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A discussion featuring a senior leader from OpenAI examined how artificial intelligence could reshape knowledge work, product development, and the skills required for future technology roles.
Nick Turley, Head of ChatGPT at OpenAI, joined a fireside chat at Harvard Business School hosted by MBA candidate Boris Ostrovskiy as part of programming run by the Harvard Business School Tech Club.
Ostrovskiy later shared highlights from the discussion on LinkedIn, outlining key ideas around AI product development, agent systems, and how AI tools may change the nature of professional work.
Conversation explores how AI is reshaping knowledge work
In his LinkedIn post, Ostrovskiy wrote: “Last week, I had the privilege of hosting Nick Turley, Head of ChatGPT at OpenAI, for a fireside chat at Harvard Business School in front of a packed room of 200+ classmates as part of our programming at Tech Club at Harvard Business School.”
He explained that the conversation covered product development philosophy and the direction of AI systems, writing: “We talked about his ‘maximally accelerated’ product iteration philosophy, ChatGPT’s business model and AI agent vision, and what this shift means for knowledge work and product management.”
According to Ostrovskiy, Turley pointed to software engineering as an early indicator of how roles may evolve as AI systems become embedded in daily workflows. He wrote: “Nick used software engineering as a leading indicator of where the job evolution is headed – even as more work moves to AI, top teams are still hiring aggressively, with a growing premium on people who know how to get leverage from AI tools, not have their work done for them.”
Product management roles may shift toward evaluation and systems thinking
The discussion also explored how the responsibilities of product managers could change as generative AI systems become part of the development process.
Ostrovskiy wrote: “The job becomes less about coordination and more about 1) understanding real user problems, 2) defining what ‘success’ means in an AI system, and 3) building evals and feedback loops so you can tell if a new model configuration is actually better than the last one.”
He added that curiosity about how AI systems behave may become a core skill across multiple roles: “The advantage goes to people who are curious about system behavior and who like building, regardless of whether their title says PM, engineer, designer or something else.”
The conversation also included advice for students learning how to evaluate AI systems: “Build something with one foundation model, then swap in a different model or prompt configuration and force yourself to decide if it’s better. When you’re a student looking to become a better PM, even a simple spreadsheet of use cases plus a qualitative rubric counts as an eval.”
AI assistants and voice interfaces discussed as future opportunities
The fireside chat also explored how AI assistants could develop beyond current question-and-answer interactions.
Ostrovskiy wrote: “Most people experience AI as one-off Q&A. In domains like health or education, the bigger opportunity is an assistant that tracks goals over time and helps with the day-to-day decisions that compound.”
The discussion also addressed the challenges involved in building realistic voice interfaces for AI systems: “Even though it feels close, voice is really a hard problem to solve and make it sound real. Getting there is dozens of small fixes: turn-taking, interruptions, timing, tone, and personality, and we’ll see more exciting innovation to come from across the industry.”
Reflecting on the event, Ostrovskiy wrote: “Thanks to Nick for being so candid about how these products are built and where the hard problems still are.” He also acknowledged support for the event, writing: “And special thanks to Nick for being so candid about how these products are built and where the hard problems still are. And special thanks to Leigh Ann Montano for her support, the event would not have happened without her.”
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