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AI Design Still Feels Like a Slot Machine
Stefano Corazza says Canva’s custom models and agents make design AI faster, more editable and built for iteration.
The TWIML AI Podcast · Sam Charrington
- Stefano Corazza is head of AI research at Canva.
The best minutes
- 5:09Canva's data makes smaller design models more economical3 min
- 16:34Detailed scene descriptions help smaller models create better designs5 min
- 39:35Owning the model stack creates more room to cut inference costs4 min
Also worth watching
- 10:58Layered designs let people move elements before regenerating pixels4 min
- 22:51Design editing needs benchmarks beyond single-shot generation4 min
- 27:04Style mismatches can be fixed without retraining from scratch3 min
- 31:14Brush revises designs through tools and visual feedback5 min
- 35:48A simpler agent harness lets the model use familiar code paths4 min
Skip the video-model ecosystem detour, the learning analogy about children, and the closing research outlook.
Full analysis · 7 min read
Real Alpha · Nov 10
Stefano Corazza says Canva is 40X cheaper on the same task
- Corazza says Canva is 40X cheaper than the most expensive frontier models on the same design task.
- He ties that figure to smaller models, design data and vertical integration, but gives no comparison method.
- For teams, the test is cost and quality across revisions, not image price alone.
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6 sections, the 5 other voices it tests Corazza against, and what it means for you. Every episode we cut gets one.
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Creative AI must make revision cheap
Corazza's product thesis is that image generation has to fit a revision loop. He says AI may get 70%, 80% there, while a human handles the last mile.
He contrasts this with the expectation of a perfect single shot, which he says breaks down on creative tasks. A draft that looks impressive but resists correction can still be unusable.
That puts the product test beyond the first image. Teams need to see how quickly a designer can adjust an element, retain the rest, and reach an approved asset.
For example, Corazza says asking a flat image model to move an object a little right is imprecise. A designer can instead move the element directly, then regenerate pixels.
Canva's claim is that faster iteration can make the process more predictable and fun. That is a product ambition, not evidence that every kind of design is already controllable.
“for the last mile, you need a human in the loop”
Our read
The episode's clearest product thesis is not that models finish creative work. It is that AI earns a place when people can correct it quickly.
Canva's 40X cost claim rests on narrower models
The headline number is Corazza's statement that Canva is 40X cheaper than the most expensive frontier models for the same design task. He also says smaller models can deliver similar quality, sometimes better.
His explanation is economic and specific. With unique data, he says, Canva can train or fine-tune models with two orders of magnitude fewer parameters, which can be faster and cheaper.
He says the two ingredients, unique data and deep vertical integration, may apply to similarly sized companies, even smaller ones, where the fit makes sense. That is broader than a Canva-only playbook.
Still, the interview does not specify which frontier model, price basis, test set, or serving conditions underpin the 40X figure. Corazza does say it is the same design task, but no full comparison method is described.
Danila Shtan said on Unsupervised Learning in November 2026 that 95% of Nebius customers do not run vanilla open-source models. He described modified and fine-tuned models, reinforcing the case for task-specific deployments, not Canva's ratio.
David Sacks said on All-In in October 2026 that a fine-tuned 9-billion-parameter model cut Coinbase fraud by about 30% in testing. That example shows specialized models can work, but a different task cannot verify design quality or cost.
“40X cheaper than the most expensive frontier models”
- DSDanila Shtan · Unsupervised Learning · Nov 2Open-model inference still has major room to improve
David Sacks · All-In · Oct 17A small model cut Coinbase fraud by about 30%
Our read
The 40X figure is a company-specific comparison, not a universal price rule. Smaller models can make economic sense when data and workload fit, but buyers need the comparison details.
Craft moves revisions into editable structure
Craft splits generation into prompt expansion, a structured scene description, and pixel rendering. Corazza says the layout can be changed before pixels are made, with the stages trained individually and checked end to end.
That structure preserves editability. A person can move or resize an element, then regenerate, rather than asking a flat image to infer spatial intent.
Take his example of 10 layout options for one page. Craft planning can create different plans for human confirmation before an LLM generates pixels.
That creates a workflow choice, not an assurance that every layout will need less compute. The value is that people can review the plan before committing to a rendered result.
Magic Layers extends a related idea to flat images, including external images. Corazza said most images Canva processes through Magic Layers come from outside Canva.
That matters for products that inherit finished assets from elsewhere. But scene structure helps only when models recover layers reliably and revisions preserve what users intended to keep.
Our read
Craft's distinction is not simply a better image generator. It moves some creative decisions into a representation people and software can still change.
Current benchmarks still underweight design editing
Corazza said benchmarks mostly focus on single-shot first generations. He acknowledged image-editing benchmarks, but said design editing is different and lacks an established benchmark.
That gap matters for purchasing decisions. A first-pass score cannot tell a team how well a model edits an existing layout, or how many manual repairs follow.
Canva uses structured VLM rubrics for readability and composition, alongside human evaluation. He says models can score clearly articulated criteria, but humans remain the ultimate test.
Herbie Bradley said on MTS in November 2026 that GDPX took months of work and still had flaws. It is not a design benchmark, but it illustrates the labor and limits involved in building useful evaluations.
Teams can borrow the method, not the benchmark result. Start from an existing asset, specify one change, then score whether the layout survives and how much correction remains.
“There is no established benchmark for how good a model is editing a design”
Our read
Canva's account is strongest where it names the benchmark gap. Structured model judging can scale checks, but human review still tests whether a design succeeds.
Brush uses a tool loop, not one prompt
Brush is Canva's vision-language model and design agent, not just another image generator. Corazza says it can operate on layered documents with text, vectors and images, and handle high-level instructions.
He says it can also create a cohesive deck of 30 slides. That is a broader task than producing one image, though the episode gives no independent measure of deck quality.
The agent loop can inspect what it created and revise. Corazza said an LLM might make 10 iterations before showing a person, whereas earlier iterations could take a minute each.
Faster inference therefore affects more than waiting time in his account. It permits more internal attempts, which could lift quality, but Canva gives no task-level comparison here.
Canva is on its third Brush harness generation and has made it thinner, so the model can use tools and write code. The team chose code, APIs or editing operations partly according to what LLM training data makes familiar.
That is a reminder that agent performance belongs to the model, harness, tools and workflow, not model weights alone. It also makes the advertised capability dependent on a well-scoped tool environment.
“the faster you make that iterative loop, the more effective everything is”
Our read
Brush's promise depends on the whole loop, including tools, visual feedback and the harness. Repeatable performance on varied editing requests matters more than a polished demo.
Full-stack control can lower serving costs
Corazza frames inference efficiency as part of affordability, especially because most Canva users are free users, including students and nonprofits. He says lower inference costs let them use more AI.
Canva runs models on its cloud and manages inference, but Corazza said it does not own the physical hardware. The host restated that Canva uses GPU instances from cloud providers, and Corazza agreed.
He described optimizations across hardware, kernels, model steps, batching and caching. More control depends on access to model weights through self-hosted open models or models built in-house.
He cited a two-order-of-magnitude fall in the cost of generating a second of 720p video over a year, from about half a dollar per second. The transcript's current price is garbled, so its endpoint cannot be stated.
He thinks another order of magnitude down is probably ahead, but offered no date. That is a forecast, distinct from the reported past reduction.
Eric Glyman said on Inside Blackstone in November 2026 that CFOs need controls for AI token spending. That supports tracking unit economics, but not any specific Canva service cost.
Canva Connect and MCP expose capabilities to developers and agents. Corazza said enterprise was Canva's fastest-growing business, with triple-digit year-over-year growth, and described API demand from enterprise customers alongside agent use.
Han Wang said on MTS in October 2026 that human visitors grew as agent traffic rose, and documentation must serve both. That fits Canva's stated plan to expose capabilities to agents while keeping humans in the editing path.
Buyers should budget cost per approved asset, including calls, revisions and human repair. The interview offers a cost thesis and deployment plan, not an independent account of quality per dollar.
“we are as far down as we can go other than owning the actual hardware ourselves”
Eric Glyman · Inside Blackstone · Nov 3Token spend needs its own controls
Han Wang · MTS · Oct 16Agent traffic does not make human-friendly docs obsolete
Our read
Cost optimization supports Canva's iteration thesis when cheaper calls lead to more useful work. Buyers should test the completed workflow and separate measured savings from forecasts.
What it means for you
- Price finished work. For one design workflow this quarter, count generations, edits and human repair alongside model charges. Compare cost per accepted asset, not just cost per image.
- Track revision time. Measure time from first draft to approved output, plus manual fixes. Separate latency from quality so faster does not automatically mean better.
- Write an editing rubric. Choose an existing design and define checks for readability, layout and the requested change. Pair a VLM score with human review.
- Test model fit. Compare a smaller specialist model with your current approach on a bounded task. Record quality, latency and serving costs, and note which data or tools each option requires.
What to watch
- Whether Canva publishes measured quality, latency and cost for Craft variants against stated tasks.
- Whether design-editing benchmarks begin testing revisions from existing assets, not only first generations.
- Whether Corazza's forecast of probably another order of magnitude lower inference costs appears in measured serving data.
Send this to product, design, engineering and finance leaders budgeting creative AI.
Watch the minute where Corazza gives the 40X comparison
Written by Real Alpha from the episode's transcript and the other moments it links, then checked against the transcript. Quotes are the speaker's exact words.
Minute by minute
- 1:15Design AI needs iteration
Corazza says Canva had roughly 100 AI models in production, with Craft and Brush marking larger steps in its design workflow. He argues that AI may get 70% or 80% of the way to a creative result, so people need a fast, enjoyable way to make revisions.
- 4:21Custom models can cut costs
Corazza says Canva’s unique design data and deep integration of models into its product made building its own models worthwhile. He says a model with two orders of magnitude fewer parameters can beat a larger one when trained on the right data, and that Canva’s models cost 40X less than the most expensive frontier models for the same design task.
- 9:34Craft separates planning from pixels
Corazza says Craft was trained from scratch, while Brush started from an open-weights model. Craft’s three stages amplify the prompt, convert it into structured scene data, then generate pixels; an LLM can intervene in the scene plan or produce layouts without generating images.
- 12:27Layers make edits more controllable
Corazza says Magic Layers decomposes flat images into elements that people can move or resize before regenerating pixels. He says this gives designers more control than asking an image model to reposition something when the image itself has no information about its size or location.
- 14:57Design data shapes model quality
Corazza says Canva’s decade of design data, including licensed material and user data used with permission, helped produce a strong training set. The team also expanded its structured scene descriptions, adding details such as bounding boxes and relative positions, and found that a larger diffusion model could capture them.
- 21:40Diversity and editing need better tests
Corazza says Craft’s training mix includes stock images, synthetic data, user-generated data and millions of Canva-created or licensed designs, with diversity intended to avoid repetitive outputs. He says most benchmarks test single generations, not design editing, and calls for tests that start from existing designs and assess edits, speed and cost.
- 24:57Rubrics help assess design quality
Corazza says vision-language models can judge qualities such as readability and composition when given clear rubrics, alongside human evaluation. He says Craft initially made cartoon images too realistic and 3D-looking, and that the team can tune the model’s taste and style through fine-tuning and reinforcement learning.
- 31:02Brush works across design tools
Corazza describes Brush as an agent that understands layered designs and can edit their elements, make them more readable or professional, and respond to broad instructions such as making something feel more modern. He says it can also handle higher-level tasks, including creating a cohesive 30-slide deck.
- 35:37A simpler harness gives agents room
Corazza says Brush is on its third harness, and the team has worked to make it thinner and simpler so the LLM can use design tools more freely. Brush can write code for tool operations and API calls; the team chose representations partly by considering what kinds of code LLMs are already trained on.
- 39:35Inference efficiency expands access
Corazza says lower inference costs can let Canva’s free users, including students and nonprofits, use more AI. He says the team has seen improvements such as fivefold speed gains over two weeks and lists optimization across hardware, kernels, model steps, distillation, serving, batching and caching.
- 43:03Model ownership unlocks optimization
Corazza says Canva runs its models on cloud infrastructure and manages their optimization and serving, without owning the hardware. He says access to model weights and collaboration across AI research, graphics and cloud engineering enable more optimization; he expects inference costs to keep falling.
- 47:56Distilled Craft models trade speed for fidelity
Corazza says Canva plans to release Craft Fast and Craft Ultra Fast, using distillation and other optimizations. He says owning both parts of Craft lets the team tune the language and diffusion models together, while giving users a choice between faster, cheaper versions and higher fidelity.
- 50:20Canva is opening models to agents
Corazza says Craft is available through Canva Connect APIs and Canva’s MCP, alongside other capabilities. He describes demand from enterprise customers building custom workflows and from people using assistants through MCP, and says agents need structured information, easier previews and less friction to test services.
- 54:18Canva will adapt as AI changes
Corazza says Canva’s direction is to make AI more accessible, embed it in iterative creative work and remove workflow barriers. He expects specific projects and models to change every few months as the team looks for bottlenecks and opportunities.
Published Nov 10, 2026.
Transcript · 39 min read
The TWIML AI Podcast, hosted by Sam Charrington, with Stefano Corazza.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
As good as modern image generation models have become, designing with them can still feel like playing games. Put the right magic words in the prompt and you might win at the slot machine. Try to iterate on the result and you're playing whack-a-mole. That's fine for memes, but when design is your job, the game gets old. Canva's AI research team wants to make that process more predictable, with greater control over layouts, easier revisions, and models fast enough to keep up with the creative process. To get there, they've trained their own family of models using Canva's design data and integrated those models right into their tools.
Here's Stefano Corazza, head of AI research at Canva, on what his team is trying to change. You have to build around iteration. AI will get 70%, 80% there. It will never get to 100%. And so you have to factor in that for the last mile, you need a human in the loop and be able to iterate in a way that is fast and also fun. I'm Sam Charrington, and this is the TwiML AI podcast. For over a decade, I've been exploring the ideas and innovations shaping the future of AI through conversations like this one that help you understand what's real, what's next, and what matters. Let's jump in. This has been a pretty busy week for you and your team.
Well, first of all, the team was announced. Was this like the debut of AI research at Canva? Yeah, that was the big debut last week, right? I think 12 hours before we actually announced Canva Craft, the model. And we also announced Canva Research. We had like a blog post, a website with a lot of information about who we are and what we do. So it was really fun. And then this month has been the busiest month probably of my life, where like every week we are announcing a new model. And so exactly 40 minutes ago, we had this announcement about Canva brush. So very exciting. This visual language model for design, creation and editing.
Read the whole transcript
Excellent. And so have there been more models than just those two over the course of the month? Yeah, so we have a total of 100 AI models in production roughly right now. And some are smaller and may not be a huge large language model or anything like that. But we started injecting, infusing AI in all parts of the product and to really help this design workflow in every possible way. But now we are taking bigger steps with Craft, the foundational model for image generation. And then with Brush, we are completely revolutionizing the way people can create content And also, we are revolutionizing how fast and affordable that is.
Because those two parameters, pioneers using AI are okay waiting a minute for the magic moment where something magical gets generated. But if you want to really be using the day-to-day, that's not an option. In game development, there's a rule. Anything that takes more than 10 seconds is completely out of the question. and we wanted to get to like instantaneous AI. Yeah, I mean, particularly with the way that you need to iterate when you're creating, like those delays really stack up. Yeah, absolutely. And people, I think there's a little bit of Silicon Valley BIOS that I've seen myself being part of that BIOS that you think like the new tech comes and it's going to be single shot, perfect, and then people would just adopt it. But the reality is never the case, especially if the task has some creativity in it.
It's not just a purely technical task. And so you have to build around iteration. AI will get 70%, 80% there. It will never get to understand. And so you have to factor in that for the last mile, you need a human in the loop and be able to iterate in a way that is fast and also fun. So that's a key part, I think, of what we do. Until the moment when we will have telepathic helmets that you can wear and you can transfer the full amount of your intent and your vision. But that is a little bit far out, I think. You know, we're going to dig into the models and what you've created there, talk a little bit about how, but I'm trying to decide how interesting it is that you even did this.
Like, on the one hand, you know, there are lots of, you know, their Frontier Labs have models and they, a big part of their businesses are allowing companies like Canva to use those models as part of broader processes. There are open weights models that you can build on. And so there's a lot that you, Canva, could have chosen to build on. On the other hand, it's like, maybe it's obvious that a Canva with all of the data and user interactions that you have access to would build a model.
“you can fine tune or train a model that is two orders of magnitude fewer parameters if you have the data and you can beat the bigger model.”
I think video is a little bit of a different ecosystem. Actually, this will be probably an episode on its own, how the ecosystem for video model and image generation model is completely different, sometimes upside down.
It's kind of fascinating. In what sense? Without making it a full episode, in what sense? Something happened about, I think, four months ago where some of the companies developing video models started releasing open-weights versions. And that had a fantastic domino effect where it forced all the competitors to do it. And so now you have an ecosystem where a lot of the image models are not open-weights, but the video models are. And then because of that, I think, also because of that, you see the cost per second of AI video generated like really plummeting because there's a whole community that is trying to figure out how to crack that problem.
And because there's a lot of models that are open weights, more people can apply to that problem. And so it's a fascinating different dynamics on the two ecosystems, if you want. With Kraft, did you start from an open weights model and train it, tune it, or is it a from scratch train? So for craft, we started, it's like train from scratch because we realized that there were some several breakthroughs that we believe were going to unlock a much more powerful model and those were not just there in the existing model. So that one we trained from scratch. For Canva Brush, which is our vision language model, We started from an open source, open weights model, and then we built on top of that.
So you're using whichever of those makes the most sense given the time or given the particular use case. And it sounds like with Kraft, I have covered this idea of a kind of two-stage generation with layout and then rendering. It was in a conversation with Qualcomm, who has some papers in that area. But I don't believe, to my knowledge, there are no popular open weights versions of that. Are the two parts of a model like that, are they trained independently or end-to-end? Or what's the system approach to training a model like that?
“And so it's a lot easier for a designer to just move that element to the right and then regenerate.”
Talk a little bit about each of those.
We can start with quality. The model performs well, like on Design Arena is one of the number four now. Is that right? Yeah. What you brought to bear to create a high quality model, what went into that result? I think we underestimated the power of like a decade of design generated and design data that we had. I think we were even ourselves a little bit surprised to rank that high because it was also the first submission that we did. So we have, of course, internal evals and we do a lot of also human evaluation internally, but you never know until you actually get into the real world arena. But yeah, it was basically a lot of work over the last year, really collecting data that makes sense for designers and really harvesting data that Canva has been.
created for over a decade now, including also data from users with their permission or data that we license from the outside. And I think we put together like a pretty good body or a pretty good training set. And I think deliver a lot of value. And also we had like several breakthroughs about how to use the data and how to train the model. So I think that was another, you know, the ability to run, hundreds of experiments and really figured out what worked and what provided the quality. I think that was a great
“we underestimated how detailed of a scene description you can give to a diffusion model”
workforce. To what degree did you use synthetic data in the training process? Is that a big part of your recipe? Yeah, it is. I think like in every data set of this size, we also want to make it as diverse as possible. And so we have all kinds of data coming in from stock images and that kind of stuff to synthetic data, to user-generated data, to designs that we created. We have millions of designs that Canva created or licensed over the last decade. And so that was also, of course, a big part of it. It kind of helps also with the taste, right? Because we have seen AI slope and AI generation where everything looks very similar.
And so I think we put so much effort on diversity, which also aligns with our company value. And so we are a company that wants to empower everybody to design. We are global. We serve every market, every language. And so we wanted to really push the diversity to make sure that the output also of the model is as diverse as possible. Talk a little bit about the way you think
“And you have benchmarks for image editing, which is great, but design editing is different than just image editing.”
When you think about this two model approach in craft, what are the qualities of an image come from having the two separate systems or models and what just come from having better training data? Yeah, that's an interesting question. We have seen just different behavior depending on different tasks, for example, which we are always trying to understand. Is the limitation the diffusion model or is it the, you know, the length side of things? And sometimes it's hard to know.
“the cartoon images generated with craft initially were a little bit too realistic and too 3D looking.”
Sometimes you'll find this maybe a little bit weird, but sometimes I have two small kids and sometimes I learn things about learning as a high-level concept that I learn by training the models.
I'm like, oh, this I'm sure applies also to humans. you know and so it's kind of a fascinating especially on pre-training if you see pre-training is basically like teaching a human as many words as possible about vocabulary right but has nothing to do with the style of talking performance or like if that human becomes i don't know a stand-up comedian the the how many words it knows has nothing to do with like the style of the delivery Right. And so I think we, the concept is so true for pre-training and post-training and AI model. And it's also true for like humans where like in the early years, you want as much variety and as much exposure to different type of activities, sensory information, experiences, languages, and so on.
Right. And so the parallel actually is fascinating. I think I'm sure this book's already written about it. Let's talk a little bit about Brush. Is Brush, what's the best way to think about it?
“the faster you make that iterative loop, the more effective everything is”
“trying to leverage how good these LLMs are at writing code and at the same time simplifying the harness in the most basic possible way”
“you can only make it really efficient if you own the full stack”
Do you have hardware in a data center or do you delegate part of that to the cloud but you're running everything on top of cloud providers, clusters?
How far down do you go? Yeah, we are as far down as we can go other than owning the actual hardware ourselves. So we run those models on our cloud, we optimize them, we manage how we optimize the inference, how we serve it, all that. So the raw thing that you're getting from someone else's GPU instances and you're clustering them, you're loading them with whatever your inference. is you're tuning LLVM and all the bits and pieces to batch sizes and all the stuff you talked about. Yeah, exactly. Talk about how has your knowledge about that space evolved? Were there things that you found surprising once you started to work on these new models or did it kind of roll out as expected?
Yeah, it's interesting because it was an industry that was completely dominated by AI researchers, if you want, where you had graphics engineers or cloud engineers coming in and being like, oh my God, there's so much more juice to squeeze here. And so it's been really phenomenal to see how, and here in San Francisco, I saw a whole wave of startups actually focusing on inference optimization. I mean, because there is so much money left on the table right now. And so there's been this like massive wave. I think I can probably name like a dozen of companies that do that because, you know, people that have a different background realize, okay, these models are really not run efficiently.
We should rethink about how we feed all this data and these jobs into the GPU. So there's been like really like a flurry of interest because it's also a very simple value proposition to explain. This thing costs you now like a billion dollars and if I do this optimization, it's going to cost you one-tenth of that. Everybody gets it. It's very easy to explain and it's a very easy ROI to put in place. And we are seeing now, as AI adoption is still skyrocketing, we are seeing a lot of pressure on reduction of cost and making it scalable. And so it's been fascinating to see a lot of those startups and also in big companies bring in knowledge that is more about graphics. And graphics has been optimizing things for graphic cards for decades. And now that knowledge is brought to combine with cloud scalability knowledge and AI models. All those three components together are really delivering wonders.
It's quite exciting. And are there prerequisites to being at the steep part of the optimization curve meaning do you have to have a certain degree of scale so that you can manipulate batch sizes or do you have to like do you have to build your own models such that you can take advantage of you know deep levels of control where do you think it makes sense to like own that full stack it was really like an interdisciplinary type of endeavor where we actually put together different teams that had different skill sets, right? And when we saw the combination of those come together, that's where like the best gains came.
And yeah, absolutely. Like if you have to host, you know, you have to self-host your model and then you have to have access to the weights of the model. You know, if you have, you know, if you're using like an open weights model, that's, you know, that's good. But in some cases, you don't have access to that. So in that case, you build your own model. But when you're self-host and when you have access to the weights, there's a lot more that you can do and a lot more juice that you can squeeze out of those models. And again, we found that by bringing together a very interdisciplinary team with different backgrounds, we were able to really tackle the problem with a different perspective.
And again, it goes back to diversity, right? in this case it was a very diverse mindset from the AI researcher that we brought in and I think it really delivered. Going back to Kraft and that kind of two-part model does There's... having a unique model like that get in the way of some types of optimizations like distillation? No, distillation is something that we do pretty regularly on every model that we develop. And so for Kraft specifically, we have announced that there's going to be a quick follow on Craft Fast and Craft Ultra Fast. And those are basically models where we are doing also the distillation among many other optimizations.
And again, by having all the models in-house, we can also make sure that the distillation that we do on the LLM side and the one that we do on the diffusion model side are also tuned to work optimally together. So again, it goes back to that concept of making sure that the input and output are really tuned for the best quality. And so we're going to have more distilled the versions of Craft coming out in the next few weeks. Because again, we want to make it even more affordable and we want also to leave the choice to the user, whether it is a developer using an API or a designer actually using to the UI or to MCP. We launched it. I think now it's also available on MCP as of yesterday, Craft. And that's another place where if I'm building a prototype app, I don't want to run on the most expensive model, of course.
And so the ultra-fast and faster version of the model I also designed. For that, they may become kind of the default, but then when you want quality, then you switch to the highest fidelity model. I'm sorry, and so the MCP is a Canva MCP or a Craft MCP? Through the Canva MCP, you will be able to also access Craft. Okay. Yeah, there's basically a bunch of like AI capabilities that we are exposing to MCP and the craft is being added. And then as we add more variants of craft, we're going to keep adding to MCP, which by the way, we are seeing like the usage of MCP on camera like really skyrocket.
I think people are finding a lot of value. So we want to give more capabilities, you know, access to more capabilities to MCP going forward. Do I understand correctly that beyond the MCP access to Kraft, you are also making Kraft and some of the other models directly available to folks? I don't know the specifics, but I'm envisioning like via an open router or via Canva directly, but just per use access to the model? Yeah, absolutely. So we released it through our API called Canva Connect. And so now it's available. You can access through the API as well as MCP. So for developers that want to do that.
That feels like a very different business, like running a super low latency, very narrow model inference service, as opposed to this is an API for our product. Is this the first model that you're hosting in this way? We have exposed APIs for various capabilities. I think also you can access Magic Layers and some of the image generation models that we had before, like Lucid Origin. But I think now the level of interest we are getting is a lot higher. And yes, you're right. It is a type of different business, if you want, right? But we are getting so much demand coming from mostly like two categories.
So enterprise customer, our enterprise business is the fastest growing business that we have in the company. really triple-digit growth year-over-year. And we are getting a lot of demand also from those enterprise customers to access the capability. to, you know, an API because they may build their own custom workflow. Now that people can vibe code anything, right, there's going to be more demand for that. And then we are getting also a lot of demand from people using, I don't know, Cloud or ChargeGPT through MCP. They now want to access more capabilities, right? So the agents can go there and say, oh, you want to generate an image?
I can use Canva. And then they can always then open in Canva for further editing. And so we are seeing these two type of use cases really growing. And sometimes those users are already using Canva and they just want to use it in a different way or they just want to build scalable applications using some of the Canva technology. And so we have seen a lot of demand. And yes, it's a new and exciting business and we're still learning a lot about how to best serve it. But I think this, we can say, is not unique to Canva. It's across the industry, right? There's a lot of companies that all of a sudden are like, oh, we are basically building not only for humans, but also for agents, right?
Even though humans is our focus even for AI features, but there's going to be in the future just more agents using some of those capabilities. I think that's unavoidable. What do you think those agents need that's different from what humans need? I think they need more structure. They need documentation defined in a certain way so they can digest more things. They need the ability to try to preview things without having to subscribe. And so a lot of things that basically are designed to remove friction, finding information more easily, streamline workflows because ultimately many times it will be the agent that will decide what solution to use as opposed to the human.
And so we have to basically make sure, for example, the agent is able to like spin up with all friction and not a lot of burden on like initial credentials or credit or purchase. And it can spin up something that the agent can test, verify it's working. And then, you know, go back to the human and be like, hey, I created this like a prototype app using this API. I tested it, it works. You and I give it a shot. Okay, now it's approved. Now we can go for the real scalable thing. So that's the kind of approach. Just to kind of wrap things up, where do you see Canva evolving in terms of research in particular?
It's a world where typically you plan a year ahead and now it's completely impossible. It's like just a month ahead is already like a pretty foggy kind of thing. But at a high level, we have a very clear mission and vision, that one is not changing. So we want to make AI more accessible. We want to make it more part of the iterative creative process. And we want to remove any barrier. And we want to reimagine all those workflows. So that's the direction that we've been pursuing. And what we're actually doing, what models we work on, specifically at Canva, yeah, this is going to change every few months.
And so I, you know, I told my team, basically, don't fight, you know, this ever-changing, constantly changing ecosystem, but just like embrace it. And so I think it's really like a mind shift of like everything is going to change, which means also like what is important today may not be important tomorrow, which opens up also for opportunities, right? Like, it's a double-edged sword to some extent, but at the same time, we see that as a big opportunity. And so we are constantly, you know, analyzing our business, see where there are opportunities, where there's bottlenecks, where there are. technical parts of our workflow that we can automate and then we prioritize those and we work on them.
Like a year ago, none of the projects, the models that we ship now was underway, you know? And so it's logic to think that in one year we will probably release models that we haven't even started working on it today. And that's, you know, it could be terrifying or could be very exciting. So I think researchers are probably the most exposed to the speed at which AI is changing And so it takes really like some major adaptation and mindset shift, but at the same time, it's quite exciting. Well, Steph, thanks so much for taking the time to share a bit about what you and your team are up to. It's very cool to hear more about it.
Thank you, Sam. It was a true pleasure and honor to be on your podcast. Thank you so much. Thank you. Thank you.
