Lynn Comp

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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.

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When OpenAI unveiled Jalapeño, its first custom AI inference chip developed with Broadcom, the announcement represented more than a hardware milestone. It highlighted a broader shift in the AI industry: the race to make intelligence faster, more affordable and more accessible at scale. As the cost of running large language models declines, product leaders face a new question—not simply what AI can do, but what products become possible when intelligence is inexpensive enough to operate continuously.For much of the generative AI era, product teams have designed around scarcity. They have limited model usage, shortened context windows, reduced reasoning steps and carefully managed AI interactions because every inference call carries a cost. But as custom silicon and AI infrastructure improvements drive down those constraints, AI can move from an occasional feature users activate to an always-present capability embedded throughout workflows. Research from McKinsey & Company estimates that generative AI could create trillions of dollars in annual economic value, but capturing that opportunity will require organizations to integrate AI into core business processes rather than treat it as a standalone tool.Members of the Senior Executive AI Think Tank believe the next generation of AI products will not simply be faster versions of today’s copilots. Below, they explore how OpenAI’s Jalapeño chip could reshape product design, unlock previously uneconomical AI applications and redefine the competitive landscape for organizations building the next generation of intelligent products.

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Artificial intelligence is rapidly redefining the cybersecurity battlefield, shifting the balance between defenders and attackers at a pace many organizations are struggling to match. As enterprises embed generative AI, autonomous agents and machine learning into critical workflows, the attack surface is expanding just as quickly as defensive capabilities evolve.This tension is at the center of discussion among members of the Senior Executive AI Think Tank, a curated group of leaders specializing in enterprise AI, machine learning and responsible AI deployment. To them, AI is not just a technology upgrade—it is a structural shift in how cyber risk is created and managed.According to the National Institute of Standards and Technology’s AI Risk Management Framework, organizations adopting AI face heightened risks related to system reliability, security vulnerabilities and adversarial manipulation, even as they gain powerful new defensive tools. At the same time, a Google Threat Intelligence Group analysis on AI-enabled threat activity warns that adversaries are increasingly using generative AI to accelerate vulnerability discovery, exploit development and initial access—signaling a shift toward more automated and scalable cyber intrusion models.With this knowledge, senior executives are asking a pressing question: Over the next five years, should we be more optimistic about AI’s role in cybersecurity—or more concerned? And more importantly, what concrete actions should leaders take today to stay ahead of the curve?Their insights suggest the answer is not binary—but it is urgent.

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The current AI conversation has been dominated by software. Organizations have raced to deploy chatbots, copilots and generative AI tools that promise to boost productivity, improve decision-making and automate knowledge work. But what happens when AI leaves the screen and enters the physical world?That future is already taking shape. AI-powered robots are moving beyond controlled factory environments and into warehouses, hospitals, retail operations and even homes. Companies including Amazon, Tesla and Figure AI are investing billions in autonomous systems capable of navigating complex environments, collaborating with humans and performing tasks that once required manual labor. At the same time, labor shortages, rising operating costs and demographic shifts are creating strong economic incentives for automation. According to the International Federation of Robotics, global demand for industrial robots has more than doubled over the past decade, with more than 4.6 million robots now operating in factories worldwide.Yet despite the excitement, fundamental questions remain unanswered: What milestone will signal that AI-powered robotics has evolved from a promising technology into a mainstream commercial reality? Will it be a breakthrough in capability? A dramatic reduction in cost? Regulatory approval? Or something less obvious?To explore these questions, we turned to members of the Senior Executive AI Think Tank, a curated group of leaders and practitioners specializing in machine learning, generative AI and enterprise AI applications. Below, they share the signals they believe executives should be watching and the conditions that will determine when AI-powered robotics truly crosses into the mainstream.

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Artificial intelligence is often framed as a race: faster models, bigger investments, larger datasets and more powerful infrastructure. But beneath the headlines lies a more consequential question for business leaders, policymakers and investors alike: Who gets to compete?A growing share of the AI ecosystem is controlled by a relatively small number of organizations with access to the world's largest compute resources, proprietary datasets and distribution channels. This means the debate is no longer simply about what AI can do but about whether the next wave of innovation will emerge from an open marketplace of ideas or from a handful of dominant ecosystems.To explore that question, we asked members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI, digital transformation and enterprise AI applications—what single rule they would change to improve AI competition.While their recommendations differ, a clear theme emerges: The future of AI should be shaped by innovation, trust and customer value rather than lock-in, opacity or concentrated control. The following insights offer a timely look at how technology and business leaders believe a more competitive—and in many cases safer—AI ecosystem can be built.

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Artificial intelligence has become the fastest-moving investment category in the corporate world. Boards are asking about it, investors expect it and competitors are announcing new initiatives seemingly every week. For many Fortune 500 CEOs, however, the challenge isn't deciding whether to invest in AI—it's deciding where to place the first major bet.The stakes are high because the wrong investment can consume millions of dollars while delivering little business value. Organizations across industries are launching AI labs, experimenting with custom models and deploying new tools at scale, yet many still struggle to achieve measurable returns.That reality raises an important question: If you were making your first significant AI investment today, where would you focus—and what would you avoid?To find out, we asked members of the Senior Executive AI Think Tank, a community of leaders and practitioners specializing in machine learning, generative AI and enterprise transformation. Their answers reveal a striking consensus about where AI creates value, why so many organizations get their priorities wrong and the foundational investments that should come before any large-scale AI deployment.