AI-Led Reflation and Bitcoin in a Post-Labor Growth Regime

Summary

The January 04, 2026 episode of the Jordi Visser Podcast features Jordi arguing that the global economy is entering an early-cycle reflation regime driven by AI-enabled productivity rather than labor growth. He points to back-to-back quarters of strong real GDP with zero job creation as evidence that traditional macro signals are breaking down, while capital spending shifts from software toward energy, hardware, and physical AI infrastructure. Visser contends that under these conditions Bitcoin historically performs well, even as political backlash, geopolitical shocks, and uncertainty around AI capex returns define the main risks for 2026.

Take-Home Messages

1. Growth Without Jobs: AI-driven productivity is allowing GDP to expand even as hiring stagnates, complicating macro interpretation.
2. Reflation Signals Aligning: Rising PMIs, commodity strength, exports, and a weaker dollar point to expansion rather than recession.
3. AI Goes Physical: The next phase of AI centers on hardware, power, and edge deployment, not just cloud software.
4. Bitcoin and Liquidity: Bitcoin has historically benefited from rising PMIs and dollar weakness, despite current negative sentiment.
5. Volatility From Non-Market Risks: Political backlash, geopolitics, and capex payoffs pose the largest threats to an otherwise constructive backdrop.

Overview

Visser frames the 2026 outlook as a misread macro environment, where fears of bubbles and stagflation obscure signs of an early-cycle reflation. He emphasizes that recent GDP surprises matter less for their magnitude than for their composition, with strong output growth occurring alongside flat job creation. This combination, he argues, reflects firms substituting AI and automation for labor rather than overheating demand.

He supports this view by pointing to corroborating indicators across markets and regions. Visser highlights rising copper prices, improving export data from Asia, strengthening PMI proxies, and a renewed downtrend in the dollar as consistent with easing financial conditions. He argues these patterns fit historical reflation regimes where real assets and growth assets rise together rather than signaling an imminent downturn.

A central theme is the transition from software-centric AI adoption to physical-world deployment. Visser describes data centers, power generation, advanced packaging, and edge devices as the real constraints and opportunities shaping the next phase of AI-driven growth. In his view, this shift alters sector leadership and anchors AI’s economic impact in tangible infrastructure rather than abstract model capability.

Visser applies this regime logic to Bitcoin by contrasting weak sentiment with what he sees as supportive liquidity conditions. He argues Bitcoin tends to perform best when PMIs rise and the dollar weakens, and he treats recent underperformance as cyclical rather than structural. At the same time, he flags geopolitical shocks, AI capex credibility, and political backlash to job displacement as the key sources of instability.

Stakeholder Perspectives

1. Central Banks: Struggling to interpret growth and inflation signals when productivity rises without corresponding employment gains.
2. Governments and Labor Agencies: Facing political pressure as AI-driven efficiency reshapes jobs faster than reskilling systems adapt.
3. Corporations and Investors: Assessing whether massive AI capital spending will translate into durable revenues and cash flows.
4. Energy and Infrastructure Providers: Managing rising demand for power and hardware required to operationalize AI at scale.
5. Bitcoin Holders and Financial Institutions: Re-evaluating Bitcoin’s role in portfolios amid shifting liquidity and macro regimes.

Implications and Future Outlook

If AI continues to decouple growth from hiring, policymakers will face growing tension between strong headline GDP and public perceptions of economic health. Visser’s framework implies that political response, not inflation, may become the binding constraint on monetary and industrial policy. How governments interpret and communicate these shifts will shape market stability through 2026.

From a market perspective, the credibility of AI-driven reflation depends on follow-through from capital spending to realized revenues. Visser argues that failure on this front, rather than valuation alone, would pose the greatest downside risk to equities and related assets. Monitoring enterprise adoption, agent deployment, and execution-layer economics becomes more important than tracking model announcements.

For Bitcoin, the outlook hinges less on narrative momentum and more on macro plumbing. Rising PMIs, a weaker dollar, and positive global liquidity have historically favored Bitcoin, even when sentiment lags. The main uncertainty is whether political or geopolitical shocks interrupt these conditions before liquidity effects reassert themselves.

Some Key Information Gaps

1. How durable are AI-driven productivity gains without corresponding job growth? Clarifying this is critical for labor policy, social stability, and long-term growth expectations.
2. How can policymakers distinguish reflation from speculative excess in real time? Better diagnostics would reduce policy error and mispriced risk across markets.
3. At what point must AI capital expenditure translate into revenues to remain sustainable? Understanding this threshold is essential for assessing systemic investment risk.
4. How should monetary policy adapt when employment weakens despite strong GDP growth? New frameworks may be needed to guide rate decisions and communication.
5. How persistent are Bitcoin’s historical correlations with PMIs and dollar weakness? Testing these relationships informs portfolio construction and macro strategy.

Broader Implications for Bitcoin

Monetary Policy in a Post-Labor Growth Economy

AI-driven productivity challenges the assumption that employment is the primary transmission channel between growth and social welfare. As output rises without broad-based hiring, central banks and governments may be forced to reconsider how they define economic success and policy targets. Over the next several years, this tension could accelerate experiments with alternative metrics, fiscal redistribution, or new social contracts.

Infrastructure as the New AI Bottleneck

The shift from software to physical AI implies that power, hardware supply chains, and energy policy will increasingly shape technological progress. Jurisdictions that fail to expand grid capacity or streamline permitting may lose competitiveness, while those that succeed attract capital and talent. This dynamic has cross-border relevance and will influence how digital transformation intersects with industrial policy.

Bitcoin as a Liquidity-Sensitive Monetary Asset

In a world of reflationary growth and shifting policy signals, Bitcoin’s role increasingly resembles that of a macro-sensitive monetary asset rather than a purely speculative instrument. Its performance under rising PMIs and dollar weakness suggests relevance as a hedge against policy miscalibration and currency debasement. Over a multi-year horizon, this positioning could deepen Bitcoin’s integration into global portfolio strategies.

Political Economy of Automation

Widespread AI adoption raises the probability of political backlash as voters confront job redesign and displacement. Regulatory interventions aimed at slowing automation could create uneven adoption across regions, reshaping global competitiveness. Bitcoin’s neutral, borderless nature may gain relevance as trust in domestic policy responses diverges across jurisdictions.