Yves here. See, your intuition about artificial intelligence has been confirmed! But how could it not have been, at least by someone independent? First “artificial intelligence” means getting rid of humans. We live in a neoliberal system, after all! We’re seeing it already in knowledge businesses like the law, where a lot of what used to be yeoman work for young attorneys to learn their trade is now performed by software. Second, the people hyping its benefits have been Silicon Valley members or adjacent. Rather a big tell, don’t you think?
By Anton Korinek, Associate Professor of Economics, Darden School of Business of the University of Virginia and Joe Stiglitz, Professor of Economics, Columbia University. Originally published at the Institute for New Economic Thinking website
The past-half century has spurred unprecedented economic growth for many developing countries – brought about by the rapid adoption of technological advancements – and has led to widely shared increases in prosperity, lifting billions out of poverty. But there is no guarantee that this process will continue in future decades.
Our new working paper on “Artificial Intelligence, Globalization, and Strategies for Economic Development,” challenges the long-standing assumption that technological progress will necessarily continue to advance broadly shared prosperity in developing countries. We argue that new developments in Artificial Intelligence (AI) may create the perfect storm to decouple technological advancement from broadly shared increases in living standards.
Dozens of developing countries have escaped from poverty in recent decades through a strategy of export-led growth, fueled by manufacturing goods produced with their abundant supply of cheap labor or their richness in natural resources. But artificial intelligence poses a triple threat to this strategy. First, artificial intelligence automates labor, which strips developing countries of their comparative advantage in cheap labor. Second, artificial intelligence produces more output with fewer natural resources. And for nations that rely on exporting their natural resources to pay for food and other essentials, artificial intelligence advancement could have deadly consequences. Third, artificial intelligence operates in a “winner-takes-all” market.
Consider the example of a hypothetical village cobbler. In the past, a village’s best cobbler only mended a few more shoes than his less adept rival down the lane. In contrast, today’s best artificial intelligence producers can easily achieve monopoly over their slightly worse competitors, because unlike a cobbler, an AI-producing firm has no physical restrictions on how many products it can create or how widely these products can be shared. This would be as if the best cobbler could produce limitless shoes — everyone would immediately switch to her product. These “winner-takes-all” markets privilege the most successful firms in artificial intelligence-powered economies, a dynamic that could also widen income inequality between developed and developing nations.
As long as the winners and losers from technological progress are located within the same country, there is at least the possibility that domestic policy measures can be employed to compensate the losers. However, when technological progress disadvantages entire countries through global trade, domestic policy measures are insufficient to provide compensation to the losers of progress. This could leave entire nations worse off.
While proponents of neoliberalism have long-preached that technological progress benefits all, this dogma is not supported by theory nor by evidence. We cannot be sure that the wealth generated from technological advancement will “trickle down” to benefit developing countries, and especially the most vulnerable within developing countries. In fact, economic theory has always held that advances in technology create winners as well as losers.
And it’s worth remembering that for large parts of the post-Industrial Revolution era, advances in technology have not been associated with broadly shared prosperity: the early decades of that era saw declining standards of living. Read any Dickens novel for a portrait of the brutal realities laborers confronted. In fact, during the first decades of the Industrial Revolution, life expectancy actually decreased. And there is a wealth of evidence that there has not been shared prosperity within at least the US in recent decades. It was only in the 19th century that advanced countries broke from eons of subsistence living; and only from the middle of the 19th to the middle of the 20th century that inequalities were reduced. The half-century of growth that enabled the East Asian Miracle, when we zoom out, looks even more like a brief anomaly. There’s no reason to believe our comparatively brief success in advancing living standards for the average worker will continue forever, or that the convergence between the developed and the less developed will continue. Without appropriate public policy interventions, changes in technology can worsen inequalities within and between countries.
So how do we avoid that future technological progress reduces worker incomes and impoverishes the poorest among us further? We may be able to learn a lesson from history. The Industrial Revolution provides a case study in the labor challenges arising from technological transformation and suggests that collective action may be an antidote to some of our challenges.
Society initially struggled to navigate the Industrial Revolution’s rapid technological transition. But while it took time for workers to mitigate these challenges, eventually, widespread technological progress led to large increases in the marginal product of labor and harkened in an “Age of Labor.” Many of the Industrial Revolution’s innovations increased the productivity of workers, especially of the low-skilled segment. For example, mechanized cotton spinning increased the output of an individual worker by a factor of around 500 while reducing the skill requirements. Increased efficiency meant that market wages of workers rose. In economic language, technical progress during the Industrial Revolution was labor-using – it increased overall demand for labor and therefore raised wages. Additionally, labor organizing resulted in a new wave of protections for workers, including increased safety standards. Furthermore, the Industrial Revolution needed a productive workforce, which incentivized public schooling and in turn equalized education. However, the analogy to the Industrial Revolution may only go so far.
One of the defining challenges posed by the Artificial Intelligence Revolution is that AI could be labor-saving overall, meaning that it could reduce demand for labor, and especially the types of unskilled labor that forms the basis of growth in many developing countries. This would simultaneously reduce workers’ pay and decimate their political power. Artificial intelligence might undermine the chance for workers in developing countries to ever acquire the same rights and protections that their counterparts in rich countries have been afforded through collective action during the “Age of Labor.”
Experts vary widely in their predictions about AI’s impact: some believe artificial intelligence will be less impactful than indoor toilets; others believe the steam engine will pale in comparison to future advances in artificial intelligence.
How do we navigate this uncertain future? In the same way we buy fire insurance when posed with a low chance of a devastating house fire, we need to concentrate our resources now into mitigating scenarios that could be extremely damaging. Research, and in particular, economic analysis, is one of the best means we currently have available to invest against what may potentially be devastating economic effects of AI in the future, such as widespread poverty resulting from the displacement of workers.
So what does our research suggest as the best course of action? Over the coming decade, although automation certainly threatens many jobs, we believe there’s still so much work that society needs done that can only be performed by humans, and there are many people who want to work. When markets fail to make the match between workers and jobs, governments should step in.
The whole world would benefit from the large investments required to retrofit the global economy for climate change—and it would pay the developed countries to provide financial assistance to the developing countries to help them make these investments.
A development strategy for governments of both developing and rich countries is to promote digital infrastructure improvement. This would feed two birds with one crumb: first, by providing unskilled laborers with ample work, and second, by better positioning their country to participate in the digital economy. Other potential projects include investing in public transit, which would allow low-income workers to commute to jobs, and strengthening healthcare, which would disproportionately benefit those made vulnerable by unemployment.
Additionally, developing countries may need to shift toward new economic models, particularly as AI strips them of their traditional comparative advantages in manufacturing and natural resources. One course of action could be to focus on strengthening agriculture or developing a service-based economy. Luckily, both agriculture and service sectors can co-exist with artificial intelligence. Emerging technologies offer small farmers opportunities to optimize their yield, while digital platforms let those same farmers trade their products at fair-market prices, increasing their earnings. Additionally, services-based industries like healthcare or tourism provide an arena for investment where artificial intelligence replacement is a more distant prospect.
While countries certainly should employ individual strategies, they also must collaborate internationally. If we’ve learned anything from the Industrial Revolution, it’s that collective action can be a powerful antidote to transformational technologies that increase inequality. Yet the Artificial Intelligence Revolution may need collaboration on a much broader scale to avoid that whole countries lose power and risk getting left behind. For example, global tax regimes have recently received much-needed attention focused on closing tax loopholes for multinational corporations.
Another area ripe for reform is global competition policy. When the EU began investigating Google in 2010, or when Germany investigated Facebook in 2016, the US took these inquiries as a political affront rather than a legitimate examination of economic competition norms. If the EU and Germany, both powerful political actors, find it difficult to control the digital giants, how much more difficult would it be for developing countries to exert control over them? Countries must coordinate competition policy together, for example via a common authority, to ensure that global winner-takes-all markets truly remain competitive.
Another promising avenue is in updating intellectual property policy for the Age of AI. We could, for example, reduce patent lengths for the use of digital technologies to ensure that the gains from AI-based innovations are shared broadly. There is a strong case for granting developing countries access to patented technologies: most patents are produced in rich countries and generate most of their income from sales and licenses within rich countries, so innovators would not incur significant losses if developing countries could use their technology for free or with limited royalties before their patents expire in high-income countries.
Relatedly, the direction of innovation has largely been shaped by those in advanced countries. It has been “steered” in ways that have not even served well the majority of those in these countries, let alone those in developing countries and emerging markets. The developing countries and emerging markets might cooperate to steer innovations in ways that advance their interests, and, in particular, the interests of their unskilled workers. Given the potential of AI, the extent to which this can be done remains an open question.
Data and information policy is perhaps the most pressing realm for regulation, and accordingly, has moved to the top of the policy agenda. Currently, there is a risk that global AI firms are setting the agenda before democratic institutions have a chance to weigh in. For instance, the most recent United States-Canada-Mexico agreement provided protections for tech giants that may have been stronger than would have naturally emerged from a more open, democratic debate. What developing countries can do in these power plays may be limited. Monopolized data may mean that newcomers in developing nations can’t catch up to create their own AI giants. One promising development has come from the EU’s proposal to require data sharing to prevent data from being monopolized, though power asymmetries between tech giants and individuals may mean that the impact of this on global inequalities will be negligible. Another virulent externality, misinformation, also needs to be addressed through increasing internet companies’ accountability.
There are reasons for cautious optimism. The global rules are still being set and in any case have to be constantly rewritten, so there is hope that international institutions and civil society may have a positive impact on the shape of these rules and what they imply for how the fruits of future progress in AI will be shared.
We thank Sophia Nevle Levoy for superb research assistance in writing this post.
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This must be the 100,000 feet view. I couldn’t make out any of the details.
Looks like you have reading comprehension issues. For grins, I glanced at only one para and found this:
Oh, and this too:
And you seem to not understand the importance of this big picture: it is totally at odds with “The future is so bright I’m gonna need shades” AI cheerleading, which is typically at the same level of detail as this piece.
Timbuk 3
https://www.youtube.com/watch?v=8qrriKcwvlY
Love those 80’s music videos.
Computer networks have eliminated so many law firm supporting jobs that it only makes sense they will eventually come for the associates too. Thirty years ago, a rain maker had two secretaries, the firm had librarians, receptionists, document runners, a typing pool, and people who ran the financial side of the business; 90% of those jobs are gone now.
Not all AI’s are on the same level based on what is fed into the model – a car dealership bot is a complete moron when compared to AI’s that try to replace rainmakers.
I dunno. I’m not always a fan of the NC commentariat mass mind, but in this case almost all the commenters strike me as more intelligent — and on point about the specific issues — than the original post. David’s comments further down the thread about how it generalizes and wanders are justified, IMO.
Yves S. to Fresh Cream.:you seem to not understand the importance of this big picture: it is totally at odds with “The future is so bright I’m gonna need shades” AI cheerleading, which is typically at the same level of detail as this piece.
You have to feed the NC machine every day with new content, of course. Still, as you say, you wouldn’t give exposure to a piece of AI cheerleading with the level of unjustified generalization that goes along with that. This piece — setting aside the Stiglitz byline — is about as useful/useless as the AI cheerleading.
Odd that you discover a mass mind in NC commentariat. I read the posts and comments on Naked Capitalism because they deviate along so many different directions of opinion.
As for the post above, though I do agree with your characterization of its content as strings of “unjustified generalization” I differ with you on your notion that it is a “piece of AI cheerleading”. I think the author(s) has swallowed too much from many different punch bowls of KoolAide.
[1] I’m not saying the OP is a piece of AI cheerleading, but that it’s almost as full of unjustified generalization as a piece of AI cheerleading.
Seventy years there were critiques of AI from the likes of Norbert Wiener (THE HUMAN USE OF HUMAN BEINGS) and even Kurt Vonnegut (PLAYER PIANO) that were more astute. (Though they didn’t call it AI then.)
{2] Jeremy G: Odd that you discover a mass mind in NC commentariat. I read the posts and comments on Naked Capitalism because they deviate along so many different directions of opinion.
Yes, NC is a relative haven of intelligent, informed opinion. But it is all relative, isn’t it?
“NC commentariat mass mind” sounds absolute, not relative. Where do you find relatively more “intelligent, informed opinion” after sampling the “NC commentariat mass mind”?
Where do you find relatively more “intelligent, informed opinion” after sampling the “NC commentariat mass mind”?
Oh boy. So, for instance, this is someone worth paying attention to —
https://en.wikipedia.org/wiki/George_Church_(geneticist)
Sorry. I’m not trying to be snotty, but you asked.
A year ago, for another example, nobody here was discussing mRNA vaccines. Right now, similarly, nobody here is discussing in vitro gametogenesis (and what that implies for in vitro organogenesis and what that in turn will mean) or quantum computing to model protein-folding (which is just about ready for prime time).
Nevertheless, these things will have big social effects over the coming decades. The last half-century was dominated and transformed by computers and network technologies. The next will be dominated by synthetic bio-based biotechnologies, but the effects will be far more profound because these technologies have the capability to reframe the terms of life itself.
But if one discussed these things here: firstly, most people here would have no comprehension of them; secondly, they have no idea these things are already in process; and, thirdly, to the extent they did get their minds around them, they would mostly have the usual reactionary, neo-Luddite horror that normal folks have — that you probably have? — that what they’ve taken to be the permanent, unchanging verities of society, the human condition, and blah blah blah are no such thing.
I will toss out my two cents. Discussion of mRNA vaccines only became of interest because of Big Pharma’s push to make a Corona vaccine at warp speed. In spite of claims to the contrary I doubt the researchers pushing mRNA vaccines understand the cellular processes they are monkeying with — and if they do understand them so well I wonder why not pump mRNA into a vat of yeast cells or E. coli and harvest Corona spicules to make a more conventional vaccine for humans. Besides I would expect a vial of Corona spicules would be much more durable than lipid encapsulated mRNA.
I expect discussion on the impacts of in vitro gametogenesis or in vitro organogenesis will become of interest as those technologies mature and find application — most probably in efforts to repopulate vanishing species. But there are definitely a few ethical issues and social implications. The protein folding problem and quantum computing have been just about ready for prime time almost as long as fusion power. The initial impetus for working on the protein folding problem was the belief that knowing the three-dimensional structure of proteins would reveal deeper understanding of how they function. A large catalog of protein structures has been collected but I have not noticed remarkable advances in understanding how proteins function. I recall mention in NakedCapitalism of the 2020 CASP (Critical Assessment of protein Structure Prediction) contest and the success of the Google DeepMind AI. There was little discussion of the topic because the impacts remain undeveloped.
For discussions of synthetic bio-based biotechnologies I think the University of Washington Institute for Protein Design might be a better place to look. Try “Audacious Project” [https://www.ipd.uw.edu/audacious/ ] There is even a TED talk there and an effort to collect donations and outside investors thinly disguised as reaching out for public participation and ideas. Join the Foldit project.
As for reactionary, neo-Luddite horror, I am indeed very conservative in my regard of processes with potential far-reaching impacts on the human condition. Humankind has already experienced too many unforeseen and unforeseeable consequences resulting from processes with potential far-reaching impacts on the human condition — consider the manifold impacts of burning fossil fuels. I do believe in unchanging verities of society — change and the impossibility of foretelling how a given change might impact the human condition. I believe the second verity I mentioned counsels conservatism to the wise.
This is why you should go forwards, not backwards.
The Americans repeat the mistakes they made in the 1920s.
Policymakers couldn’t see what Glass-Steagall did, as they thought banks were financial intermediaries.
It separates the money creation side of banking from the investment side of banking, and stops bankers producing securities; they buy themselves with money they create out of nothing.
https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2014/money-creation-in-the-modern-economy.pdf
(There are intermediaries involved so it’s not obvious, but this is effectively what is happening)
The whole thing turns into a ponzi scheme and you get a 1929 or 2008 type event.
1929 and 2008 look so similar because they are.
https://www.youtube.com/watch?v=vAStZJCKmbU&list=PLmtuEaMvhDZZQLxg24CAiFgZYldtoCR-R&index=6
At 18 mins.
1929 and 2008 – Minsky Moments, the financial crises where debt has over whelmed the economy.
They did save the banks this time, which avoided another Great Depression.
They left the debt in place, which caused a balance sheet recession.
As a CEO, I can use the company’s money to do share buybacks, to boost the share price; get my bonus and top dollar for my shares.
What is there not to like?
Share buybacks were found to be a cause of the 1929 crash and made illegal in the 1930s.
What lifted US stocks to 1929 levels in 1929?
Margin lending and share buybacks.
What lifted US stocks to 1929 levels in 2019?
Margin lending and share buybacks.
A former US congressman has been looking at the data.
https://www.youtube.com/watch?v=7zu3SgXx3q4
What is the fundamental flaw in the free market theory of neoclassical economics?
The University of Chicago worked that out in the 1930s after last time.
Henry Simons was a founder member of the Chicago School of Economics and he had worked out what was wrong with his beliefs in free markets in the 1930s.
Banks can inflate asset prices with the money they create from bank loans.
https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2014/money-creation-in-the-modern-economy.pdf
Henry Simons and Irving Fisher supported the Chicago Plan to take away the bankers ability to create money.
“Simons envisioned banks that would have a choice of two types of holdings: long-term bonds and cash. Simultaneously, they would hold increased reserves, up to 100%. Simons saw this as beneficial in that its ultimate consequences would be the prevention of “bank-financed inflation of securities and real estate” through the leveraged creation of secondary forms of money.”
https://www.newworldencyclopedia.org/entry/Henry_Calvert_Simons
Margin lending had inflated the US stock market to ridiculous levels.
Real estate lending was actually the biggest problem lending category leading to 1929.
Richard Vague had noticed real estate lending balloon from 5 trillion to 10 trillion from 2001 – 2007 and went back to look at the data before 1929.
They did real estate for 2008.
“The Great Crash 1929” John Kenneth Galbraith
“By early 1929, loans from these non-banking sources were approximately equal to those from the banks. Later they became much greater. The Federal Reserve Authorities took it for granted that they had no influence over these funds”
He’s talking about “shadow banking”.
They couldn’t control the lending from shadow banks in the 1920s either.
They thought leverage was great before 1929; they saw what happened when it worked in reverse after 1929.
Leverage acts like a multiplier.
It multiplies profits on the way up.
It multiplies losses on the way down.
Today’s bankers seem to have learnt something from past mistakes.
They took the multiplied profits on the way up.
Taxpayers picked up the multiplied losses on the way down.
Mariner Eccles, FED chair 1934 – 48, observed what the capital accumulation of neoclassical economics did to the US economy in the 1920s.
“a giant suction pump had by 1929 to 1930 drawn into a few hands an increasing proportion of currently produced wealth. This served then as capital accumulations. But by taking purchasing power out of the hands of mass consumers, the savers denied themselves the kind of effective demand for their products which would justify reinvestment of the capital accumulation in new plants. In consequence as in a poker game where the chips were concentrated in fewer and fewer hands, the other fellows could stay in the game only by borrowing. When the credit ran out, the game stopped”
Wealth concentrates until the system collapses.
“The other fellows could stay in the game only by borrowing.” Mariner Eccles, FED chair 1934 – 48
Your wages aren’t high enough, have a Payday loan.
You need a house, have a sub-prime mortgage.
You need a car, have a sub-prime auto loan.
You need a good education, have a student loan.
Still not getting by?
Load up on credit cards.
“When the credit ran out, the game stopped” Mariner Eccles, FED chair 1934 – 48
…… etc …..
thanks for working up a reminder of some hard History lessons.
Leading the Authors 3rd Paragraph, is a dandy explanation of How ReArranged Deck Chairs will work:
Dozens of developing countries have escaped from poverty in recent decades through a strategy of export-led growth, fueled by manufacturing goods produced with their abundant supply of cheap labor or their richness in natural resources.
Paragraph 14 acknowledges climate change may be an issue–at some time.
Paragraph 16 nods this way: Additionally, developing countries may need to shift toward new economic models, particularly as AI strips them of their traditional comparative advantages in manufacturing and natural resources.
Your comments and Links are about Profits; Profiteers, and Profiting, using our Chosen Economic System to the detriment of, amongst many other things, our Life Sustaining Environment for the sake of Per$onal (mo$tly) Profit.
When AI is marketed and targeted to redress the Climate Crises and these specific things, it would be an endeavor I could see more value in.
Thank you for quoting that third paragraph – elitist, Larry Summers stuff and demonstrably untrue. Where are these dozens of countries which have escaped poverty via resource extraction and exporting cheap goods? A handful in Asia, none in Africa, none in Latin America.
From the standpoint of the poor, the most successful countries in Latin America are those which seek to manage poverty and provide basic education and healthcare for free – Cuba, Nicaragua and Venezuela.
In all three cases the priority is food sovereignty, not exports.
The fundamental unit of value is the calorie.
You could look at 2008 as being analogous to the “rich man’s” panic of 1907, which was saved by JP Morgan walking through the NYSE calling out buy orders.
Warren Buffet played that role on TV in March 2009. He put in the bottom on that day. I recall it clearly because I saw Buffet on a restaurant TV with my son, who needed new shoes, and we had just bought shoes for both of us in a hurry because there was good chance that the next day all the credit cards would be turned off and retail would be a crisis.
My point being if 2008 is 1907, then 1929 is 2030.
I dunno — maybe one thing we do better this century is move faster. . . .
NC has got to be the only site where the comment section turns into more articles.
Great post, thanks!
Interestingly, I think AI might be more of a threat to knowledge workers than manual laborers.
I will give a couple of examples to support this view.
First, I have seen several attempts that can do radio-logical analysis well. We recently saw advances in predicting protein folding that are rivaling the best research chemists and biochemists.
Second, if you look at Google’s attempt to get robots to do relatively simple tasks, the results are extremely unimpressive. Some commercial companies, such a automatic dish washers, or clothes folding robots, are really pretty pathetic and impractical devices.
Right now, we can get AI to solve quite complex strategy type problems, but we can’t get a robot to clean a toilet effectively.
There are some exceptions, such as the little floor vacuum robots, but they are really not that intelligent. They are solving a fairly constrained problem with a clever idea.
In short, my prediction is professions where you have gone through lots of training to solve a specific problem, makes you much more vulnerable to AI that a general manual laborer.
Professionals in certain industries are well organized, and can use the law to protect themselves from competition. They are likely to survive the onslaught of AI better, than professions that are not politically well connected.
As an example, I don’t think radiologists will get displaced easily by technology. However, chemists are not a well organized political group, so they may suffer more disruption.
Predicting the future is hard, not least because the system is very complex, and we don’t know what advances in AI might happen next. However, currently I would be more worried about AI hurting professionals than general blue collar labor.
I suspect that the big losers in the knowledge field could be the mid range workers. Top people in their field will always be in demand, if nothing else to give the thumbs up to the solution provided by AI.
To give a hypothetical example, if you had an AI that could trawl millions of legal judgements, regulations and decisions to produce a core summary of relevant precedents for every civil action, you would wipe out the work of an army of paralegals and young aspiring lawyers. But the senior lawyer, the person who presents to the judge, would not just keep their job, they’d probably make even more money. So you’d end up with a tiny number of winners on vast enumerations, while everyone else loses their jobs.
Historically, you can make an argument its when the lower middle and professional classes find themselves shut out of advancements that revolutions occur. AI could be the perfect storm to allow that to happen.
“if you had an AI that could trawl millions of legal judgements, regulations and decisions to produce a core summary of relevant precedents for every civil action, you would wipe out the work of an army of paralegals and young aspiring lawyers.”
On its way.
I met a guy at a party a few years ago that had a small company that did this for patent law. At that point his servers where in his basement. I also met the guy a year ago who owns a company that processes about 25k pages of disability claims a day for SS. The idea here is that if you claim you lost a leg, the software figures that out and routes it to someone with some expertise in that area along with some ‘peer’ data.
Both had some interesting stories about how they had gotten to the point where they are. Both realized there was long way to go.
This is going to hit accountants too, and already is to a growing extent. There is an entire industry already trying to replace accounts payable/purchasing functions with AI. Having been on the receiving end of some of this, I can attest that some of the ‘automation’ is not true AI – it’s merely using software to slough off one company’s AP duties to the AR departments of those they purchase from.
Larger companies are already using these systems and by extension, they force smaller companies to use them too. So far I have been unable to tell from my position far from the C suites if this will result in far less labor or if it’s simply rearranging the deck chairs so some middle managers can claim a bonus. For now I suspect it’s the latter – big companies can claim large cost cutting in AP, however at some point that will be offset in more labor needed for AR unless that gets automated as well, but so far I’m not seeing that. And of course having dealt with many of these systems, I’ve found that the tech is crap and often doesn’t work, resulting in the need for more customer service and IT work.
One thing that does seem clear – largescale use of AI in accounting will make allow the fraud to be scaled up too. Currently the accounting department I work in has all of us checking each other’s work to a large extent. That would not be the case with AI programmed and supervised by a few at the top.
I’ve seen similar things.
Most recently I’ve seen a company wanting to automate AR to improve the AR function, however, the reason why AR performed badly was due to errors in the billing. The errors in the billing came from bad inputs from sales often due to bad inputs from customers. The automation of the AR just made the sending out of incorrect billings, incorrect statements and incorrect reminders faster. GIGO.
The failed implementations that I’ve seen have all had one thing in common – poor preparation and poor planning. Sooner or later it will be done better, based on what I’ve seen then it might not be soon.
Traditional ERP systems like SAP weren’t a threat to labour because they relied heavily on manual data entry. Junior and mid-level employees, who did/do the bulk of data entry, feed information upwards via the system to upper management who gain visibility into company-wide operations via dashboards.
Enterprise AI systems however will in my opinion, not only threaten mid range workers, but will likely shrink the C-Suite too by first chipping away at, then eventually eliminating, the operational functions within it. Roles like Chief Financial Officer, Chief Risk Officer, Chief Compliance Officer etc, traditionally the lieutenants of the CEO, are squarely in the cross hairs of AI. Where CEOs used to tap their lieutenants for critical insights about the health of the business, AI dashboards will assume the role and will, at the click of a button, give a consolidated view of both company-wide and divisional performance, making the lieutenants all but obsolete. It won’t be a failure of technology that makes adoption of enterprise AI less of an existential threat for operational roles within the C-Suite, but the political clout of the lieutenants and their cosy relationship with the board and CEO that will likely see them kept beyond their usefulness (a cushion that unfortunately won’t be available to junior and mid level workers).
100% of those AI dashboards you suggest would be producing garbage in, garbage out nonsense that would not actually correspond to the health of the business. Of course, the majority of the C-levels you mention are also garbage in, garbage out seat-warmers who produce little to no value. However, they are a somewhat powerful political block and the sorts of positions that CEO’s want their kids to end up in, so I could imagine those roles may be pretty resistant to being eliminated.
We could buy those de-jobbed mid-level people off with a Universal Basic Luxury Income.
Calls to mind Charlie Chaplin’s madcap “intelligent eating machine” in Modern Times: https://www.youtube.com/watch?v=17PkUsTVa7g
This is a really key point. The author lists manufacturing and natural resource extraction as industries likely to be disrupted by AI (which I guess the author is using synonymously with machine learning), but I have a very tough time seeing how that’s going to happen in the near-term. Machine learning is a tool that is well suited for certain types of tasks where there are large labeled datasets, or tasks where there are very easy mechanisms for providing a lot of feedback to an algorithm without exerting much physical energy. The robot vacuum cleaners have at most only a small subset of their “intelligence” as machine learning.
I’ve seen machine learning used for some end-of-line quality checks and things like that, but turning over important tasks to a black box prone to breaking in untraceable ways in an enterprise where uptime is critical to profitability sounds like a very dubious proposition to me.
So basically, pattern recognition, data processing, and repetitive movements in confined spaces are the low hanging fruit for the advanced automation to grab.
I think of “AI” as a marketing term. But I can agree that there will be more advanced types of automation.
natural resources -> synonymously with machine learning
I seen that in the oil industry field surveys and also used in oil refineries for quite a while. The oil refinery guy moved into the stock market. BioComp Systems
in general there is already a lot of this stuff going on at a low level. Given the continuing advances in computing hardware married with 5G, it needs someone’s idea to create one of those killer apps to kick this into big time.
Perhaps one of these existing things will be the VisiCalc and someone else will come along with Lotus 123.
One thing that the article missed, I think, is that the actual labeling of large datasets is very much a low-skills task that has been primarily offshored, including to countries that missed the initial wave of industrialization through low-price consumer goods production. I would not be shocked to find big African and other still-developing economies getting jump started by lots of jobs annotating edge cases for machine learning to ingest. In fact, I have read of such labeling farms already being constructed in places as disparate as Kenya and Nepal.
AI fundamentally is only ever going to be as good as the quality of data going in, and will always depend on some level of curation. As the data needs scale up, even curating and checking a tiny portion of the data flow will still mean literally millions of hours of human labor per advanced model.
such a automatic dish washers…
I want to see a robot that will also empty a dish or clothes washer and put the contents away.
“automatic dish washers”
Maybe a number of these things need to be looked at in a different way.
Sort of like, for example, why do you need to put the dishes away? Some houses have two dishwashers. Dishes come out of the clean dishwasher to the table and then back into the other dishwasher. This process eliminates a lot, but not all of the need to put the dishes away.
As do I, but we are going to have a bit of a wait I am afraid. If they could clean the bathroom too, life would really have moved forward.
The bathroom might need to be remodeled. While in Africa I stayed in a few places that the combined the shower, toilet and sink into one thing. When you showed everything got wet.
The water pressure was generally not much and there wasn’t much of a door, sometimes just a piece of plastic. But add water pressure and a watertight door the place would just about clean itself.
Not so sure. I’ve stayed in places like that too… mouldy, smelly… Then again in Paris they have self-cleaning toilets on street corners that work pretty well.
>>>self-cleaning toilets on street corners that work pretty well.
We could use those right now in San Francisco and then to other places in the Bay. Actually, we could use them in a lot more places around the country.
We all have certain bodily functions, but it seems that after the restroom stalls’ coin meters were made illegal, all the effort at providing ways to deal with these functions hast been
actually reducedstopped. Even the mirrors in most public places have been removed.Despite the recent epidemics in Los Angeles, and IIRC San Francisco, I do not see any efforts at getting more public restrooms. I guess the only way to get some restroom stalls in these United States is to make them profitable again. Mere death is not important enough.
A quote from the paper, page 14:
is what I consider to be the most central. and from that quote then this bit is what I’d focus on:
There are at least two ways of reducing the amount of capital being accumulated, one is taxation and another is improving the bargaining position of labour.
I’m in favour of improving the bargaining position of labour by reducing the amount of hours of labour for sale – longer paid vacations, paid parental leave, early retirement etc
And from the article:
that is already happening in some white-collar professions. One example might be how many companies one accountant can create and report for in a tax-haven. The deskilling is real, the accountants now are of two kinds: the ones who follow a set of instructions carefully and the tax-accountants. The first kind are in a squeeze and the second kind is not too far behind.
The might be reasons for cautious optimism, however, waiting for global rules being set? I wouldn’t pin any hopes on that, global rules, if they ever will be set, will be set for and by the global elite.
.
The rules will be set when people wake-up and realize the ramifications. As long as people think it’s only people, they regard as beneath them–the “undeserving,” if you will–nothing will change, as they’ll simply write these people off as losers who didn’t try hard enough and subsequently deserve their fate.
If people wake-up and one or both of the following, things will change:
(1) From a self-interested point of view, there are a lot of high-skilled, high-paying jobs that, if they cannot be completely rendered obsolete, can certainly be deskilled.
(2) If we don’t handle this issue properly, we could easily end up with a large permanent underclass of people–it may not mean outright revolution but could mean the United States declines as a country and power–much more so than the state of things are in now.
The July/August 2015 issue of Foreign Affairs magazine “Hi Robot”, has an article by the FT’s Martin Wolf, in which he pushes back a bit against Stiglitz’s views.
I can’t think about any of the historical eras discussed in the article without thinking of all the wars.
AI appears to threaten Knowledge workers, who are also customers.
Good luck with a business which has a diminishing pool of customers.
I suspect AI could be very successful at eliminating Doctors, Politicians, Lawyers, Accountants, Stock Brokers and many C suite (management) positions.
If AI did not not eliminate the professions, AI could greatly reduce their value, and eliminate a great swath of the upper middle class.
Manual work is more difficult to eliminate, because people are adaptable, current robots very specialized, and not yet as adaptable as people.
I also always think about that when I hear these claims.
And when they talk about the service economy, financial services has been one of the main areas pointed at when economists and assorted apparatchiks talk about “growth” from a service economy…the kind mentioned as a future for developing countries.
That’s buying “insurance”, taking out loans, banking and brokerage accounts…all things no robot is going to need.
So there is some plan that’s not mentioned to get to this alleged tech utopia. And it ain’t “sharing” the wealth among 7+ billion people.
That’s already starting to happen. For example, there is now technology that can do a lot of the discovery work entry-level lawyers used to. Although this has not eliminated the need for lawyers, it means you don’t need as many of them.
Finance is one that is on the verge of major disruption, including what have traditionally been well-paying jobs. While traders still exist, there are lot fewer of them than there were even 10 years. While people many have the image of a large number of people yelling in phones all day, like in Hollywood movies, it is now largely done been a relatively small number of people writing computer algorithms.
More recently, there is now AI that can manage investments for you for a fraction of the cost of the human version. With enough computing power and a few advances in machine learning, this could probably be as good as the human version (if it’s not already there) in terms of getting a good return on investments.
As far as manual work, some many be safe but there have also been advances in robotics as well that could potentially replace some of the more repetitive stuff–like a lot of other automation it won’t necessarily eliminate all jobs but mean you don’t need as many people as you used to.
I think this is right for manual work, but think of what we’ve seen over the past few decades – anything that can be produced in quantity is dirt cheap. If you’re ok owning the same bookcase as everyone else, you can get a Billy for under $100. Want a different one? Expect to pay 5x, because it isn’t mass produced in the same quantity. Children’s toys have migrated to plastic which is easily molded, and are ridiculously inexpensive to make.
For services, I mean we have spelling and basic grammar checks in word processors but the NYT still manages to publish with errors in their headlines. The overlooked problem with AI is that each specific problem requires a completely different AI to handle it. And it’s all software, so requires updates in perpetuity.
My company is relatively small, handling analytics for a very specific industry. Complex algos are our moat. The reason for our success is vastly outperforming the humans, because we do something that wasn’t the core competency of the industry/people who use our software. Anywhere I see AI replacing people is because the humans were just bad at that activity. That’s the whole reason we use computers – people aren’t good at doing a million arithmetic calculations quickly and without errors.
The thing about this, of course, is that orthography and language rules are not handed down from on high on stone tablets, but are constantly evolving, shifting, changing, being invented, &c.
Over time, when applied to language tasks, AI could change the nature of the language itself in subtle and overt ways.
Speaking from painful experience, if you have a unique niche, complex algos and high expertise, your competitors or customers are likely already engaged in IP theft. Having trade secrets and airtight confidentiality contracts help, but they’re still going to try and rip you off. Litigating IP claims are horrendously expensive and time consuming. A small business going into federal district court against corporations in decidedly not a level playing field. And even if you win, as we did, you still lose…
That’s the whole reason we use computers – people aren’t good at doing a million arithmetic calculations quickly and without errors.
The problem is the requirement that a million arithmetic calculations be done quickly and without errors. Or at all. This is the great regression of humanity. It is not progress.
Walter Reuther’s (perhaps apocryphal) retort comes to mind. During a tour of an auto factory featuring some of the earliest robots the following exchange is reputed to have taken place:
Auto Executive: “Hey Walter, good luck getting these robots to pay union dues.”
Walter: “Good luck getting them to buy cars.”
This article speaks to future issues of technology and equity . What’s missing here, is it matters not what the capacity and scale are if you kill your “customer” in the process.
The biggest issue is what do we do with those who are displaced?
Yes there will be some jobs but how many and, perhaps more importantly, will they be the kind of jobs that those displaced can realistically retrain for. I emphasize the word “realistically” because there is a certain segment of the population that thinks we can just magically press a button and magically retrain everyone affected and we’ll all be holding hands and singing Kumbaya. Okay, I’m being a bit facetious but hopefully you get the idea.
People who are advocating things like universal income or a universal job guaranteed are not Luddites or against retraining as some like to claim, but rather acknowledging the reality that
(1) Retraining has not historically had a great success rate to begin.
(2) The gap between where somebody was, in the job that was displaced, and where somebody needs to be if they have any hope to be employed is serious issue as well. I don’t mean this in a disrespectful way but simply a reality we’re going to have to contend with.
It’s an issue that doesn’t have any easy or good answer.
This is a real concern. I remember this was Clinton’s attitude around NAFTA. We would just retrain all those displaced factory workers whose jobs were being shipped to the Developing countries. We know THAAT turned out.
With any new technology, society should be able to ask itself: Just how helpless do we, as individual human beings, want to be? Along these lines I highly recommend Matthew B. Crawford’s latest book, “Why We Drive.”
As for AI, years ago when I was a low-ranking editor, my superiors introduced a complex software system that kept the editing function in human hands but added to our workload a typesetting and layout function that previously was handled by others. So editors reluctantly had to learn the new system. The managing editor defended this annoying addition to our daily tasks by observing that AI might eventually perform word editing, in which case we would be well equipped with our software training to perform other tasks.
Days later I thought of the reply to this that I would have liked to make at the time. I would’ve begun by noting that editing is a fairly complex affair and while, yes, AI might eventually be that capable, it was far more likely that AI would first displace managers, since their binary and often bonehead decision-making amounted to a far simpler challenge. I still fantasize that in some obscure computer lab a brilliant AI engineer is working on just this proposition, aiming at displacement of not those productive and relatively inexpensive human workers on the bottom rungs but rather those grossly over-compensated nitwit hacks at the top.
It’s hard to know what to make of an article that talks all the time in generalisations, doesn’t define what it means by any of the terms it uses, and doesn’t give any examples.
The article seems, at least, to be about there “developing” world, although the authors don’t say what they mean by that. We’re told that the “past-half century has spurred unprecedented economic growth for many developing countries – brought about by the rapid adoption of technological advancements”, which is something that would surprise most development economists, and in any case is essentially meaningless since most “developing” countries had only just become independent in 1970. Likewise, if “dozens” of countries “have escaped from poverty in recent decades through a strategy of export-led growth” based on manufacturing, then it wouldn’t have hurt to give an example or two. I suspect the authors are vaguely thinking of China, South Korea, maybe Singapore and what was that other one, oh yes Taiwan. (They make chips don’t they?) I wonder if the authors have ever visited any of these “developing” countries.
We then dive into AI, which is a totally different subject, not to mention a technology that doesn’t actually exist and which, if it did, would change the world in ways that we can hardly imagine. It’s not obvious why AI would be “labour-saving” or indeed why an AI would actually agree to labour at all. I have some difficulty imagining an AI screwing parts of cheap telephones together more cheaply than a human: it’s not the intelligence, it’s the dexterity which is the problem. And no, “digital infrastructure improvement” is not going to help “developing countries”, whatever they may be if those countries suffer from a lack of physical infrastructure, road and rail links between countries and a reliable electrical grid. (Which is why much internet access in Africa, for example, is by mobile phone.) First things first.
The ability exists to 3D print circuit boards. There may be no need to screw that cheap phone together. None of that would be “AI” but a combination of advances in a lot of places.
The term Artificial Intelligence should be a tell for hype. Professionals only dare refer to the more accurate and mundane term ‘Machine Learning’. And WTF is an AI cobbler? Do they mean robotic? What a horrid example, if you neglect the investment in the robot and the labor of production and maintenance, of course it can outcompete a guy fixing shoes in his village with a needle and thread. And once it is that efficient, it will take over the world and everyone will have their shoes repaired virtually for free. I think this is Criti-Hype. And do countries really escape poverty with an IMF-style export economy that will crumple if their exploiters waste less? Or do they remain in poverty because they survive on letting themselves be extracted cheaply? Asking for a friend.
Pick and Place machines are way better than any human. They will handle hundreds of parts per minute, some so small they cannot be identified by the naked eye.
They also cost millions of EUR.
And once procured or leased will have to be paid for, serviced, provided with a very clean environment, controlled temperature and humidity, and of course a steady supply of Work. A lot of commitment, with robots.
Humans, OTOH, are very cheap, and does not require financing. They will still function in a bad environment and they can be disposed off as “market conditions” changes or when they are wearing out. No service needed, “society” takes care of all that.
In my opinion, the reason humans are used instead of robots is because all of the responsibilities for the robots lies with the owners, whereas there is little responsibility involved in employment.
“They also cost millions of EUR.”
Well, the big industrial ones that produce millions of EUR of product do. For less there’s $5000 pick and place.
The robotic side isn’t difficult or complicated.
How much of AI is simple data collection, collation, tracking and data sets sorting? This article, for instance, shows how data tracking and sorting can be a wealth creator for large corporations paying for the data in the real estate market. Where does that leave the ‘little guy”?
https://wolfstreet.com/2021/02/20/getting-artificially-intelligent-in-commercial-real-estate-using-consumers-smartphone-data-to-negotiate-commercial-transactions/
The authors confuse AI and robotisation. Admittedly, there’s connection, but at the moment very weak.
AI as we have it right now is really a pattern recognition/recombination tool. It is not, in any way “intelligent” (which is why I hate the AI moniker. Machine learning is a bit better, but not much. PRR is not sexy enough).
It cannot, at the momement, cope with a 3D world except in some very specific situations – a trained animal can do better in many, many situation.
What it excels at is sifting through a tons of data, looking for patterns (real and spurious, including patterns based on our biases), recombining the patterns and testing them. That is a definition of what many low-level to mid level “knowledge” workers do, they are the people with shovels before heavy mechanisation, and similarly to them, it’s very likely the “AI” will make them redundant. Not all at once, but reasonably quick.
And yes, that will remove people like trainee lawyers. That will create an interesting situation – if you remove the sifting through old cases as part of their training, what lawyers will it produce?
In one extreme, you can see where it could produce lawyers you really could replace with the AI – ones that will just repeat the stuff the law-search engines found for them.
In the other, I could see the shift from mindless searching to being ones who still search, but in much smaller set of cases (preselected by the law-search-engine), and do the work of providing some fundaments for the senior lawyers. In not just rote searches, but engaging their brains much earlier on.
Most likely it will fall somewhere in between, or I’d say with robo-lawyers for “trivial” cases, and much more paid human lawyers for everything else.
“The poor, have nothing to stir them up to be serviceable but their wants, which it is prudence to relieve, but folly to cure.”
Anonymous 1714.
The village cobbler example in the fourth paragraph offers a harbinger of some lack of depth and precision waiting in the analysis in the rest of the post — although award diversity and ‘political-correctness’ points for the usage “her product”.
The village cobbler was a shoe maker as well as a shoe repairer, but the example seems for a moment to focus only the shoe repair function. In the next sentence the productive capability — not the repair function — of the cobbler is contrasted with the productive capability of an “AI-producing” firm. Should I assume that an “AI-producing” firm means a firm that produces AI as a product which accounts for the lack of “physical restrictions” in producing products and distributing them? The next sentence returns to a physical product, shoes, and takes some short-cut to liken the limitless “AI-product” to limitless shoes made by the best cobbler. I skipped over the claim that somehow an AI-producing firm can achieve monopoly over slightly worse competitors because of unlimited production capability. Should I assume the competitors are also “AI-producing” firms with similar unlimited production and distribution capability, and if so what does “slightly worse competitors” mean — worse AI-product or worse marketing and sales or worse what in whose estimation … the Market’s estimation? The last sentence of the paragraph jumps from “artificial intelligence operates in a “winner-takes-all” market” in the third paragraph to “winner-takes-all” markets more generally across “artificial intelligence-powered economies” and suggests this could widen income inequality between developed and developing nations. What is an “artificial intelligence-powered economy”? Besides, I would think there are already many mechanisms in place to assure widening the inequality between developed and developing nations without worrying about new “winner-takes-all” markets for “artificial intelligence-powered economies” in that context.
The post continues with similar depth and insight to an oddly portrayed review ‘progress’ and some conflicts in the world’s economic advance through the Industrial Revolution and the “Age of Labor” to a present now faced with a vaguely portrayed “uncertain” future with artificial intelligence and “artificial intelligence-powered economies” continuing the dynamic of ‘progress’ and conflict into the vaguely portrayed uncertain future. Proposals for navigating this uncertain future follow in a succession that appear to be loosely ranked in order of their increasing unlikeliness. But wait! “…there is hope that international institutions and civil society may have a positive impact on the shape of these rules and what they imply for how the fruits of future progress in AI will be shared.” At least there is still hope.
Need it be said that the intelligence that is the model for the “artificial” version is seriously lacking and besides the surplus energy to run such schemes will eventually be lacking, even given some desperate depopulation scheme by the elites, who seem not to recognize they are the great consumers of depleting energy resources through militarization and individual travel schemes.
The word Artificial (Fake) says it all, and there’s already mountains of evidence that the most vulnerable – i.e. the least predatory, and most caring (versus forever profit seeking) – have been, and will be, eaten alive when there is no human input outside of the amoral Billionaires and Millionaires who most subscribe to and patent their particular AI™, never acknowledging that AI can only exist with their input of – [sub]human- subjective, amoral, and discriminatory derived algorithms.
I almost hope for a massive Carrington event (solar storm) to bring people to their senses about forfeiting their humanity, wisdom, kind humor, intelligence and dexterity to a handful of homogenous sociopathic, power obsessed megalomaniacs – Ray Kurzweil (The ‘Father’ Of [Transhumanism] The Singularity; of course currently presiding at Google in his seventies), Larry Ellison, and Billy Gates come to mind first, simply because they’ve been around longer – all on quite good terms and first name familiarity with the Military Industrial Complex, the Executive Office™, and High Power, California Congress™ Critters, for decades.
gotta run
Yes, incredibly important topic here and many thanks for posting. It is important to note that the above is only a portion of the full paper, which is why it may come across to some as disjointed.
That being said:
…developing countries may need to shift toward new economic models, particularly as AI strips them of their traditional comparative advantages in manufacturing and natural resources. One course of action could be to focus on strengthening agriculture or developing a service-based economy. Luckily, both agriculture and service sectors can co-exist with artificial intelligence.
Emerging technologies offer small farmers opportunities to optimize their yield, while digital platforms let those same farmers trade their products at fair-market prices, increasing their earnings.
Additionally, services-based industries like healthcare or tourism provide an arena for investment where artificial intelligence replacement is a more distant prospect.
1. ‘developing countries may need to shift’ could readily be restated as ‘all nations, communities and individuals’, as nearly all of the OECD deindustrializes and ages into senescence and detached rentierdom.
2. ‘…digital platforms let [xxxxx] trade their products at fair market prices…..’ Platforms. They do? Oh yes, of couuuurse they do! [rolleyes emoji]
3. ‘like healthcare and tourism’. So basically, diligent cheerful servants then? serving the global Masters engaging in intensive bouts of conspicuous consumption in carefully manicured artificial environments? It’s All According to Planplanplanplanplan…..
I made a quick look at the introduction to the fifty page full paper. The introduction is several pages long and and as you suggest offers a more coherent read — but it appeared to make the same questionable generalizations, assumptions, and jumps in logic that appear in more naked form in this post. I agree with you that AI is an important topic, along with several other topics the authors subsumed under their AI rubric.
“First “artificial intelligence” means getting rid of humans.” Or enslaving the few they may need.
I have listened to numerous interviews with existential experts regarding AI, General Intelligence and Super Intelligence. It is not pretty. Most of these experts point to the General Intelligence level as the real tipping point, tipping Humanity into that famous “dustbin of history”.
We now live in the Anti-Human Age. I see all around me, anti-human activity. Data exploitation, loss of privacy, loss of personal freedom, loss of consciousness, mind mushing.
Evidence? What used to be the main subject of this site, the Financial System, has been destroyed by machines: computerized and AI driven trading manipulation. It is now moving into machine driven hyperdrive. And maybe ask Texans about their machine managed environment.
It is the Machines that will choose if THEY go to Mars, not Humans.
how does one go about becoming an existential expert?
You survive to reach old age.
*forlornly crosses ‘existential expert’ off prospective careers list*
This is a feature, not a bug. A sustainable planet must have considerably less « DNA humans » : the 21st century 8-11 billion population is 10, or may be more, too much people. We can keep or even increase economic output per unit of energy if we can have more « artificial dogs » (for repetitive tasks like delivery of parcels or industrial assembly) and maybe some « Artificial humans » because their overall energy consumption will be an order of magnitude less than «DNA human », and they will be more adaptable to extreme conditions like space or deep under the ocean.
Note that I prefer the term « Artificial humans » over « Artificial General intelligence » because I don’t see how it is possible to get to AGI without the full gamut of what we consider human experience, and the associated human rights that should be granted to them (in particular, the right to not be a slave). The right analogy is that creating « Artificial humans » will be another way for human to have kids, with all the obligations that it implies (being loving parents, not overburdening kids with our personal agenda, etc…)
“Artificial dogs”
Funny that you should mention this. In Switzerland, last year, the Postal service was experimenting with a robot to deliver parcels and letters. He was so helpless in real life traffic, that he had to be accompanied by a real dog! For example, the robot kept turning back whenever in the middle of a crosswalk the light turned red.
Hi,
I think that AI will have a huge impact on white collars, not so much where physical activities are involved. When I mean huge I really mean enormous. The new generation of expert systems that are under development is able to automate most of office activities. We have to expect an incredible acceleration in adoption in the next few years.
Will all this gain in “productivity” improve the life of the 99%? Looking at the past few decades, does not support this outcome.
The globalists found just the economics they were looking for.
The USP of neoclassical economics – It concentrates wealth.
Let’s use it for globalisation.
Mariner Eccles, FED chair 1934 – 48, observed what the capital accumulation of neoclassical economics did to the US economy in the 1920s.
“a giant suction pump had by 1929 to 1930 drawn into a few hands an increasing proportion of currently produced wealth. This served then as capital accumulations. But by taking purchasing power out of the hands of mass consumers, the savers denied themselves the kind of effective demand for their products which would justify reinvestment of the capital accumulation in new plants. In consequence as in a poker game where the chips were concentrated in fewer and fewer hands, the other fellows could stay in the game only by borrowing. When the credit ran out, the game stopped”
This is what it’s supposed to be like.
A few people have all the money and everyone else gets by on debt.
How can you achieve sustainable growth with neoclassical economics?
That’s not going to be easy because policymakers don’t know what real wealth creation is.
Policymakers usually go for the economic growth model of the US in the 1920s.
Why is neoclassical economics so bad?
The classical economists identified the constructive “earned” income and the parasitic “unearned” income.
Most of the people at the top lived off the parasitic “unearned” income and they now had a big problem.
This problem was solved with neoclassical economics, which hides this distinction.
It confuses making money and creating wealth so all rich people look good.
Neoclassical economics is a pseudo economics, which is more about hiding the inconvenient truths discovered by the classical economists than telling you how the economy works.
Neoclassical economics was the economics of the roaring 20s, the Wall Street Crash and the Great Depression.
Sooner or later policymakers try and drive their economy into a Great Depression.
Bankers make the most money when they are driving your economy into a financial crisis.
They will load your economy up with their debt products until you get a financial crisis.
On a BBC documentary, comparing 1929 to 2008, it said the last time US bankers made as much money as they did before 2008 was in the 1920s.
https://www.youtube.com/watch?v=vAStZJCKmbU&list=PLmtuEaMvhDZZQLxg24CAiFgZYldtoCR-R&index=6
At 18 mins.
The bankers loaded the US economy up with their debt products until they got financial crises in 1929 and 2008.
As you head towards the financial crisis, the economy booms due to the money creation of bank loans.
https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2014/money-creation-in-the-modern-economy.pdf
The financial crisis appears to come out of a clear blue sky when you use an economics that doesn’t consider debt, like neoclassical economics.
The Thatcher Revolution was our roaring twenties, which led a financial crisis and would have caused a Great Depression if we hadn’t saved the banks.
Bankers make the most money when they are driving your economy into a financial crisis.
They will load your economy up with their debt products until you get a financial crisis.
https://www.housepricecrash.co.uk/forum/uploads/monthly_2018_02/Screen-Shot-2017-04-21-at-13_53_09.png.e32e8fee4ffd68b566ed5235dc1266c2.png
The bankers loaded the economy up with their debt products until we got a financial crisis in 2008.
As we headed towards the financial crisis, the economy boomed due to the money creation of bank loans.
https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2014/money-creation-in-the-modern-economy.pdf
The financial crisis appeared to come out of a clear blue sky as we used an economics that doesn’t consider debt, neoclassical economics.
Neoclassical economics is bad, so they wrapped it in a fluffy, new ideology called neoliberalism.
Global elites swallowed it hook, line and sinker.
What
does this have to do with
Artificial intelligence??