Podbit · Nerd Snipe with Theo and Ben
Luna isn't for subagents calling other subagents — it's for your code calling an LLM directly. Permission gating, title generation, classification tasks: anything you'd hardcode an API call for, Luna is now one of the best options ever for that use case.
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.
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.
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.
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.
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.
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.
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.
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