprised at how little of a splash Power and Progress seems to have made. This is anecdotal of course, but in the 9 months since it came out, I\u2019m not sure I\u2019ve once heard someone reference the book or any idea in it. The authors clearly intended it to be a handbook for people who are scared about AI putting humans out of a job, the way Thomas Piketty\u2019s Capital in the Twenty-First Century became a handbook for people worried about inequality, or Robert Gordon\u2019s The Rise and Fall of American Growth became a handbook for people concerned about technological stagnation. But I don\u2019t think it did.

Why not? One reason might be that the timing wasn\u2019t as favorable as it might appear. Contrary to Acemoglu and Johnson\u2019s assertion (on p.24 of the hardcover edition) that we live in an age of \u201Cblind techno-optimism\u201D, the internet is absolutely chock-full of arguments and warnings about the downsides of AI. Concerns over the risk of rogue Artificial General Intelligence resulted in a boardroom coup attempt that almost drove Sam Altman out of OpenAI. Worries that AI wouldn\u2019t uphold diversity led Google to implement some pretty hilarious countermeasures. Fears of mass surveillance, deepfakes, etc. are widespread. And of course the idea that AI is going to lead to mass unemployment is absolutely ubiquitous \u2014 so much so that practically every San Francisco tech event I go to features discussions about exactly this subject. Yes, even dance parties.

On top of that, though, I have to say that this book\u2026well, I just don\u2019t think it\u2019s very good. I winced while I wrote that sentence, because Simon Johnson is a personal friend, and Acemoglu is a celebrated genius, and because both of them have written such good books in the past. This is the first broadly negative book review I\u2019ve written since 2014, and I\u2019m a lot less combative of a blogger than I was a decade ago. I did not want to pan this book, especially because I think the topic is a good and important one, and I think the authors are brilliant people whose hearts are in the right place.

3a8082e126