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Sourcery · 2 min instead of 55 min · Oct 12, 2026

The real bottleneck is deciding what to build, not writing code

Aravind Srinivas

Founder & CEO, Perplexity

  • Molly O'SheaHost · Founder, Sourcery

    Ex-Upfront Ventures investor; has interviewed Alex Karp and Palmer Luckey.

About this segment

Srinivas argues coding isn't what constrains the best engineers; problem framing, specs and constraints are. He wants AI that challenges your idea before building it.

From “Perplexity CEO: SMBs Could Drive the Next Economic Boom”, Oct 12, 2026. Watch the full episode

“I don't want an AI that just spits out slop that makes a vibe-coded clone.”
Aravind Srinivasat 43:23 · Sourcery43:23

Takeaways

  1. Writing code is nearly solved once specs and constraints are clear
  2. Want AI that questions the business model before building a clone
  3. Perplexity targets research and problem solving, not coding tokens
Watch 2:14 from 42:23

In this episode

Full episode
  1. 29:26Long-context agents make local hardware cost-competitive with data centers3:28
  2. 26:26AI products compound through recursive usage, not network effects1:57

  3. 42:23The real bottleneck is deciding what to build, not writing code2:14

Up next · My First MillionNarcissism means seeking validation you should fill yourself4:55

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 42:27

    we are trying to build our own market. That's why we like to think we don't actually have any competitor because number one, we're not going after like coding just like everybody. We actually don't think the bottleneck is in producing more code. I actually think you talk to any engineer today, like the best engineers, and ask, are you bottlenecked on writing more code? I think most of them would say no.

  2. 42:53

    They rather say we're really bottlenecked on problem solving. Like there's not enough time to actually problem solve. Like when you actually know exactly what problem to solve and clearly the specs and system design and like all the constraints, writing code is actually dead simple with AIs today. It's almost already a solved problem. But figuring out all these constraints, that is the hard problem.

  3. 43:19

    Like figuring out what problem to even solve, what are the constraints? Let's say you go and ask an AI build me a clone of this product. I don't want an AI that just spits out slop that makes a vibe-coded clone. I actually want the AI to come and tell me like it's stupid of you to even go and build this clone. Like what is even the business model? Why do you even have a right to build this? How does it even fit into your strategy? I think you mean this. You probably want to build this.

  4. 43:49

    This is what I suggest you build. This is the spec. These are the constraints. These are the security risks. These are the go-to-market risks. This is what I suggest you build. Do you want me to build it? I said, sure, build it. And then writing the code is dead simple. I think the bottleneck's actually the first. Clarity of thought, problem solving, deep research analysis, and scoping out things. That's what we're all bottlenecked on. If I had more people like that in our company, I think we could be 10x more in market cap. So I think that's the problem we want our products to do and solve. And when everybody else is in search of more coding tokens or more neoclots, we kind of want to be the best at research and analysis and problem solving.