Scaling the ‘Cursor for Slides’ to $50M ARR: Gamma founder Jon Noronha

Contents

Jon Noronha: We all start some new project with this blank page saying “Enter presentation title,” and it’s like, “My God, where do I start? I have to outline all the slides. I gotta figure out my story, what’s the hook? What are the key moments, what it’s gonna look like, what fonts am I gonna use, what colors, what imagery do I need?” And you get stuck in this spiral of, like, thinking through all these things.

Sonya Huang: Totally.

Jon Noronha: And so when we actually built AI that solved our blank page problem, it turned out that it solved everybody else’s blank page problem, too. Suddenly they could just go from a vague idea to a fully worked out rough draft and suddenly their job was editing, not starting from scratch.

Sonya Huang: Today we’re joined by Jon Noronha, founder of Gamma, a Cursor for slides. Gamma is a canonical example of application layer success in AI. Image generation models were the initial product breakthrough, and these AI tailwinds have now carried Gamma to 50 million plus in ARR, 50 million users and cash flow positive. Jon discusses practical lessons for builders on multimodal orchestration, running extensive A/B tests, fine tuning versus prompt engineering, cost efficiency and more. He also shares thoughts on how Gamma is solving the cold start problem, and their future roadmap towards more agentic editing experiences. Enjoy the show.

Sonya Huang: Jon, thank you so much for joining us on Training Data today.

Jon Noronha: It’s great to be here. Thanks for having me.

What is Gamma?

Sonya Huang: Let’s start with what is Gamma? Why’d you start the company?

Jon Noronha: So we started the company back in 2020, and the original vision has not really changed up ‘til now: We wanted to reinvent presentations. My co-founder, Grant, came to me with the idea back in 2020, and he was talking about how he was just spending all of his time sitting outside on a park bench trying to call into conference calls because his wife was using the one spot in their house with a desk. This was the depths of COVID when we were all in our houses, and he was joining all these conference calls, staring at his little tiny phone, trying to follow along to these PowerPoint decks people were doing on Zoom.

Sonya Huang: Wow, the good old days.

Jon Noronha: I know. I was like, “That’s all I do too. All I do is just sit on Zoom calls all day and look at Google Slides decks alternating with maybe making Google Slides decks along the way.” Neither one’s very fun for me. And so as soon as he mentioned it, I was like, “Man, this is such an exciting opportunity to reinvent this thing.” Which on the one hand, slides are the language of business. They are the way that we all communicate at work, especially around high stakes ideas. Like, for you, anytime somebody pitches you, I’m sure it’s with a slide deck. And yet, if you ask anybody in almost any walk of life, “Do you enjoy making slides? This is how you want to communicate?” You generally get some kind of ick reaction, like a, “Ugh, I hate having to do that. I hate—I hate how slides make me look bad. I hate how I spend 90 percent of the time on formatting and just 10 percent on the content. I hate how everyone’s judging me for how they look and not what I’m saying.” And so Gamma’s vision came out of this almost universal problem. People need to do visual communication, they want to look great, but current tools don’t cut it. And so we wanted to reinvent that not just by making a new editor for making PowerPoints, but for actually rethinking the format itself.

Sonya Huang: That’s so cool. By the way, I’m one of those shameful people that actually likes making slides. So I guess I’ve now realized that I’m a weirdo.

Jon Noronha: Yeah, you are the one percent. Congratulations.

Sonya Huang: [laughs] So Gamma today is an amazing success story in the AI application layer. I think you guys announced you’re at 50 million-plus of ARR, cash flow positive, team of 30 people. So congratulations.

Jon Noronha: Thank you.

Sonya Huang: Did it always feel like this?

The long road to get here

Jon Noronha: No, absolutely not. It’s been a long road to get here. There are no overnight successes. As I said, we started the company in 2020, before all this AI stuff had taken off.

Sonya Huang: And did you know the AI stuff was going to happen when you started the company?

Jon Noronha: Absolutely not. I actually remember trying the first version of GPT-3, which actually also came out in 2020, and I was one of the first people, I think, to get access. And we tried it out, and I just wanted to see could this make slides? We were already working on it. I wanted to see if I gave it a document, could it summarize the key bullet points? Could it do the opposite? Take bullet points and, you know, turn it into paragraphs of text? And the answer was no at the time. It just wasn’t there yet.

And so I kind of thought, wow, this is neat. But I dismissed it. And for several years we went down a very different path. We were very much focused on this future of work thesis, thinking about remote async work, trying to build an alternative to slides that was more interactive, more webpage-like, more responsive. And we ended up in this zone of sort of medium traction, some product market fit, but not enough to be probably like a VC-scale business, not enough to really take off with exponential growth. And got to a pretty difficult point maybe two years into our journey where we had that middling product market fit, we had a dwindling runway, and we probably wouldn’t have made it, especially because the economy around us was just going crazy. It was a period of inflation going up, interest rates going up, banks crashing—including our own Silicon Valley Bank. So we had some very rough periods along the way. And frankly, I would say what saved us was generative AI getting good just in time.

So we were already at this point of facing some, I’ll say, existential angst of are we going to make it as a company? But we started checking back in on all this AI stuff over a period of two years. And for me, what really caught my eye was actually not language models, it was image models. Stable diffusion had come out that summer, and I was seeing everybody on Twitter posting these silly little things they were making in stable diffusion. It wasn’t serious work yet, but there was this magic and this virality to seeing what AI could do. I think Dall-E was also taking off at the same time. And I started to realize this is the thing that can really change presentations, because what are we doing when we’re presenting our ideas? We’re doing a lot of visual designs, and we’re often decorating things sort of pointlessly to fill space. Probably PowerPoint’s key innovation was actually clip art maybe 30 years ago, but nobody’s really come up with what’s the next clip art? And I saw stable diffusion in Dall-E and I’m like, this is by far the most compelling thing I’ve ever seen that could do it.

The image models took off

Sonya Huang: That’s a really interesting insight. So it wasn’t even the language model side that made you think there’s a why now for your business, it was the visual—the image model side.

Jon Noronha: Absolutely. They’re just so magnetic, so magical. But ironically, the image models taking off made us revisit the text models and say, “What’s been happening with this GPT-3 thing that we heard about a while ago?” And I remember trying it out and just trying some of the same early prompts I tried two years earlier, and suddenly they worked. And they actually worked quite well. Everyone sees ChatGPT as an overnight success as well. But it turns out OpenAI had been improving the models all that time as well. And in particular, the thing they’d been working on was instruction tuning, so making it so that you could actually give a prompt the way a human would, like, “Please write this thing,” or “Format this thing,” and it would listen to you.

Sonya Huang: Yeah.

Jon Noronha: Very simple innovation that I think underlied the whole ChatGPT takeoff. And so suddenly we realized we had all the ingredients to not just be a tool for making presentations, but actually just make the presentation for you.

The blank page problem

Sonya Huang: That’s super cool. Can you say a word on—you know, I think you guys mentioned pre-AI, you had this cold start problem. How do you think AI is changing that problem for you all?

Jon Noronha: Well, it’s funny you say that, because yes, we had a cold start problem, which was that we had launched this product into beta with middling traction. And the reason it wasn’t taking off was the activation rate at the very start. So a hundred people would sign up, and 95 or so would fall off in the first few minutes of using the product, because they get dropped into this new innovative tool that was actually just a blank page. And we’d say, “Look how cool it is. It’s got so many features.” And they’d say, “Yeah, but what can I make? What can I do with it?”

Sonya Huang: Yeah.

Jon Noronha: And they would pretty much all fall off along the way, except for the few diehards who would manage to power through that blank page. And so the reason we seized on AI early on was we thought it could solve our blank page problem. We thought it could speed us through showing something magical where a user would get it. I think what we failed to appreciate until we actually launched it was that it wasn’t our blank page problem being solved, it was the user’s blank page problem. And if you then think back to, like, why are presentations so intimidating and frustrating, it’s because we all start some new project with this blank page saying, “Enter presentation title,” and it’s like, “My God, where do I start? I have to outline all the slides. I gotta figure out my story. What’s the hook? What are the key moments, what it’s gonna look like, what fonts am I gonna use, what colors, what imagery do I need?” And you get stuck in this spiral of, like, thinking through all these things.

Sonya Huang: Totally.

Jon Noronha: And so when we actually built AI that solved our blank page problem, it turned out that it solved everybody else’s blank page problem, too. Suddenly they could just go from a vague idea to a fully worked out rough draft. And suddenly their job was editing, not starting from scratch.

Sonya Huang: Yeah. No, I’ve loved that. And I’ve actually used Gamma a lot. I get asked to give a lot of presentations about AI, and it’s so refreshing to just give Gamma a document and be like, “Turn this into a slide deck,” versus having to sit there in front of a blank page and figure out what I’m going to talk about.

Jon Noronha: Well, thank you for trying it. We always appreciate it. And yeah, that’s the magic, too. We use it every day internally, and that’s part of our success is I love having a dog food-able product, a thing you can use every day.

Sonya Huang: Totally. Tell me about the hard work that goes into—and this plays into the whole debate of how much value is in the application layer. Tell me about the stuff that you do in between the foundation models and your users.

Adding value in the application layer

Jon Noronha: Yeah, really good question. I think this is where so much of our work the last few years has gone. And I would say there’s a couple of different categories of it. The first is actually the medium itself. So I mentioned that where we started was we didn’t want to just be a presentation clone, a PowerPoint clone, we wanted to be our own unique format. And so we came up with something that our users describe as if Notion and Canva had a baby. So what does that mean? The Notion part is it’s block based and writing based. So you’re not actually dragging boxes around and moving them the way you are in Google Slides. You’re actually just typing. And we try to bring whatever you type to life and feel visual. But the Canva side is we have all this visual range. So we have diagrams and charts and visuals and crazy theming and templates that sort of all make it feel a bit more magical. But the key thing is that we do that part for you. You don’t need to actually know what’s going to look right. You just type and you get something great out. And so actually a huge part of what we do as a company is not anything to do with prompts or fine tuning, it’s just actually tuning those core building blocks. How do we have the best diagrams, the best layouts, the best visual themes? And those become our arsenal that AI has let loose on. It’s the building blocks that it works on.

Sonya Huang: And what goes into that? Like, what allows you to create the best visual themes or the best diagrams?

Jon Noronha: Well, one thing that goes in is taste. So this is probably a big difference from when, you know, people think about, “What if I just type the same thing into, say, ChatGPT?” We actually have a pretty big team of designers. In fact, for a while, our company was one-third designers. It was four out of twelve designers, which is very unusual, I think, in Silicon Valley, especially at the early stage. But we put a ton of care into what will great output look like. Our whole philosophy is that we do the great design so that you don’t have to. But it’s also quite data driven. We spend a lot of time looking at what users are trying to do in our product and where things get lost. And also a lot of analysis of, like, what do presentations out there in the world have? AI is great for this, by the way, at churning through, for example, thousands of external slide decks and telling us what are the common layouts and designs and things that are there?

Sonya Huang: Yeah. What are some of the insights you’ve learned from the data of where people tend to get lost and where you’ve solved that with your product?

Jon Noronha: You know, if you think about the process of making a presentation, there’s actually this whole series of hurdles that come along the way. There’s sort of a storytelling and structure phase where it’s like, how do I even want to say this? And then there’s a formatting and design phase. And we are certainly looking to improve on both, but in particular in the formatting and design phase, there’s actually just certain common tricks that for an AI are so easy, but for a human are so tedious. One simple trick is don’t just try one thing, try ten different designs and pick the best one. Easy to say when you’re an AI that can do it all in about three seconds. For a human, that’s incredibly time consuming.

Sonya Huang: Yeah.

Jon Noronha: Another simple trick is with imagery, making all the images in your presentation the same color palette. Easy to say when you’re an AI that can generate the image from scratch. Hard to do when you’re a human pulling from Google image search or your own library, and making everything just perfectly tuned. So I would say that we’re trying to stack up a lot of those tricks into something that actually no human could do.

Sonya Huang: And when you generate ten different options, how do you figure out what’s the best one?

A/B testing everything

Jon Noronha: Oh, such a good question. So my background—I don’t think I shared—is before Gamma, I worked at Optimizely in a product role. Optimizely, for those who don’t know, was A/B testing you’ll actually use. So we sort of pioneered A/B testing as a universal practice in how marketers tune their ideas, and then also how products get launched. And that’s actually been a throughline through my whole career in product management. I did a ton of A/B testing before that at Microsoft. And now we do a huge amount of it at Gamma. So every time a new model comes out, so for example, as we’re recording this today, Sam Altman is on stage announcing GPT-5.

Sonya Huang: Yeah.

Jon Noronha: Literally right now, there’s somebody on our team who’s actually making a code update on our side so that we can implement GPT-5 and run a test on it. And we go through a couple different phases of testing when a new model comes out. We do have evals, so we try to automatically measure is this thing better than previous models. But it turns out we’re in a creative domain where there’s often no right answer. And so it’s really hard to run evals and just say, “Oh, GPT-5 is better than Claude Sonnet by 2.7 percent.” There’s actually not a metric for that. But what we do have are a bunch of real users, and we’re lucky to now have millions of users that we can actually run these experiments on. And so sometime this afternoon, we’re going to start an A/B test of GPT-5 against Claude, against Gemini, and we’re going to actually measure a whole series of user behaviors when we generate a presentation. We’re going to actually ask users what they think through ratings, but we’re also going to measure how much did they edit the final output, how much did they export it to other tools, did they share it, and finally did they convert from a free customer to a paying customer? And we have run probably hundreds of these experiments, and we have learned very carefully which models work well for which tasks, and on top of that, what aspects of our prompts work well. So I would say we skew pretty heavily on the idea of prompt engineering. We have quite complex prompts in our products, and quite complex orchestration of prompts. And every piece of those is the product of some degree of A/B testing and experimentation to find out what actually resonates.

Sonya Huang: Data flywheel in action. Nobody better to build the data flywheel than the Optimizely guys.

Jon Noronha: Absolutely.

Evaluating models

Sonya Huang: So it’s primarily prompt engineering as opposed to fine tuning. Is there a reason for that?

Jon Noronha: Honestly, we just haven’t found that fine tuning works all that well. Fine tuning is something that I’ve seen so much hype around in the industry, but mostly coming frankly from companies that do fine tuning for you. But in our own experience, fine tuning seems to hobble the model’s actual intelligence. And also, frankly, what we found is that fine tuning seems to be popular among open source models, but we actually have not seen the open source models come nearly close to the closed source foundation models.

Sonya Huang: Interesting.

Jon Noronha: So for us, our daily drivers are, like, Claude, Gemini and GPT, rather than some of these open source models that get a lot of news.

Sonya Huang: Okay, so name your favorite child: Claude, Gemini, GPT. Like, what is each best at?

Jon Noronha: You know, if I had to choose, I’m a Claude-stan. I would say we are very, very heavy Claude users. And in particular, I think what we found about Claude was it just has a certain taste to it about what actually looks good. There’s this creativity, and it’s remarkable because it doesn’t show up in any benchmark. When all these companies talk about their models, first of all, they’re always talking about software engineering. At this point, coding is the dominant use case of AI. It’s the killer app. And so all these companies are optimizing for, like, reasoning, tool use and coding. We are not a coding platform; we are a visual expression platform. And so all the benchmarks are useless. We have to throw them out and run our own experiments. Claude has been the best, although funnily enough, earlier versions of Claude sometimes work better than new ones as they’ve kind of gone down this coding-optimized path. I think the one that’s most slept on is probably Gemini, though. Gemini is by far our most heavily-used model. And that’s because they really win on cost efficiency. When it comes to intelligence per dollar, nothing beats Gemini Flash, which is probably our daily driver. And maybe this ties into another thing about our company, which is we actually really care about margins, unlike maybe a lot of other AI companies in this space. We have always tried to operate, since we had AI, at cash flow positive, and think about how we can offer these things sustainably.

Sonya Huang: Yeah. What are the reasoning models? Have those been an unlock for you all?

Jon Noronha: Surprisingly, no. We’ve actually run these experiments, and what we’ve found is that the longer a model thinks, the less creative it gets. I think reasoning works really well on questions that have a right answer, like creating a math proof or solving a coding problem. They really don’t seem to work well in any kind of creative writing domain.

Sonya Huang: Huh. That’s so interesting.

Jon Noronha: And in particular, an unfavorite child, we just found that DeepSeek was a total dud for our use case, despite all the hype of, like, “Oh my God! These open source models can reason.”

Sonya Huang: Huh.

Jon Noronha: Yeah.

Sonya Huang: What about on the image generation side? Any favorite models?

Jon Noronha: We have a lot of favorite children in image generation. I think we have around 20 different image models that we support in our product, and we take a similar approach of A/B testing them all. I think in image models, there’s not as much of one right answer. I think our most heavily-used models come from Ideogram and Flux. They have some really great models and amazing progress. But we’ve also been really amazed by what OpenAI’s GPT image can do. It is so powerful at text and infographic type content. And so we’re mixing in where we can, but also trying to figure out how to serve it cost effectively.

Sonya Huang: Totally. I want to go back to this data flywheel and kind of prompt tuning concept. Are you familiar with DSPy?

Jon Noronha: Yeah.

Sonya Huang: Are you guys using that, or any other methods to kind of automatically engineer your prompts?

Jon Noronha: We’re actually not, no. We’ve tried DSPy a little bit, but we haven’t found it helpful for us. I think there’s a couple reasons for that. We’ve actually really valued sort of the legibility and readability of our own prompts. We almost view prompts as a user experience, as things that a designer can work on, things that a PM can work on, not just things that engineers write and optimize. I think another thing is that we really care about cross-model reusability. So I mentioned that, for example, Claude and Gemini and GPT each have their own strengths. We use all those models in concert. We test them against each other constantly. Also, at any given time, one of these models is broken and you have to use a different one. And so we care a lot about that portability. And we found that tools like DSPy are really good at micro optimizing one very specific, sensitive combination. But we care a lot about generality.

Sonya Huang: Hmm. And so are you handcrafting your prompts then?

Jon Noronha: Well, Claude is handcrafting them for us, I would say. We use a lot of Claude code to iterate on our prompts, but we do a lot of human review and iteration and guidance of what to do.

Sonya Huang: Yeah, super interesting. I want to ask about the elephant in the room, which I think is competition.

Jon Noronha: Yeah.

The 800-pound gorillas

Sonya Huang: Like, I mean, Slides, I agree with you. Visual communication, it’s one of the most pervasive forms of communication, especially in the business world. And therefore you have a lot of 800-pound gorillas in the room that are going to be competing with you. Who scares you?

Jon Noronha: The one that scares us most is still PowerPoint and to a lesser extent Google Slides because they are still the dominant incumbents. We’re proud to have millions of monthly active users, but the stat that I heard is that PowerPoint and Google Slides each have 500 million monthly active users. So there’s, like, a billion or so slides users out there, and we are still only the tiniest fraction of that. So we still view ourselves very much as an insurgent trying to take on these very big incumbents. Luckily for us, those companies have been fairly slow moving with AI. I think we’re actually lucky that agentic coding has taken up so much of everyone’s attention, because these productivity use cases have actually been neglected by some of the big players.

I think the other big source of competition for us is the foundation models themselves. So we saw ChatGPT agent come out just a few weeks ago. It makes PowerPoints. Thankfully for us, not very good ones. But we know that’ll change, and we know these will get better. And so I think for us, we’re still trying to lean into our differentiation. How can we be as distinct as possible from PowerPoint, from ChatGPT? How can we bring a huge amount of our own taste on top of just what the models can do? But it’s this delicate dance, because on the one hand, we care a lot about being different. We don’t want to just be a PowerPoint builder at the end of the day, because PowerPoint will eventually become a great PowerPoint builder. We want to become a great Gamma builder. We want to be our own visual storytelling platform, and really our own medium. And yet we also have to be familiar, because we’re kind of crossing this chasm where our first 50 million users were early adopters who want to try the newest thing. I think our next hundred million or so users are going to be maybe this early majority that need things to be convenient, need things to be familiar, need things to fit into existing workflows, especially as we also make this transition from pure B2C to start to do B2B as well. And so we’re kind of on this tightrope, trying to figure out how to be familiar and yet innovative.

Sonya Huang: Totally. What about Canva? I view them as somebody that’s been beloved for their taste in design. Obviously, some of their usage is slide driven. How do you think about them?

Jon Noronha: First and foremost, I would say we view them as an inspiration. I think they’re a very inspiring company in terms of what they’ve achieved. They’ve really paved a path that we’re trying to follow. I think they’ve proven a couple really interesting things about how you can build a company. They’ve proven that PLG can work at enormous scale. So they have—the stat that I’ve heard about Canva is that they didn’t hire a single salesperson until they had $500 million of ARR. Which is crazy, because I think most companies aspire to ever reach 500 million using any strategy whatsoever. Certainly we do. And yet they’ve managed it with this sort of SMB-focused strategy, and a huge library of sort of templates. And so we want to follow that trail that they blazed. I think we’re taking a somewhat different path than they are though, because Canva is first and foremost a design tool where you drag and drop things into place and use templates to do it.

We’ve always had this different DNA from where we started in the pandemic of actually being a writing-based tool. And that’s why we kind of also compare ourselves to Notion as maybe another index point of this company, where you didn’t need to have any design skill whatsoever to make something really beautiful and magical. And I think Notion’s also an inspiration to integrating AI into their platform as well. And so yes, I think we’ll compete with Canva more and more head on over time, but I’m hoping we can forge our own path versus just trying to be a copycat.

Sonya Huang: Totally. I remember at some point a couple years ago there were three or four of these AI slide creation tools, for lack of a better word, and you guys really won out, and a couple of others have pivoted or gone sideways. Why do you think that is?

Jon Noronha: That’s true. It’s been a very competitive space in the past, and I think we’ve managed to make it a little bit less competitive for now at least. I would point to a couple differentiators. The first one is that we’ve just always had a pretty lean, paranoid mindset. So I think some of the other companies you’re referring to raised quite a lot of money and built pretty big teams. And we always had the mindset that AI world is changing really, really fast, and the most important trait that will determine our success is our ability to pivot and adapt. And so that’s led us to have quite a small team, I would say, relative to our traction. We’ve cared a lot about keeping that sort of lean mechanic.

It’s also impacted our spending just in a very tactical way in terms of how many dollars we’re willing to spend on inference. So many AI companies now are operating at a loss. I think that includes many of our competitors who were running these tools like GPT-4 and DALL-E that were just tremendously expensive when they came out. And I think it’s this classic mechanic of selling dollars for 75 cents. You can get a lot of growth, but it’s not sustainable. And I think a lot of companies really hit the crunch of doing something unsustainable. And so that’s also become a part of our DNA is we really care a lot about cost efficiency in everything that we do. We really care about maintaining that cash flow positivity, because we think it’s the foundation that will propel us to keep moving faster than everyone else.

Advice for AI founders

Sonya Huang: What advice do you have for other founders building in the application layer?

Jon Noronha: First and foremost, I would think about what is your unique perspective on the market that you’re tackling? For us at Gamma, our unique perspective is all about differentiating the medium itself. So we’ve always been very clear to ourselves: We are not just trying to make a PowerPoint builder, we’re trying to create a new format to replace the slide deck. And that’s guided many, many of our product choices. It’s also angered our users sometimes. It’s not sort of a panacea, but I think it’s helped us to navigate what is a very competitive market.

And so I would definitely encourage other founders in this space to think about their unique lens. So for example, I’d be wary of making yet another vibe coding startup. There are so many companies that are doing pretty much the same thing in this space. I would think about what is the neglected area where people are not applying AI? And even potentially you want to go against the grain of what the foundation models are training for. So the reason these vibe coding tools are taking off is that the foundation models themselves are so good at coding, and it’s so clear that the foundation models are optimizing for that. I think if you work on something they’re not optimizing for as much but is still adjacent, you’ll have a little bit better luck at making progress, without being smushed by the giant dinosaurs out there.

I would also really try to incorporate some form of experimentation, and really encourage trying multiple different models. I think people tend to lock into one model provider and just assume that it’s the best out there. But the innovation out there is so rapid and uneven and unpredictable that I think you have to plan for a world where on any given day of the week, there’s a different best model out there.

The road ahead for Gamma

Sonya Huang: Yeah, we got the Optimizely DNA coming out here. I’d love to close by talking a bit about the future of how you see Gamma. So right now you all have something like 250 million Gammas have been created on your platform. What are you seeing in terms of the usage so far? What are folks using you for, and how has that evolved over time as the underlying models have gotten better and as your platform has gotten better?

Jon Noronha: Yeah. So, you know, we started and really reached product market fit with this use case of presentations. And even presentations turn out to be a very wide world. Everything from a very visual TED Talk type presentation to a very wordy consulting or iBanking slide. And so we actually see quite a lot of room within presentations to just keep improving and doing better.

But we also discovered all these interesting adjacencies that we hadn’t necessarily planned on from the start. The first one was simply documents. And when I say documents, I don’t mean, like, plain text Google Docs. I mean things like PDF proposals and brochures and shiny reports that you give to a customer and white papers. There’s actually all of these things. You know, if the market share of PowerPoint is like 500 million people, I think for PDF it’s like a billion people. I think there’s just an enormous number of these visual outputs people are creating that don’t quite fit the classification of a slide deck. And we’ve actually seen tremendous pull there and built out a lot of product for that.

The next one that really surprised us was websites. So a lot of people started making these things in Gamma, and realizing that the Gamma they made worked well as a personal website or their small company or agency website, similar to how Notion docs actually kind of get used as these little mini …

[CROSSTALK]

Sonya Huang: I was going to make the exact same comparison.

Jon Noronha: Yeah, yeah. Again, they’re a big inspiration to us. And we realized this is a great market to compete in because websites are so sticky, and they’re so meaningful to people in terms of being their public face to the world. And so we launched a sites product last year as well. And so we keep finding these adjacencies and pulling, and it’s making us realize that we are much more than an AI presentation tool. We’re still thinking about how to formulate it, but I think our latest mindset is that we’re a visual storytelling platform. And what that means is that anytime you have something high stakes and important you want to convey, and especially when you’re kind of like a book being judged for your cover, where people really care how it looks, Gamma is a really great place to go. So we want to serve that through many different use cases.

Sonya Huang: I love that. Say a word on what should users expect from you over the near to medium term? How will the products evolve?

Jon Noronha: We have some really big things coming out just next month. So in September, maybe just to give a bit of a tease of what’s coming, we are doing a lot to improve our core editing experience, really expanding our visual range and variety. So we have this goal of making every Gamma feel wildly different, and anything that you sort of imagine that you want to achieve with it, you can just get through easy prompting. So that’s a big one for us.

We’re building a lot more agentic editing, so you can just delegate to the AI and say, “You know, this five-slide presentation should really be twenty. And can you expand more on section three, and maybe redo the visuals in this other part?” just by talking to it.

And then a big one that I’m really excited about is that we’re launching an API. So we’ve had a really surprising amount of interest in this. I think there are so many companies that as part of their work just need to create some kind of visual output based on some sort of workflow. So a simple example is a sales team where a new prospect comes in and you want to tee up a pitch to that prospect. And so what if you could just take your CRM, pipe in all the notes you’ve had from them and get a pre-canned pitch deck in your template that you can just go and take to that prospect without the hour of salesperson time, and let’s just say mangling that might have gone on when they did it. And I’m actually surprised by how many investors I hear from that say, “Actually, I have, like, 10 startups in my portfolio that need something like this,” because there’s so many startups that do really valuable work for people and just need a way to show it off. So we’re really excited for that to launch, too.

Sonya Huang: Super exciting. I remember Karpathy tweeted, “What is the Cursor for slides?”

Jon Noronha: That’s right.

Sonya Huang: Everybody showed up in the comments saying Gamma. It’s really cool to hear the story of both how you guys have gotten so far, the combination of vision, of scrappiness, of everything you’re doing in the model orchestration and experimentation layer, and also kind of how you see the future of agentic editing and what’s to come. So thank you so much for sharing the story of Gamma today, and I can’t wait to see you guys continue to change the future of visual communication.

Jon Noronha: Thanks so much for having me. It was so great to be here.

Mentioned in this episode:

Mentioned in this episode:

• Language Models are Few-Shot Learners: Original paper for GPT‑3 that Jon tried early for slide generation and found lacking at the time.
• Training language models to follow instructions with human feedback: Cited as the key improvement that made models follow natural prompts and unlocked Gamma’s AI path.
• Ideogram: Image-generation service; one of Gamma’s most-used image models.
• FLUX (Black Forest Labs): State-of-the-art image model family; another top performer in Gamma’s image stack.
• DSPy: Stanford framework for programmatic prompt/chain optimization. Gamma tested it but prefers human-legible, cross-model prompts for portability.