AI Acceleration, Software Repricing, and Bitcoin’s Structural Crossroads

Summary

The February 15, 2026 episode of the Jordi Visser Podcast features Jordi arguing that AI has entered a recursive self-improvement phase that is structurally undermining SaaS, hyperscalers, and overowned growth equities. He contends that collapsing intelligence costs and rising dispersion, combined with elevated hedge fund gross leverage, create a higher-than-priced risk of forced deleveraging. Visser links this regime shift to a rotation toward scarce physical assets and frames Bitcoin as both vulnerable in a short-term unwind and strategically advantaged in a longer-term capital reallocation.

Take-Home Messages

1. Recursive Self-Improvement: AI is now accelerating through self-reinforcing model upgrades, compressing development cycles and destabilizing legacy software moats.
2. SaaS as Supply Explosion: Margin pressure stems from a surge in software supply as coding costs collapse, not from immediate demand weakness.
3. Leverage and Covariance Risk: Elevated hedge fund gross leverage combined with rising dispersion increases the probability of nonlinear deleveraging.
4. Physical Scarcity Repricing: Energy, materials, and infrastructure constraints become strategic advantages as the digital economy collides with real-world bottlenecks.
5. Bitcoin’s Path Dependence: Bitcoin may decline in a broad unwind, but a post-shock environment of policy easing and capital rotation could reinforce its scarcity premium.

Overview

Jordi Visser argues that AI has crossed from incremental productivity gains into a “supersonic tsunami” phase characterized by recursive self-improvement and rapidly compounding capability. He maintains that recent model upgrades demonstrate a transition from AI assisting humans to AI building infrastructure for other AI systems. In his view, this marks a structural inflection point rather than another technology cycle.

He rejects the idea that recent SaaS weakness represents a traditional buying opportunity, contending instead that software faces a supply explosion as development costs approach zero. Visser explains that when software becomes cheaper to create, competition multiplies and pricing power erodes, even if short-term revenue metrics appear resilient. He describes this as a value trap dynamic driven by deflationary pressure rather than collapsing demand.

Turning to market structure, Visser highlights elevated hedge fund gross leverage and rising cross-asset dispersion as underappreciated fragilities. He points to turbulence in covariance relationships and early signs of credit weakness as potential accelerants if forced deleveraging begins. In his framework, a sideways index can mask growing systemic stress beneath the surface.

Finally, Visser shifts to the physical economy, arguing that hyperscalers have transitioned from asset-light software firms to asset-heavy infrastructure builders. He emphasizes bottlenecks in power, transformers, memory, and data center construction as strategic constraints that can impair revenue realization. He situates Bitcoin within this broader creative destruction cycle, noting its correlation with growth assets but suggesting that longer-term capital structure shifts may ultimately favor scarce digital assets.

Stakeholder Perspectives

1. Public Equity Investors: Reassessing whether software drawdowns reflect cyclical dislocation or structural margin compression driven by AI-enabled supply expansion.
2. Hyperscalers and AI Labs: Balancing aggressive capital expenditures against infrastructure bottlenecks and competitive threats from lower-cost open models.
3. Hedge Funds and Risk Managers: Monitoring dispersion, covariance shifts, and gross leverage exposure that could amplify forced selling.
4. Regulators and Financial Stability Authorities: Evaluating cross-asset transmission channels that could transform sector rotation into broader financial stress.
5. Bitcoin Allocators and Treasurers: Weighing short-term beta risk against longer-term scarcity dynamics as capital reallocates in an AI-transformed economy.

Implications and Future Outlook

If AI capability continues compounding through recursive self-improvement, competitive cycles in software may compress from years to quarters, intensifying margin volatility and capital reallocation. Firms that cannot defend differentiated data, distribution, or infrastructure advantages could experience prolonged earnings pressure despite headline growth in AI adoption. Investors will need to distinguish between cyclical overshoot and structural erosion by closely tracking pricing power, agent deployment rates, and enterprise switching behavior.

At the market level, elevated gross leverage combined with unstable correlations increases the probability that localized shocks become systemic events. Credit spreads, leveraged loan performance, and financial sector leadership will be critical early-warning indicators of contagion. Policymakers may face difficult tradeoffs if financial instability emerges amid rapid technological disruption.

For Bitcoin, path dependence remains central: a broad deleveraging could temporarily compress prices alongside growth equities, reinforcing its short-term correlation profile. However, a post-shock environment of policy easing, dollar weakness, and capital flight from overbuilt software could re-anchor interest in scarce, non-dilutable assets. Over a multi-year horizon, the interaction between AI-driven creative destruction and monetary regime stress may define Bitcoin’s next adoption phase.

Some Key Information Gaps

1. What triggers would convert elevated gross leverage into a systemic deleveraging event? Identifying these triggers is essential for financial stability planning and institutional risk management.
2. How does collapsing software production cost alter long-term SaaS margin structures? Clarifying this dynamic will determine whether current valuations reflect temporary fear or durable structural repricing.
3. At what capex threshold do hyperscalers face balance sheet stress from infrastructure overbuild? Understanding this inflection point is critical for assessing credit risk and equity dilution scenarios.
4. How rapidly will low-cost open-source AI models displace paid enterprise solutions? Measuring displacement rates informs competitive positioning and policy responses around technology leadership.
5. Can Bitcoin function as a durable capital reallocation vehicle in an AI-agent-driven economy? Evaluating this role will clarify how digital scarcity assets perform amid technological and monetary regime shifts.

Broader Implications for Bitcoin

Monetary Regime Stress and Digital Scarcity

As AI accelerates competitive turnover and compresses corporate profit pools, traditional equity-based wealth concentration may become less stable over time. In such an environment, capital may seek assets insulated from earnings volatility and dilution, reinforcing demand for fixed-supply instruments. Over a three-to-five-year horizon, recurring technology-driven repricing cycles could strengthen the appeal of Bitcoin as a non-sovereign reserve complement.

Capital Market Structure in an AI Economy

If hyperscalers and AI labs become capital-intensive infrastructure operators, financial markets may experience sustained tension between growth narratives and balance sheet constraints. This shift challenges the asset-light paradigm that dominated the past decade and could reconfigure portfolio construction norms globally. Bitcoin’s transparent issuance schedule and absence of corporate leverage make it structurally distinct within this evolving capital architecture.

Sovereignty, Open Models, and Decentralization

The rise of lower-cost open AI models introduces geopolitical and competitive complexity that transcends individual firms. As software becomes commoditized and distributed globally, states may confront diminished control over digital innovation pipelines. In parallel, Bitcoin’s decentralized consensus and open participation model illustrate how digital networks can operate beyond traditional jurisdictional hierarchies.

Energy and Infrastructure Competition

AI’s demand for power, cooling, and advanced materials underscores the strategic importance of energy and infrastructure policy. Jurisdictions that secure reliable, scalable power may attract both AI data centers and Bitcoin mining operations, intertwining digital compute and monetary networks. Over time, competition for resilient energy systems could reshape industrial policy and align Bitcoin more closely with long-term grid modernization strategies.