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
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28:44
Developer Experience and Automation
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
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25:02
FinOps – Managing Cloud and AI Costs
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
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30:20
Datacenter Slowdown?
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
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32:36
Personal Data
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
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