Inside Canva’s $3B ARR AI Design Rocketship — CTO Brendan Humphreys on Magic Studio & Canva Code

Canva’s Mind-Blowing Growth and Profitable Journey

Matt Turck [1:14] Brendan, welcome.

Brendan Humphreys [1:15] Thank you.

Matt Turck [1:33] Thanks for doing this. I thought a fun place to start would be to share a few metrics about the company because obviously people know that Canva is a fantastic company, but I don't know that people have quite internalized how fantastic a business it is that you all have built over the last 10-plus years. So, in terms of numbers, you announced literally yesterday in a press release that your annual recurring revenue is now surpassing $3 billion, which means that your growth rate is around 40% to 50% and actually accelerating at scale.

Matt Turck [2:11] And on top of that, you're doing so profitably. You've been consistently profitable for the last seven-plus years. You have 230 million monthly active users, including 24 million paying subscribers in 190 countries. And your customer base, especially as you've been pushing into the enterprise, includes 95% of the Fortune 500, with teams at Atlassian, the New York Stock Exchange, and HP. So, incredible stats and incredible business all around.

Brendan Humphreys [2:18] Yeah, it's an amazing journey to be on, and we're at a fantastic point right now. Absolutely.

Matt Turck [2:34] How does that feel? You've been on this rocket ship for over 10 years. Do you sometimes get to take a step back and realize how incredible a company you all have built, or is that a succession of fire drills from the inside?

Brendan Humphreys [3:03] It does feel a little surreal, but I do recall when I joined the company, when I made the decision to join, I could really understand that there were multiple pathways to revenue. It was a really simple idea that was very captivating. I'd actually tried to build something similar in my spare time. And so I really believed it was very easy to believe in the business model. And that made it very easy to get on board 100%. And I think I've been there ever since.

Matt Turck [3:14] And the original idea of Canva was simple design in the browser in 2012. What was it?

Brendan Humphreys [3:40] That's right. Yes. So, Mel recognized that the entirety of the design process involved so many different tools, each of them with a steep learning curve. There was often real friction between the tools, and that kept the barrier to entry really high. So she saw this opportunity to consolidate it into a platform that was web-based, the source of our success, that great insight.

Why Brendan Left Atlassian to Join a Tiny Startup

Matt Turck [3:46] And you joined in 2014, when the company had only 12 employees.

Brendan Humphreys [3:46] Yeah, something like that.

Matt Turck [3:49] You were the 12th employee, something like that?

Brendan Humphreys [3:50] Yeah, something like that. Yeah.

Matt Turck [4:13] And you joined from Atlassian, which at the time was already an incredibly successful company. What was the thought process? And perhaps to generalize it, how does one, as an up-and-coming engineering leader, pick the next company to go to? Because clearly that was an incredible pick.

Brendan Humphreys [4:43] How do you pick the winners? Yeah, look, I've been very lucky. I joined Atlassian back in 2007 through an acquisition. Atlassian bought my company, and around about that time, 2014, I kind of recognized that I was in a little bit of a rut. I had certainly enjoyed the ride at Atlassian. They'd gone through their own hypergrowth phase, and they were just leading up to IPO. But I really wanted to get back down to just building something.

Brendan Humphreys [5:00] And through a friend, I met Mel and Cliff, and I instantly fell in love with the business model. I don't think I have great advice on how to pick winners.

Matt Turck [5:22] Perhaps less picking winners, more what do you optimize for? I mean, it sounds like you optimize for the founders and believing in the mission. Some people may optimize for, well, that's a famous company, therefore I will learn to grow as an engineer because it's scaling, so I'm going to see scale. But you deliberately chose a small company.

Brendan Humphreys [5:56] Right. So there was a combination of things that were really attractive from the first time I walked in the door. Certainly Mel, Cliff, and Cam are really impressive founders, both with the expansiveness of their ambition, but also they're just really nice people, and that helps a lot. The engineering team back then was, I think, four other engineers, the CTO, and the first engineer that was hired, Dave Hearnden, ex-Google engineer, just a prodigiously talented engineer, very, very smart, very Zen engineer. I would describe him as the engineer's engineer.

Brendan Humphreys [6:16] And meeting him and chatting with him, I realized this is someone that I could learn from, someone I could partner with. And that, combined with that goodwill in the founders, the ambition in the founders, I thought, yep, I'm going to take a plunge here.

What Being a Founder Taught Brendan About Leadership

Matt Turck [6:29] And you mentioned that you joined Atlassian through the acquisition of your company. How has having been a founder helped you as an engineering leader?

Brendan Humphreys [6:48] I think it helps create agency. I think that any organization that grows beyond a handful of people, the bystander effect is a real problem, that it's too easy to think, you come across a problem and you think, that's not my problem. But when you own a company, everything's your problem. And so having that kind of high agency when you're attacking problems doesn't mean you're going to solve every problem, but you recognize the problems and you're very engaged and you're happy to cross over the swim lanes a lot.

Brendan Humphreys [7:23] Certainly, at early-stage companies, you've got to be doing that a lot. You've got to be really a bit of a generalist. And so I think that's a really powerful thought that you really get ingrained when you're kind of on the line, on the hook for everything when you're running your own company.

Growing with Canva: From 12 Employees to 2,300 Engineers

Matt Turck [7:47] And riffing on that, how was your progression from employee number 12, or whatever the number was, to today? Getting from such a small company to a company at this level of scale, it means that you've done very different jobs across the years. What was your journey, and how did you scale yourself up as the business itself scaled?

Brendan Humphreys [8:16] So at Atlassian, I had worked my way up into, I guess, middle management. I had spent some time there looking after a bunch of teams, and then I realized I really wanted to get back on the tools. So I kind of fought my way back down into engineering and was a principal engineer there for a while, and then jumped across to Canva as an engineer. And I worked at Canva for a good three or four years, just primarily on the tools, just writing code.

Brendan Humphreys [8:47] As we started to double in size every year in our engineering org, there needed to be some management of that kind of horde of engineers. And because I had that experience, it was very natural for me to kind of step away from the tools a little bit and find myself more in a management role. And I've always tried to keep myself active in the tools. As we've grown, we've continued to double in size. Now we have about 2,300 engineers. It's extremely difficult.

Brendan Humphreys [8:50] But—

Matt Turck [8:52] So you do that on weekends, or how does one do that?

Brendan Humphreys [9:23] Yeah, try and quarantine some time to play. And certainly the AI tooling helps with my rusty coding skills. But I actually find a lot of intellectual enjoyment out of solving different intellectual problems, like problems of organizational complexity, figuring out how to organize an organization to get a particular problem solved. That's actually quite an interesting challenge.

Matt Turck [9:31] 2,300 engineers is a high number relative to the total size of the company. I don't know what the total number is.

Brendan Humphreys [9:33] We have 5,000 employees.

Matt Turck [9:43] Yeah, so that's almost half, which for a company at this scale presumably speaks volumes to the innovation pace of the company, how much you're constantly building.

Brendan Humphreys [10:01] I think it speaks to the ambition of the founders. This broad mission to empower the world to design, it has meant that the product is quite expansive. And as the feature set's expanded, we've had to expand engineering to stand up teams that can own these features and operate them in production.

How Canva Runs a Global Team from Sydney to Europe

Matt Turck [10:21] And you were saying right before we started recording this that you're based in Sydney, but the team is still largely distributed, working from home, with some hubs. Talk to that a little bit. Where are folks, and what's your in-office or out-of-the-office stance?

Brendan Humphreys [10:50] Yeah, so our origins are predominantly in Sydney, Australia. We have a large office there. In fact, we're building a new one. That's very exciting. We have a hybrid working culture, so people can come into a local hub and work. We set up hubs when there is a large enough number of people in a particular geography. Through COVID, we were still doubling in size in our engineering org every year, and we kind of realized that we can keep that pace and hire people that are just time zone compatible.

Brendan Humphreys [11:15] So that meant that we spread up and down the eastern seaboard of Australia, across to Perth, across to New Zealand. Through acquisition, we have acquired engineering teams, so we have about 400 engineers right through Europe through our acquisitions.

Matt Turck [11:41] Any lessons learned managing people in a bunch of different time zones? One of the key questions of the moment: it sort of feels like everybody went remote during COVID. It sort of feels like the pendulum has swung the other way, and now there's very much an office culture, at least in some pockets of the tech world. But you're clearly showing that it's possible to scale globally. How do you do it?

Brendan Humphreys [12:08] I will say it's not without challenge. The key is to find ways for teams in kind of incompatible time zones to be as autonomous as possible. Usually, that means just finding the right seams in the product so there's real ownership in these locations. And that's what we've strived to do. There will be the occasional need for a meeting, and when you have to meet between Sydney and, say, Prague, that's not pleasant. Someone's going to get the short end of the stick on that one, right?

Brendan Humphreys [12:31] An early morning or a late night every now and then is going to be needed. But where possible, we focus on a lot of written technical communication, a lot of async communication. So we just minimize the need for face-to-face contact.

Matt Turck [12:38] And do you have regional leaders, VPs in different locations? How does that work from an org chart perspective?

Brendan Humphreys [12:52] We have leaders, we have geography leaders. They just tend to be kind of general manager types. They're not engineering-specific. All of engineering reports up through me in Sydney.

Matt Turck [13:02] And for that collaboration and the async part that you mentioned, what's the stack? Are you a Slack shop? Are you Jira? What do you use?

Is AI a Threat or a Superpower for Canva?

Brendan Humphreys [13:16] Canva is an enterprise tool, so it has a full doc suite. So you can use it for sharing and collaborating on docs, presentations, whiteboards, and that's a very effective tool for asynchronous collaboration.

Matt Turck [13:41] All right, let's switch to AI. Each time a big lab releases an AI feature, especially a visual one, there's somebody somewhere on the internet that says, "Well, that's going to be terrible news for Canva." And it seems that, one, it hasn't been much of an issue so far, given the growth of the company. And two, in general, you guys seem to have a very different stance on that. So how do you all think about AI as an opportunity or possibly a threat?

Matt Turck [13:56] Have there been any moments where, despite being bullish on AI, you all looked at each other and said, "Oh, this one actually might be a problem"?

Brendan Humphreys [14:32] Look, we're certainly alert to the rise in visual AI, but we feel we're actually really well positioned. We can use third-party models when they become available, integrate them into our platform very, very quickly, and provide a really rich, cohesive experience. We can facilitate collaboration. We can let you organize your content. We can provide brand kit controls, all of these tools that perhaps your average AI-centric startup can't. And indeed, we have a very rich API layer that is available for third-party developers, our ecosystem offering.

Brendan Humphreys [15:07] A lot of niche AI startups are finding us a fantastic distribution channel. So they've got their niche product. They can come and build a rich integration into Canva, and then suddenly they get distribution, they get a lot of users. We've had a lot of success, and that's a win-win for us and for them.

Matt Turck [15:14] Oh, interesting. So beyond the models and the sort of infrastructure part, there are actual products which are effectively OEM'd through Canva?

Brendan Humphreys [15:18] Well, they're literally apps available in an app store that we provide. Oh, the app store.

The Real Story Behind Canva’s AI and Machine Learning Team

Matt Turck [15:46] So the App Store. Okay. Distribution through the App Store. Okay, got it. Okay. You have been using AI and deploying AI for quite a bit. And just to provide general context, I think the first feature was a background remover. Maybe there was an acquisition. Talk to this: how long have you all been thinking about AI, even as far as I can tell, before ChatGPT really appeared on the scene?

Brendan Humphreys [16:14] So we certainly—Cliff likes to say—we've been talking about AI and dabbling in AI long before it was cool. We stood up our own machine learning team many, many years ago, like 2017, I think, was when we first had a serious crack at building an ML team. And that was all about propensity modeling and building recommendation engines. We have a vast library of content that we want to serve to users, and we want to be able to understand their intent and then be able to serve exactly the right content for the right context.

Brendan Humphreys [16:52] So we have quite sophisticated ML models that help with that. We did acquire Kaleido. Kaleido was actually integrated through our App Store as Remove.bg. And we loved their product. Their product was so popular on the Canva platform. We loved it so much. Then we met the team. We loved them. We acquired them. We've since brought all of their expertise in-house. We've continued to build a lot of in-house AI expertise. We have over 200 machine learning engineers on staff.

How Canva Ships New AI Features So Fast

Brendan Humphreys [17:23] We've just started to form our own, or coalesce a lot of that expertise into, more or less, a standalone research organization that is kind of protected somewhat from the product cycle. And we'll continue to build on the great work that both research teams in Canva and inside one of our more recent acquisitions, Leonardo, have been doing, which is more based around foundational model work and deeper research.

Matt Turck [17:33] So that's very interesting. So it's 200 machine learning/AI folks, and so you're saying there is a central lab that does R&D research?

Brendan Humphreys [18:03] Yeah. So we take a hybrid approach there. We certainly have teams that are dedicated to more foundational research. But we also have folks embedded in product teams who are doing research, and they tend to be on shorter product cycles. Their output is usually a prototype which product will look at and make judgments about, and then we'll see if we want to turn that into something that's a production feature. And we'll try to turn those around very, very quickly.

Brendan Humphreys [18:41] We've invested enormously in putting a platform abstraction over a lot of the third-party AI that we use, whether that's kind of like base-layer infrastructure like Bedrock or SageMaker, or whether it's third-party AI providers like OpenAI. And that allows us to rapidly switch out models and experiment with different models. It allows us to augment models with our own proprietary tech. And it allows us to really iterate very, very quickly when new models come onto the market that do something exciting. We can get them in front of users in really cohesive experiences very, very quickly.

Brendan Humphreys [19:11] So we were able to, for instance, Canva Code is an interesting feature that we've just launched, just back in April. That's a feature that allows users to build little intelligent, interactive widgets in their designs that went from first idea through to production in about three months. And we're now serving 100 million users and climbing.

A Tour of Canva’s Latest AI-Powered Products

Matt Turck [19:45] Fantastic. You anticipated some of my questions. Let's unpack some of it. So let's start with features. You just mentioned Canva Code. You guys had a very impressive last 12 months in terms of shipping velocity. And I wrote down Magic Studio, Dream Lab, Canva AI, Canva Code, Canva Sheets. Maybe give us a little bit of a tour of what those different things do at a high level? So, Magic Studio to start.

Brendan Humphreys [20:13] Yeah, so Magic Studio is a collection of AI-powered tools, both text and visual. There are text-based tools that allow you to, for instance, modify text to be in your particular voice or in a brand voice, which is really powerful. There's text summarization, text expansion. We've got a whole suite of image editing that's really powerful, whether that's infill or outfill. We've got Magic Grab, which is an amazing feature, which basically allows you to treat a raster image as something that you can decompose.

Brendan Humphreys [20:54] You can pick objects up and move them around. Magic Erase uses similar technology to be able to remove and then infill in a very smart way. We did launch Canva Sheets in April. That's our take on a spreadsheet product. It is designed to be very, very easy to use, fully integrated into the visual communication experience of Canva. And it will be our kind of data backbone going forward. So when we want to do more data-driven products, we'll be using Canva Sheets as the kind of data source for that.

From Design Tool to All-in-One Productivity Platform

Matt Turck [21:20] It does feel from the outside as a significant expansion, going from a visual suite to starting to get into effectively data infrastructure. But is the goal ultimately for the company just all-around productivity, starting with design but then expanding to just about everything?

Brendan Humphreys [21:50] We see ourselves at the intersection between productivity and creativity. And we think that increasingly in the workplace, visual communication is just coming to the fore. And so we have been rounding out our feature set. We have a Docs product that allows users to create online documents, collaborate on them, but do so in a really visual way so they can embed rich visuals, they can embed graphics very easily, and they can change different doc types within the one design. So you can have a quite text-heavy document on one page, and then on the next page, you can have a whiteboard where you can be doing some collaborative whiteboarding.

Brendan Humphreys [22:21] You can have sections of a presentation in the same design. And that's an amazingly powerful way to collaborate on a particular project because different contributors to a project need different assets. And normally it's quite hard to organize all those assets. And here they're all just in one design space.

Matt Turck [22:24] What about Dream Lab and Canva AI? What do those do?

Brendan Humphreys [22:46] So Dream Lab was our first integration with Leonardo. So Leonardo, we acquired last year. They're an amazing Australian-based AI company, visual AI company. Dream Lab is prompt-based image generation and uses its own foundational model.

Matt Turck [22:58] And all of those products work in a copilot kind of experience where you interact with the software and the platform, asking the product to do something for you?

Brendan Humphreys [23:14] So we do have a chat interface, Canva AI, and we are expanding that. At the moment, there are some features within Canva that are powered conversationally, but others that are just more of a traditional software experience.

Matt Turck [23:31] And where I'm going with this is the inevitable question of 2025 around agents. To what extent do you have agents? Do you currently plan on having agents where some of those design functions could be outsourced to an AI coworker?

Brendan Humphreys [23:58] Yes, certainly we are paying attention to the current excitement around agents. I think there's a bit of a definitional problem at the moment as to what exactly an agent is. I have my own idea around what agents are, but maybe we don't get into the definition. We certainly have behind Canva AI quite sophisticated orchestration that you could call an agentic solution. It's inferring intent at a very abstract level from a user prompt and then planning a course of action and then executing on that action.

Brendan Humphreys [24:43] Internally, we have agentic solutions deployed, for instance, in our customer support. We're using a lot of agentic solutions there where first-line support, when a query comes in from a user, goes through an agentic flow. And quite often we can handle user requests purely with that agentic flow. But within the product itself, yes, we're definitely looking at that. We're also thinking about a future where access to Canva might be mediated by some other agent. And so we're definitely looking at Model Context Protocol.

Brendan Humphreys [24:59] We have our own Model Context Protocol that we've stood up just internally, and we're certainly talking to the big vendors about interactions there that are possible.

Matt Turck [25:24] And you view this both as an opportunity, but also something to be potentially concerned about. In double-clicking on what you just said, where Canva could be a part of somebody else's chain as opposed to people coming to Canva, logging in, having a Canva experience, is that something that you worry about?

Brendan Humphreys [25:50] It's not actually a concern. We see Canva as a destination. If you are doing any kind of visual communication, then there are going to be natural limits to a conversational interface. And people are going to want to be able to collaborate. They're going to want to be able to control for brand. They're going to want to be able to organize their content. There are going to be limits to how you want to interact with a design conversationally. So we think a future that we see is that if you do have your personal chatbot and you want to do some design, you might get a one-shot design there that then you can be transported into a Canva experience where you're actually able to stop talking about the design or asking a machine to do something and actually just play with it as you would normally, with a mouse and picking things up and moving them around.

Keeping Up the Pace: How Canva Moves So Quickly

Brendan Humphreys [26:21] I think that is the essence of kind of human creativity. And I think that that's not going to go away.

Matt Turck [26:42] That's an incredibly impressive list of products and features around AI that was shipped literally over the last 12 to 18 months. How do you guys do it? Like, how do you manage to have this kind of velocity, especially at this kind of scale? And is there something different about shipping AI features versus regular features?

Brendan Humphreys [27:11] So thank you for saying we have high velocity. I will take that compliment. It certainly feels like breakneck speed at times. The technology org, we're very focused on really thinking hard about the domain model that we are implementing and then building a set of composable components with powerful APIs internally that enables product teams to move very, very quickly because they're sitting on top of a rich set of functionality. It takes real care and diligence to build out that internally consistent set of surfaces and services that back those surfaces, keeping control of the domain model so the domain model doesn't just explode into this kind of combinatorial explosion of complexity.

Brendan Humphreys [27:57] And so we spend a lot of time thinking about that. We want the service teams to be thinking very carefully about what APIs we provide product teams so that product teams can move very quickly. With AI, it's more of the same. It's thinking very deeply about what's the AI platform layer that we can provide. Product teams can be getting the benefits of AI without necessarily having to go deep on the technology. Having said that, we are very focused on this kind of democratization of knowledge around AI in our technology org.

Brendan Humphreys [28:34] We think that once upon a time in software teams, you'd have a DBA on the team, but now a DBA is not really a skill anymore because the abstractions above databases are such that you don't really need that deep skill unless you're dealing with very specific circumstances. We like to think that the same is going to happen with AI technologies. And we're seeing it now with the tooling that's out there. It's a real democratization. A lot of engineers who have no formal training in AI are picking up these skills and figuring out how to use these tools and then being able to build AI-first features very, very successfully.

Brendan Humphreys [29:10] So we want to enable that. We invest a lot in education of our engineers, lifting them up to understand how these LLMs work, how to interact with them, how to effectively build product around them, which is not always intuitive. There are a lot of things that you have to wrap your head around, particularly the non-determinism, which is a tricky thing. I think a lot of software engineers are trained, particularly the more senior engineers, in wanting to write a whole set of unit tests based on a deterministic system.

Brendan Humphreys [29:35] But when you've got a non-deterministic system, you have to kind of move your mindset to now we're going to test with evals, which are much more probabilistic. So it's a little bit of a mind shift. There's a lot of other shifts you have to make as a software engineer, but we're very much about empowering engineers to understand the technology, firstly to help in our product, but also, and maybe we're going to go in this direction in the conversation, to understand that their jobs have changed now that these AI tools in the coding space are so powerful. Their jobs have essentially changed.

Brendan Humphreys [30:21] So we really want to enable that learning and that rediscovery and let each engineer have their moment of realization that, oh, wow, I now have a different way of working, and it's a more productive way of working because I have these very powerful tools in the loop.

The Future: AI Agents, Copilots, and Smarter Workflows

Matt Turck [30:48] You wrote on LinkedIn a blog post a couple of months ago where I wouldn't say that you were necessarily super excited about the concept of vibe coding, if that's a fair way of putting it. So maybe walk us through how you think about AI tools, GitHub Copilot, Cursor, vibe coding, where that fits in the organization or not.

Brendan Humphreys [31:06] Firstly, some definitions. So when I wrote that blog post, I was referencing perhaps a very tight definition of vibe coding. This idea of giving into the vibes, it very much suggests that you only focus on the prompts. In effect, the prompt becomes the source of truth. In the same way that when you write source code today and you feed it to a compiler and you don't really worry about the compiler output, vibe coding seemed to suggest that, well, now the prompts are the source code.

Brendan Humphreys [31:48] And the actual generated source code is something you don't have to worry about. We don't think the tools are there yet. I'm not sure the tools will ever get there, but at the moment they're certainly not there. That's not to say that they aren't powerful. They are extremely powerful, and we have deployed them at scale at Canva. And in my travels talking to senior engineers within Canva, you couldn't pry these tools out of their hands. They are far more productive with these tools.

Brendan Humphreys [32:18] But we have a very important rule that you need to own the output of the tool. That means you have to understand it as if you'd written everything yourself, because ultimately the generated source is the source of truth, and it's going to go into the repository under your name. It'll go via peer review. We have a strong culture of peer review. And so another human's going to have to read it and understand it. I'm very excited by the productivity lift that we've seen with these tools.

Brendan Humphreys [32:25] And I think we've got a ways to go.

Matt Turck [32:29] So it's real. You're seeing a real productivity lift in terms of—

Brendan Humphreys [32:47] Yes. Well, we could probably spend an hour talking about how do you measure productivity in engineering teams, but we certainly are seeing, in some metrics that we measure that we consider to be good proxies for productivity, a significant, like a double-digit percentage increase in productivity.

Matt Turck [32:55] So without getting into the definition, but in this case defined as speed to product, just producing more faster?

Brendan Humphreys [33:12] If there's any Canva engineers listening, this is not how your performance is rated. This is the number of PRs merged in a week. We're seeing about 30% more PRs merged from engineers who are using these tools predominantly.

How AI Tools Are Changing the Way Engineers Work

Matt Turck [33:30] And for peer review, is AI-generated code as easy to review as human-generated code, or is there a question of volume—that it's just more of it, therefore it takes longer to review it?

Brendan Humphreys [33:58] I think this is the real challenge with a peer review culture, is that it's very easy to write a relatively small prompt and end up with volumes and volumes of code. We are seeing some success with AI-assisted code review. I think that is inevitable, that author with AI superpowers will be matched by reviewer with AI superpowers. I think that's an inevitable path that we'll go down at scale. There are anti-patterns that we see. We did have one enterprising engineer who vibe-coded his way to, I think, a 50,000-line pull request and then lobbed it over the fence to a reviewer.

Matt Turck [34:09] Good luck.

Brendan Humphreys [34:35] Yeah, good luck. We have a guideline that we really want to see PRs in the order of hundreds of lines of diff, not 50,000 lines, which is not an easy task for anyone to review in any reasonable amount of time. So there's challenges there. But I think, again, if you've got AI on one side in the authoring, then I think you can certainly have AI assisting and guiding a reviewer. Then I think that works at scale as well.

Matt Turck [34:40] Does the productivity jump impact your hiring plans in any way?

Brendan Humphreys [35:04] That's a good question. We have slowed down hiring a little bit for a number of reasons. We wanted to just take a pause and understand where the market's going with these AI tools. We are still hiring, but we're not hiring as fast. There's a challenge for our industry in that we do see slightly bipolar results for these tools. In the hands of a senior engineer who can tell good code from bad code, they're very, very powerful.

Brendan Humphreys [35:36] But for graduate engineers, for junior engineers, they can be quite dangerous because they just don't know what they're looking at when they've generated a bunch of code. I think that's a challenge for the industry. We do have a graduate intake program. We will still be taking grads in at the beginning of the year next year, but we're just taking a pause now. I think the way that I'm thinking about it personally is that engineers are more productive. If you've got a lot of work to do, then why wouldn't you want more productive engineers to throw at that work?

Brendan Humphreys [35:46] So I think we've got room to grow our engineering org.

Rethinking Hiring and Training in the Age of AI

Matt Turck [36:06] And after the pause, how do you think about that problem of junior engineers that will be vibe coding, or whatever the better term is, sort of natively? Do you need to have an internal program where you train them on some core logic that they will just not naturally know?

Brendan Humphreys [36:30] I think so. I think there's this kind of—I read somewhere this kind of advice: what do you do if you're graduating? Just focus on critical thinking. But critical thinking is something you can't really teach. It's something that you need to build around a domain of knowledge. And the only way you get a domain of knowledge is to work in that domain. So I think at Canva, we'll be focusing on probably just more careful onboarding, more careful peer review of grads.

Brendan Humphreys [37:00] We will be more picky. We will be looking for the grads who bring deep first-principles thinking, strong, really strong CS fundamentals, but also show us some of those human qualities. Empathy in engineering is a vastly underrated skill. And it is a skill. It's not a talent. It's something that you can teach engineers.

Why Empathy Matters in Engineering at Canva

Matt Turck [37:04] How does that manifest? What is empathy in engineering?

Brendan Humphreys [37:33] So empathy, I think it's got this kind of wishy-washy, soft, fluffy feeling to it when you say empathy. But really, it's about getting in someone's head and understanding, when you're having any kind of collaboration with someone, what's their point of view? Why are they saying the things they're saying? Why are they acting in the way that they're acting? In a large engineering organization, it's absolutely essential that we have productive collaboration. And a key to productive collaboration is being able to empathize with your collaborators so that you can very quickly get on the same page and then all be rowing in the same direction, just to mix my metaphors.

Brendan Humphreys [38:12] So we teach skills internally around this. There's a great improv trick to keep a conversation going, which is just the rhetorical device of saying, "Yes, and," when you're contributing to an argument, so that you're not shutting your mind off to possibility, continuing the conversation, opening your mind to what's been said, but also with the "and" allowing you to add more context. We also educate people about techniques like steelmanning, that is, listening to someone else's argument that may be different to yours and then being able to restate it in the strongest possible terms with genuine intent.

Brendan Humphreys [38:55] One of two things happens when you do that. You may change your own mind when you've forced yourself to take on board all of their context and then restate their argument in the strongest possible way. That might convince you that their way is actually correct, but at the very least, it will build genuine trust with the person that you're collaborating with. And if they're doing it back to you, then it's a very, very productive way to resolve differences. Technical differences come up all the time.

Brendan Humphreys [39:21] It's a great way of resolving those technical differences. Related is the principle of charity, just really taking people, understanding that most people are reasonable and want to do the right thing. They just have different contexts and different incentives. So building empathy for their context and their incentives, again, will help you be productive in a team setting.

Matt Turck [39:29] It feels particularly important in the setting that you were describing, having a 40—what was the number? 4,200? No.

Brendan Humphreys [39:31] 2,300 engineers.

Building vs. Buying: How Canva Chooses Its AI Tech

Matt Turck [40:05] 2,300 engineers. In the context you describe, having a 2,300-engineer organization spread across multiple locations and continents, very fascinating. Okay, going back to AI behind the scenes at Canva, you mentioned some elements of the stack. So let's start with models. It sounds like you're using some OpenAI and Anthropic for some things, and then you have your own foundation model efforts through Leonardo and others. How do you think about the build versus buy?

Brendan Humphreys [40:34] We have an engineering value: strive for pragmatic excellence. And so we are ruthlessly pragmatic in whether to build versus buy. And that goes across all elements of functionality. I'm a huge believer in, I think it was Jeff Bezos who came out with that famous saying of, don't spend time on undifferentiated heavy lifting. Certainly, we want to leverage best-of-breed models. If they're third-party and they're available via an API, then we will. If they're third-party open-source models, then we can host them ourselves, then we will do that.

Brendan Humphreys [41:10] But we do provide a platform. As I said, we put a platform abstraction over that so that we can chop and change quite quickly. We are investing heavily in our own model development. Leonardo has given us a great foundation for that, no pun intended. And we have billions of very unique opt-in data points around design creation, design intent, ingredient selection. We're mining to produce real insights into how to improve people's designs, how to help people in their design activities.

Matt Turck [41:14] As in a data flywheel of reinforcement learning?

Brendan Humphreys [41:15] Yeah.

Matt Turck [41:22] Where positive actions, or any action, indicates how people use the product and whether they like the outcome or not.

Brendan Humphreys [41:22] Okay.

Lessons Learned: Technical Debt and Scaling Pains

Matt Turck [41:30] Leonardo that you mentioned was an Australian company that, what, 150 people or something like that?

Brendan Humphreys [41:32] Yes. Don't quote me. Yes, I think around about 150.

Matt Turck [41:35] Doing foundation models for visual stuff.

Brendan Humphreys [41:36] That's correct, yeah.

Matt Turck [42:13] Interesting. And so the decision to acquire was to continue to build foundation models, which there is certainly a current of thinking in the current AI landscape where people say, well, stop building your own models. It makes no sense because the foundation models, by becoming ever more general, are going to do a lot of things that any kind of specialized model does. But not everybody agrees with that. But clearly you guys want to maintain at least independence through your own model. How do you think about it?

Brendan Humphreys [42:49] So Leonardo's got a very successful end-user product in market, and so we're keeping that running independently as well. We primarily acquired Leo for their research capability, though. I mean, their research capability is phenomenal. They've managed to build a world-class research team that's spread all around the world. On the question of foundation models, I don't think that race has been won, certainly. And again, we're radically pragmatic there. Leo have just shipped an amazing set of features that uses Google's Veo 3 model.

Brendan Humphreys [43:25] It uses the latest Flux models. So even Leo is using third-party and internal models. We do think we are uniquely positioned to be in that foundation model space. So maybe that advice of, kind of like, don't be in the foundation model space is good general advice, but we think that we are in quite a unique position in terms of the data that we have to pursue that particular avenue.

Matt Turck [43:27] Do you use any open-source model?

Brendan Humphreys [43:45] We do. We use Meta's models. We use models from Stable Diffusion. Segment Anything is an open-source model that we use. I know we've tried Llama. So yeah, we have certainly looked at those models.

Matt Turck [43:54] How about the tooling layer, orchestration, all the things? Any tips, recommendations, things that work, don't work, that you've tried, that you like, that you don't like?

Brendan Humphreys [44:32] So we have our own proprietary platform layer that sits on top of tools like Vertex from Google or Bedrock from Amazon. I think certainly Vertex or Bedrock have really impressive inference serving. We don't have a strong preference. We try and maintain a footprint across both to give us flexibility. And we have our own model-serving capabilities internally, which are part of that AI platform that I mentioned.

Matt Turck [44:44] And you mentioned eval a few minutes ago. How do you go about it? Same thing: do you use third-party tools? Do you do your own evals for the specific job to be done here?

Brendan Humphreys [44:51] We use Weights & Biases for our evals, and we have our own eval framework that we've built.

Matt Turck [45:18] How do you think about hallucinations and AI being stochastic and not deterministic, which perhaps you could argue for creation of visual matters less, except you all made a big push in the enterprise. So presumably when it comes to things like brand guidelines, you need to be pretty accurate. So yeah, how do you think about hallucinations and then guardrails and that kind of stuff?

Brendan Humphreys [45:44] Yeah, it's an interesting question. It's an unsolved problem. If a model gets it right 90% of the time, then 10% of the time it's getting it wrong, right? That's going to be acceptable in some cases and not in others. For some of our AI implementations, we decompose the problem and use much more orchestration that's based on heuristics, with elements that are solved by LLMs. That gives us much more determinism and much more control. So if it's really important, that's the way we're going to solve it.

Brendan Humphreys [46:07] There'll certainly be AI in the mix, but it's not like, hand the problem to the AI and then hope for the best. For other spaces, as you said, when it's more creative, then perhaps you can do that. Yeah, the hallucination problem is interesting. It's minimized, but it's still there, and I'm not sure it's going to go away.

Matt Turck [46:12] Is AI security something that you think about? Prompt injections?

Brendan Humphreys [46:30] Absolutely. We think very carefully about prompt injection. We are very careful. We have a world-class security team that has rapidly upskilled on security as it applies to AI. So we're very careful in that regard, very sensitive to being in that enterprise space.

Matt Turck [46:59] Talking about enterprise, that seems to be one key strategic initiative that you've had over the last couple of years in particular. Was there anything specific, other than what you would imagine around security and sort of enterprise-grade features, that was part of that push? We have a number of companies that we all work with as VCs, early-stage startups that at some point graduate from that early kind of PLG inbound motion to being outbound and enterprise-focused. From an engineering standpoint and product standpoint, any sort of tips and tricks about what took longer than you thought or was harder than you would have imagined?

Brendan Humphreys [47:42] Catering to an enterprise market, it certainly pulls the product teams in different directions. Enterprise customers want sophisticated admin controls, they want sophisticated auth, they want data residency, these kinds of considerations. If you haven't baked them into your product early, they become quite expensive to retrofit. Very early in Canva's architectural journey, we put quite a good abstraction in over our AWS infrastructure. But I do think back now and wonder, oh, we could have just spent a little bit more time really abstracting region-based storage.

Brendan Humphreys [48:20] It would have been cheap to do then. We have got it now, but it was a massive engineering effort. Back when it was 10 of us and a few services and a few databases, it would have been relatively cheap to kind of build that culture in there, build that kind of tax, if you like, on every product that you have to think about what region shard you're going to store the data in. Having to retrofit it across hundreds of teams, hundreds of services, was a monumental lift.

Matt Turck [48:44] Any other sort of scalability lesson going from that 10-, 12-person team to the scale today in terms of architectural decisions, technical debt, anything that in retrospect you wish you had done earlier?

Brendan Humphreys [49:06] Technical debt is, I mean, it's a dirty word. It shouldn't be a dirty word. Technical debt is an extremely useful thing when you're a startup. And we had the high-interest credit card of technical debt for a few moments in Canva's history. And I'll tell you about two of them. One was our original editor. We call it E1. It was a JavaScript, kind of jQuery big ball of mud that was kind of hero-coded by a few people.

Brendan Humphreys [49:36] And it got so big and so unwieldy that it became very, very difficult to add new features without kind of non-deterministic behavior cropping up in it. I mean, it was an opportunity cost. We could have re-engineered that, but we were busy building essential services that were going to power the next generation of features that we were trying to get to market. By the time we were ready to rewrite it, we did have to pay a lot of technical debt down.

Brendan Humphreys [50:04] We went through a huge re-engineering effort, two years to rebuild this editor without adding really much of any new features. Well, we did get collaborative editing out of that, I should say. But it was, I think, an example where we just ran up the technical debt to get this critical mass of functionality built around the product. And we could see that this thing was groaning under the weight of just basically poorly architected front-end code, but we were willing to take that on.

Brendan Humphreys [50:30] And then we found product-market fit, we got a revenue stream, and that gave us a lot more runway to then go back and do the big rearchitecture. I think we made the right choice. It was pretty scary, but I think we made the right choice there. And we've got a number of examples where we've done that, where we've kind of really run up technical debt. I think the important thing is to, as an engineering team, recognize it, see it, make intentional decisions around it.

Brendan Humphreys [51:15] And that goes to our engineering value of striving for pragmatic excellence. It can be a pragmatic choice to be expedient in engineering quality, to take shortcuts to get to market quickly because you just have to. And as long as you are intentional, you're making really good engineering decisions, and then you're keeping the receipts so that you can come back to product and say, well, now we need time to go and reengineer it properly, then I think that can be quite powerful.

Shipping Fast Without Breaking Things

Matt Turck [51:48] And in the same vein, and not from a technical debt perspective, but from a how fully baked should a feature be perspective, how do you all think about the tension between shipping stuff quickly, which again, you've done admirably, particularly in the last couple of years, but on the other hand, having hundreds of millions of monthly active users that expect some level of consistency and quality? That tension as you scale between going fast and being reliable?

Brendan Humphreys [52:14] Yeah. Wow. Look, everyone at Canva, everyone from product right through to engineering, is a product owner. They really own the product. They care about the product. They think deeply about the product. And so there's a lot of internal dogfooding culture at Canva. When we do ship something, if it's a big new feature, we will always put it through some kind of beta testing with a select group of users. We will have the feature behind a feature flag that allows us, for instance, to just expose it to staff and have staff dogfood it.

Brendan Humphreys [52:49] We have the advantage that our product is very applicable to our work, so we use it every day. And there is that constant feedback loop between the product teams who are building it and the entire company that's using the features. And we have good pathways of communication that collect that information from that dogfooding and feed it back. Look, it's a tricky balance. We don't always get it right. But I think the key there is just the passion that engineers have for shipping quality product, that they really want to ship something awesome.

Brendan Humphreys [53:00] So they sweat the detail.

What’s Next: AI Video, New Features, and Big Ambitions

Matt Turck [53:14] Fascinating conversation. To zoom out, maybe to close: next few years at Canva, what's on the roadmap? What does success look like? What's your ultimate ambition as a team?

Brendan Humphreys [53:37] So Mel has a two-step plan for Canva, which is to create the world's most valuable company and then do the most good in the world that we can. And I have to say that really helps me get up in the morning and keep coming into work. I think it's a really noble mission that is something different from a lot of companies who are just focused on commercials. Practically speaking, I think we've got such a long way to go in building out the product vision that is in Mel's head.

Brendan Humphreys [54:15] And I'm really excited to go on that journey. I think AI is really, really exciting. I think from a ways-of-working point of view, I'm looking forward to a new era of productivity. I don't think software engineering goes away. I think software engineering changes to actually a much more fun job, where a lot of the mundane kind of plumbing of software engineering is handled for you, and you're able to orchestrate software with these agents helping you and become much, much more productive.

Brendan Humphreys [54:49] So I would expect our productivity as an engineering org to lift significantly still. You'll see that I can't tell you too much about what's coming down the pipe in terms of features, but actually there's an announcement coming out this week, which is pretty exciting. Actually, I probably can tell you about that. We are integrating Veo 3, which is Google's new amazing video generation model, natively into Canva. So that will be exciting to put into the hands of users. We've got a host more AI features that are coming down the pipe around the October timeframe.

Brendan Humphreys [55:03] And then into next year, there's so much more, but I'm not going to mention any of that because I will get in trouble.

Matt Turck [55:30] Incredible. And since you mentioned it, just to double-click on it quickly, that's one of the things that makes Canva even more of an incredible company, is precisely this commitment to doing good in the world. So we can't cover everything in this discussion, but quickly, I believe the founders pledged their ownership in the company to charity.

Brendan Humphreys [55:43] So there is the Canva Foundation. They've given the vast majority of their equity in the company to the foundation, and the foundation supports charitable causes all around the world.

Matt Turck [55:54] Yeah. And then there is a commitment to do the right thing across the board, including in AI, where I believe you have a fund for creators or content.

Brendan Humphreys [56:13] We do have a creators fund. I think it's $200 million, and it's designed to reward creators. We recognize that it's a challenging environment for creators, but we want to see creators recognized as contributors to these AI models. So that's how we're doing it. Wonderful.

Matt Turck [56:16] Brendan, thank you so much. This was terrific. Really appreciate it.

Brendan Humphreys [56:17] My pleasure. Thank you.

Matt Turck [56:38] Hi, it's Matt Turck again. Thanks for listening to this episode of The MAD Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing, if you haven't already, or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build the podcast and get great guests. Thanks, and see you at the next episode.