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Grit · 2 min · Jul 27, 2026
AI can expand the deals a PE firm can assess
Parag Agrawal
He says AI lets a PE firm simulate far more potential buyouts before choosing where to spend human and financial capital.
In this episode
Full episode- 35:23AI agents need incentives that keep the web open4 min
- 22:54Agrawal wants engineers to move work into learning loops3 min
- 13:09Agrawal would rather overuse tokens than be too cautious6 min
- 8:39Agrawal built a personal agent to catch dropped priorities5 min
- 2:49Agrawal protects proactive work with three daily priorities4 min
- 18:46AI productivity can increase engineering demand4 min
- 29:12A strong close rate revealed a sales pipeline problem4 min
- 58:18AI can expand the deals a PE firm can assess2 min
“Now people do work beyond what was previously happening.”
Parag Agrawalat 58:32 · Grit
Takeaways
- Use models to screen more opportunities before committing resources.
- AI may widen the set of deals a firm can seriously evaluate.
- The bigger gain may be work that was previously out of reach.
About this segment
He says AI lets a PE firm simulate far more potential buyouts before choosing where to spend human and financial capital. The opportunity is expanding work, not merely speeding up old analysis.
Parag Agrawal is founder and CEO of Parallel, former CEO of Twitter.
Founded Parallel Web Systems, which builds agentic web search for AI agents; former Twitter CEO.
Joubin Mirzadegan, Host · Partner, Kleiner Perkins
Took Palo Alto Networks' cloud business from $2M to $50M+ ARR in a year.
From “Ex-Twitter CEO on Why AI Needs a New Internet | Parag Agrawal”, Jul 27, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
From the transcript Grit publishes with the episode.
- 58:24
thing that excites me is people often frame this as like, oh, we were already doing this work, now we are going to make it more efficient and we can do the same work, but it's going to be faster and it's going to be cheaper and more automated. Now people do work beyond what was previously happening. I'll give you a very simple example. Let's say you are a semi hypothetical, a PE firm thinking about buyouts. And in the previous world you'd say, okay, I could buy out these categories of businesses in these kinds of neighborhoods and these kinds of cities, and you'd use some gut and human judgment to narrow down to like 4 or 5 interesting opportunities. And then you'd go do extra work on them. Right. Their biggest limited resources, how many of these they can do through capital and human capital in a year? And
- 59:20
the biggest job is to figure out which ones to do and prioritize that well. Now they can use parallel and models to really run what feel like almost simulations on various criteria. Did they set and do a lot of compute to exhaustively come up with a rational list of opportunities to actually explore, and then pick the ones they do better? They're previously pruning the space of what they would explore to this tiny, top down, intuitive thing. And now they get to see real information, real data about what it could be and if it makes them pick one better. There's so much value created that it's worth token maxing in that context. Right. And so when you see these use cases about like just entirely new ways of operating businesses that have existed forever, that's really exciting to me. And I think that's the next year of growth, which isn't just like replace what
- 1:00:22
we're doing today. It's going back to what does it take to make it 1000 x, right? It is work that was not happening yesterday. Well, sir, thank you for doing this. Well said. Uh, when you hear the word