Episode Summary

Replay Episode! When this conversation with Lorikeet co-founder Steve Hind first aired, the company had just raised $14M. Fast forward a few short months and they’ve doubled their valuation, become the hottest name in Australian AI, and poached some of the best engineers in the country. With so many new subscribers since then, we’re bringing this one back.

In this episode, Georgie Healy sits down with Steve to unpack how Lorikeet built Rae, an AI platform powering complex support for banks, healthcare, SaaS, and beyond. Steve explains why Retrieval-Augmented Generation (RAG) isn’t the silver bullet everyone thinks, what “agentic frameworks” really mean (and why it’s mostly marketing), and how starting with the hardest customer problems creates a defensible product.

They also dive into what makes AI support feel truly human, why empathy matters more than “personality,” and how to balance technical brilliance with marketing clarity. Along the way, Steve shares his favourite AI tools, what he looks for when hiring, and why founders should stop being insecure about not training their own models.

Whether you’re building in AI, hiring AI talent, or just curious about where the hype ends and real customer value begins—this replay is a masterclass in scaling smart AI support.

Time Stamps

01:52 – What makes Lorikeet different from other AI support tools

02:49 – Why banks and healthcare companies need AI-driven customer support

09:00 – What is RAG, and why does it matter for AI-powered customer service?

14:09 – The biggest mistake AI chatbots make in customer support

16:26 – What does "agentic reasoning" really mean?

19:12 – The cost of running AI models and why RAG reduces compute power

27:46 – AI tools Steve uses every day (and his take on Perplexity and Replit)

30:36 – How to stand out when applying for an AI job

39:18 – What people get wrong about AGI and AI scaling laws

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