Next in Tech

We're in the early days of cost impacts for AI applications. While there are some cautionary tales, current spending seems to be a small fraction what's to come. Analysts Jean Atelsek and Melanie Posey return to the podcast to talk about what they heard at the FinOps X conference with host Eric Hanselman. The need for cost management in AI is seen as so great that the FinOps Foundation, a project of the Linux Foundation, is talking about morphing its conference into Tokenomicon and pivot into token economics. The portmanteau of tokenomics is sweeping across cloud and AI services providers, as well as IT vendors, as enterprises wrestle with dueling forces of AI acceleration and management constraints for access and cost.
Unlike FinOps for cloud operations, the costs and metrics for AI are fairly opaque. Some enterprises are trying to manage costs by limiting access, but that risks stifling the innovation and democratization that is supposed to come with AI transformation. Request routing is promising, but it requires understanding the nature of the request and the suitability of available infrastructure to fulfill it, something that is not well understood by many. Most are just getting comfortable with managing model lifecycles and the step up to cost management can be a large one.
More S&P Global Content:
Compute sovereignty: The strategic importance of digital infrastructure
Next in Tech | Ep. 222: FinOps – Managing Cloud and AI Costs
AI in action: unleashing agentic potential
Hyperscaler earnings quarterly: What price inference?
For S&P Global subscribers:
FinOps in the age of agentic AI
FinOps Foundation expands FinOps discipline beyond cloud to technology value management
Service providers race to meet surging enterprise demand for AI infrastructure
FinOps Market Monitor & Forecast
Host/Author: Eric Hanselman 
Guests: Jean Atelsek, Melanie Posey
Producer/Editor: Dylan Scheible
Published With Assistance From: Feranmi Adeoshun, Sophie Carr, Kyra Smith,

There's great momentum in moving to greater levels of agentic automation, but there are critical areas where deeper consideration is required in how it's applied. In capital markets, trust is a foundational element on which transactions are built and Krisha Vinjamuri, Head of Technology, Enterprise Solutions at S&P Global Market Intelligence, joins host Eric Hanselman to talk about how this can be achieved and the important aspects of successful implementations.
One of the useful things in capital markets, is that there are open standards on which to base data ontologies. It's not exciting, but it's the basis of a semantic foundation that can not only ensure that there is depth in data definitions, but can also reduce errors generated by agents. The larger question that looms beyond the construction of foundational architecture, is how the operational envelope that bounds agentic action will be established. This has to be built from policy definitions that take those actions into account. There is great promise and much work that needs to be done.
More S&P Global Content:
AI-enable Corporate Actions
AI-driven Portfolio Monitoring with iLEVEL
AI-automated SSI
Equity Bookbuilding AI Assist
Market Intelligence AI Hub
For S&P Global subscribers:
FinOps in the age of agentic AI
AI Infrastructure Market Monitor & Forecast
Service providers race to meet surging enterprise demand for AI infrastructure
In 2026, the telecom network becomes code
Credits:
Host/Author: Eric Hanselman
Guest: Krishna Vinjamuri
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible

Networking can be an invisible part of IT infrastructure, but AI is creating demands that make it a critical part of keeping AI application fed with data. Mike Fratto returns to the podcast to discuss both the long haul and local requirements for AI networking with host Eric Hanselman. It's always been important to link chunks of infrastructure efficiently, but AI's voracious need for data has dramatically increased the scope and scale of the need. The risk that any gap in performance or capacity presents is that precious GPU resources will be idled, an increasingly expensive proposition.
The realities of AI application architectures is that infrastructure is ever more hybrid, requiring access to repositories of data both on-premises and in various clouds and models scattered across various providers. The need for dynamic connectivity is driven by the rapid evolution of preferences for new models and the diversifying needs of agents to reach new data sources. It's not only forcing network expansion, but it's also driving M&A activity as network providers look to enhance automation in response to customer demands.
More S&P Global Content:
Compute sovereignty: The strategic importance of digital infrastructure
AI won't solve its own energy problem – and that might be fine
AI in action: unleashing agentic potential
AI infrastructure results in 2025 top expectations, forecast upgraded
For S&P Global subscribers:
MWC 2026: Agentic AI as the next operating model for networks and network operations
AI Infrastructure Market Monitor & Forecast
Service providers race to meet surging enterprise demand for AI infrastructure
In 2026, the telecom network becomes code
Credits:
Host/Author: Eric Hanselman
Guest: Mike Fratto
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible

The shifts that the agentic revolution is driving are felt in many areas of technology and Dell Technologies spans some of those that are seeing the greatest upheaval. The 451 Research team was in attendance at the annual Dell Technologies World conference and Brian Partridge, William Fellows, Henry Baltazar and Greg Macatee joined host Eric Hanselman to talk about their perspectives on the conference, agentic advancement and the technology market. Dell has positioned itself as a purveyor of not only the compute infrastructure needed to build the foundation for AI, but also as the custodian of AI's most critical raw material – data.
The rapid evolution of agentic applications has created a need for new capabilities that is being complicated by both technology infrastructure demands and geopolitical events. Supply chains are being challenged by increasing demand for storage at a time when silicon pipelines were already under tremendous pressure. All of this is happening as the costs of AI are starting to have a material impact on businesses. Tokenomics, the impact of the cost of producing AI tokens, has taken center stage.
More S&P Global Content:
Compute sovereignty: The strategic importance of digital infrastructure
AI won't solve its own energy problem – and that might be fine
AI in action: unleashing agentic potential
AI infrastructure results in 2025 top expectations, forecast upgraded
 
For S&P Global subscribers:
Dell Technologies' unified private cloud strategy: IT environments reimagined
AI Infrastructure Market Monitor & Forecast
Quantum Computing Market Monitor & Forecast
Service providers race to meet surging enterprise demand for AI infrastructure
Credits:
Host/Author: Eric Hanselman
Guest: Brian Partridge, William Fellows, Henry Baltazar, Gregg Macatee
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible

One of the more interesting areas where AI is having an impact is in building operations. While building automation technology has made significant advances, AI is bridging gaps in building system controls. Analyst Zöe Roth has been researching the approaches and achievements from various smart building automation technologies and joins host Eric Hanselman to discuss what she's seen and what opportunities lie ahead. Some of the advances from data center information management (DCIM) systems are being applied to smart buildings. A typical building has multiple control capabilities for HVAC, lighting, and mechanical that often aren't interconnected and smart building systems can unify visibility and operations.
The integration of AI in smart buildings has to balance a number of different aspects of both the human in the loop for control and the scale that the introduction of cloud-based management offers. Fully autonomous operations may be some time off, but vendors are working to build trust and capability. Cloud-based systems can provide a consolidated view of a portfolio of buildings and improve maintenance operations, but face reluctance in some sectors where on-premises preferences prevail. AI is advancing rapidly and will accelerate further as it matures.
 
More S&P Global Content:
451 Digital Industries Insider
Next in Tech | Ep. 208: Smart Spaces
 
For S&P Global subscribers:
2026 Trends in IoT, Edge & Digital Industries
Smart buildings 2025: Market trends, vertical strategies and vendor positioning
Redefining OT security and ensuring operational continuity in the age of hybrid, AI-driven OT
 
Credits:
Host/Author: Eric Hanselman
Guest: Zöe Roth
Producer/Editor: Feranmi Adeoshun with Barb Dalumpines
Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible