Key Takeaways
- The AI revolution should prove a massive growth opportunity for the technology sector, with AI-related revenues forecast to grow at a compound annual growth rate (CAGR) in the high 20% range to nearly $650 billion by 2028.
- Large cloud service providers are leading the AI investment cycle, but increasing AI spending by enterprises, investment in AI at the edge, and the refreshing of endpoint devices, will deliver a multi-year investment cycle that will benefit the entire IT industry.
- Semiconductor makers have been the early winners, though the hardware sector, which provides infrastructure for AI's massive data flows, is also benefiting. The software and services sectors will take longer to generate meaningful AI revenues as they must create use cases that customers find demonstrably beneficial.
- AI will affect technology sector's credit quality at different speeds, reflecting rated issuers' ability to deliver sustainable increases in revenues and cash flow at different stages of the AI investment cycle. Many issuers will benefit, but some will be disrupted.
Jensen Huang, CEO of chipmaker NVIDIA Corp., has likened OpenAI's ChatGPT, a conversational chatbot powered by artificial intelligence (AI), to the iPhone in terms of its potential to transform and affect the broader economy and solve previously intransigent problems. S&P Global Ratings think he is being conservative.
It took the smartphone industry more than 15 years to reach annual device sales of about $500 billion, and related mobile app sales of about another $200 billion. We expect the market for AI, including traditional AI (such as machine learning) and generative AI (Gen AI, which creates text and other media), will expand from less than $200 billion in 2023 to nearly $650 billion by 2028, equivalent to a compound average growth rate (CAGR) in the high 20% range, and will account for nearly 15% of total global IT spending by 2028 (see chart 1). We further believe spending on IT as a percentage of global GDP will increase meaningfully over the next decade, leading to improved growth prospects and better rating trajectories for many of the technology sector issuers that we rate.
Chart 1
Cloud Service Providers Are Leading The AI Investment Cycle
The AI-driven IT spending cycle is just beginning. Significant investment has been mostly limited to large cloud service providers (CSPs) and consumer internet companies. Gen AI's development and commercialization requires large upfront investment and has been led by companies with significant financial resources such as Microsoft Corp., Amazon.com Inc., Alphabet Inc., and Meta Platforms Inc. Widespread adoption of AI by other business sectors will come only once AI infrastructure, large language models (like ChatGPT), and product-enhancing software applications are in place.
Most CSP investment has been focused on AI training, which involves analyzing raw data in large language models to detect patterns. According to NVIDIA, more than 50% of its data center revenues during the fourth quarter of 2023 came from CSPs that were building AI infrastructure in expectation of future demand.
Inferencing, where trained models use live data to make predictions and conduct real-world tasks, will gradually move beyond CSPs and be adopted by enterprises and sovereigns, transforming it into a much larger market than the current training-focused iteration. Starting about 2025, we expect investment will shift significantly towards accelerating capabilities for AI at the edge (where data will be captured and analyzed on local devices) and will thus expand AI's adoption in various end markets requiring increased security and lower latency. Enterprises involved in this edge transition will continue to rely on CSPs' established training and inferencing capabilities, typically building bespoke AI systems on CSPs' foundational models.
We believe AI spending by large CSPs that are acting as AI enablers to the wider economy will continue to grow significantly for the next several years. Total capital spending by the big four (Alphabet, Amazon, Meta, and Microsoft) is expected to increase 26% in 2024, while (undisclosed) growth in AI-specific spending will likely be higher still (see chart 2).
Chart 2
Enterprises' AI Spending Will Increase, Slowly But Steadily
AI investment growth has proven slower among the wider business community than among CSPs, reflecting enterprises' strategizing over competing AI priorities. A recent survey by Boston Consulting Group, a management consultant, found that 90% of business executives cited AI as a top priority, yet more than two-thirds were focused only on small-scale proof of concept explorations. We believe those projects are mainly focused on automating repetitive tasks, improving employee productivity, and gaining insights from data analysis.
Despite that currently tentative approach, companies expect a near 2x return on their AI investments, primarily due to productivity gains in areas including software development, sales and marketing, customer service, and content creation, according to a survey by IDC, a technology markets data provider. Businesses also expect significant cost savings through operational efficiency beyond employee productivity, according to the same survey.
We expect business applications of AI will, over the next two to three years, focus on Gen-AI tools, like ChatGPT and Microsoft Copilot, streamlining current workloads, while non-Gen AI technologies replace (or assist) lower-skilled and repetitive functions. These products will be developed by a few companies, used by many, and will lead to immediate productivity improvement. The stage after that is likely to include more specialized AI use cases, notably as smaller companies make industry-specific tools and applications built on models developed by the big CSPs. Smaller companies who do not integrate AI into their products during this period will face a challenge to remain competitive.
As enterprises move from trialing AI technologies to deployment over the next decade (cajoled by CSPs and software vendors seeking to monetize AI investments), we expect spending on AI infrastructure and related software and services is likely to both grow and prove sustained. That is supported by evidence, including a March 2024 survey by KPMG, which polled 220 companies with more than $1 billion in revenues and found that 43% of them expected to invest at least $100 million in Gen AI over the following year. A second study, this one by IDC, claimed that spending on Gen AI solutions will double to $40 billion in 2024, then increase another 75% to nearly $70 billion in 2025, (see chart 3). NVIDIA's recent comment that 40% of fiscal 2024 revenue from its data centers segment was related to AI inferencing also suggests enterprise AI spending will soon accelerate.
Chart 3
Investment In AI Will Affect All Technology Sectors, But Not All At Once
We break the IT industry into four major sectors: semiconductors, hardware, software, and services. Each of those sectors has a unique set of risks and opportunities that will result from the adoption of AI, and as a result will be affected in different ways, and at different times.
Semiconductors
Semiconductor companies have been the most prominent beneficiaries during the early stage of the AI investment cycle. NVIDIA's graphics processing units (GPUs), which are mostly used by large CSPs, have grabbed headlines, yet the makers of bespoke application specific integrated circuits (ASICs), such as Broadcom Inc. and Marvell Technology Inc., are providing differentiated products to major CSPs. Broadcom's networking chips are also in demand, while sales of high bandwidth memory (HBM) chips from Micron Technology Inc. are likely to increase meaningfully by late 2024, though demand will still lag that of the HBM market leader, SK hynix Inc.
Hardware
Hardware suppliers are reaping the benefits of AI investment too, with rising demand, from tier 2 CSPs and enterprise customers, for IT infrastructure upgrades driving orders (and creating backlogs) at Dell Technologies Inc. and Hewlett Packard Enterprise Co. (HPE) over the past few quarters. This demand should be sustainable for the next several years. Elsewhere, makers of personal computers (PCs) and smartphones (such as Dell, HP Inc., and Apple Inc.) could see new demand as their products enter a replacement cycle driven by AI, starting in 2025.
Software
We expect the AI transition's effect on the software sector will prove, generally, more gradual. Businesses recognize that AI-enabled software can improve productivity and reduce costs, notably in software development, content creation, sales and marketing, and customer service. Yet, software vendors still need to convince enterprise customers of the value of the differentiated data sets underpinning their AI technology, and demonstrate an ability to translate that data into value-enhancing output. Some software makers, like Microsoft, are already realizing notable revenue increases from AI-enhanced products, but the sample size remains small. Potential software use cases could yet include financial software that utilizes AI to automate reporting tasks, links to analytics for revenue generation, and insurance software that can process quotes, expedite leads, and improve the customer experience.
Services
We expect the services sector will also see gradual benefits from AI investment, likely over the next two years. Organizations running on legacy IT systems will look to IT service providers for proof-of-concept testing and insights on how best to prioritize IT investments to incorporate AI. Large IT-service providers will benefit by acting as system integrators, although we expect that Gen AI related revenue from exploratory projects will ,generally, remain a small portion of contracted revenue through 2025. On the other hand, outsourcing services could find themselves in competition with generative AI, which might lower demand for human-based customer services, though this is likely to be a slow shift over many years.
The AI Investment Cycle Comes With New Risks
While AI investments hold the potential to revolutionize industries, augment efficiencies, and foster unprecedented levels of productivity, they also come with multifaceted risks that warrant investor consideration (see table 1) . Companies and investors are, or should be, considering such risks as they navigate AI-specific decisions, while the technology industry itself should consider them, given their potential to slow, but not derail, rapid AI adoption.
Table 1
| Selected risks posed by rapid AI adoption | |
|---|---|
| Potential risk | Our view |
| End user disruption | IT and business process outsourcing (for example, at call centers, in coding, and in business analytics) could rapidly change with AI capabilities. This may have an outsized effect on smaller, lower-rated issuers that lack product diversification, or don't have the resources to incorporate AI in a timely fashion. |
| Demand fails to materialize | Businesses' experimentation with AI technology may, in many cases, end in the pilot phase due to insufficient return on investment, performance inaccuracy, and data security. Resultant delays in AI investment could have serious implications for CSPs and technology vendors that invested in expectation of enterprise demand. |
| Restrictive regulation | Rapid AI growth has fueled concerns about privacy, bias, misinformation, manipulation, and cyber criminality--leading to state regulation and voluntary industry-based initiatives. Adequate compliance requires cooperation from all stakeholders and failure could result in stricter regulation of AI that increases compliance costs, raises entry barriers, diminishes innovation, and reduces adoption. Global regulations could also impact competition and innovation in different markets. US AI regulation is likely to lag that of the EU, although we note recent congressional hearings signal a push for greater federal oversight. While large US enterprises support collaborating with regulators to address AI risks, they also caution against strict regulations which could stifle innovation, especially against smaller firms looking to innovate. We believe corporations and regulators can cooperate to limit AI’s risks and minimize regulatory impediments to the technology and for the companies developing it. |
| IT budget cannibalization | Corporations’ near-term AI investment will likely focus on process efficiencies and cost cutting rather than the creation of new products and services. As AI investment proves its worth (through increased sales or reduced cost) we expect the resulting profits will be used to fund spending on AI tools and talent, leaving funding for other IT projects mostly intact. On the other hand, should AI investments fail to clear return on investment (ROI) hurdles, companies are likely to sharply curtail AI spending while mostly preserving traditional IT budgets. |
| Supply chain disruption | Taiwan Semiconductor Manufacturing Co. (TSMC) sits at the heart of the world's semiconductor supply. It is the key foundry partner to NVIDIA, and thus central to the current GPU supply issues, as well as a major source of semiconductors to chipmakers AMD and Broadcom, and to large CSPs. TSMC’s potential inability to procure sufficient water and electricity to meet production demands, an inability to transition to lower nodes in a timely fashion, and geopolitical tensions are all tail risks to the AI ecosystem. |
| Exacerbated IT spending volatility | CSPs are currently investing to procure the GPUs required to expand their AI infrastructure, yet their spending patterns are historically lumpy. They have, in the past, extended the useful life of data centers and pushed back product upgrades when faced with weak demand or budget constraints. Failure to monetize AI investments in a timely fashion could result in CPSs delaying new orders for multiple quarters. This would create inventory management challenges for semiconductor and hardware providers and increase volatility across the IT sector. |
The AI Investment Cycle Will Demand Patience From Credit Investors
Investors' enthusiasm for AI's potential is evident in the rich equity valuations of companies generating revenue from the new technology (and of some companies that are spending in expectation of revenues). Yet it remains uncertain when, and often if, investments will deliver meaningful revenues. There is no must-have AI software application for enterprises, at least today. Microsoft's Copilot, for example, improves the Office experience, but it remains uncertain that enterprises will pay a monthly premium for access. Yet if CSPs do not see sufficient demand for AI services, their investment could slow rapidly. That would have knock on effects for semiconductor and hardware providers, and potentially equity investors' AI convictions.
Credit investors will require patience. It will take time for the AI investment cycle to create stable cash flows at most rated issuers. Previous technology transitions, including the shift to cloud computing and software-as-a-service (SaaS), didn't lead to immediate improvements in credit worthiness. In the case of SaaS, for example, credit quality improvements that were reflected in ratings emerged only once SaaS accounted for a significant portion of overall revenues and thus reduced revenue volatility associated with software licenses. Our initial rating on Salesforce.com Inc. was BB/Stable/--, in April 2013. It took 10 years for that to reach A+/Stable/--, as we reflected the positive effect of the market's adoption of the company's SaaS model and its resulting credit quality improvement.
New uses will be found for AI, which will increase the total addressable market for the technology industry and benefit a wide cohort of companies. Market participants will inevitably point to incremental AI-related revenues as proof of success, though we counsel credit investors to focus on the sustainability of issuers' AI portfolios and to monitor market share for shifts that will separate long-term winners from pretenders. We will also watch for proof that issuers can generate incremental profits from AI-driven sales. If every software vendor has AI capability there will be no competitive advantage, and market forces could be expected to reduce pricing power accordingly.
Yet, where AI improves an issuer's competitive positioning, leading to sustained above-industry revenue growth and cash flow expansion, we would expect improved credit-quality to follow. We have upgraded ratings on semiconductor and hardware issuers where we viewed AI-driven improvement to be sustainable (see table 2). Software and services issuers could benefit from similar dynamics over the longer term if they succeed in introducing AI-enabled software or services that deliver long-term improvement to productivity or cost reductions.
On the other hand, some issuers (mostly smaller software and services providers in the speculative-grade category) could be negatively impacted by AI-related disruption and competitive weakness should they fail to successfully launch AI products in a timely manner. We consider that the current environment disadvantages small and highly-leveraged tech issuers which tend to have shorter-term investment horizons driven by owners that are often focused more on near term cost profit expansion rather than long-term investment.
Table 2
| AI-related rating changes | ||
|---|---|---|
| Issuer | Date | |
| NVIDIA Corp. (A+/Stable/A-1+) | 6/5/2023 | NVIDIA was upgraded to A+/Stable/A-1+, from A/Stable/A-1 due to significant and immediate revenue growth opportunity from the Gen-AI investment cycle. Its performance since that upgrade has been much stronger than we had anticipated but we also remain somewhat cautious of potential revenue volatility should GPU undersupply reverses or if CSPs pause new purchases while digesting existing inventory. |
| SK hynix Inc. (BBB-/Stable/--) | 12/14/2023 | We revised our rating outlook to 'Stable' from 'Negative' on expectations that operating results will improve meaningfully over the next six to 18 months, given its lead in the rapidly expanding Gen-AI memory-chips market, including high-bandwidth memory 3 (HBM3), and despite weakness in its NAND flash business. |
| Advanced Micro Devices Inc. (A-/Positive/A-2) | 2/21/2024 | We revised our rating outlook to 'Positive' from 'Stable' at the long-term rating of 'A-'. This was partly a result of the company’s growing revenues from GPU and accelerators for AI use cases. We view AMD’s products to be an increasingly viable alternative to NVIDIA’s range. |
Semiconductor: Early Beneficiaries Have Strong Growth Opportunity
Semiconductor companies have been the earliest beneficiaries of AI, profiting from large upfront investments needed to develop and commercialize Gen AI. Much of that spending has been by major CSPs on GPUs, which have provided the processing power for Gen AI as its training grows in scale and algorithmic complexity. That has proven a boon for leading GPU maker NVIDIA, and to a lesser extent its rival AMD. Elsewhere, custom chip providers, such as Broadcom and Marvell, are likely to see material AI-related revenues in 2024, while HBM providers, such as SK hynix, Samsung, and Micron, should benefit from AI server's growing memory requirements.
As AI shifts to inferencing, from training, we expect NVIDIA will face increased competition, including from AMD and Intel Corp, but should remain a leader given its entrenched position. We expect AI-enabled endpoint devices, including PCs and smartphones, to drive additional semiconductor demand, starting from late 2024 and into 2025. Our forecast is for AI-related semiconductor revenues to grow at a CAGR of about 25% over the next five years, expanding to well over $200 billion per annum.
We remain cautious of the potential for increased volatility in the semiconductor market. CSPs' currently aggressive investments could stall for multiple quarters if revenue growth disappoints or is delayed. The resulting impact on credit metrics would be significant given AI's contribution to semiconductor companies' recent revenues and growth. Volatility of demand and increased competition will also come from other sources. Large CSPs are developing their own chips in order to reduce reliance on external vendors. Deployment of custom AI chips will grow due to their cost advantages over GPUs. Latecomers to the AI market, such as Micron, should be expected to aggressively seek market share, which is necessary for it to remain competitive with larger, better capitalized rivals.
Overall (and excluding unexpected industry consolidation) we expect credit metrics will generally improve for many of our semiconductor issuers over the next several years as AI increases its share of overall industry revenues.
Table 3
| The semiconductor industry has been the big winner in the AI gold rush | |
|---|---|
| Issuer/Ratings | AI's impact |
| NVIDIA Corp. (A+/Stable/A-1+) | Near term: Nvidia has been the biggest winner in the Gen-AI race. Revenues from its Data Center segment grew over 400% year-over-year to $18.4 billion for its fiscal Q4 2024, driven by demand for its Hopper GPU computing platform and InfiniBand networking solution. We expect its Data Center segment to grow more than 75% in fiscal 2025 given still strong demand and various product ramps, and despite ongoing export restrictions to China. The high prices of Nvidia’s highly complex and supply-constrained chips should mean the company maintains industry-leading EBITDA margins of above 60%. NVIDIA, along with Texas Instruments Inc. is the highest-rated U.S. semiconductor company and a notch behind TSMC and Samsung Electronics, globally. |
| Long term: Nvidia has an early and meaningful lead in AI training and a strong position in inferencing. This should deliver a five-year revenue CAGR of well above 20%. We will monitor for potential revenue volatility as supply constraints ease and CSPs digest their GPU purchases. We expect revenues will continue to shift towards inferencing, which should be about 40% of fiscal 2024 revenues, and towards software, which has an annualized run rate of $1 billion annualized run rate. | |
| Advanced Micro Devices Inc. (A-/Positive/A-2) | Near term: AMD said it expects its data center GPU business to reach revenue of at least $3.5 billion in 2024. |
| Long term: While AMD’s data center GPU is experiencing strong demand, it remains a distant second to NVIDIA’s formidable AI GPU and accelerators. The data center market has secular growth characteristics, especially as Gen AI continues its evolution from mainly model training to a much broader inferencing, where AMD and Intel are considered incumbents and have competitive product offerings. We expect AMD and Intel’s GPUs and x86 central processing units (CPUs) to be competitive alternatives to NVIDIA’s products for inferencing use cases and that they will thus attract significant customer interest. | |
| Intel Corp. (A-/Negative/A-2) | Near term: Intel has built a portfolio of discrete accelerators and has a sales pipeline that is expected to exceed $2 billion in 2024. The company expects to ship about 40 million AI PCs this year. |
| Long term: Similar to AMD (see above), we expect Intel’s GPU and x86 to compete with NVIDIA’s AI products. However, we also recognize that Intel faces increasing threats in its core PC and data center chip markets, from AMD and ARM, while management is also focused on growing its foundry (third-party manufacturing) business. | |
| Broadcom Inc. (BBB/Stable/A-2) | Near term: Broadcom’s management noted increasingly strong demand for its customized AI accelerators on the company's Q1 fiscal year 2024 earnings call. Management also revised guidance on AI-related semiconductor sales to over $10 billion for fiscal 2024, equal to over 35% of total semiconductor sales and up from the 25% forecast provided at the end of fiscal year 2023. Broadcom’s AI-specific semiconductor sales should continue to support the company’s industry-leading profitability and free cash generation, despite weakness in its non-AI segments. |
| Long term: We expect Broadcom to benefit from continued enterprise investment in custom ASICs as its ethernet controllers, network switches, digital signal processors (DSPs), and optical components will be needed to facilitate the transmission and processing of large volumes of data in data centers and cloud computing environments. The recent acquisition of VMWare provides Broadcom with software-related AI upside through virtualization software that could allow organizations to deploy and manage AI models more efficiently. NVIDIA plans to enter the custom AI chips market, yet we do not believe this represents a near- to mid-term threat for Broadcom given its significant lead. | |
| Micron Technology Inc. (BBB-/Stable/--) | Near term: Micron has nominal market share in high-bandwidth memory (HBM), and lags the market leader SK hynix. That position reflects Micron's late entry into the HBM market, though its recent mass production of HBM3E, for use in NVIDIA’s H200 GPU, should start to generate meaningful revenues by the end of calendar 2024. |
| Long term: We view AI as both an opportunity and risk for Micron. HBM is in the early stages of a multi-year cycle, with grow at a forecast CAGR of over 20% over the next five years. Because the technology is data-center focused it should provide a more durable source of demand, compared to consumer-focused smartphone and PC end markets, potentially reducing revenue volatility. It also requires more bit capacity to produce and will carry much higher prices than existing products. Yet, we believe Micron's competitive position is weaker than its rivals, at this time, due to its late entry and smaller scale. Our view of Micron's long-term credit trajectory will be influenced by its success, or lack thereof, in the AI investment cycle. | |
| Marvell Technology Group Ltd. (BBB-/Stable/--) | Near term: Marvell's revenues from the data center end-market grew 54% year-over-year in the fourth quarter of 2024, driven by demand from AI markets that partly mitigated current weakness in its other end markets. Demand for higher bandwidth networking is benefitting its electro-optics products and ethernet solutions, which currently make up most of its AI-related revenues (equal to over $550 million, or about 10% of total fiscal 2024 sales). The company’s data center compute solutions (largely its custom ASICs) and, perhaps to a lesser extent, storage solutions should also benefit from AI demand. Marvell expects annual revenues from its cloud-optimized AI custom ASIC programs to increase to well over $800 million per year by the end of fiscal 2025. |
| Long term: We expect Marvell’s data center segment to strongly benefit from AI-related demand for multiple years due to its significant investment in leading edge process designs. Nonetheless we believe there is a risk of increasing competition from new entrants, especially in the custom ASIC product category, where new suggests that NVIDIA will join Broadcom as Marvell’s main competitor. | |
| Wafer Fab Equipment (WFE) Companies: Applied Materials Inc. (A/Stable/A-1), Lam Research Corp. (A-/Stable/A-1), KLA Corp. (A-/Stable/--) | Near term: We expect limited impact from AI on providers of wafer manufacturing equipment, as most customers are currently paring back capital spending due to overcapacity, following a record post-pandemic investment cycle. Furthermore, while AI-specific chips are capturing a growing share of semiconductor-industry revenue, they are a small fraction of all wafers produced and have yet become a major driver of foundry investment decisions. |
| Long Term: AI represents an additional demand driver for leading edge nodes that should drive incrementally greater capital intensity and provide a tailwind to the WFE sector. While the overall sector should benefit, we see particular benefits for process control providers, like KLA Corp., as greater die sizes on GPUs compared to CPUs and other logic chips raise the risk of defaults making yield management more critical. ASML should, meanwhile, see upside from a greater share of industry activity at leading nodes. | |
Hardware: An AI Revenue Boost, With More To Come
The hardware industry is already benefitting from a shifting of the investment emphasis to AI, with possibly more benefits to come for hardware vendors exposed to on-premise data centers, makers of enterprise and consumer products at the edge, and producers of endpoint devices. Elsewhere, the importance of power efficiency and cooling requirements for AI servers, means traditional server vendors (such as Dell, HPE Enterprises (HPE), and Lenovo Group Ltd.) and specialty server vendors (such as SuperMicro) have recaptured market share they had lost to original design manufacturers (ODMs).
Unlike the public cloud-transition, which mostly benefitted large CSPs and ODMs, AI offers opportunity to ODMs and original equipment makers (OEMs) such as Dell and HPE, whose core customers include enterprises and tier-2 CSPs. As enterprises gradually adopt AI inferencing, we expect a sustained increase in AI-related spending on servers, storage, and network equipment. Further out, as AI proliferates into the edge and endpoint devices, we expect a modest pickup in smartphone and PC unit growth starting in late 2024, and a broader adoption of augmented reality/virtual reality (AR/VR) devices to enhance productivity or for consumer entertainment purposes. Edge AI features may accelerate this refreshing of consumer electronics and further differentiate companies by increasing some average selling prices, profit margins, and potentially creating new revenue streams.
Shifts in credit quality will be determined by hardware vendors' ability to offer sustainably differentiated product and services, while an ability to procure AI chips is the biggest near-term revenue driver. By 2025, AI-server revenues are likely to surpass that of general-purpose servers, with the customer mix shifting to smaller CSPs and enterprises from large CSPs. The resulting larger total addressable market will be a net positive for OEMs (Dell and HPE) whose AI-related sales are still a modest percentage of overall revenues. We expect lower operating margin on AI sales but expect they will contribute to higher overall cash flows over the longer term.
Table 4
| Hardware providers should reap near- and long-term benefits from the AI investment cycle | |
|---|---|
| Issuer/Ratings | AI's impact |
| Apple Inc. (AA+/Stable/A-1+) | Near term: Growing AI use cases will be a catalyst for Apple to refresh its products, potentially at higher prices due to new hardware and component requirements. Apple is reportedly exploring a partnership/licensing deal with Google for Gemini-powered features on iPhones and has held discussions with OpenAI to potentially use generative pre-trained transformer (GPT) models. In this early stage of AI use cases at the edge, we are not surprised by Apple leveraging third-party AI technology to provide the best experience for its users. However, we also expect Apple to continue to assess internal and external tools to power Gen AI and thus differentiate its products from its competitors. |
| Long term: Apple appears to be in a good position to capitalize on AI thanks to an estimated 1.2 billion-plus (and growing, albeit slowly) iPhone users, and the over 2.2 billion users of its other devices (including iPod, Mac, etc.) who are likely to adopt AI-enabled devices over time. Monetization of AI at Apple’s App Store could be meaningful given its above-average profitability. | |
| Cisco Systems Inc. (AA-/Stable/A-1+) | Near term: Cisco's business should be only modestly affected by AI over the next two years. Management has discussed a target for $1 billion in AI-related orders, the majority of which will recorded as revenue in fiscal 2025 (ending in July), which would amount to less than 2% of total revenue. |
| Long term: Cisco’s expects it will benefit from AI data centers' increasing adoption of Ethernet switching, rather than InfiniBand. We consider this a credible expectation. InfiniBand has, so far, dominated AI switching due to its better reliability, but Ethernet enhancements are in the works. AI datacenter customers would like to use Ethernet because of its scale, industry-wide interoperability, and massive installed base, which should result in more competitive pricing and reduced maintenance complexity. Customers would also like to avoid vendor lock-in--NVIDIA is the main supplier of InfiniBand switches whereas the Ethernet ecosystem has many vendors. In addition to improved performance for high-end training, Ethernet should benefit from increasing lower-end training and inferencing workloads. Cisco, in June 2023, shared research suggesting that the Ethernet for AI-TAM (technology acceptance model) would grow to about $6.4 billion in 2027, from about $500 million in 2023, making it a growing but still modest component of the company’s more than $60 billion of revenues. | |
| Dell Technologies Inc. (BBB/Stable/--) | Near term: Dell is starting to see strong momentum in AI servers with a backlog of $2.9 billion exiting the fourth quarter of fiscal 2024.AI-server revenues also rose 60% and orders jumped 40% quarter-over-quarter. We think Dell’s AI-server revenues could account for a high single-digit percentage of total revenues in fiscal 2025, a significant amount given its massive revenue base of about $90 billion. |
| Long term: We remain cautious about the long-term sustainability of AI-related demand because AI workloads generally favor public cloud providers. Yet we acknowledge that demand from Dell's core customers, consisting mostly of tier-2 CSPs and enterprises, has been much stronger than we had anticipated. If this momentum is sustained over the long term, we expect it will result in stronger overall growth prospects tempered by potentially greater revenue volatility as customers pause and digest purchases. We would consider Dell’s business risk profile to be modestly stronger under this scenario. | |
| HP Inc. (BBB/Stable/A-2) | Near term: We forecast sales of AI-enabled PCs to gradually increase, starting in 2025 and into 2026, as PC makers introduce new product lines in late 2024. We believe HP's AI Studio solution will benefit from early adoption as it enables private AI models built from on-prem infrastructure to be scaled onto the cloud. HP also recently acquired Poly, a communication technology platform that uses AI in its video-related hardware and software offerings. We believe HP can benefit from enhancing its PCs' video and audio offerings with Poly’s AI-enabled products. |
| Long term: AI spending is likely to have limited impact on HP Inc. We expect it will spur a modest level of PC refresh cycle, albeit at higher ASPs, but will not materially alter PC industry fundamentals, which we believe will still grow at a low single digit rate over next three to five years. We believe PC’s ability to run LLMs will be limited, though the advanced security capabilities and the ability to run local LLMs more securely and at less cost than cloud-based alternatives can benefit HP if there is a shift away from LLM training and inferencing by CSPs to the edge and end point devices. We also consider that HP's mature printing business could benefits from increased use of AI-enhanced 3D printing. | |
| Hewlett Packard Enterprises Co. (BBB/Negative/A-2) | Near term: HPE’s AI-related revenue was modest in fiscal 2023, but is growing. Cumulative accelerated processing units (APU) orders (including APUs in the Compute segment and the Cray EX and Cray XD businesses within the High Performance Compute & Artificial Intelligence (HPC & AI) segment), totaled $4 billion over the period from fiscal Q1 2023 to fiscal Q1 2024. That represented about 25% of all server orders over the period. We expect HPC & AI to post the strongest growth within HPE, at near mid-single to high-single-digits over the next two to three years. |
| Long term: We expect HPE to capture some AI-related demand from its enterprise customers, which will be looking for AI solutions for a hybrid environment and at the edge. Overall, AI will modestly improve HPE’s revenue growth prospects, especially from AI servers with high ASPs, but we do not expect its long-term competitive position to improve given competition from Dell as well as the rapidly growing Super Micro Computer Inc. | |
Software: Vendors Will Have To Prove Their AI Worth
As enterprises evaluate AI use cases and software vendors develop new AI-enabled applications, we expect AI-related software revenues to start inflecting positively towards the end of 2024 and into 2025. In our view, software vendors' primary focus is to protect their competitive position as AI penetrates the marketplace. Developing and infusing AI capabilities into their existing products in a timely manner is a top priority. Software vendors seeking to charge for new AI applications, or raise prices on existing products that incorporate AI, will have to demonstrate tangible benefits to customers. We are already seeing some software-specific AI monetization (including from Microsoft's Copilots, and the growing value of ServiceNow Inc.'s new annual contracts), but we do not expect this transition to significantly alter the revenue trajectory for most rated software vendors over the next couple of years. Layering AI functionalities on top of existing software should improve end-user productivity and offer cost savings that support a transition to higher revenues for vendors. We are cautious regarding the extent to which enterprises will be willing to undertake new investment to enhance productivity without a clear ROI path. Modest revenue guides by industry leaders, including Salesforce and Adobe Inc., suggest we are still early in the stages of software's industry's AI evolution, and that monetization opportunities are not guaranteed even for the largest vendors with stickiest customers. The effects on credit quality for the majority of rated software issuers are therefore likely to be gradual.
At the same time, the AI evolution has the potential to disrupt pockets of the software industry, especially in cases where new AI capabilities replace or reduce reliance on legacy software. Adobe, for example, has faced investor concerns regarding OpenAI's introduction of a new text-to-video generator. We also consider small-to-medium sized software providers, whose product offerings compete with larger vendors, to be at risk as the latter have greater ability to invest ahead of demand and possibly bundle AI within their overall product portfolios to capture new market share. We view software that seeks to reduce complexity to be the most at-risk of potential disruption from AI-enhanced competition Examples include software used to manage supply chains, data and workflow, and provide business analytics.
Table 5
| Software providers should experience gradual AI benefits | |
|---|---|
| Issuer/Ratings | AI's impact |
| Microsoft Corp. (AAA/Stable/A-1+) | Near term: Microsoft is leading the industry in developing, deploying, and monetizing AI-enabled solutions. In its most recent quarter, the Azure cloud business grew 30% year-over-year, and management credited 6% of that growth to AI services. Over the near term, we expect Microsoft-backed OpenAI to be the top Gen-AI model provider, Azure to be a top three training and inferencing AI model, and CoPilot to be a top five Gen-AI application. We also expect Microsoft's GitHub Copilot business to be a near-term growth driver for the company, building on its 30% quarter-over-quarter growth to reach 1.3 million paid subscribers as of the end of December 2023. |
| Long term: Microsoft’s willingness to aggressively increase capital spending to capture AI opportunities will make it hard for competitors to match its rate of innovation. Layering AI across new and existing products and continuing to form strategic partnerships will support rapid user and revenue growth. Its investments in custom Maia and Cobalt chips should allow Microsoft to reduce costs and supplier reliance, leading to industry-leading margins and strong cash flow generation commensurate with its credit ratings. | |
| Adobe Inc. (A+/Stable/A-1+) | Near term: Adobe’s AI-related revenue hasn’t been clearly articulated, yet activity for its Gen-AI-enabled Firefly product portfolio has, and it’s been exceptional. Firefly was released in June 2023, and users had generated over 3 billion images by the end of October 2023. The company, on March 14, 2024, highlighted its strength in monetizing its AI solutions over the first quarter of its 2024 fiscal year, though we believe most Firefly activity has used free credits and will continue to do so through the of rest 2024. Therefore, we don’t expect the product to have a material impact on fiscal 2024 revenue growth, which we forecast to increase by 10%. |
| Long term: We expect Adobe to be a long-term leader in Gen-AI-enabled digital content creation. Adobe could generate significant AI-related revenue by upselling Firefly-enabled products and converting users from free to paid. About 90% of Firefly users are new to Adobe’s suite of products, providing massive upside grow the potential for monthly paid subscriptions. We expect Adobe’s revenue growth to expand in the double-digit percentages annually, at least for the next five years, supported by Firefly's monetization. While we believe Adobe will face strong competition from smaller companies, like Open AI and Midjourney, its scale and long history as a digital-content-creation leader provide it with the data and usership to succeed. | |
| Salesforce.com Inc. (A+/Stable/--) | Near term: We don’t expect AI to have a significant impact on Salesforce's revenue over the next year, even as customers begin to pilot its newer AI embedded offerings. Revenue growth will likely take time; however, we expect Salesforce will help customers experiment with and understand the benefits of AI on its platform. We believe the company’s product strategy may attract new customers and drive uptake of the Data Cloud product, which reached $400 million of annual recurring revenue (ARR) as of the fourth quarter of fiscal 2024. |
| Long term: Salesforce is uniquely positioned to help customers use their company data to implement AI strategies and improve organizational productivity. We view Salesforce’s customer relationship management (CRM) platform's breadth, and AI integration across products in sales, marketing, and support, as supportive of a strong market position. | |
| International Business Machines Corp. (A-/Stable/A-2) | Near term: IBM has made AI, and particularly Gen AI, a central pillar of its strategic focus, and has aligned its software and consulting offerings to capitalize on customers' desire to deploy AI in business processes. Nonetheless, we believe that the near-term impact of AI will be limited and at best a small positive to the company's credit quality. IBM said its Gen-AI book of business was in the “low hundreds of millions of dollars” in the third quarter of fiscal 2024 and was double that by the end of the year. While impressive on a standalone basis, this represents a small incremental contribution to revenue in the context of IBM’s broader business, particularly given that longer-term contracts will delay the recognition of sales and earnings from consulting contracts. |
| Long term: IBM’s longer-term AI opportunity set remains unclear in our view. The company wants to be a significant player in assisting clients to develop, deploy, and manage generative (and other) AI tools, but we see uncertainty around execution, particularly given a track record of limited success capitalizing on emerging technologies, including mobile and hyperscale cloud infrastructure. We don't discount IBM’s possibilities in AI, however, given their deep and trusted relationships with the largest enterprises globally, willingness to invest heavily in acquiring technology, and increased nimbleness and ability to make bold strategic decisions under the current CEO--including the purchase of RedHat and divestiture of Kyndryl. | |
| ServiceNow Inc. (A-/Stable/--) | Near term: ServiceNow has a suite of NOW Assist products for IT service management (ITSM), customer success management (CSM), HR, and creator workflows. Customers can buy Professional Plus or Enterprise Plus add-ons to get access to Gen-AI capabilities in Now Assist. We expect ServiceNow's Gen-AI products to complement its core products and support upselling through its Professional Plus products. The company is seeing strong early demand for its Pro Plus/Enterprise Plus products. We expect ServiceNow will benefit from improving profitability during 2024. |
| Long term: We expect ServiceNow to continue to gain market share due to its Gen-AI capabilities, though it’s too early to quantify the impact of Gen AI on long-term performance. | |
| Intuit Inc. (A-/Stable/--) | Near term: Intuit has launched multiple AI offerings across its portfolio over the past year, with a focus on enhanced user experience, process automation, and personalized insights. Intuit has integrated AI/machine learning (ML) algorithms into QuickBooks to help small businesses with tasks such as expense categorization, invoice creation, cash flow forecasting, and identifying potential tax deductions. QuickBooks Assistant is an AI-powered virtual assistant that helps users navigate the QuickBooks platform, answer questions, and perform tasks using natural language commands. Within TurboTax, the company utilizes AI to simplify the tax filing process for users by providing step-by-step guidance, identifying potential deductions, and ensuring compliance with tax laws. Within Mailchimp, the company is rolling out two new Gen AI experiences including AI-driven audience segmentations and marketing automations.. We expect these products to provide near-term upside to current company guidance and to drive adoption of AI features among existing customers. The company has a strong track record of attracting new customers and selling to its customer base of 90 million consumers and 75 million small businesses. |
| Long term: The forecast impact of AI on Intuit's revenues varies depending on several factors, including the adoption rate of AI-powered features by customers, the effectiveness of these features in improving user experience and efficiency, and Intuit's ability to monetize AI-driven enhancements. Intuit's sizable install base supports our expectation of 10% to 20% growth of average revenue per customer (ARPC) due to AI adoption over the longer term. | |
| CrowdStrike Holdings Inc. (BB+/Stable/--) | Near term: CrowdStrike, a leading cybersecurity company, integrates AI/ML technologies into its products using Charlotte AI, its Gen AI security-analyst solution. Announced in the second quarter of 2023, the company rolled out its AI assistant as part of its Falcon platform. While it's still early days for Charlotte AI, the company is experiencing good traction. We estimate that Charlotte AI can add at least $100 million in ARR by 2025, assuming limited penetration (less than 20%) of CrowdStrike's installed base. |
| Long term: CrowdStrike believes it has a sustainable data advantage, with 10 years of attack data and a rich set of security telemetry, which it could pair with human input from its Overwatch teams to help build out Charlotte AI’s features. We also expect that as AI becomes more prevalent, it will likely make it easier for attackers to gain access to companies' systems, which will likely lead to additional demand for security solutions, eventually benefitting CrowdStrike. The company is focusing on the cost advantage of Charlotte AI versus competitors, like Microsoft, in this space. Over the longer term, we expect CrowdStrike to continue to integrate AI features into its cyber security products, thereby driving long-term profitability. | |
| Uber Technologies Inc. (BB+/Positive/--) | Near term: We expect ongoing proof-of-concepts and experimentation in autonomous vehicles (AVs), but consider this unlikely to affect Uber’s earnings trajectory. Autonomous will have a profound impact on Uber’s core ride-share business and is a risk worth monitoring, yet we believe the company will benefit from its strong market position and newer strategies in the core ride-share segment that will support profit and free cash flow growth. |
| Long term: We view autonomous as both an opportunity and threat to Uber’s business model. The technology has the potential to revolutionize the industry, and overall autonomous industry revenues could exceed $300 billion over the next decade, according to a study by management consultants McKinsey & Co. Mobility as a service and robotaxis will be central to the industry’s development, yet adoption of this technology is highly uncertain. According to our Mobility colleagues, sales of level 4 global light vehicle, which can operate in self-driving mode, will represent just 6% of total sales in 2035, while their growth expectations remain cautious. Furthermore, deployment has been limited in scale due to complex regulatory and safety backdrops and is unlikely to become widespread over the next several years. AVs' commercialization and rapid adoption may threaten Uber's business model if it lowers the entry barriers or costs related to driver supply and potentially removes local regulatory hurdles, bringing new entrants. AV technology developers may partner with ride-hail networks other than Uber or develop their own. Conversely, this technology may benefit Uber through its scaled technology platform, and by reducing driver supply costs and thus cost for consumers. | |
| Project Alpha Intermediate Holding Inc. (“Qlik”) (B/Stable/--) | Near term: Negative impacts from AI are expected to be minimal in the near term, given that Gen-AI solutions remain nascent and have limited ability to analyze and visualize data for business purposes. Near-term tailwinds are a possibility, however, considering Qlik’s partnership with OpenAI and new functionality that could embed Gen AI into Qlik workflows. |
| Long term: We believe Gen AI poses potential risk to Qlik’s business due to its ability to generate relatively sophisticated graphics, and the possibility to pair that with data analytics/contextualization for business needs. This risk is mitigated to an extent, however, by the company’s ongoing Gen AI integrations, which gives Qlik a first mover advantage in AI embedded data visualization and analytics solutions. | |
| Globetrotter Intermediate LLC ("Quickbase") (B-/Stable/--) | Near term: Quickbase is expected to benefit from its investment in AI enhancements. For instance, with the help of Gen AI, Quickbase's customers can describe the required custom business application in natural language and Quickbase can build that custom application within one to three minutes. |
| Long term: AI is a risk as well as an opportunity for Quickbase due to Gen AI's potential to disrupt no-code/low-code platforms. Considering AI's current ability to assist in code writing, and its potential to continue learning, Gen AI may take share in the no-code/low-code market if its capabilities are enhanced to the point that a non-technical user is able to build applications using natural language with minimal debugging. However, AI could also be a tailwind if integrated into Quickbase's no-code/low-code platforms such that it further enables/ simplifies the building of complex applications that may need a developer today. | |
| Contact Center as a Service (CCaaS) companies: RingCentral, Inc. (BB/Stable/--), Genesys Cloud Services Holdings II LLC (B/Stable/--), Avaya LLC (CCC+/Stable/--) | Near term: Contact center-exposed companies like Avaya, Genesys, and RingCentral appear highly exposed to AI displacement. Yet we don’t anticipate significant business pressures in the near term and believe the impact of AI will be gradual given their legacy exposure to traditional contact center technologies. |
| Long term: We consider these companies to be highly exposed to AI displacement. The pandemic likely prompted significant investment related to resiliency and efficiency as businesses sort to reduce costs and, more importantly, improve customer experiences to a point of differentiation. Spending in areas like Gen AI platforms and software is poised to grow at a CAGR of about 100% through 2027, according to tech-intelligence group IDC Corp. We view investments in areas like digital transformation, public and private cloud contact centers, and chatbots as paramount for competitive positioning. Newer technologies, like virtual agents and assistants, that provide human-like interactions 24/7 will likely reduce employee headcounts over several years. Contact center groups that can scale and effectively integrate AI automation may be able to limit damage from technology disruption, gain competitive advantages, and demonstrate added value in customer services. | |
Services: AI Will Have A Modest But Generally Positive Effect
AI related initiatives will likely be a modest credit positive for most large IT-service providers, leading to accelerated revenue growth due to higher demand and investment to enhance longer-term competitivity. We believe, with most IT still running on legacy systems, organizations will need to accelerate their digital transformation efforts and will depend on IT service providers with robust consulting practices to assist proof-of-concept testing and with insights on how best to prioritize IT investment. Clients' main focus will likely be on improving employee productivity and enhancing customer service. Large IT service providers will act as system integrators and are well positioned to validate accuracy of output from LLMs, foundational models, and enterprise-grade models (with better traceability of data and logic to avoid hallucinations). We expect most Gen-AI related revenue from these mostly-exploratory projects to remain a small portion of contracted revenue through 2025, with more upside thereafter.
Gen AI is also likely to shift competitive dynamics in a sector that has seen an abundance of offshoring to reduce labor costs. Service providers could leverage AI to enhance the quality and lower the cost of services, reducing the advantages enjoyed by offshore providers and potentially reducing pricing power industry wide. While some of these benefits could eventually accrue to customers through price competition, we believe service providers' margins will also improve because of efficiency gains. AI-related services could cannibalize some services currently offered to clients and IT service providers will likely seek to offset this risk with higher pricing and new service offerings which could improve productivity for the clients. AI poses a mounting risk to those with significant exposures to legacy IT infrastructure, and outsourcing services will face secular pressure from accelerating digital transformation efforts.
We expect Gen AI will reduce the demand for human-based customer service in areas of traditional business process outsourcing linked to customer experience (CX) activities. We do not consider this to be imminent as clients across sub-sectors remain cautious, and acknowledge the requirements for large amounts of reliable, well-organized data for fully-automated Gen-AI solutions. This presents a barrier that will likely limit broad adoption of AI for all customer relationship management services.
Over the next 12 to 24 months, we expect CX providers to adopt Gen-AI technology to improve productivity. This will notably be in training and supporting their own employees, and for quality assurance purposes, as opposed to widespread replacement of direct customer interaction. They will also increasingly partner with clients to utilize AI as a means to improve service quality and productivity, typically through projects that are currently at proof-of-concept stage. CX providers will also need to make meaningful investments to stay competitively positioned to win future large-scale Gen AI deployments. We currently view AI as credit neutral for CX providers, but this could evolve rapidly as we monitor developments of the risks, threats, and opportunities relating to the proliferation of Gen AI.
Table 6
| The AI transition will bring benefits and risks for IT service providers | |
|---|---|
| Issuer/Ratings | AI's impact |
| Accenture PLC (AA-/Stable/--) | Near Term: We see Gen AI as an extension of Accenture's digital core strategy. Leveraging its existing competitive advantages, identifying emerging technologies, and investing at scale to build services to support them, Accenture has already committed to investing $3 billion in AI over three years (2023) while doubling its skilled data and AI practitioners to 80,000. While we consider Accenture's fiscal 2023 as a year of experimentation for many of its clients, these investments have already yielded approximately 300 projects, contributing roughly $300 million to FY '23 revenue, with rapid acceleration to $450 million in Q1 of FY 2024. Although this remains a small proportion of revenue relative to total revenue, it has helped cushion against an IT spending environment ripe with budgetary constraints and sales cycle elongation. |
| Long Term: We expect that revenue contributions from GenAI-related and enabling offerings will continue to accelerate, particularly as its customers look to realize value at scale. We also believe that as a first mover, Accenture should be well-positioned to achieve and maintain a long-term leadership position as a provider of generative AI-enabled services. | |
| Concentrix Corp. (BBB/Stable/--) | Short term: Concentrix is engaged in more than 140 projects (mostly at proof-of-concept phase) with clients. These aim to implement various Gen-AI tools in its offers in order to improve productivity and customer experience. The company is adopting Gen-AI technology as part of its toolkit for training and supporting its employees and for quality assurance purposes. These initiatives may help improve business productivity, contribute modestly to top line, and drive margin expansion over the next one to two years. |
| Long term: Advancements in Gen-AI technology could disrupt the CX industry broadly if accuracy concerns and other flaws are mostly eliminated and Gen AI increasingly substitutes for traditional client engagement. This could lead to lower demand for human-based customer service. Alternatively, Concentrix could use its customer engagement expertise to position itself as a trusted intermediary between tech providers and clients, ensuring Gen-AI tools are implemented effectively while enhancing customer engagement. | |
| DXC Technology Co. (BBB-/Stable/--) | Near term: We expect a limited near-term effects from AI given that the bulk of DXC's services address mission-critical IT systems, it is currently utilizing AI to deliver some of these services, and because IT requirements and investment requirements will likely provide opportunities to partner with customers looking to adopt AI technology in their business. |
| Long term: We see Gen AI presenting a higher-level risk over the longer term as a significant portion of its Global Infrastructure Services (GIS), which represents about 50% of total revenue, is tied to legacy on-premise and IT outsourcing services. We expect broader adoption of AI will accelerate customers' digital transformations, hastening a decline in demand. We believe that customers shifting to smaller managed-services contracts or exhibiting higher churn levels will result in a sustained revenue decline, potentially creating profitability pressure and negatively impacting cash flow generation. | |
| Foundever Group SA (BB-/Negative/--) | Near term: Foundever is increasing its investments in AI to secure operating efficiencies and better meet clients' needs. We consider this critical to it maintaining its current market position. Resulting efficiencies could improve profitability over time, though other dynamics, including price competition may offset gains. We do not expect incremental EBITDA from Gen-AI initiatives in 2024, and only assume a modest expansion in 2025. |
| Long term: Advancements in Gen-AI technology could disrupt the CX industry broadly if accuracy concerns and other flaws are mostly eliminated and adoption of Gen AI increases as a substitute for traditional client engagement. This could lead to lower demand for human-based customer service. Furthermore, Foundever is a distant third in CX management. Larger competitors, Teleperformance and Concentrix, have significantly greater means to invest in AI offers, and are thus a significant competitive risk for the company. | |
| Unisys Corp. (B+/Negative/--) | Near term: Unisys' multi-year contractual recurring revenue base offers some protection from near term risks. The company is developing artificial intelligence applications integrated with machine learning, hyper-automation, and quantum computing. |
| Long term: Longer term, we see Gen AI as a greater source of risk as it could accelerate declines in Unisys’ legacy business, which could further strain cash flow generation that is needed to service post-retirement liabilities, and support research and development, sales, and marketing investments necessary to scale AI offerings. | |
Related Research
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- AI for Security, and Security for AI: Two Aspects of a Pivotal Intersection, Nov. 29, 2023
- Machine Learning: The Fundamentals, Nov. 29, 2023
- Gen AI Is Writing A New Credit Story For Tech Giants, Nov. 14, 2023
- NVIDIA Corp. Upgraded To ‘A+’ As It Is Set To Benefit From A Strong AI Investment Cycle; Outlook Stable, June 5, 2023
Additional Contributors: Christian Frank, James Thomas, Minesh Shilotri, Kevin Chen, Tuan Duong, Ejikeme Okonkwo, Gul Kundra, Sid Kolli, Pasha Azadmard, Steven Mcdonald, Daniel Pianki, Andy Sookram, Ben Hirsch, Shailendra Pamnani.
Writer: Paul Whitfield
This report does not constitute a rating action.
| Primary Credit Analyst: | Andrew Chang, San Francisco + 1 (415) 371 5043; andrew.chang@spglobal.com |
| Secondary Contacts: | David T Tsui, CFA, CPA, San Francisco + 1 415-371-5063; david.tsui@spglobal.com |
| Nishit K Madlani, New York + 1 (212) 438 4070; nishit.madlani@spglobal.com | |
| Contributors: | Jack J Tortora, New York 5163857212; jack.tortora@spglobal.com |
| Saurabh B Tarale, Pune; saurabh.tarale@spglobal.com |
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