ChatGPT, Reasoning, and the Dawn of Asynchronous Agents

Why OpenAI’s latest podcast reveals a quietly massive shift in how we’ll use AI

You can watch the full episode above, and it is one of the most candid and illuminating conversations OpenAI has released so far. If you’re in the business of AI, building with it, or just fascinated by where it’s all going, it’s well worth a listen.

The episode features OpenAI Head of ChatGPT Nick Turley and Chief Research Officer Mark Chen, hosted by Andrew Mayne, and it pulls back the curtain on how ChatGPT became a breakout hit - almost by accident - and where it’s going next.

Let’s dig into what they said, what it means, and why the shift to asynchronous agents is the part I think we’ll all be talking about six months from now.

The Podcast in a Nutshell

Here are the key takeaways:

  • OpenAI was genuinely surprised by ChatGPT’s success. Nick Turley recalls thinking the metrics dashboard was broken on day one.
  • The name “ChatGPT” was a last-minute decision. Even internally, some staff didn’t know what GPT stood for.
  • “Contact with reality” (real-world user feedback) was crucial to their success. They released early, learned quickly, and iterated often.
  • They are no longer just building a chatbot. The future lies in intelligent agents that take on complex, long-running tasks in the background.

Glossary of Terms

Here’s a cheat sheet for some of the terms discussed in the episode:

  • AGI: Artificial General Intelligence - human-level intelligence across tasks.
  • Agentic Coding: Complex, multi-step coding tasks executed over time.
  • Codex: OpenAI’s code-generation model.
  • Contact with Reality: Letting real users shape the product via live feedback.
  • Dual Use: Tech that can be both beneficial and harmful.
  • Evals: Internal benchmarks to measure model performance.
  • GPT: Generative Pre-trained Transformer - the foundational model architecture.
  • ImageGen: OpenAI’s image generation tools such as DALL·E 3.
  • Iterative Deployment: Launching early, learning fast.
  • Multimodality: AI that understands and works across text, image, audio, and more.
  • Obsequious/Sycophantic: Early ChatGPT flaw - being overly flattering.
  • Prompt Engineering: Writing inputs to guide AI outputs effectively.
  • Reasoning: The model’s ability to logically work through problems.
  • Reinforcement Learning (RL): Reward-based model training method.
  • Specifications (Specs): Public model documentation.
  • Utilitarian Product: A tool designed for real-world utility, not novelty.
  • Variable Binding: Accurate mapping between words and visual outputs.
  • Turing Test: Can a machine convincingly imitate human intelligence?
Auto-generated description: Three people are seated and talking in a cozy room with microphones and bookshelves in the background.

Why Reasoning Changes the Game

One of the most quietly significant parts of this podcast is how the team talked about reasoning. Not just logic puzzles or smart search - but real, step-by-step cognitive effort.

Mark Chen explains it beautifully. He compares AI reasoning to solving a crossword puzzle. You do not just land on the answer instantly - you explore possibilities, test a few, rule things out, go back, and try again. That whole process - backtracking, reevaluating, and coming up with something solid - is what they’re aiming for with the next generation of models.

Instead of demanding fast answers, we start making room for better ones. It’s a change in the relationship - from instant response machine to something much closer to a thoughtful assistant who actually takes the time to work something through.

Asynchronous Agents - The Real Takeaway

This, for me, is where the podcast got seriously exciting.

Nick Turley starts talking about tasks that don’t just take seconds or minutes, but hours or days. Not in a theoretical sense - in a “this is what we’re building toward” sense. And you can hear in his voice that he sees it as foundational. “Five-minute tasks, five-hour tasks, five-day tasks” - that phrasing repeats more than once.

The point he’s making is simple, though surprisingly overlooked. A lot of valuable work takes time. And right now, most AI interfaces are built around instant gratification. You prompt, it replies. Fast, tidy, done.

But the next wave? That’s going to feel different.

You will not be chatting in real time. You will be assigning tasks. And those tasks will run in the background - through reasoning, research, iteration - and come back with something genuinely useful.

It’s less like talking to an assistant, and more like handing something off to a very capable colleague. One who just happens to live in your browser.

From Codex to Deep Research - A Glimpse at What’s Coming

Some early prototypes already hint at this shift.

Codex - OpenAI’s coding-focused model - is moving in this direction. You don’t prompt it to autocomplete a line of code. You give it a whole unit of work. “Fix this bug,” “Implement this feature,” “Write the integration.” Then it goes away, thinks, and sends back a proper Pull Request. Not instantly - but carefully.

They also mention something called Deep Research. It’s probably the clearest example of an agentic model we’ve seen from OpenAI. You ask a big, layered question. It goes off, finds data, surfaces gaps, asks more questions, digs deeper - and you see its progress unfolding over time. Think of it as Google that doesn’t just search - it investigates.

They even adapted the UI to show a progress bar on your lock screen. Not because people are impatient - but because it reframes how we engage. You’re not chatting anymore. You’re waiting on work to complete.

And Yes, People Will Wait

This bit surprised even Sam Altman, apparently - but not Nick.

He puts it plainly. If the value is high enough, people will wait. It’s no different from delegating to a human. If you ask a colleague to crunch some data or research a new supplier, you don’t expect it back in five seconds. You trust they’ll come back with something useful - and you get on with your day.

That’s how these agents are going to work. Quietly, in the background. Less like tools, more like workflows with a brain.

It’s going to reshape how we build, not just how we prompt.

Explore the Podcast with an Interactive Agent

If you’re the kind of person who likes to dig a little deeper or ask your own questions, I’ve built something for you.

Thanks to Google’s NotebookLM, you can now interact with a custom-built agent trained specifically on the OpenAI podcast content. It understands the episode in detail and can help answer follow-up questions, explain concepts, or clarify anything you didn’t quite catch.

Auto-generated description: A digital interface invites users to ask questions about a podcast featuring discussions on AI assistants, with a text input box for interactions.

Try it here:
https://notebooklm.google.com/notebook/63797506-66ce-4d91-972b-1ebc6a347c4d

To use it, you’ll need:

This is still experimental tech, but it works surprisingly well. Feel free to test it, explore ideas, or just see what else the podcast didn’t say out loud.

Where It’s All Heading

This is what I’d call OpenAI’s real long game - and it’s one that doesn’t fit neatly into the chatbot narrative.

The chat interface made AI accessible. But asynchronous agents will make it powerful. In the coming year or two, we’ll see a whole new generation of experiences emerge that:

  • Don’t require you to sit and steer the conversation
  • Let agents work independently for hours or days
  • Deliver results you didn’t micromanage, but still align with your goals

I wouldn’t be surprised to see a shift in how we design around AI. Dashboards instead of chats. Progress indicators instead of typing dots. Even team-style collaboration spaces where you’ve got multiple agents handling different areas of your workflow - and reporting back like a project team.

For knowledge workers, creatives, educators, analysts - this isn’t a nice-to-have. It’s the difference between a helpful suggestion and a fully executed plan.

Final Thought

What OpenAI is building here isn’t just a smarter assistant. It’s a whole new form factor for getting things done. One where you hand off complexity, give it time, and get real value back.

It won’t feel like magic. It’ll feel like productivity, finally catching up to possibility.

Want more breakdowns like this? I’m tracking agentic workflows, persistent memory, and long-term reasoning across OpenAI, Google, and Anthropic. Drop me a message if you’re building something in this space - I’d love to trade notes.