Latest news with #AIEnterprise
Yahoo
6 days ago
- Business
- Yahoo
Goldman Sachs revamps Nvidia stock price target ahead of earnings
Goldman Sachs revamps Nvidia stock price target ahead of earnings originally appeared on TheStreet. Wall Street's favorite AI chipmaker, Nvidia () , faces a key earnings test later this month, and Goldman Sachs just recalibrated its expectations. The reset comes at a time when investor sentiment has swung bullish, with hyperscaler spending and China market developments being a core focus. Goldman Sachs' analysts are flagging key levers that could potentially dictate Nvidia stock's path ahead as it continues racking up even bigger gains. Big expectations for Nvidia's Q2 Nvidia is set to report its fiscal Q2 (July quarter) earnings after the closing bell on August 27, 2025, with big expectations in play. Wall Street expects Nvidia to post a non-GAAP EPS of $1.00, GAAP EPS of $0.93, and revenue of $45.70 numbers represent a massive jump from last year's $30.04 billion in revenue and $0.68 EPS. For perspective, Nvidia guided Q2 sales to around $45 billion, plus or minus 2%, factoring in lost sales from previously stalled H20 GPU shipments to China. Here's what investors will be watching in particular: Blackwell ramp: Investors will want the scoop on GB200/NVL systems rollout, particularly when customers will get their first units. They'll also want to know how quickly the large rack-scale systems (NVL36/NVL72) are being adopted and whether there's a positive impact on sales. These systems are bigger and attract higher average selling prices (ASP). China licensing: The U.S. greenlit H20/MI308 GPU sales to China, but will be taking a 15% cut of those sales. Investors will want to know how much China could add to sales in Q3 and beyond, and the impact on profit margins in Q2. Gross margins: After a large one-time charge linked to unsold H20 chips in the previous quarters, Nvidia's adjusted gross margin was 71.3%. Management expects margins to claw back to the mid-70% range later this year, which makes their guidance all the more important. Networking: Q1 networking revenue came in at a superb $4.9 billion. Growth here could be a sign of broader adoption of GB200 systems and AI data center infrastructure. AI capex signals: Big tech remains relentless in its AI spending, with Google raising full-year capex to $85 billion, and Microsoft expecting $30 billion in Q3. Hence, any link to Nvidia's order backlog will be critical. Software monetization: Updates on Nvidia's AI Enterprise platform and NIM microservices, especially through Amazon's AWS Marketplace, could offer more insights into a new recurring revenue engine. So with the sentiment already sky-high, Nvidia will need to deliver a strong beat-and-raise quarter to keep the rally going. Goldman lifts Nvidia price target as earnings loom Goldman Sachs just gave a bullish nod to Nvidia heading into its fiscal Q2 results. Analyst James Schneider raised his price target from $185 to $200, representing roughly a 9.5% potential upside from current levels while keeping a buy rating. Schneider feels the investor sentiment is highly bullish, with most upbeat over upcoming results. This elevated optimism, though, sets a much higher bar for Nvidia's second-half commentary and guidance to the tougher comps and sky-high expectations, Schneider expects a 'clean beat-and-raise quarter.' He feels Nvidia's stock reaction will have everything to do with guidance exceeding expectations and whether sales to China are part of the estimates. Schneider also raised Datacenter sales estimates by nearly 8% on average, citing stronger-than-expected hyperscaler spending and mid-quarter data showing robust AI demand. Also, his Q2 and Q3 revenue projections of $41.9 billion and $51.5 billion are 2% and 8% above Mr. Market's consensus, respectively. He adds that with China sales back online, Nvidia could see an extra $20 billion in revenue and $0.40 in EPS by FY27. In the near term, Schneider feels that investors will be keenly following how Blackwell chips ramp outside China. Moreover, they'll also be interested in how China sales might impact margins, and the direction of gross margins in the second half. Looking further ahead, Schneider expects the market's focus in late 2025 will shift toward the potential direction for 2027. His FY27 EPS forecast is $6.75, and he believes much of 2026's upside is already priced in, making future growth signals critical. Nvidia's rally fueled by a string of blowout quarters Nvidia's stock has been on quite the run this year, up roughly 54% in the past three months and almost 40% over the past six. For the year, Nvidia stock is up an emphatic 72%, with its powerful momentum having everything to with its incredible earnings streak. The company delivered 10 straight quarterly EPS beats, consistently beating revenue estimates as AI infrastructure demand continues to impress. More News: Jim Cramer delivers straight talk on tricky S&P 500 market Bank of America drops shocking price target on hot weight-loss stock post-earnings JPMorgan drops 3-word verdict on Amazon stock post-earnings In fiscal Q1 2026 (April 2025), Nvidia posted earnings of $0.81 per share, beating by $0.06, on sales of $44.06 billion (up 69% year-over-year). Similarly, in fiscal Q4 2025, the company delivered $0.89 EPS (beat $0.04) on $39.33 billion (up 77.9% YOY), while fiscal Q3 2025 hit $0.81 on $35.08 billion (up 93.6% YOY). The real momentum kicked off in mid-2023, with year-over-year sales surging at triple-digit growth rates. Also, even in softer markets, Nvidia blew past expectations. Now, quarterly sales are more than seven times early-2023 levels, highlighting the sheer scale of its Sachs revamps Nvidia stock price target ahead of earnings first appeared on TheStreet on Aug 11, 2025 This story was originally reported by TheStreet on Aug 11, 2025, where it first appeared. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
6 days ago
- Business
- Yahoo
Goldman Sachs revamps Nvidia stock price target ahead of earnings
Goldman Sachs revamps Nvidia stock price target ahead of earnings originally appeared on TheStreet. Wall Street's favorite AI chipmaker, Nvidia () , faces a key earnings test later this month, and Goldman Sachs just recalibrated its expectations. The reset comes at a time when investor sentiment has swung bullish, with hyperscaler spending and China market developments being a core focus. Goldman Sachs' analysts are flagging key levers that could potentially dictate Nvidia stock's path ahead as it continues racking up even bigger gains. Big expectations for Nvidia's Q2 Nvidia is set to report its fiscal Q2 (July quarter) earnings after the closing bell on August 27, 2025, with big expectations in play. Wall Street expects Nvidia to post a non-GAAP EPS of $1.00, GAAP EPS of $0.93, and revenue of $45.70 numbers represent a massive jump from last year's $30.04 billion in revenue and $0.68 EPS. For perspective, Nvidia guided Q2 sales to around $45 billion, plus or minus 2%, factoring in lost sales from previously stalled H20 GPU shipments to China. Here's what investors will be watching in particular: Blackwell ramp: Investors will want the scoop on GB200/NVL systems rollout, particularly when customers will get their first units. They'll also want to know how quickly the large rack-scale systems (NVL36/NVL72) are being adopted and whether there's a positive impact on sales. These systems are bigger and attract higher average selling prices (ASP). China licensing: The U.S. greenlit H20/MI308 GPU sales to China, but will be taking a 15% cut of those sales. Investors will want to know how much China could add to sales in Q3 and beyond, and the impact on profit margins in Q2. Gross margins: After a large one-time charge linked to unsold H20 chips in the previous quarters, Nvidia's adjusted gross margin was 71.3%. Management expects margins to claw back to the mid-70% range later this year, which makes their guidance all the more important. Networking: Q1 networking revenue came in at a superb $4.9 billion. Growth here could be a sign of broader adoption of GB200 systems and AI data center infrastructure. AI capex signals: Big tech remains relentless in its AI spending, with Google raising full-year capex to $85 billion, and Microsoft expecting $30 billion in Q3. Hence, any link to Nvidia's order backlog will be critical. Software monetization: Updates on Nvidia's AI Enterprise platform and NIM microservices, especially through Amazon's AWS Marketplace, could offer more insights into a new recurring revenue engine. So with the sentiment already sky-high, Nvidia will need to deliver a strong beat-and-raise quarter to keep the rally going. Goldman lifts Nvidia price target as earnings loom Goldman Sachs just gave a bullish nod to Nvidia heading into its fiscal Q2 results. Analyst James Schneider raised his price target from $185 to $200, representing roughly a 9.5% potential upside from current levels while keeping a buy rating. Schneider feels the investor sentiment is highly bullish, with most upbeat over upcoming results. This elevated optimism, though, sets a much higher bar for Nvidia's second-half commentary and guidance to the tougher comps and sky-high expectations, Schneider expects a 'clean beat-and-raise quarter.' He feels Nvidia's stock reaction will have everything to do with guidance exceeding expectations and whether sales to China are part of the estimates. Schneider also raised Datacenter sales estimates by nearly 8% on average, citing stronger-than-expected hyperscaler spending and mid-quarter data showing robust AI demand. Also, his Q2 and Q3 revenue projections of $41.9 billion and $51.5 billion are 2% and 8% above Mr. Market's consensus, respectively. He adds that with China sales back online, Nvidia could see an extra $20 billion in revenue and $0.40 in EPS by FY27. In the near term, Schneider feels that investors will be keenly following how Blackwell chips ramp outside China. Moreover, they'll also be interested in how China sales might impact margins, and the direction of gross margins in the second half. Looking further ahead, Schneider expects the market's focus in late 2025 will shift toward the potential direction for 2027. His FY27 EPS forecast is $6.75, and he believes much of 2026's upside is already priced in, making future growth signals critical. Nvidia's rally fueled by a string of blowout quarters Nvidia's stock has been on quite the run this year, up roughly 54% in the past three months and almost 40% over the past six. For the year, Nvidia stock is up an emphatic 72%, with its powerful momentum having everything to with its incredible earnings streak. The company delivered 10 straight quarterly EPS beats, consistently beating revenue estimates as AI infrastructure demand continues to impress. More News: Jim Cramer delivers straight talk on tricky S&P 500 market Bank of America drops shocking price target on hot weight-loss stock post-earnings JPMorgan drops 3-word verdict on Amazon stock post-earnings In fiscal Q1 2026 (April 2025), Nvidia posted earnings of $0.81 per share, beating by $0.06, on sales of $44.06 billion (up 69% year-over-year). Similarly, in fiscal Q4 2025, the company delivered $0.89 EPS (beat $0.04) on $39.33 billion (up 77.9% YOY), while fiscal Q3 2025 hit $0.81 on $35.08 billion (up 93.6% YOY). The real momentum kicked off in mid-2023, with year-over-year sales surging at triple-digit growth rates. Also, even in softer markets, Nvidia blew past expectations. Now, quarterly sales are more than seven times early-2023 levels, highlighting the sheer scale of its Sachs revamps Nvidia stock price target ahead of earnings first appeared on TheStreet on Aug 11, 2025 This story was originally reported by TheStreet on Aug 11, 2025, where it first appeared.
Business Upturn
6 days ago
- Business
- Business Upturn
GPU as a Service Market Set to Hit $26.62 Billion by 2030: What's Driving the Growth?
By GlobeNewswire Published on August 11, 2025, 05:30 IST Delray Beach, FL, Aug. 10, 2025 (GLOBE NEWSWIRE) — The report 'GPU as a Service Market by Service Model (IaaS, PaaS), GPU Type (High-end GPUs, Mid-range GPUs, Low-end GPUs), Deployment (Public Cloud, Private Cloud, Hybrid Cloud), Enterprise Type (Large Enterprises, SMEs) – Global Forecast to 2030″ The GPU as a Service market is expected to grow from USD 8.21 billion in 2025 and is estimated to reach USD 26.62 billion by 2030; it is expected to grow at a Compound Annual Growth Rate (CAGR) of 26.5% from 2025 to 2030. The surge in AI and machine learning applications is a primary driver for the GPU as a Service (GPUaaS) market. Industries such as healthcare, finance, and automotive require high-performance computing for tasks like data analysis, image recognition, and autonomous driving. Download PDF Brochure: Major Key Players in the GPU as a Service Industry: Amazon web Servies, Inc. (US), Microsoft (US), Google (US), Oracle (US), IBM (US), Coreweave (US), Alibaba Cloud (China), Lambda (US), Tencent Cloud (China), (India), among others. GPU as a Service Market Segmentation: By deployment, hybrid cloud segment is projected to grow at a high CAGR during the forecast period. Hybrid cloud deployment in the GPU as a Service (GPUaaS) market will grow at a high CAGR in the forecast period because of its ability to balance data security, cost-effectiveness and flexibility. Hybrid cloud models are being adopted increasingly by businesses to take advantage of both on-premises infrastructure and public cloud resources. This solution is especially helpful for AI inference and training workloads that demand scalable GPU capabilities without compromising data privacy. Financial institutions and healthcare organizations, for example, leverage hybrid cloud deployments to process sensitive data locally while utilizing cloud GPUs for training AI models. Companies such as NVIDIA offers DGX Cloud and AI Enterprise, enabling seamless deployment of AI across hybrid environments. By enterprise type- small and medium-sized enterprises (SMEs) segment will account for the high CAGR in 2025-2030. The small and medium-sized enterprises (SMEs) segment in the GPU as a Service (GPUaaS) market is anticipated to grow at a high CAGR during the forecast period, driven by the increasing adoption of AI, machine learning (ML), and data analytics. SMEs often lack the capital to invest in expensive on-premises GPU infrastructure, making cloud-based GPUaaS a cost-efficient and scalable option. AWS, Azure, and Google Cloud provide SMEs with on-demand access to high-performance GPUs, which can be used to speed up AI model training, video rendering, and data analysis without having to make huge initial investments. GPUaaS supports pay-as-you-go pricing, which enables SMEs to efficiently manage operational costs. North America region will hold highest share in the GPU as a Service market. North America holds the maximum market share of the GPU as a Service market because of its strong technological infrastructure, advanced AI ecosystem, and presence of leading cloud service providers in the region. Amazon Web Services (AWS), Microsoft Azure and Google Cloud have headquarters in the region and provide scalable and reliable GPUaaS solutions. The adoption of artificial intelligence (AI) and machine learning (ML) among various industries like healthcare, finance, and gaming drives strong demand for GPU resources. Ask for Sample Report: GPU as Service Market Key Takeaway By 2025, the GPU as a Service market is projected to reach USD 8.81 billion, and is expected to grow to USD 26.62 billion by 2030, at a CAGR of approximately 26.5% By market dynamics, the growing need for parallel computing in AI, machine learning, deep learning, and data science applications is fueling the adoption of GPUaaS, offering scalable performance without infrastructure costs. By deployment model, the hybrid cloud segment is projected to grow at the highest CAGR during the forecast period, as enterprises seek to balance data control and cost-effective GPU scaling. By service model, the Infrastructure as a Service (IaaS) segment dominates the market due to its flexibility in providing GPU power to startups, research institutions, and enterprises on demand. By vertical, the media & entertainment segment is expected to witness significant adoption of GPUaaS, particularly for real-time rendering, video editing, game development, and animation. By regional market, North America is estimated to hold the largest market share due to the presence of major cloud service providers, early technology adoption, and robust AI research and deployment. By regional growth, the Asia Pacific region is expected to register the highest CAGR during the forecast period, driven by cloud computing growth, AI adoption, and increasing investments in GPU infrastructure. By competitive outlook, the market is moderately consolidated, with major players focusing on partnerships, product innovation, and cloud-based GPU infrastructure to maintain a competitive edge. Disclaimer: The above press release comes to you under an arrangement with GlobeNewswire. Business Upturn takes no editorial responsibility for the same. Ahmedabad Plane Crash GlobeNewswire provides press release distribution services globally, with substantial operations in North America and Europe.
Korea Herald
08-07-2025
- Business
- Korea Herald
WEKA Debuts NeuralMesh Axon For Exascale AI Deployments
New Offering Delivers a Unique Fusion Architecture That's Being Leveraged by Industry-Leading AI Pioneers Like Cohere, CoreWeave, and NVIDIA to Deliver Breakthrough Performance Gains and Reduce Infrastructure Requirements For Massive AI Training and Inference Workloads PARIS and CAMPBELL, Calif., July 8, 2025 /PRNewswire/ -- From RAISE SUMMIT 2025: WEKA unveiled NeuralMesh Axon, a breakthrough storage system that leverages an innovative fusion architecture designed to address the fundamental challenges of running exascale AI applications and workloads. NeuralMesh Axon seamlessly fuses with GPU servers and AI factories to streamline deployments, reduce costs, and significantly enhance AI workload responsiveness and performance, transforming underutilized GPU resources into a unified, high-performance infrastructure layer. Building on the company's recently announced NeuralMesh storage system, the new offering enhances its containerized microservices architecture with powerful embedded functionality, enabling AI pioneers, AI cloud and neocloud service providers to accelerate AI model development at extreme scale, particularly when combined with NVIDIA AI Enterprise software stacks for advanced model training and inference optimization. NeuralMesh Axon also supports real-time reasoning, with significantly improved time-to-first-token and overall token throughput, enabling customers to bring innovations to market faster. AI Infrastructure Obstacles Compound at Exascale Performance is make-or-break for large language model (LLM) training and inference workloads, especially when running at extreme scale. Organizations that run massive AI workloads on traditional storage architectures, which rely on replication-heavy approaches, waste NVMe capacity, face significant inefficiencies, and struggle with unpredictable performance and resource allocation. The reason? Traditional architectures weren't designed to process and store massive volumes of data in real-time. They create latency and bottlenecks in data pipelines and AI workflows that can cripple exascale AI deployments. Underutilized GPU servers and outdated data architectures turn premium hardware into idle capital, resulting in costly downtime for training workloads. Inference workloads struggle with memory-bound barriers, including key-value (KV) caches and hot data, resulting in reduced throughput and increased infrastructure strain. Limited KV cache offload capacity creates data access bottlenecks and complicates resource allocation for incoming prompts, directly impacting operational expenses and time-to-insight. Many organizations are transitioning to NVIDIA accelerated compute servers, paired with NVIDIA AI Enterprise software, to address these challenges. However, without modern storage integration, they still encounter significant limitations in pipeline efficiency and overall GPU utilization. Built For The World's Largest and Most Demanding Accelerated Compute Environments To address these challenges, NeuralMesh Axon's high-performance, resilient storage fabric fuses directly into accelerated compute servers by leveraging local NVMe, spare CPU cores, and its existing network infrastructure. This unified, software-defined compute and storage layer delivers consistent microsecond latency for both local and remote workloads—outpacing traditional local protocols like NFS. Additionally, when leveraging WEKA's Augmented Memory Grid capability, it can provide near-memory speeds for KV cache loads at massive scale. Unlike replication-heavy approaches that squander aggregate capacity and collapse under failures, NeuralMesh Axon's unique erasure coding design tolerates up to four simultaneous node losses, sustains full throughput during rebuilds, and enables predefined resource allocation across the existing NVMe, CPU cores, and networking resources—transforming isolated disks into a memory-like storage pool at exascale and beyond while providing consistent low latency access to all addressable data. Cloud service providers and AI innovators operating at exascale require infrastructure solutions that can match the exponential growth in model complexity and dataset sizes. NeuralMesh Axon is specifically designed for organizations operating at the forefront of AI innovation that require immediate, extreme-scale performance rather than gradual scaling over time. This includes AI cloud providers and neoclouds building AI services, regional AI factories, major cloud providers developing AI solutions for enterprise customers, and large enterprise organizations deploying the most demanding AI inference and training solutions that must agilely scale and optimize their AI infrastructure investments to support rapid innovation cycles. Delivering Game-Changing Performance for Accelerated AI Innovation Early adopters, including Cohere, the industry's leading security-first enterprise AI company, are already seeing transformational results. Cohere is among WEKA's first customers to deploy NeuralMesh Axon to power its AI model training and inference workloads. Faced with high innovation costs, data transfer bottlenecks, and underutilized GPUs, Cohere first deployed NeuralMesh Axon in the public cloud to unify its AI stack and streamline operations. "For AI model builders, speed, GPU optimization, and cost-efficiency are mission-critical. That means using less hardware, generating more tokens, and running more models—without waiting on capacity or migrating data," said Autumn Moulder, vice president of engineering at Cohere. "Embedding WEKA's NeuralMesh Axon into our GPU servers enabled us to maximize utilization and accelerate every step of our AI pipelines. The performance gains have been game-changing: Inference deployments that used to take five minutes can occur in 15 seconds, with 10 times faster checkpointing. Our team can now iterate on and bring revolutionary new AI models, like North, to market with unprecedented speed." To improve training and help develop North, Cohere's secure AI agents platform, the company is deploying WEKA's NeuralMesh Axon on CoreWeave Cloud, creating a robust foundation to support real-time reasoning and deliver exceptional experiences for Cohere's end customers. "We're entering an era where AI advancement transcends raw compute alone—it's unleashed by intelligent infrastructure design. CoreWeave is redefining what's possible for AI pioneers by eliminating the complexities that constrain AI at scale," said Peter Salanki, CTO and co-founder at CoreWeave. "With WEKA's NeuralMesh Axon seamlessly integrated into CoreWeave's AI cloud infrastructure, we're bringing processing power directly to data, achieving microsecond latencies that reduce I/O wait time and deliver more than 30 GB/s read, 12 GB/s write, and 1 million IOPS to an individual GPU server. This breakthrough approach increases GPU utilization and empowers Cohere with the performance foundation they need to shatter inference speed barriers and deliver advanced AI solutions to their customers." "AI factories are defining the future of AI infrastructure built on NVIDIA accelerated compute and our ecosystem of NVIDIA Cloud Partners," said Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA. "By optimizing inference at scale and embedding ultra-low latency NVMe storage close to the GPUs, organizations can unlock more bandwidth and extend the available on-GPU memory for any capacity. Partner solutions like WEKA's NeuralMesh Axon deployed with CoreWeave provide a critical foundation for accelerated inferencing while enabling next-generation AI services with exceptional performance and cost efficiency." The Benefits of Fusing Storage and Compute For AI Innovation NeuralMesh Axon delivers immediate, measurable improvements for AI builders and cloud service providers operating at exascale, including: "The infrastructure challenges of exascale AI are unlike anything the industry has faced before. At WEKA, we're seeing organizations struggle with low GPU utilization during training and GPU overload during inference, while AI costs spiral into millions per model and agent," said Ajay Singh, chief product officer at WEKA. "That's why we engineered NeuralMesh Axon, born from our deep focus on optimizing every layer of AI infrastructure from the GPU up. Now, AI-first organizations can achieve the performance and cost efficiency required for competitive AI innovation when running at exascale and beyond." Availability NeuralMesh Axon is currently available in limited release for large-scale enterprise AI and neocloud customers, with general availability scheduled for fall 2025. For more information, visit: About WEKA WEKA is transforming how organizations build, run, and scale AI workflows through NeuralMesh™, its intelligent, adaptive mesh storage system. Unlike traditional data infrastructure, which becomes more fragile as AI environments expand, NeuralMesh becomes faster, stronger, and more efficient as it scales, growing with your AI environment to provide a flexible foundation for enterprise and agentic AI innovation. Trusted by 30% of the Fortune 50 and the world's leading neoclouds and AI innovators, NeuralMesh maximizes GPU utilization, accelerates time to first token, and lowers the cost of AI innovation. Learn more at or connect with us on LinkedIn and X.
Forbes
07-07-2025
- Business
- Forbes
L'Oréal's AI Weapon Could Kill Traditional Beauty Industry Forever
L'Oréal has announced a major partnership with NVIDIA to deploy generative AI across its beauty ... More empire, creating personalized marketing campaigns and 3D product visualizations that could revolutionize how cosmetics are designed and sold Every industry is finding and deploying use cases for generative AI, from automating routine workflows to designing entirely new products and services. The beauty industry, traditionally always highly tech-focused, is no exception. Now, L'Oréal, the world's largest cosmetics company, has announced a major deal with Nvidia that will supercharge its ambitions to roll out generative AI across its business. L'Oréal is no stranger to AI, and I've been following its progress in the field since 2019. However, it clearly believes the game has moved on and that generative AI, capable of creating entirely new content from existing data, will be hugely significant to its future. Through the partnership announced this month (June) at the VivaTech Paris Expo, L'Oréal will join Nvidia's AI Enterprise microservices ecosystem. One of the first initiatives breaks new ground for the industry by spanning design and marketing functions, bringing 3D product visualizations to life for both marketing and product research purposes. So, is L'Oréal getting set to unleash the ultimate AI glow-up on the industry? And how is the beauty and cosmetics industry as a whole adapting to the game-changing opportunities? Let's take a look. How Is L'Oréal Using Generative AI? L'Oréal's partnership with Nvidia will enable it to scale its CreAItech Generative AI platform, which creates visualizations and assets from product models. The aim here is to reduce the time it takes creative teams to design and render images and 3D models for marketing or research purposes. This process can be automated, enabling its teams to quickly test and iterate different marketing strategies. Images could be personalized for individual customers or for different local markets. Localizing products in this way is hugely important but also resource-intensive for manufacturers of consumer packaged goods, and generative AI promises efficiencies here. Aside from that, the plans announced so far under the partnership focus on laying the foundations for future generative AI initiatives across the company. This takes the form of a new AI development tool called the AI Refinery that will power experiences behind Noli, the AI-powered beauty marketplace recently launched by L'Oréal. This uses the mountains of data on skin types, hair colors and product formulations held by the business to create AI-powered search engines and other consumer-facing tools. Another standout example of L'Oréal's AI innovation is Beauty Genius, its generative AI-powered personal beauty assistant. Designed to tackle decision fatigue in a crowded beauty market, Beauty Genius offers 24/7 personalized guidance based on L'Oréal's proprietary knowledge, dermatological data, and real-time virtual try-on tools. It uses generative AI, augmented reality, and computer vision to recommend routines, answer intimate questions, and help users discover ideal products tailored to their skin tone, hair type, and beauty concerns, all in a secure, private, conversational interface. These are just the early use cases that make up the low-hanging fruit, however. The big picture is that L'Oréal plans to utilize its partnership with NVIDIA to iterate generative AI across various enterprise functions and gain a competitive edge over its competitors. However, they also have their own plans. What Are Other Industry Leaders Doing? Beauty and cosmetics industry leaders have been keen early adopters of AI, and that trend is continuing into the generative AI era. In addition to L'Oréal, we recently saw Estee Lauder partner with Adobe to reduce the time it takes to create and launch digital marketing campaigns, utilizing Adobe's Firefly generative AI platform and its Firefly Services APIs. It also created a Voice-Enabled Makeup Assistant designed to make beauty more accessible for the visually impaired by guiding the user through the application process and suggesting improvements. Unilever has also used generative AI to create "ultra-personalized experiences," such as a virtual scalp therapist for its Dove brand, which provides expert skincare advice. This aligns with trends across other industries, where generative AI is being used to build more personal connections with customers. It also tallies with Salesforce research, showing that 76 percent of marketers now use generative AI for basic content creation, and 63 percent use it to analyze marketing data. Further innovation comes from AI-native startups and scaleups operating in the beauty and cosmetics space. One notable example here is Revieve, which offers a range of AI-powered tools for skincare and makeup over Google Cloud. This means smaller players in the industry that don't have the resources to engineer their own AI infrastructure can still offer AI services to their customers. Generative AI And The Future Of The Beauty And Cosmetics Industry I believe we have only just started to see the tip of the iceberg in terms of the impact of generative AI. Marketing and customer-facing functions often serve as the testbed for these applications. But as businesses become more confident in their AI strategy, we will start to see it used in product design and testing. For example, another generative AI initiative announced by L'Oréal involves building and training a foundation model that it hopes will reduce material and energy waste during the formulation process. As with other industries, we will increasingly see AI used to streamline and drive efficiencies in business processes, from hiring and onboarding staff to compliance and managing legal tasks. And, just around the corner, a new generation of agentic tools and platforms promise to automate even more complex tasks in the next wave of AI innovation. With the emergence of as-a-service tools and platforms, generative AI is no longer restricted to big names like L'Oréal and Estee Lauder. This means the next big disruptive force in cosmetics and beauty could be someone unexpected, and the opportunities are huge for businesses that are ready to ride the wave.
