AI’s Kinetic Turn and the New Digital Market Regime

Summary

The December 07, 2025 episode of the Jordi Visser Podcast features Jordi explaining why apparent AI “bubble” dynamics may actually represent a durable capital expenditure regime rather than a fragile mania. Visser argues that markets are transitioning from a cognitive era of cloud-based large language models to a kinetic era in which AI is embedded in physical devices, industrial systems, and transportation networks. He links this shift to a K-shaped economy, aggressive infrastructure and compute buildouts, expanding tokenization, and an emerging role for Bitcoin within faster, more interconnected financial markets.

Take-Home Messages

1. AI as the macro anchor: AI now frames the macro narrative more than traditional themes like inflation or rates, shaping sector leadership and investor psychology.
2. From cognitive to kinetic AI: Embedding AI into cars, phones, PCs, robots, and industrial equipment will trigger synchronized upgrade cycles that support real-economy demand.
3. K-shaped economic stress: Small businesses are experiencing recession-like conditions even as AI-linked large caps, transports, and banks break out, increasing political and policy tensions.
4. Physical stack repricing: Long-overlooked industrials in photonics, glass, networking, and sensors as potential new “compounders,” while warning that code-centric software winners face rerating risk from AI-driven commoditization.
5. Tokenization and Bitcoin’s next phase: Tokenized assets, faster trading rails, and a base-building phase for Bitcoin will become key features of the next market regime layered on top of the AI infrastructure buildout.

Overview

Visser opens by asserting that AI has become the defining driver of markets, arguing that investors who see only a bubble risk missing a structural shift in how capital expenditure and productivity interact. He situates the current environment within a longer history of technology-led regimes, suggesting that AI blends elements of the internet and mobile eras but with broader reach into both services and manufacturing. This framing sets up his main claim that what looks like speculative excess may instead be the early stages of a multi-year investment cycle with deep macro implications.

He draws a distinction between a “cognitive” phase of AI, dominated by cloud-hosted large language models, and an emerging “kinetic” phase where AI “brains” live in edge devices and machines. Visser emphasizes visual language models and agents running on neural processing units in everything from smartphones and laptops to cars, trucks, and factory robots. In his view, this device-level deployment will force households, firms, and logistics networks into synchronized hardware upgrade cycles that boost purchasing managers’ indices and underpin more resilient demand for components and infrastructure.

The conversation repeatedly returns to a K-shaped pattern in the real economy, where small businesses and many consumers face tightening credit and job losses while large AI-linked companies surge. Visser notes that transportation, banks, and semiconductor names are breaking out even as small business indicators resemble recession conditions, creating a disconnect between equity indices and lived experience. He frames this divergence as a key channel through which AI, interest rates, and industrial dynamics will exert pressure on central banks to cut, even if headline markets remain strong.

To illustrate where value might be mispriced, Visser highlights firms such as Corning and Marvell as examples of long-ignored industrial and component players that now sit in the critical “physical AI stack.” He argues that demand for optical fiber, glass, photonics, and advanced connectivity will rise as data centers, devices, and eventually humanoid robots require more bandwidth, lower latency, and better sensors. At the same time, he warns that code-based software “compounders” face a tougher future as AI-enabled “vibe coding” allows enterprises to build tailored tools in-house, compressing valuations for traditional packaged software and shifting market leadership toward hardware, infrastructure, and select digital assets including Bitcoin.

Stakeholder Perspectives

1. Institutional investors: Rebalancing portfolios toward AI infrastructure, device manufacturers, and overlooked industrials while managing the risk that capex overbuild or demand shocks could unwind current leadership.
2. Central banks and financial regulators: Assessing how AI-driven investment, K-shaped labor dynamics, tokenization, and rapid trading infrastructure may alter financial stability, policy transmission, and market volatility.
3. Small and medium-sized enterprises: Facing competitive pressure from large firms deploying AI at scale, while contending with higher borrowing costs, tighter margins, and potential displacement in service and logistics sectors.
4. Technology and industrial firms: Deciding how aggressively to integrate NPUs, sensors, robotics, and autonomous capabilities into product lines and supply chains, given uncertainties around demand timing, standards, and energy availability.
5. Bitcoin-focused and digital asset stakeholders: Positioning around a future in which tokenized assets, AI-driven analytics, and faster market rails may change liquidity patterns, portfolio construction, and the perceived role of Bitcoin as a scarce digital asset.

Implications and Future Outlook

In the near term, much hinges on whether kinetic AI deployment delivers the real-economy uplift that Visser anticipates or instead exposes overconfident capex decisions. If synchronized upgrade cycles across autos, smartphones, PCs, and industrial systems materialize, they could sustain manufacturing activity, support employment in key sectors, and validate the equity rerating of infrastructure and component suppliers. A weaker-than-expected uptake, by contrast, would leave balance sheets stretched, pressure banks exposed to AI-heavy borrowers, and invite sharper regulatory scrutiny of capital allocation.

The K-shaped pattern that Visser describes suggests growing political pressure as small businesses and wage earners experience stress while AI-linked winners consolidate power and margins. Policymakers will need to navigate calls for rate cuts, industrial policy, and possibly targeted support for smaller firms, all while managing the risk that easy financial conditions further inflate AI and digital asset valuations. How governments respond will influence not only sector performance but also public perceptions of fairness and the legitimacy of the AI-led growth narrative.

Looking further ahead, the convergence of AI, tokenization, and digital assets points toward a faster and more tightly coupled financial system, in which trading, collateral, and risk transfer can move at machine speed. If tokenized securities, structured products, and Bitcoin-linked instruments become widely integrated into mainstream platforms, shocks in one corner of the system may propagate more rapidly than in past cycles. Building resilient market infrastructure, clear regulatory frameworks, and robust risk models will be critical to harness these innovations without amplifying instability.

Some Key Information Gaps

1. How can investors and regulators distinguish between sustainable AI infrastructure capex and overbuild that will later threaten financial stability? Clarifying this boundary is essential to guide prudent capital allocation, set supervisory expectations, and reduce the risk of systemic stress from failed AI buildouts.
2. How will the emerging small business recession interact with AI adoption and high interest rates to shape labor markets and local economies over the next five years? Understanding this interaction will help policymakers anticipate distributional impacts, target support measures, and calibrate rate policy in a K-shaped environment.
3. What empirical methods can quantify the contribution of kinetic AI deployment to future PMI increases across autos, electronics, and industrial equipment? Robust measurement tools are needed so that firms, investors, and governments can distinguish AI-driven growth from cyclical noise when planning investment and policy.
4. What governance frameworks are needed when national security and macroeconomic stability depend on a small set of private AI and space-infrastructure firms? Designing such frameworks will shape the balance of power between states and platforms, while influencing innovation incentives and crisis management options.
5. How will tokenization and faster trading rails alter market microstructure, liquidity cascades, and retail investor behavior during periods of stress? Addressing this question is vital for market-structure regulation, investor protection, and risk models that encompass Bitcoin and other digital assets in an AI-accelerated environment.

Broader Implications for Bitcoin

AI-Led Capex Cycles and Bitcoin as a Macro Hedge

As AI-driven capital expenditure becomes a central macro force, investors will likely seek robust hedges against the risk that overbuild or policy error triggers sharp corrections. Bitcoin may benefit from this search for protection, particularly if it is increasingly framed as a scarce asset that sits outside the traditional equity and bond complex tightly linked to AI and infrastructure cycles. Portfolio allocation decisions could hinge on how convincingly Bitcoin demonstrates its independence from AI-related boom–bust dynamics while still participating in digital asset adoption trends.

K-Shaped Growth, Inequality, and Monetary Regimes

The K-shaped pattern described in the episode points toward widening disparities between AI-enabled winners and more traditional firms and workers, raising questions about how long current monetary and fiscal approaches remain politically sustainable. If larger segments of the population perceive financial markets as decoupled from their own prospects, demand may grow for alternative savings technologies and parallel economic communities built around Bitcoin. This trajectory would give Bitcoin an expanded socio-political role as both a protest asset and a coordination tool for regions or groups seeking greater autonomy from centralized monetary policy.

Tokenization, Market Speed, and Bitcoin’s Financial Plumbing Role

Tokenization and faster trading rails promise more granular ownership structures and real-time settlement, but they also risk amplifying liquidity cascades when sentiment turns. In such an environment, Bitcoin could evolve into a key piece of financial plumbing, serving as a high-liquidity bridge asset, collateral layer, or settlement medium across otherwise fragmented tokenized markets. Over time, this role would make Bitcoin’s technical resilience, governance, and regulatory treatment central issues for market stability far beyond the narrow domain of digital asset trading.

Compute Concentration, Energy Demand, and Bitcoin Infrastructure

Concentration of compute in a few AI and data-center hubs will intensify energy demand and infrastructure buildouts, reshaping power markets and grid planning. Bitcoin’s existing experience at the intersection of high-density compute, flexible load management, and energy monetization positions miners and energy partners as important counterparties in this evolving landscape. In the medium term, cross-fertilization between AI and Bitcoin infrastructure—such as shared sites, grid-balancing services, or financing structures—could reshape both sectors’ cost bases and geographic footprints.

Governance of Private Infrastructures and Parallel Bitcoin Institutions

As national security and macro stability depend more heavily on privately controlled AI, networking, and space infrastructures, questions about accountability and public oversight will intensify. If formal governance mechanisms lag behind the power of these platforms, some actors may look to Bitcoin-based institutions—such as multisignature treasuries, open-source standards bodies, and transparent settlement rails—as partial counterweights. Over a multi-year horizon, this could spur new hybrids where state, corporate, and Bitcoin-native governance structures coexist and compete to define credible, trusted rules for digital and physical infrastructure.