How to Build a Consumer AI App That 10 Million People Can't Put Down
The Nebius for Startups Podcast
Who’s talking
2 speakers1Transcript68 turns · 26 min
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?
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?
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.
That's insane, a trillion tokens a day. So, 10 million users, 100 different models that you're doing post-training on. Per
week.
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?
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.
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.
So our users are spending more than 150 minutes per day per user. How many minutes? 115 minutes?
More than 150.
Per
day, per
user.
That's incredible stickiness.
That's one of the top. time-consumed
apps on the app stores. Congratulations.
Thank
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?
I see. So, the use case is more on the entertainment side. Okay. Which we call role-play.
Okay. Entertainment role-play. Okay.
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
yeah
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