Speaker
Anton Osika
Appearances over time
1 episodes
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1
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Lovable reached $500 million in annualized revenue by May 2026, just 20 months after launch, growing by roughly $100M every 6 months.
Lovable users are creating one million new projects every single week on the platform, with over 50 million apps built in total since launch.
Applications built on Lovable collectively receive more than 700 million visits per month, demonstrating significant real-world deployment and usage.
Four out of five Lovable users have no technical or engineering background, showing the platform is primarily empowering non-developers to build software.
A company called Narsa replaced more than 10 internal tools with bespoke Lovable-built applications, saving over $1 million per year in software costs.
Approximately 60% of Lovable's lowest-tier subscribers hit their usage caps and pay extra top-up fees, indicating high addictiveness and ROI for users.
Intel's decline wasn't about bad luck — it was structural. When technical founders gave way to finance-driven business leaders, every major investment decision started running through a spreadsheet instead of an engineering vision. The result: no new factories for a decade, no EUV machines, and $100B returned to shareholders instead of compounded into the future.
When Intel offered to help Apple port macOS to x86, Jobs dropped a bombshell: 'I've been working on that for the last 4 releases.' Jobs had been quietly preparing Apple's core technology for a potential future switch for years before anyone at Intel suspected. This foresight — building optionality in secret — is what made Apple Silicon inevitable.
TSMC's original vision — become the factory for the entire semiconductor industry — seemed so niche that Intel didn't take it seriously. Intel was vertically integrated and proprietary; TSMC standardized everything and welcomed any customer. Steady progress and a demanding Apple drove them from irrelevant to producing 5x Intel's wafer volume by 2021, now 7x.
The AI buildout has a natural circuit breaker: energy. Nobody builds data centers without power, and global energy capacity is expanding at just 4–5% per year. That constraint limits how overheated the market can get. Meanwhile the value of a token — a unit of intelligence — is nearly infinite if it improves logistics, finance, and labor. Gelsinger sees two decades of growth, not years.
Quantum has been '5 years away' for 25 years — but Gelsinger says this time is different. We now know how to build qubits, error-correct them, and write algorithms against them. Multiple modalities (trapped ions, photonic, spin) are all showing results. Engineering scale is the only remaining challenge, and he expects quantum supremacy across multiple industries before 2030, with encryption-breaking Q-Day around 2032–2033.
Four out of five Lovable users have zero engineering background. They're first-time founders, enterprise employees building side hustles, and small business owners figuring out what to build. The 20% who are technical use Lovable because it enforces best practices automatically — secure payments, security scans, architecture guardrails — things even developers appreciate not managing manually.
Lovable is evolving beyond software creation into full business operations. In pre-release, users can access an AI co-founder that monitors their business overnight and delivers strategic recommendations each morning. With all your apps running on the platform, Lovable has access to all the data — usage, revenue, customers — to recommend optimizations without being asked. The product moat just got a lot deeper.
When a US nursing education company used Lovable to build custom scheduling, certification management, and admin tools, they didn't just save money — they replaced 10 separate software subscriptions and now save over $1 million per year. As building costs approach zero, every company will evaluate whether Salesforce, Slack, and HubSpot are still worth their price — or whether a bespoke alternative built in days beats them on fit and cost.
Lovable uses multiple frontier models and increasingly its own fine-tuned open-weight models, routing each request to whatever is most suitable. The team explicitly refuses to optimize for cost by using measurably worse models. A research team in Stockholm does post-training using reinforcement learning on the specific mistakes frontier models make inside Lovable's agent harness. The data flywheel from a million weekly projects is the core competitive moat.
Taiwan has less than three weeks of energy reserves. A Chinese blockade — no military engagement, just cutting off oil and LNG — would brown out the island. Fabs take 90 days to restart once shut down. The global economic impact would exceed the Great Depression. This isn't hypothetical: China has blockaded the Taiwan Straits 7 times in the last 4 years.
When Jason's team built two separate intranets in Japan and the US without coordinating, he worried they'd diverged. Anton said: that's the right approach. At CERN, Anton witnessed isolated teams working on the same particle accelerator publish separately to avoid anchoring bias — free markets work by competition, not consensus. Now that building is cheap, running parallel experiments beats planning a single perfect product.
Jason Calacanis's Founder University program needed an intranet. Two years ago that would have been a $500,000 project — off the table. Instead, a non-technical program manager built it herself on Lovable in 4 to 8 hours, including economic impact calculators, without asking permission. Total cost: under $2,000. The software now powers the program in Saudi Arabia and Japan.
One million new projects every week. Over 50 million total apps built. 700 million monthly visits to those apps. $500 million in annualized revenue by May 2026 — all in 20 months since launch. These aren't vanity metrics; they represent a platform where businesses are actually running. Lovable isn't a prototype tool anymore.
At Intel's CPU peak, the company literally laughed at NVIDIA's machines. GPUs were for gamers, not serious compute. But Jensen Huang kept building a deeper software stack — CUDA, SIMD, multi-threading — until Japanese HPC researchers realized these 'graphics cards' could tackle the world's hardest workloads. Intel even had its own answer, Project Larrabee, killed the week Gelsinger left.
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