From RLHF to RLHB: The Case for Learning from Human Behavior - with Jeffrey Wang and Joe Reeve of Amplitude

**Alessio** (0:07)
Hey everyone, welcome to the Latent Space Podcast. This is Alessio, Partner and CTO of Residence at Decibel Partners. Today, we have a special episode that we recorded live at the Amplitude offices. They hosted a hackathon over the weekend with over 150 people. We went there to check out some of the projects and meet all of the talented builders there and spent some time with Jeffrey, who's the co-founder and chief architect of Amplitude, as well as Joe, who runs the R&D team. We talked about using data and especially product data in larger language models, both in terms of how do you make it easy for your team to understand some of the data, as well as how do you use these models to maybe synthesize some of the learnings from it? For example, why is this cohort of users turning at a higher rate? Are there any patterns using the data? So there's a lot of interesting research they're doing.
We didn't record this in the studio, obviously, so certainly the audio quality is a little lower. We apologize for that, but it was a great discussion, so we wanted to make sure everyone could have access to it. I'll let you jump in.
Thank you, everyone, for coming. Hopefully, some of you have listened to the podcast before. If you haven't, we focus on AI research and application, so we don't focus on AI is going to kill us all. We don't think about virtual girlfriends. We don't think about all of these more societal things. We're focused on models. How do you build them? How do you train them? How do you use them in production? What are some of the limitations on getting these things from demos to things that millions of users use?
And obviously, a lot of you are building things, otherwise you wouldn't be here.
And some of you have been building things for a long time, and now have a new paradigm that you want to build on top of, so I'm excited to dive in here. I'm sure most people know you, but maybe you want to do intros and give a little background.

**Jeffrey Wang** (2:04)
Sure. Yeah, hey, everyone. I met you all this morning, but I'm Jeffrey. I'm one of the co-founders and chief architect here at Amplitude.
I've been working on this product analytics thing, helping people understand user behavior data and make great product decisions and build better products for the last decade or so. And obviously, AI is a technology that we've been leveraging for a long time, but the recent trends are particularly exciting.
And we have a lot of thoughts on how to apply that to our space, what we're doing in our product, and what we think the future of AI and product development and product data is. So excited to talk through some of those.

**Joe Reeve** (2:38)
Yeah, I'm Joe, Joe Reeve.
I've got a background in sort of startups and tech, been a professional software engineer since I was 16, quit college. And at the moment, I'm running sort of AI R&D efforts here at Amplitude. Super excited about all the new stuff, but also all the stuff that Amplitude has been doing for a long time. And how we're sort of getting renewed interest and excitement and abilities to push that even further forwards.

**Swyx** (3:03)
So I think it's useful for people listening on the podcast and also some people here. Can you contextualize Amplitude as an AI company?
What does that mean to you? What unique opportunities do you guys have?

**Jeffrey Wang** (3:21)
Sure. Yeah. Happy to speak to that.
So we think about the fundamental thing that our customers of Amplitude try to do. It's they want to look at their product data and they want to figure out how do I make my product better?
And the really cool thing about product data is that one, it's often very high fidelity. Digital products compared to, let's say, physical products before them have way more information about what's going on. And so that's why product data is even a thing at all. You finally have that feedback loop of, hey, I built this thing. This is how people are using it. Now, let me learn from that and make my product better. Now, one of the downsides of that is that the data is massive. If you look at any of the internet scale products out there, they generate enormous amounts of data. And the ability of humans to kind of sift through that data is obviously limited. At Empathy, we try to give people as many tools, whether AI or not, in order to process that. But at the end of the day, if you could get from the data and what user behavior is happening in your product to the insights of how to make your product better without as much manual work, that's kind of the holy grail of product analytics. And so in some sense, Empathy has always been a company on the path to AI because figuring out how to make your product better from data is ultimately an AI problem. And so we're kind of just solving all the barriers in the way, like getting data in first, building good models for short-term things.