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Goldman Sachs analyst Kash Rangan discusses how AI will act as a 'force multiplier' for the software industry, dispelling concerns that it will eviscerate software companies, and provides insights on companies like Salesforce, CoreWeave, Oracle, and Snowflake amidst ongoing discussions about an AI bubble. Technology Sector Navigates AI Transformation as Goldman Sachs Projects 'Force Multiplier' Effect U.S. equities are closely observing the evolving narrative surrounding Artificial Intelligence and its profound implications for the software industry. Goldman Sachs analyst Kash Rangan has positioned AI not as a disruptive force, but as a "force multiplier" for software, a perspective that aims to alleviate prevailing concerns about the technology's potential to diminish established software companies. This assessment comes amidst an ongoing market debate regarding an "AI bubble," a discussion particularly pronounced in private investment markets. The Event in Detail: AI as a Catalyst for Software's Rebirth Kash Rangan's analysis draws a compelling parallel between the current AI wave and the advent of web browsers in the 1990s. At that time, many feared browsers would dismantle the existing software ecosystem; however, they ultimately became a foundational gateway that significantly expanded the industry's scope. Rangan asserts that AI will similarly "synergize with software," simplifying its logic and enhancing its intrinsic value. He maintains that the software industry is poised for a "rebirth" driven by AI integration. Several key players in the software landscape are under scrutiny as the AI transformation unfolds: Salesforce (CRM): Goldman Sachs views Salesforce as possessing investment value, contingent on the growth of its AI product, Agentforce. A substantial increase in its customer base could alleviate the "valuation pressure" that has weighed on its stock price. Oracle (ORCL): The company recently reported robust fiscal year 2026 first-quarter results. Total Remaining Performance Obligations surged 359% year-over-year to $455 billion, with total quarterly revenues rising 12% to $14.9 billion. Cloud revenues demonstrated significant growth, up 28% to $7.2 billion. However, software revenues experienced a slight decline of 1%. CoreWeave (CRWV): Highlighted as a high-leverage bet on AI infrastructure, CoreWeave showcased explosive revenue growth of 207% in Q2 2025. Yet, the company reported widened net losses of $290 million, primarily due to substantial interest expenses associated with its capital-intensive expansion. Its market capitalization soared from $14.2 billion at its March 2025 IPO to $57.76 billion by August 2025, reflecting a price-to-sales (P/S) ratio of 18.5x, significantly exceeding the 6.2x average for its AI cloud peers. Analysts express caution regarding its debt sustainability and potential margin compression. ServiceNow (NOW): The company reported outstanding Q2 2025 results, with subscription revenue growth of 21.5% and expanded operating margins. Management underscored the increasing adoption of AI-powered solutions, with CEO William McDermott stating, "ServiceNow's Q2 results were outstanding. They continue our long track record of elite level execution, and we are at the forefront of enterprise AI." The company raised its full-year outlook, targeting over $15 billion in subscription revenue by 2026. Analysis of Market Reaction: Differentiating AI Valuations The discussion around an "AI bubble" continues to resonate within financial markets. However, analysis suggests this phenomenon is more pronounced in private markets, where venture capital funding has driven AI company valuations to elevated levels, with median revenue multiples ranging from 25x to 30x Enterprise Value to Revenue for 2025. In contrast, many publicly traded application software companies are currently experiencing "valuation troughs" or are "valuation depressed." Citi analysts emphasize that AI is not causing a complete disruption of the software industry but rather an "acceleration of industry differentiation," leading to a "winner-takes-all" environment. This shift poses a significant challenge to traditional Software-as-a-Service (SaaS) models reliant on seat-based charging. Data illustrates this growing divergence: software companies with growth rates exceeding 20% have seen their median enterprise value-to-revenue (EV/Revenue) valuation almost double since 2022 to 11.7 times, while those with growth rates below 10% have stagnated around 4.9 times. Concerns about overvaluation in some segments of the AI market have been echoed by prominent figures. OpenAI CEO Sam Altman has voiced apprehension regarding investor "overexcitement," suggesting that current sentiment may be pushing valuations beyond sustainable levels. Companies such as Palantir (PLTR) and CrowdStrike (CRWD), with P/E ratios of 501 and 401 respectively, are cited as examples of potentially overvalued entities. Broader Context and Implications: The Road Ahead for Software The "AI revolution" represents a culmination of years of development, with the launch of generative AI models like ChatGPT serving as a pivotal moment that reoriented investor and founder focus. The current market environment necessitates a clear distinction between companies that effectively integrate and commercialize AI and those that do not. Those that adapt are expected to experience reaccelerated growth and rising valuations. Infrastructure providers, such as CoreWeave, are positioned for substantial benefit from the AI boom, but their path is marked by considerable capital intensity and associated debt risks. The current public market valuations for many existing software companies indicate a period of recalibration, where the long-term impact of AI on profitability and business models is still being assessed. Looking Ahead: Growth, Profitability, and Strategic Adaptation As the software industry continues to evolve under the influence of AI, key factors for investors to monitor include the reacceleration of growth for existing software firms, the ability of AI to translate into tangible bottom-line profits, and the ongoing investment in AI infrastructure. The intersection of AI with other transformative technologies such as IoT, robotics, and machine learning is also expected to drive future innovation, efficiency gains, and new revenue streams. The coming quarters will likely provide further clarity on how companies are navigating this transformative period, with a continued focus on those that can demonstrate sustainable growth and profitability through strategic AI adoption and innovation. Oracle's impressive RPO backlog and CoreWeave's ambitious expansion plans underscore the significant capital and strategic maneuvers underway in the AI-driven tech landscape.
Goldman Sachs managing director Kash Rangan posits that artificial intelligence will act as a "force multiplier" for the software sector, ushering in a period of rebirth rather than obsolescence. This perspective suggests a significant shift in investor focus from AI hardware to application layers, potentially impacting valuations of public software companies and prompting scrutiny of private AI firms. Goldman Sachs Analyst Bets AI Will Give Software Companies a 'Rebirth' U.S. equities are poised for a notable shift in investment strategy within the technology sector, as a leading voice from Goldman Sachs champions artificial intelligence (AI) as a catalyst for a renaissance in software. Kash Rangan, a managing director at the investment bank, argues that AI will serve as a "force multiplier" for software, contrary to fears of its disruptive potential, ultimately leading to a rebirth of the sector. The Event in Detail: AI as a Force Multiplier for Software Rangan’s thesis suggests that the integration of AI into software will drive unprecedented growth, potentially reversing years of stagnation. He draws a parallel between AI’s current impact and the internet browser’s effect on software in the 1990s, where it expanded the industry significantly. This perspective comes as investors begin to look beyond the initial hype surrounding AI hardware to focus on the application layers where AI can deliver tangible value. In this evolving landscape, Rangan highlights several established SaaS companies as promising investment opportunities, recommending Salesforce (CRM), Intuit (INTU), Adobe (ADBE), and ServiceNow (NOW). These firms, he suggests, are well-positioned to leverage AI to enhance software functionalities, automate complex tasks, and revitalize their offerings. Additionally, Rangan identifies CoreWeave (CRWV) as a speculative, high-leverage bet on the AI infrastructure theme. CoreWeave, which recently debuted on NASDAQ, has seen its shares more than double since its March 2025 initial public offering. The company reported strong performance in Q2 2025, with revenue surging 207% year-over-year to $1.213 billion, marking its first quarter exceeding a billion dollars. A substantial 98% of this revenue stemmed from long-term contracts, contributing to a robust revenue backlog of $30.1 billion. Analysis of Market Reaction and Broader Implications Rangan’s analysis could significantly re-orient investor sentiment, directing focus and capital towards public software companies. This shift is predicated on AI’s ability to make software more intuitive and efficient, generating new monetization avenues and expanding user bases. Goldman Sachs research supports this view, with estimates suggesting generative AI technologies could boost global GDP by 7% over a decade. While optimism surrounding AI is high, Rangan clarifies that concerns about an "AI bubble" are more pertinent to private markets than to established public software firms. Valuations for private AI startups, such as OpenAI and Anthropic, have indeed soared, with OpenAI reportedly boosting its valuation to $500 billion and Anthropic reaching $183 billion through recent funding rounds. These figures underscore intense investor appetite for early-stage AI plays, contrasted with the more mature and financially established public software sector. However, CoreWeave presents a unique blend of hyper-growth and financial risk. The company has aggressively pursued debt financing to fuel its expansion, with total capital commitments now exceeding $25 billion, including a $2.6 billion delayed draw term loan facility in July 2025. This strategy has resulted in a significant debt load of $11 billion against $1.1 billion in cash, incurring high interest expenses, projected at approximately $1 billion annually. Despite its rapid revenue growth, CoreWeave’s valuation has seen some normalization, compressing from over 35x forward sales at IPO to approximately 12x as of September 2025, though this remains elevated compared to the cloud infrastructure sector median of roughly 3x EV/sales. Risks for CoreWeave include customer concentration, with one client accounting for 72% of sales, and the inherent challenges associated with heavy leverage tied to aggressive capital expenditure plans. Expert Commentary > "AI will be a 'force multiplier' for software, not a destroyer," stated Kash Rangan, emphasizing his belief that AI will lead to a software renaissance. His perspective is echoed by Goldman Sachs economists Joseph Briggs and Devesh Kodnani, who highlight how AI breaks down barriers between humans and machines, fostering innovation in software. Marco Argenti, Goldman Sachs' chief information officer, foresees AI evolving into hybrid workers and expert models by 2025, transforming software into intelligent partners. Looking Ahead Investors will be closely monitoring the adoption rates of AI-driven software solutions, with Goldman Sachs projecting accelerated AI investments exceeding 2024 expectations. While the "rebirth" narrative positions software at the forefront of the AI revolution, potential risks remain. Goldman Sachs analysts have previously warned of an "inevitable slowdown" in AI spending, which could pressure stock valuations, potentially leading to a market dip if major tech firms reduce capital expenditures. For companies like CoreWeave, long-term sustainability will depend on improvements in its capital structure and diversification of its customer base. The interplay between innovation, market sentiment, and prudent risk assessment will ultimately define the tech economy's trajectory in the coming years.
The global technology landscape is undergoing a significant transformation driven by the increasing adoption of Small Language Models (SLMs). These compact yet powerful AI models, characterized by fewer than two billion parameters, are proving instrumental in delivering efficient and accurate natural language processing, particularly in resource-constrained environments and sensitive data applications. A recent comprehensive evaluation report highlights the strategic positioning and leadership of key players such as Microsoft (MSFT), IBM (IBM), and Infosys (INFY.NS), indicating a pivotal shift towards optimized, on-device artificial intelligence solutions. The Event in Detail The "Small Language Models (SLM) Companies Quadrant" report, based on an assessment of over 100 companies, underscores the growing influence of SLMs across critical sectors including Healthcare, Finance, and Manufacturing. Unlike their larger counterparts, SLMs are designed for efficiency, making them ideal for integration into low-power devices and environments where data privacy is paramount. Microsoft is strategically leveraging its Azure AI platform to deploy scalable and customizable SLMs, facilitating seamless integration across edge devices while maintaining high performance. The company's collaboration with OpenAI further enhances its access to advanced language models, enabling hybrid AI systems that combine cloud-based and edge AI capabilities. IBM solidifies its position in the SLM market through its robust enterprise AI solutions, with a particular focus on industries demanding stringent security measures. The company's hybrid cloud offerings support the deployment of adaptable AI models, ensuring compliance and data privacy. IBM Watson continues to be a core component in bringing machine learning and innovative insights to enterprise activities. Infosys is actively expanding its SLM offerings, catering to the rising demand for domain-specific applications. Its expertise in fine-tuning and providing enterprise-grade AI tools positions it favorably to serve industries requiring specialized AI models. Analysis of Market Reaction and Infrastructure Impact The widespread adoption of SLMs is generating substantial ripples across the broader AI ecosystem, significantly impacting the demand for high-end AI chips and cloud computing services, while simultaneously accelerating the shift towards on-device processing. The overall AI infrastructure market was valued at approximately $87.6 billion in the third quarter of 2025 and is projected to grow at a Compound Annual Growth Rate (CAGR) of 17.71% to reach $197.64 billion by 2030, with hardware accounting for a significant 72.1% of current spending. The total addressable market for AI accelerator chips alone is forecasted to reach $500 billion by 2028, underscoring the immense investment flowing into this sector. NVIDIA Corp. (NVDA) continues to hold a dominant position in the high-end AI accelerator market, reportedly commanding an 80-90% share. This leadership is primarily driven by the robust demand for its Graphics Processing Units (GPUs) and the foundational CUDA software ecosystem, which are critical for large language models and AI infrastructure. The full-scale production of NVIDIA's Blackwell platform, which commenced in early 2025, is expected to sustain significant revenue generation for the company. The cloud computing market is also experiencing explosive growth, propelled by AI-related workloads. Valued at $0.86 trillion in 2025, it is forecast to expand to $2.26 trillion by 2030, reflecting a brisk 21.20% CAGR. Amazon Web Services (AMZN) is projected to generate $126.5 billion in revenue in 2025, an 18.3% increase year-over-year, largely fueled by its expanding AI-related workloads and infrastructure investments. Microsoft Corp. (MSFT) plans to commit approximately $80 billion to enhance its data center infrastructure in 2025, alongside an additional $3 billion over two years to expand cloud and AI capacity in India. These investments highlight the strategic importance placed on foundational AI infrastructure by technology giants. The Rise of Edge AI and On-Device Processing A notable trend emerging from the SLM paradigm is the burgeoning demand for Edge AI, or on-device processing. This involves processing data locally on devices such as mobile phones, IoT devices, and autonomous vehicles, thereby reducing reliance on centralized cloud servers. This segment presents substantial growth opportunities for companies like Advanced Micro Devices Inc. (AMD) and Intel Corp. (INTC), which are actively developing specialized Neural Processing Units (NPUs) for these applications. By 2025, AI inference — the process of applying an AI model to new data — is increasingly expected to reside on Edge AI devices due to the inherent benefits of faster response times, reduced bandwidth consumption, and enhanced data privacy. The Edge AI chip market is projected to reach $12.2 billion in revenue by 2025, potentially surpassing cloud AI chip revenues, which are estimated at $11.9 billion for the same period. This growth is driven by the increasing need for low-latency processing and the availability of cost-effective, ultra-low-power chips. Hyperscalers' Custom Silicon Push and Market Competition While NVIDIA maintains its leadership in high-end AI training, the landscape is evolving with AMD emerging as a formidable contender. AMD's Instinct MI series accelerators, such as the MI300X, are proving competitive for large language model inference, positioning AMD to solidify its role as a strong second player. Hyperscale cloud providers, including Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT), are making substantial investments in developing their custom silicon. This strategic move impacts their cloud service margins but fosters competitive differentiation, signaling a future multi-platform AI ecosystem where NVIDIA may face increasing competition and potential market share adjustments in segments beyond high-end training. The decentralization of AI infrastructure through Edge AI and Sovereign AI initiatives is expected to further diversify demand and stimulate innovation in the development of low-power, high-efficiency chips. Implications for Sector Growth and Investment Outlook The rapid advancements and deployment of SLMs are poised to drive continued growth across the Technology Sector, with particular emphasis on AI, Healthcare, Finance, and Manufacturing. The shift towards efficient, on-device AI solutions is anticipated to fuel demand for specialized hardware and software, creating new avenues for market participants. The competition among tech giants for market share in various industry applications is expected to intensify, leading to further innovation and strategic partnerships. Companies with robust SLM offerings and strong integration capabilities are likely to experience increased adoption and revenue, while those that lag in adapting to these technological shifts may face significant challenges. The emergence of Agentic AI — systems that autonomously pursue goals and make decisions — signifies another frontier in AI innovation, with projected market growth reaching trillions by 2030, particularly in areas like decentralized finance (DeFi). Investment vehicles such as the SoFi Agentic AI ETF are already providing investors access to companies leading in this transformative space, underscoring the market's confidence in the long-term potential of autonomous AI systems. The interplay between SLMs, Edge AI, and Agentic AI will be crucial factors to monitor in the evolving stock market landscape.
Snowflake (NYSE: SNOW) delivered robust fiscal second-quarter 2026 results, reporting accelerated product revenue growth and improved customer metrics. The strong performance led to a significant increase in its stock price post-earnings, though persistent concerns regarding profitability and a high valuation continue to be key discussion points for investors. Technology Sector Performance U.S. equities saw a notable advance in the technology sector following a series of strong earnings reports from key players. Among them, Snowflake (NYSE: SNOW), a prominent AI Data Cloud company, announced its fiscal second-quarter results for 2026, ending July 31, 2025. The report spurred a positive market reaction for the company, with its stock surging approximately 14% in the aftermath, contributing to its year-to-date gain exceeding 45%. Fiscal Second Quarter Performance Highlights Snowflake reported total revenue of $1.1 billion for the quarter, marking a 32% year-over-year growth. Product revenue, a critical metric for the company, also grew 32% year-over-year to $1.09 billion. These figures underscore a re-acceleration in growth rates, which was a positive signal for investors. The company also demonstrated strength in its customer metrics. The net revenue retention (NRR) rate remained robust at 125%. Snowflake expanded its base of large customers, reaching 654 customers with trailing 12-month product revenue exceeding $1 million. Furthermore, remaining performance obligations (RPO), an indicator of future revenue, increased by 33% year-over-year to $6.9 billion. Looking ahead, Snowflake raised its full-year fiscal 2026 product revenue guidance to approximately $4.4 billion, representing 27% growth. For the third fiscal quarter, product revenue is projected to be between $1.125 billion and $1.130 billion, indicating 25% to 26% growth. Market Reaction and Underlying Dynamics The positive market reaction to Snowflake's earnings was primarily driven by its strong top-line growth and improved operational metrics. The company's increasing integration with artificial intelligence (AI) initiatives also played a significant role in investor optimism. CEO Sridhar Ramaswamy highlighted the impact of AI on business development: > "Snowflake delivered yet another strong quarter, with product revenue of $1.09 billion, up a strong 32% year-over-year, and remaining performance obligations totaling $6.9 billion." He further noted that AI is influencing nearly 50% of new logos won in Q2 and powers 25% of deployed use cases, with over 6,100 accounts utilizing Snowflake's AI weekly. This demonstrates the company's growing reputation as a foundational platform for AI workloads. However, despite the operational gains, the company continues to face scrutiny regarding its profitability. Snowflake reported a GAAP net loss of $298 million in Q2, an improvement from previous quarters but still a significant loss. The company relies on non-GAAP adjustments to present a more favorable profitability picture, with a non-GAAP operating margin reaching 11% in Q2, up from 9% in Q1. Valuation and Broader Context Snowflake's valuation remains a critical point of contention among analysts. With a market capitalization of approximately $77 billion, the company trades at roughly 19 times sales. This is a considerably higher multiple when compared to tech giants like Alphabet (NASDAQ: GOOG), which trades at approximately 8 times sales, and Microsoft (NASDAQ: MSFT), at about 13 times sales. Both Alphabet and Microsoft boast significantly stronger cash flow, established profitability foundations, and more diversified revenue streams. This high valuation implies that investors are pricing in near-perfect execution and substantial future growth, leaving little margin for error, particularly given Snowflake's ongoing GAAP losses. Industry analysts, such as those from William Blair, have praised Snowflake's progress in developing an "end-to-end solution for customers looking to build AI applications," while Bank of America views Snowflake as "a true data platform in the large enterprise." These observations underscore the company's strategic positioning but do not entirely alleviate valuation concerns. Looking Ahead Investors will continue to closely monitor Snowflake's progress toward achieving GAAP profitability and the sustained impact of its AI integrations on revenue growth and customer acquisition. The company's guidance for a full-year fiscal 2026 non-GAAP adjusted free cash flow (FCF) margin of 25%, implying approximately $1.152 billion in FCF, signals confidence in its recurring cash flow generation capabilities. However, the market will assess whether Snowflake's performance continues to justify its premium valuation multiple, particularly as macroeconomic conditions evolve and competition within the data cloud and AI analytics space intensifies.
Mr. Sridhar Ramaswamy is the Chief Executive Officer of Snowflake Inc, joining the firm since 2024.
The current price of SNOW is $225.79, it has increased 0.06% in the last trading day.
Snowflake Inc belongs to Technology industry and the sector is Information Technology
Snowflake Inc's current market cap is $75.3B
According to wall street analysts, 49 analysts have made analyst ratings for Snowflake Inc, including 15 strong buy, 32 buy, 9 hold, 1 sell, and 15 strong sell
Looks like SNOW is catching a strong bid today because Fortune just crowned them #1 on their "Future 50" list, basically calling them the prom king of AI stocks for the second time in three years. The price action is confirming the hype, with the stock pushing up over 2% on solid volume.
Let's break down the alpha behind today's move.
1. Fundamental Catalyst: The Fortune #1 Ranking
The main driver is a major PR win. Snowflake announced it was ranked number one on the 2025 Fortune Future 50 list.
2. Technical Picture: Bullish Momentum
The chart is backing up the news. SNOW is showing solid technical strength across multiple timeframes.
3. Social & Sentiment Analysis
The narrative is strong and the chart looks constructive. Just don't be the one who FOMOs in after the news is already priced in. Maybe track the key resistance levels on Edgen Radar before you ape in, bro.