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Knuckle Up with Nakul · 3 min · Nov 10, 2026
He left ads after forecasting growth would stall below 100 million DAU
Parag Agrawal
Looking at Twitter's growth engine, Parag Agrawal projected it could stall below 100 million daily users.
In this episode
Full analysis- 39:02Agents need search built for different inputs and latency5 min
- 19:01A CEO should shape the company around how they lead3 min
- 51:41Agent-scale web access needs a way to pay for contentPro pick 7 min
- 25:57A contested sale forced Twitter into a dual operating reality7 min
- 33:20Agents' expected web use became the thesis for Parallel6 min
- 16:34He left ads after forecasting growth would stall below 100 million DAU3 min
- 58:50Agrawal expects agents to shift the web from pull to push3 min
- 1:15:04He hires for potential, then looks for proven outlier-team builders3 min
“it could stall out at under a hundred million DAU”
Parag Agrawalat 17:27 · Knuckle Up with Nakul
Takeaways
- The projection came from examining Twitter's growth engine.
- He moved from ads to the core product to address durable growth.
- Small-team execution helped turn the idea into company direction.
About this segment
Looking at Twitter's growth engine, Parag Agrawal projected it could stall below 100 million daily users. He left ads to push machine learning into the consumer product, where a small team proved the case.
Parag Agrawal is Founder of Parallel Web Systems.
Founded Parallel Web Systems, which builds agentic web search for AI agents; former Twitter CEO.
Nakul Mandan, Host · Host, Knuckle Up with Nakul
General partner at Audacious Ventures; his show goes deep with founder CEOs on how they build.
From “Ex-Twitter CEO's $2B bet on search for AI agents | Parag Agrawal (Founder of Parallel)”, Nov 10, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
From the transcript Knuckle Up with Nakul publishes with the episode.
- 16:37
I think these were simple things. Like these are not like some crazy big deep insights, right? Like I used to work on ads. Ads was this separate siloed team. Slightly siloed, not fully siloed. And you build ads, you collect a bunch of, you build a bunch of interesting machine learning system because what is ads? It's like matchmaking with machine learning. Twitter at the time used to do a reverse chronological timeline.
- 17:02
And there was almost a religion at the company to not use too much machine learning when it came to the core product, right? Now, so we, I'm there sitting around building ads. I am, I was like a data geek, right? So, I am looking at Twitter is going public. We've built ads. Now you can see if you do the math on the growth engine of the company, it to me appeared that it could stall out at under a hundred million DAU, the growth engine of the business.
- 17:36
So we spent years dealing with, oh, Twitter is growing so fast. We can't roll servers in or keep the site up. And all of a sudden we were going to end up in a world where Oh God, the product is now flatlining in its growth potential, right? And it didn't get there, but you could forecast that in a year, that's where we would be if you looked at the data carefully enough, right? So I'm like, okay, no one seems to be paying attention to this.
- 18:04
There's religion against machine learning. I'm going to abandon ads. I'm going to figure out how to show up on the product side. And work with a collection of people where we would go transform the product to drive long-term durable growth. It's like very simple. It wasn't like the top-down strategic direction for the company yet. But a year later, that's what it sort of became. Right? So it's like these kind of obvious-ish ideas.
- 18:40
But the right people... Focused execution with a small group to prove and change the rest of the company around it. So I did that with other people and showed a lot of initiative there. Fought, as you can imagine, some internal battles because these cultural things are ingrained.