2026 Market Rotation Excesses: AI Disruption Fears Overblown, Stock-Picking and EM Opportunities Emerge

The stock market in early 2026 presents a curious spectacle. While macro fundamentals—robust corporate earnings, resilient economic data—argue for forward momentum, equities have largely stalled, churning within the trading ranges established in late 2025. Beneath this surface calm, however, a violent rotation is underway. Leadership is shifting, and over $700 billion in market value has evaporated from sectors deemed most vulnerable to the looming shadow of Generative AI. Including the hyperscale enablers of this new compute build-out, the total市值 loss swells to a staggering $1.8 trillion. This is not merely a sectoral rebalancing; it is a fundamental repricing driven by narrative-driven fear, creating pockets of profound market “dissonance” where price action has dramatically decoupled from underlying business fundamentals. The central question for investors is whether this is the beginning of a structural disruption that will render entire business models obsolete, or a classic case of market overshoot, creating exceptional opportunities for those who can separate signal from noise.

The Anatomy of a Rotation: From AI Euphoria to Existential Dread

The current market rotation is a direct response to the maturation of the Generative AI trade. After three years of relentless upward momentum, investors are grappling with three intertwined anxieties that are now dictating capital flows.

First, there is growing skepticism over the sustainability of the capital expenditure (capex) boom. Over the past six months, expectations for tech giant capex in 2026 and beyond have continued to accelerate. The market is beginning to question the return on these massive investments. Will the promised productivity gains and new revenue streams materialize quickly enough to justify the spend, or are we witnessing a bubble in compute infrastructure?

Second, and more consequentially for stock prices, is the fear of GenAI’s disruptive impact. The market is rushing to discount potential obsolescence, leading to a broad-based derating of companies in software, financial data, consulting, and business services. Money is fleeing these “AI-disrupted” sectors and flowing into perceived safe havens and tangible assets: consumer staples, mining, materials, and infrastructure. This is a bet on displacement over adoption.

Third, this equity weakness is bleeding into credit markets, particularly leveraged loans and private credit, sparking concerns about a reflexive financial tightening. Some fund net asset values (NAVs) have been marked down, raising anxiety over the valuations of many private holdings with significant software exposure.

This triad of fears has pushed the performance and valuation dispersion within the S&P 500 to near-historic wides. It is, in one sense, a stock picker’s paradise. Yet, the rotation is not monolithic. Some moves appear knee-jerk and may disappoint, while others—particularly into areas linked to the physical build-out of AI and renewed industrial policy—seem to have more durable foundations. The easy-money phase of this bull market is unequivocally over. Navigating the next leg requires discriminating between justified caution and hyperbolic fear.

Deconstructing the AI Disruption Narrative: A Case of Market Illogic?

The market’s current behavior bears a striking resemblance to a previous episode of narrative-driven dissonance. In 2023, investors simultaneously bid up shares of GLP-1 drug manufacturers while also rallying packaged food and beverage stocks—a logical impossibility if one believed the obesity drugs would significantly alter consumption patterns. The dissonance broke decisively following a major clinical trial in May 2023. The packaged food sector fell 21%, while biopharma surged 50%. GLP-1 makers saw profits more than double, while food company earnings stagnated against the backdrop of a projected $100 billion annual Total Addressable Market (TAM) for the drugs.

Today, a similar, perhaps even broader, contradiction is at play. The market is punishing both the perceived disruptors and the disruptees. Consider these paradoxes:

• How can the market simultaneously derate software stocks to multi-year lows on fears of AI tools, while also pushing credit spreads for the hyperscalers building those very AI tools to multi-year highs on financing concerns?
• Why are the shares of rating agencies and capital markets providers lagging, even as debt issuance to fund AI data center construction continues to grow?
• With data center construction accelerating and power bottlenecks becoming extreme, why are utility stocks—the essential power providers—underperforming?
• If AI use cases among Fortune 100 companies are increasing (seemingly validating ROI), why are the “Magnificent Seven” tech giants lagging the broader market?

This collective action suggests the market is extrapolating a worst-case disruption scenario far into the future, without a nuanced view of how adoption will actually unfold. The base-case view emerging from a fundamental analysis is that GenAI adoption is a far higher probability outcome than GenAI displacement. Corporate America is more likely to forge a collaborative relationship with these tools—a chronic interdependence—rather than face targeted obsolescence.

A closer examination of the hardest-hit sectors reveals why the fear may be overdone:

1. Wealth Management & Brokerage: This sector is actually benefiting from the strong post-COVID wealth effect in equity and housing markets. More importantly, data access in finance is highly limited and regulated. The advisor-client relationship is built on trust, comprehensive planning, and regulatory oversight, making it exceptionally difficult for a large language model (LLM) to disintermediate. The "robo-advisor" wave of the 2010s was likely Act 1 of digital disruption, and it has already been absorbed into the industry fabric.

2. Software: Current fundamentals remain strong, with consensus expecting 15% annualized earnings growth from 2026 through 2028. While competitive moats may moderate and pricing power could ease, software engineering itself is identified as the most concrete AI productivity use case. The potential for material margin improvement from R&D efficiencies and faster go-to-market strategies for new products is a powerful offset to any top-line pressure. As Nvidia CEO Jensen Huang remarked, “The idea that software is going to get deprecated and replaced by AI is… the most illogical thing in the world.” Software companies own entrenched distribution and, crucially, proprietary data—assets not easily replicated or given away.

3. Healthcare & Financial Data: Companies in insurance, clinical trials, and real estate operate in domains requiring extreme levels of trust, authentication, and face high legal risk from AI “hallucinations.” While LLMs are proficient with public data, firms that own and control distribution around proprietary, non-public datasets are unlikely to cede that advantage. The more probable outcome is that GenAI becomes another paid, value-added distribution channel for these data aggregators, not their executioner.

The fundamental data supports this skeptical view of the disruption narrative. The trailing twelve-month (TTM) net income for the basket of “AI-disrupted” companies is up 1.5 times since ChatGPT’s launch in November 2022. Furthermore, 2026 earnings revisions for the group remain slightly positive at 0.2%, indicating analysts do not yet see AI materially weighing on near-term profits. If this trend holds, valuations for the most affected stocks should find a floor.

The Credit Conduit: When Equity Fear Infects Debt

The anxiety is not confined to public equities. It is transmitting rapidly into the leveraged credit market, revealing a different set of risks centered on capital structure and vintage.

Year-to-date through February, the S&P Software Index is down 21%, dramatically underperforming a flat S&P 500. This weakness is now evident in credit, particularly in US leveraged loans and Business Development Companies (BDCs), which contrast with positive returns in less-exposed High Yield bonds. The credit exposure, however, is distinct: it is heavily skewed toward private, sponsor-backed companies, not the public issuers dominating equity analysis.

Key stress indicators are flashing:

• Roughly 21% of software industry loans have seen double-digit price declines YTD.
• 22% of the software loan universe (by count) recently traded at distressed levels below $80, up from 12% at year-end 2025.
• In Collateralized Loan Obligations (CLOs), underperformance is concentrated in BB tranches and equity due to NAV erosion concerns.

The following table maps the software exposure across the leveraged credit ecosystem, highlighting where the risks are most concentrated:

The sector-specific challenges are acute. A significant portion of software sector debt was underwritten in the weak 2021 vintage, characterized by higher-than-typical leverage for LBOs and very low interest rates. Furthermore, software loan maturities are significantly more front-loaded than the rest of the borrower base, creating a near-term refinancing wall.

The historical parallel is instructive. Before its downturn, the energy sector represented about 15-16% of the high-yield index—a concentration similar to software’s today. As oil prices collapsed in 2014-2015, energy spreads widened by roughly 1,200 basis points (bps). Initially contained, the sell-off eventually became broad-based, with HY ex-energy spreads widening from ~400 bps to about 750 bps by early 2016. High concentration in a single sector can create portfolio dislocations that reverberate widely.

The near-term outlook for credit is for weak sentiment to persist. AI disruption is a “new” risk for investors to navigate. While defaults are likely to stay low in the immediate future, price declines could broaden. Companies may use sponsor cash injections for liquidity, and any initial defaults would likely skew toward distressed exchanges rather than bankruptcies. The medium-term risk is spillover: software is a large enough sector that significant stress could infect the broader loan market.

The Global Canvas: Diverging Paths in Emerging Markets

Against this backdrop of US sectoral turmoil, emerging markets (EM) are telling a different story. EM assets are performing strongly, underpinned by fundamentally robust economic positions. A key determinant for continued outperformance will be the trajectory of the US dollar. The baseline view assumes the Federal Reserve cuts rates twice more this year, supporting a generally weaker dollar. The risk is persistently strong US growth that questions these cuts if inflation fails to trend down.

The regional performance is not uniform:

• Latin America: Major 2026 elections loom in Brazil, Colombia, and Peru. The fundamental story, however, is one of resilience—contained inflation and orthodox central banking provide a stable foundation. The political question is whether the prior leftward shift will reverse, but the economic setup suggests little incentive for dramatic policy shifts regardless of outcome.
• Asia: The external story is complicated. Export-driven economies face US tariff headwinds but enjoy a massive tailwind from the global AI capex boom. Korea and Taiwan have accelerated to above-trend growth on strong tech exports. Moving past peak US tariffs is supporting an acceleration in non-tech exports. China remains the outlier: growth is subdued but stable, with persistent deflationary pressures. Its exports exert a downward pull on regional growth. The view is constructive but less robust than for Latin America.
• CEEMEA (Central & Eastern Europe, Middle East, Africa): Positioned between Asia and Latin America in performance. Orthodox monetary regimes have restrained inflation, supported by stable fiscal outlooks. South Africa exemplifies this: its central bank has de facto lowered its inflation target to 3%, reflecting deep confidence, reinforced by a coalition government’s restrained fiscal stance.

The disparity is stark in equity performance. Over the past year through February, the MSCI China Index is up about 15% in USD terms, massively underperforming the 66% gain in the MSCI Emerging Markets ex-China Index. Chinese equities have less exposure to the global AI capex cycle and global banks (which returned 40%) and more exposure to structurally softer domestic consumer demand.

Policy Crosscurrents: Tariffs, Japan, and the US Growth Engine

The investment landscape is further shaped by significant policy developments.

US Tariffs & Fiscal Implications: The Supreme Court’s striking down of the “Liberation Day” tariff under the IEEPA was a seismic event, invalidating an estimated $175 billion in collected revenue and potentially reducing revenues by $1.9 trillion through 2036. The swift replacement with Section 122 tariffs (10%-15%) mitigates but does not eliminate the fiscal hole. Even if these lapse, the average effective US tariff rate remains near 13.7%, three times higher than at the start of the Trump administration’s second term. The structural implication is clear: this introduces fresh uncertainty and points toward higher long-term Treasury yields due to greater deficit expansion and debt issuance.

Japan’s Stability Dividend: A landslide election victory for the LDP—the first single-party two-thirds majority since WWII—combined with a “complete and total endorsement” from the US President, sets the stage for a stable, long-term administration. This reduces policy uncertainty and is a tailwind for domestic capital expenditure, with three initial US investment projects worth $550 billion already coordinated. The focus is shifting from inflation countermeasures to national security and strategic investments in 17 sectors, including AI and semiconductors. Monetary policy is expected to normalize with a rate hike likely in June, though headline CPI is poised to fall below 2% soon.

The US Productivity Imperative: Underlying all of this is a critical debate on US potential growth. Net international migration has plunged from 2.7 million to 1.3 million, with projections falling to 321,000—the sharpest slowdown since the pandemic. The contribution of immigrant labor to potential growth has fallen from over 1 percentage point to about 0.6. As a result, potential GDP growth has already slipped from 3.0% in 2022 to 2.3% today, with risk of falling below 2% in 2026 if immigration controls persist. The offset must come from productivity, currently trending at a respectable but unspectacular 1.9%. The hope is that AI can drive a 1990s-style acceleration of 100-150 bps per year. The upcoming benchmark revision to jobs data will be crucial; a significant downward revision would imply stronger productivity growth is already occurring.

The Macro Regime Shift: A Conversation with KKR’s Henry McVey

Synthesizing these cross-currents points to a broader macro “regime change,” a view articulated by KKR’s Henry McVey. The post-2010 world of low growth, low inflation, tight fiscal, and easy monetary policy is over. The new regime is defined by:

1. Higher geopolitical risk.
2. Rising deficits (especially in developed markets).
3. A more complex energy transition intertwined with national security (and AI).
4. Inflation finding a higher “resting heart rate.”

This has profound investment implications. It favors credit as an asset class, suggests a continuing steepening yield curve, and demands a focus on “collateral-based cash flows” and securities that perform well in a higher nominal GDP environment—infrastructure and asset-backed finance. The old playbook of seeking ultra-long duration in bonds and stocks is obsolete. Commodities and defense stocks are early manifestations of this shift, which is expected to persist through the end of the decade.

McVey notes that while the US outlook for 2026 is positive—driven by the “Make it Great” bill, consumer rebates, and productivity gains—much optimism is already priced in. S&P 500 implied earnings growth is around 16% versus a historical average of 11%, and valuations are rich. The room for error is narrow. This environment favors a broadening market, where the equal-weighted S&P 500 continues to outperform the cap-weighted index.

On AI, the current discerning phase is healthy. It separates companies with poor capital structures from those with genuine quality and free cash flow conversion. AI will transform parts of healthcare, industrials, and financial services, creating both losers who cannot build moats and extraordinary winners who harness the technology. The volatility itself is creating opportunity; an index of high-quality companies has already entered a bear market, down 20%.

Tactical Asset Allocation: Navigating the Narrow Path

In this complex environment, the tactical stance emphasizes selectivity and quality across asset classes.

• US Equities (Overweight): The “disinflationary boom” narrative is consensus, with ambitious forecasts for a broadening of earnings growth from 6%-8% in 2025 to 14%-16% in 2026 for the “S&P 493.” The market is expensive, concentrated, and complacent, leaving little room for upside surprise and vulnerability to shocks. The preference is for stock selection over the cap-weighted index, favoring Financials, Healthcare, and select Industrials and Energy names. A year-end S&P 500 target range of 7,500 to 7,800 remains, but the path will be choppy.
• International Developed Markets (Underweight) and Emerging Markets (Overweight): Japan’s prospects are improving, but the greater opportunity lies in EM, where a beneficial goods disflation from Chinese exports, a mix of rising industrial commodity prices and stable-to-falling energy costs, and a potentially weaker dollar create a favorable backdrop. Latin America benefits from “pro-business political stability,” and India remains a core long-term growth holding.
• US Fixed Income: With two more 25 bps Fed cuts priced in and “stealth QE” loosening financial conditions, the focus is on reducing short-duration exposure and moving into the “belly of the curve” for better carry and lower price volatility. The long end remains challenged by expanding term premiums, keeping the 2s/30s curve steep.
• Alternatives: Real Assets (Overweight) for diversification benefits, focusing on industrial metals and energy infrastructure. Hedge Strategies (Overweight) are favored in an environment of elevated idiosyncratic risk, lower borrowing costs, and extreme stock-level dispersion, which plays to the strengths of fundamental, active managers.

The Editor’s Lens: Dissonance as Opportunity

The core investment insight from this mosaic of data and analysis is that the market is currently rewarding a simplistic, fear-based narrative over a nuanced, fundamentals-based one. The $1.8 trillion valuation shock is a measure of anxiety, not a precise forecast of economic destruction. History shows that such periods of “dissonance”—where prices disconnect violently from still-intact business fundamentals—often create the most compelling opportunities for disciplined investors.

The market is asking, “Who will AI kill?” A more productive question is, “How will AI be adopted, and who will be paid in the process?” The evidence points toward collaboration, interdependence, and productivity enhancement far more than outright displacement. Software companies with proprietary data and entrenched distribution are not giving away their crown jewels; they are more likely to monetize AI as a new channel. Data-rich industries bound by regulation and trust are not easily disintermediated.

The risks are real, particularly in the leveraged credit market where weak vintages and high concentration could amplify stress. Yet, for public equity investors, the extreme dispersion has created a landscape where high-quality companies with durable competitive advantages are being sold indiscriminately alongside truly vulnerable ones. This is the stock picker’s moment. The easy, broad-based gains of the early AI boom are past. The next phase belongs to those who can identify the collaborators and enablers in the new AI ecosystem, distinguish cyclical fears from structural threats, and have the fortitude to act when the market’s narrative overshoots reality. The rotation’ excesses are not just a risk to be managed; they are the raw material from which alpha is built.