Mapping AI's Rate of Change: From Buildout to Broadening
Thematics
AI leadership is entering a new phase: from a concentrated enabler trade toward a broader, barbelled opportunity set spanning select enablers and emerging adopters. Our 6th global AI stock mapping across ~3,600 stocks provides us with quantitative backing.
Executive Summary
A cycle in transition: Expect a "barbell" of outperformance of key infrastructure "enablers" and early software players & AI "adopters." Cycles typically play out with semis outperforming first, followed by infrastructure, and then software and services. We see AI leadership entering a new phase: from a concentrated enabler trade toward a broader, barbelled opportunity set spanning select enablers and emerging adopters. With semis and infrastructure having outperformed the S&P 500 by 500-700 pts since the beginning of the cycle, we believe now is the time to start adding exposure to early software enablers and AI adopters. However, we also believe the scale of the AI capex buildout and key bottlenecks like power will elongate upside for select players — as a result, we don't expect the same linear handoff in market leadership seen in prior computing cycles.
For this transition period, we expect a "barbell" of outperformance and recommend to: 1) own power names addressing key data center bottlenecks, 2) remain selective across semis/infrastructure, and 3) broaden exposure to early software enablers and AI adopters.
Margin benefits are not fully reflected in adopter out-year estimates: Consensus expects high-materiality adopters to deliver 460bps of EBIT margin expansion in 2025-26, nearly twice the MSCI ACWI IMI. Yet that advantage fades in subsequent years. We expect productivity gains to prove more durable, creating potential upside to out-year earnings especially as adoption deepens.
Earnings are broadening as Adopter valuations reset: Consensus NTM EPS for our adopter cohort has risen ~70% over two years, well ahead of the broader market. Estimates have more than doubled for the enablers. Adopters’ median forward P/E has fallen to 18x versus 22x for enablers. Improving fundamentals and lower valuations create a more attractive entry point, in our view.
AI exposure is spreading while deepening remains concentrated: New AI entrants increasingly come from outside Tech, including Real Estate, Autos, Consumer Durables, Transportation, and Consumer Services. Rising materiality remains concentrated in Software, Semiconductors, Utilities, and Media. The opportunity set is expanding without a clean handoff from existing beneficiaries.
Materiality matters. Among enablers and adopters, companies where AI is "core to thesis" have outperformed those where it is "significant." Performance gaps are even wider among AI-challenged stocks. We see materiality as critical to identifying where AI can meaningfully reshape fundamentals, on both sides of the coin.
Please click here for the full database of ~3,600 stocks mapped by AI exposure and materiality. In addition to our AI mapping, we also map all stocks under Morgan Stanley coverage on exposure to subthemes within our four key themes: AI & Tech Diffusion, the Multipolar World, Societal Shifts, & the Future of Energy. Please see The Thematic Map - Scaling Themes in AlphaSignals for more information.
We believe the AI trade is at an important transition. We're calling for a "barbell" approach as we move deeper into the cycle — a broadening into software enablers and AI adopters, while still owning players addressing key data center bottlenecks. In historical computing cycles, we've seen a similar pattern of stock outperformance across three categories of technology "enablers": semis, infrastructure, then software/services. Together, these companies provide the technology stack that enables each computing cycle, from the chips, infrastructure, and devices that form its physical backbone to the software layered on top. There is also a technology diffusion component to each cycle, and these companies are the "adopters" that leverage the new technology to drive productivity gains and revenue growth. We expect the diffusion of AI into other sectors to drive greater outperformance for the adopter category than in tech cycles that preceded it. Why? The exponential scaling of LLM capabilities and AI's full potential productivity impact suggest a broad, and meaningful opportunity for value creation.
We believe now is the time to start adding exposure to early software enablers and AI adopters. We have clearly seen this outperformance pattern play out this cycle, with semis and infrastructure outperforming the S&P 500 by 500-700 pts. Now, we are calling for a broadening into early software enablers, particularly categories like infrastructure and security software.
However, we also believe this cycle is different, given the unprecedented scale of the AI capex buildout and key bottlenecks that are constraining its pace. We believe demand for compute is likely to significantly exceed supply for years to come. We continue to see constraints across the "3 P's": People, Power, and Politics. Power remains in short supply (our team forecasts a 57 GW shortfall in 2026-28) while labor constraints and regulatory hurdles are also slowing the pace of data center development (see our work on data center moratoriums). As a result, we don't expect the same linear handoff in market leadership seen in prior computing cycles. Instead, we are calling for a "barbell" of outperformance over the next 6-12 months as the cycle transitions, with further outperformance of 1) power names addressing key data center bottlenecks and select semis/infrastructure names, and 2) early software enablers and AI adopters beginning to translate the technology into measurable productivity and revenue benefits. We expect late-stage software enablers (like application software) and services companies to benefit later in the cycle. Our proprietary AI mapping database provides the quantitative backing for this call.
5 Key Takeaways from our 6th AI Mapping
(1) AI Adoption Benefits Are Making Their Way Into Margins, but Aren’t Yet Embedded in Out-Year Estimates. Consensus expects AI Adopters to deliver 4.6% of EBIT margin expansion in 2025-26, modestly ahead of the S&P 500 and nearly twice the MSCI ACWI. Yet that advantage fades quickly in out year estimates, with expected margin expansion for Adopters converging toward the broader market in 2026-27 and 2027-28. Put differently, while estimates are beginning to reflect near-term margin benefits among companies with the most material AI adoption, analysts are not yet underwriting a sustained or compounding productivity benefit. This is especially notable given the meaningful margin inflection expected this year; consensus appears to recognize the initial benefit, but not its persistence. We expect AI driven productivity gains to prove more durable than currently embedded in estimates, creating potential upside to out year earnings as those benefits become more evident. Please note, companies may be classified as both AI Adopters and AI Enablers, but analysts assess and tag the materiality of each exposure separately.
(2) AI Earnings Momentum Is Broadening Beyond Enablers. Consensus forward earnings trends reinforce our view that the next phase of AI leadership is more likely to broaden than completely rotate. Enablers continue to show the strongest earnings momentum, with consensus NTM EPS more than doubling over the past two years. Importantly, however, Adopters are also seeing a meaningful earnings inflection, with NTM EPS rising roughly 70% over that period, ahead of the broader market. The divergence has become more pronounced over the past year as Adopter estimates have accelerated alongside continued strength in the Enablers. In our view, this is the fundamental setup for the barbell: earnings momentum remains strong among select Enablers, but is increasingly extending to Adopters as AI benefits begin to translate into company fundamentals.
(3) Valuation Divergence Is Improving the Risk/Reward on the Adopter Side of the Barbell. While select Enablers will continue to command a valuation premium, reflecting their more established earnings exposure to the AI buildout, Adopter valuations have reset meaningfully over the past year. The median forward P/E for our high-materiality Adopters has fallen to 18x, broadly in line with the MSCI World and well below the 22x multiple for Enablers. In our view, this divergence creates a more attractive entry point for Adopters at a time when the fundamental benefits of AI adoption are beginning to emerge in margins and earnings, but may not yet be fully reflected in out-year expectations. This supports our barbell approach: retain exposure to select Enablers where earnings durability can justify a premium, while increasing exposure to Adopters where improving fundamentals are now paired with a more favorable valuation setup.
(4) AI Exposure Is Broadening Across the Market, but Deepening Remains Concentrated. Broadening captures the expansion of the AI opportunity set, as companies move from no prior AI exposure to positive exposure today, while deepening captures rising AI materiality among companies already exposed to the theme. New AI entrants are increasingly coming from outside the traditional technology complex, with industries such as Real Estate, Autos, Consumer Durables, Transportation, and Consumer Services seeing a meaningful broadening of AI exposure. However, the companies where AI is becoming more material to the investment thesis remain concentrated in areas such as Software, Semiconductors, Utilities, and Media. AI is diffusing across the broader market, creating a growing pool of potential Adopters, while simultaneously deepening within select Enablers and other industries where AI is already material.
(5) The Market Is Increasingly Pricing the Magnitude of AI Exposure. Simply having exposure to AI, either positive or negative, is not enough; the degree to which AI matters to the investment thesis is increasingly driving relative performance. Among enablers and adopters, companies where AI is "core to thesis" have outperformed those where it is "significant" by 107%. The differentiation is even more pronounced among companies exposed to disruption: businesses where AI represents a core threat have underperformed those facing moderate disruption by ~161%. These performance gaps have widened suggesting investors are becoming more discerning about where AI can meaningfully reshape company fundamentals. As we recommend our barbell approach, looking at materiality is critical when assessing which area of adopters to increase exposure to.
What to Own
#1: Adopters: Adopters with Positive Rate of Change
- Rate of Change: Positive Increase in Materiality (eg Moderate to Significant)
- AI Adopter Materiality: Moderate to Core to Thesis
- Exposure: AI Adopter with Adopter Exposure More Significant than Enabler Exposure
- Stock Rating: Overweight Rated by Morgan Stanley Analyst
- Market Cap Ranking: 10 largest stocks by market cap in each region meeting criteria above
#2 Adopters: Adopters with High Pricing Power
- Pricing Power: High
- AI Adopter Materiality: Moderate to Core to Thesis
- Exposure: AI Adopter
- Stock Rating: Overweight Rated by Morgan Stanley Analyst
#3 Enablers: Power Bottlenecks
- Stocks across the Powered Shell Providers (PSPs), Data Center REITs, Off Grid Power Generation, Energy Storage, Renewable Energy Developers, Midstream Energy, and Equipment chosen in coordination with our industry analysts
#4: Enablers: Semis/Hardware Names with Durability
- Stocks across semiconductors/semi equipment, networking, and hard disk drives chosen in coordination with our industry analysts
#5: Enablers: Early Software
- Early cycle software chosen in coordination with our industry analysts
AI's Rate of Return in Stock Performance Charts
About the Data Set:
This is the sixth mapping of our global coverage (~3600 stocks )as it pertains to the speed and breadth of AI. See our prior mapping notes at AI 1.0, AI 2.0, AI 3.0, AI 4.0, and AI 5.0. We surveyed Morgan Stanley Research analysts across the firm's global stock coverage with the following questions:
- How do you currently classify the company's AI exposure? AI Adopter, AI Enabler, AI Challenged. Note: We no longer have an "Enabler/Adopter" category but stocks can't be tagged as both adopters and enablers with varying materiality to each.
- How material do you believe AI will be to the investment thesis in the next 12-24 months? 5 categories from Core to Thesis down to Insignificant.
- How do you perceive this company's pricing power (i.e., ability to retain cost savings vs. passing through to customers)? 3 categories High, Neutral, and Low.
A complete list of Adopters, Enablers and AI Challenged stocks is available upon request: Please click here if you would like to receive that database. In addition to our AI mapping, we also map all stocks under MS coverage on exposure to subthemes within our four key themes: AI & Tech Diffusion, the Multipolar World, Societal Shifts, & the Future of Energy. Please see The Thematic Map - Scaling Themes in AlphaSignals
AI Mapping Data by Sector & Geography
Tech Cycle in Transition: Recommend Selectivity Across the Stack
We're calling for a "barbell" approach as we move deeper into the AI cycle; a broadening into software enablers and AI adopters, while still owning players addressing key data center bottlenecks. In every historical computing cycle, we've seen a similar pattern of stock outperformance across three categories of technology "enablers": semis, infrastructure, then software & services ( Exhibit 30 ). Together, these companies provide the technology stack that enables each computing cycle, from the chips, infrastructure, and devices that form its physical backbone to the software layered on top. There is also a technology diffusion component to each cycle — these companies are the adopters that leverage the new technology to drive productivity gains and revenue growth. We expect the diffusion of AI into other sectors to drive greater outperformance for the adopter category than in tech cycles that preceded it. Why? The exponential scaling of LLM capabilities and AI's full potential productivity impact suggest a broad, and meaningful opportunity for value creation. This is supported by the fact that companies are now citing quantifiable benefits from AI.
Now is the time to start adding exposure to early software enablers and AI adopters. We have clearly seen this outperformance pattern play out this cycle, with semis and infrastructure outperforming the S&P 500 by 500-700 pts. Now, we are calling for a broadening into early software enablers in the US — particularly infrastructure and security software, as well as select applications. We believe services will be a later-cycle beneficiary, once companies find a repeatable path to AI returns that services firms can help implement.
However, we also believe the cycle is different, given 1) the size of the AI capex build-out, and 2) key bottlenecks that are slowing the cycle. We continue to believe the demand for compute is likely to significantly exceed supply for many years to come. As we've written, we see key bottlenecks across the "3 P's" — People, Power, and Politics. We continue to be short power (our team forecasts a 57 GW shortfall in 2026-28, or 33 GW net of time-to-power solutions), labor remains a challenge, and regulatory hurdles are also slowing the pace of the data center buildout (see our work on data center moratoriums). As a result, it's likely we don't see as clear a linear handoff as in past cycles. Thus, we believe it's time to be selective across multiple parts of the stack during this transition period and are recommending continued outperformance of 1) power bottleneck players and select semis & infrastructure names, as well as broadening outperformance of 2) early software enablers and AI adopters.
This cycle is already playing out like others … but faster and larger than before. Since November 2022, we've seen the classic compute cycle playbook materialize — semis have outperformed the S&P 500 by ~700pts, followed by infrastructure outperforming by ~500pts and software/services by ~200pts ( Exhibit 31 ). Compared to past tech cycles, we are seeing faster adoption and larger equity value creation than ever before. AI has penetrated US households at 2x the rate of the Mobile Era, reaching 50% penetration at just three years ( Exhibit 5 ). Semis and infrastructure relative stock performance is already 3-4x higher than peak stock outperformance in the Mobile Era, and we are still in earlier innings ( Exhibit 6 ). We expect to see the same — if not even greater — levels of outperformance for software/services and AI adopters, given the transformational and productivity-enhancing applications of AI.
In past tech cycles, we’ve seen a hardware-to-software handoff when hardware growth slows to single digits. Historically, once hardware penetration reaches maturity, growth begins to slow and we see value creation shift to the software/services built on the underlying technology. An example is the iPhone and Apple Services. Once the iPhone installed base reached ~650M and represented ~60% of the global high-end smartphone installed base, iPhone unit growth began to slow to a CAGR nearing GDP growth ( Exhibit 34 ). Growth then shifted to Services, which compounded at an 18% CAGR in FY16-25. We've also seen similar dynamics play out with smartphones penetration and Meta revenue once global smartphone penetration reached 55%. In both of these cases, there was an inflection in software/services revenue once hardware penetration reached 50- 60% and hardware growth slowed to single digits.
How does this apply to the AI cycle? Our teams forecast a hyperscaler capex growth deceleration in 2028, indicating we could be entering a transition from hardware to applications. Morgan Stanley teams forecast hyperscaler/neocloud capex Y/Y growth decelerating from 93% growth this year to 14% in 2028 ( Exhibit 10 ). This indicates we could soon see a transition from hardware to software/services in 2028, and we expect this shift heading into next year as this is priced in.
We want to be clear that we still expect outperformance of select enablers, as AI bottlenecks are bigger than ever before. We still see power as the key bottleneck preventing faster data center buildout and AI adoption — we forecast as 57 GW shortfall from 2026-28 (translates to a 30-40% power shortfall) and recent data points from PSPs indicate 1) an urgency from customers to energize sites by year-end 2027, and 2) heightened competition for deals with firm power grid access. Local opposition that has delayed or cancelled $286B of US data center projects since 2025 are also slowing the pace of the buildout, with communities fearing rising power bills, environmental issues, and quality of life concerns. These dynamics favor power bottleneck players (including PSPs, data center REITs, off-grid generation / renewables, grid equipment, data center electrical equipment, and others), and this cohort has outperformed the S&P 500 YTD, but underperformed semis and infrastructure by 100pts and 40pts, respectively.
We also expect gains to extend to US software, as enterprise adoption accelerates... We see large growth opportunities across pockets of software/services enablers and expect leadership to rotate into these types of categories, as enterprise adoption rises from low levels. As our US Software team has written here, the software cycle generally progresses from infrastructure software to security and finally applications. We've seen the same dynamics play out this cycle, as companies focus on data infrastructure, observability, and orchestration required to provide models with enterprise context, as well as security due to the recent AI breaches. We expect outperformance to then shift to application software and like select names in this space (more below). We believe an uplift to software enabler names in Europe and Asia will be longer-tailed, given 1) there are less of them in general, and 2) the software names that do exists are skewed more toward applications than infrastructure/security.
...and early adopters, as companies prove they're generating quantifiable benefits and clear market leadership with AI. Today, market cap is concentrated in AI enablers vs. adopters, with over 1/3 of S&P 500 market cap concentrated in the top 10 AI enablers by market cap vs. 1/5 for the top 10 AI adopters. We expect performance to broaden to adopters over time. This is important, as our prior work indicates that companies who have best adopted new technologies have tended to rise ahead of peers and become the largest market cap companies — examples include Ford and GM aggregating share with the assembly line, as well as software players leveraging the internet and iPhone platform ( Exhibit 39 ). Why now? While we have been talking about adopters for a while, it has taken time for enterprise adoption to materialize. We believe we are at the early stages of an inflection point where companies generate tangible ROI from AI adoption. Nearly 25% of the S&P 500 cites quantifiable benefits from AI in earnings transcripts, and consensus expects AI Adopters to deliver 4.6% of EBIT margin expansion in 2025-26, modestly ahead of the S&P 500 and nearly twice the MSCI ACWI ( Exhibit 40 , Exhibit 3, Exhibit 42 ). We believe this adopter trade starts to work when benefits hit the P&L, especially when companies can prove durable revenue and market share leadership due to AI.
Services is the sector where performance is yet to come. While we recommend the shift to software, we are not yet recommending a shift into services. We expect services to be a late cycle beneficiary, as it comes into play when there is accelerated deployment of a technology. In order for services companies to help companies with AI implementations and transformations, we have to see a repeatable path to generating ROI from AI. Given many companies remain in experimentation or pilot phases, this creates a difficult environment for services names, as IT budgets are pressured in non-AI areas.
How to play our call for a "barbell" of outperformance across data center bottlenecks, select semis/infrastructure names, early software enablers, and early AI adopters. Below are our ways to play the transition:
- Power Bottleneck Players ( Exhibit 43 ). We continue to "Power Bottleneck Players" that are addressing data center energization. This group includes the PSPs, data center REITs, off-grid generation / renewables, grid equipment, data center electrical equipment, and others.
- Select Semis/Infrastructure Names We Still Like ( Exhibit 14 ). While power is the biggest bottleneck, we also highlight select AI semis/infrastructure names that are either 1) attacking secondary bottlenecks after power, or 2) can still see outperformance due to idiosyncratic reasons. Semis names (SOX index) have de-rated by from ~8x since late February to ~21x NTM P/E today, and we still see
windows of opportunity with select names and sub-segments like components, substrates, and testing equipment.
- Early Software Beneficiaries ( Exhibit 45 ). For software/services, we expect leadership to rotate into companies monetizing and building new applications on top of AI — this includes the hyperscalers, infrastructure software, security software, and select applications.
- Early AI Adopters ( Exhibit 11 ; Exhibit 12 ). For adopters, please see our AI Adopters Screen — we believe we will see the first value creation across AI Adopters where AI is "Core to Thesis."
Where could we be wrong? The capex cycle proves more durable than we expect — driven by much higher demand or greater bottlenecks — leading to stronger-for-longer semis and infrastructure growth. We have historically underestimated the size of past tech cycles ( Exhibit 46 ), so it's possible that demand continues to exceed supply for many years to come, driving more elevated and consistent capex.
Key Views from Our Tech Teams
US Semiconductors (Joe Moore)
Demand remains exceptionally strong for all of the data center–related end markets, with NVDA/AVGO both looking for 70-100% y/y growth in 2027 from a broad range of spenders. Across hyperscalers, neoclounds, frontier model developers, sovereigns, and enterprise we are not seeing any slowing in AI spend or adoption. Token consumption continues to grow exponentially, and with increased product momentum in agenetic applications (SpaceXAI's Grok Bot and META's Muse) we could see a broadening beyond chat bots and coding agents to broader consumer use cases — further increasing the demand for compute.
Within the enterprise there still remains a long runway for growth, and measures like the Ramp AI index show the average business is still spending very little on AI today. Either enterprise or consumer could be another catalyst for further semiconductor demand, and should serve to lift the multiples of the stocks most pricing in a slowdown, which looks like Nvidia at 14x next year's EPS and Broadcom at 11x its FY28 EPS guidance, with both meaningfully supply constrained at those levels.
However as the constraint to AI increasingly moves to data center land and powered shell availability we expect leadership within semis to shift away from pricing leverage– driven stocks (INTC, MU, SNDK) and toward (1) the highest quality franchises at already trough multiples (Nvidia/Broadcom), along with (2) data center content stories (CBRS, ALAB). To us, Nvidia remains the most attractive, with exposure to all the most exciting content stories: fast inference with groq, agenetic CPU demand, CPO solutions etc. and the most compelling product cycle in Vera Rubin, offering 35x performance improvements vs the current generation Blackwell. Nvidia is also in a position to gain share if lead times come in due to constraints elsewhere, which played out in 2023.
US Internet (Brian Nowak)
Agentic workflows are making compute an increasingly scarce asset… We believe compute itself remains a bottleneck that the hyperscalers will continue being able to monetize. Agentic products that plan, browse and execute multi-step tasks consume materially more compute per user than prompt-and-response chat. The launch of SpaceXAI's Grok Bot and META's Muse, which provision users with a persistent cloud VM that keeps working after closing the app, will add to this dynamic. In effect, single tasks fan out into chained inference calls and tool/browser use (e.g., Meta checking a user's Gmail to know when a flight is so it can order something that ships in time), thus tokens per task continues to rise and GPUs are utilized even without a human in the loop between sessions. As we have written here, this is also a memory story. Agents that carry context across sessions need somewhere to keep that latent state resident, and higher KV cache/long-context requirements raise memory capacity needs at both the chip and rack level...already visible in our bottom-up work, where memory moves from HSD% of our Blackwell rack estimates to ~25% of our Vera Rubin rack cost estimates.
...with the hyperscalers set to 4X compute capacity from '25 to '28. We see ~80 GW of capacity coming online across the hyperscalers and neoclouds in '27/'28 (~38 GW/~44 GW), taking total capacity to ~145 GW by '28, up ~4X from ~36 GW in '25, with GOOGL adding the most in both years (~9 GW/~11 GW). Compute capacity pricing is as simple as supply and demand, and as discussed in Internet: The Paths to 25-50% GenAI ROIC (27 Jul 2026), 1 GW of GB300 rented as IaaS supports $19-27bn of revenue at 25-40% ROIC with ~70% incremental EBIT margins. Total hyperscaler margins will be lower in aggregate due to 1) lower monetizing custom silicon and 2) hardware sales in the case of GOOGL, but we view these businesses as attractive given the demand at the top of the stack.
We prefer META, AMZN, and GOOGL as best ways to play compute scarcity. We believe META has multiple call options not currently reflected in the stock at ~18X our ’28 EPS. We have written about these upside nodes here, and the recent release of Muse (its personal assistant/agent, see here) has made us incrementally positive on their ability to serve their 3.6bn DAUs and address the $30tn TAM of consumer spend across agentic offerings. Beyond META, we believe GOOGL/AMZN/MSFT are all set to benefit from compute pricing remaining high as newer chips (with higher token throughput) come online, as we model 52% hyperscaler revenue growth in ‘27 across AWS/Azure/GCP, up from 43% growth in ’26. Please let us know if interested in our bottoms up capex per GW or capacity deployment models.
US Software (Adam Wood)
AI Value Capture Starts in Infrastructure + Cyber, with Select Application Winners Emerging. Across our software coverage, we continue to prefer Infrastructure Software (MSFT, SNOW, DDOG, NET, DT) and Cybersecurity (PANW, CRWD) as the earlier beneficiaries of the next phase of AI tooling, consistent with our view that the software cycle should progress from infrastructure to security and ultimately applications (see Software: The Moat & The Journey – Assessing Software Durability + Growth in the Age of AI (21 Jul 2026)). As enterprise AI adoption moves into production, we expect spending to first accrue to the data infrastructure, observability and orchestration layers required to provide models with enterprise context, while the proliferation of agents and AI-enabled workflows expands the attack surface and creates incremental security requirements. Further, the infrastructure software vendors are positioned to provide the data/context required by AI applications, while cybersecurity becomes increasingly important in securing agentic workflows. Within Application Software, we remain selective and continue to prefer TEAM, NOW, SHOP, NAVN, and TTAN, where recent results provided increasingly tangible evidence of AI acting as a growth enabler. In particular, TEAM is benefiting from strong cloud momentum and emerging monetization through Collections and its Teamwork Graph, while NOW is among the earliest application vendors showing a measurable AI-driven growth inflection. SHOP has also delivered early evidence that AI interfaces can act as an incremental merchant channel, with internally-developed tools improving merchant performance and productivity. Separately, sentiment has also recently improved around CRM following better-than-expected Q2 results (see Salesforce, Inc.: Q2 Results – Better Leading Indicators, but 2H Execution Still Matters (27 Aug 2026)), given rate of change improvement in leading indicators, stronger AI monetization signals, and greater evidence that its system-of-record and workflow position can remain durable in an agentic world. However, we remain EW as we need more evidence that headless monetization and AI momentum can offset legacy portfolio drags to accelerate overall organic growth.
Regional Views
North America
Andrew Pauker & Michelle Weaver
Our earnings broadening thesis continues to play out. Russell 3000 median stock EPS growth has accelerated to 14%, the strongest level since 2021, while median stock sales growth is running at 8%, around the best level since 2023. At the same time, 88% of S&P 500 companies beat on EPS in 2Q, up from 82% in 1Q. S&P 500 earnings revisions breadth stands at 25%, while 88% of S&P industry groups are now showing positive revisions breadth, with both measures near a cycle high. The key point is that earnings strength is no longer confined to a narrow group of mega-cap stocks. Broader participation at the median-stock level, coupled with improving revisions breadth, remains consistent with our view that the earnings recovery is progressing and should support greater index resilience as the cycle matures.
Against that backdrop, we believe the US equity market is transitioning from an early- to mid-cycle regime. Historically, this is a period when leadership shifts from lower- to higher-quality as cyclically driven operating leverage begins to moderate and the market places greater emphasis on earnings durability, margin stability, free cash flow generation and operational efficiency. The maturation of the business and earnings cycles is consistent with this view, as is a Fed that is moving from active easing toward a more hawkish stance. The shift toward quality should be supportive of the S&P 500 given the index’s quality bias — over 40% of the S&P screens as high quality based on our factor framework. In addition, a greater focus on margin expansion and operational efficiency should continue to favor companies demonstrating tangible benefits from AI adoption.
Work from our thematic research colleagues shows that adoption is moving decisively from experimentation toward measurable enterprise value. In 2Q26, 40% of identified AI adopters cited at least one quantifiable benefit, up from 21% a year earlier and 37% in the prior quarter. Across the broader S&P 500, roughly 25% cited measurable benefits, versus 14% a year ago. Those gains are concentrated in revenue generation, cost savings, capital efficiency and productivity, with the strongest momentum in Technology, Communication
Services and Financials. Companies also reported an average 9.6% net productivity increase over the past year, led by software development, customer service, finance and operations. The labor impact is therefore less about wholesale replacement and more about role redesign, higher revenue per employee and durable operating leverage — suggesting the adoption cycle remains early. See here and here for more.
Interestingly, several industries often viewed as vulnerable — including Transports, Software & Services, and Professional Services — also rank among the more attractive adopter groups in our thematic research team’s AI mapping work and our industry-level analysis. Earnings revisions are beginning to reflect this opportunity across the market, with forward margin expectations improving most clearly for companies where AI is material to the investment thesis and pricing power is neutral to strong. We continue to view AI adoption as an important source of earnings growth and operating leverage, with roughly 100 basis points of net margin expansion through 2027 expected to be tied to adoption.
Our factor work continues to show quality-oriented factors such as high free cash flow yield and low accruals leading the market. Interestingly, factors tied to operating efficiency are also seeing clear outperformance, with the high sales-per-employee factor up 5% over the last month. In our view, this ties directly to our constructive call on AI adopters. Consistent with this rotation, the closely followed momentum factor is also seeing its leadership shift toward quality and toward services-oriented, asset-light and fee-based industries. Unsurprisingly, these industry groups are also among the best-positioned areas of the market from an AI adoption perspective.
Survey Highlights
This round of the AI Mapping, we are able to view exposure to AI on a more flexible level, allowing for any combination of Adopter, Enabler and/or Challenged exposure. Across North American names, AI exposure is close to universal, only ~11% carry no tag at all. Adoption is the dominant lens: 84% are flagged as Adopters in some combination, versus 37% as Enablers and ~29% as Challenged. The single largest bucket by count is pure Adopter at 38%, followed by Adopter + Enabler (19%), Adopter + Enabler + Challenged (14%) and Adopter + Challenged (13.5%). Pure Enabler and pure Challenged are thin tails, and Enabler + Challenged without adoption is empty.
On a market-cap weighted basis the picture shifts materially. Weighting by cap, Adopter + Enabler jumps to ~40% of the universe (from 19% by count) and the triple-tagged bucket to 21% (from 14%), while pure Adopter falls to 29% from 38%. In other words, the largest companies are the ones sitting in multiple AI roles enabling and adopting simultaneously and the single-role names skew small. Mega-cap tech continues to dominate the multi-role cohorts: NVDA, AAPL, META, and AVGO are examples of Adopters + Enablers. These multi exposures make it critical to also assess materiality when thinking about a company's role within the broader AI landscape.
The same size skew also appears in the materiality ranking. Core to Thesis covers just 12% of names but ~44% of market cap; adding Significant, roughly a quarter of the universe by count accounts for 58% of capitalization. Companies with large market caps are also the ones with AI as an important part of their investment thesis. The bulk of names sit lower down: Moderate is the largest bucket at 36% of count and 21% of cap, and Insignificant at ~25% and 17%.
Looking at the materiality attached to Challenged exposure tells a different story from the headline ranking. Twenty-nine percent of the universe, 25% of market cap, carries a Challenged tag, but the severity assigned to that disruption risk is overwhelmingly low. Insignificant is the single largest at 38% of Challenged names, and Wildcard accounts for a further 26%, together roughly two thirds of the cohort. Only 18% of the Challenged group are rated Significant or Core to Thesis on the Challenged axis, and those represent under 6% of Challenged market cap. In other words, analysts are willing to flag that AI could hurt a business far more often than they are willing to underwrite it as a base-case driver of the investment thesis. The market-cap cut sharpens the point. Wildcard alone is 50% of Challenged market cap and Moderate a further 34%.
Materiality matters for performance. Among positively exposed cohorts, Core to Thesis AI Adopters in North America have continued to outperform those Significant. The performance gap began in earnest after the liberation day lows seen last year.Companies are becoming more effective at communicating the benefits from AI adoption and the benefits are becoming more tangible to drive this performance spread.
Pricing Power Continues to Be a Differentiator. Adopters and/or Enablers with Core to Thesis or Significant materiality that also have high or neutral pricing power continue to outperform those with low pricing power.
Asia & EM
Daniel Blake & Kristal Ji
Previously Identified AI Enablers and Enabler/Adopters Have Led YTD Performance in Asia/EM
We revisit the stocks identified in our January 2026 AI mapping survey (Version 5) to assess subsequent performance. The companies previously identified by our analysts as AI enablers and enabler/adopters have outperformed the MSCI AC APAC benchmark, while broader AI adopters (stocks that have at least a "Moderate" materiality to AI adoption in the AI mapping survey, regardless of pricing power) and AI-disrupted stocks have underperformed. This reflects the concentration of the Asia/EM indices in AI supply chain beneficiaries, where strong performance to 22 June drove index returns.
Previously identified AI enablers in Asia/EM continue to command a clear valuation premium, trading at ~20x NTM P/E versus ~16x for the MSCI AC APAC benchmark. Enablers saw the strongest re-rating through June before consolidating thereafter, while enabler/adopters followed a similar, albeit less pronounced, trajectory. Broader AI adopters have modestly de-rated YTD and now trade below the benchmark, while AI-disrupted stocks have seen further multiple compression and remain at a significant discount.
Selectively Broaden Exposure to AI Adoption Beneficiaries, While Retaining Core AI Enablers
In Asia Thematic Alpha: Broaden Exposures to AI Adoption Beneficiaries, but Hold onto Core AI Enablers (4 Sep 2026), we shared our views around broadening out which are seen in developed markets, particularly the US. In Asia/EM, however, earnings momentum remains concentrated in upstream/capex beneficiaries, while revisions for consumer/services remain broadly negative.
AI adopters are beginning to show signs of improving earnings momentum, supporting our view to retain core exposure to AI enablers while selectively broadening into adoption beneficiaries that appear ahead of peers in monetization (more details below). Our new Asia Pacific sub-theme ranking framework reinforces this view. In this framework, we incorporated the latest thematic tagging database to capture the up-to-date classificaion by our analysts; see Global Thematics: The Thematic Map – Scaling Themes into AlphaSignals (16 Aug 2026).
AI & Tech Diffusion sub-themes rank broadly in the top third of our framework, supported by strong growth and profitability profiles. Upstream themes, including AI Compute Infrastructure and Powering AI, generally trade at higher valuations, but continue to benefit from strong sales and earnings estimate revisions. Specifically, earning revision breadth for AI Compute Infrastructure continues to trend higher, while valuations have de-rated through the recent crowding unwind between late June to early August.
AI Adopters are trading at cheaper valuations than most sub-themes, with NTM P/E of 17.2x standing at the 16th percentile of its 10-year history. Recent earnings have surprised on the upside, according to our analysts, and both revenue and earnings have been raised over the last three months. Technically, AI Adopters have also just marked a golden cross on a 13w/52w technical.
Increasing Transcript Mentions of Quantifiable Benefits from AI Adoption in Asia/EM, with Broadening Revenue Contribution
We screen company commentary or transcripts in Asia/EM to look for quantifiable benefits from AI adoption.
The trajectory has shifted from early exploratory phases in 2023 to systematic cost savings and revenue expansion in 2026. Analysis of corporate earnings transcripts, filings, and investor disclosures across thousands of listed equities in Asia/EM reveals an exponential rise in transcript mentions that report specific metrics such as labor hours saved, cost reductions, expansion in output per employee, and incremental revenue contributions. Total quantifiable mentions expanded from approximately 100 in 1Q23 to 2,950 in 2Q26, reflecting a fundamental shift toward agentic AI deployment and outcome-based monetization.
AI benefits are broadening from cost savings to revenue growth: Early corporate AI adoption was primarily focused on efficiency, with margin and cost savings accounting for ~75% of quantifiable AI benefit mentions in early 2023. That mix is gradually shifting: revenue benefits have increased from 25% of mentions in 1Q23 to 40% in 2Q26 (Exhibit 13), suggesting that companies are increasingly moving beyond cost optimization toward using AI to support revenue growth.
We see this evolution playing out in two phases across Asia/EM corporates:
Early-Stage Cost Optimization (2023–24): Initial deployment centered on internal productivity, workflow automation, and labor optimization. Enterprise surveys indicate that early AI adopters achieved EBIT margin expansion through process automation, software engineering acceleration, and reduced customer support handle times.
Monetization and Revenue Expansion (2025–26): Top-line benefit mentions expanded from 25% of total quarterly citations in 1Q23 to 40% by 2Q26. Rather than focusing on headcount reductions, corporate commentary increasingly emphasizes "revenue-headcount decoupling," where existing teams leverage AI toolkits to drive higher average revenue per employee and reallocation of workflows to higher-ROI tasks.
Asia/EM AI Adoption Leaders v4
In the Thematic Alpha report, we utilized our thematic tagging framework to filter for companies identified as AI Adopters with Core to Thesis, Significant, or Moderate materiality. We also narrowed the universe to those with a market cap over US$5bn and Overweight or Equal-weight ratings by Morgan Stanley analysts. During the process, we further looked into each key vertical and worked with our industry analysts to select the companies that are ahead of peers in AI adoption and more likely to monetize on AI Adoption, via productivity benefits and/or revenue expansion.
The list trades with a median stock valuation of 19.6x NTM P/E, representing a 23% premium to the index on an equivalent equal-weighted basis.
For prior versions 1-3, please see:
- Mapping AI Adoption and Disruption Opportunities (11 Feb 2026)
- Asia/EM AI Mapping - Adoption Leaders Emerging, but Pricing Power Crucial (27 Aug 2025)
- Asia/EM AI Adoption Leaders - Identifying 38 Potential Winners in the next Phase of AI Diffusion (10 Mar 2025)
Europe
Leoni Externest, Marina Zavolock & Regiane Yamanari
Europe's AI exposure is relatively skewed toward adoption. ~40% of MSCI Europe has material exposure to AI adoption (excluding AI Enablers & AI Challenged), if we also include those with "Insignificant" AI Adoption exposure, this rises to ~60% of the index by weight. By contrast, the S&P 500 has less than 30% exposure to "pure play" AI adopters on the same basis (excluding Mag 7 stocks). Europe's enabler exposure is narrower but still meaningful at just over a fifth of the index (and growing) — its composition is very different to the US, weighted toward the physical layer of the AI buildout in Semis, Cap Goods, Utilities and Mining rather than the hyperscalers. The AI Challenged group is the smallest of the three at under 6% of index weight, though close to double the S&P 500 on a comparable basis, and sits mainly in Software, Business Services, and Medtech.
The percent of stocks with material exposure to AI continues to rise, with just over half of European companies having at least "moderate" exposure to AI, compared with around a third in our last survey, and just over 10% in our first survey. The proportion of companies classified as AI Challenged has seen the most significant increase, although this is largely due to a change in methodology — the three groups are no longer mutually exclusive, so analysts can now flag a company as both a beneficiary of, and potentially disrupted by, AI. This is evident in the sector data, with Media, Discretionary Retail, Transport, Business Services and Div Fins sectors all carrying a large proportion of companies tagged as both AI Adopters and AI challenged, as well as Software where ~90% of companies are tagged in all three categories.
Materiality has deepened alongside the broadening in exposure. AI is at least moderately material for 51% of European companies, versus 43% in the last survey and 24% in the first, with the Insignificant bucket shrinking by a comparable amount. Core to Thesis remains a small group but has continued to rise with each survey. At the sector level, Semis and Software maintain the most material AI exposure, while Tobacco, Luxury, HPC, and Energy sectors consist of stocks with insignificant or no exposure to AI.
Performance has tracked the survey categories closely, with enablers ahead and challenged stocks lagging. Enablers have led European performance since the start of last year and widened that lead through to a peak in June, with performance rolling over more recently amid the momentum unwind. Adopters are only modestly ahead of the index, while Challenged stocks are the clear laggards, albeit rising off lows in recent months.
Earnings tell the same story and are still moving in favour of Enablers. NTM EPS for AI Enablers has grown at roughly three times the pace of the index since the end of 2022 and continues to move higher. Meanwhile, earnings trends for AI Adopters have recently reversed, with the group's NTM EPS underperforming relative to the index since the start of the year. Similarly, earnings for AI Challenged stocks have been trending downward on a relative basis over the last 12 months. Earnings revision breadth is positive for all three groups, with Enablers ahead of the index and Adopters broadly in line. Valuation shows a similar story, with Enablers trading at a clear premium, albeit below the peak reached earlier this year, and Adopters broadly in line with MSCI Europe. The largest move has been in the AI Challenged group, which has de-rated from a substantial premium to roughly market multiples.
Other companies mentioned: Adobe (ADBE.O, US$238), Akamai Technologies (AKAM.O, US$118), Appian (APPN.O, US$37), Astera Labs (ALAB.O, US$363), Cerebras Systems (CBRS.O, US$212), HubSpot (HUBS.N, US$218), Intel (INTC.O, US$124), Intuit (INTU.O, US $292), Micron Technology (MU.O, $1,096), Salesforce (CRM.N, US$233), SanDisk (SNDK.O, US$1,887), SAP SE (SAP.N, US$211), Toast (TOST.N, $30), and Workday (WDAY.O, $188).
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Analyst Certification The following analysts hereby certify that their views about the companies and their securities discussed in this report are accurately expressed and that they have not received and will not receive direct or indirect compensation in exchange for expressing specific recommendations or views in this report: Daniel K Blake; Stephen C Byrd; Leoni Externest, CFA; Anna Feldman; Sabrina E Hao; Qingyi Huang; Kristal Ji; Brian Nowak, CFA; Andrew B Pauker; Elizabeth Porter, CFA; Chris Quintero; Sanjit K Singh; Michelle M. Weaver, CFA; Adam Wood; Regiane Yamanari; Marina Zavolock; Wen Zhang, CFA. . Global Research Conflict Management Policy Morgan Stanley Research has been published in accordance with our conflict management policy, which is available at www.morganstanley.com/institutional/research/conflictpolicies. A Portuguese version of the policy can be found at www.morganstanley.com.br Important Regulatory Disclosures on Subject Companies The analyst or strategist (or a household member) identified below owns the following securities (or related derivatives): Daniel K Blake - Alphabet Inc.(common or preferred stock), Hut 8 Mining Corp.(common or preferred stock), Riot Platforms Inc.(common or preferred stock), Snowflake Inc.(common or preferred stock); Stephen C Byrd - Apple, Inc.(common or preferred stock); Jonathan Eisenson - Adobe Inc.(common or preferred stock), Bloom Energy Corp.(common or preferred stock), Micron Technology Inc.(common or preferred stock), NVIDIA Corp.(common or preferred stock); Sabrina E Hao - Amazon.com Inc(common or preferred stock), Apple, Inc.(common or preferred stock), ASML Holding NV(GDR), Microsoft(common or preferred stock), NextEra Energy Inc(common or preferred stock), NVIDIA Corp.(common or preferred stock), Taiwan Semiconductor Manufacturing Co Lt(GDR); Qingyi Huang - Eli Lilly & Co.(common or preferred stock), Meta Platforms Inc(common or preferred stock), Micron Technology Inc.(common or preferred stock), NVIDIA Corp.(common or preferred stock), RTX Corp(common or preferred stock); Nikhil Javeri - SanDisk Corporation.(common or preferred stock), Seagate Technology(common or preferred stock); Kristal Ji - Amazon.com Inc(common or preferred stock), ASML Holding NV(GDR), Eli Lilly & Co.(common or preferred stock), Micron Technology Inc.(common or preferred stock); Brian Nowak, CFA - Bloom Energy Corp.(common or preferred stock), Micron Technology Inc.(common or preferred stock), NVIDIA Corp.(common or preferred stock), Seagate Technology(common or preferred stock); Andrew B Pauker - RTX Corp(common or preferred stock); Elizabeth Porter, CFA - Micron Technology Inc.(common or preferred stock), Microsoft(common or preferred stock); Chris Quintero - Bloom Energy Corp.(common or preferred stock), Broadcom Inc.(common or preferred stock), Cummins Inc(common or preferred stock), RTX Corp(common or preferred stock), Seagate Technology(common or preferred stock), Walmart Inc(common or preferred stock); Sanjit K Singh - Alphabet Inc.(common or preferred stock); Marina Zavolock - Bloom Energy Corp.(common or preferred stock), Hut 8 Mining Corp.(common or preferred stock), Micron Technology Inc.(common or preferred stock), NVIDIA Corp.(common or preferred stock), TeraWulf Inc(common or preferred stock). As of August 31, 2026, Morgan Stanley beneficially owned 1% or more of a class of common equity securities of the following companies covered in Morgan Stanley Research: Adobe Inc., Advantest, AIA Group Ltd, Airbus SE, Akamai Technologies, Inc., Alibaba Group Holding, Alphabet Inc., Amazon.com Inc, Appian Corp, Apple, Inc., AppLovin Corp, ASML Holding NV, Aspeed Technology, Astera Labs Inc, AstraZeneca Plc, Atlassian Corporation PLC, AXA, Bayer AG, BHP Group Ltd, Bloom Energy Corp., Broadcom Inc., Cerebras Systems, Cipher Mining Inc., Clearway Energy Inc, Cloudflare Inc, Coca-Cola Co., Contemporary Amperex Technology Co. Ltd., Costco Wholesale Corp, CrowdStrike Holdings Inc, Cummins Inc, Datadog, Inc., Delta Electronics Inc., Dynatrace Inc, Eli Lilly & Co., ENGIE, Equinix Inc., Furukawa Electric, GE Aerospace, GE Vernova, Home Depot Inc, HubSpot, Inc., Hut 8 Mining Corp., Ibiden, Informa, Intel Corporation, Intuit, Kao, KIOXIA Holdings, Legrand, Liberty Energy, Lowe's Companies Inc, Lumentum Holdings Inc, MasterCard Inc, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., Micron Technology Inc., Microsoft, MiniMax, Mitsui Kinzoku, MongoDB Inc, Montage Technology Co Ltd, Murata Manufacturing, Nan Ya PCB, Naturgy, NextEra Energy Inc, NIO Inc., Nokia Oyj, Northrop Grumman Corp., Novartis AG, Nu Holdings Ltd., NVIDIA Corp., Okta, Inc., Palo Alto Networks Inc, Philip Morris International Inc, Ping An Insurance Group Co of China Ltd, Procter & Gamble Co., Rakuten Bank, Rexel S.A., Riot Platforms Inc., Rolls-Royce Holdings PLC, RTX Corp, Salesforce, Inc., Samsung Electro-Mechanics, SanDisk Corporation., SAP SE, Sartorius Stedim Biotech SA, Schneider Electric, Sea Ltd, Seagate Technology, ServiceNow Inc, ServiceTitan Inc, Shopify Inc, Siemens Energy AG, SK hynix, Snowflake Inc., Soitec SA, Solaris Energy Infrastructure, Sony Group, Standard Chartered, Standard Chartered Bank, Taiwan Semiconductor Manufacturing Co Lt, Target Corp, TeraWulf Inc, Toast, Inc., Unimicron, Visa Inc., Walmart Inc, Welltower Inc., Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp. 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MasterCard Inc, MediaTek, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., MiniMax, Montage Technology Co Ltd, Navan Inc, NEC, NextEra Energy Inc, NVIDIA Corp., Philip Morris International Inc, Procter & Gamble Co., Salesforce, Inc., Samsung Electronics, SAP SE, Schneider Electric, ServiceNow Inc, Solaris Energy Infrastructure, Sony Group, Standard Chartered, Standard Chartered Bank, Tencent Holdings Ltd., TeraWulf Inc, Unimicron, Walmart Inc, Williams Companies Inc, WuXi AppTec Co Ltd. Within the last 12 months, Morgan Stanley has received compensation for investment banking services from Airbus SE, Akamai Technologies, Inc., Alibaba Group Holding, Alphabet Inc., Amazon.com Inc, AstraZeneca Plc, AXA, Bank of China Limited, Bayer AG, Bloom Energy Corp., Broadcom Inc., Cerebras Systems, Cipher Mining Inc., Clearway Energy Inc, Cloudflare Inc, Coca-Cola Co., Contemporary Amperex Technology Co. Ltd., Eli Lilly & Co., ENGIE, Equinix Inc., ERock Inc, GE Aerospace, GE Vernova, Hitachi, HSBC Holdings, HubSpot, Inc., Informa, Innio N.V., Intel Corporation, Intuit, Liberty Energy, LY Corporation, MasterCard Inc, MediaTek, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., Micron Technology Inc., Microsoft, Midea Group Co Ltd., MiniMax, Montage Technology Co Ltd, Navan Inc, NEC, NextEra Energy Inc, Nu Holdings Ltd., NVIDIA Corp., Philip Morris International Inc, Ping An Insurance Group Co of China Ltd, Procter & Gamble Co., Riot Platforms Inc., Salesforce, Inc., SAP SE, Schneider Electric, Seagate Technology, ServiceNow Inc, Singapore Telecom, Solaris Energy Infrastructure, Sony Group, Standard Chartered, Standard Chartered Bank, Tencent Holdings Ltd., TeraWulf Inc, Unimicron, Walmart Inc, Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp. 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Ltd., Costco Wholesale Corp, CrowdStrike Holdings Inc, Cummins Inc, CXMT Corp, Datadog, Inc., DBS Group Holdings, Delta Electronics Inc., Discovery Limited, Eli Lilly & Co., ENGIE, Equinix Inc., ERock Inc, Fast Retailing, Fujitsu, Furukawa Electric, GE Aerospace, GE Vernova, HD Hyundai Electric Co Ltd, Hitachi, Home Depot Inc, Hon Hai Precision, HSBC Holdings, HubSpot, Inc., Hut 8 Mining Corp., Ibiden, Industrial Bank Co. Ltd., Informa, Innio N.V., Intel Corporation, Intuit, Japan Tobacco, JSW Energy Limited, Kao, KE Holdings Inc, King Yuan Electronics Co Ltd, KIOXIA Holdings, Legrand, Liberty Energy, Lowe's Companies Inc, Lumentum Holdings Inc, LY Corporation, MasterCard Inc, MediaTek, Meitu Inc, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., Micron Technology Inc., Microsoft, Midea Group Co Ltd., MiniMax, Mitsui Kinzoku, MongoDB Inc, Montage Technology Co Ltd, Murata Manufacturing, Naturgy, Navan Inc, NEC, NextEra Energy Inc, NIO Inc., Nokia Oyj, Northrop Grumman Corp., Novartis AG, Nu Holdings Ltd., NVIDIA Corp., Okta, Inc., Palo Alto Networks Inc, Philip Morris International Inc, Ping An Insurance Group Co of China Ltd, Postal Savings Bank of China Co Ltd, Procter & Gamble Co., Rakuten Bank, Recruit Holdings, Riot Platforms Inc., Rolls-Royce Holdings PLC, RTX Corp, Salesforce, Inc., Samsung Electronics, Samsung SDI, SanDisk Corporation., SAP SE, Sartorius AG, Schneider Electric, Sea Ltd, Seagate Technology, ServiceNow Inc, ServiceTitan Inc, Shopify Inc, Siemens Energy AG, Singapore Telecom, SK hynix, Snowflake Inc., Soitec SA, Solaris Energy Infrastructure, Sony Group, Standard Chartered, Standard Chartered Bank, Taiwan Semiconductor Manufacturing Co Lt, Target Corp, Tencent Holdings Ltd., TeraWulf Inc, Toast, Inc., Unimicron, Visa Inc., Walmart Inc, Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp, Z.AI CO., LTD.. 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Ltd., Informa, Innio N.V., Intel Corporation, Intuit, KE Holdings Inc, King Yuan Electronics Co Ltd, Legrand, Liberty Energy, Lowe's Companies Inc, Lumentum Holdings Inc, LY Corporation, MasterCard Inc, MediaTek, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., Micron Technology Inc., Microsoft, Midea Group Co Ltd., MongoDB Inc, Naturgy, NextEra Energy Inc, Nokia Oyj, Northrop Grumman Corp., Novartis AG, Nu Holdings Ltd., NVIDIA Corp., Palo Alto Networks Inc, Philip Morris International Inc, Ping An Insurance Group Co of China Ltd, Postal Savings Bank of China Co Ltd, Procter & Gamble Co., Rolls-Royce Holdings PLC, RTX Corp, Salesforce, Inc., Samsung Electronics, Samsung SDI, SAP SE, Schneider Electric, Sea Ltd, Seagate Technology, ServiceNow Inc, ServiceTitan Inc, Siemens Energy AG, Snowflake Inc., Sony Group, Standard Chartered, Standard Chartered Bank, Taiwan Semiconductor Manufacturing Co Lt, Target Corp, Tencent Holdings Ltd., TeraWulf Inc, Toast, Inc., Visa Inc., Walmart Inc, Welltower Inc., Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp. 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Ltd., Costco Wholesale Corp, CrowdStrike Holdings Inc, Cummins Inc, CXMT Corp, Datadog, Inc., DBS Group Holdings, Delta Electronics Inc., Discovery Limited, Dynatrace Inc, Eli Lilly & Co., ENGIE, Equinix Inc., ERock Inc, Fast Retailing, Fujitsu, Furukawa Electric, GE Aerospace, GE Vernova, HD Hyundai Electric Co Ltd, Hitachi, Home Depot Inc, Hon Hai Precision, HSBC Holdings, HubSpot, Inc., Hut 8 Mining Corp., Ibiden, Industrial Bank Co. Ltd., Informa, Innio N.V., Intel Corporation, Intuit, Japan Tobacco, JSW Energy Limited, Kao, KE Holdings Inc, King Yuan Electronics Co Ltd, KIOXIA Holdings, Legrand, Liberty Energy, Lowe's Companies Inc, Lumentum Holdings Inc, LY Corporation, MasterCard Inc, MediaTek, Meitu Inc, Mercadolibre Inc., Meta Platforms Inc, MetLife Inc., Micron Technology Inc., Microsoft, Midea Group Co Ltd., MiniMax, Mitsui Kinzoku, MongoDB Inc, Montage Technology Co Ltd, Murata Manufacturing, Naturgy, Navan Inc, NEC, NextEra Energy Inc, NIO Inc., Nokia Oyj, Northrop Grumman Corp., Novartis AG, Nu Holdings Ltd., NVIDIA Corp., Okta, Inc., Palo Alto Networks Inc, Philip Morris International Inc, Ping An Insurance Group Co of China Ltd, Postal Savings Bank of China Co Ltd, Procter & Gamble Co., Rakuten Bank, Recruit Holdings, Riot Platforms Inc., Rolls-Royce Holdings PLC, RTX Corp, Salesforce, Inc., Samsung Electronics, Samsung SDI, SanDisk Corporation., SAP SE, Sartorius AG, Schneider Electric, Sea Ltd, Seagate Technology, ServiceNow Inc, ServiceTitan Inc, Shopify Inc, Siemens Energy AG, Singapore Telecom, SK hynix, Snowflake Inc., Soitec SA, Solaris Energy Infrastructure, Sony Group, Standard Chartered, Standard Chartered Bank, Taiwan Semiconductor Manufacturing Co Lt, Target Corp, Tencent Holdings Ltd., TeraWulf Inc, Toast, Inc., Unimicron, Visa Inc., Walmart Inc, Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp, Z.AI CO., LTD.. 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Inc, Siemens Energy AG, SK hynix, Snowflake Inc., Sony Group, Standard Chartered, Standard Chartered Bank, Taiwan Semiconductor Manufacturing Co Lt, Target Corp, Tencent Holdings Ltd., TeraWulf Inc, Toast, Inc., Visa Inc., Walmart Inc, Welltower Inc., Williams Companies Inc, Workday Inc, WuXi AppTec Co Ltd, Xiaomi Corp. An employee, director or consultant of Morgan Stanley is a director of Cummins Inc, MetLife Inc., Walmart Inc. This person is not a research analyst or a member of a research analyst's household. Morgan Stanley & Co. LLC makes a market in the securities of Appian Corp, Clearway Energy Inc, Cummins Inc, Equinix Inc., MetLife Inc., Northrop Grumman Corp., Solaris Energy Infrastructure, Welltower Inc.. Morgan Stanley & Co. International plc is a corporate broker to AstraZeneca Plc, HSBC Holdings, Informa, Rolls-Royce Holdings PLC. The equity research analysts or strategists principally responsible for the preparation of Morgan Stanley Research have received compensation based upon various factors, including quality of research, investor client feedback, stock picking, competitive factors, firm revenues and overall investment banking revenues. Equity Research analysts' or strategists' compensation is not linked to investment banking or capital markets transactions performed by Morgan Stanley or the profitability or revenues of particular trading desks. Morgan Stanley and its affiliates do business that relates to companies/instruments covered in Morgan Stanley Research, including market making, providing liquidity, fund management, commercial banking, extension of credit, investment services and investment banking. Morgan Stanley sells to and buys from customers the securities/instruments of companies covered in Morgan Stanley Research on a principal basis. Morgan Stanley may have a position in the debt of the Company or instruments discussed in this report. Morgan Stanley trades or may trade as principal in the debt securities (or in related derivatives) that are the subject of the debt research report. Certain disclosures listed above are also for compliance with applicable regulations in non-US jurisdictions. STOCK RATINGS Morgan Stanley uses a relative rating system using terms such as Overweight, Equal-weight, Not-Rated or Underweight (see definitions below). Morgan Stanley does not assign ratings of Buy, Hold or Sell to the stocks we cover. Overweight, Equal-weight, Not-Rated and Underweight are not the equivalent of buy, hold and sell. Investors should carefully read the definitions of all ratings used in Morgan Stanley Research. In addition, since Morgan Stanley Research contains more complete information concerning the analyst's views, investors should carefully read Morgan Stanley Research, in its entirety, and not infer the contents from the rating alone. In any case, ratings (or research) should not be used or relied upon as investment advice. An investor's decision to buy or sell a stock should depend on individual circumstances (such as the investor's existing holdings) and other considerations. Global Stock Ratings Distribution (as of August 31, 2026) The Stock Ratings described below apply to Morgan Stanley's Fundamental Equity Research and do not apply to Debt Research produced by the Firm. For disclosure purposes only (in accordance with FINRA requirements), we include the category headings of Buy, Hold, and Sell alongside our ratings of Overweight, Equal-weight, Not-Rated and Underweight. Morgan Stanley does not assign ratings of Buy, Hold or Sell to the stocks we cover. Overweight, Equal-weight, Not-Rated and Underweight are not the equivalent of buy, hold, and sell but represent recommended relative weightings (see definitions below). To satisfy regulatory requirements, we correspond Overweight, our most positive stock rating, with a buy recommendation; we correspond Equal-weight and Not-Rated to hold and Underweight to sell recommendations, respectively.
Data include common stock and ADRs currently assigned ratings. Investment Banking Clients are companies from whom Morgan Stanley received investment banking compensation in the last 12 months. Due to rounding off of decimals, the percentages provided in the "% of total" column may not add up to exactly 100 percent. Analyst Stock Ratings Overweight (O or Over) - The stock's total return is expected to exceed the total return of the relevant country MSCI Index or the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis over the next 12-18 months. Equal-weight (E or Equal) - The stock's total return is expected to be in line with the total return of the relevant country MSCI Index or the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis over the next 12-18 months. Not-Rated (NR) - Currently the analyst does not have adequate conviction about the stock's total return relative to the relevant country MSCI Index or the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months. Underweight (U or Under) - The stock's total return is expected to be below the total return of the relevant country MSCI Index or the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months. Unless otherwise specified, the time frame for price targets included in Morgan Stanley Research is 12 to 18 months. Analyst Industry Views Attractive (A): The analyst expects the performance of his or her industry coverage universe over the next 12-18 months to be attractive vs. the relevant broad market benchmark, as indicated below. In-Line (I): The analyst expects the performance of his or her industry coverage universe over the next 12-18 months to be in line with the relevant broad market benchmark, as indicated below. Cautious (C): The analyst views the performance of his or her industry coverage universe over the next 12-18 months with caution vs. the relevant broad market benchmark, as indicated below. Benchmarks for each region are as follows: North America - S&P 500; Latin America - relevant MSCI country index or MSCI Latin America Index; Europe - MSCI Europe; Japan - TOPIX; Asia - relevant MSCI country index or MSCI sub-regional index or MSCI AC Asia Pacific ex Japan Index.
Stock Price, Price Target and Rating History (See Rating Definitions)
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