Steve Taplin

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

Writing code has always taken time. Now, with AI, developers can produce in minutes what might once have taken hours. For companies under pressure to move faster and do more with less, that sounds like a clear win. But getting code written is only one part of building software. Someone still has to review it, test it, make sure it’s secure and keep it running once it reaches customers.So if teams are producing more code than ever, how do they know they’re actually getting more productive? Faster development doesn’t mean much if work piles up waiting for approval, bugs make their way into production or the cost of maintaining new software keeps climbing. And the more code a company produces, the more important it becomes to know what’s worth building in the first place.Members of the Senior Executive AI Think Tank, a curated group of experts in machine learning, generative AI and enterprise AI applications, weigh in on what AI-driven productivity really looks like, where the biggest bottlenecks are emerging and how leaders can tell whether faster coding is translating into better business results.

expert panel

In artificial intelligence, yesterday’s expertise can quickly become tomorrow’s outdated knowledge. A framework that once gave a professional an edge may be superseded by a new model, a familiar workflow may become automated and the skills that helped someone advance may no longer be enough to move them forward. For professionals at every stage of their careers, staying relevant means rethinking not just what to learn, but how to learn.But how do you keep pace with a field that never seems to stand still without spending every waking hour chasing the next breakthrough? And how can experienced professionals distinguish between skills worth investing in for the long term and those they need to continually refresh?Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, explore what it takes to build lasting expertise in a fast-changing field—from knowing when to challenge established assumptions to recognizing which skills to leave behind.

expert panel

Artificial intelligence is forcing companies to rethink not just how work gets done, but who makes decisions, how success is measured and what leaders are ultimately responsible for. The qualities that help an AI company break through—technical brilliance, ambitious vision and the ability to move fast—can become liabilities when the business grows beyond the reach of a handful of experts. Suddenly, the decisions that once fit in a conference room carry consequences for customers, employees, infrastructure and public trust.So what happens when the technology moves faster than the leadership model built to support it? Who gets to challenge a launch when the market demands speed? How do companies develop experienced talent when AI agents take on the work that once taught people the ropes? And how can leaders preserve the freedom to experiment without losing sight of the risks?Members of the Senior Executive AI Think Tank, a curated group of experts in machine learning, generative AI and enterprise AI applications, are confronting these questions from across the AI landscape. Together, they offer a fresh look at what leadership must become as AI companies grow—and why the industry's next phase may depend as much on how leaders think and organize as on what their technology can do.