Anish Acharya

speaker

8,262 appearances 20 recordings 3 series first heard Mar 2025 last heard 12 Sep

Anish Acharya’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 15 in all, peaking in Jun 2026 with 4.

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The a16z Show · Why Companies Are Becoming a Series of Loops | Anish Acharya on Lenny’s Podcast · 12 Sep 2026 podcast

My question is what are the business loops, right?

So if you're the GM of a business, you're looking across many job functions and you've got loops running in coding and marketing and sales and support and legal, the output of all of those loops is something that is itself a loop that you should be able to optimize for.

And I think the strong form of this is that it sends a message to the CEO saying, hey, we need to actually make a change to one of the physical aspects of the business or to our business model or to our strategy.

So I think we're going to see this sort of cascading set of everything from a loop per person, loop per job function, loop across entire business units to loops that can run large parts of the company.

With that said, I think humans are a critical ingredient.

I just don't think that most work in the organization can be done fully autonomously.

When you think of what a human will do in this like AI native company, sales, support, strategy, and exceptions, right?

And all those things are super critical.

We've seen one thing, Lenny, it's that the ability for models to do new thinking out of distribution thinking is still really limited.

And I don't actually take the point that some of the new thinking in math is actually representative of new thinking in domains like business.

So you're still going to need a person to say, hey, here's the thing I think we should make and have them be right about it.

I mean, a great example is a growth team.

You worked on the growth team at Airbnb, right?

right.

So you remember those, like, I don't know how you ran your team, but I'm guessing it was something like you got everyone together, you built a list of possible experiments, you prioritized them, you built them, you shipped them,

So the loop version of that should be that every variant gets generated, every variant gets measured.

Once you get to stat sig with a high enough p-value, you converge and ship that variant.

You then have a long-term holdout and you start working on the next experiment.

And then you're going to hit some local maxima.

And I think this is really important.

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