Bitcoin’s future in an artificial intelligence-driven world may depend less on code and more on central banks.
In a new note, Greg Cipolaro, global head of research at financial services and infrastructure firm NYDIG, argued that artificial intelligence will affect bitcoin mainly through macroeconomic channels and its impact on the labor market.
The key variables are growth, employment, real interest rates and liquidity. Bitcoin, he writes, sits downstream of those forces.
If automation cuts jobs and wages, consumer demand could weaken and, in a severe case, falling incomes would strain debt payments and pressure asset prices.
Those fears appear to be well-grounded. Just this week, Jack Dorsey’s fintech firm Block unveiled its shrinking back toward its pre-pandemic size, cutting staff by about 40%. Dorsey cited AI-enabled efficiency for the job cuts, something that was theorized in Citrini’s research on the AI-doom that spooked the market this week.
In such a scenario, policymakers might respond with lower rates or fiscal spending to stabilize the economy. That wave of liquidity could support bitcoin, which has often tracked shifts in global money supply.
A different outcome would look less friendly for the cryptocurrency. If AI boosts productivity and economic growth without major job losses, real yields could rise, and central banks might keep policy tight.
Higher real rates have historically weighed on bitcoin by raising the opportunity cost of holding it and making risk assets less attractive.
Shift in demand
Anxiety around AI echoes past moments of upheaval in Human society.
The steam engine displaced manual labor in factories and on farms. Electrification then rewired entire industries. Later, computers and the internet automated clerical work and reshaped retail, media and finance.
Each wave triggered fears of permanent job loss. In the early 1900s, factory mechanization sparked labor unrest as machines replaced skilled craftsmen. In the 1980s and 1990s, personal computers cut typist pools and back-office staff. More recently, e-commerce helped hollow out brick-and-mortar retail roles.
Yet aggregate demand did not collapse. Productivity rose. New industries absorbed displaced workers, even if the transition proved uneven and painful. Nowadays, we have industries that were unthinkable before the dawn of the internet. Think cloud computing.
Cipolaro argued AI may follow a similar pattern. As a general-purpose technology, it requires firms to redesign workflows and invest in complementary tools. Over time, that process tends to expand productive capacity rather than shrink it.
“The implication is not that disruption will be painless, but that the equilibrium response to new technology has historically been integration, not obsolescence,” Cipolaro wrote. “Society’s response to AI will likely follow the same pattern.”
For bitcoin, that distinction matters. If AI ultimately lifts long-term growth, the structural backdrop could differ from the short-term shocks that often drive liquidity injections.
Meanwhile, adoption may also rise thanks to agentic payments, which would essentially see software pay other pieces of software without human involvement. One of Bitcoin’s earliest visions centered on machine-to-machine payments, and AI may be the necessary tool to make them a reality.
Still, incentives aren’t currently there for a widespread rollout. Credit cards bundle rewards and short-term credit, features that stablecoins do not yet match, Cipolaro noted.
Ultimately, while the rise of AI brings new challenges, what matters is the human response to the disruption it brings. If AI triggers a deflationary shock and forces the money printer to turn back on, or if it fuels a productivity boom that raises real yields, bitcoin will reflect that.
Context
Current positioning around DeFi & Stablecoins remains sensitive to primary-source updates, policy interpretation, and execution risk across major venues.
What To Watch
Key confirmation signals include sustained spot demand, funding stability, and whether price can hold reclaimed levels after headline-driven volatility.
If momentum weakens, traders will likely prioritize downside liquidity zones and risk-control positioning before adding new directional exposure.
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