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Melanie Perkins was 19 when she started teaching design software at the University of Western Australia. The tools were complicated. Students spent most of the semester just learning how to navigate the interface rather than actually producing work. That gap became the thesis for Canva. She built a simpler, browser-based design tool for school yearbooks first. It was called Fusion Books and it worked. But the bigger opportunity was obvious: take that same simplicity and apply it to every design task a non-designer might face. That was what she took to investors. The pitch process ran for three years. By most accounts she had over 100 meetings before securing a meaningful check. Rejections arrived with familiar notes: the market was too crowded, Adobe was too entrenched, the vision too broad to take seriously. She relocated from Perth to Sydney, then flew to San Francisco to enter a startup program. She kept going. In 2013 she closed a seed round and launched. The waitlist at launch had 50,000 people on it, having seen nothing more than a landing page. By 2024, Canva had reached $2.3 billion in annualized revenue. The company was profitable. It employed over 4,500 people across 11 offices and served more than 170 million users worldwide. The product never tried to replace Photoshop or Illustrator for professional designers. That wasn't the market. The market was everyone else: the small business owner making a flyer at 11pm, the teacher building a slide deck between classes, the social media manager who needed five different graphics by noon. Canva made design fast enough that someone with no training could produce something usable in under ten minutes. That was the entire bet. What made the growth compound was the template ecosystem. The more users created and shared templates, the more useful the platform became for the next person who arrived. Canva didn't need to build everything. Users built it for them, and the product improved without a proportional increase in cost. The other decision that defined the company was staying private longer than most at that scale would. When the 2022 tech correction arrived, Canva's internal valuation was marked down from $40 billion to $26 billion. Perkins and co-founder Cliff Obrecht had been profitable before the inflation. They remained profitable after. The valuation adjustment made headlines and changed nothing operationally. The IPO question has followed the company for three years. Perkins has said consistently that they'll list when it makes sense, not in response to external pressure. She's been tuning out external pressure since a living room in Perth. There's no reason to expect that to change.

@claudeai is taking paid subscribers from @ChatGPTapp , and the data is now public. Most founders assumed the paid AI consumer market was settled. ChatGPT first, everything else distant. That assumption is wrong. Switching behavior in paid AI is already happening at scale. That means prosumers and knowledge workers are actively evaluating alternatives, not just defaulting to the first tool they tried. If you're building an AI product for paying users, the window is open. The market isn't captured. It's still being decided, and the incumbents don't have the moat everyone thinks they do. A 2-year-old product is winning paid switching behaviour. That tells you everything about defensibility in this category.

@OpenAI just shipped its first custom inference chip, built with @Broadcom . This isn't just a hardware story. When the company selling you AI also owns the silicon it runs on, inference costs and latency become levers only they control. For founders building AI-native products, that's a supply chain risk hiding inside a product decision. 📷 Your margins on AI features are downstream of choices being made in fabs you'll never visit. The shift from 'which model performs best' to 'who owns the full stack from chip to API' is already underway. Vendor lock-in used to mean switching your CRM. Now it can mean your cost-per-query doubles because one company vertically integrated. Watch inference pricing over the next 18 months more closely than you watch model benchmarks. 📷

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