Bitcoin’s AI Rally Faces Risk If Workers Lose Out

In an Oct. 14 memo, Bitwise Chief Investment Officer Matt Hougan outlined three possible economic paths linked to artificial intelligence that could support Bitcoin. He acknowledged that the cryptocurrency could still suffer temporary declines and face risks unrelated to the broader economy.

In the first scenario, an AI productivity boom makes intelligence-based services cheaper and increases demand for scarce assets such as Bitcoin. In the second, an AI investment bubble bursts, causing a recession and leading to policy support and monetary expansion that could increase demand for the cryptocurrency. In the third, moderate productivity gains support continued Bitcoin adoption, exchange-traded fund demand and larger institutional allocations.

The scenarios assume that greater scarcity, economic instability or easier monetary conditions would increase demand for Bitcoin. The cryptocurrency’s supply is fixed, while its price depends on investor demand, purchasing power and market liquidity.

On Oct. 12, the Bank for International Settlements published working paper No. 1381, “Transformative AI and demand bottlenecks,” by Phurichai Rungcharoenkitkul. The paper modeled an economy in which AI automates more work and raises output, while consumer demand that funds further innovation gradually weakens.

In the paper’s main calibrated scenario, consumption falls from about 69% of economic output before widespread AI adoption to 62.1% when additional innovation is no longer profitable. Sustainable long-term growth declines from about 2% to 1.6%. Output growth initially reaches just under 2.4% about 15 years after the start of the AI era and remains above 2% for about 35 years as companies continue investing in machinery and computing capacity.

The model estimates that labor’s share of tasks could fall from 60% to between 15% and 50% over three decades. In the demand-bottleneck scenario, real wages eventually settle about 15% below the level projected without that degree of automation. The estimate assumes a fixed labor supply, an unchanged range of tasks and wages based on workers’ marginal productivity.

Interest rates rise during the early investment boom in the model as demand for capital increases, then fall when weaker consumption becomes a constraint. The pre-AI reference rate is 1.65%, compared with a median projection of 1.2% after 20 years. The model’s 90% range extends from minus 0.6% to 4.4%.

The Federal Reserve’s Oct. 14 Beige Book reported that businesses in five of its 12 districts had reduced staffing through attrition, delayed hiring or expected fewer jobs after AI-related productivity gains. Consumer spending was broadly flat, and more than half of the districts reported that higher-income households accounted for most spending. Employment was broadly stable, with three districts reporting modest increases, five reporting slight increases and four reporting no change.

The U.S. trade deficit widened to $105.6 billion in August, partly because of strong imports of AI hardware. Economist Dean Maki estimated that imports represented about 70% of AI-related capital spending.

AI-linked stocks fell on Oct. 14 after inflation data came in hotter than expected. CoreWeave declined about 16%, while Nebius and Dell fell about 11% each and Oracle dropped about 9%. Borrowing costs for AI infrastructure companies remained elevated.

Bitcoin was trading at $80,924.48, down 2.91% over 24 hours and 2.11% over seven days, while up 5.01% over 30 days, according to the source data. The Federal Reserve’s next meeting is scheduled for Oct. 27-28. ETF flows, AI investment and credit conditions will be monitored alongside longer-term changes in growth, wages and consumption.