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Grit · 3 min · Jul 27, 2026
Agrawal wants engineers to move work into learning loops
Parag Agrawal
Parag Agrawal says engineers should turn measurable problems into models that improve with data, rather than hand-code every step.
The episode’s best minutes
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
- 5 more moments in the full episode
“Figure out what a good output would look like. Figure out what model architecture is right.”
Parag Agrawalat 25:18 · Grit
Takeaways
- Collect data and define what a good output looks like.
- Use models where outcomes can be measured.
- Build feedback loops so the model keeps improving.
About this segment
Parag Agrawal says engineers should turn measurable problems into models that improve with data, rather than hand-code every step. That lets teams focus on solving problems across functions.
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.
- 23:01
high impact work, which happens to be hard. So if you have great engineers, you have to kind of trust their judgment and their taste that if they can automate something away with the existing tools, they will. And so we just we have a few simple things we talk about, which is like beyond the meta layer, if you can like use an AI to automate something like there are big parts of our system which are loops with models in them, which in an old world you could have thought that this was a.
- 23:40
Like an engineer would have done that, right. Like if you're doing quality work, like if I think back to our time at Twitter when we were building, like these sort of recommendation systems, and you were doing a bunch of work to improve its quality and run, build better models, improve them. And there were engineers, a bunch of them, iterating on models by using intuition, running a bit as finding new features, building data pipelines. You can take some of those things and now have them be in a more automated loop, because wherever there is a nice, clean way of measuring things, right, you can automate a lot of those things, and not a lot breaks because they're going to go into opaque models anyways, right? So those things are in our system and we don't even think about them as, oh, this was work that an engineer did earlier which
- 24:34
are being done by models now. Does that make sense? And so to me, it's like what an engineer does become more and more meta over time as you find feedback loops that you trust and can automate and make measurable, and you can trust on your models and then you don't, you have to look less under the hood of how it's working. Right. And our goal is to push more things inside learned models rather than in code.
- 25:07
Right. So if there is a I've been saying one sentence internally quite a lot like the answer to every problem is a model. So and which is sort of how we work. So you have a problem. You're tempted to write a bunch of code for it? No. Instead, collect some data. Figure out what a good output would look like. Figure out what model architecture is right. Train the model and put it in a loop so that you can keep improving the model. The more data you can collect and the where do you want to point it? So it's simple. Once you sort of just internalize that worldview and the whole debate of like, is this worth like? I can't sit there and curate what work is worth doing versus not. And so that mindset just allows you to get out of that decision making process and hire people who are very versatile and problem solvers. How many engineers do you
- 26:04
have? 30 odd. And how many did you have six months ago? 15