MS - DeepSeek and US Power Infrastructure Implicationspdf
MS - DeepSeek and US Power Infrastructure Implicationspdf
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  1. M IdeaFuture of Energy | North AmericaDeepSeek: US Power Infrastructure ImplicationsThe DeepSeek news caused an over-reaction among US stocks with exposure to AI growth. We highlight data points indicative of strong AI inference growth, show the (low) degree of AI upside priced into many stocks, and focus on key open questions that will drive our views on US AI infrastructure. Morgan Stanley & Co. LLCStephen C ByrdEquity StrategistStephen.Byrd@morganstanley.com+1 212 761-3865Brenda DuverceEquity StrategistBrenda.Duverce@morganstanley.com+1 212 761-4772David Arcaro, CFAEquity AnalystDavid.Arcaro@morganstanley.com+1 212 761-1817Andrew S PercocoEquity AnalystAndrew.Percoco@morganstanley.com+1 212 296-4322Christopher Snyder, CFAEquity AnalystChris.Snyder@morganstanley.com+1 212 761-4470Angel CastilloEquity AnalystAngel.Castillo@morganstanley.com+1 212 761-1931Brian Nowak, CFAEquity AnalystBrian.Nowak@morganstanley.com+1 212 761-3365Matt BombasseiResearch AssociateMatt.Bombassei@morganstanley.comThe DeepSeek news (LLMs developed by a Chinese startup at a reportedly low cost of training, and with a range of AI inference efficiencies) drove a massive correction among many stocks with exposure to US AI infrastructure growth. Key points we explore within this note: (i) our analysis of the US data center pipeline suggests the vast majority of the known pipeline is AI Inference and non-AI use cases, rather than AI Training (as our colleagues have noted, there are strong arguments that AI Inference demand will grow rapidly), (ii) based on our analyses contained in our Powering GenAI model, we see the cost of compute (capital cost of data centers, divided by compute power measured in teraFLOPs) falling by ~90% over the next 6 years, resulting in a “Jevons Paradox” in which underlying demand for AI compute will rise rapidly as AI adoption grows, (iii) our discussions with corporates shows recent evidence of significant AI infrastructure spend in the US (Stargate is the most visible of many large AI infrastructure projects that we expect to see), (iv) post the sell-off we experienced today, many stocks with exposure to US AI infrastructure growth are pricing in little AI growth. We also highlight key questions we are focused on as we continue our assessment of the implications of DeepSeek on the growth in US AI infrastructure, and (v) ~60% of our forecasted US power demand growth comes from non-AI drivers (onshoring of manufacturing, electrification, EV’s, etc.), which are not impacted by these recent developments.Overview of DeepSeek: Last week, Chinese startup DeepSeek released two models (DeepSeek-R1-zero and DeepSeek-R1) which explored the potential for LLMs to Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision.For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report.Key TakeawaysWe believe the market reaction among US power names with AI exposure is overdone, and highlight stocks with mis-priced AI infra growth upside.US AI infrastructure will continue to grow rapidly, in our view - we include analyses indicative of a high % of US data centers focused on non-training tasks.We expect to see large US data center announcements, using a range of nuclear and natural gas-fired power, with renewables to reduce net carbon emissions.We developed an analysis of the cost of compute, which shows a ~90% reduction in a 6 year period, in turn driving rapid growth in AI adoption (and inference).Within this note, we highlight key uncertainties/open questions that could drive us to be more pessimistic on the magnitude of AI infrastructure growth.January 28, 2025 04:58 AM GMT
  2. M Idea2develop reasoning capabilities without any supervised data, focusing on self-evolution through a pure reinforcement learning process. Notably, DeepSeek-R1-zero has reached performance levels comparable to OpenAI-o1 (using solely reinforcement learning in post training) and at a substantially lower cost to inference as reflected in pricing. DeepSeek-v3 (the base model for DeepSeek-R1-zero and DeepSeek R-1) was reportedly trained on an estimated $5.6m of compute based on 2.8mn H800 GPU hours (substantially lower than the ~30.8mn H100 GPU hours required to train Meta Llama 3 405B). While the actual cost of training is open for debate, this level of training and GPU time cost reduction (driven by Multi-head latent attention, MoE, FP8 Mixed Precision training, GRPO, etc.) is set to be an important driver of faster LLM-enabled and GenAI product innovation, consumer and enterprise adoption, and GenAI enabled ROIC and economic productivity to come. For more on our on analysis of GenAI early adoptions and diffusion winners from here, please see our latest work in Uncovering Alpha in AI's Rate of Change.It is critical to understand the capex intentions of the major LLM developers –what is the magnitude of risk that these companies will significantly reduce their AI infrastructure capex? We would highlight a few data points with respect to statements from LLM developers regarding their AI infrastructure spending commitments: Microsoft issued guidance of $80b on data center spend through fiscal 2025 (ending in the middle of 2025); Meta CEO Mark Zuckerberg stated this past Friday that Meta intends to spend $60-65 billion in capex in 2025 as the company ramps up its AI development capabilities, noting that the company would end 2025 with 1.3 million AI GPUs; and The Project Stargate was announced last week (our note here) a new company that intends to invest $500 billion to build new AI infrastructure for OpenAI in the United States. The initial equity funders in Stargate are SoftBank, OpenAI, Oracle, and MGX. Arm, Microsoft, NVIDIA, Oracle, and OpenAI are also mentioned as key technology partners. A long history of algorithmic improvements suggest we shouldn't understate the incremental demand that comes from lower costs, more advanced capabilities, and continued scaling. In the paper that describes the invention of the transformer the authors write "our base model surpasses all previously published models...at a fraction of the training cost " (here). And Nvidia has said that they have seen algorithmic efficiencies over the last decade of >1000X, outpacing their single chip inference performance gains. In that context the possibility of a 10x reduction in training compute suggested by DeepSeek does not affect the LTGR significantly when per model training requirements have been growing at 10x annually - with the potential for lower costs to accelerate inference adoption and thus inference demand. The key AI power demand questions we are focused on: 1. What is the mix between AI training and inference? Some data points regarding US data center developments would suggest that >75% of the power and computational demand over the next several years is for Inference, but there is significant uncertainty regarding the exact percentage. For example, Cushman & Wakefield estimates that >47 gigawatts of US data centers are under development, and of that total, only ~4 gigawatts are “hyperscale” projects (see Exhibit 1).
  3. M IdeaMorgan Stanley Research3Exhibit 1:Cushman & Wakefield estimates that >46 gigawatts of US data centers are under development, and of that total, only ~4 gigawatts are “hyperscale” projectsSource: Cushman & WakefieldOf the “Colo” (colocation) projects that comprise the vast majority of the US data center development pipeline, we believe these projects are primarily for Inference and other non-AI applications, rather than for LLM Training. Why? These projects tend to be much smaller than the large US AI Training facilities we have seen (typically 100-300 megawatts, much smaller than the 2,000 megawatts Meta is building in Louisiana, and much smaller than the 15,000 megawatts that we calculate would ultimately be built by the Stargate consortium – see our Stargate note: Powering GenAI: Assessing Near- and Long-Term Implications of Stargate. Exhibit 2Exhibit 2Exhibit 2provides an illustrative comparison of US data center demand, and the potential mix of Inference-focused data centers relative to AI-focused data centers. Exhibit 2:The following table provides an illustrative comparison of US data center demand, and the potential mix of Inference-focused data centers relative to AI-focused data centers:Source: Morgan Stanley Research
  4. M Idea42. Are we experiencing a “Jevons Paradox” dynamic in which the rapidly falling cost of compute will lead to rapidly accelerating demand for AI Inference? We believe there are multiple indications that the answer is yes – our model shows a ~90% drop in the unit cost of compute (capital cost of a data center, divided by the computational power of the data center) over a 6 year period, and our recent survey of corporate AI Adoption suggests increases in the magnitude of AI use cases (see Exhibit 3). Exhibit 3:We believe there are multiple indications that the answer is "yes" when asked whether we are experiencing "Jevons Paradox" as our model suggests rapidly falling cost of computeSource: Morgan Stanley ResearchAs the cost of compute drops, and as LLMs become more efficient in using computational resources, there is in our view the potential for an acceleration in AI adoption (and the associated demand for AI inference). For more on our analysis of GenAI early adoptions and diffusion winners from here, please see our latest work in Uncovering Alpha in AI's Rate of Change.
  5. M IdeaMorgan Stanley Research5Exhibit 4:We See Declining Training/Inference Costs Driving Faster GenAI Adoption and Tech Diffusion as We Move Along the Multi-Year CycleSource: Company Data, Morgan Stanley ResearchPerformance represents the median return as of 1/27/2025 since the launch of ChatGPT 11/30/22 and excludes OpenAI given its private status3. Will there be increasing levels of separation between Chinese and US LLM development and access to GPUs/TPUs, such that demand for (and access to) chips and computational power in the US is much greater than in China? While this is a subject with much uncertainty, we would note statements from President Trump, including at the Stargate press conference last week, that would suggest that AI leadership is an important strategic objective of the Trump administration.4. At a strategic level, which path will LLM developers take: (A) committing large amounts of capital (likely in the hundreds of billions of dollars) in vertically scaling of GPU clusters to try to find emergent capabilities in larger scale foundational models (aka Stargate) or (B) invest a much smaller amount of capital on engineering teams looking to build more effective and efficient models? Stepping back, we continue to expect to see a large number of data center project announcements in the US. Why? •Our Powering GenAI model is built from the chip level, and note that our model currently assumes that 55% of global AI chips are deployed in the US (we believe this percentage could rise to the extent that the Trump administration takes action to restrict AI technology transfer to non-aligned countries). •A large number of US data center projects in the development pipeline (>40 gigawatts under a recent C&W market assessment – by comparison, our total US data center demand projections total 57 gigawatts for 2025-2028) appear to be related to inference (rather than training), given the smaller size of these sites (often 100-300 megawatts) relative to LLM training sites (such as Meta’s 2,000 megawatt training facility in Louisiana). •The Stargate announcement last week (see our note here) is indicative of an approach to LLM training in the US, at least among some players, that is highly capital- and power-intensive.In terms of the magnitude of AI upside priced into US AI infrastructure stocks
  6. M Idea6after yesterday’s stock moves, many of the “grid de-bottlenecking” power stocks price little to no such upside. Exhibit 5highlights Overweight-rated US stocks with AI power/infrastructure exposure, and degree of impact and upside excluding this AI exposure. Exhibit 5:The following table highlights Overweight-rated US stocks with AI infrastructure exposure, and degree of impact and upside excluding this AI exposureSource: Morgan Stanley Research. *Cumulative deployed fleet, about 25% is with data centers. ** Represents our estimate of direct sales to data centers by GEV but direct exposure is likely higher given their presence within the utility market. PT without AI for CMI is for illustrative purposes and assumes ~20x P/E on data center related earningsIndependent Power Producers (IPPs): We still see meaningful upside if the urgency level in the industry remains high and gigawatt-scale data centers remain part of the strategy for AI training: VST, PEG, CEG, and others have been working on contracting power with data centers for nearly a year and we have been expecting deals to be announced soon. We think VST could announce colocation deals with gas plants in Texas in the near-term, and a large-scale data center at the Comanche Peak nuclear plant in TX in the next few months. We tend to think the gas plant deals, which are smaller and likely to garner less scrutiny, would still potentially move forward while large nuclear plant deals could be pushed out somewhat if the industry revisits plans for capex spending. We have been expecting a nuclear deal to be announced by CEG in the coming months as well in the mid-Atlantic region, and a deal by PEG mid-year. We see these opportunities as among the fastest ways to power new data centers and avoid major infrastructure bottlenecks, so if the urgency level in the industry is still high, and GW-scale data centers remain part of the strategy for AI training, we still see collocation deals like these as very attractive and likely. The power markets also remain tight. Both the mid-Atlantic and Texas markets are facing shrinking reserve margins for a number of reasons - Retirements in the mid-Atlantic, industrial growth in TX, so even if the pace of data center growth slowed we see a need for new power generation in both markets, which require higher power prices - a positive for stocks across the space Renewable Developers: nothing priced in, so any data center demand represents upside to growth and profitability: We don't see any downside in NEE and AES given guidance targets don't incorporate meaningful growth from data center demand, and we believe investors haven't priced in this opportunity given uncertainty around the policy backdrop. We still see strong renewables demand in the US regardless of the outlook for Federal policy and even if data center power needs slow, given rising load growth from other sources, tight utility supply-demand for power, and the speed and economic attractiveness of renewables. Regulated Utilities: Most companies have not incorporated data center growth into their load forecasts, capex plans, and earnings targets so data center demand generally represents an upside opportunity. Within the regulated utility space, our conversations with companies have already indicated ~120 GW of new data centers
  7. M IdeaMorgan Stanley Research7in the advanced stage high probability pipeline across our coverage universe. This compares to the 110 GW we have modeled in total incremental data center capacity through 2030 and the utility numbers have been rising fast. It seems like near-medium term data center plans with utilities have increased in commitment level, but there is still a risk that these plans could shift, be cancelled, or delayed. To the extent data center power needs slow sooner than expected, the load growth boost for utilities could be shorter-lived and overall infrastructure needs less than expected as near-term projects gravitate toward areas around the country where limited upgrades are necessary. To the extent we see a quicker shift to inference, this could move the incremental data center buildout back to some of the core inference markets like VA, CA, IL, and TX, and away from the more remote training locations. US Clean Tech: As mentioned earlier, we still expect a large number of data center project announcements in the US associated with inference and non-AI workloads, which will require the buildout of new renewables + natural gas generation as well as significant investments in the grid. It is also important to emphasize that ~60% of our forecasted US power demand growth comes from non-AI drivers (onshoring of manufacturing, electrification, EV’s, etc.), which are not impacted by these recent developments. As a result, we believe the reaction in the key AI-levered clean tech stocks, namely GEV and BE, is likely overdone. GEV, in particular, shed a remarkable ~$25bn of market cap in trading following the DeepSeek announcement. Assuming a low-to-mid-teens multiple, this implies $1.5-$2bn of “lost” EBITDA on a run-rate basis (20%-30% of our 2028 EBITDA estimate) which we do not believe is representative of the strong underlying fundamentals, even ex-AI. BE was also a significant laggard, down 25% and shedding $1.7bn of market cap. Following yesterday’s move, the stock is trading at 20x our conservative 2026 EBITDA estimate (70% of the volumes embedded within our 2026 estimates are already under take-or-pay or supply agreement), however we still expect significant volume and earnings upside as the company announces additional contracts with utilities and hyperscalers in 2025. Recall, BE is currently delivering ~300-400 MW per annum of fuel cell product. Even if there were a modest shift in the ultimate power demand outlook for training, we would not expect it to meaningfully change the earnings potential for BE as its product particularly well-positioned to provide power to smaller inference data centers. All of that said, we recognize that the DeepSeek announcement, given the somewhat opaque details, injects uncertainty and takes some air out of the power demand narrative which likely weighs on valuation for both companies in the near-term. US Industrials: While Data Center accounts for just 2% of the US industrial economy, the vertical has been a material growth driver over the last 2yrs w/ associated tailwinds across Utility + Power Gen. News that DeepSeek has achieved frontier AI performance at a fraction of the cost (capex + power) presents a very material risk for our US Industrial coverage where Data Center demand is driven by facility power requirements, most notably Electrical (ETN, HUBB) and HVAC (CARR, JCI, TT, VRT). Following Monday’s sell-off, our analysis suggests the market is now baking in HSD forward growth for Data center exposure across our coverage, a sharp step down from prior expectations of high-teens growth. This screens conservative when compared to historical growth rates, communicated NTM plans (META, Stargate). Industrials have been unable to keep pace with Hyperscale capex
  8. M Idea8to date, resulting in excess backlog (2-3x normalized) and serving as a forward buffer. An ability to create and run AI models at a lower should accelerate real world adoption including Embodied AI which we view as a structural tailwind for US Mfg and in line w/ our US Reshoring thesis ($10 Trill).US Machinery and Back-up Power Generators: Historically a more commoditized market, the field for large diesel back up power generators has become one of several key bottlenecks for data center construction with backlogs for engines out to 2027. DeepSeek news we think raises questions that are likely to have implications good or bad on at least two important considerations: 1) Existing OEMs’ desire to invest in incremental large diesel engine capacity and willingness of new entrants to invest, and 2) ability of OEMs to continue to raise prices on engines over the coming years given rising focus on costs. Ultimately, our view is that the combination of greater uncertainty, multi-year lead times (i.e. backlogs out to 2027) and the risk that investments into data center capacity accelerate rather than slow on the back of the DeepSeek news, continues to put Cummins and Caterpillar in an advantaged competitive position. Assuming 20x multiple on our 2025 earnings estimates for each of CAT and CMI's data center related power gen businesses, to assume that these do not get the full credit of a SOTP (i.e. >30x P/E on AI exposed businesses) given the relatively low % of total sales we estimate our PTs would be influenced as shown in the table in Exhibit 5. Ultimately, underscoring our view that CMI has multiple ways to win, while we simultaneously believe the market is overestimating secular tailwinds at CAT.Latest on EQT: Rising power consumption is a source of longer-term gas demand upside, but LNG is a much larger near-term growth driver. As we have previously highlighted, US LNG export capacity is set to increase by ~85% over the next four years, adding ~11 bcf/d of domestic consumption (see here). Furthermore, President Trump’s executive order to lift the “LNG pause” (see here) could support additional FIDs (final investment decisions), boosting LNG feedgas demand towards the back-end of the decade and into the 2030s. On power, AI is just one of the many drivers of power demand growth, with is also supported by onshoring of manufacturing and a broader trend of electrification. Total data center growth (including AI) represents ~0.8% of our Utes team's ~1.8% electricity demand CAGR 2025-30. Our base case price targets and $3.75 long-run Henry Hub forecast assume gas demand from the power sector remains roughly flat through 2030, with any growth a source of incremental upside. For Devin McDermott's latest thoughts on EQT, please see his note here.
  9. M IdeaMorgan Stanley Research9Disclosure SectionThe information and opinions in Morgan Stanley Research were prepared by Morgan Stanley & Co. LLC, and/or Morgan Stanley C.T.V.M. S.A., and/or Morgan Stanley Mexico, Casa de Bolsa, S.A. de C.V., and/or Morgan Stanley Canada Limited. As used in this disclosure section, "Morgan Stanley" includes Morgan Stanley & Co. LLC, Morgan Stanley C.T.V.M. 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M Idea10Acuity Brands Inc., AES Corp., AGCO Corp, Allison Transmission Holdings Inc, Altus Power Inc, American Electric Power Co, Antero Resources Corp, APA Corp, Array Technologies Inc, Bloom Energy Corp., Carrier Global Corp., Caterpillar Inc, Chord Energy Corporation, CNH Industrial NV, CNX Resources Corp, Comstock Resources Inc., ConocoPhillips, Constellation Energy Corporation, Coterra Energy Inc., Cummins Inc, Deere & Co., Devon Energy Corp, Diamondback Energy Inc, Donaldson Company Inc., Eaton Corporation PLC, Emerson Electric Co, EOG Resources Inc, EQT Corp., Expand Energy Corp, First Solar Inc, Fluence Energy Inc, Fortive Corp, Gates Industrial Corporation PLC, GE Vernova, Hess Corp., Honeywell International Inc, Hubbell Inc., Johnson Controls International Plc, Kennametal Inc., Lennox International Inc, Lincoln Electric Holdings Inc, Martin Marietta Materials Inc, Matador Resources Co, Maxeon Solar Technologies Ltd., MGE Energy, Inc., Murphy Oil Corporation, NextEra Energy Inc, Northern Oil & Gas Inc., NRG Energy Inc, Occidental Petroleum Corp, Oshkosh Corp, Otis Worldwide Corp, PACCAR Inc, Permian Resources Corp, Plug Power Inc., Public Service Enterprise Group Inc, Range Resources Corp., REV Group Inc., Rockwell Automation, Shoals Technologies Group, Solaredge Technologies Inc, Stanley Black & Decker Inc, Stem Inc, Summit Materials Inc, Sunnova Energy International Inc, Sunrun Inc, Terex Corp., Timken Co, Tourmaline Oil Corp., TPI Composites Inc., United Rentals Inc., Vertiv Holdings Co., Vistra Corp, W.W. Grainger Inc., Westinghouse Air Brake Technologies Corp, WillScot Holdings Corporation.Within the last 12 months, Morgan Stanley has either provided or is providing non-investment banking, securities-related services to and/or in the past has entered into an agreement to provide services or has a client relationship with the following company: 3M Co., AES Corp., AGCO Corp, Allison Transmission Holdings Inc, Altus Power Inc, American Electric Power Co, Antero Resources Corp, APA Corp, Array Technologies Inc, Bloom Energy Corp., Carrier Global Corp., Caterpillar Inc, Chord Energy Corporation, CNH Industrial NV, CNX Resources Corp, Comstock Resources Inc., ConocoPhillips, Constellation Energy Corporation, Coterra Energy Inc., Cummins Inc, Deere & Co., Devon Energy Corp, Eaton Corporation PLC, Emerson Electric Co, EOG Resources Inc, EQT Corp., Expand Energy Corp, First Solar Inc, Fluence Energy Inc, Fortive Corp, Gates Industrial Corporation PLC, GE Vernova, Hess Corp., Honeywell International Inc, Hubbell Inc., Ingersoll Rand INC, Johnson Controls International Plc, Murphy Oil Corporation, NextEra Energy Inc, Northern Oil & Gas Inc., NRG Energy Inc, Occidental Petroleum Corp, Oshkosh Corp, Otis Worldwide Corp, Ovintiv Inc, Permian Resources Corp, Plug Power Inc., Public Service Enterprise Group Inc, Range Resources Corp., Rockwell Automation, Shoals Technologies Group, Solaredge Technologies Inc, Stanley Black & Decker Inc, Stem Inc, Summit Materials Inc, Sunrun Inc, Terex Corp., Timken Co, Tourmaline Oil Corp., TPI Composites Inc., United Rentals Inc., Vertiv Holdings Co., Vistra Corp, Vulcan Materials Company, W.W. Grainger Inc., Westinghouse Air Brake Technologies Corp, WillScot Holdings Corporation.An employee, director or consultant of Morgan Stanley is a director of Caterpillar Inc, CNH Industrial NV, Cummins Inc, Occidental Petroleum Corp. 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 3M Co., Acuity Brands Inc., AES Corp., AGCO Corp, Allegion Public Limited Company, Allison Transmission Holdings Inc, Altus Power Inc, American Electric Power Co, Antero Resources Corp, APA Corp, Array Technologies Inc, Bloom Energy Corp., Caterpillar Inc, Chord Energy Corporation, Civitas Resources Inc., CNX Resources Corp, Comstock Resources Inc., ConocoPhillips, Coterra Energy Inc., Cummins Inc, Deere & Co., Devon Energy Corp, Diamondback Energy Inc, Donaldson Company Inc., Eaton Corporation PLC, Emerson Electric Co, EOG Resources Inc, EQT Corp., Expand Energy Corp, Fastenal Co., First Solar Inc, Fluence Energy Inc, Fortive Corp, Gates Industrial Corporation PLC, Hess Corp., Honeywell International Inc, Hubbell Inc., Ingersoll Rand INC, Johnson Controls International Plc, Kennametal Inc., Lennox International Inc, Lincoln Electric Holdings Inc, Martin Marietta Materials Inc, Matador Resources Co, Maxeon Solar Technologies Ltd., MGE Energy, Inc., Murphy Oil Corporation, NextEra Energy Inc, Northern Oil & Gas Inc., NRG Energy Inc, Occidental Petroleum Corp, Oshkosh Corp, Otis Worldwide Corp, Ovintiv Inc, PACCAR Inc, Plug Power Inc., Public Service Enterprise Group Inc, Range Resources Corp., REV Group Inc., Rockwell Automation, Shoals Technologies Group, Solaredge Technologies Inc, Stanley Black & Decker Inc, Summit Materials Inc, Sunrun Inc, Terex Corp., Timken Co, TPI Composites Inc., Trane Technologies PLC, United Rentals Inc., Vulcan Materials Company, W.W. Grainger Inc., Westinghouse Air Brake Technologies Corp.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 RATINGSMorgan 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 December 31, 2024)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.
  11. M IdeaMorgan Stanley Research11Coverage UniverseInvestment Banking Clients (IBC)Other Material Investment Services Clients (MISC)Stock Rating CategoryCount% of TotalCount% of Total IBC% of Rating CategoryCount% of Total Other MISCOverweight/Buy146339%36345%25%66939%Equal-weight/Hold170345%36946%22%81048%Not-Rated/Hold50%00%0%20%Underweight/Sell59616%749%12%22213%Total3,7678061703Data 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 RatingsOverweight (O). The stock's total return is expected to exceed 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). The stock's total return is expected to be in line with 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 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). The stock's total return is expected to be below 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 ViewsAttractive (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.Important Disclosures for Morgan Stanley Smith Barney LLC CustomersImportant disclosures regarding the relationship between the companies that are the subject of Morgan Stanley Research and Morgan Stanley Smith Barney LLC or Morgan Stanley or any of their affiliates, are available on the Morgan Stanley Wealth Management disclosure website at www.morganstanley.com/online/researchdisclosures. For Morgan Stanley specific disclosures, you may refer to www.morganstanley.com/researchdisclosures.Each Morgan Stanley research report is reviewed and approved on behalf of Morgan Stanley Smith Barney LLC. This review and approval is conducted by the same person who reviews the research report on behalf of Morgan Stanley. This could create a conflict of interest.Other Important DisclosuresA member of Research who had or could have had access to the research prior to completion owns securities (or related derivatives) in the AES Corp.. This person is not a research analyst or a member of research analyst's household.Morgan Stanley Research policy is to update research reports as and when the Research Analyst and Research Management deem appropriate, based on developments with the issuer, the sector, or the market that may have a material impact on the research views or opinions stated therein. In addition, certain Research publications are intended to be updated on a regular periodic basis (weekly/monthly/quarterly/annual) and will ordinarily be updated with that frequency, unless the Research Analyst and Research Management determine that a different publication schedule is appropriate based on current conditions.Morgan Stanley is not acting as a municipal advisor and the opinions or views contained herein are not intended to be, and do not constitute, advice within the meaning of Section 975 of the Dodd-Frank Wall Street Reform and Consumer Protection Act.Morgan Stanley produces an equity research product called a "Tactical Idea." Views contained in a "Tactical Idea" on a particular stock may be contrary to the recommendations or views expressed in research on the same stock. This may be the result of differing time horizons, methodologies, market events, or other factors. 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  12. M Idea12who receive it. Morgan Stanley recommends that investors independently evaluate particular investments and strategies, and encourages investors to seek the advice of a financial adviser. The appropriateness of an investment or strategy will depend on an investor's circumstances and objectives. The securities, instruments, or strategies discussed in Morgan Stanley Research may not be suitable for all investors, and certain investors may not be eligible to purchase or participate in some or all of them. Morgan Stanley Research is not an offer to buy or sell or the solicitation of an offer to buy or sell any security/instrument or to participate in any particular trading strategy. The value of and income from your investments may vary because of changes in interest rates, foreign exchange rates, default rates, prepayment rates, securities/instruments prices, market indexes, operational or financial conditions of companies or other factors. 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