Evidence receipt / belief
Published · transcript-backedKrithika Shankarraman: belief
25 May 2025 Lenny's Podcast Growth tactics from OpenAI and Stripe’s first marketer | Krithika Shankarraman
“I think there was a very visceral example where we decided to bring our free product into the hands of more users and sort of what was available in the free plan.”
Source trail
Everything needed to verify it.
- Speaker
- Krithika Shankarraman
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 25 May 2025
- Publisher
- Lenny's Podcast
Transcript context
…That reminds me. To sort of close out our conversation, I wanted to come back to pricing strategy. I have that in my notes here and I haven't gone back to it. So let's focus on the AI and pricing strategy. Just say someone is trying to figure out pricing for their product and they have some kind of AI product. What are some tips, some piece of advice to think this through? Any general frameworks you use? Again, there's no playbook. I feel like it's such a non-answer, but I think the real answer is experimentation. And we found this firsthand multiple times at Stripe, but also at Retool. I think there was a very visceral example where we decided to bring our free product into the hands of more users and sort of what was available in the free plan. And then there was another one that we tested out as a pricing function where we decided to do something quite controversial, which is to take the thing that our sales team was gated on, a self-hosted version of Retool, and made that available self-serve to anybody who wanted it. They didn't have to talk to a salesperson. And that kind of blew up the funnel, right? Because the amount of pipeline that the sales team saw had diminished considerably, but it also helped them focus up market, on higher ACV deals. And so that trade-off is really hard to make, so the only way we could do it was through experimentation and piloting to build conviction. So I would say AI is no different in that you kind of have to test the market to see what works. Is it a seed-based model? Is that where people are deriving value? Or is the way that they speak about the value of the product something quite different? Is it hours saved? Is it the amount of things that they could do now that they couldn't do before? And so there might be a metric there to go off of, and I don't think anyone solved it, especially with agents coming into play. How you pay for AI workers is going to be very different. What is that unit of completion for things like code generators? It's going to be a Wild, Wild West before we come up with something that is as internalized now as seed-based pricing or usage-based pricing. Wild indeed. I want to actually follow this insight you had around Retool. That's really interesting. Yeah, so you opened up self-hosted Retool. What was the insight there, because this might be useful to people, that convinced you to play with that? Seems like a big deal change to how you price and do trials.…
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