AI Agents, Stablecoins, and Bitcoin Positioning for 2026

Summary

The January 03, 2026 episode of The Pomp Podcast features Jordi Visser arguing that “AI bubble” fears will recur as a market narrative even if real adoption keeps widening. Visser links AI to rising productivity and profit margins, warns that investors overreact to episodic shocks such as tariffs or the Deepseek news cycle, and questions whether standard GDP statistics can capture intangible efficiency gains. He also connects stablecoins and AI agents to faster transaction velocity and highlights Bitcoin as a long-run position where the main hazard is behavioral error during volatility.

Take-Home Messages

1. Recurring risk narrative: “AI bubble” framing may keep resurfacing and moving markets even if adoption remains strong.
2. Shock-driven overreaction: Tariffs and tech-news scares can trigger poor timing decisions when investors chase headlines.
3. Productivity without hiring: AI can lift margins while job growth stays weak, complicating how policymakers read the cycle.
4. Faster financial plumbing: Stablecoins and AI agents could increase transaction volume and the speed of money by reducing friction.
5. Bitcoin execution risk: The long-run thesis can still be undermined by position sizing mistakes and emotional selling during drawdowns.

Overview

Anthony Pompliano frames the conversation around how investors should think about 2026 when many market participants worry about an “AI bubble.” Visser argues that the label persists because it offers a simple story for a complex transition involving capex, revenue timing, labor effects, and infrastructure constraints. He expects the narrative to recycle repeatedly, creating recurring episodes of fear and repricing.

Visser points to tariffs and the Deepseek news cycle as examples of events that can dominate attention even when underlying trends remain intact. He describes these moments as overreactions that tempt investors to overtrade and abandon positions at the wrong time. Pompliano uses the discussion to emphasize decision discipline as the practical advantage in a fast-moving market.

Visser argues that AI’s most important macro effect may be productivity gains that show up more in profit margins than in payroll expansion. He suggests that standard GDP metrics struggle to capture intangible efficiency gains, so official data may lag the operational reality inside firms. This measurement gap, in his telling, contributes to confusion about whether the economy is “booming” or “stalling.”

The discussion extends to financial rails, where Visser links stablecoins and AI agents to higher transaction volume and faster money velocity. He also expects enterprise agent adoption to increase demand for on-premise infrastructure because firms will prioritize security and control over fully cloud-dependent workflows. Visser then connects the theme back to Bitcoin by treating the main threat as self-sabotage during volatility rather than a failure of the long-run thesis.

Stakeholder Perspectives

1. Regulators and central banks: They will focus on whether stablecoin growth and agent-driven activity alter oversight, settlement risk, and monetary transmission.
2. Large enterprises: They will weigh security, governance, and liability when deciding whether AI agents run in the cloud or on-premise.
3. Energy and infrastructure providers: They will prioritize power availability, grid buildout, and permitting as AI compute demand rises.
4. Investors and asset managers: They will debate how to manage repeated “bubble” scares without whipsawing portfolios or missing durable adoption.
5. Bitcoin holders and corporate treasuries: They will focus on sizing, liquidity planning, and rebalancing rules that reduce the chance of forced selling.

Implications and Future Outlook

Visser’s core implication is that markets may keep oscillating between enthusiasm and skepticism because the AI transition lacks a single decisive datapoint. The most decision-relevant signals will come from concrete adoption evidence, margin durability, and the pace at which infrastructure constraints get resolved. Policymakers and analysts who rely on lagging aggregate statistics risk misreading both the benefits and the distributional strains.

The episode also implies that stablecoins and AI agents could tighten feedback loops in finance by reducing friction and increasing transactional throughput. Faster rails can improve efficiency, but they can also stress supervisory frameworks if automated activity grows faster than monitoring and control systems. This tension will matter for payment policy, market-structure debates, and the perimeter between regulated and lightly regulated intermediaries.

For Bitcoin-oriented decision-makers, the discussion translates into an execution problem rather than a conviction problem. Volatility remains the mechanism that transfers Bitcoin from weak hands to strong hands, so process discipline determines whether holders capture the long-run upside. Clear guardrails around leverage, liquidity, and rebalancing become the practical line of defense against narrative-driven whipsaws.

Some Key Information Gaps

1. How should GDP accounting frameworks adapt to better capture intangible productivity and efficiency gains attributed to AI? Better measurement would reduce policy error when AI changes output without showing up cleanly in headline statistics.
2. What labor-market metrics most credibly separate true job creation from AI-driven productivity that reduces hiring needs? Clear diagnostics would help governments target responses if growth persists while hiring weakens.
3. What are the most binding power constraints for AI data centers and enterprise deployments over the next 12–24 months? Reliable constraint mapping would improve infrastructure planning and reduce market volatility driven by uncertainty.
4. Under what security and governance conditions will large enterprises prefer on-premise AI agents over cloud-based deployments? Understanding decision thresholds would clarify capex direction and the pace of agent adoption.
5. Which guardrails (sizing, rebalancing rules, liquidity plans) best reduce behavioral mistakes for long-run Bitcoin holders? Practical risk controls would help households and institutions avoid self-sabotage during drawdowns.

Broader Implications for Bitcoin

AI-Driven Automation Raises the Premium on Hard Settlement

If AI agents increase the pace of economic activity, settlement finality becomes more important than branding or convenience. Bitcoin’s base layer offers a global, rules-based settlement asset that does not depend on a single operator’s uptime, policy choices, or access rules. Over a 3–5+ year horizon, more automated commerce could increase the value of neutral settlement and increase demand for clearly auditable reserves.

Stablecoin Expansion Increases the Demand for Verifiable Backing

As stablecoin volumes grow, markets will place greater weight on whether reserves remain liquid, transparent, and enforceable under stress. Bitcoin can function as a reserve asset, collateral asset, or credibility anchor for issuers that want to distinguish themselves in a crowded field. This dynamic could push policymakers toward clearer disclosure standards while rewarding structures that prove solvency without relying on narrative trust.

Productivity Gains Without Broad Wage Growth Intensify Monetary Tension

If AI raises margins and output while hiring stays weak, political pressure will rise to “do something” about distribution and perceived fairness. Bitcoin’s fixed supply and predictable issuance may become more salient in environments where governments respond with discretionary stimulus, credit support, or financial repression. Over time, this tension can make Bitcoin’s monetary rule-set more attractive as a hedge against policy volatility.

Energy and Infrastructure Bottlenecks Increase Interest in Cost Floors

When power becomes a constraint, energy pricing and availability shape which technologies scale and where they cluster geographically. Bitcoin mining’s sensitivity to electricity prices makes it a visible participant in debates about grid buildout, demand response, and local permitting. Over a multi-year horizon, jurisdictions that expand generation and modernize grids can simultaneously enable AI growth and attract Bitcoin-related capital, while restrictive regimes may lose both.

Narrative Volatility Strengthens the Case for Rule-Based Allocation

Repeated “bubble” scares and headline shocks increase the odds that investors overtrade and damage long-run outcomes. Bitcoin’s volatility makes this problem more acute, but it also makes systematic allocation rules more valuable for households, institutions, and corporate treasuries. Over 3–5+ years, better governance around custody, liquidity, and rebalancing may become as important as thesis formation in determining who benefits from Bitcoin adoption.