Next in Tech

Effective security management has never been simple and the expanded threat landscape enhanced by AI has only widened the gap between defenders and attackers. Scott Crawford and Greg Zwakman return to discuss the managed security services market with host Eric Hanselman. Security services have been an option for enterprises since the earliest days of IT, but the need for more depth and breadth in security operations has changed market dynamics and the enterprise security calculus. Part of this shift is driven by the scope and scale of security operations. Expanded attack surfaces with more devices, more SaaS applications and hybrid infrastructure can be all the more difficult to secure. Integrated security operations patterns, like Managed Detection and Response (MDR) can provide more comprehensive services and ones that are targeted at delivering improved security outcomes, rather than just operational support.

Changing security requirements are driving M&A activity, as well. Where services had been an augmentation to vendor products, some vendors now see them as a critical part of their portfolio and a key to platform aspirations. Zscaler's acquisition of Red Canary and Sophos' pick up of SecureWorks are some of the more notable transactions in this trend. Security management has shifted front of security markets.

More S&P Global Content:

• AI for security: Agentic AI will be a focus for security operations in 2025
• The evolution of security platforms – 6 centers of gravity shaping the market

For S&P Global subscribers:

• Managed Security Services Market Monitor & Forecast
• For sale: MDR vendors
• 2025 Trends in Information Security
• Ending SecureWorks' search for a suitor, Sophos takes it off Dell's hands
• Zscaler expands its platform play into a new market by reaching for Red Canary

Credits:

• Host/Author: Eric Hanselman
• Guests: Scott Crawford, Greg Zwakman
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The annual Kagan Media and Telecom Summit in New York is in the books and it not only covered a lot of market activity, but it was also surrounded by a number of media and sports rights headlines. Justin Nielson, Eric Keith, Scott Robson and Seth Shafer joined host Eric Hanselman to look at what was covered in the expansive event and the time around it. There were big shifts in media rights and a couple of sports team sales that led to lively discussions. Networks are balking at some of the lofty prices for sports media rights in the face of streaming services' higher demand. The realignment of regional sports coverage is still shaking out, as well.

Media consolidation keeps hovering, but will it always be next year's prospect? Macroeconomic uncertainty seems to be playing a role and the pendulum on cord cutting seems to be swinging back around.

In broadband, the reset of the BEAD program has states scrambling to reapply under the new, much tighter deadline. Will this drive fixed wireless access (FWA)? Satellite is still a possibility in remote regions, but costs may hold it back. Private equity activity in fiber networks could be reaching a tipping point, as their investments mature and operators' partners shift strategies. There was a lot in store and much more to come.

More S&P Global Content:

• Kagan Media and Telecom Summit event link

For S&P Global subscribers:

• A sneak preview of the Kagan Media & Telecom Summit 2025
• See it in charts: TV Networks, June 2025
• Comcast strikes sports network deal; Streaming services expand sports viewing
• New media rights deal a slam dunk for the NBA
• Kagan Media & Telecom Summit 2025: Broadband panel discussion

Credits:

• Host/Author: Eric Hanselman
• Guests: Justin Nielson, Erik Keith, Scott Robson, Seth Shafer
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The Model Context Protocol (MCP) specification has helped to accelerate access to a wide range of data sources for AI applications. But there are questions about the security and trust implications around a protocol that is still in its infancy. Scott Crawford and Justin Lam return to the podcast to examine the concerns that have been raised and changes that are underway in the specification with host Eric Hanselman. The previous episode introduced MCP and some of the market forces that are in play. Security considerations didn't appear to be fully sorted out in the first version of the specification, but more work is being done to move beyond the OAuth-based approach. Automating the data access process can be powerful, but also fraught with the potential for abuse.

The larger questions in MCP revolve around understanding risk and establishing trust. Data exposure has been a constant concern in AI, but the more complex issues exist in the integrity of the data that's being used. AI technology is moving forward rapidly and adversaries that are looking to compromise it and moving right along with these advances.

More S&P Global Content:

• The 2025 Generative AI Outlook
• Next in Tech | Ep. 224: Context Around MCP

For S&P Global Subscribers:

• Technology Primer: Model Context Protocol explained
• Databases and analytic services get the agentic AI treatment at Google Cloud Next 2025
• IT Insider 3: A roundup for IT decision-makers

Credits:

• Host/Author: Eric Hanselman
• Guests: Scott Crawford, Justin Lam
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Linking generative AI models to the agents that are going to use them now has a standard for queries, the Model Context Protocol (MCP). Suddenly all sorts of things are vying to be your data source of choice. What could go wrong? Analysts Jean Atelsek, James Curtis and Henry Baltazar join host Eric Hanselman to provide an introduction to what MCP is, what all the fuss is about and where this is headed. Since Anthropic released the standard in November of 2024, there has been significant progress in expanding and clarifying it, but it is still very much a work in progress. Database providers and storage vendors have been the obvious participants and many others are joining in. The larger questions are around ensuring trust and transparency in this market. Basic authentication has been worked out, but more sophisticated authorization mechanisms need to be defined.

The mad dash is already leading to M&A activity, with storage vendors looking to expand their opportunities and maintain their relevance. Microsoft has released its MCP definition and Google has created an agent-to-agent (A2A) protocol for direct agent interactions. There are many pieces that are coming together to enable fully agentic operations and there is still a lot of work to be done.

More S&P Global Content:

• The 2025 Generative AI Outlook

For S&P Global subscribers:

• Technology Primer: Model Context Protocol explained
• Databases and analytic services get the agentic AI treatment at Google Cloud Next 2025
• IT Insider 3: A roundup for IT decision-makers

Credits:

• Host/Author: Eric Hanselman
• Guests: Jean Atelsek, James Curtis, Henry Baltazar
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Developer experience is one of the areas where AI applications are showing significant return on investment, but there are significant hurdles to overcome in both changing established development patterns, as well as integrating AI tooling. Analyst Jean Atelsek and AWS vice president for developer experience Deepak Singh join host Eric Hanselman to explore the current state of AI code assistance and look at where it's headed. Auto-complete, where the next bit of a line of code is filled in for a programmer, has been evolving over a number of years, but the arrival of agents to augment code generation and task automation is being to revolutionize software development. Changing development patterns is hard, but the benefits offer strong incentives to change habits. Where early uses had AI engines generate smaller code snippets that developers integrated, that's changing to having AI tackle full functions that are then reviewed and corrected. Tooling around AI implementations are tailoring they way in which they interact with individual developers, enhancing their experience.

Application modernization is an area where AI can shine, as it can assess a massive codebase whose authors are no longer available and provide not only documentation, but also prioritize recoding efforts. It's a task where the hours required for manual assessment can be daunting and error prone. Leveraging AI code generation securely requires that organizations have sufficiently secure development pipelines. Mitigating risks from confabulation and errors in AI generated code is the same process as ought to be in place for human coders, an area where some less mature organizations may have some catching up to do.

More S&P Global Content:

• The 2025 Generative AI Outlook

For S&P Global subscribers:

• Can generative AI modernize legacy code bases? It depends
• Tech Trend in Focus: Generative AI in programming
• Generative AI Market Monitor & Forecast

Credits:

• Host/Author: Eric Hanselman
• Guests: Jean Atelsek, Deepak Singh
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Managing IT costs has always been challenging and the FinOps movement is working to tackle this problem. Analysts Jean Atelsek and Melanie Posey return to the podcast to discuss their research and what they saw at the FinOps X conference with host Eric Hanselman. Cost angst has been exacerbated by the move to cloud. The shift from capital expense to operational expense has been a complicated transition for many. The infrastructure spending growth that being driven by AI initiatives has only just begun, but is already concerning. As cloud costs have become material expenses, more focus has been put on managing them. There are a host of startups that are looking to provide tools and cloud providers all have some form of cost management tooling.

The shift to virtualization opened the door to optimization, but was one that only few walked through. Efforts at charge-back and show-back accounting created better visibility, but FinOps is looking to take things a step further by identifying costs early in the development cycle. Putting the vast quantities of operational data that cloud throws off to work requires a new focus. It's another area where AI can help businesses work their way out of the looming costs that AI applications create. It requires bringing together lines of business, development teams and IT operations, but the benefits could be significant.

More S&P Global Content:

• Webinar: AI Has Swallowed the Tech Industry: Indigestion to Follow?
• Cloud Shifts podcast

For S&P Global Subscribers:

• FinOps Foundation showcases Scopes initiatives, user deployment experiences at FinOps X Day
• FinOps adoption expands as generative AI amplifies public cloud cost challenges – Highlights from VotE: Cloud, Hosting & Managed Services and Cloud Native
• FinOps Market Monitor & Forecast

Credits:

• Host/Author: Eric Hanselman
• Guests: Jean Atelsek, Melanie Posey,
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

A set of questions are being asked about whether the velocity of datacenter build outs is wavering. Analyst Dan Thompson returns to the podcast to explore the realities of the market with host Eric Hanselman. There are some facts that raised the questions, including Microsoft demurring on some datacenter projects, and they have fueled concerns that the heady pace might slacken. The DeepSeek model's debut with claims of lower training requirements spooked the tech markets in January. There have been layoffs. Are these harbingers of AI doom?

In the same way that DeepSeek signaled the next phase of optimization in AI models, datacenter markets can be expected to adjust over time. The difference is that the time scales for the two areas are vastly different. With AI ecosystems seemingly cycling daily, the multiyear scheduling of datacenters might appear glacial by comparison. But it's a market influenced by many factors, including a complex supply chain. Capital spending across hyperscale cloud providers remains healthy and maintains an eye on a future with much greater density of AI functionality. We're still a long way from a correction.

More S&P Global Content:

• Powering AI – Opportunities, tensions in datacenter and energy markets

For S&P Global subscribers:

• Truths about how the power sector can (and cannot) respond to datacenter needs
• Potential impacts of DeepSeek on datacenters and energy demand
• 2025 US datacenters and nuclear energy report

Credits:

• Host/Author: Eric Hanselman
• Guests: Dan Thompson
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

In the rush to build out AI applications, a full understanding of the dynamics of personal data management can be difficult to achieve. As we kick off Pride Month, the complexities of personal data handling deserve more attention and Emily Jasper and Alan Moore join host Eric Hanselman to discuss the concerns and approaches to address privacy issues. Enterprises accumulate both operational and self-reported personal data, some with regulatory requirements for collection and reporting and some in support of employee development. Is that data the new oil that can fuel their efforts, the new water that can leak or the new plutonium that can be powerful, but also dangerous?

With many systems accumulating data, it can be difficult to ensure that right data is in the right places. Data migration is hard, but can be necessary in technology transitions. Data is the raw material that builds AI value, but personal data increases the risks of not only expose, but of creating presumptions by AI models of association and affiliation. There are additional risks in inadequate datasets for training. As we've pointed out in previous episodes, organizations need to be aware of how well their training data reflects the populations they intend to serve.

More S&P Global Content:

• Webinar: Winning the Ad Dollar: Data-Driven Sales Enablement
• Next in Tech | Ep. 213: AI and Privacy
• Next in Tech | Ep. 204: Ethical AI Data

For S&P Global subscribers:

• Safeguarding privacy in the AI era – Highlights from VotE: Data & Analytics
• 2025 Trends in Workforce Productivity & Collaboration
• Ask the Analyst: Retail tech — personalization without friction
• 2025 Trends in Data, AI & Analytics

Credits:

• Host/Author: Eric Hanselman
• Guests: Emily Jasper, Alan Moore
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Around the globe, municipalities are facing a set of challenges managing the impacts of more extreme weather, while addressing aging infrastructure and more chronic issues, such as rising heat and sea levels. There are a set of technologies that can help them to both be aware of the risks and improve planning to work on mitigating them. The Internet of Things (IoT) can play a role in sensing and advances in digital twins can aid in simulating climate-driven events. It's a set of tools that, when applied well, can help to better build resilience.

At the same time, the resilience of utility systems are being challenged by tech advances. Power consumption by datacenters is impacting the load on electricity grids. The transition to greater use of renewables is changing grid dynamics and investment is needed to maintain stability. Climate impacts can have differing impacts across society and efforts manage equity are critical. The upcoming webinar will dig into all of this in more detail – join us to continue the conversation!

Join the webinar: Risk to Resilience: How Technology is Reinventing Urban Preparedness

More S&P Global Content:

• Climate physical risk insights for the U.S. municipal bond market
• Enhancing government resilience with technology amidst uncertainty
• Sustainable cities: Open data portals for community-driven AI apps, sustainability validation

For S&P Global subscribers:

• Lessons from the Iberian blackout: The starfish and the spider
• Potential impacts of DeepSeek on datacenters and energy demand
• Adoption of automation capabilities could drive consolidation in the smart buildings space
• Smart spaces must address privacy concerns, deliver experience enhancements – Highlights from VoCUL: Smart Spaces

Credits:

• Host/Author: Eric Hanselman
• Guests: Therese Feng, Zoë Roth, Johan Vermij
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

With all of the drama associated with AI, it's easy to miss the need to understand the foundations that deliver the data that is the raw element from which AI value is built. Databases and storage infrastructure are critical components that have to work in concert with AI plans and returning guests Henry Baltazar and James Curtis join host Eric Hanselman to discuss what's been happening and what enterprises need to know about the future. Databases and storage management systems have been intertwined for a long time and AI pressures are tightening that connection. Storage systems perform analytics on the data they store to optimize its handling, tracking use and characteristics. The same insights that aid in compression and tiering are also useful in classifying data for AI. Data classification has always been a challenge for enterprises, as storage systems are typically disconnected from the data owners and applications that use them. Intelligent storage systems have been able to intuit the nature of content, including mapping databases and virtual machines. Databases have been able to leverage storage capabilities like snapshotting for resilience.

Into this mix a new set of AI focused storage and database offerings arrive that target AI uses. The question is whether the native database and storage systems can do enough of what's needed. They already store key data and have valuable insights and classification capabilities. Some vendors are attaching GPU clusters to storage systems to provide high performance AI model training functionality. The major issue for most, is the matter of data placement. Shifting petabytes of data is no small task and concerns about data security and the costs involved now loom much larger.

More S&P Global Content:

• Next in Tech | Ep. 213: AI and Privacy
• Next in Tech | Ep. 203: NRF conference shows AI challenges and rewards

For S&P Global Subscribers:

• Rapid data growth and migrations increase storage burdens
• Amazon S3 Tables and automated metadata highlight recent AWS Storage enhancements
• 2025 Trends in Cloud and Cloud Native

Credits:

• Host/Author: Eric Hanselman
• Guests: Henry Baltazar, James Curtis
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith