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The $500 Trillion AI Bet Depends on Energy, Infrastructure, and Policy, Not Just Code

The $500 Trillion AI Bet Depends on Energy, Infrastructure, and Policy, Not Just Code

Jensen Huang’s recent remarks on AI’s economic trajectory are as bold as they are inevitable. “There’s a belief that the world’s GDP is somehow limited at a hundred trillion dollars,” he said. “AI is going to cause that hundred trillion dollars to become two hundred, three hundred, five hundred trillion… Everybody’s jobs will change.”

The pitch is seductive, and on the micro level, largely correct. AI will not simply replace jobs; it will strip away friction. Workers will spend less time wrangling spreadsheets or typing prompts and more time orchestrating, deciding, and creating. Productivity will surge. Those who fail to integrate AI will lose to those who do.

But macroeconomics rarely bends to technological optimism. The real question is not whether AI expands the economic pie. It is how that expansion prices out, and who captures the gains.

Pressure-testing Huang’s $500 trillion vision reveals two sharply different futures. One  to structural deflation and abundance. The other leads to inflationary distortion.

Scenario A: The Nominal Bubble

If the $500 trillion figure is driven more by financial engineering than physical output, the result could be an inflationary shock.

A booming AI sector would generate enormous paper wealth across companies such as NVIDIA, Microsoft, and OpenAI. Investors and founders would recycle those gains into real-world assets: housing, energy, food, and commodities. That is classic demand-pull inflation, amplified by unprecedented .

At the same time, AI’s digital promise collides with physical bottlenecks. Training models requires vast amounts of copper, semiconductors, data centers, and electricity. Competition for those constrained resources pushes up costs across the broader economy while non-AI sectors struggle to keep pace.

In this scenario, the $500 trillion economy is not real growth. It is a valuation bubble chasing finite real-world supply.

Scenario B: The Deflationary Engine

The counterargument is that AI could create genuine GDP expansion while driving structural deflation.

Jensen Huang, Founder and CEO of Nvidia, Source: Wikipedia

GDP is ultimately price multiplied by quantity. If AI removes the constraints of human labor and intelligence, the quantity of goods and services could scale dramatically even as prices fall.

When AI automates coding, legal work, diagnostics, research, and eventually physical production through robotics and automated manufacturing, the marginal cost of creating products and services collapses. Software, logistics, energy optimization, and even manufacturing become radically cheaper.

If output expands severalfold while costs decline, the economy grows in real terms. Living costs fall, purchasing power rises, and abundance—not inflation—defines the outcome.

This is the future Huang is implicitly betting on. And mathematically, it is possible.

The Dangerous Transition Gap

The real risk lies between those two scenarios.

Markets may price in AI-driven abundance long before the physical infrastructure exists to support it. Building advanced energy grids, semiconductor fabs, robotics supply chains, and transmission networks could take 10 to 15 years.

That creates a dangerous mismatch. Capital floods into AI today, asset prices surge, and resource competition intensifies before supply-side abundance arrives. Energy, housing, metals, and essential goods could all become more expensive during the transition.

In effect, the path to abundance may first pass through inflation.

Central banks would face an impossible balancing act between suppressing inflation and supporting growth. Workers in disrupted industries could face displacement before new AI-augmented roles scale fast enough to absorb them. Social and political friction could undermine the productivity boom AI promises.

Abundance is not automatic. It has to be engineered.

The Real Question

Huang is probably right that GDP is not capped at $100 trillion. He is also right that AI will fundamentally change how people work.

But whether the world reaches $500 trillion through abundance or distortion will depend less on algorithms and more on institutions.

The outcome will hinge on energy policy, industrial capacity, monetary discipline, and labor adaptation. Technology creates productive capacity. Governments, central banks, and markets determine whether that capacity translates into stability.

AI will reshape the global economy. The real question is whether society can manage the transition as effectively as it trains the models powering it.

 

Source: https://www.financemagnates.com/institutional-forex/the-500-trillion-ai-bet-depends-on-energy-infrastructure-and-policy-not-just-code/

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
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Are institutions ditching Bitcoin for AI-themed products?

Are institutions ditching Bitcoin for AI-themed products?

Bitcoin sits at US$76,638.55, and I still see a range play. The price action reflects a market digesting competing forces rather than breaking into a new trend. Institutional capital is not fleeing digital assets but rotating with purpose. Money is moving out of mainstream Bitcoin and Ether ETFs and into AI-themed funds and select altcoin products. This shift tells a nuanced story about risk appetite, narrative momentum, and the search for growth in a macro environment that favours selectivity over broad exposure.

Recent flow data makes this rotation unmistakable. Between 18 and 22 May, US spot Bitcoin ETFs recorded about US$1.26 billion in net outflows. Ether ETFs lost roughly US$216 million over the same window. At the same time, Solana, XRP, and Hyperliquid HYPE products attracted inflows of about US$15.6 million, US$22 million, and US$72.4 million, respectively. Reports show total BTC and ETH ETF redemptions reached nearly US$2.7 billion over two weeks.

These numbers do not signal a retreat from crypto. They show capital reallocating within the asset class toward ecosystems with idiosyncratic growth drivers, such as network adoption and derivatives activity. The flagship funds remain massive. CMC aggregate data still puts Bitcoin ETF assets at around US$106.22 billion and Ether ETF assets at nearly US$13.8 billion. The system is large but is currently experiencing a net trickle-out from the core holdings.

Outside crypto, the AI infrastructure trade commands intense attention. An AI-linked memory chip ETF, DRAM, gathered more than US$6.5 billion of assets within 27 trading sessions after its April launch. It surpassed US$10 billion within 30 sessions. That pace makes it one of the fastest-growing and most traded ETFs in the United States. Institutions express AI conviction through familiar equity wrappers rather than more volatile coins. Hedge funds have ramped up their exposure to tech and AI stocks, reinforcing this preference. The narrative around chips and model-training infrastructure offers a compelling growth story that aligns with current macro expectations. Managers appear to use crypto price rebounds to trim exposure to rate-sensitive benchmark assets such as BTC and ETH while keeping risk on the table through altcoins and AI themes.

Macro expectations have shifted toward higher-for-longer interest rates. This backdrop shapes how institutions position across digital assets and equities. When rates stay elevated, investors favour assets with clear near-term catalysts and visible adoption curves. Within crypto, products tied to more sustainable ecosystems fit that bill. They offer exposure to specific network effects and derivatives activity that can drive outsized returns even when large caps face headwinds. The rotation reflects enthusiasm for growth narratives in AI infrastructure and higher beta altcoins, not a total exit from digital assets. Risk appetite has not vanished. It is being reallocated toward perceived higher growth and more targeted narratives, both inside and outside crypto.

Global markets provide important context for this flow dynamic. On Tuesday, May 26, 2026, equities worldwide pare early gains as Middle East geopolitical developments compete with optimism over an interim diplomatic breakthrough. US equity-index futures trade higher by 0.6 per cent, with S&P 500 futures up one per cent and Nasdaq 100 futures up 1.4 per cent compared to Friday’s close. This follows an eight-week consecutive winning streak for the S&P 500. Investors return from the Memorial Day holiday, focusing on upcoming PCE inflation and GDP figures.

In the Asia-Pacific, benchmarks show mixed performance. Japan’s Nikkei 225 surged 2.87 per cent to 65,158.19 points, driven by technology and component manufacturers. Australia’s S&P/ASX 200 slid 0.4 per cent to 8,656.6, weighed down by losses in large banks and real estate players. Hong Kong’s Hang Seng gained 0.86 per cent, tracking recovery in local property markets and optimism around Chinese tech listings. These moves matter because crypto increasingly correlates with traditional risk assets. When tech equities rally, crypto often follows. When macro uncertainty rises, correlations can tighten further.

Energy and commodities add another layer. Brent Crude trades around US$97.54 to US$98.00 per barrel after volatile swings tied to US-Iran diplomatic developments. WTI Crude hovers near US$91.00 per barrel. Spot gold rose 0.75 per cent to US$4,550.18 per ounce amid lingering safe-haven demand. Iron Ore edged down slightly by 0.11 per cent to US$109.67 per tonne.

The US Dollar Index prints a touch stronger at 99.34 against its Group-of-10 peers. Cash trading of US Treasuries resumed with a minor rally, leaving the 10-year Treasury yield at 4.55 per cent as investors await core inflation indicators. These variables influence institutional positioning across all risk assets. A stronger dollar and sticky yields can pressure rate-sensitive holdings. Geopolitical tensions can boost safe havens while creating volatility that benefits high-beta names.

For Bitcoin and Ethereum, sustained ETF outflows could cap upside or increase sensitivity to negative macro surprises. These vehicles remain a primary channel for institutional demand. Persistent redemptions signal caution among large allocators. The DRAM ETF’s explosive growth demonstrates how powerful the AI infrastructure narrative can be when wrapped in a familiar vehicle. Concentration risk rises if narratives fade or liquidity reverses. Investors paying for growth today expect delivery tomorrow.

Practical signals deserve close monitoring. Watch daily net flows into BTC, ETH, and major altcoin ETFs. Track relative performance between crypto ETFs and AI equity ETFs. Observe changes in the probability of rate cuts or hikes implied by Treasury yields and Fed funds futures. If macro conditions ease and AI enthusiasm broadens back into digital assets, flows could rotate again, potentially back toward BTC and ETH. The interplay between these factors will determine whether the current shift becomes a lasting regime change or a temporary tactical adjustment.

Breakouts require either a macro catalyst that reignites broad institutional demand or a narrative breakthrough that pulls capital back into the flagship assets. Until then, selective exposure and careful flow monitoring offer the clearest path forward.

 
Source: 
 

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
j j j

The Automation Paradox: Why Replacing Humans With AI Is An Economic Suicide Pact

The Automation Paradox: Why Replacing Humans With AI Is An Economic Suicide Pact

The recent announcement from Meta regarding the layoff of 8,000 employees is more than just another headline in the tech sector’s ongoing volatility; it is a signal of a structural shift that should alarm anyone who understands the foundational mechanics of a consumer economy. When Mark Zuckerberg admitted that the massive capital expenditures on artificial intelligence have directly contributed to the need to scale back the company, he laid bare a cold, mathematical reality that is beginning to play out across the globe.

Automator’s Paradox

We are witnessing the first major tremors of what economists are now calling the Automator’s Paradox. While it is entirely rational for an individual firm to replace a hundred-person team with ten people aided by advanced AI, the collective result of this behavior across the entire market is nothing short of economic cannibalism. If we continue on this path of wholesale human replacement, we are not building a more efficient future. Instead, we are dismantling the very engine of consumption that keeps the global economy alive.

The logic presented by Big Tech leadership is deceptively simple. Meta, Amazon, and Google are on track to spend a staggering $750 billion on AI this year alone. To justify these astronomical investments to shareholders, these companies must find efficiencies. In the corporate lexicon, efficiency is almost always a euphemism for reducing headcount. Zuckerberg’s observation that a team once requiring a hundred people might now only need ten is a testament to the sheer power of modern generative AI. This microeconomic victory masks a macroeconomic catastrophe. A company that automates its workforce saves on wages, but it also removes those wages from the pool of disposable income that fuels the rest of the economy. When this happens in isolation, the impact is negligible. When it happens simultaneously across the Fortune 500, we face a systemic collapse of demand.

The AI Layoff Trap

This brings us to the most chilling realization of our current era, which was highlighted in a landmark economic research paper titled “The AI Layoff Trap” released in March 2026. The study models a scenario in which companies automate faster than the broader economy can absorb displaced labor. It identifies a Prisoner’s Dilemma at the scale of the entire global economy. Each individual CEO is incentivized to automate to stay competitive and protect margins. As every company follows this rational path, they collectively destroy the consumer base that buys its products. We are approaching a tipping point where the supply side of the economy, powered by tireless AI, becomes hyper-productive, while the demand side, comprised of unemployed humans, withers away. Zuckerberg himself noted that Meta’s ad revenue fluctuated based on consumer discretionary spending linked to oil prices. He should perhaps be more concerned that his own internal efficiencies are removing the very consumers who would click on those ads in the first place.

This is particularly haunting because it tested every conventional safety net we have spent the last decade debating. We have long been told that universal basic income, worker equity participation, or massive upskilling programs would bridge the gap. They do not. Upskilling fails when the AI evolves faster than a human can be retrained. Universal basic income, while helpful for subsistence, does not replace the robust discretionary spending required to sustain a growth-oriented economy. Even capital income taxes and Coasian bargaining were found to be insufficient to stop the downward spiral. The more capable the AI becomes and the more competitive the market remains, the worse the economic outcome for society. It is a terrifying irony that the more we improve our technology, the more we accelerate our own economic obsolescence.

The only intervention that the study found to be effective is a Pigouvian automation tax. This is a direct tax on the act of replacing a human role with a machine. In economic terms, a Pigouvian tax is intended to discourage an activity that creates a negative cost for others, much like a carbon tax. By taxing the replacement of humans, we force companies to internalize the social cost of unemployment and lost consumption. This is not about being Luddites or fearing progress. It is about acknowledging that the market, left to its own devices, will not self-correct. The market is currently rewarding companies for cutting their own throats by firing their future customers. Only a rigorous policy intervention can break the cycle and ensure that AI serves as a tool for human prosperity rather than a replacement for human existence.

Recirculation, Not Replacement

The vision we must advocate for is one of recirculation rather than replacement. The goal of an AI-driven economy should not be a world where humans are discarded, but one where AI works to generate wealth that is then paid out to humans, who in turn spend it to keep the ecosystem circulating. We need a system where AI passes the money to the human. This is not just about charity; it is about systemic survival. If AI can do the work of 90 people, the value generated by that AI must still find its way into the pockets of those 90 people so they can remain active participants in the economy. If the wealth generated by AI is merely hoarded in the capital expenditures of a few tech giants or returned to a shrinking pool of investors, the circulation stops, and the economy dies.

The current trajectory at Meta is a warning of what happens when we prioritize infrastructure over people. The company’s capital expenditure guidance has climbed as high as $145 billion, which marks a significant increase from previous years. This is a massive bet on compute at the expense of community. When Meta’s chief people officer, Janelle Gale, speaks of offsetting investments by laying off staff, she is describing a transfer of wealth from human labor to silicon hardware. This might look good on a quarterly earnings report, but it is unsustainable in the long term. A world of perfect AI and zero workers is a world with no customers. The tech giants are currently building the most sophisticated stores in history, but they are inadvertently firing everyone who has the money to walk through the doors.

We must shift the narrative from asking how we use AI to cut costs to asking how we use AI to expand human capacity. Zuckerberg’s point that AI can help employees spin up more new projects is the right sentiment, but it is currently being used as a justification for downsizing rather than expansion. If AI makes a team ten times more efficient, the answer should be to do ten times more things with those 100 people, not to keep the output the same and fire 90% of the staff. We are currently stuck in a scarcity mindset regarding human labor, viewing it only as a liability to be minimized. We need to view it as the ultimate engine of demand.

The Choice

Ultimately, the choice before us is a political one, not a technological one. The automation wave is already running, and as the data shows, it is picking up speed. We cannot wait for the invisible hand to fix this, because the invisible hand is currently busy coding its own replacement. We need a global consensus on an automation tax and a fundamental redesign of how wealth is distributed in an era of post-labor productivity. The ecosystem must remain circular. Humans must be paid, and humans must spend. If we allow AI to break that circle, we are not just losing jobs; we are losing the very foundation of our modern civilization. The 8,000 people leaving Meta this month are not just a statistic. They are a symptom of a systemic fever that, if left untreated, will break the global economy.

The scale of this challenge is unprecedented because the rate of change is exponential. In previous industrial revolutions, the economy had decades to adjust, and new sectors emerged to absorb displaced workers. In 2026, the speed of AI deployment is measured in months. This leaves no room for natural market corrections. If every major corporation decides to automate 10% of its workforce this year to fund AI development, the resulting drop in consumer confidence and spending will trigger a recession that no amount of algorithmic trading can stop. We are effectively watching a high-speed chase where the destination is a brick wall. The only way to avoid the crash is to put a price on the displacement itself, ensuring that the transition to an automated world is slow enough for the social fabric to remain intact.

Policy makers must realize that the current corporate strategy of high capex and low headcount is a race to the bottom. While companies like Meta, Nvidia, and Amazon might see their stock prices soar in the short term due to AI hype, those valuations are built on the assumption of future growth. That growth requires consumers with disposable income. If the middle class is hollowed out by automation, the very products these AI models are designed to sell will have no market. We must champion a future where AI works for us, not instead of us. This requires a radical rethinking of the relationship between capital and labor. The idea that humans should be paid because AI works is not radical; it is the only logical conclusion for a society that wishes to remain a society. We must demand that the gains from automation are used to fund human life, ensuring that the economy remains a tool for human flourishing rather than a playground for autonomous machines.

 

Source: https://www.benzinga.com/Opinion/26/05/52664041/the-automation-paradox-why-replacing-humans-with-ai-is-an-economic-suicide-pact

 

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com
j j j