How Gamma pulled off their AI pivot | Jon Noronha (Co-founder and CPO of Gamma)

**Jon Noronha** (0:00)
We weren't even monetizing, and people were emailing us begging to pay for the product.

**Brett Berson** (0:03)
For today's episode, I'm sitting down with Jon Noronha, co-founder and chief product officer of Gamma, the AI presentation platform that has over 100 million users.

**Jon Noronha** (0:13)
We knew this AI thing was going to be big. We put all of our resources into it. It was kind of our Hail Mary moment as a startup.

**Brett Berson** (0:18)
They bet everything on solving one problem, the blank page. And when it worked, it worked almost too fast.

**Jon Noronha** (0:25)
Servers going down every other week, support volume we couldn't keep up with, best definition I've heard of product market fit is you stop pushing the rock up the hill and you start chasing the rock down the hill.

**Brett Berson** (0:34)
Before that launch, Jon says that looking back, that weakness may have been one of the luckiest things that ever happened to the company.

**Jon Noronha** (0:41)
A lot of my naivete came from looking at some product and saying, I can make it better. Just because I think it's better doesn't mean the user think it's better.

**Brett Berson** (0:48)
In our conversation, Jon unpacks why Gamma worked.

**Jon Noronha** (0:51)
We bet early on and pretty consistently on building a horizontal product rather than a vertical one. It was actually a source of cost intention in fundraising.

**Brett Berson** (0:58)
He gets into the sudden unplanned sprint into enterprise and why he now believes the very guardrails that made Gamma work in 2023 are the same things holding back what today's smarter AI models can do.

**Jon Noronha** (1:11)
Gamma's early success came when LLMs weren't very good yet. All those guardrails we put in place are holding these much more intelligent AIs back. We're now in a mode not of even adding functionality, but trying to throw things out, loosen the requirements and let AI do more overall.

**Brett Berson** (1:26)
Let's dive in. Maybe a way to frame part of the discussion is for Gamma, if you go back to like six months before the company was started all the way through today, how would you define the distinct phases of product market fit for the company?

**Jon Noronha** (1:45)
Well, let's start with the none era of product market fit. So we started the company in 2020 This was obviously peak pandemic, and it was this period when we saw all these companies like, let's say, Zoom and Loom and Slack really blowing up because people were taking a new approach to work. And there was this incredible why now moment of people realizing we're not all in the same room, we're not using the same tools. And so that was the era in which Gamma was born. The early hypothesis was not about AI. It was all about remote work driving this transformation. And so we had a lot of prototyping, a lot of beta concepts.

**Brett Berson** (2:16)
Did you start working on the company without a product in mind?

**Jon Noronha** (2:21)
No, well, we had a problem in mind, I should say. And we had a target. The target was always PowerPoint. We knew we wanted to reinvent presentations. Because first of all, it had a massive TAM. That was something I took from my previous experiences. I want to work on something that had a huge TAM if you were successful, with a product that nobody liked. And so PowerPoint checked those two boxes really well. It also felt well suited to our team skills. It was really about front end design, creativity, productivity. I knew there was a great product to be built here. There was like a billion people that use PowerPoint Google Slides every month, and they're not happy with it. So that was kind of always the driving force. We thought we had a why now. And so we dove in and we said, all right, let's prototype, let's explore, let's see what we can do here. We built a product that did okay, I would say we had some traction. But if we're talking 2020 to 2022-ish, we had basically no product out, just prototypes.
22, we launch on Product Hunt, we get our first thousands of users, maybe we're in the hundreds of monthly actives. We have slightly linear growth, but I would say we're in this era of weak to middling product market fit, definitely not red hot. At this point, runway starts dwindling, we're starting to figure out where do we go from here, how do we drive more of this. And this is when the first green shoots of AI are starting to show up. We're seeing stable diffusion as a model out there. We're seeing GPT-3, not even ChatGPT yet.