Bitcoin's future is increasingly tied to macroeconomic trends influenced by artificial intelligence (AI), rather than its technological fundamentals. According to NYDIG Research, the cryptocurrency's trajectory will depend heavily on how AI affects growth, employment, real interest rates, and central bank liquidity. As Greg Cipolaro, NYDIG's global head of research, suggests, Bitcoin's position in the market is fundamentally downstream of these economic forces. Job displacement caused by AI could weaken consumer demand, leading to reduced incomes that strain debt payments and asset prices.

This concern is underscored by recent developments, such as Block's decision to cut its workforce by 40%, which CEO Jack Dorsey attributed to AI-driven efficiency. If widespread job losses occur, policymakers might respond with lower interest rates or increased fiscal spending, creating a wave of liquidity that could support Bitcoin, which has historically tracked global money supply changes. Conversely, if AI enhances productivity without significant job losses, real yields may rise, prompting central banks to maintain tight monetary policies. Higher real rates typically diminish Bitcoin's appeal by increasing the opportunity cost of holding the asset, making riskier investments less attractive.

The contrasting outcomes of AI's impact on the economy underscore the critical relationship between macroeconomic stability and Bitcoin's value. Historically, technological advancements have caused upheaval in labor markets, from the steam engine to the internet. Each wave of disruption sparked fears of permanent job loss; however, new industries often emerged to absorb displaced workers, even if the transition was uneven. Cipolaro believes AI could similarly transform the economy, requiring firms to redesign workflows and invest in complementary technologies, potentially expanding productive capacity over time.

The implications for Bitcoin are significant. If AI promotes long-term economic growth, the structural environment for Bitcoin could shift away from the immediate shocks that typically prompt liquidity injections. Meanwhile, the rise of agentic payments—where software autonomously transacts with other software—could promote Bitcoin adoption, aligning with the original vision for machine-to-machine payments. However, the current incentives for a broad rollout of such systems remain limited, as credit card offerings still outpace those of stablecoins.

The interaction between AI and macroeconomic factors presents a complex landscape for Bitcoin. If automation leads to job losses, consumer demand could weaken significantly. In such scenarios, economic strain could lead to policymakers resorting to lower interest rates or fiscal stimulus, effectively increasing the liquidity in the market. A surge in liquidity has historically been a boon for Bitcoin, which has often tracked shifts in global money supply closely.

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This means that Bitcoin could benefit from an influx of capital in a liquidity-driven environment. On the other hand, if AI-driven productivity improvements occur without major job losses, the economic landscape could shift towards higher real yields. In this scenario, central banks might opt to keep monetary policy tight to combat inflationary pressures that could arise from increased productivity. Historically, higher real rates have been detrimental to Bitcoin, as they heighten the opportunity cost of holding the asset compared to traditional investments.

Anxiety surrounding AI's impact on employment echoes historical moments of technological upheaval. The introduction of the steam engine, for example, led to significant changes in labor dynamics, displacing manual labor in various sectors. Electrification transformed entire industries, while the rise of computers and the internet automated clerical work, reshaping sectors like retail and finance. Each wave of technological advancement triggered fears of permanent job loss, yet history has shown that these disruptions often lead to new opportunities and industries that absorb displaced workers, albeit through painful transitions.

Cipolaro argues that AI may follow a similar trajectory. As a general-purpose technology, it necessitates that firms redesign workflows and invest in complementary tools, which could ultimately expand productive capacity rather than diminish it. The historical pattern indicates that rather than obsolescence, the equilibrium response to new technologies tends to be integration into existing frameworks, creating new roles and opportunities in the process. For Bitcoin, this distinction is crucial.

If AI leads to sustained economic growth, the structural backdrop for Bitcoin could differ significantly from the short-term shocks that have historically driven liquidity injections. The emergence of agentic payments could further bolster Bitcoin's utility. This concept envisions software autonomously executing transactions with other software, aligning with Bitcoin’s foundational vision of enabling machine-to-machine payments. Despite this potential, the current incentives for widespread adoption remain limited, as traditional credit systems continue to offer attractive features that stablecoins do not currently match.

Ultimately, the human response to the rise of AI will play a pivotal role in shaping Bitcoin's future. Should AI trigger a deflationary shock, it could compel central banks to increase liquidity, favoring Bitcoin as an alternative store of value. Conversely, if the rise of AI fosters a productivity boom that raises real yields, Bitcoin's appeal may diminish, reflecting the economic realities of a changing landscape.