Shilpa Ranganathan

Alphabet Inc. just posted its Q2 results and outlook for the remainder of the year. There is a lot to unpack here, and I think that my colleagues will have their own assessments. So, I will keep my comments specific to the Google Cloud division which aligns with my areas of research. If you want to hear or see more about other parts of the business please check out Anshel Sag, Matthew Kimball and Patrick Moorhead . POSITIVES 1) Financially, Google Cloud hit two major milestones $10B in quarterly revenue and $1.2 B in profit contribution. That's a bit north of 20% of total revenue for the quarter. 2) AI is a driver for Cloud with the majority of it's top 100 customers using AI capabilities. There was also a mention of over 2M developers using Google AI 3) Despite a strong emphasis on its own Gemini models, Google is continuing to expand its offers through partnerships with other AI and enterprise players. OPEN QUESTIONS 1) Spending looks to remaining flattish in terms of Head Count and CAP/EX. It seems like there may be some lag in terms of the market catching up to what has been an aggressive build up the past few years. However, it might also be a sign that new technology (chips and models) is more efficient. 2) When asked about areas of fastest AI growth and slower AI growth. CEO Sundar Pichai indicated that search and consumers had faster growth. And enterprise use was slower. Since Alphabet serves both areas that's good. But, it does beg the question about what we may hear from other enterprise only vendors. We shall see.

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More than 60% of all funded generative AI startups, and nearly 90% of today’s generative AI unicorns are Google Cloud customers. At #GoogleCloudNext we announced how we're making it easier for them to run on Google Cloud. Learn more ⬇️

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In this podcast, Google Cloud's Oliver Parker vice president, global generative AI GTM, and Miku Jha, director, AI/ML and generative AI partner engineering, sit down with Bob Evans to talk about how businesses can safely and efficiently scale their generative AI efforts, focusing on high-value opportunities while leveraging Google Cloud’s open platform, extensive ecosystem, and advanced AI/ML capabilities to deliver meaningful results for clients. Scaling Generative AI The Big Themes: Prioritizing cost and impact: Businesses are focused on the dual priorities of cost efficiency and impactful results when implementing generative AI. With numerous potential applications for generative AI, organizations need to evaluate which projects will deliver the highest value. This approach involves collaborating with partners to identify the opportunities that offer the most substantial benefits. Open ecosystem advantage: Google Cloud’s emphasis on openness and flexibility is a key differentiator in the AI landscape. Its commitment to open systems and platforms allows for various models and technologies, including first-party and open-source offerings. This open ecosystem approach enables businesses to leverage various AI models and infrastructure options, enhancing scalability and adaptability. End-to-end offerings: Google Cloud offers a comprehensive AI stack that supports a wide range of applications, from foundational models to advanced infrastructure. This end-to-end tool facilitates the development and scaling of sophisticated AI applications. The integration of various layers, including model capabilities, hardware options, and platform services, enables partners to deliver efficient and effective AI offerings. The Big Quote: “Having great models is one thing, but having differentiated infrastructure and a platform — all those three things come together, and I think we are unique in that sense.” More from Google Cloud: Learn more about Google Cloud and AI.

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🤓 No time to read the full white paper on NetApp's innovative RAG solution on docs.netapp.com, "Doc"? I've got you covered! This blog post breaks down the key insights so you can grasp how NetApp is revolutionizing AI and enhancing user experiences with technical content. Get a quick and informative overview without diving into the entire paper. Check out the blog post here: Read here: https://lnkd.in/epQBknnz #NetApp #ArtificialIntelligence #AIInnovation #RAGSystems #MachineLearning #UserExperience #TechnicalDocumentation #ContentExperience #Innovation

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Hear from Sachin Gupta, VP and GM, Infrastructure and Solution Group, Google Cloud to learn customer-proven strategies for AI-powered innovation and modernization, leveraging the best of public cloud, edge, sovereign, and cross-cloud infrastructure and services. See how leaders are achieving real business outcomes faster and more cost-effectively in industries like retail, healthcare, and even the most stringent regulated industries. Plus, see how Gemini is dramatically simplifying and reimagining cloud as we know it.

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Join Google Cloud CEO Thomas Kurian and industry leaders to discover how AI is reshaping businesses across the globe. Learn from leading customers how they're harnessing Google's generative AI to drive innovation and gain direct insights into the latest AI advancements and their practical applications. Register now!

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Leading your company’s AI strategy? Then stop scrolling. Check out Google Cloud's new ebook—a guide to help technical leaders architect robust, scalable, and cost-effective #generativeAI systems. What will you discover inside? 🔵 Actionable strategies to evaluate AI platforms 🔴 Best practices for optimising resources with cost-efficient AI technologies 🟡 Examination of cost, scalability, security, and performance dimensions 🟢 And much more!

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Microsoft's chief scientist, Jaime Teevan, says get ready for another AI change... Soon AI will start prompting us, instead of the other way around! For more on her vision for how this change will help spur new ways of thinking, read her article linked below. #msftadvocate #ai #copilot https://lnkd.in/guyDhJfM

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Accelerate your cloud journey with our new generative AI assistant on AWS re:Post https://repost.aws/. Collaborate with re:Post Agent and community experts to build your cloud workloads with confidence. #AWSrePost #GenerativeAIAssistant #CloudDevelopment 🤖💡🚀

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One of my favorite quotes from Sundar Pichai during the Google - Alphabet earnings call this week: “Our commitment to innovation, as well as our long-term focus and investment in AI, are paying off and driving success for the company and for our customers.” Every day, I see first-hand the tangible business impact that Google Cloud's gen AI solutions can have for our customers – from increased revenue and reduced costs, to better customer engagement and employee satisfaction. Given our customers’ success in this space, it’s no surprise that our AI/ML capabilities were listed as our # 1️⃣ strength in the Critical Capabilities assessment as part of Gartner's 2024 Magic Quadrant™ for Strategic Cloud Platform Services. We made a notable jump in the Leader category this year – coming in ahead of AWS in Vision and ahead of MSFT in Execution. https://lnkd.in/gFhsG9vU

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As an AWS employee, I'm always eager to expand my skills and knowledge through hands-on experiments during my weekends (fun stuff). One of the things that has truly impressed me is the ease of use and access to models, including open-source ones, provided by Amazon SageMaker and Bedrock. I truly believe that the future of Generative AI lies in adopting a multi-model approach, and the recent launch of Llama 3 further reinforces this idea. In the past, I found Amazon SageMaker to be challenging to use and lacking access to the latest models. However, that's no longer the case! This past weekend I had a really good experience using SageMaker and Bedrock to effortlessly connect Llama 3 to a chatbot. The process was straightforward and user-friendly. I had previously done the same with Claude 3, and the ability to choose any model and use it with the same simple UI is a significant advantage. My next goal is to do some use case comparisons between various models. If you're interested in exploring the capabilities of Amazon SageMaker and Bedrock, I highly recommend giving them a try. The seamless integration of various models, including cutting-edge ones like Llama 3, opens up a world of possibilities for GenAI enthusiasts and professionals alike. Here is my step by step approach that anyone can follow in minutes! 😀 https://lnkd.in/evgNUb2v Feel free to reach out if you have any questions or would like to discuss this further. I'm always happy to share my experiences and insights! #AmazonSageMaker #Bedrock #GenAI #Llama3 #MultiModelApproach

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📣 📣 📣 - Important partnership announcement! GitHub and JFrog have joined forces to deliver enterprises a seamless, unified experience for generating, managing, securing, and delivering software across the software supply chain. 🤝 #GitHub #JFrog #partnership 🐸 More details below 👇🏼

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Discover how Oracle Cloud's computer vision and data science services improve the speed of #AI predictions by utilizing the NVIDIA Triton Inference Server: https://lnkd.in/dDwTSCQy

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A core tenet for #AWS has always been that, if it was possible to take care of scaling, resilience, availability, security, and cost management of our customer's infrastructure components, we should do so. Eventually, this became known as "serverless", which encompasses much more than executing code without the need for servers. Amazon SQS and S3 were the first AWS services launched, and they were "serverless" from day one. A good example of this philosophy in practice is Aurora Serverless, which provides on-demand auto-scaling for Amazon Aurora databases. It proactively scales up resources during peak periods to ensure the predictable performance when you require it. When demand subsides, it seamlessly scales back down, reducing waste. And with granular, second-by-second billing, you can truly optimize for cost. Remember, the essence of frugal architecture is robust monitoring driving the ability to optimize costs. To dive deeper into how Aurora Serverless exemplifies this principle, I recommend reading the latest paper here: https://lnkd.in/e4uPK6wv #AWS #serverless #databases

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