Christian Masset coaches founders to speak to the media, to investors and to their own teams. The new wave of AI founders is younger, he says, but it brings the same problem as every founder before it: they talk too much. One founder he has coached for almost two years used to drown investors in technical detail. Last week he said the absolute minimum, and the investor loved it. Augustin asks Christian how a one-man AI consultancy should position itself when everyone in AI says the same things. They also talk about AI safety and steering AI agents with the I Ching, how Steve Jobs rehearsed, Wittgenstein and reframing, and why curiosity is a better motivation than money.

Online, every impression is measured. Step outside and the whole industry goes blind. Steve Walsh is CTO of Smart Retail, an edge computer vision platform that brings web-style analytics to physical space — and he is careful to explain how it counts people without identifying them. The engineering is the interesting part: a 30-to-40-million-parameter model small enough to run on a standalone device with no connection, uploading only aggregates. Also: what four years of AI coding tools actually changed about how his team ships.

Jason Xu has built product on both sides of the divide — years at Google, then years at Alibaba — and he is unusually precise about how differently the two think. Asked to name two good things about each, he does, and the answers say more than a critique would. Then the conversation turns philosophical: if AI collapses the cost of reorganising a team, what happens to the relationship between a company and the people in it? Jason reorganises his own workforce every three months. He explains what that means when most of the workforce is a model.

Most of our guests arrive already sold on AI. Daniel Sarosi did not. A JVM specialist with twenty years in Hong Kong, he watched people generate code that broke every practice he had spent a career defending — and said so. This is the conversation about what changed his mind and what did not. Along the way: what bad code actually is in plain language, why he still will not trust non-engineers to vibe code anything that matters, why data security is the part nobody talks about, and what foreign companies get wrong about entering China. Then the second half: what Great Wall Connect does day to day, what living in Taiwan and travelling China taught him about rules and flexibility, and where he actually lands on AI — a sophisticated pattern matcher he would not trust to build a secure system alone.

Andy Green has spent thirty years building systems that cannot be allowed to fail — GSM anti-fraud for mobile networks, the early terminals at the Hong Kong Exchange, and now Three Points Compliance, which builds AI tools for SFC-licensed firms. He argues compliance is the one place where a plausible answer is worse than no answer, and explains how you build AI that knows when to stop. Also: why he runs Qwen at home on modified hardware, why 'never' is a dangerous word, and what improv comedy taught him about engineering.

From Yahoo Mail in the 90s to building EA's online games business out of Shanghai, to leading a prototyping team on Apple Vision Pro — Hanley Leung has worked through every major platform shift of the last 30 years. Now in Hong Kong running an AI startup and the Lunatechs community, he argues this AI cycle rhymes with the 90s internet but moves faster than mobile. Why he calls AI 'clay, not Lego', why ex-Apple founders frustrate VCs, why HTML now beats PowerPoint, and why focus — saying no to good ideas — is the real currency of the AI era.

Dr. Bruce Cheung (張維) has been doing AI in Hong Kong since before most people had a computer. One of the first Computer Science graduates from HKU in 1985, he built statistical language models on floppy disks using LISP and Prolog — decades before anyone said 'LLM.' Now Head of the College of Life Sciences and Technology at HKU SPACE, he bridges AI with law and education, arguing that Hong Kong's universities have been quietly world-class in AI research since the 1980s while the public only noticed when ChatGPT arrived. Plus: why Manus costs too much, whether Gemini will ever serve Hong Kong, ADHD in the AI age, and Aug's theory that the I-Ching is the 'bones' for structured AI thinking — which Bruce agrees with.

A neuroscience PhD who once mapped neonatal mouse brain wiring under a microscope, Marcus Leiwe now runs an AI consultancy in Hong Kong applying the same academic rigor to business problems. From building a private, on-device AI pipeline for Branches of Hope (so refugee records never touch the cloud) to a passion-project Cantonese tone visualizer that runs entirely in the browser, Marcus shows what AI looks like when 'move fast and break things' isn't an option. Plus: Air Canada's chatbot lawsuit, the Vedic 17 levels of consciousness, and why HK is misread as just fintech.

Jacky Man (文子軒) is a 22-year-old HKU double-degree student who co-founded Mastermock — an AI-powered exam question generator that helps teachers save four hours a week on paper-setting. He drives a taxi at night to fund his zero-revenue startup, pitched his way through dozens of rejections to land a government grant, scaled from ten employees down to two (and got better results), and still finds time to hunt for a girlfriend. A raw, honest look at what it's like to be young, broke, and building in Hong Kong's AI scene.

Jenny Wong is a corporate marketer with a creative writing background and zero IT experience. Six months ago she set out to build an AI that could write tonally-correct Cantonese pop lyrics. Every major LLM failed at Cantonese tone-matching. So she taught herself through Dify, n8n, Manus, and Genspark, found the local HK AI community on Telegram and Instagram, met 黃志華 — the godfather of the 0243 tonal notation system — and shipped Cantolyrics.ai, which now generates Hong Kong-quality lyrics in around four minutes. Our second fully-Cantonese episode.