Breaking Analysis with Dave Vellante

Discussion on the effects of cloud optimization on cloud companies, Snowflake's renewal with Microsoft, Google's focus on building its own data cloud stack, and a deceleration in spend momentum for Snowflake. The chapter also explores Snowflake's spending momentum and data consolidation challenges, the evolution of cloud apps and the data stack, Snowflake's opportunity on Google Cloud and the role of DevOps, Google's efforts to extend application services and control plane beyond Google Cloud, and differentiating Google Cloud by emphasizing solutions.

This podcast discusses the delays in the FTC's approval of Broadcom's acquisition of VMware and the potential impact on customers. It explores the challenges of multi-cloud computing and VMware's role in solving multi-cloud chaos. The discussion also includes VMware's range of products and partnerships, the potential separation of Carbon Black as a standalone security product, and VMware's decision on security and potential asset sell-off.

The data from enterprise customers is clear but conflicted. While 94% of customers say they’re spending more on AI this year, they’re doing so with budget constraints that will steal from other initiatives. As well, the choice of where customers plan to run generative AI is split almost exactly down the middle in terms of public cloud vs. on-premises/edge. Further complicating matters, developers report the experiences in the public cloud with respect to feature richness and velocity of innovation has been outstanding. At the same time, organizations express valid concerns about IP leakage, compliance, legal risks and cost that will limit their use of the public cloud. In this Breaking Analysis we’ll share the most recent data and thinking around the adoption of large language models and address the factors to consider when thinking about how the market will evolve. As always, we’ll share the latest ETR data to shed new light on key issues customers face balancing risk with time to value.Google memo - we have no moat and neither does OpenAIhttps://www.semianalysis.com/p/google-we-have-no-moat-and-neitherJanelle Teng - AI in the Cloud article on Substack:https://nextbigteng.substack.com/p/ai-model-layer-the-new-frontline-of-cloud-warsA16z on the economics of AI:https://a16z.com/2023/08/03/the-economic-case-for-generative-ai-and-foundation-models/Wall St Journal Article citing AWS, Google, MSFT, Dell & HPE POVhttps://www.wsj.com/articles/the-ai-boom-is-here-the-cloud-may-not-be-ready-1a51724d?reflink=mobilewebshare_permalinkTechnalysis GenAI study of 1,000 ITDMS:https://www.technalysisresearch.com/downloads/TECHnalysis%20Research%20Generative%20AI%20in%20Enterprise%20Survey%20Highlights.pdfAWS Outposts at the edge with Sagemaker - Circa 2021https://aws.amazon.com/blogs/machine-learning/machine-learning-at-the-edge-with-aws-outposts-and-amazon-sagemaker/

After a tough 2022, the first half of 2023 has shown impressive strength and many technology bets have paid off. For sure investors in the so-called Magnificent Seven, i.e. Apple, Alphabet, Amazon, Meta, Nvidia and Tesla have been rewarded. But sharp investors have sought alpha beyond these issues, riding the wave of secular trends in AI, cybersecurity, cloud infrastructure and software as well as other emerging spaces like cleantech and robotics. As we enter the second half of 2023, the runup in tech combined with macro uncertainty has many investors taking a cautious posture. But we believe the long term outlook for firms that can capitalize on the AI wave remains extremely attractive as an unstoppable force.   Hello and welcome to this week’s Wikibon CUBE Insights, Powered by ETR. In this Breaking Analysis we’re pleased to have back, founder and Chief Investment Officer of Spear Invest, Ivana Delevska to assess the current state of the market and explore how this investor is playing AI’s rising tide.  Spear Investment Deckhttps://19544476.fs1.hubspotusercontent-na1.net/hubfs/19544476/Spear%20Alpha%20Investment%20Case%20Approved.pdfSpear Advisors Fund Letter from its CIOhttps://seekingalpha.com/article/4599648-spear-advisors-q1-2023-fund-letterPlatformonomics Repatriation Indexhttps://www.platformonomics.com/2023/05/platformonomics-repatriation-index-q1-2023-surfs-up/Revised Wikibon Cloud Forecasthttps://wikibon.com/breaking-analysis-what-leaked-court-docs-tell-us-about-aws-azure-google-cloud-market-shares/

Recently leaked court documents during the Microsoft Activision hearing require us to revisit our cloud forecasts and market share data. The poorly redacted docs, which have since been removed from public viewing, suggest that Microsoft’s Azure revenue is at least 25% lower than our previous estimates. As a result, we’ve cut and revised our Azure revenue figures which in turn increases AWS’ big 4 hyperscale cloud market share. Our new estimates show that AWS maintains a greater than 50% share of revenue through 2023. While the change also helps Google Cloud, its market share is only modestly affected. In this Breaking Analysis we update our hyperscaler cloud revenue estimates and market share data. We’ll also explain how the ETR data on cloud should be interpreted in this context and look forward to potential catalysts for cloud growth, including acceleration in Q4 attributable to generative AI.Microsoft annual 10K:https://microsoft.gcs-web.com/static-files/e2931fdb-9823-4130-b2a8-f6b8db0b15a9Wikibon repatriation report:https://wikibon.com/breaking-analysis-desperately-seeking-cloud-repatriation/SiliconANGLE article on leaked court documents with Azure revenue data:https://siliconangle.com/2023/06/29/court-filing-shows-microsoft-azure-generated-lower-expected-34b-revenue-2022/Constellation report on cloud optimization:https://www.constellationr.com/blog-news/private-cloud-compelling-option-cios-insights-new-research

Cloud complexity, tools sprawl and the AI awakening further tip the balance in favor of cyber attackers. Combined with corporate inertia, AI-washing, LLM inconsistency and the pace of change, we believe for now anyway, adversaries have the advantage over defenders. Moreover, macro spending headwinds continue to force organizations to make budget tradeoffs, not the least of which is how to fund AI experiments and deployments. Notably, however, 45% of organizations are using LLMs in production for use cases that may very well improve the productivity of SecOps teams in the long run and accelerate the cat and mouse game back to a state of quasi-equilibrium. In this Breaking Analysis we share key takeaways from Supercloud 3 – AI meets cloud security – and put forth new spending data from the latest ETR survey that shows which security firms are best positioned in the AI race to capitalize on the wave. Cybersecurity in the AI age: The power, the promise, the perilhttps://siliconangle.com/2023/07/03/cybersecurity-ai-age-power-promise-peril/How organizations can combat AI-equipped attackershttps://siliconangle.com/2023/07/03/organizations-can-combat-ai-equipped-attackers/Supercloud 3 - AI meets Cloud Security Supercloud.world

The AI heard ’round the world has put the machine intelligence sector back in the spotlight. But when you squint beyond the press hype, the data shows that artificial intelligence is now the number one sector in terms of relative spending velocity in the ETR taxonomy. Normally market hype leads deployments, but the data suggests that spending activity and market penetration for AI are coinciding with the hype. While hyperscale cloud players are reaping the rewards, we think this is a rising tide that’s going to lift all AI ships, those both plainly in sight and others that may not be so visible.In this Breaking Analysis we dig deeper into the AI space with spending data from ETR and one of the best minds in tech generally, and AI specifically, Jeff Jonas, CEO, founder, and chief scientist at Senzing.

The recent Databricks Data+AI Summit attracted a large audience and, like Snowflake Summit, featured a strong focus on large language models, unification and bringing AI to the data. While customers demand a unified platform to access all their data, Databricks and Snowflake are attacking the problem from different perspectives. In our view, the market size justifies the current enthusiasm seen around both platforms but it’s unlikely that either company has a knockout blow for the other. This is not a head on collision. Rather Snowflake is likely years ahead in terms of operationalizing data. Developers can build applications on one platform, like Oracle when it won the market, that perform analysis and take action. Databricks likely has a similar lead in terms of unifying all types of analytic data – e.g. BI, predictive analytics & generative AI. Developers can build analytic applications across heterogeneous data, like Palantir today. But they have to access external operational applications to take action. In this Breaking Analysis we follow up last week’s research by connecting the dots on the emerging tech stack we see forming from Databricks. With an emphasis on how the company is approaching generative AI, unification and governance…and what it means for customers.  To do so we tap the knowledge of three experts who attended the event, CUBE analysts Rob Strechay and George Gilbert and AI market maven Andy Thurai of Constellation Research. 

Over the past several months we’ve produced a number of in-depth analyses laying out our mental model for the future of data platforms. There are two core themes: 1) Data from people, places, things, and activities in the real world drives applications, not people typing into a UI; and 2) Informing and automating decisions means all data must be accessible. That drives a change from data locked in application silos to application logic being embedded in a platform that manages an end-to-end representation of an enterprise in its data.  This week’s Snowflake Summit further confirmed our expectations with a strong top line message of “All Data / All Workloads” and a technical foundation that supports an expanded number of ways to access data. Squinting through the messaging and firehose of product announcements, we believe Snowflake’s core differentiation is its emerging ability to be a complete platform for data applications. Just about all competitors either analyze data or manage data. But no one vendor truly does both. To be precise, managing data doesn’t mean running pipelines or serving analytic queries or AI/ML models. It means managing operational data so that analytics can inform or automate operational activities captured in transactions. With data as the application foundation, the platform needs robust governance.In this week’s Breaking Analysis, we try to connect the dots between Snowflake’s high level messaging and its technical foundation to better understand the core value it brings to customers and partners. As well, we’ll explore the ETR data with some initial input from the Databricks Data + AI Summit to assess the position and prospects of these two leaders along with the key public cloud players. 

HPE’s announcement of an AI cloud for large language models highlights a differentiated strategy that the company hopes will lead to sustained momentum in its high performance computing business. While we think HPE has some distinct advantages with respect to its supercomputing IP, the public cloud players have a substantial lead in AI with a point of view that generative AI is fully dependent on the cloud and its massive compute capabilities. The question is can HPE bring unique capabilities and a focus to the table that will yield competitive advantage and ultimately, profits in the space?In this Breaking Analysis we unpack HPE’s LLM-as-a-service announcements from the company’s recent Discover conference and we’ll try to answer the question: Is HPEs strategy a viable alternative to today’s public and private cloud gen AI deployment models, or is it ultimately destined to be a niche player in the market? To do so we welcome to the program CUBE analyst Rob Strechay and Vice President / principal analyst from Constellation Research, Andy Thurai. World’s Top Performing Supercomputers: https://twitter.com/wholemarsblog/status/1671721744623874048?s=46&t=AqnHczjrru-dQaVNpPGp7wHPE’s Slingshot interconnect architecture:https://www.nextplatform.com/2022/01/31/crays-slingshot-interconnect-is-at-the-heart-of-hpes-hpc-and-ai-ambitions/How Cray makes Ethernet suitable for HPC: https://www.nextplatform.com/2019/08/16/how-cray-makes-ethernet-suited-for-hpc-and-ai-with-slingshot/The generative AI stack according to Andreesen Horowitz:https://a16z.com/2023/06/20/emerging-architectures-for-llm-applications/