Inside the Secret Labs Where AI Learns to Work
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What is the main topic discussed in this episode?
Welcome, humans, to the Neuron AI Explained. I'm your host, Corey Knowles, and I'm here as always alongside us with a spring in his step and a button as hard as our own Grant Harvey. How are you today, my friend?
I'm doing well. I'm doing well. I love that. That's a very visual flowery description of me, which I welcomely embrace. How are you? Well, what are we talking about today, Grant? Today, we are talking about reinforcement learning environments, the training grounds where AI agents learn to plan, use tools, adopt, and make judgment calls across multi-step tasks. These environments are quickly becoming the bottleneck for real-world AI performance. And in 2025, they were one of the most aggressively funded and least understood parts of the AI stack.
Our guest today is Nick Heiner, head of RL Environments at Surge AI, where he leads the company's reinforcement learning environments team. Before Surge, Nick was a founding engineer at Fixie, a senior engineer at Netflix working on UI platforms, and part of the U.S. Digital Service.
At Surge, he's helped build large-scale simulated workplaces like CoreCraft, environments used by frontier labs to test whether models can actually do knowledge work end-to-end. He's also the co-author of Surge's recent research showing that even the best models fail roughly 40% of the time on real workplace tasks, with failures clustering around planning, adaptability, groundedness, and common sense.
Nick, welcome to The Neuron. Glad to have you.
Thank you. Happy to be here. You mentioned that it's one of the best funded but also least understood areas right now.
Do you agree with that assessment?
So there's a tweet that, you know, I love to poke fun at my VC friends with. I said a lot of VCs have become reinforcement learning experts in the last three months. And they're like, yeah, yeah, we know.
What are reinforcement learning environments and why are they important?
Like we're just we're piling in here and there's still a lot to learn about the space.
Well, hopefully we can help out all of your VC friends and teach them a bit more direct from the horse's mouth here. I guess to start, how did you end up at Surge AI building RL environments? And what was the moment that you realized that this was the future of AI training?
So, I mean, the moment that I sort of left Netflix and went into AI startups in general was basically the moment, the first moment I used ChatGPT. And it was just, I'm sure everyone remembers where they were when they first used ChatGPT. I remember the moment. Right. It's just immediately obvious that this is something totally different. And, you know, I love Netflix, but it just didn't feel like a time to be at a 25-year-old company in a well-established space. It's like this is a whole new field. So I came over to Surge, and at the time, everything was scaling up really fast. Like, you know, Llama 2, GPT-3, sort of those initial models had come out, and everyone's looking at the scaling laws and saying, okay, now we just need to bump it up in order of magnitude.
which means the entire supply chain needs to get bumped up in order of magnitude, of which we at Surge were a part. So when I first joined, I focused a lot on building out our expert network. And that has a bunch of pieces to it. There's the actual recruiting, but then there's things like, how do you vet people? How do you see who's the best at what type of tasks? How do you apply quality checks to work at broader and broader scales? Then I transitioned to lead several of our client engagements. And that was during 2024. And the big thing there was like, again, in 2023, a lot of these models were being produced by scrappy small bands of researchers at these labs. And then 2024, all those orgs scaled up from 20 geniuses to an org of 1000 people.
And, you know, in much the same way, we had to scale up too. And they sort of had new expectations of us, you know, high-level enterprise maturity, and then just being able to produce super high-quality data for, you know, 40 different research tracks at once instead of like a team of 20 that was focused on three.
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:00–1:51
2
What are reinforcement learning environments and why are they important?
1:51–5:32
3
What challenges do AI models face in real workplace tasks?
5:32–9:50
4
How does Nick Heiner's experience shape his views on AI training?
9:50–21:42
5
What factors contribute to the effectiveness of RL environments?
21:42–24:53
6
What is the significance of reward signals in AI training?
24:53–27:50
7
How do reinforcement learning environments differ from traditional training methods?
27:50–1:02:16
Speakers
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