A New Regime Shift
All investors care about regime shifts. There are cyclical regime shifts and secular regime shifts. Cyclical shifts are shorter-term deviations from the larger trend, like we saw earlier this year when DeepSeek briefly disrupted the AI trade and tariff headlines stoked fears of the end of U.S. exceptionalism. Once tariffs shifted and compute needs accelerated, the broader AI regime reasserted itself.
A secular regime shift, however, is far more powerful. It usually reflects dramatic changes in monetary policy, fiscal policy, and innovation. I believe we are in the early stages of one right now.
From 2007 to 2009, the U.S. experienced such a shift, driven by fiscal austerity, unconventional monetary policy, and disruptive technology. Post-GFC, austerity dominated fiscal decisions, while QE reshaped central banking. At the same time, the smartphone revolution ignited a software boom and gave rise to the “Magnificent 7.” Software and growth dominated investment returns over the last 17 years.
Back then, U.S. national debt hovered around $9 trillion, and deflation was the central fear. Today, debt has ballooned to $37 trillion, the deficit runs near 7% of GDP, and inflation, not deflation, is the dominant concern. Public anger over wealth inequality and declining opportunity is real. The current administration was elected on promises to restore balance and has responded quickly and aggressively with fiscal policy: tariffs and trade wars, onshoring manufacturing, expansive energy and nuclear policy, a broad-based Crypto Plan, and most of all, an AI Action Plan positioning artificial intelligence as the defining innovation of our time. Yet despite these moves, the administration does not believe that monetary policy and the Fed is aligned with the urgency of the moment.
In a March 2024 paper, Stephen Miran, Chair of the Council of Economic Advisers and recently nominated to join the Federal Reserve Board in a temporary capacity, argued with Daniel Katz that the Federal Reserve and the Treasury need clearer structural alignment to avoid working at cross purposes. The authors warned that the Fed’s independence has drifted into insularity and groupthink, while the Treasury’s debt management decisions can undercut monetary policy. Their proposed reforms including shorter Fed governor terms, greater presidential accountability, congressional oversight of the Fed’s budget, and democratizing the governance of regional Reserve Banks were designed to bring the Fed and Treasury into a more accountable, coordinated framework. By better aligning fiscal and monetary institutions, Miran contended, policy would be both more effective and more responsive to the needs of the broader economy.
Scott Bessent has described Jerome Powell’s Federal Reserve as “too academic.” At first, this might sound benign after all, who doesn’t want careful, model-driven rigor? But in the age of exponential innovation, being academic is a liability. An academic Fed looks backward, anchored to pre-AI models that assume the future will resemble the past. Today, those assumptions are obsolete. In this environment, “too academic” means backward-looking, slow, reactive, and dangerously disconnected from reality.
The administration views the consequences of this stance as very real. Lower rates are clearly needed to help fund the deficit, but voters, particularly those who put Trump back in office, are feeling the pain of high rates. Housing affordability has become a major political issue heading into the midterms, and prolonged elevated rates are the driving factor: homeownership is slipping further out of reach.
At the same time, small businesses are suffocating under credit costs, while large corporations with access to capital markets, are thriving, their profit margins supported by AI-driven efficiency gains.
- Labor’s share of national income has steadily declined, from roughly 64% in 2001 to about 55.8% in 2024, showing how profits are capturing a larger share of growth.
- Housing affordability is collapsing: homeownership now requires about 45% more of a typical buyer’s income compared to 2019, and median home prices hover at six times median income, up from 4–5× two decades ago. Mortgage costs have surged requiring an annual income of $126,670 to afford a median-priced home, compared to a median household income of just $80,610.
And here lies the paradox: even when the Fed attempted to ease in September 2024, the transmission mechanism to long-term yields appeared broken. Despite a surprise 50 bps cut, 10-year Treasury yields rose rather than fell. Mortgage rates stayed stuck, offering no relief to households. The old academic assumption that cutting short rates would lower long rates may no longer hold under the weight of today’s debt and deficits.
Recognizing this, the administration has floated trial balloons around cutting capital gains taxes on housing and creating channels for households to borrow at subsidized lower rates if long yields remain sticky. They have also loosened capital rules on banks to encourage more Treasury purchases, undoing parts of the GFC-era framework to help cap long-term yields. During the chaos after Liberation Day, stocks, bonds, and the dollar fell simultaneously, underscoring the reality that long-term yields will be harder to manage than in the post-GFC years. These moves underscore the political intent and urgency around bringing down yields and improving housing affordability and the recognition that an academic Fed alone cannot deliver it.
On a recent All-In Podcast following the AI Action Plan, Bessent argued that the U.S. may be entering an environment reminiscent of the 1990s under Alan Greenspan, when rapid technological innovation allowed the Fed to “run the economy very hot” without sparking inflation. Just as the IT boom fueled non-inflationary growth three decades ago, today’s AI buildout could produce a similar dynamic. As Bessent put it: “Allan Greenspan was able to run the economy very hot in the 90s and because of the IT boom we had this very powerful non-inflationary growth. I think it’s highly likely we could have that now.” He emphasized that such growth would not only sustain higher output but also help “bring down the deficit very quickly.”
The combination of wanting to run the economy hot now and lower rates due to future deflationary pressures of AI is about being forward-looking. The critique of the “too academic” Fed is targeting its rigid adherence to the 2% inflation target an orthodoxy born in the 1990s and treated as gospel ever since. Powell’s Fed has maintained that policy must remain tight until inflation returns to that level. His term as Chair runs until May 15, 2026, but the administration has signaled it will announce its nominee for the next Fed Chair in the coming weeks. Early contenders, in recent interviews, have delivered a consistent message: the Fed must evolve, becoming more forward-looking rather than bound by outdated models.
This was emphasized by Bessent in a recent interview with Nikkei. He stressed that the Fed’s role is now expanding far beyond traditional interest-rate management. Because of this, the next Fed Chair must be someone capable of thoroughly examining the institution itself, not just inheriting old frameworks. Bessent argued the Fed’s leadership should be proactive and forward-looking, able to anticipate how structural changes such as AI-driven productivity, fiscal dynamics, and geopolitical pressures will reshape the economy. The next Fed Chair, in short, must be an adaptive leader prepared to rethink the Fed’s mission in light of rapid innovation and fiscal realities.
Bessent’s belief that AI-driven productivity shifts demand a forward-looking Fed has real merit. AI is progressing at an unprecedented pace, and in a world defined by exponential innovation, relying on backward-looking models is too slow. As digital employees begin to replace human workers across industries, deflationary pressures from automation will be powerful and persistent. The labor vs capital imbalance will likely worsen. This creates a paradox: the labor market may weaken even as overall economic output and profitability remain strong, fueled by productivity gains. In such a regime, clinging rigidly to a 2% inflation target risks policy mistakes, whereas embracing exponential innovation allows the Fed to harness growth without unnecessarily stifling it.
All of this points to a subtle but significant shift: by signaling that the next Fed Chair will be chosen for alignment with the administration’s forward-looking views, the White House is effectively laying the groundwork for higher inflation tolerance, a quiet change in the target without ever formally announcing it. While no official statement has been made, markets are being conditioned to expect a Fed that will allow inflation to run hotter in the short term, confident that productivity gains and technological deflation will offset the risks going forward.
This represents not just a monetary adjustment but a profound policy realignment to match the fiscal transformation already underway. For decades, monetary and fiscal policy often moved at cross-purposes. Today both are converging around the realities of exponential innovation. As the Fed signals a quiet shift away from strict inflation orthodoxy, AI itself is transitioning into a new phase: from software-driven LLMs to a hardware-intensive cycle defined by data center expansion, surging electricity demand, and massive investment in rare earths and copper to power electrification. This next leg of the AI buildout is industrial in scale, requiring the kind of long-term capital coordination once seen in railroads or highways. In parallel, the deployment of physical AI is set to accelerate with robotaxis rolling out next year, humanoid robots moving from prototypes to scaled production, and AI reshaping military capabilities. Aligning monetary flexibility with fiscal investment ensures the financial system can support, rather than hinder, this unprecedented transformation. As Peter Diamandis put it on his Moonshots podcast, America’s AI strategy is “potentially the broadest U.S. industrial strategy we’ve seen since President Eisenhower… a plan to turn the U.S. into one huge AI factory.”
We are entering a new regime where fiscal and monetary policy are converging on a pro-growth stance, implicitly willing to run inflation hotter in order to sustain momentum. This policy realignment coincides with AI’s acceleration into a transformative phase, one that threatens to dismantle the moats around software companies that have dominated markets for the past 17 years adding to the regime shift. As coding becomes ubiquitous and increasingly surpasses human capabilities AI will democratize innovation, empowering every company to build its own solutions. The result is a structural shift: a macro environment designed to fuel growth paired with a technological revolution that broadens access to productivity, reducing dependence on legacy software giants and redistributing competitive advantage across the economy.
Eric Schmidt recently emphasized this threat to software moats, warning that enterprise software and middleware business models are at risk of being hollowed out by AI. With tools like Google Cloud and Model Context Protocol, enterprises can connect databases directly to large language models, which in turn generate the necessary code automatically. This eliminates the need for the connective layers that thousands of ERP, MRP, and middleware firms have relied on for decades. Schmidt added that AI will also displace junior and journeyman programmers, leaving only senior engineers to supervise AI-generated code until even those roles diminish. He described this as part of the “San Francisco consensus”: the belief that programming and math will largely be replaced by AI within one to two years. Because coding underpins physics, chemistry, biology, and material science, Schmidt stressed that this automation will accelerate innovation across industries, eroding software moats and forcing adaptation.
This disruption is no longer hypothetical. Schmidt has warned for a year that autonomous AI agents could replace most programmers, and early signs are already visible. Industry surveys show that 53% of senior developers believe AI now codes better than most humans, with 45% noting that AI lowers entry barriers meaning non-programmers can now “code.” Startups are embracing “vibe coding,” where ideas paired with AI generate usable software quickly, though questions about accountability and security remain. Developers also report that reviewing AI-generated code can sometimes slow them down.
The implications are profound. Software is shifting from line-by-line human craftsmanship to idea-driven prompts and AI engines. Competitive advantage is moving from execution to vision, oversight, and productization. This is the early stage of a regime shift, signaling both the embodiment of AI and the rise of coding competition from machines.
This hardware vs. software divide is already showing up in the stock market within the tech sector. The S&P 1500 equal-weight semiconductor index is up 14% YTD through last Friday, while the S&P 1500 equal-weight software index is down 7% YTD. Salesforce.com, despite its Agentforce initiative, is down 27% YTD. Amazon destroyed most brick-and-mortar retailers after the GFC; I expect AI disruption to have a similar impact on the broader software universe as adoption accelerates.
As AI erodes the moats around anything built on code, we are poised for a revival in the physical world, areas chronically underinvested in during nearly two decades of software dominance. The massive power needs of AI and the data center buildout will demand a surge of investment in energy infrastructure, while mining for rare earths and copper will be critical to electrification. The government will support these efforts with policies and deregulation as this is a race to military supremacy. At the same time, new industries are set to scale: robotaxis rolling out next year, humanoids in the years to follow, and a military buildup fueled by AI. Each of these trends requires tangible assets, steel, energy, minerals, transportation, and industrial capacity that have lagged since the post-GFC software boom. As capital shifts from digital moats to physical foundations, the imbalance created by 18 years of neglect could spark a powerful revaluation across these hard-asset sectors where demand will outstrip supply like we are already seeing with gas turbines, transformers and cooling systems and the capex numbers are now estimated at close to 1 trillion dollars for 2025 and 2026 up significantly since the end of 2024.
This powerful convergence of fiscal and monetary policy with the disruptive force of AI marks the beginning of a secular regime shift that will define markets for years to come. Unlike cyclical shifts, which fade with the business cycle, this change is structural rooted in a policy framework that prioritizes growth over inflation orthodoxy and a technological revolution dismantling software dominance. The software-driven era of the past 17 years is giving way to one where physical infrastructure, energy, and industrial capacity take center stage. AI’s deflationary impact on labor, combined with fiscal expansion and a more tolerant Fed, creates conditions for sustained productivity-driven growth even as inflation runs hotter. This regime shift will not resolve quickly; instead, it will reshape capital allocation, corporate strategy, and government policy for the next decade and beyond. Investors who recognize this transition early will be best positioned to capture opportunities in sectors overlooked for nearly two decades.
The era of the “academic Fed,” with its rigid devotion to outdated models and a 2% inflation target born in the 1990s, is giving way to an “inflation target of the future”, one shaped by fiscal expansion, technological acceleration, and the realities of exponential innovation. Just as railroads, industrialization, and the IT revolution redefined markets in past centuries, today’s alignment of fiscal, monetary, and technological forces marks the start of a secular regime shift unlike any before it. AI is dismantling the moats of software incumbents while reviving demand for physical infrastructure, energy, and hard assets that have been underinvested in for nearly two decades. For investors, the implication is clear: the winners of the next era will not be those who cling to yesterday’s orthodoxy, but those who recognize that higher inflation tolerance, productivity-driven growth, and a capital rotation toward the physical world are reshaping the opportunity set. Those who adapt early will capture the upside of this transformation, while those bound to the past risk being left behind.
