David Obasiolu

expert panel

The AI industry has embraced a simple idea about data centers: If AI needs more computing power, build bigger facilities and pack them with more chips. But as the infrastructure race accelerates, that assumption is becoming harder to defend. Power availability, grid capacity, cooling, water and the physical limits of getting new facilities online are emerging as constraints that raw compute cannot solve.The question matters because data centers are long-term investments being built around a technology that is changing at remarkable speed. The infrastructure designed for training enormous models may not be what businesses need as inference workloads grow, models become more efficient and AI moves closer to where data and users actually reside.There are also business questions hiding underneath the engineering one: How much compute does a company actually need? Where should it run? What happens when power costs change or a new generation of hardware makes today's architecture less attractive? And how should CEOs think about infrastructure that needs to remain useful even as the AI workloads it supports evolve?Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, offer a range of perspectives on what should come next. In the discussion that follows, they examine whether bigger data centers are really the answer, where power and efficiency fit into the equation, why inference deserves a different infrastructure strategy and which overlooked factors—from memory and networking to reliability and data governance—could determine whether an AI investment delivers lasting business value.

expert panel

Early-career employees have always learned by doing the work that more experienced colleagues have moved beyond: building the first draft, reconciling data, troubleshooting code, preparing analyses and sitting in on important decisions. Much of it is repetitive, but it also gives new professionals something essential—exposure to how problems unfold, how mistakes get fixed and how judgment develops.As AI takes over more of that work, companies face a problem hiding inside a productivity opportunity: If the first rung of the career ladder disappears, what replaces it?Members of the Senior Executive AI Think Tank—experts in machine learning, generative AI and enterprise AI applications—are seeing this challenge across industries. They point to a new approach: Let AI handle more of the execution while giving early-career employees more responsibility for evaluating work, making decisions, solving problems and learning from experienced leaders.That means rethinking more than training programs. In the sections that follow, members of the Think Tank explain how leaders can redesign junior roles around judgment and verification, use simulations and AI-generated work as training tools, preserve mentorship and hands-on experience and give emerging talent meaningful accountability earlier in their careers—ensuring that as AI makes work faster, it doesn't make the path to becoming a capable leader disappear.

expert panel

Open-weight large language models have become one of enterprise AI's hottest topics. Their promise is compelling: greater control, improved privacy, customization and freedom from vendor lock-in. Yet many executives remain focused on one number—the licensing cost—while overlooking the far larger operational commitment required after deployment.Members of the Senior Executive AI Think Tank, a community of executives and practitioners leading AI strategy across industries, agree that organizations should evaluate open-weight models the same way they would any other mission-critical technology platform: through total cost of ownership, long-term operational resilience and measurable business outcomes.McKinsey has emphasized that successful AI adoption depends not only on selecting the right models but also on building the data infrastructure, operating models and organizational capabilities needed to scale them. For enterprises adopting open-weight models, those ongoing investments can quickly become a larger cost factor than the model itself.In this article, AI Think Tank members examine the hidden costs and strategic considerations behind open-weight AI adoption, sharing how executives should think about infrastructure, talent, security, governance, maintenance and the business cases where owning and operating these models can create a meaningful advantage—and when the hidden costs outweigh the benefits.

expert panel

For years, enterprise technology leaders have wrestled with a familiar dilemma: Embrace the speed and innovation of a vendor platform or invest in building enough internal capability to maintain strategic control. Generative AI has made that trade-off far more consequential. As organizations move beyond chatbots to autonomous agents that retrieve information, invoke tools and make decisions across business systems, the focus is increasingly on who controls the pathways connecting models, knowledge, applications and enterprise data.That challenge is driving renewed interest in customer-owned AI control planes—enterprise-managed gateways that sit between AI applications and the rapidly expanding ecosystem of models, Model Context Protocol (MCP) servers, agent hubs and knowledge sources. Rather than relying entirely on vendor-specific ecosystems, these architectures promise centralized governance, stronger security, greater architectural flexibility and the ability to adopt new AI capabilities without redesigning the entire technology stack. Yet they also introduce an important question: Does adding another layer simplify enterprise AI or simply shift complexity from vendors to internal engineering teams?Members of the Senior Executive AI Think Tank, a community of leaders shaping enterprise AI strategy across architecture, governance, cloud computing and digital transformation, largely agree that customer-owned control planes represent an important evolution—but only if organizations approach them with discipline. Below, they discuss why centralized gateways can help organizations reduce vendor lock-in without slowing innovation, what security and architecture teams need to see before they'll trust agentic AI at scale and why governance should be built into every model and tool interaction rather than bolted on later.

expert panel

For decades, the technology industry's infrastructure strategy has been remarkably straightforward: Build bigger data centers, add more fiber and deploy more compute capacity closer to users. But what if the next major leap in AI infrastructure happens above the planet rather than on it?That question is gaining attention as SpaceX continues expanding its Starlink satellite network and explores ways its orbital infrastructure could support AI-related computing and global data movement. While the concept of space-based AI infrastructure remains in its early stages, it represents a potentially significant shift in how organizations think about compute, connectivity and data distribution. Instead of relying exclusively on terrestrial networks, future AI systems could leverage orbital infrastructure to extend services into remote regions, improve resilience and create entirely new competitive dynamics.The idea is gaining traction at a time when demand for AI infrastructure is accelerating rapidly. According to a Goldman Sachs analysis, AI-related data center power demand is expected to increase dramatically through the end of the decade as organizations race to secure the compute capacity needed to support next-generation AI applications. As those investments accelerate, executives are increasingly asking whether future infrastructure strategies will be limited to Earth—or whether space will become a critical extension of the global AI stack.To better understand the opportunities and risks, members of the Senior Executive AI Think Tank shared their perspectives on how space-based AI infrastructure could reshape cloud providers, telecommunications companies and AI platform vendors over the next decade. Their insights reveal both extraordinary possibilities and significant challenges, from global connectivity and distributed computing to governance, economics and the growing concentration of infrastructure power.

expert panel

Artificial intelligence remains one of the most consequential forces reshaping business, yet many organizations still struggle to distinguish meaningful breakthroughs from attention-grabbing headlines. While public discussion often centers on increasingly powerful models, digital assistants and speculation about artificial general intelligence, many enterprise leaders are discovering that the most transformative AI developments occur behind the scenes.Ask 10 AI experts what will matter most a year from now, and you might expect 10 different answers. Instead, members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—arrived at a strikingly similar conclusion: The biggest opportunities—and risks—aren't tied to the next model release. Across industries, they point to the infrastructure that makes AI useful in practice, from governance and security to evaluation, trust and workflow integration. At the same time, many are skeptical of some of today's loudest predictions, particularly around fully autonomous agents replacing human judgment at scale.As recent research from McKinsey suggests, organizations are increasingly finding that AI success depends less on access to cutting-edge models and more on the ability to operationalize them effectively. The experts featured here—those on the front lines of AI innovation—share the developments they believe leaders are underestimating, the trends they think are overhyped and where executives should be investing now to create lasting competitive advantage.