Podcast Episode

How to Build a Consumer AI App That 10 Million People Can't Put Down

The Nebius for Startups Podcast

Jul 7, 202626 min

Who’s talking

2 speakers
Lifan Wang19 min · 76%
Joshua Liss6 min · 24%

1Transcript68 turns · 26 min

LW
Lifan Wang0:00

We're processing more than a trillion tokens per day. A lot of people think for consumer AI, PMF, product market fit, is very important. But I have a different opinion. You're no longer a tag. AI knows you. We can rank 100 models in one hour, two hours. What have you learned about yourself?

JL
Joshua Liss0:27

Hi, I'm Joshua Liss. I lead media entertainment here at Nebius, and I am thrilled to be your host for the Nebius for Startups podcast series. Today, I am joined by Lifan Wang. Lifan is the co-founder at Keon Labs, formerly known as FlowGPT. Keon Labs is building the infra layer for data and creativity. Why don't you tell us what that means?

LW
Lifan Wang0:53

Yeah, sure. Previously, we're known as Flow GPT. We built an app called Emoji, which has more than 10 million users. But what we're actually building, we're actually working on every day, is the infrastructure that actually scales the product. We're actually testing more than 100 different models every week. using the real-time signal from the users, and we're processing more than a trillion token per day.

JL
Joshua Liss1:25

That's insane, a trillion tokens a day. So, 10 million users, 100 different models that you're doing post-training on. Per

LW
Lifan Wang1:32

week.

JL
Joshua Liss1:33

Per week. Yeah. And a trillion tokens a day. Yeah. That's amazing scale. So, what does it mean to be the infra layer at that scale for data and creativity use cases?

LW
Lifan Wang1:46

Yeah, so I think at the beginning, when we were building this product, we feel like we just need to provide a really good experience for users. That's our original belief. And then we figured out, why don't we try different models? And that actually turns out very important. The retention, I think trying different models, varies more than 10%. We figured out that we need to build better models. The conflict between consumer app and AI model is that AI model is improving, keep improving, but the cost is just so high and we cannot afford the pretargeting model for the consumer use cases.

JL
Joshua Liss2:38

And especially with your business, because you were telling me how much time your users are spending with you. Tell me what that number is and what that means for how you scale a business like yours.

LW
Lifan Wang2:49

So our users are spending more than 150 minutes per day per user. How many minutes? 115 minutes?

JL
Joshua Liss2:57

More than 150.

LW
Lifan Wang2:57

Per

JL
Joshua Liss2:59

day, per

LW
Lifan Wang2:59

user.

JL
Joshua Liss3:00

That's incredible stickiness.

LW
Lifan Wang3:01

That's one of the top. time-consumed

JL
Joshua Liss3:04

apps on the app stores. Congratulations.

LW
Lifan Wang3:07

Thank

JL
Joshua Liss3:08

you. Okay. So scaling a business like that is quite challenging. Maybe you can give me a bit of an overview of the different types of users of your platform. What are they coming and doing with Kion Labs on a typical day?

LW
Lifan Wang3:25

I see. So, the use case is more on the entertainment side. Okay. Which we call role-play.

JL
Joshua Liss3:32

Okay. Entertainment role-play. Okay.

LW
Lifan Wang3:34

Yeah. So, for traditional content consumption, it's very restricted, I would say. Okay. For example, when people are reading a novel, people have to wait for the author to update. People cannot control the novel, where it's going. And also, it's not super immersive. You are actually just seeing others doing things. but um for us for for the years on emoji people are actually role playing in the story people can you know like say what they want to do yeah in the in the story

JL
Joshua Liss4:10

yeah

LW
Lifan Wang4:10

and they

2Episode notes

Henry Wang built a consumer app that 10 million people use for 150 minutes a day. For most of that time, the assumption in the industry was that you needed a frontier model to deliver a great AI experience.

Henry found a different answer.In this episode, Joshua Liss, Head of Media and Entertainment at Nebius, sits down with Henry (Lifan) Wang, Co-founder of Kaon Labs, to get into how you actually scale a consumer AI product. Not just find product-market fit, but build the infrastructure that lets you serve tens of millions of users, test 100 models a week, and generate a trillion tokens a day without it costing you everything.

Henry explains why personalization at consumer scale isn't a recommendation system anymore, how Kaon Labs replaced two-week A/B tests with Elo ranking that resolves in two hours, why entertainment is the right beachhead for consumer AI right now, and what in-experience advertising could look like.

He also shares his contrarian take on what actually separates the consumer AI companies that break out from the ones that spike and die.

0:00 - Intro teaser: 10M users, 115 minutes a day & 1 trillion tokens

0:20 - How Qiyan Labs built AI infrastructure for 10 million users

3:02 - Why AI roleplay is changing entertainment forever

5:10 - Hyper-personalization vs. recommendation algorithms

8:18 - Why scaling consumer AI is harder than enterprise AI

11:33

Showing a preview — retrieve the full transcript via the Exa API.

Powered by the Exa API

See something wrong or missing on this page?