Breaking Analysis with Dave Vellante

The podcast grades their 2023 predictions on IT spending, cost optimization, security, generative AI, cloud, blockchain, data platforms, automation, and tech events. They discuss accuracy of previous predictions, including acquisitions, advancements in AI, and zero trust security. They also discuss AI hype, cloud expansion, and blockchain struggles. Finally, they make predictions for 2023, including the embrace of Ethereum, momentum of snowflake and Databricks, automation comeback, and upcoming predictions for 2024.

This podcast explores the challenges of achieving full stack observability, evolution of observability and market segmentation, changes in observability tools and AI adoption, difficulties and opportunities for full stack observability and AI, and the market shift towards platforms and potential consolidation.

OpenAI's concept of 'super alignment,' using less capable AI to supervise advanced AI models, changing relationship with Microsoft, restructuring OpenAI into two separate companies, and concerns about AI's true intentions are discussed in this podcast.

The podcast discusses the need to evolve current data and data center architectures to support emerging use cases and AI-infused apps. It explores integrating unstructured and structured data, the transformation of data centers with LLMs, challenges of unifying metadata in AWS, shared persistent memory and all-flash storage in data management, and the new approach to data center architectures.

In this episode of Breaking Analysis, theCUBE analyst Dave Vellante focuses on the evolving landscape of enterprise tech, driven by generative AI, and its implications for AWS. The discussion emphasizes how AWS must adapt its powerful playbook, known for agility, developer choice, power, scale, reliability and security, to accommodate the growing demand for simplicity and coherence among mainstream customers.Follow theCUBE's live event coverage https://www.thecube.net/Vellante discusses the challenges that AWS must tackle in the face of changing customer preferences, market pressures and increased competition, particularly from Microsoft and Nvidia. Also, up for discussion, AWS's recent initiatives, such as its gen AI stack and partnership with Nvidia, as well as the technical challenges it faces in providing a unified data platform for developers.Read the full article https://siliconangle.com/2023/12/02/reinvent-2023-underscores-new-simplicity-mandate-aws/For daily news for CIOs, check out our parent publication at https://siliconangle.com/Be sure to follow Dave's weekly Breaking Analysis podcast as well, for the deep data dives on enterprise computing trends, from spending patterns to Wall Street implications.https://www.youtube.com/playlist?list=PLenh213llmcZMTRZKjnAwesSCiuLQT21E

Conventional wisdom says Microsoft Corp. is the big winner in the recent OpenAI saga. We don’t quite see it that way.Both Microsoft and OpenAI are in a worse position today than last Thursday, prior to the firing of OpenAI Chief Executive Sam Altman and the ongoing public drama that ensued. Microsoft and OpenAI had a huge lead in market momentum, artificial intelligence adoption and feature acceleration, and were setting the narrative in AI.Our discussions with customers and industry insiders leads us to conclude that the duo has put its substantial lead at risk. Although Microsoft CEO Satya Nadella is making lemonade from lemons, the window was just cracked open for the competition and it’s more clear than ever that one large language model will not rule them all.

IBM's transition from Watson 1Dotto to Watson 2Dotto, their leading technological foundation in AI, and deep research positioned them as a major player in generative AI. The podcast also explores IBM's ecosystem and partnerships, their changing focus under new leadership, the significance of audio in podcasts, and their roadmap and key challenges.

Power Law Explained Wikipediahttps://en.wikipedia.org/wiki/Power_lawHeavy tailed power laws:Why are Power Law distributions called 'Heavy-tailed'?Explainer video for power laws in statistics:https://www.youtube.com/watch?v=JC71ZazlMR0Power law in economics:https://dash.harvard.edu/bitstream/handle/1/34651705/68262294.pdfWhat is retrieval augmented generation (IBM Research)https://research.ibm.com/blog/retrieval-augmented-generation-RAGLlama2 technical paper from Meta:https://scontent-dfw5-1.xx.fbcdn.net/v/t39.2365-6/10000000_662098952474184_2584067087619170692_n.pdf?_nc_cat=105&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=o6bnSMYXMT8AX8QaArm&_nc_ht=scontent-dfw5-1.xx&oh=00_AfD4K2tm94KqZZ6vm0bmor-zmbJ5z5ENslOx60fJRmMO9A&oe=6548D47F

In 1987, Nobel Prize-winning economist Bob Solow famously observed, “You can see the computer age everywhere but in the productivity statistics.” This proclamation became known as the productivity paradox. Ironically, Solow’s statement preceded the greatest productivity boom since the dawn of the computer age which subsequently came to fruition in the 1990’s. It can be argued that a similar pattern is being seen today where AI is everywhere but generally not showing up in earnings numbers or productivity statistics…yet. In this Breaking Analysis we squint through the latest earnings reports from Microsoft, Alphabet and Amazon to understand what’s happening in cloud, evaluate the impact or lack thereof of AI on cloud earnings momentum and explain how we think about the future impact of generative AI and cloud.