Unprecedented! A report from 2028 on the "AI Economic Plague" reveals: When machines no longer need you, how much "human premium" remains for $BTC and $ETH?

We have always believed that every breakthrough in AI would drive asset prices higher. However, a macro memo set in June 2028 depicts a completely opposite scenario: an unexpectedly booming AI productivity ultimately triggers an “economic plague” driven by demand collapse.

The report fabricates a scenario where the U.S. unemployment rate rises to 10.2%, and the S&P 500 index declines by 38% from its peak in October 2026. The market has become numb to bad news; six months prior, similar data would have triggered circuit breakers. The crisis is broken down into two mutually reinforcing chains.

The first is the real economy chain. The leap in AI capabilities leads to systemic displacement of white-collar jobs. Real wages turn negative growth, and high-income groups are forced to downscale. Consumption, the “human-centered engine” accounting for about 70% of GDP, begins to shrink. A sharp question is posed: how much will machines spend on optional consumption? The answer is zero. This gives rise to “ghost GDP”—output recorded in national accounts but unable to circulate in the real economy.

The second is the financial system chain. The structural deterioration of white-collar income expectations begins to erode assets built on stable cash flow assumptions. The first to fall are the software industry. By the end of 2025, AI programming tools experience a step change in capability, prompting companies to build in-house replacements for SaaS purchases. Fortune 500 companies use self-developed solutions as bargaining chips, cutting renewal costs by 30%. Industry moats shift from functional differentiation to brutal cost and financing endurance battles.

More reflexively, the disrupted companies do not resist but accelerate AI adoption to save themselves. For example, a process automation firm sees its contract value growth halve due to client layoffs and then announces a 15% reduction in its own staff. The individual rational behaviors of each company, when combined, dismantle the entire economic system’s brakes.

When AI agents become the default configuration in early 2027, transactions shift from human discrete decisions to continuous 24/7 optimization. The friction rent layer based on “human limitations” begins to collapse: travel booking platforms, insurance policies reliant on renewal inertia, financial advisors, real estate agents. Buyer commissions are compressed below 1%.

Deeper impacts occur at the payment layer. When agents dominate transactions, interchange fees of 2-3% for card organizations become glaring. In the setup, agents start settling in $SOL or stablecoins on Ethereum Layer 2, with costs approaching fractions of a cent. This directly impacts the profitability models of institutions like Mastercard and American Express.

This is far from an industry boom issue. About 50% of U.S. employment is white-collar, yet they drive roughly 75% of optional consumption. The top 10% of income earners contribute over 50% of consumption. Therefore, even a 2% decline in white-collar employment could trigger a 3-4% drop in optional consumption. This leverage effect was already hinted at in early October 2026, as deteriorating job vacancy data led to the bond market pricing in consumption shocks, with the 10-year U.S. Treasury yield dropping from 4.3% to 3.2%.

Meanwhile, AI investment has not slowed, as it is fundamentally an operational expense replacement rather than traditional capital expenditure. Companies shift budgets from salaries to AI infrastructure (like Nvidia and TSMC), creating a stark divergence: high prosperity in AI hardware and infrastructure versus consumption decline. At the national level, divergence also appears: South Korea, as a pure beneficiary, outperforms, while India’s $200 billion+ annual IT services exports suffer a heavy blow, with the rupee depreciating 18% against the dollar in four months.

The first domino in financial risk is private credit. Its scale exceeded $2.5 trillion by 2026, with large sums based on the assumption of “long-term stable compound growth” of SaaS revenues, invested in leveraged software acquisitions. When AI shatters this assumption, losses begin to surface. In April 2027, Moody’s downgraded the debt ratings of 14 issuers totaling $18 billion. A $5 billion Zendesk loan based on recurring revenue was marked down to 58 cents on the dollar, becoming a landmark default case.

Private credit itself is a closed structure, which could be controlled. But the problem lies in large asset managers acquiring life insurance companies, turning annuity liabilities into financing bases for private credit. As software defaults spread, insurance regulators tighten risk capital requirements, forcing institutions to add capital or sell assets, creating a vicious cycle in a harsh market environment. Complex structures like offshore reinsurance further obscure loss attribution.

The truly deadly part is the housing mortgage market, with a size of about $13 trillion, underpinned by the assumption of borrowers’ stable income over the next 30 years. The risk is terrifying because the loans are high quality: high credit scores, substantial down payments, verifiable income. But AI-driven downward revisions of white-collar income expectations cause borrowers to lose confidence in their future cash flows. Early signs appear in the use of home equity lines of credit and early withdrawals from retirement accounts, followed by rising delinquency rates in tech hubs like San Francisco and Seattle.

Policy-wise, the dilemma deepens. Traditional tools like interest rate cuts and quantitative easing can rescue the financial engine but cannot fix the underlying real economy problem of “human intelligence being less valuable.” Fiscal policy faces a structural paradox: transferring payments to households, but the tax base (mainly from taxing labor hours) is shrinking. Labor income’s share of GDP has fallen from 64% in 1974 to 56% in 2024, and further plummeted to 46% during the four years of accelerated AI.

Political debates shift toward taxing AI compute power and establishing “public intelligence royalties” similar to sovereign wealth funds, but the divide between left and right is huge. Social friction intensifies, with protesters even blocking AI labs. The pace of institutional change far outstrips the feedback loop of technological iteration.

The underlying logic of all this is the historic retreat of the “intellectual premium.” The modern economic and financial system’s pricing anchor—the scarcity of human intelligence—is being passively undermined. When machine intelligence becomes a cheap substitute, the entire system must painfully reprice itself. The report ends with a self-check question: how much of your assets and cash flows are bets on the old assumptions—“frictions won’t disappear, white-collar income remains stable, households continue to be demand engines”?

For assets like $BTC and $ETH, their long-term valuation narratives include a “hedge” component against distrust in the traditional system. But if the root cause of shocks is a societal restructuring of production relations rather than illegal currency issuance or institutional default, then their “safe haven” attributes need to be reevaluated under new logic. As the economic plague spreads, all assets built on old cash flow assumptions are subject to revaluation.