Jon Noronha: How Gamma’s big bet on AI paid off
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Hey, everyone, and welcome to Generative Now. I am Michael Magnano. I'm a partner at Lightspeed. And

And this week, I'm talking with John Nerona. He's the co-founder of Gamma, an AI-powered platform that helps users create interactive and engaging presentations, websites, and social media assets in minutes. Gamma started in 2020, but made a massive pivot to AI in 2023. We talk about what that shift looked like and how it led the company to quickly skyrocket to many millions of users around the world. So let's get into it.

Hey, John. Mike, great to be here. Thanks for having me. Yeah, thanks for coming on. I've really, really been looking forward to this. I think Gamma is one of these really fascinating AI companies. You know, obviously over the past decade,

however many years now, two, three years, there's been so much fascination with the model layer of AI. So much innovation has been happening. But now everyone is talking about the application layer and trying to make sense of where there's value, what companies and great products will emerge. And one of the products that I think of immediately is Gamma. I know you didn't start as an AI company. So

Tell us a little bit about like, you know, founding journey in 2020 to, you know, it's almost like the chat GPT moment and how you got to where you are today. Yeah, totally. It really feels like the application layer is having its moment. But as you said, we didn't start as an AI application. And I still don't know if we could truly call ourselves AI native, although we're always trying to be more AI native.

So yeah, we founded the company in 2020 out of sort of the wreckage of another company, Optimizely. So that's where my co-founders and I and where a lot of our founding team came from. Optimizely got acquired in 2020, and it was a great opportunity to try something new. But it was also a very weird time because it was the depths of the COVID pandemic. The whole world was changing and feeling very shaken up.

Our original thesis for the company, we've always been going after presentations as our initial market. We thought that PowerPoint is just so ripe for disruption. PowerPoint's coming up on 40 years old. 40? It was created in 1987. Yeah, 1987 is when the first PowerPoint came out. If you think about how the product has changed over that time, there have been some big changes. Color was one they added in the early 90s and a few other things like that. They launched clip art at some point.

But based on the paradigm has been unchanged and other great slide tools have come along, but they're mostly just PowerPoint on different platforms. So Google Slides is PowerPoint on the internet, Keynote is PowerPoint for Macs, so on and so forth. We saw this opportunity to rethink presentations from the fundamentals. So don't just try to make a better editor for PowerPoints, try to make a new format for how people present ideas. That was the whole original thesis. It was our original pre-seed bitch tech. It was we're building the anti-PowerPoint. That was kind of the whole idea.

And we thought that COVID provided this amazing why now. It was this period, if you kind of cast your mind back to 2020, when the future of work was a really hot VC thesis. And in fact, there were companies like Zoom, Loom, Slack, just going crazy with usage, incredible growth coming out of nowhere. It felt like the door was finally open to revisit all of these sort of like standard workplace tools. And so we thought it was an amazing time to actually dive in and go after presentations and

And so we did those first couple of years. We were all about rethinking presentations for an era of remote work where people are not all in the same room, maybe not even all experienced in the content at the same time. So what does it mean to design presentations for recording? Our vision was actually create something that was a hybrid presentation and document, something that you could send around before the meeting and we'll have all the information sent around after the meeting of even capture all the notes in the discussion. So a living document that had kind of the rich visuals of presentations, but also they're like

density and real information that a presentation would have. So like really leaning into like new format, like you said. Yeah, really leaning into new format, leaning into different, trying to be as different as we possibly could versus sort of like same as possibly could. And we'll come back to that because that has been both good for us and bad for us. Okay. That was sort of the original thesis. I think we did our first beta launch in 2021, launched on Product Hunt in a bigger way in 2022.

And we ended up right around 2022 in this sort of dead zone of like partial product market fit. I'm sure you see a lot of startups end up in this zone where we'd launched something that had, you know, a number of diehard fans, but our number of active users was in the thousands. Maybe if we're being really strict, maybe it was in the hundreds. So we had a couple of passion people, but it just wasn't there. Certainly it wasn't there for like a venture sized outcome, not there to get us to be able to raise a series A. And so we

We were starting to look at our burn rate and see that we were probably going to go out of business in the next year or so, just running out of runway, if we didn't change something drastically. And so we had the choice of do we pivot the company or do we make any one more big bet on our current path, which is this sort of like presentations path.

And frankly, it was lucky timing that right around that time of mid 2022 is when generative AI was finally on the cusp of getting good. Yeah. These models had all been around in some form for a while, but they weren't really good yet. We even dabbled with them earlier on. But 2022, the key moment that happened actually was not chat GPT-4S. It was stable diffusion. So stable diffusion was the first really popular AI image model that came out.

And it blew up on Twitter. I remember might even have been called Twitter at the time. It was people sharing these crazy pictures they created. And to be honest, they still weren't that good yet. They had all the weird hands and fingers and garbled text, but still there was something magical about people creating illustrations for anything and

And so we kind of had this realization that like, gosh, what is painful about making presentations? So much of it is formatting and decorating. You're just putting all this time into making up clip art and pretty pictures to sort of like fill the space. And so we thought maybe this is the moment to really bet on AI. And so we started doing exactly that in summer 2022. Really?

really investing in it, really kicking the tires of what large language models can do. And I don't think we even fully realized this would be the direction for our company. I think we had this narrow idea, which was AI can help us with our cold start problem in our product. So we have this kind of new format. It's a bit hard for people to grasp our activation rate of like the percent of new signups that actually see the value is quite low. What if we could speed run you through that whole first step?

you know, hour of using the product and make it a first five minutes instead by using AI to generate that first presentation. That was kind of our initial narrow idea. And so we really bet on that. We threw sort of the whole team at it, a bunch of different ideas, not all of which worked, generating presentations, generating images, editing presentations. This idea of generating a presentation from a prompt turned out to be magical. And we basically put everything into launching that.

And then we kind of had this another incredible stroke of luck, which was that year in November, ChatGPT came out. And we all know what happened next in terms of the whole world went crazy for AI, created this sort of insatiable demand for what can I do with AI, people talking about AI and social media, sharing what they made.

It also meant that just in time for us to basically run out of money, the models got good. And so OpenAI released a new version of their model, GPT 3.5. So we ended up launching basically first few months in 2023, our AI generator, and it was total inflection point for us. We went from this sort of like middling zone of product market fit, like, I don't know if this is really a thing to, oh my gosh, people are begging us to pay. They're begging us to monetize. And

And it's just been a totally different trajectory ever since. That's incredible. Yeah, it's funny. Like a couple of times, I think you mentioned the word luck. You know, one thing I often think about with startups and definitely when I was building my company prior to being an investor, like I feel like we too got lucky and want to take a step back and think about it. It's not really just luck, it's timing, right? Like you give yourself the space to exist at a certain moment in time when lightning strikes. And it sounds like you did a really good job of

of keeping the company around and keeping it going long enough for these moments to happen. Thank you. Yeah, I think you're totally right. It's the metaphor I've always heard that I really like is that luck in startups is like sailing. So you have to catch the right wind, but you also have to have rigged up your sail to put your boat in the right place to be able to ride the wind. And so for us,

Yeah, it was timing. In fact, we had to be there when lightning struck. But we also had to have been building for basically three years ahead of that. Building a product, building a team, building a technology base so that when it came, we were in the best position to seize that moment. You can build a product really quickly that takes advantage of tailwinds. But if it's not robust and the infrastructure is not stable and you can't scale, those three years probably ended up being...

immensely valuable for Gamma before ChatGPT really took hold. Totally. So, you know, one of the things that I find really interesting about Gamma, not really even just Gamma, just sort of the space of creative tools is what you find often, you know, if you look back at a lot of the successful companies, is if you just stick to kind of like one job or one format, it ends up not being

a frequent enough use case to like really support the company. You've expanded beyond presentations. And I wonder if that has anything to do with why you've expanded beyond presentations. You're totally right. And it's funny you say that because when we were in sort of particularly those dark times of wondering if we had a path as a company, one of the biggest things standing in our way was that so few investors wanted to invest in a presentations companies.

A lot of bodies buried in presentation.

where people aren't even paying for the presentation's product. Like, you'll never sort of overcome that wall. Yeah, they just bundle it. They bundle it. And going up against bundling is a hard thing, no doubt. And so, actually, from very early on, even before we got into AI, we were already thinking about this idea of expanding the offering and going broad. I think maybe the best inspiration for us has been Canva, which has really shown the ability to build a, like...

blockbuster business by sort of diversifying across different assets and diversifying both in terms of like making different stuff, but also winning over the same customer at different points in their week or their month. So taking someone from a weekly active user to a daily active user because they have five or six different things they use your product for. So we very much taken that approach. Presentations was sort of our starting point. It's still our biggest hook

But we've now added all these other formats that we support. So we have a document builder. And when we say document, think of something more visual than a Google Doc. So we make really shiny PDFs and brochures and eBooks and that sort of thing. Websites is one of our biggest ones. And it sounds very different than presentations. Like, oh my gosh, websites is a whole market in its own right. But

A lot of websites are often quite simple and they are kind of just an online brochure. If you think about like a small business owner or like a coach or something who's trying to represent themselves online, actually their pitch deck and their website are almost the same thing, just in slightly different shapes. And so we kind of lean into this idea of malleability, which AI really enhances. Start with a presentation, turn it into a website, turn it into a PDF that you can leave behind by printing out at a client's site.

And we just had another one in the last few months, which was social media. So basically when people create social media graphics, like a LinkedIn carousel,

And I think our goal is to go from two to four to maybe 10 of these different formats to turn people from those like monthly to weekly to daily active users. Have you found so far that the user for these different formats is the same? Like somebody who needs a presentation is the same person that needs a website, who needs social media assets, or are these actually bringing in like different customers completely? We have been amazed by the surprising similarity across different parts of our product offerings.

and the ability to go horizontal. This is actually the other thing that came up in fundraising, which was so interesting is so many investors, especially at that early sort of like pre-seed, seed stage, don't want to hear about your horizontal product. They want to hear about your vertical focus. So, you know, they say, tell me about the one persona that you're winning over. And I understand where this advice comes from, because if you try to serve everyone, you serve no one. And I think most founders who go after the horizontal thing crash and burn. And in fact, we almost did crash and burn. So I like, I get where it's all coming from.

Nonetheless, from the very beginning, when we've kind of pursued this vision, we have been shocked by the sort of like horizontalness of the space. We got the very start of Gamma, actually the very first thing we did before building anything was user research. We just reached out to our own networks and we basically talked to a hundred people and we just asked them,

tell me about the last presentation you made. And this was people in very different walks of life. It could be teachers or students or consultants or, you know, doctors, whatever it is. It turns out everybody had made a presentation because it's just common across many jobs. And they actually all had the same gripes. So it wasn't like the doctor needed very different features than the student. They all had trouble formatting things to look like. They all struggled with being judged for how visual their content was. They all struggled with keeping attention. It was like kind of all the same stuff.

And now that we've expanded to more formats, it's even more true. The people who are making websites, now there is a distinction of like the designer developer versus everybody else. Obviously, designers and developers care a lot about getting all the details right. But 98% of people are in the everybody else and they have the same needs. I just want to get something professional looking live without stressing about code and needing to pay somebody to maintain my site or change my phone number on it.

And so we've been surprised by the regularity and surprised by, I would say, the level of crossover. When ChatGPT launched, and obviously you talked about stable diffusion early on, powering a lot of the images, obviously that was so magical to so many people. And I think it is still magical. But now, across all of these formats, whether it's presentations or website, how much are you finding that people are relying

on the actual AI versus just, hey, Gamma's a beautiful new format that makes it easy. I'm going to choose that over PowerPoint. Not because of the AI features, just because I love Gamma. What is pulling people in now? Is the AI or the product? The AI is pulling them in, but the core product is making them stay. So it's kind of this dynamic of come for the AI. The AI is what drives the virality. It's something we can impress you with in the first five minutes of signup. And it truly does answer that question of how is this 10x better than what came before?

You can literally do something in two hours that took you 20 hours before. So that is still the promise. But I think this gets to your point around how focus has shifted from the model layer to the application layer. And I think in the application layer, a lot of this debate has come over this concept of a GPT wrapper. So are all these people just wrappers around GPT-3 or whatever it is? I think maybe what that perspective misses this idea that often the wrapper is much thicker than the thing that is being wrapped. So

it's almost like you have this like magical, like jewel or something that you're putting into like Thanos's arm, but you have to have the whole rest of the stuff too. And so for us, yeah, that's like core product. We spend much more of our time on features like having really good PDF export than we do on making the AI smarter, quite frankly. And that is maybe a misunderstanding about the AI application layer. I think a particular example

When people think AI application layer, they think of these coding tools. They think of stuff like Cursor that's totally blowing up, where actually the UI is pretty thin. It really is mostly just a way of getting clod into your code base as fast as possible. That's right. Yeah. We have a bit of a different approach, where presentations are a highly user experience intensive product. So is website building. Most of our work is actually UX design in front of engineering. It's how do we actually make it all work together?

You see that actually in the composition of our team, which our team is like one third UX designers. Oh, wow. Zero ML engineers. So very different than maybe your typical AI company, but probably more similar to what you would see out of like a consumer software company. That's super interesting. So how do you think about like...

Are you guys doing any sort of research or developing your own models or even fine-tuning your own models? Or is it really just all off the shelf and then make the product great with UX and design talent? It's off the shelf, but really heavy prompt engineering. So we've kind of dabbled in things like fine-tuning, but we've actually found that prompt engineering is where a lot of the real power comes up. The challenge is that most users don't know how to prompt and don't want to learn to prompt. And so any interface that is just a prompt box with an output is not going to be very successful. And so...

There's an enormous amount of stuff in between what the user types into our product and what the AI model sees, which is where I think a lot of our value comes in. And that is prompt engineering, but prompt engineering is almost inseparable from user experience. It's like, what knobs are we choosing to expose?

That also shows up in how we worked, which is our UX designers are also prompt engineers, are also front-end developers. We have these very messy overlapping roles where people have multiple skill sets that they combine to provide the right experience at the end of the day. So we're doing quite a lot of that. We do a lot of pre-processing and post-processing of what AI does to make it all feel sort of magical and consistent. A simple example of this is we now do a lot of AI image generation. Actually, we do almost...

We've generated almost a billion AI images. So we do an insane amount of image generation. But when we do that, we do a lot of work on top to make sure that those images feel coherent and consistent. So when you make a presentation that has 15 images in it, we do a lot of work to make sure they all have the same visual style and feel like they all came from the same place versus just a bunch of random prompts being sent off to Dolly or whatever it is. That's really, really cool. You're doing a bunch of great prompt engineering. I

I feel like one thing I keep hearing, I mean, I've been hearing it for two years now, but I feel like the voice just gets louder and louder is that if your defensibility is a prompt or a system prompt or prompting, like you're toast. Like the models are just going to figure this out. Personally, I don't agree with this, to be clear. I invested the application layer. And so...

How do you think about that? Like, how do you think about the defensibility against, you know, something that can be recreated and just a prompt? I guess part of the answer, not to answer for you, is the UX. But like maybe talk about building a traditional software company like pre-AI because it feels like that is what is defensible here. It's like just a great product. I think if all your product did was, for example, a blog post generator where you type in a line of text and you get out like, I don't know, a 10 paragraph blog post.

then I would tend to agree that you're toast. All you are doing is standing in between a really good text generator and the output. But I think it's different when you talk about a real application that somebody lives in to do sophisticated work. And so I'll just focus on our use case, although I think this applies more broadly in the application layer.

Actually generating the text of the presentation is not the hard part. And in fact, ChatGPT is already really good at it. Models just do that. Yeah. But we see all of our value in being what you do with that text. So for us, that includes some things that we use AI for, but it's very domain specific. So actually laying out content in a presentation. So that means structuring ideas into multiple slides, figuring out where content goes on each slide, coming up with different ways of visualizing stuff. So not just...

boilerplate AI images, but actually our own structured visualizations, things like a timeline that are very PowerPoint-y that we do, abstracting away all of that so that the user can give very simple prompts. And also I think crucial for us, it all still needs to be human editable at the end of the day. So there's currently this craze of all these web app builders, like your Bolts and your Loveables. I think they're really cool products, but they generally help somebody go from idea to prototype.

And then they have the prototype and it's like, okay, but can I really use this as my full app? Then there's a disconnect because it's not really a fully editable app. And in fact, to make it a fully editable app, you almost need an interface like Webflow or something where you have this full point and click no code type interface.

A lot of what we have really invested is that no code interface all around it, where you can manipulate the content, edit very easily and quickly, and then do a bunch of stuff with it. So like we have a present mode, we have PowerPoint export, we have publishing a website to a real custom domain. That stuff is actually where all of the pain goes into. I see all of the sort of like,

you know, coding work that our team is doing every day, all the pull requests and 99% of it is that kind of thing. Not to mention making the whole application scale and all the things that can go wrong when you actually have to make this work across, you know, a hundred countries, across very different user types and with very large numbers of requests every day. Am I hearing you correctly in that you can't just build a product that takes something that's in a model,

and gives it to the user. Like that's a wrapper, right? Like a wrapper is a thing that basically just takes ChatGBT and makes it look a little different. What you're saying is just like in pre-AI times, you have to provide value by cheating together workflows. That's what you're doing. You're building a product

Yeah, sure. It's getting some content from AI, but at the end of the day, that's not why people are using it. They're using it because it's a great product. Absolutely. And that product is great because of the way that it fits into their life and their workflow. It's

It's because you are establishing a trusted relationship with that user that they can come back again and again and get a predictable result by working with you that they'll be happy with. And that's hard because they are working with unproductive technology under the hood. And so, so much of the magic is smoothing over the bumps of what AI can do and exposing it in a way that has like nice clean handles for people to grab onto. They're not using it because it's AI. In fact, I would argue customers probably don't even care about that. Like they're not, nobody uses a product for the technology.

right? Absolutely right. One of the things that I often think about is the fact that every company wants to leverage AI now and every company wants to be an AI company. So when you talk about using presentations as a way to then ladder into websites and as a way to ladder into social media assets, all with sort of AI as the wedge and the thing that you mentioned like drives the virality, you know, you could see a world in which this product

does look a lot like Canva or, I don't know, name some other creative suite. But as we all know, these companies want to go in the other direction as well. They want to take the suites they have and they want to inject AI everywhere they exist.

So they can get the benefit of the wedge also. So, yeah, just how do you think about that? Are you are you headed sort of directly at these really, really large incumbents now? And how do you think about differentiating yourself? I think inevitably, yes. You know, you either die an obscure startup or live long enough to fight the big suites. And so our hope is to live long enough to fight the big suites.

And so far, I would say we're doing well in that. And I think what that really comes down to is the advantage of being lean and nimble. Imagine if AI development were to totally pause, so all capabilities just freeze at the moment they are now. I agree with many folks who believe that there would still be a huge amount of AI innovation to be squeezed out just from what we've already gotten.

But I think in that world, what you would see is that the PowerPoints and Google Slides of the world would then sort of like have the predictability to be able to invest and plan something. But that's not quite the world we live in. We live in a world where the capabilities are changing actually quite drastically and quite frequently. So every maybe two months, something new happens.

where you have to sort of rethink what the product's capable of. And I've worked in large organizations. I got my start of my career working at Microsoft, actually. And I just know that large organizations don't have the kind of tight decision-making loop where they can change their core products that quickly. And that's for a lot of good reasons. Most PowerPoint users don't want PowerPoint to change that fast. Enterprise sellers of PowerPoint don't want the product they're selling to change under their feet. There's many reasonable restrictions these companies face. And

Really what their buyers are buying is the predictability of, I just want the same thing that I learned when I was in high school in 2005 or whatever it is. Like that's, that's what PowerPoint should always be. So.

It creates a really nice window when there is technology change for new companies to come in who are not bound by the past and who can make very quick decisions about pivoting their product. And I think that's going to happen to us. I think our product will still change drastically over the next year, two years, three years. If it is, we're not doing our job. And it's really informed how we build the team as well. We've really optimized for being a small team.

So we are about 35 people right now, where many companies, I think with our scale of users revenue would already be in the hundreds, maybe two or 300 people. But it's a lot easier to move a 30 person organization or 300 person organization, and that's way easier than 30,000. So that's kind of the mindset that we've taken there. It's also where we've made maybe this counterintuitive play of focusing pretty purely on product led growth and not really going after B2B yet.

Many companies in our position really feel the pressure, internal and external, of like sell to really big businesses. And we do feel that pressure now. We feel that pressure because those companies are knocking on our door saying, "Hey, we want to use your product." And of course, we ourselves see the revenue opportunity, the retention opportunity of going enterprise. But once you go enterprise and once you really build those very long-term contracts and relationships,

you start optimizing more predictability. And I think things will simmer down where that may make sense. But right now, we think the most important existential thing for us is staying ahead and eliminating. And that means preserving our chance to be very, very nimble. You mentioned that you wouldn't be surprised if the company...

company makes more pivots or iterative adjustments over the coming years as technology keeps advancing. Obviously, if you knew what those were, you'd probably do them now. But maybe what are some of the directions you think this thing could go in? What are some of the things you see sort of coming down the pike? And you're like, oh, wow, we're going to have to react to that. One of the big ones that we're working on right now, actually, is this shift from maybe tool-based interfaces to agentic interfaces. And I

I don't really like to use the word agentic because it's such a sort of meaningless buzzword across different places. But the core of it for us or the core sort of product design trade-off that we face is how much should Gamma be conversational in the sense that you talk to it, though you would talk to ChatGPT, versus how much should it feel more like a traditional software tool where you just sort of like push some button and, you know, put inputs and then generate something.

And we've actually flip-flopped on this a few times in our life already. I think we're going to keep flip-flopping because they each have their advantages. The advantage of the sort of tool-based approach is it's very familiar for someone who doesn't really care about AI and doesn't want to become a prompt engineer. They just specify what they want and they get something good out. But

And a lot of people have said that chat interfaces are dead because more AI will converge in this direction. I don't think it's true, though. And it's certainly not borne out by the numbers, which is like chat GPT being by far the dominant consumer AI product by probably a factor of 10 compared to anything else. I think the reason it may not be true is, number one, the models themselves are really optimizing for these conversational things. And the entire AI application layer floats on top of this innovation layer happening in the models themselves and what they are tuning for.

And the second reason is that the conversational approach lets people really clarify their intent. So when they say, make me a presentation about X, Y, Z, there's a chance for AI to say, when you say you want a presentation about this, are you picturing something more professional focused or more whatever? Are you thinking like 30 slides or 15? It kind of lets you draw out those questions up front. And it also enables correction afterwards.

So you generate the thing and the user can say, "That's not quite what I wanted. I'd actually like to fix it." We're all still figuring out what it means to support that kind of interface. Coding tools have really gone whole hog into just give it a chat interface and make it call 100 different tools and go ham on your code base.

I think they're also going to see saw a bit in terms of like how far they go in that direction. This debate has been going on really since the beginning. I think rightfully, you know, we started from a place of maybe apprehension around the chat interface and comparing it to, oh, you know, we've always had terminals. This is why we evolved to having the GUI, right? And tactile buttons, et cetera. Why are we going backwards? Why are we just chatting with everything? But I think you're making a really good point. And, you know,

the promise of almost like a dynamic interface that's basically evolving in real time based on what the user's putting into the model and the model's spitting back out is, it's probably a lot harder to pull off than it sounds, both technically and also just from a user experience standpoint, right? The best product designs are the ones that

you know, have a lot of clarity and the user has a deep understanding of where things are and their orientation in the world, that's just going to be hard to pull off, I think. And so a chat interface, an agentic sort of experience, you're right. I mean, it's,

We all understand it. We understand it not just because we use ChatGPT, but we message with each other all day, every day on iMessage and Slack and Discord and all these things. And so I wonder if that is the way that people are going to be computing moving forward on an indefinite basis. There's kind of this open question, I think, in the application layer of what is the end state? And one answer is that the end state is...

These super heavily designed software applications, like imagine a Slack or a Notion, which really sort of dominated the application layer of the 2010s and even before that. I mean, the 2000s and the 1990s as well. It was these like really rich graphical applications that became a core part of how we get our work done.

I think the other question is, is the A application layer going in the direction of a junior employee who works for you, where the interface is you talk to them? You've got these software products like, say, Devon, where it's just your junior employee whose whole interface to you is you're just messaging them on Slack. You've got voice interfaces where you just talk to it and tell it what you want. And

You know, we don't push buttons on our employees. We send them emails and phone calls. It's a giant open question of where we go. That also creates a lot of other side questions. An example is pricing. So GUI software apps are priced per seat, but employees are priced per hour. And depending on which one of those paths we go, we might end up with a different business model as well.

So true. Yeah. How do you think about voice? I mean, do you think that that will be a way that people will work with with agents and AI? I mean, there are some obvious cases for it, but, you know, let's say I'm interacting with Gamma. Like, is there is there a world in which I'm talking to Gamma and that's actually more efficient? I think there is. I think that's a pretty likely thing that we will do on our roadmap. The reason I think so is that conversation is such a natural medium.

Speaking is often easier than writing. And we kind of have this mindset of we are trying to build your AI design partner. And with presentations, there's this really obvious analog, which is not most of us, but CEOs, high-level executives often don't make their own slides. Instead, they have people who do that for them. They have staff who are these presentation designers who are very good at what they do.

And they just give these very vague intentions. They're like, I want something that shows the three products we're launching, and it needs to feel like a Steve Jobs keynote. So put some animation over there and make it go kind of like that. And the poor human has to come up with like, well, here's three ideas of what I think you meant. Let me show you some. And they say, no, no, no, not like that at all. And by the way, the middle one, the launch is delayed. So rework the whole thing. That's kind of what the interface looks like. And I have a feeling working with AI to make presentations will converge on a very similar flow.

Except that instead of it being a human who takes a day and a half to get the results back and is stressed out the whole time, it's an AI who gives you those three options in a matter of minutes and shows them to you. When it's not three options, it's like 10 options because variety is so free when it comes to AI. And the fact these models are multimodal just makes it even more analogous to interacting with a human on the other end.

end, right? Like, oh, I'm answering your questions, but also like, let me show you this thing. Like you said, let me show you 10 ideas. Here are the actual images, right? Oh, and I also built the actual product. Here's the software. Go try it out. Right. So by the way, I lost the product for you. Yeah, exactly. Yeah. I mean, I really, I think you're making a compelling case for it. I could totally see every product going in this direction.

So the way we got there in the conversation was, what are some of the things, the pivots you anticipate could be coming at you? What about the things that you're already just planning for? What are the things you know Gamma needs to do and you're working on right now that you might be willing to give us a little flavor of in advance? We alluded to a bit of it already, which is this more agentic experience of working with AI. And for us, what that really means in terms of concrete product launches is

We focused a lot on the initial generation of a presentation. So you give us your notes, we turn it into a rough first draft of slides. That's all well and good, but the rough first draft is only a starting point. Where do you go from there? And we kind of have this disappointing interface right now where we get you that first draft, but it's still up to you to get it the rest of the way yourself.

We want to invest much more in the editing experience so that you can rework it in either large or small ways with sort of tasteful use of AI. And not only AI, also plenty of human tools as well. So some examples of that are being able to give feedback on what was generated and get another version that adjusts what you wanted.

But also being able to make high level changes. For example, I want to make this 20 slide deck be 30 slides instead. Or I want you to put less emphasis on topic A and more emphasis on topic B. Or imagine if it's on a website. You have this website with five or six pages and you say, I think we should split out this section to be its own page on the website. We plan to do sort of much more with the overall editing process.

We also plan to do much more in just our core product experience. And so what that means for us is slides are all about visuals. So we're doing much more with sort of like visualization and making ideas come to life visually. There's also a lot to do with styling. So making things look very distinct and match your brand. We've already done a lot in that vein, but there's a lot more still to do.

And then finally, we kind of have these four big formats. So, you know, websites, documents, social presentations. I think we're gonna see more. I don't know when, but one that we're really intrigued by is video as a new output format. So we all spend a lot of time on YouTube and a lot of the stuff that you find on YouTube

is actually people presenting things. It's people talking through ideas. Sometimes literally, it's like a talking head over a Google's Lives presentation. But sometimes it's even just these more nuanced pseudo-presentation real things where they're just walking through a couple of ideas and

And AI opens up so much in that vein. Pretty simple stuff like just, you know, write the notes for me of what to say in the video, but also pretty neat stuff like upload my AI avatar presenting my content for me, but without all the ums and ahs that would happen if I actually presented it. Wow.

I don't know if you ever had to try to record like a 30 minute presentation. You always mess up in the middle of like slide four and have to start again and then stitch it together. And it's like a huge pain. So being able to just make that all really seamless would be a huge deal. That sounds really cool. Thanks for sharing that. You talked about the fact that the company started in 2020 and then, you know, around the time of ChachiBT, well, Stable Diffusion and ChachiBT, you got lucky. You got this massive tailwind, obviously growth,

since then has been incredible. What's that been like for the team and for you as founders? A couple of years sounded like we're really hard. Now it's just been explosive growth. How do you manage that with the team? How does the team handle it? How does the culture evolve and adapt?

How do your different processes evolve and adapt as the team grows and the revenue grows? Just tell us a little bit about that. You know, it's still been hard, but it's a totally different kind of hard. And it's helping to really understand what product market fit really is. I actually saw someone on Twitter joke that not having product market fit is really fun because there's the existential dread, but you also just get to do whatever you want. You just build the things you believe in at kind of whatever pace you feel like.

Once you get on the roller coaster of having a huge number of customers demanding your product, you no longer control the pace of the direction in many ways. You are being pulled by the market in various ways. And so for us, that market pull has looked like

well first of all scaling to i think we announced recently we crossed 50 million users so wow that's like multiple thank you multiple orders of magnitude beyond what we ever thought this thing would do and so many systems break we spend a lot of our times just you know keeping up with uh

demand. There's also things like we discovered that more than half those 50 million users didn't even speak English, weren't in the US. And so you have to shift your priorities around and suddenly build internationalization because it's just like a no-brainer at some point. And that hurts other things on your roadmap. I think our team has been very resilient and adaptable. It's a lot of change, but it's also really exciting. I think we all know it's a privilege to work on such a fun product that is going through such intense growth. It's the kind of thing we all only get to experience maybe

a couple of times in our career, if ever. So we all kind of know it's a roller coaster. In terms of what it's meant for the team, one of our engineers described it as Gamma's puberty that we're going through. So we've gone from our basic childhood, but we're not the big grownup company either. We are figuring out what it means to grow responsibly and how to build in a lot of structure we might not have had before.

but really keep those roots of this nimble company that's still paranoid it's going to die. What big model innovation are you looking forward to for Gamma? What are you really hoping...

Anthropic does or ChatGPT does or any of these other model companies? What are you hoping they build? One of them actually just happened, which is our holy grail for the longest time is images with text in them. And I actually thought with the pace of AI, this would have already happened. We would get really good at making images with text in them, but it's always felt just out of reach. And OpenAI's new GPT image model is the first one that really seems like it's there.

It's still not perfect and it's really slow. So I think what we are looking forward to is a world where it's almost effortless and very quick in a matter of seconds to make sophisticated images with text in them. And that's an example of a paradigm shift for us because in some ways, all a slide is images with text in it or vice versa. Text with images in it. And so depending on how the technology evolves, it might retool everything about how we approach our product roadmap. Then there's another innovation on top of that, which is take those images with text in them

and then turn them into videos. And all of a sudden, things like slide animations could be done in a totally different way. They could be totally procedurally generated. And so we're also very interested in the AI video space and how it evolves. But it's funny. That's a lot of the shiny, cool stuff. There's also just basic stuff. We are students of model quality because we are so dependent on it. And we pass that quality on to our users. And it's interesting that for all the talk of like, oh my gosh, AGI and the world's going to blow up in 2027,

Large language models still struggle with a lot of the same stuff that they always have. And for us, the capabilities are actually very basic. They're things like, can it follow multiple instructions at the same time?

Can we feed it 20 pages of content and make sure that the output it has doesn't make up any facts that weren't in the original content? And they're largely unsolved. It's made it really interesting to work with foundation model companies because we actually have the privilege of being able to choose between multiple foundation models. Coming from our kind of roots in Optimizely, we have our own evals, but we also A/B test them quite heavily. So we're constantly testing, does this new anthropic model beat this new Gemini model or this new OpenAI model?

we kind of have this interesting quantitative sense of model capabilities changing based on user preferences. And they are moving forward, but maybe not as drastic as many of the foundation model companies want us to think it is. I'm sure these models are going to evolve dramatically over the next 12 months. So maybe if we do this again sometime, we'll be surprised by how different they are or maybe how different Gamma is by then. I guess we'll see. Yeah, I would love to. Hopefully both are much improved. Yeah, definitely.

Well, Gamma's doing a great job, obviously, so I don't think you have much to worry about there. But John, thank you so much. This has been incredible. Really appreciate the time and congrats on everything with Gamma. Thanks. I really appreciate it, Mike, and it was so awesome to be here.

If you liked this episode, please do us a favor and rate and review the show on Spotify and Apple Podcasts. This really does help. And if you want to learn more, follow Lightspeed at Lightspeed VP on YouTube, X, LinkedIn, and everywhere else. Generative Now is produced by Lightspeed in partnership with Pod People. I am Michael McDonough, and we will be back next week with another conversation. See you then.