In a rapidly evolving economic landscape driven by artificial intelligence, the future of Bitcoin may hinge less on its technological infrastructure and more on macroeconomic trends influenced by central banks. Greg Cipolaro, the global head of research at NYDIG, highlights that the intersection between AI and Bitcoin is primarily shaped by growth, employment rates, real interest rates, and overall liquidity in the market.
As automation and AI reshape the labor market, potential job losses and declining wages could weaken consumer demand. In a particular scenario where incomes fall considerably, individuals may struggle to meet debt obligations, ultimately putting downward pressure on asset prices, including cryptocurrency. Recent developments reflect these concerns; for instance, Jack Dorsey’s fintech company Block has announced a significant workforce reduction of approximately 40%, attributing the cuts to efficiencies gained through AI—a situation echoed in recent market analyses.
In light of these changes, policymakers may respond by lowering interest rates or increasing fiscal spending to stabilize the economy. Should this occur, an influx of liquidity into the market could potentially bolster Bitcoin, which has historically mirrored shifts in global money supply and liquidity.
Conversely, a more favorable scenario for the cryptocurrency could arise if AI enhances productivity and drives economic growth without leading to substantial job losses. In such a case, central banks might maintain tighter monetary policies, resulting in higher real yields. Historically, elevated real interest rates can diminish the attractiveness of holding Bitcoin, as they increase the opportunity cost associated with the asset and make other risk assets more appealing.
Concerns surrounding AI echo historical moments of societal upheaval when technological advancements dramatically altered labor dynamics. Events such as the introduction of the steam engine, the advent of electrification, and the rise of computers have each caused significant shifts in employment patterns, often sparking fears about permanent job losses. However, it’s important to note that despite these disruptions, aggregate demand did not collapse; instead, productivity improved, and new sectors emerged to utilize displaced workers, albeit with varying degrees of difficulty.
Cipolaro posits that AI may mirror this historical trend. As a general-purpose technology, it compels companies to modify workflows and invest in complementary tools, which can ultimately increase productive capacities. He suggests that while the transition may not be seamless, history suggests that the societal response to such technological waves is generally one of integration rather than obsolescence.
For Bitcoin, this distinction carries significant implications. A potential long-term growth boost from AI could create a different structural environment compared to the immediate shocks that often prompt liquidity responses from central banks. Moreover, the rise of programmable payments could enhance Bitcoin’s adoption, facilitating automated transactions between software without human intervention—a vision that aligns with Bitcoin’s initial concept of machine-to-machine payments.
However, obstacles remain. Currently, the incentives for widespread use of Bitcoin and other cryptocurrencies are lacking, especially when compared to credit cards, which offer rewards and short-term credit benefits that stablecoins currently do not.
Ultimately, the impact of AI on the economy highlights the importance of human responses to the shifts it creates. Whether AI leads to a deflationary environment that revives monetary stimulus or propels a surge in productivity that increases real yields, Bitcoin’s value and relevance will likely reflect these overarching economic trends.
