Inside ChatGPT: The fastest growing product in history | Nick Turley (Head of ChatGPT at OpenAI)

How a Hackathon Sparked the Most Influential AI Product

Nick Turley, head of ChatGPT at OpenAI, walks through a candid origin story and an operational playbook that turned a research demo into a product used by hundreds of millions. What began as a volunteer hackathon project—originally called “chat with GPT‑3.5”—morphed into ChatGPT after a rapid, ten‑day push to ship and a fortuitous tweet. Turley describes the surprising mix of engineering improvisation, user feedback loops from public launch, and iterative model improvements that created one of the fastest rising consumer products in history.

GPT-5 feels different: faster, smarter, and more useful

GPT‑5 is presented as a categorical step change: better reasoning and math performance, dramatic improvements in coding and front-end generation, and a more convincing, helpful writing voice with what Turley calls “taste.” He emphasizes the practical improvements—speed, dynamic internal "thinking" when needed, and broader utility across health, education, and developer workflows. Importantly, OpenAI made the frontier model widely available rather than gated, accelerating discovery and adoption.

Design Philosophy: Ship Fast, Learn Faster

Turley repeats a core belief: in AI, you cannot know what to polish until you ship. The model and the product are one and the same; iterative releases expose real failure cases that benchmarks miss. That approach—rapid prototyping, public feedback, and daily iteration where possible—enabled the team to find emergent use cases and prioritize the right product investments, from search integration to personalization and memory.

Is it maximally accelerated?

A cultural motto at OpenAI asks whether a project is "maximally accelerated"—a forcing question to remove unnecessary blockers and focus on critical path work. Turley clarifies that acceleration is contextual: speed matters for product discovery, but rigorous processes are essential for frontier model safety, red‑teaming, and external review.

From Research Lab to Global Product: Distribution, Retention, and Use Cases

ChatGPT’s early public exposure created viral discovery channels and unpredictable use cases, with TikTok and community threads acting as a large‑scale feedback loop. Turley highlights three retention levers: model improvements tuned to user use cases, new product capabilities like search and personalization, and traditional product reductions in friction such as removing login barriers. The result is unusually strong retention—what he calls a “smiling curve”—where cohorts return and use the product more over time.

Emergent human uses

Beyond productivity, Turley notes surprising adoption in relationship coaching, mental health support, education, and everyday decision making. These high‑stakes applications demand careful model behavior work, connection to trusted external resources, and clear measurement of harms and efficacy.

Culture, Hiring, and Teamcraft

OpenAI’s hybrid research–product culture matters. Turley credits interdisciplinary collaboration, hiring for curiosity, and small high‑leverage teams as keys to keeping velocity and product quality. He also explains how product managers increasingly need to write evals—explicit, testable success criteria—to communicate real‑world objectives to research teams.

Where this is headed

Turley envisions moving beyond chat as the primary interface: natural language endures, but the future likely includes AI that can render interfaces, take actions, and build long‑term relationships with users through memory and widened action spaces. This would shift ChatGPT from a conversational shell to a personal AI that helps manage goals across work and life.

In summary, the conversation maps a pragmatic path through technological breakthroughs, public experimentation, and product craft: launch quickly to surface real problems, invest in model behavior and safety, and design for long‑term relationships rather than one‑off interactions. The story is part origin myth, part operational manual for building products on frontier models and for stewarding capabilities as they scale to billions of people.