In-Depth Research Analysis:
1 Executive Summary:
This report examines the core tension facing US equities in the second half of 2026: structural opportunities remain, but the market has moved from liquidity- and AI narrative-driven upside into a stricter phase of earnings delivery, capital efficiency, and cash flow quality. The first-half market was resilient at the index level, but internal dispersion was significant. Leadership was concentrated in AI infrastructure, semiconductors, industrial and power infrastructure, energy, and selected high-quality technology companies. Market breadth improved, but sustained pricing support remained concentrated in companies with positive earnings revisions, strong balance sheets, high-quality cash flow, and credible AI monetization paths.
At the macro level, the key question is not simply whether the Fed will cut rates, but whether higher rates are sustainable. Under the Warsh Fed, forward guidance has been reduced, and the policy path will depend more directly on inflation, employment, and financial conditions. At the same time, US fiscal deficits, Treasury supply, and long-term rates continue to constrain equity valuations. AI is improving the earnings numerator for selected companies, but it has not yet materially lowered the discount-rate denominator. As a result, investors should not rely on a single assumption that AI will push rates lower or that policy will ease quickly.
From an earnings and valuation perspective, US equities are not cheap at the index level. Further upside is more likely to come from earnings upgrades than from broad multiple expansion. AI remains the most important structural theme, but its pricing logic has shifted from demand imagination to return verification. The market will increasingly focus on whether AI capital expenditure can translate into revenue, margins, and free cash flow. Companies controlling compute, advanced manufacturing, HBM, data centers, power infrastructure, data governance, cybersecurity, and core workflows should retain relative advantages. By contrast, assets with AI narratives but limited monetization visibility will face a stricter valuation test.
In sector allocation, the report favors a shift from index beta to structural alpha, and from broad growth to high-quality growth. Technology, semiconductors, AI infrastructure, data software, and high-capital-efficiency platforms remain long-term themes. Industrials, power infrastructure, and selected materials benefit from AI capex spillovers. Energy, dividend-oriented assets, and stable cash-flow businesses can serve as hedges against inflation, geopolitical risk, and market volatility. Consumer, financials, healthcare, and small- and mid-cap equities require more disciplined fundamental screening. The key is not to take more risk, but to own better-quality risk.
Overall, US equities still offer opportunities in the second half of 2026, but the margin for error has declined. In the base case, if inflation continues to ease, long-term rates remain stable, and corporate earnings are revised higher, the market can retain structural resilience. However, if reflation pressure rises, fiscal and Treasury supply risks push long-term rates higher, or AI capex returns disappoint, volatility in high-valuation assets could increase. Strategy should remain anchored in earnings quality, cash flow, returns on capital, and control over value-chain bottlenecks, rather than in a single macro assumption or thematic narrative.
2. First-Half Review: Structural Repricing Behind Index Resilience
Looking back at the first half of 2026, the US equity market continued to move higher, but its internal structure was far more complex than the headline index performance suggested. As of mid-to-late June, the S&P 500 had gained roughly 10% year-to-date, the Nasdaq had risen by around 14%, the Dow Jones Industrial Average was up approximately 7%, while the Russell 2000 had advanced by about 20%. Based on ETF performance, the S&P 500 ETF delivered a year-to-date total return of roughly 10%, indicating that the market did not experience a broad-based retreat in risk appetite. On the surface, this appeared to be a market characterized by index resilience and some improvement in breadth. However, from the perspective of sector performance, stock-level contribution, and earnings logic, the real driver of the market was not indiscriminate liquidity expansion, but a structural repricing around earnings revisions, the AI value chain, capital efficiency, and reflation-related trades.
Sector divergence was pronounced. From the beginning of the year to mid-June, information technology was the strongest-performing sector, with the technology sector ETF delivering a year-to-date total return of more than 30%, significantly outperforming the broader market. Energy and industrials also performed strongly, with energy generating a total return of more than 20% and industrials approaching 17%. Materials also delivered a double-digit return. In contrast, healthcare, communication services, financials, and consumer discretionary materially lagged, with some sectors still posting negative year-to-date returns. This divergence shows that the first half of the year was not a broad-based bull market in which all sectors rose together, but rather a structurally driven market led by a limited number of high-conviction growth chains and sectors with visible earnings recovery logic.
Stock-level performance further amplified this structural divergence. Several companies within the AI infrastructure and semiconductor value chain generated substantial excess returns. Micron benefited from the combination of a memory cycle recovery and AI server demand, delivering a sharp year-to-date rally. AMD also performed strongly on the back of AI compute demand and the dual narrative around CPUs and GPUs. Broadcom outperformed the broader market, supported by AI ASICs, networking chips, and data center demand. NVIDIA, despite facing a high base and periodic investor concerns over the return on AI capital expenditure, remained positive year-to-date and continued to serve as a core pricing asset for the AI compute cycle. Large platform technology companies such as Microsoft also maintained solid performance, supported by cloud, enterprise software, AI tools, and strong cash flow quality. By contrast, parts of traditional consumption, healthcare, financials, and software companies that have yet to demonstrate a clear AI monetization path significantly underperformed. This indicates that the market is no longer simply rewarding the “AI narrative”; instead, it is placing greater emphasis on revenue realization, margin improvement, and cash flow quality.
Therefore, the first-half market should not be described simply as a “narrow leadership market,” nor should it be characterized as a broad-based expansion. A more accurate description is that market prices showed periodic signs of broadening, especially in small caps, energy, industrials, materials, and certain cyclical assets, while the core logic of earnings and capital allocation remained highly concentrated. Capital did not flow indiscriminately into all risk assets. Instead, it was concentrated in several categories where fundamental improvement was easier to prove: first, AI infrastructure and semiconductor bottlenecks; second, industrial and materials assets benefiting from the capital expenditure cycle and reindustrialization; third, energy assets supported by oil prices, geopolitical risk premiums, and inflation expectations; and fourth, large technology companies with strong cash flow, platform capabilities, and visible AI monetization paths.
The underlying reason for this structure is that US equities have moved from the broad pricing of AI narratives over the past two years into a stricter phase of capital efficiency and earnings verification. From 2023 to 2025, investors were more willing to pay valuation premiums for the long-term imagination associated with AI. Entering 2026, however, the market has begun to require companies to prove that AI investment, data advantages, compute resources, and organizational efficiency can be translated into real revenue growth, margin expansion, and cash flow returns. The number of companies able to pass this test remains limited, which naturally keeps the pool of assets receiving sustained valuation premiums relatively narrow.
This also explains why the information technology sector significantly outperformed the broader market. Investors were not simply chasing technology stocks. Rather, they were pricing the most certain parts of the AI value chain. Compute chips, advanced manufacturing, HBM, memory, advanced packaging, AI networking, cloud infrastructure, and data center equipment are all hard constraints within the AI capital expenditure cycle. As long as demand for AI training and inference continues to expand, the market will prioritize the revenue visibility and pricing power of these bottleneck segments. The strong first-half performance of semiconductor and memory-related companies reflects precisely this logic.
At the same time, the strength in energy, industrials, and materials suggests that the market was not trading AI as the only theme. The outperformance of energy mainly reflected oil prices, geopolitical risk premiums, and inflation expectations. The rise in industrials and materials reflected a repricing of the capital expenditure cycle, supply chain reshoring, manufacturing investment, and the spillover demand from AI data center construction. AI does not only affect chips and software; it also transmits into a broader industrial chain through electricity, cooling, data center construction, equipment procurement, infrastructure upgrades, and physical capital expenditure. This is why some traditional cyclical sectors also performed well in the first half.
However, this broadening does not mean the market has entered a true broad-based bull market. The underperformance of healthcare, consumer discretionary, financials, and communication services indicates that earnings pressure, policy uncertainty, consumption divergence, rate sensitivity, and valuation constraints remain in place. The weakness in consumer discretionary reflects the internal divergence within the US household sector: the wealth effect continues to support high-end consumption among higher-income groups, while lower- and middle-income households face pressure from credit costs, inflation, and the depletion of excess savings. Weakness in financials is related to the yield curve, credit quality, regulatory pressure, and commercial real estate risk. Healthcare, despite its defensive characteristics, was constrained by policy pricing, drug pricing pressure, and valuation adjustments among certain leading companies. The lagging performance of communication services also shows that platform assets themselves are being reassessed in terms of growth, regulation, and returns on AI investment.
From a style perspective, small caps performed well in the first half, with the Russell 2000 even outperforming the broader market. This suggests that market breadth has improved compared with 2025, as capital began to rotate back into assets previously suppressed by high interest rates. However, the small-cap rebound should be understood more as a function of easing rate expectations, valuation repair, and earnings recovery from a low base, rather than a broad re-rating of lower-quality assets. For small- and mid-cap companies, sustained outperformance still depends on two conditions: financing costs must continue to decline, or at least stop rising; and earnings revisions must show genuine improvement. If the rebound is driven only by valuation repair from a rate trade, without follow-through in profits and cash flow, its sustainability remains uncertain.
As a result, the first-half market was best characterized as “conditional broadening.” In terms of price action, the rally did expand from a small number of mega-cap technology stocks into small caps, energy, industrials, materials, and selected cyclical assets. But in terms of underlying logic, capital continued to revolve around relatively high-conviction earnings signals. In other words, the core asset universe did not simply expand from “a few technology giants” to “all assets.” Instead, it expanded from “AI leaders” to “AI bottlenecks, capital expenditure beneficiaries, assets with clear earnings recovery, and companies with strong cash flow quality.” This was a quality-driven broadening, not a liquidity-driven broad rally.
From the perspective of our strategy framework at the beginning of the year, the market’s first-half performance broadly validated the “capital efficiency test” we previously highlighted. At the start of the year, we argued that the investment logic for US equities in 2026 would shift from grand narratives toward ROIC, cash flow, and earnings delivery. The market would not indiscriminately reward all AI-related assets, but would instead focus more closely on whether companies could convert capital expenditure into high-quality revenue and profit. First-half market performance confirmed the validity of this view. Assets that received sustained pricing support generally shared three characteristics: strong earnings revision momentum, resilient cash flow, and disciplined capital allocation.
Our view on liquidity was also largely validated. The market did not enter a systemic risk-off phase despite high interest rates and fiscal pressure, indicating that liquidity conditions and earnings resilience continued to support US equities. However, liquidity did not flow evenly into all assets as it often did during low-rate cycles. Instead, it was directed primarily toward companies with higher earnings visibility, stronger business models, and healthier balance sheets. In other words, the first-half market did not lack capital; it lacked enough high-quality assets capable of absorbing that capital on a sustained basis.
That said, our earlier framework also requires some mid-year refinement. First, we need to be more cautious about the pace of AI application-layer diffusion. AI has not automatically led to a broad re-rating of the software sector. On the contrary, the internal divergence within the application layer has been more pronounced than expected at the beginning of the year. Traditional SaaS models face multiple challenges, including slower seat growth, pricing model adjustments, and competition from AI-native solutions. By contrast, data platforms, cybersecurity, observability, governance, and vertical workflow platforms have demonstrated more evident relative advantages. Second, AI has not yet sufficiently improved the macro rate environment. Although AI is enhancing productivity and margins for some companies, it has not meaningfully pushed long-term interest rates lower, nor has it materially eased market concerns over the US fiscal deficit and Treasury supply pressure. This means that AI has improved the “numerator” of earnings for certain companies, but has not yet systematically improved the “denominator” for equity valuation.
This point is crucial for understanding the second half of the year. US equities maintained resilience in the first half because earnings improvement among certain companies was strong enough to offset the pressure from elevated discount rates. However, if long-term rates rise again in the second half, or if the market begins to question the return cycle of AI capital expenditure, the margin for error for high-valuation growth assets will decline. Conversely, if earnings revisions continue to broaden across more industries while long-term rates remain relatively stable, market breadth may improve in a more sustainable and higher-quality manner.
Overall, the essence of the US equity market in the first half of 2026 was a renewed screening process around earnings quality, capital efficiency, and real AI monetization capability amid persistent macro uncertainty. Index performance masked the complexity beneath the surface, and the AI narrative alone can no longer explain all areas of market strength. The starting point for second-half strategy should therefore be built on this review: US equities are not without opportunities, but opportunities are shifting from broad risk appetite to structural earnings verification. The market is not simply rewarding large-cap size; it is rewarding assets that can continue to demonstrate efficiency, cash flow, and pricing power in a high cost-of-capital environment.
3. Macro and Policy Framework: The Warsh Fed and the Sustainability of Higher Rates
Entering the second half of 2026, the macro environment facing US equities can no longer be summarized simply as a “rate-cut trade” or an “AI bull market.” The more important shift is that the Federal Reserve’s policy framework is entering the Warsh era: policy communication has become more restrained, forward guidance has been materially reduced, and monetary policy is refocusing on price stability, data dependency, and the market’s own price-discovery function. This shift does not necessarily mean policy will remain restrictive indefinitely, but it does mean that markets can no longer rely excessively on the Fed to provide clear easing signals. Asset pricing will increasingly need to adjust on the basis of inflation data, labor-market data, long-term interest rates, and fiscal constraints.
Compared with the past few years, the most visible feature of the Warsh Fed may be “fewer commitments, stronger constraints.” During the latter part of the Powell era, although the Fed continued to emphasize data dependency, markets could often infer a relatively clear policy bias from the policy statement, the dot plot, and the press conference. Since Warsh took office, the policy statement has become more concise, forward guidance has been weakened, and the central bank has avoided providing excessive path commitments to the market. This change may increase short-term market volatility, as investors need to reprice the policy path directly in response to changes in inflation, employment, and financial conditions. Over the medium to long term, however, it also suggests that the Fed aims to reduce the market’s dependence on a policy backstop and re-anchor its inflation target and institutional credibility.
For the market in the second half of the year, the key implication of this policy style is that trading rate-cut expectations will become more difficult. Even if the US economy does not weaken materially, as long as inflation remains above target and energy, service prices, or wage stickiness create renewed pressure, the Fed will lack a strong basis for a rapid pivot toward easing. Warsh should not be viewed simply as a dovish replacement. His policy focus is more likely to be observing whether productivity gains and supply-side improvements can create room for future rate cuts while preserving price stability. Therefore, the market should not interpret the new Fed leadership as the start of an easing cycle, but rather as a transition into a policy regime that places greater emphasis on data validation and inflation discipline.
This is highly important for equity valuations. Over the past two years, US equities have remained resilient in a high-rate environment mainly because of two conditions: first, corporate earnings have outperformed expectations, especially among large-cap technology companies and AI value-chain beneficiaries; second, the market has retained expectations of an eventual policy pivot, treating high rates as a temporary constraint. Under the Warsh Fed, if forward guidance becomes more limited and the policy path is no longer signaled in advance, the market’s confidence in future rate cuts will decline, and discount-rate assumptions for high-valuation assets will become more sensitive. In other words, US equities can still rise, but further upside will depend more on earnings delivery than on valuation expansion driven by rate-cut expectations.
In terms of the rate path, the most important question for the second half is not whether policy rates will fall immediately, but whether higher rates are sustainable. If the economy slows moderately, inflation continues to ease, and long-term rates remain stable, a restrictive-rate environment does not necessarily undermine the US equity story. In this scenario, earnings resilience and AI-related capital expenditure can continue to support structural opportunities, particularly among companies with strong cash flow, healthy balance sheets, and flexible margins. However, if inflation rebounds, or if fiscal deficits and Treasury supply pressure push long-term rates higher, the market will re-enter a phase of valuation compression. Long-duration growth stocks, small caps, and externally financed assets would face greater pressure.
The impact of the Warsh policy framework will not be symmetric across assets. For large-cap technology and high-quality growth companies, the real test is not whether rates decline, but whether earnings growth can continue to offset higher discount rates. If cloud computing, semiconductors, AI infrastructure, and enterprise software continue to demonstrate revenue growth, margin improvement, and cash flow quality, higher rates do not necessarily eliminate their valuation premium. For companies with unstable earnings, weak cash flow, and elevated refinancing pressure, however, the absence of clear easing signals will limit the scope for valuation repair. For small caps and selected cyclical assets to sustain outperformance, they will need not only improving rate expectations, but also earnings revisions and a better financing environment.
At the sector level, the policy environment will reinforce the market’s preference for quality and cash flow. The extent to which financials benefit from higher rates depends on interest margins, credit quality, and capital-market activity. If policy remains excessively tight and leads to a flatter yield curve or rising credit risks, financial stocks may not benefit systematically. Consumer discretionary is more sensitive to interest rates and credit costs, and household-sector divergence will continue to affect its performance. Industrials, materials, and energy will be more influenced by the capital expenditure cycle, oil prices, geopolitical risk, and the pace of fiscal spending. Within technology, differentiation will also deepen, as the market places greater emphasis on capital expenditure returns, free cash flow, and pricing power rather than simply assigning valuation premiums to all AI-related assets.
Fiscal constraints form an unavoidable backdrop for the Warsh Fed. The US fiscal deficit remains elevated, while interest expense and Treasury supply pressure make it difficult for long-term rates to return easily to pre-pandemic lows. Even if the Fed gains room to cut rates in the future, long-term yields may remain elevated due to term premium, fiscal risk premium, and the structure of Treasury supply and demand. Therefore, the market needs to distinguish between policy rates and long-term rates in the second half. The former is directly controlled by the Fed, while the latter reflects inflation expectations, real rates, fiscal supply, and global capital demand. For equity valuation, the stability of long-term rates is often more important than whether short-term policy rates are reduced modestly.
Within this macro framework, AI serves primarily as support for the earnings side, rather than as a source of rate-side easing. AI is indeed improving productivity, revenue growth, and margins for certain companies, especially in semiconductors, cloud computing, data centers, electrical equipment, data platforms, cybersecurity, and selected software businesses. However, AI has not yet meaningfully pushed long-term rates lower, nor has it materially eased market concerns over fiscal deficits and Treasury supply. In other words, AI has improved the “numerator” of earnings for certain companies, but it has not yet systematically improved the “denominator” for equity valuation. This is why the market still needs to focus on long-term rates and fiscal constraints in the second half, rather than assuming that AI can offset all macro pressures.
Therefore, the macro theme for the second half of 2026 can be summarized as follows: US equities remain in an environment where earnings resilience coexists with higher-rate constraints, while the Warsh Fed’s reduction in forward guidance will make markets more dependent on data and fundamentals for price discovery. As long as inflation continues to ease, long-term rates remain stable, and corporate earnings continue to be revised upward, structural opportunities in US equities should remain intact. However, if inflation or fiscal pressure pushes rates higher again, the market’s tolerance for high-valuation assets will decline materially. Second-half strategy should not be built on the single assumption that policy is about to ease quickly. Instead, it should return to stricter asset selection: companies that can demonstrate earnings resilience, cash flow quality, and capital efficiency in a high cost-of-capital environment are the ones most likely to retain durable pricing support.
4. Earnings and Valuation: From Multiple Expansion to Earnings-Driven Returns
After the first-half rally, the key question for US equities is no longer whether the market can move higher, but whether current valuations can continue to be supported by earnings growth. Over the past two years, market upside was driven by a combination of valuation recovery, AI enthusiasm, and resilient earnings. Entering the second half of 2026, however, the margin for error has narrowed. With forward valuation multiples already above historical averages and long-term Treasury yields still elevated, the market will require stronger evidence of revenue growth, margin expansion, and free cash flow delivery.
At the index level, US equities are no longer inexpensive. Even after accounting for the superior profitability, cash flow quality, and return on capital of large technology companies, current valuations already embed considerable optimism. This does not mean the market is overvalued in a simple or uniform sense. Rather, it means valuation dispersion will matter more. Companies with strong earnings revisions, pricing power, and cash flow durability can still justify premium valuations through profit growth. By contrast, companies whose valuations depend primarily on distant earnings, unclear capital returns, or weak cash flow quality will face greater pressure.
This shift is especially important for AI-related assets. AI remains the most important structural growth theme in US equities, but the market is moving from narrative expansion to earnings verification. In the early stage of the AI infrastructure cycle, investors were willing to pay for orders, compute demand, cloud capex, and long-term potential. As AI capital expenditure continues to expand, investors are increasingly focused on returns: whether chips, servers, data centers, and cloud infrastructure can generate durable revenue; whether training and inference demand can sustain growth; whether depreciation, power, land, and financing costs will pressure margins; and whether platform companies can monetize AI through subscriptions, advertising efficiency, enterprise tools, or developer ecosystems.
As a result, the AI value chain is likely to see further valuation stratification. The strongest pricing power should remain with companies that control scarce bottlenecks, have visible demand, and can preserve margin flexibility, including compute chips, advanced manufacturing, advanced packaging, HBM, memory, networking chips, power equipment, and critical software infrastructure. A second group of beneficiaries includes platforms that can use AI to improve customer productivity and are deeply embedded in data, workflows, and enterprise processes. By contrast, companies with AI narratives but limited monetization visibility, weak customer ROI, or continued dependence on traditional seat-based growth will find it harder to sustain valuation expansion.
More broadly, US earnings growth remains concentrated in a limited number of high-quality sectors and large companies. Large-cap technology, AI infrastructure, and selected cyclical recovery areas have contributed a disproportionate share of positive earnings revisions, while earnings recovery in defensive sectors, consumer discretionary, financials, and parts of healthcare remains uneven. If earnings upgrades remain concentrated, the index may stay resilient, but internal market divergence will persist. A more durable improvement in market breadth would require earnings revisions to broaden across a wider set of industries.
Margins will be another critical variable in the second half. AI tools, automation, and operating efficiency may continue to support margins for selected companies. At the same time, wages, energy, power costs, data center depreciation, memory inflation, tariffs, and supply chain adjustments may create new cost pressures. For investors, the key issue is not simply whether revenue can grow, but whether that growth can translate into high-quality earnings and free cash flow.
This is why capital efficiency will remain central to valuation differentiation. In a low-rate environment, the market was more willing to tolerate high capital expenditure, limited near-term profits, and distant cash flow stories. In a high-rate environment, capital is more expensive, and investors will focus more closely on the return profile of each dollar invested. For AI infrastructure companies, rapid capex growth is both a signal of demand and a source of future depreciation and free cash flow pressure. For cloud and platform companies, the market will monitor whether AI investment leads to higher ARPU, stronger retention, increased workload migration, and better margins. Capital expenditure itself no longer automatically represents value creation; return on invested capital does.
Free cash flow quality should also become more important. With policy rates still restrictive and long-term rates unlikely to fall sharply, strong cash flow gives companies greater flexibility to invest, repurchase shares, pursue strategic acquisitions, and withstand volatility without relying on external financing. Companies with weak cash flow, by contrast, remain more exposed to refinancing costs, funding conditions, and changes in risk appetite.
For the broader market, second-half upside is more likely to come from earnings revisions than from further multiple expansion. If corporate earnings continue to surprise positively and long-term rates remain stable, current valuations can be absorbed gradually through profit growth. If earnings expectations are revised lower or long-term rates rise again, valuation pressure could increase even without a recession. For high-valuation growth assets, the pace of earnings delivery will matter more than the macro narrative. For lower-valuation cyclical assets, continued re-rating will depend on whether earnings recovery and financing conditions improve together.
Therefore, the key framework for the second half should shift from “whether multiples can expand further” to “which companies can grow into their valuations.” The former depends heavily on lower rates and stronger risk appetite; the latter depends on operating quality. In a policy environment where the Warsh Fed provides less forward guidance, long-term rates remain constrained by fiscal supply, and AI has not yet materially lowered discount rates, US equities are more likely to enter an earnings-revision-driven phase rather than a broad multiple-expansion phase.
Overall, the earnings and valuation message for the second half of 2026 is clear: index-level valuations are not cheap, but earnings resilience continues to support the market. Multiple expansion may be limited, but structural opportunities remain where earnings revisions, cash flow quality, and capital efficiency continue to improve. The market will not indiscriminately reward growth, nor will it mechanically punish premium valuations. The companies most likely to retain durable pricing support are those that can continue to demonstrate revenue growth, margin stability, free cash flow quality, and strong returns on capital in a high cost-of-capital environment.
5. AI Value Chain: From Infrastructure Expansion to Application Monetization
AI remains the most important structural theme for US equities in the second half of 2026, but the market’s pricing framework is changing. Over the past two years, investors focused mainly on the size of AI demand and the most direct beneficiaries. Entering the second half of 2026, the key questions have shifted to which companies can retain control over scarce bottlenecks, convert AI capital expenditure into revenue and cash flow, and build sustainable monetization at the application layer. In this sense, the AI theme is not ending; it is moving from the first stage of narrative expansion and infrastructure buildout into a second stage defined by earnings verification, supply constraints, and business model differentiation.
AI should not be viewed as a single sector. It is a multilayered system consisting of compute, memory, networking, data centers, cloud platforms, data governance, enterprise software, and end devices. Each layer has a different pricing logic. Segments closer to physical bottlenecks, supply constraints, and visible capital expenditure tend to have stronger near-term earnings visibility. Segments closer to applications, customer budgets, and business model transition are likely to see greater dispersion, with valuations becoming more sensitive to revenue conversion and customer ROI. Therefore, the second-half AI framework should shift from broad AI exposure to layered selection.
The first layer is compute and semiconductor bottlenecks. This remains one of the most visible parts of the AI value chain. Continued growth in training and inference demand makes GPUs, AI ASICs, advanced manufacturing, advanced packaging, HBM, high-performance memory, networking chips, and semiconductor equipment critical bottlenecks. Unlike traditional semiconductor cycles, this AI cycle is not driven by a single end market. It is shaped by compute density, memory bandwidth, chip interconnects, packaging capacity, and data center cluster efficiency. Representative companies in this layer include NVIDIA, Broadcom, AMD, Micron, Marvell, TSMC, ASML, Applied Materials, Lam Research, and KLA.
Within semiconductors, however, the market will continue to differentiate. The most important assets are not all chip companies, but those combining technology leadership, scarce supply, customer lock-in, and margin resilience. GPUs and AI accelerators remain central to the compute cycle, while AI ASICs, networking chips, HBM, and advanced packaging are becoming increasingly important. As inference demand expands alongside training, compute demand is moving from concentrated high-end training clusters toward broader deployment environments, raising the importance of networking, memory, low-latency interconnects, and power efficiency. The AI semiconductor theme is therefore evolving from single-point compute toward system-level compute.
The second layer is data centers, power, and physical infrastructure. As AI capital expenditure expands, investors are increasingly recognizing that the constraints are not only in chips, but also in power availability, land, powered shells, cooling, construction timelines, transformers, electrical equipment, and grid access. Compared with traditional cloud data centers, AI data centers require higher power density, more advanced cooling, greater redundancy, and more complex construction. Representative companies and beneficiaries in this layer include Vertiv, Eaton, Schneider Electric, GE Vernova, Quanta Services, Fluor, Comfort Systems, Trane Technologies, Carrier, and selected data center REITs such as Equinix and Digital Realty.
This shift has important implications for US sector allocation. AI has often been treated as a technology-sector growth story, but its impact is increasingly spreading into physical capital expenditure. Data center construction requires grid upgrades, engineering services, equipment procurement, and energy infrastructure. It may also raise regional power demand and affect energy pricing. As a result, the link between AI, reindustrialization, energy security, grid investment, and manufacturing capex is becoming stronger. Investors should therefore pay closer attention to the infrastructure spillovers of AI demand, rather than focusing only on chips and cloud services.
The third layer is cloud capital expenditure and infrastructure delivery. Large cloud providers remain the core buyers of AI infrastructure and the main platforms through which AI capabilities move from model development into enterprise adoption. However, cloud capex expansion has a dual meaning. On the one hand, it reflects strong demand and confidence in future AI workloads. On the other hand, it also implies depreciation pressure, cash flow consumption, power constraints, and longer payback periods. Representative companies in this layer include Microsoft, Amazon, Alphabet, Oracle, Meta, CoreWeave, and other cloud and AI infrastructure providers.
At this stage, orders and remaining performance obligations are only the first layer of information. The more important questions are whether these orders can be delivered on schedule, converted into recognized revenue, supported by stable margins, and funded without damaging free cash flow quality. Some companies may report strong orders and demand, but revenue realization could be delayed if data center construction, power access, or equipment deployment lags. If financing costs, depreciation, and operating expenses rise too quickly, margins and cash flow may come under pressure. In the second half, the market is likely to place greater emphasis on balance sheet capacity, project execution, and capital discipline.
The fourth layer is data, governance, cybersecurity, and observability software. This may be the first part of the AI software layer to show incremental demand. As enterprises deploy more AI agents and automated workflows, demand should rise for data access, model permissions, identity controls, log monitoring, cost management, security detection, and compliance governance. AI will not simply reduce enterprise software spending. In certain infrastructure software categories, it may create new usage-driven demand. Representative companies in this layer include Snowflake, Datadog, CrowdStrike, Palo Alto Networks, Zscaler, Cloudflare, Okta, MongoDB, Confluent, AvePoint, and ServiceNow.
The key logic of this layer is that these companies are closer to AI usage itself. The more AI agents are deployed and the more frequently models are called, the greater the enterprise need for data quality, permission management, security auditing, and system observability. Unlike traditional seat-based software, these categories are more directly linked to data traffic, usage volume, workload complexity, and rising security risks. Within software, infrastructure software, data governance, and security platforms are therefore likely to retain relative advantages.
The fifth layer is enterprise applications and SaaS business model repricing. This is the most complex and most differentiated part of the AI value chain. AI can improve enterprise software products by generating content, supporting sales, improving customer service, accelerating software development, enhancing marketing conversion, and automating workflows. At the same time, AI agents may weaken the traditional seat-growth logic. If enterprises can use AI tools to allow fewer employees to complete more work, conventional seat-based SaaS models may face structural pressure. Representative companies in this layer include Salesforce, Adobe, Atlassian, Workday, HubSpot, ServiceNow, Palantir, Procore, Wix, Shopify, and Intuit.
Application software should therefore not be treated as a uniform AI beneficiary. The companies with stronger positioning tend to have at least one of several advantages: high-quality proprietary data, deep workflow embedding, system-of-record status, control over business processes, the ability to shift from seat-based pricing toward consumption-based, outcome-based, or hybrid pricing, and clear evidence that AI improves customer revenue, reduces costs, or enhances operating efficiency. By contrast, companies that only add AI features without strong data moats, workflow stickiness, or monetization visibility may struggle to sustain valuation re-rating.
The central question for application software is not whether a company has an AI product, but whether AI changes customer budget allocation and willingness to pay. Enterprise customers will not pay indefinitely for concepts; they will pay for measurable productivity gains, revenue uplift, risk reduction, and cost savings. In the second half, investors are likely to focus more closely on actual AI adoption, ARPU uplift, renewal rates, net revenue retention, credit consumption, customer ROI, and sales cycle changes. Platforms that can turn AI from a feature upgrade into a business model upgrade should have stronger pricing power, while companies unable to demonstrate commercialization may face valuation pressure.
The sixth layer is edge AI and consumer electronics. The logic of edge AI is different from cloud AI. Its core lies not in large-scale training, but in device upgrades, ecosystem stickiness, privacy-preserving computing, local inference, and control over user access points. As AI capabilities move from the cloud to smartphones, PCs, wearables, and other devices, hardware upgrade cycles may receive a new catalyst. Representative companies in this layer include Apple, Qualcomm, Intel, AMD, Microsoft, Dell, HP, Lenovo, Samsung, and selected component suppliers across sensors, memory, and power management.
For consumer electronics companies, AI may create value in two ways: first, by supporting hardware replacement and premiumization; second, by strengthening service ecosystems and user retention. However, near-term expectations should remain disciplined. Edge AI should not yet be treated as a fully realized profit cycle. Investors need to observe whether AI features can genuinely drive device shipments, average selling prices, service revenue, and user engagement, rather than focusing only on product launches or feature demonstrations. Compared with cloud infrastructure, edge AI may commercialize more gradually, but its long-term value could be meaningful once user habits and ecosystem loops are established.
Overall, as AI enters its second pricing stage, the market will focus on four standards: control over bottlenecks, revenue conversion, margin stability, and free cash flow quality. The first stage of the AI trade was driven mainly by demand imagination and capital expenditure expansion. The second stage requires companies to prove that their position in the value chain can translate into real financial outcomes. Companies controlling hard constraints may retain strong pricing power, but they will also face high bases and future supply expansion. Cloud and data center companies will continue to benefit from demand growth, but must prove capex returns. Software companies have long-term monetization potential, but internal dispersion will widen. Edge AI offers a potential replacement-cycle story, but commercialization still requires time.
Therefore, the second-half AI investment framework should not be about simply finding AI exposure. It should be about identifying AI cash flow conversion. The most durable beneficiaries are not all companies participating in the AI narrative, but those that control scarce resources, own customer budget entry points, possess pricing power, and can convert AI investment into revenue, profit, and free cash flow. AI remains the core engine of structural opportunity in US equities, but the driver is shifting from valuation imagination to operating delivery. Companies that can prove AI-driven returns on capital are the ones most likely to retain market premiums in the next stage.
6. Sector Allocation and Portfolio Framework: From Index Beta to Structural Alpha
Entering the second half of 2026, the core allocation question for US equities should not be whether the index can move higher, but which assets can continue to generate structural alpha in an environment of elevated rates, earnings dispersion, and AI capex verification. The first half showed that liquidity and risk appetite can still support the index, but capital does not flow evenly across all risk assets. This pattern is likely to persist. Opportunities should come more from sector-level and value-chain differentiation than from broad-based multiple expansion.
From a portfolio construction perspective, we favor a balanced framework built around high-quality growth, cash-flow assets, and hard-asset hedges. One side of the portfolio should retain exposure to AI, semiconductors, cloud infrastructure, data platforms, and high-capital-efficiency technology companies to participate in long-term technology diffusion and earnings upgrades. The other side should include energy, selected industrials, utilities, dividend-oriented assets, and other defensive cash-flow businesses to reduce exposure to long-term rate volatility, geopolitical risk, and valuation compression. This is not a simple combination of offense and defense; it is a risk-reward rebalance around earnings visibility, cash flow quality, and returns on capital in a higher cost-of-capital environment.
Technology remains the most important structural area, but selection within the sector needs to become more disciplined. AI infrastructure, semiconductor bottlenecks, cloud platforms, data governance, cybersecurity, and sticky enterprise software still have durable growth potential. However, valuation tolerance will increasingly depend on earnings delivery and returns on capital expenditure. Assets with unclear commercialization paths, unstable cash flow, or valuations driven mainly by distant AI narratives may face higher volatility. The key is not to find the largest AI exposure, but to identify companies that can convert AI into revenue, profit, and free cash flow.
Semiconductors and AI hardware should remain supported by strong demand, but expectations are now higher. GPUs, AI ASICs, HBM, advanced packaging, memory, networking chips, and semiconductor equipment remain critical bottlenecks in the AI value chain. However, some segments have already delivered strong first-half performance, and valuations now reflect substantial optimism. Further upside will depend on order durability, the persistence of supply constraints, pricing power, customer concentration, and margin performance. In this area, value-chain positioning and earnings quality should matter more than simply chasing cyclical momentum.
The software allocation framework should shift from broad SaaS exposure toward AI usage beneficiaries and workflow control. Traditional seat-based models face potential pressure from AI agents, especially if enterprises use AI to enable fewer employees to complete more work. By contrast, data platforms, cybersecurity, observability, identity governance, AI control layers, cloud cost management, and vertical software deeply embedded in core workflows are more likely to benefit from AI adoption. The key question is not which software company has AI features, but which company can prove that AI improves customer ROI and convert that value into ARPU, renewals, consumption, or outcome-based revenue.
Industrials and power infrastructure deserve continued attention. AI data center construction, US manufacturing investment, supply chain reshoring, and grid upgrades are jointly supporting a physical capex cycle. Electrical equipment, cooling systems, engineering and construction services, mechanical and electrical services, industrial automation, and selected materials companies may continue to benefit from AI infrastructure spillovers. Unlike traditional cyclical assets, this industrial cycle is not driven only by broad economic demand. It is increasingly linked to AI, energy security, and reindustrialization. Therefore, industrials should also be viewed as part of the broader AI infrastructure beneficiary chain.
Energy plays a dual role as both return source and hedge. If geopolitical risk, energy transportation disruption, or fiscal expansion raises inflation pressure, energy assets may benefit. Energy prices also transmit directly into inflation expectations and long-term rates, giving the sector portfolio-hedging value. That said, energy exposure should not rely solely on the assumption of persistently rising oil prices. If global demand weakens or supply disruptions ease, energy prices may fall. Energy is therefore better positioned as a hedge against inflation and geopolitical risk than as a single directional growth theme.
The outlook for financials is more neutral. Higher rates can support interest income for some financial institutions, but sector performance depends more on the yield curve, credit quality, capital-market activity, regulation, and commercial real estate exposure. If high rates weaken credit demand, raise default risk, or flatten the yield curve, financials may not benefit systematically. Allocation should focus on institutions with strong capital positions, sound asset quality, stable fee income, and lower risk exposure rather than on a simple high-rate thesis.
Consumer discretionary and consumer services require greater selectivity. US consumption has not weakened broadly, but household divergence remains significant. Higher-income consumers continue to benefit from wealth effects and service demand, while lower- and middle-income households face pressure from credit costs, inflation, and depleted savings. Within consumer sectors, brand strength, pricing power, customer mix, and channel efficiency will drive performance dispersion. Premium consumption, membership ecosystems, platform-based consumption, and companies with strong pricing power should be more resilient, while businesses exposed to lower-income demand, weak differentiation, or inventory pressure may remain volatile.
Healthcare retains defensive value, but its opportunities are likely to be more selective than broad-based. The sector can provide protection during slower growth and higher volatility, but drug pricing, reimbursement policy, regulatory uncertainty, and shifting competition may limit multiple expansion. More attractive areas include innovation-driven therapeutics, medical technology, healthcare services with stable cash flow, and companies using AI to improve R&D, diagnostics, or operating efficiency. Healthcare can serve as part of the defensive side of the portfolio, but policy and valuation risks need to be carefully managed.
Utilities and dividend-oriented assets offer stable cash flow and volatility control. Elevated long-term rates may constrain valuation upside, but if market volatility rises, growth slows moderately, or rate expectations stabilize, stable cash-flow assets can play a defensive role. Some utilities and power-related companies may also benefit from rising electricity demand from AI data centers, giving them a structural growth element beyond traditional defensiveness.
Small and mid caps have already shown periods of strength this year, but their ability to continue outperforming will depend on earnings and financing conditions. If long-term rates remain stable, credit conditions improve, and earnings revisions turn positive, small caps may continue to benefit from valuation repair and better market breadth. If policy remains restrictive, funding costs stay high, or credit risk rises, dispersion within smaller companies will remain significant. Rather than buying small-cap beta broadly, investors should focus on companies with healthy balance sheets, improving cash flow, clear demand drivers, and valuation recovery supported by earnings.
From a style perspective, quality should remain more important than beta. In a high cost-of-capital environment, the market should reward companies that can self-fund growth, preserve margins, improve returns on capital, and generate stable free cash flow. High-beta, low-profitability, weak-cash-flow, and externally financed assets may outperform only if rate-cut expectations strengthen materially and risk appetite expands sharply. Under current conditions, their risk-reward remains less attractive.
Overall, the sector allocation message for the second half is clear: US equities still offer structural opportunities, but the framework should shift from index beta to structural alpha, from broad growth to high-quality growth, from AI narrative to AI cash flow, and from low-valuation rebound to earnings recovery. Technology, semiconductors, AI infrastructure, and data software remain long-term themes, but valuation and capital returns matter more. Industrials, power, and selected materials benefit from AI capex spillovers. Energy and dividend assets can serve as hedges against inflation and volatility. Consumer, financials, healthcare, and small caps require more disciplined fundamental screening. The key is not to take more risk, but to own better-quality risk.
7 Scenario Analysis: Earnings Resilience, Reflation Risk, and AI Return Verification
For the second half of 2026, the US equity outlook should be framed around three main scenarios: a soft-landing scenario in which inflation continues to ease, growth slows moderately, and earnings remain resilient; a reflation scenario in which energy, fiscal, wage, or tariff pressures push long-term rates higher; and an AI return-verification scenario in which investors reassess whether AI capex can generate sufficient revenue, margins, and free cash flow. These scenarios are not mutually exclusive and may alternate in importance over the course of the year. The key is not to bet on a single path, but to identify which assets remain resilient across different macro and earnings conditions.
In the soft-landing scenario, the economy continues to grow at a moderate pace, inflation gradually declines, the labor market cools without meaningful deterioration, and long-term rates remain stable or move modestly lower. This would be the most favorable environment for US equities. The market would not need rapid rate cuts to sustain risk appetite, as earnings growth itself could support valuations. AI infrastructure, high-quality technology, semiconductors, data software, industrial automation, and selected cyclical recovery assets would likely remain well supported. Small and mid caps could also benefit if financing conditions improve and earnings revisions recover from a low base.
Even in this favorable scenario, however, the market is unlikely to return to indiscriminate upside. Valuations are no longer cheap, the Warsh Fed has reduced forward guidance, and long-term rates remain constrained by fiscal supply. As a result, the market will still require earnings quality and capital discipline. A soft landing would more likely lead to “quality broadening,” with leadership expanding from a narrow group of AI leaders to companies with improving earnings, cash flow, and returns on capital, rather than a broad re-rating of low-quality or highly leveraged assets.
The second scenario is reflation. If energy prices rise due to geopolitical or transportation disruptions, or if fiscal expansion, tariffs, wage stickiness, and service inflation slow the disinflation process, markets would need to reprice the Fed path and long-term rates. For equities, the main risk would be higher discount rates and valuation compression rather than weaker growth alone. High-valuation growth stocks, long-duration technology assets, small-cap growth, and externally financed companies would likely face greater pressure. Energy, selected materials, industrials, defensive cash-flow assets, and companies with pricing power should be relatively more resilient.
In a reflationary environment, sector rotation could become more pronounced. Energy and hard assets would have stronger hedging value, while some industrial and materials companies could benefit from pricing power and capex resilience. However, reflation does not imply that all cyclical assets will outperform. If rates rise too quickly and weaken demand, cyclical earnings would also come under pressure. The more durable beneficiaries would be companies that can pass through cost inflation, maintain balance sheet strength, and generate stable cash flow without heavy dependence on financing conditions.
The third scenario is weaker-than-expected AI return verification. This would not mean the long-term AI trend is reversing. Rather, it would reflect a market reassessment of near-term capex intensity, revenue conversion, and cash flow pressure. Over the past two years, AI infrastructure spending has supported strong demand for chips, data centers, cloud services, power equipment, and software infrastructure. As capex continues to rise, investors will focus more closely on whether these investments can generate adequate revenue, margins, and customer ROI. If AI-related cloud revenue grows more slowly than capex, or if data center deployment is delayed by power, construction, equipment, or financing constraints, valuations across the AI value chain could be reassessed.
In this scenario, the most vulnerable assets would be those whose valuations depend heavily on distant growth, whose cash flow remains unstable, whose capex intensity is rising too quickly, or whose monetization path is unclear. The market would not abandon the AI theme, but it would compress valuations for companies unable to demonstrate revenue realization and returns on capital. Companies controlling hard bottlenecks, retaining strong customer lock-in, and preserving high margins and cash flow should be more resilient. Data platforms, cybersecurity, governance, and workflow-control software may also retain relative advantages because they are closer to enterprise AI usage and risk management needs.
These scenarios have different implications for the index. In a soft landing, the index could continue to rise, with broader participation driven by earnings revisions. In a reflation scenario, volatility would likely increase, valuation multiples would face pressure, and leadership could rotate toward energy, hard assets, defensives, and pricing-power companies. In an AI return-verification scenario, index pressure could come disproportionately from large technology and AI-related stocks due to their high index weights. If underlying demand remains intact, however, the adjustment would more likely represent a correction in expectations and valuation rather than the end of the AI cycle.
Our base case remains a combination of soft landing and structural earnings resilience, but portfolios should retain buffers against reflation and AI return-verification risk. The US economy has not materially weakened, corporate earnings remain resilient, and AI capex continues to expand. These factors support ongoing structural opportunities in US equities. At the same time, inflation, fiscal pressure, long-term rates, and AI investment returns remain the main sources of volatility. The most favorable environment would be one in which earnings continue to be revised upward, long-term rates remain stable, and AI revenue conversion improves. The most adverse environment would be one in which inflation pushes rates higher while AI capex returns are questioned, creating pressure on both the valuation denominator and the earnings numerator.
Therefore, second-half portfolios should be able to participate in upside while retaining protection against tail risks. The upside side of the portfolio should focus on high-quality growth, AI bottlenecks, data software, industrial and power infrastructure, and assets with clear earnings recovery. The defensive side should include stable cash-flow assets, companies with pricing power, higher-quality dividend exposure, and assets with inflation or geopolitical hedging characteristics. The objective is not simply to reduce risk, but to improve the quality of risk exposure.
Overall, the key for the second half is not to predict a single macro scenario with certainty, but to identify assets that can maintain earnings resilience and capital efficiency across different outcomes. A soft landing would support risk assets, but not all valuations equally. Reflation would pressure long-duration assets, while increasing the value of hard assets and pricing power. AI return verification may increase volatility in technology, but it should not alter the long-term direction of the AI cycle. Strategy should therefore remain anchored in earnings quality, cash flow, returns on capital, and control over value-chain bottlenecks, rather than in a single macro assumption or thematic narrative.
8 Risks and Conclusion: Quality Still Leads, but the Margin for Error Is Lower
US equities still offer structural opportunities in the second half of 2026, but the market’s margin for error has narrowed. The key risk is no longer simply whether the economy enters recession. Rather, it is whether the balance between earnings expectations, AI capital expenditure, long-term rates, and valuation can remain intact. The first-half rally was supported by earnings upgrades among high-quality companies, but valuations now reflect a meaningful amount of optimism. Any shock that weakens the earnings numerator or raises the discount-rate denominator could lead to greater volatility.
The first major risk is inflation and the rate path. If energy prices, services inflation, wages, tariffs, or fiscal expansion slow the disinflation process, markets would need to reprice the Fed path and long-term rates. Under the Warsh Fed, reduced forward guidance also means investors cannot rely on early policy signals to stabilize expectations. A combination of resilient growth, sticky inflation, and elevated long-term rates would be particularly challenging for high-valuation growth assets.
The second risk is US fiscal pressure and Treasury supply. Elevated deficits, interest expenses, and long-duration bond issuance may keep long-term rates from falling meaningfully, even if the Fed eventually has room to reduce policy rates. A higher term premium or fiscal risk premium would constrain equity valuations, especially for assets that depend on distant cash flows, external financing, or multiple expansion.
The third risk is weaker-than-expected AI return verification. AI remains the central structural theme, but the market is moving from demand imagination to return assessment. As cloud, data center, semiconductor, and power infrastructure capex continues to rise, investors will increasingly ask whether these investments can generate sufficient revenue, margins, and free cash flow. If AI revenue conversion lags capex growth, or if enterprise adoption and customer ROI disappoint, valuations across parts of the AI value chain could be reassessed.
The fourth risk is market concentration. US index performance remains heavily dependent on a limited number of large technology and AI-related companies. This concentration supports index resilience during rallies, but it can also amplify downside if leading companies miss earnings expectations, lower guidance, or face renewed questions about returns on capital.
The fifth risk is margin pressure. Corporate profitability has been a key support for the market, but wages, energy, power costs, data center depreciation, memory prices, tariffs, supply chain adjustments, and financing costs could pressure margins in the second half. AI and automation can offset some of these pressures, but the benefits will not be evenly distributed. Companies with pricing power, scale, data advantages, and strong operating discipline should remain better positioned.
Additional risks include household and credit divergence, geopolitical shocks, energy disruptions, regulation, export controls, and intensifying technology competition. These risks matter not only because they affect sentiment, but because they can transmit through inflation expectations, long-term rates, corporate costs, and earnings revisions.
These risks do not imply that US equities lack opportunity. They imply that the market will demand higher-quality exposure. The market can still reward growth, but only growth that converts into earnings and cash flow. It can still reward AI, but increasingly AI that proves returns on capital. It can still reward premium valuations, but only where earnings revisions can justify them.
Therefore, second-half strategy should not simply reduce risk exposure; it should improve the quality of risk exposure. On the growth side, investors should focus on companies with value-chain bottlenecks, earnings visibility, capital efficiency, and cash flow conversion. On the defensive side, stable cash flow, dividend quality, pricing power, and inflation-hedging characteristics remain important. In cyclical areas, balance sheet strength and earnings recovery matter more than low valuation alone.
Overall, the message for the second half of 2026 is clear: the index remains resilient, but tolerance for disappointment is lower; AI remains the core engine, but it is entering a return-verification phase; liquidity remains supportive, but it will not lift all assets equally. Quality remains the central theme, but the definition of quality is becoming stricter. It is no longer only about size or valuation premium. It is about the ability to consistently deliver earnings resilience, free cash flow, and returns on capital in a higher cost-of-capital environment.
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