Abby Joseph Cohen Analyzes AI Stock Deceleration, Dollar Weakness Investment Strategy Amid US Policy Concerns

Abby Joseph Cohen’s expectation of a “deceleration” in AI stocks represents a significant shift from the explosive growth narrative that has characterized the sector in recent years [1]. This perspective from a veteran Wall Street strategist carries particular weight given Cohen’s distinguished career at Goldman Sachs, where she was known for her data-driven approach to market forecasting. The timing of this assessment is noteworthy, occurring as major AI companies face increasingly demanding year-over-year comparison periods and as valuations reach elevated levels that many analysts consider stretched.

The deceleration thesis aligns with observable market dynamics in the AI sector. NVIDIA (NVDA), the leading AI chipmaker, has shown sequential deceleration in quarterly revenue growth—from 55% year-over-year growth in Q2 2025 to 22% in Q3 2025 [0]. While the stock remains near its 52-week high of $212.19 and trades at $191.68 with a 1.67% daily gain, the underlying growth metrics suggest the pace of expansion is moderating [0]. Similarly, Palantir (PLTR) experienced a 1.74% pullback to $162.82 on January 28, 2026, as investors digest the sustainability of recent gains [0].

The valuation context supports Cohen’s cautious stance. NVDA trades at a price-to-earnings ratio of 47.05x, which, while justified by growth characteristics, leaves limited margin for disappointment [0]. Analyst targets suggest approximately 41.8% upside from current levels, but achieving such returns would require sustained growth rates that become increasingly difficult as the base period expands [0].

The US dollar’s weakness represents a defining macro theme that Cohen explicitly addressed on Bloomberg Open Interest [1]. The currency has declined approximately 9% during 2025 and an additional 2.2% in early 2026, reaching a 4-year low that has significant implications for corporate earnings and asset valuations [1][3]. This multi-quarter decline reflects a combination of factors, including divergent monetary policy paths, fiscal concerns, and shifting investor sentiment toward US assets.

For multinational corporations with substantial international revenue exposure, dollar weakness provides a meaningful tailwind. NVIDIA derives 49.8% of its revenue from outside the United States, meaning currency translation effects enhance reported earnings when the dollar weakens [0]. This dynamic creates a somewhat paradoxical situation where the macro currency environment supports corporate profitability even as sector-specific growth rates decelerate.

The policy dimension of dollar weakness adds complexity to the investment landscape. Recent comments from Trump administration officials have acknowledged the potential benefits of a weaker dollar for US competitiveness, with the President stating that “you make a hell of a lot more money with a weaker dollar than you do with a strong dollar” [3]. This rhetorical shift suggests dollar weakness may be tolerated or even encouraged rather than resisted, potentially extending the currency’s decline.

Cohen’s characterization of US policies as exhibiting an “erratic nature” introduces a qualitative dimension to the investment outlook that quantitative metrics alone cannot capture [1]. This assessment encompasses several policy domains with direct market relevance, including tariff negotiations, Federal Reserve independence and guidance, fiscal deficit trajectories, and AI-specific regulatory considerations.

The tariff uncertainty creates planning difficulties for businesses across multiple sectors, particularly those with complex global supply chains. For the AI sector specifically, export control policies affecting semiconductor shipments to China remain fluid, introducing operational uncertainty for companies like NVIDIA that must navigate evolving regulatory requirements [2]. Recent developments, including China’s approval of purchases for NVIDIA H200 chips, demonstrate the potential for tactical flexibility in these relationships but also highlight the underlying volatility [2].

Federal Reserve policy trajectory represents another source of uncertainty. The central bank’s path on interest rates affects both discount rates applied to growth stock valuations and the broader risk appetite that has supported AI sector multiples. The combination of fiscal concerns, with growing government debt affecting currency sentiment, and potential tensions between monetary policy independence and political pressures creates an environment where policy outcomes remain difficult to predict.

The market action on January 28, 2026, revealed important sector rotation dynamics that align with Cohen’s cautious AI sector outlook [0]. The Technology sector’s 0.78% decline ranked among the worst-performing sectors, while Communication Services eked out a marginal 0.007% gain as the lone advancing sector [0]. This pattern suggests profit-taking in AI-related names after strong recent performance, with investors rotating toward more defensive or historically undervalued segments.

Small-cap weakness, as reflected in the Russell 2000’s 1.00% decline, indicates that risk appetite is contracting at the market’s speculative edge [0]. This dynamic often precedes or accompanies periods of sector consolidation, where investors become more discriminating about valuation and fundamentals rather than pursuing broad-based exposure to thematic winners.

The AI sector’s concentration in a limited number of large-cap winners creates structural vulnerability that Cohen’s analysis implicitly addresses. NVIDIA’s continued outperformance, even within a declining Technology sector, demonstrates the extent to which AI-related enthusiasm remains concentrated in a handful of heavily-weighted stocks. Any meaningful correction in these names would have outsized impact on broader market indices given their representation in major benchmarks.

This concentration dynamic also extends to the demand side of the AI value chain. Hyperscale computing companies—Microsoft, Google, Amazon, Meta—represent the primary customers for AI infrastructure spending. Any indication of capex discipline from these entities would ripple through the entire AI supply chain, from chip manufacturers like NVIDIA to infrastructure specialists like Super Micro Computer (SMCI), which posted a 1.41% gain to $31.64 on January 28 [0][2].

The 11% aggregate decline in the US dollar over the past year creates a meaningful divider between companies based on their international revenue exposure [1][3]. For domestic-focused businesses, dollar weakness represents primarily an input cost and inflation challenge. For globally diversified corporations, particularly those in the technology sector, currency tailwinds enhance reported growth and may mask underlying demand moderation.

This dynamic complicates the AI investment thesis by introducing a variable that can distort underlying business performance. When evaluating AI companies, distinguishing between organic growth and currency-enhanced results becomes increasingly important, particularly for companies like NVIDIA with substantial international revenue exposure [0].

The AI sector faces several interconnected risks that warrant careful monitoring. Valuation compression represents a material concern, as the elevated P/E multiples currently assigned to AI leaders assume sustained growth trajectories that historical patterns suggest are difficult to maintain indefinitely. If growth deceleration accelerates beyond expectations, multiples may contract, creating double-digit declines even in fundamentally strong businesses.

Capital intensity in AI infrastructure creates financial leverage risks for companies throughout the value chain. Applied Digital’s balance sheet, carrying $2.6 billion in debt against $1.9 billion in cash, exemplifies the financial exposure some AI-related companies have assumed to fund capacity expansion [2]. Should demand disappoint expectations or competitive dynamics intensify, these capital structures could come under pressure.

Regulatory and policy risks have materialized as a meaningful concern for AI investors. Export controls affecting semiconductor shipments to China, potential domestic AI governance frameworks, and antitrust considerations for dominant platforms all represent potential constraints on growth or profitability. The policy uncertainty Cohen identified manifests concretely in these regulatory dimensions [1][2].

Sustained dollar weakness carries risks for foreign holders of US assets, particularly Treasuries. Reduced foreign appetite for dollar-denominated securities could elevate yields, increase borrowing costs, and create headwinds for growth assets that have benefited from historically low discount rates. The interaction between currency weakness, inflation expectations, and Federal Reserve policy creates a complex dynamic that requires careful monitoring.

Trade tension escalation represents another macro risk dimension. Currency moves, particularly if perceived as competitive devaluation, could provoke retaliatory trade measures from trading partners. The intersection of trade policy, currency dynamics, and geopolitical considerations creates a risk landscape where adverse outcomes, while not base case, represent meaningful probability events.

Despite the risks identified, the AI structural growth thesis remains intact even if sector deceleration occurs. The fundamental demand drivers for AI infrastructure—enterprise digital transformation, productivity enhancement, and new application development—continue to expand. For investors with appropriate time horizons and risk tolerance, meaningful corrections in high-quality AI names could represent attractive entry points.

Dollar weakness creates specific opportunity windows for internationally focused investors. US assets become more affordable for foreign currency-denominated investors, potentially increasing demand for equities at the margin. Additionally, companies with natural dollar exposure—either through revenue generation or cost structures—may deliver earnings outperformance that surprises consensus expectations.

This analysis synthesizes multiple perspectives on the investment landscape as articulated by Abby Joseph Cohen on Bloomberg Open Interest [1], supported by quantitative data from market monitoring systems [0] and contextual coverage from financial news sources [2][3].

The key takeaway from Cohen’s appearance centers on her expectation of AI stock deceleration—a view grounded in valuation considerations, growth comparison challenges, and the natural maturation of a sector that has delivered exceptional returns in recent years [1]. This perspective does not constitute a rejection of the AI investment thesis but rather a call for more discriminating analysis and realistic return expectations.

The dollar weakness theme Cohen addressed represents a significant macro development with concrete investment implications [1][3]. The 11% decline in the dollar over the past year has meaningful effects on corporate earnings, particularly for globally diversified technology companies, and introduces inflation considerations that may influence Federal Reserve policy decisions. The policy uncertainty Cohen identified encompasses not only currency management but also tariff negotiations, fiscal trajectories, and sector-specific regulatory frameworks that collectively create a complex decision environment for investors [1].

For market participants, Cohen’s analysis suggests a balanced approach that acknowledges both the continued structural opportunity in AI-related businesses and the risks that elevated valuations, policy uncertainty, and currency volatility introduce. The brief penetration of the S&P 500 above the 7,000 level represents a psychological milestone that underscores the market’s overall strength even as sector-specific dynamics warrant more cautious positioning [0].

Key upcoming catalysts that will test Cohen’s deceleration thesis include NVIDIA’s earnings report scheduled for February 25, 2026, with analyst estimates projecting quarterly revenue of $65.56 billion [0]. Any significant deviation from these expectations would provide meaningful insight into the sustainability of AI sector growth rates and the accuracy of Cohen’s market positioning guidance.