To get to the benefits that AI offers, organizations have to address their technology infrastructure in ways that are much broader than historical approaches. Senior analyst Greg Macatee joins host Eric Hanselman to delve into what’s required and what enterprises are identifying in the recent Voice of the Enterprise AI and Machine Learning study. Enterprises are struggling with raising the success levels of AI projects. Over 60% report moderate to severe challenges in achieving AI success. Bringing together the computational power and the right quality data in the right locations can be complicated in the hybrid environments that more are operating. It’s not just a matter of being more selective with use cases, AI requires a set of organizational skills that have to be honed. Starting small and iterating can reduce risk while building competency. Infrastructure has to shift in new ways, as well. Data management processes that can build the necessary data pipelines to feed AI applications bring together a broader set of tech disciplines. There are new wrinkles in AI infrastructure ecosystems, with new providers looking to address supply chain constraints, like the Neocloud or GPU as a Service (GPUaaS) providers. Even hyperscalers are looking to them to meet surging demand in a tight market. Those new options offer new choices, but enterprises need to match them with their AI goals. More S&P Global Content: Navigating the AI infrastructure landscape The path from LLMs to agentic AI Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: AI infrastructure strategies evolve amid widespread data challenges – Highlights from VotE: AI & Machine Learning Generative AI Market Monitor & Forecast AI infrastructure: Trends, thoughts and a 2025 research agenda Credits: Host/Author: Eric Hanselman Guest: Greg Macatee Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith
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Broadcom VMware Explore Conference
The annual gathering on infrastructure virtualization that is the VMware by Broadcom Explore conference has wrapped up and there are important takeaways from both the formal program and informal discussions. Analysts Jean Atelsek, Henry Baltazar and William Fellows join host Eric Hanselman to talk about their takes on the event. The newly integrated VMware Cloud Foundation 9.0 (VCF) suite has shipped and along with it a new approach to the product portfolio. AI-focused capabilities have been added as VMware aims to create a private cloud portfolio to rival the public cloud offerings. Higher level data services are the first service abstractions available, with the promise of more to come. In the new organizational structure, the Tanzu product offering is now in its own division in Broadcom. While that offers it independence, it’s also meant that in areas like AI functionality, there is some overlap between it and AI capabilities being built into VCF 9. The larger challenge for Broadcom is to motivate customers to fully implement VCF 9 and put all of its capabilities to work. More S&P Global Content: Next in Tech | Ep. 222: FinOps – Managing Cloud and AI Costs Next in Tech | Ep. 214: KubeCon and Cloud Native Next in Tech | Ep. 183: Exploring Broadcom VMware For S&P Global Subscribers: VMware Cloud Foundation 9.0 manifests Broadcom's vision for modern private cloud VMware Tanzu enhances support for generative AI and agents with Tanzu AI Solutions VMware remains dominant as organizations evaluate alternative virtualization technologies Credits: Host/Author: Eric Hanselman Guest: Jean Atelsek, Henry Baltazar, William Fellows Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith
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Data Integration for AI
Can you have too much data for an AI application? In the mad dash to collect the raw material for AI applications, it can be tempting to pull in as much as you can. Product manager Emily Jasper returns to the podcast with a set of recommendations for more strategic use of data with host Eric Hanselman. Just as it might not be wise to load up on everything on a buffet, being strategic about using the data that best suits the goals of your project can improve outcomes and help to manage risk. By understanding the data that you’re putting to work, you can bound the universe of outcomes and simplify the process of bringing it into the AI application pipeline. At the same time, the process of data governance becomes clearer when the sources are better understood. Bringing an understanding of the set of data resources that an enterprise has is critical and has to be accompanied by knowledge of the quality of that data. The principles of library sciences are back in focus in AI, as organizations work to curate data characteristics and provenance. As in so much of AI, matching the ecosystem of tools, data providers, and capabilities to the use cases being built is fundamental to project success. Managing risk in AI has become a process of bringing the right data to the right problem. More S&P Global Content: The path from LLMs to agentic AI Next in Tech | Ep. 224: Context Around MCP Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: Data Management Market Monitor & Forecast Automation pervades data management practices – Highlights from VotE: Data & Analytics Technology Primer: Model Context Protocol explained The evolution of agentic process automation: Midyear market and technology update Pace of AI agent advancement could spur M&A in the sales automation market Credits: Host/Author: Eric Hanselman Guest: Emily Jasper Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith
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The ONUG Tech Community
The typical process of deploying technology has been one of enterprises designing infrastructure around vendor capabilities. The Open Networking Users Group (ONUG) was founded to reverse that equation and define enterprise requirements for technology and work with vendors to achieve them. Co-founder Nick Lippis joins host Eric Hanselman to talk about the approach that ONUG takes, the nature of its community and the work that they’re doing today. While their roots are in the networking world, they’ve expanded to address a wide range of enterprise concerns. The current collaborations include security related work in cloud and security operations automation, WAN connectivity and a reference architecture for AI infrastructure. The ONUG fall conference takes place in New York on October 22-23. More S&P Global Content: The ONUG fall conference The path from LLMs to agentic AI Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: The evolution of agentic process automation: Midyear market and technology update Pace of AI agent advancement could spur M&A in the sales automation market 2025 will be pivotal for network APIs – Highlights from Communication and Network APIs Global Survey 2024 Use of open-source software driven by reliability, low cost – Highlights from VotE: DevOps Glimpses of cybersecurity talent development: Industry and community Credits: Host/Author: Eric Hanselman Guest: Nick Lippis, ONUG co-founder Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith
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Black Hat and DefCon
The annual “security summer camp” that is made up of the Black Hat and DefCon conferences is just past and the security analyst team, Scott Crawford, Dan Kennedy, Justin Lam and Mark Ehr, join host Eric Hanselman to examine what they saw and discuss the implications. Despite the heat of a Las Vegas summer, it’s become bigger than the two main conferences, with a number of side events, like B-Sides, there’s a lot going on. AI conversations are evolving and maturing. We’ve mostly moved beyond blaming user foibles for breaches, but AI is expanding the attack surface with new and more complex tactics for user manipulation. AI is lowering the barriers for attackers. The days of script kiddies have morphed into Claude Code-fueled attack development. The larger question is how security vendors are responding to AI risks. Claims that tier 1 security analysts should start looking for another job just seem irresponsible in the current environment. AI augmentation can reduce toil and digest the masses of events that security teams struggle to deal with today. At the same time, AI is scaling attack volumes. It’s the constant hegemony that’s always played out at the core of security. More S&P Global Content: RSAC Conference 2025: Breaking records at the threshold of uncertainty AI for security: Agentic AI will be a focus for security operations in 2025 Next in Tech | Ep. 215: RSA Conference Preview Deep Pocket Inspection: RSAC Innovation Sandbox Retrospective & Perspective Next in Tech | Ep. 227: Managed Security Services Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: Use of GenAI security solutions has spiked, continued uptake projected – Highlights from VotE: Information Security Infosec spending projected to rise 27% on average in 2025 – Highlights from VotE: Information Security CNAPP in focus after large infosec acquisition – Highlights from VotE: Information Security Data Insight: Data security market to top $26B in 2029 Data Security Market Monitor & Forecast CNAPP matures into full-spectrum security solution Credits: Host/Author: Eric Hanselman Guests: Scott Crawford, Dan Kennedy, Justin Lam, Mark Ehr Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith
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