Latest news with #WilliamStanleyJevons
Daily Maverick
21-05-2025
- Business
- Daily Maverick
Back to Economics 101: The US economy and the functions of money
Part 3 in a five-part series. Read Part 1 here, and Part 2 here. It has only recently become clearer as to what probably happened to America in the period since the Gold Standard was suspended in the early 1970s and fiat money came to rule global finance. At the risk of being academic, the unleashing of global capital flows from the 1970s fragmented the functions of money as embodied in the US dollar. In 1875, economist William Stanley Jevons expounded his four functions of money, encapsulated in the 1919 rhyme, 'Money's a matter of functions four, A Medium, a Measure, a Standard, a Store.' Of these four money functions, the two most important are money as a Medium of exchange and money as a Store of value. Money as a Measure is a metric like centimetre, litre or kilogramme: we use it for counting. Money as a Standard of deferred payment deals with the financial denomination of debt to be settled in the future, a function some modern economists subsume within the money as a store of value function. What happened after the Nixon Shock for the US dollar was that the 'medium' and 'store' functions — and more importantly the underlying values they represented — started to diverge. At the risk of overgeneralising, the US dollar's medium of exchange function became more related to trade transactions, while the US dollar's store of value function became more related to capital transactions. The value of money breathes two atmospheres A world of two financial atmospheres began to emerge: an Atmosphere of Capital centred squarely on the US, and the Atmosphere of Trade whose centre of gravity began migrating towards Asia and, in particular, China. The end result today, as Gillian Tett noted in the Financial Times: 'America (has) hegemonic power in finance, via the dollar-based system… China has hegemonic power over global manufacturing, via its dominance of supply chains.' Yes, the 'measure of money' of both these two atmospheres was and is mostly still in US dollars — trade is still overwhelmingly invoiced in US dollars — but the intrinsic value they reflected started to diverge. Today the Atmosphere of Trade largely determines movements on a nation's current account and its value is more akin to (though not precisely measured by) Purchasing Power Parity (PPP). The Atmosphere of Capital is the one with which global financial markets are most familiar: value is reflected by 'free market' exchange rates. The latter's net flows show up mostly on a nation's capital account. Thus, the valuations of each of these two functions of the US dollar — were they able to be separated — would be very different. Bloombergia and CNBC-land think mostly in terms of market values: yet most Chinese exporters effectively invoice (even if most do not realise it) in PPP dollars. China's current GDP — dominated as it is by trade flows reinforced by a closed capital account — is at market rates of about $18-trillion to the US' $30-trillion. But at PPP rates, China's GDP is rather closer to $40-trillion again to the US's $30-trillion. American politicians wearing trade-oriented glasses see China's currency as fundamentally undervalued, its free-market value manipulated lower by the Bank of China. But few have noticed that were a revaluation of the Chinese renminbi to occur (so triggering a relative devaluation of the US dollar to realign cross rates more closely to PPP values), China's economy could become quite a bit larger than the US economy. China wanted 'competitive' renminbi, US wanted 'strong' dollar Even as the exchange rate management behaviour of the Bank of China has been (and still is) aimed at keeping the value of China's renminbi lower than its 'free market' value, thereby allowing China to continue enlarging its global trade footprint, vested interests representing big capital in the US have long had a complementary yet opposite agenda: both US public and US private sector actors mostly wanted to keep the value of the US dollar higher (the 'Strong Dollar Policy') despite the pleadings of US industry (and US agriculture) for a more competitive exchange rate. Even the US Treasury long favoured a strong dollar… until now. Under Donald Trump, this era is over. In the words of US Treasury Secretary Scott Bessent, the US will now put Main Street before Wall Street, thus ending a multi-decade practice where the US capital tail has been wagging the US trade dog. Though not expressed as such, the Trump Administration is trying to narrow the difference of the store of value and medium of exchange rates of the US dollar, bringing its 'capital' value down to a rate closer to what they perceive the 'trade' value of the US dollar should be. The hope is that this will reverse the hollowing out of US industry and — to paraphrase Trump — make US manufacturing great again. How the US economy got tied up in today's Gordian knot In 1959, the Netherlands discovered natural gas in the North Sea. Over the next two decades, gas grew to have an outsized influence on Dutch exports and in the process drove up the value of the Dutch guilder. Most low-value-added manufacturing in the Netherlands simply could not compete at the higher exchange rate, and much of Dutch industry was hollowed out. Thus was born the economic term 'Dutch Disease'. Since then, commodity exporters worldwide have been wary of the fallout that would probably result from a commodity price bonanza that would increase the value of their currency and so in turn boost their terms of trade. When this appreciation was allowed to happen, export-oriented domestic manufacturing industries invariably suffered… and rarely recovered in the aftermath, even when and if commodity prices settled lower again, bringing the exchange rate down as well. Manufacturing export markets once lost were hard to recover. The US has, since the 1970s, experienced its own form of Dutch Disease, albeit driven by a unique 'commodity': the attractiveness of its currency, its capital markets and most especially its government bond market, offering as the latter did a store of value with very little downside currency risk. This was highlighted by Brendan Greeley in a 2019 Financial Times opinion titled 'How to diagnose your own Dutch Disease'. Greeley noted that 'around 1980 the United States discovered that it was the Saudi Arabia of money'. This non-resource 'commodity' supercharged the post-1980 financialisation of the American economy, particularly since the Global Financial Crisis. Since the valuation lows of 2008/2009, the St Louis Fed's broad-based value of the US dollar has risen over 46%. Already 'infected', the US contracted a 'double case' of Dutch Disease, further laying waste what remained of its industrial manufacturing infrastructure. Result? From 2020 to 2024, none of the top 10 industrial export sectors — five of which were energy-related — achieved revenue growth above inflation. [Parenthetically, in 2008 the Shale Revolution began in the US. It has since turned the US from being a net importer of oil and gas (2008: $452-billion) to being a big net exporter (2024: $176-billion). As happened in the Netherlands, carbon played its part in the post-GFC intensification of the US's Dutch Disease. But the true underlying cause of the affliction has been the 'export' of that most unusual of 'commodities': the US Treasury Bill.] What is left of US industry today? Of the top 20 industrial plants by employment in the US today, 11 are in defence and aerospace, four are in autos (of which two are Tesla), four in tech and one in pharma. Given the defence bias of this list, this is hardly what one might call a world-class, globally competitive, broad-based industrial foundation for a modern United States. Economists' defence: US consumers won more than US producers lost Many economists have dismissed the negative consequences of this seismic shift, reasoning the gains to US consumers far outweighed the losses to US producers. Hard evidence to support this cost-benefit analysis however has been sketchy, particularly because the losses to employment — not just the economic ones but the social ones as well — are hard to measure. Besides, many of those gains were in effect only secured by running up the US' national debt, owed to both domestic and foreign investors. Stock and bond market-oriented economists further rationalised the fallout with the offsetting gains from product price deflation (from cheaper imports) and even more so from the gains in stock prices — $10,000 invested in an S&P 500 index fund in 1992 would have risen to $270,000 at the end of 2024: 27 times growth in 33 years, a 10% compound annual growth rate. Bond prices performed well too, rising strongly from 1981 to 2020: over this four-decade period, bond yields fell from just under 16% to just over 2%. Contrast this gain to what happened to GDP: over the same period, it rose from $6.5-trillion to $29.7-trillion: only 4.6 times or a compound annual growth rate of 4.7%. Given these stock and bond market gains, what's not to like? For the captains of capital (including the tech-oriented companies who displaced the industrial titans of 1980 when the top 10 were composed of six oil companies, two car assemblers, GE and the 'old' IBM), capital gains from the stock market have resulted in a massive wealth windfall to both management and shareholders. Federal debt and wealth inequality The undersides to these halcyon days were many, but two need highlighting. Firstly, federal debt rose almost twice as fast as GDP, from $918-billion in 1980 to near $37-trillion today, a compound annual growth rate of 8.6%. Much of this debt went into funding defence, social security and Medicare/Medicaid with — in today's ageing US — an ever-larger share also going into pensions. Expenditure growth (favoured more by Democratic than Republican administrations, unless it was on defence) coupled with tax cuts (often favouring the rich, mostly sponsored by Republican administrations) naturally increased deficits, which thereby rolled into higher aggregate federal debt. With interest rates falling, the carried-forward overall debt resulted in a bearable debt interest burden: in Dick Cheney's words, ' deficits didn't matter '. But not when in 2022 the Fed Fund's rate began rising again: the burden grew quickly to be $881-billion in 2024. (This interest bill was capitalised and added to the outstanding federal debt load.) In 2024, this $881-billion payment overtook the US defence budget for the first time, breaching Niall Ferguson's Law: 'Any great power that spends more on interest payments on the national debt than on defence will not stay great for very long.' (It should also be noted that the US government's off-balance sheet liabilities now top $80-trillion. Entitlement programmes are rapidly approaching bankruptcy: Medicare is forecast to go under before Trump leaves office, Social Security during the tenure of his successor.) Secondly, US wealth inequality has widened, especially benefitting the ultra-rich. In 1980, the top 1% owned 24% of US wealth, the next 9% owned 44% and the bottom 50% only 2.5%. By 2024, the top 1% owned 31% of US wealth, the next 9% owned 36% and the bottom 50% still only 2.4%. The social and political implications of this concentration of wealth at the top and dearth of wealth for the 'Left Behinds' and 'Never Caught Ups' at the bottom needs little elaboration. Suffice it to say, the latter fuelled the rise of the Steve Bannon/MAGA wing of today's Republican Party. DM
Yahoo
06-02-2025
- Business
- Yahoo
Opinion: What China's DeepSeek breakthrough really means for the future of AI
Last week, the Nasdaq stock exchange — which lists significant U.S. tech stocks — experienced a big drop. This resulted from the Chinese startup DeepSeek announcing that it had developed an artificial intelligence model that performs as well as OpenAI and Meta's AI technology, but at a fraction of the cost and with less computing power. AI chip designer Nvidia lost nearly $600 billion of its market capitalization (the total dollar value of its outstanding shares of stock) — the largest single-day drop experienced by a company in U.S. market history. Although Nvidia's share price has recovered some ground, analysts continue to second-guess ambitious AI infrastructure plans, including the company's specialized graphics processing unit chips as well as massive data centers like those built and operated by Amazon. DeepSeek's creators claim to have found a better way to train their AI by using special parts, improving how the AI learns rules and deploying a strategy to keep the AI running smoothly without wasting resources. According to the company's report, these innovations drastically reduced the computing power needed to develop and run the model and therefore the cost associated with chips and servers. This sharp cost reduction has already attracted smaller AI developers looking for a cheaper alternative to high-profile AI labs. Read more: Column: OpenAI accuses China of stealing its content, the same accusation that authors have made against OpenAI At first glance, reducing model-training expenses in this way might seem to undermine the trillion-dollar "AI arms race" involving data centers, semiconductors and cloud infrastructure. But as history shows, cheaper technology often fuels greater usage. Rather than dampen capital expenditures, breakthroughs that make AI more accessible can unleash a wave of new adopters, including not only tech startups but also traditional manufacturing firms and service providers such as hospitals and retail. Microsoft Chief Executive Satya Nadella called this phenomenon a "Jevons paradox' for AI. Attributed to the 19th century English economist William Stanley Jevons, the concept describes how making a technology more efficient can raise rather than lessen consumption. Steam and electrical power followed this pattern: Once they became more efficient and affordable, they spread to more factories, offices and homes, ultimately increasing use. Nadella is right: Today's plummeting development costs for generative AI are poised to generate a similar expansion. That means the sky is not falling for Big Tech companies that supply AI infrastructure and services. Major tech players are projected to invest more than $1 trillion in AI infrastructure by 2029, and the DeepSeek development probably won't change their plans all that much. While training costs may drop, the long-term hardware requirements for massive machine learning workloads, data processing and specialized AI software remain enormous. Although chip prices might fall as model training becomes more efficient, AI-based applications — such as generative chatbots and automated industrial controls — demand powerful servers, high-speed networks to transmit massive data flows and reliable data centers to handle billions of real-time queries. Regulatory, security and compliance demands further complicate implementation, requiring advanced, sometimes costly solutions that can store and process data responsibly. Read more: Opinion: If your phone had feelings would you treat it differently? It could happen sooner than you think General-purpose technologies that transform economies typically spread in two stages. First, during a long gestation period, well-funded organizations experiment, refining prototypes and processes. Later, once standards stabilize and ready-to-use solutions emerge, more cautious firms jump in. In the case of electricity, the first stage saw factories spending years reorganizing production floors and adopting new workflows before electrification spread widely; in the case of AI, it has consisted of big banks, retailers and manufacturers making slow, piecemeal use of the technology. A century and a half ago, when the Bessemer process introduced the use of hot air to blast impurities out of molten iron and mills figured out how to produce standardized steel products, manufacturers pivoted. Steel prices plummeted and consumption soared, eventually increasing spending in that sector despite steelmakers' more efficient use of iron ore. Now that DeepSeek and other innovations promise lower costs, more companies may be ready to embrace or at least try AI, and the demand for AI infrastructure is likely to increase. A more affordable, cutting-edge model could also encourage industries, startups and entrepreneurs to use AI more widely, increasing its adoption in logistics, customer service and more. Imagine, for example, a 200-person law firm specializing in commercial real estate. Initially, it uses ChatGPT sometimes to produce quick contract summaries, but its partners grow uneasy about inconsistent quality and confidentiality risks. After testing a contracts-focused model provided by a reputable vendor, the firm adopts technology that integrates directly with its document management system. This allows associate attorneys to auto-summarize hundreds of pages in seconds, rely on AI 'clause suggestions' tailored to real estate precedents, and limit the need to seek guidance from senior partners to cases of especially ambiguous or high-stakes language. Moreover, the system design prevents client data from leaving the firm's domain, increasing security. Over time, the firm adds AI modules for advanced litigation research and automated billing notes, steadily reducing administrative tasks and letting human experts focus on strategic legal insight. It sees quicker contract turnaround, standardized billing and a new willingness among partners to explore AI-based tools in other areas. In short, AI's capital demands won't shrink thanks to DeepSeek; they will become more widely distributed. We'll see this spur expansion in power grids, cooling systems, data centers, software pipelines and infrastructure that enables more devices to use AI, including robots and driverless cars. The trillion-dollar infrastructure push may persist for years to come. Victor Menaldo is a political science professor at the University of Washington and is writing a book on the political economy of the fourth industrial revolution. If it's in the news right now, the L.A. Times' Opinion section covers it. Sign up for our weekly opinion newsletter. This story originally appeared in Los Angeles Times.
Los Angeles Times
06-02-2025
- Business
- Los Angeles Times
Opinion: What China's DeepSeek breakthrough really means for the future of AI
Last week, the Nasdaq stock exchange — which lists significant U.S. tech stocks — experienced a big drop. This resulted from the Chinese startup DeepSeek announcing that it had developed an artificial intelligence model that performs as well as OpenAI and Meta's AI technology, but at a fraction of the cost and with less computing power. AI chip designer Nvidia lost nearly $600 billion of its market capitalization (the total dollar value of its outstanding shares of stock) — the largest single-day drop experienced by a company in U.S. market history. Although Nvidia's share price has recovered some ground, analysts continue to second-guess ambitious AI infrastructure plans, including the company's specialized graphics processing unit chips as well as massive data centers like those built and operated by Amazon. DeepSeek's creators claim to have found a better way to train their AI by using special parts, improving how the AI learns rules and deploying a strategy to keep the AI running smoothly without wasting resources. According to the company's report, these innovations drastically reduced the computing power needed to develop and run the model and therefore the cost associated with chips and servers. This sharp cost reduction has already attracted smaller AI developers looking for a cheaper alternative to high-profile AI labs. At first glance, reducing model-training expenses in this way might seem to undermine the trillion-dollar 'AI arms race' involving data centers, semiconductors and cloud infrastructure. But as history shows, cheaper technology often fuels greater usage. Rather than dampen capital expenditures, breakthroughs that make AI more accessible can unleash a wave of new adopters, including not only tech startups but also traditional manufacturing firms and service providers such as hospitals and retail. Microsoft Chief Executive Satya Nadella called this phenomenon a 'Jevons paradox' for AI. Attributed to the 19th century English economist William Stanley Jevons, the concept describes how making a technology more efficient can raise rather than lessen consumption. Steam and electrical power followed this pattern: Once they became more efficient and affordable, they spread to more factories, offices and homes, ultimately increasing use. Nadella is right: Today's plummeting development costs for generative AI are poised to generate a similar expansion. That means the sky is not falling for Big Tech companies that supply AI infrastructure and services. Major tech players are projected to invest more than $1 trillion in AI infrastructure by 2029, and the DeepSeek development probably won't change their plans all that much. While training costs may drop, the long-term hardware requirements for massive machine learning workloads, data processing and specialized AI software remain enormous. Although chip prices might fall as model training becomes more efficient, AI-based applications — such as generative chatbots and automated industrial controls — demand powerful servers, high-speed networks to transmit massive data flows and reliable data centers to handle billions of real-time queries. Regulatory, security and compliance demands further complicate implementation, requiring advanced, sometimes costly solutions that can store and process data responsibly. General-purpose technologies that transform economies typically spread in two stages. First, during a long gestation period, well-funded organizations experiment, refining prototypes and processes. Later, once standards stabilize and ready-to-use solutions emerge, more cautious firms jump in. In the case of electricity, the first stage saw factories spending years reorganizing production floors and adopting new workflows before electrification spread widely; in the case of AI, it has consisted of big banks, retailers and manufacturers making slow, piecemeal use of the technology. A century and a half ago, when the Bessemer process introduced the use of hot air to blast impurities out of molten iron and mills figured out how to produce standardized steel products, manufacturers pivoted. Steel prices plummeted and consumption soared, eventually increasing spending in that sector despite steelmakers' more efficient use of iron ore. Now that DeepSeek and other innovations promise lower costs, more companies may be ready to embrace or at least try AI, and the demand for AI infrastructure is likely to increase. A more affordable, cutting-edge model could also encourage industries, startups and entrepreneurs to use AI more widely, increasing its adoption in logistics, customer service and more. Imagine, for example, a 200-person law firm specializing in commercial real estate. Initially, it uses ChatGPT sometimes to produce quick contract summaries, but its partners grow uneasy about inconsistent quality and confidentiality risks. After testing a contracts-focused model provided by a reputable vendor, the firm adopts technology that integrates directly with its document management system. This allows associate attorneys to auto-summarize hundreds of pages in seconds, rely on AI 'clause suggestions' tailored to real estate precedents, and limit the need to seek guidance from senior partners to cases of especially ambiguous or high-stakes language. Moreover, the system design prevents client data from leaving the firm's domain, increasing security. Over time, the firm adds AI modules for advanced litigation research and automated billing notes, steadily reducing administrative tasks and letting human experts focus on strategic legal insight. It sees quicker contract turnaround, standardized billing and a new willingness among partners to explore AI-based tools in other areas. In short, AI's capital demands won't shrink thanks to DeepSeek; they will become more widely distributed. We'll see this spur expansion in power grids, cooling systems, data centers, software pipelines and infrastructure that enables more devices to use AI, including robots and driverless cars. The trillion-dollar infrastructure push may persist for years to come. Victor Menaldo is a political science professor at the University of Washington and is writing a book on the political economy of the fourth industrial revolution.
Zawya
05-02-2025
- Business
- Zawya
Europe's AI bulls pin hopes on 'Jevons Paradox' after DeepSeek rout
LONDON - Artificial intelligence bulls in Europe are dusting off a 160-year-old economic theory to explain why the boom in the sector's stocks may have further to run, despite the emergence of China's cheap AI model DeepSeek. Tech stocks worldwide plunged on Jan. 27 after the launch of DeepSeek - apparently costing a fraction of rival AI models and requiring less sophisticated chips - raised questions over the West's huge investments in chipmakers and data centres. At the heart of the selloff was U.S. advanced chipmaker and AI poster-child Nvidia, which lost 17% of its value, or close to $600 billion, in the largest one-day drop in market capitalisation for any company on record. Since then, tech stocks have rebounded, with European markets hitting new highs, and a 19th century economic theory is suddenly on everyone's lips: the Jevons Paradox. Named after English economist William Stanley Jevons, it posits that when a resource becomes more efficient to use, demand can increase - rather than decrease - as the price to use the resource drops. "I hadn't discussed it until Monday (last week), and then suddenly it's everywhere," said Helen Jewell, Chief Investment Officer at BlackRock Fundamental Equities, EMEA. "This paradox highlights one of the uncertainties at the moment," said Jewell, flagging that a key question for European stock-pickers is whether data centres and their suppliers will be less in demand. "One of the big question marks from (last) Monday's news is how much energy is going to be needed for the AI revolution?" The selloff hit direct and indirect AI plays alike. Dutch semiconductor equipment maker ASML, and sector peers ASMI and BE Semi all fell 7%-12% on Jan. 27, before recouping losses later in the week, as did Siemens Energy , which provides hardware for AI infrastructure. "Jevons Paradox strikes again!" Microsoft chief executive Satya Nadella said in a post on X. "As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of." THE NEW BUZZWORD On Friday, Tomasz Godziek, portfolio manager of the Tech Disruptors fund at J. Safra Sarasin Sustainable Asset Management, said lower AI costs could exemplify the Jevons Paradox. "Ultimately, this could fuel a new wave of AI investment, creating fresh opportunities, particularly in software and inference technologies," Godziek said. Portfolio managers at Thematics Asset Management, an affiliate of Natixis IM, cited Jevons Paradox as one reason they believe demand for AI chips may remain healthy. Mark Hawtin, head of the Liontrust global equities team, also said his investment thesis on AI was reinforced by the news on Jan. 27, flagging the paradox. "Everyone has become an expert on Jevons Paradox," said Aviva Investors portfolio manager Kunal Kothari, who manages a UK equity income fund with around 2 billion pounds ($2.5 billion) in assets. "The falling cost of improved productivity through GenAI will likely benefit companies in the UK market generally, as they will predominantly be consumers of these technologies," he added, pointing to data and software names like RELX, LSEG, Experian and Sage as likely beneficiaries. DATA CENTRE NEEDS IN FOCUS The need for data centres and the vast amounts of power required to run them has driven a lot of AI investing in Europe already, given that there aren't any homegrown rivals to the likes of Nvidia, whose shares have rocketed by about 200% in under two years. "There is an implicit assumption that the adoption and usage of AI would require increasingly more chips, and more data centre capacity and power consumption," said Kasper Elmgreen, CIO of fixed income and equities at Nordea Asset Management. "What DeepSeek has done is to question what is required from that route and what can be delivered by making much better software." Not everyone is convinced of the new rationale, including Jordan Rochester, head of FICC strategy at Mizuho EMEA. "Whilst many Nvidia optimists pointed to Jevons Paradox to help them sleep better at night ... it was less convincing in the short term after what has been a meteoric rise in Nvidia shares," he wrote in a note. ($1 = 0.8122 pounds)
