Skills
About
I'm an engineer with a deep passion for AI and cybersecurity, dedicated to advancing cutting-edge, reliable, and safe applications of Artificial Intelligence across industries. BS Computer & Electrical Engineering MS Complex Information Systems Security SecAI
David Obasiolu
Published content
expert panel
AI has learned to write, reason, code and create at remarkable scale. But as artificial intelligence generates more of the content used to train future systems, the industry faces an uncomfortable possibility: What happens when AI increasingly learns from itself?A 2024 study published in Nature found that repeatedly training models on AI-generated outputs can cause them to lose information from the original data distribution, with less common patterns among the first to disappear. Researchers call this phenomenon “model collapse”—a warning that more data does not automatically translate into better AI.Synthetic data offers a compelling answer to the industry's growing demand for training material. It can simulate rare events, generate examples for specialized tasks and help developers test systems in situations that would be expensive, impractical or risky to reproduce in the real world. But synthetic data can also create a closed feedback loop, amplifying existing errors and narrowing the range of patterns a model recognizes.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, examine what it will take to keep AI learning rather than looping. Below, they offer a roadmap for executives navigating the next phase of AI adoption: how to distinguish useful synthetic data from convincing but uninformative output, how to protect the evidence that makes AI smarter and how to determine whether a new model is genuinely more capable than the one before it.
expert panel
AI progress is often reduced to a leaderboard: a model scores higher on reasoning, coding or multimodal benchmarks, and the industry moves on to the next release. But benchmarks are snapshots of what a model can demonstrate under controlled conditions. They do not always capture what happens when increasingly capable models are connected to an organization’s data, software, physical environments and decision-making processes.The 2026 AI Index from Stanford HAI reports that frontier AI capabilities are advancing faster than many established benchmarks can measure, with some evaluations reaching saturation within months.Members of the Senior Executive AI Think Tank see a similar pattern from the front lines of enterprise AI, machine learning, robotics, cybersecurity, cloud technology and digital product development. They point to a broader question for business leaders: What are today’s most capable AI models beginning to do that could matter far more than benchmark gains suggest? Here, they explore the emerging capabilities they believe deserve closer attention—and what those developments could mean for the way organizations build products, make decisions and operate in the years ahead.
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
Aug 20, 2026
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.
