Episode Summary

Oscar Wahltinez, AI engineer at Google Sydney and longtime advocate for responsible AI, joins In The Blink of AI to demystify the most hyped, and misunderstood, parts of the AI revolution. From model size myths and token prediction mechanics to the surprising truth behind model leaderboards, Oscar explains the inner workings of large language models (LLMs) in clear, human terms.

With nearly a decade at Google, including time at the legendary Mountain View campus, Oscar shares his technical insights, product gripes (sorry, Google Assistant), and reflections on what it actually means to build AI responsibly. You’ll hear about the core innovations behind modern LLMs, the rise of decoder-only models, and the power of RAG systems and embeddings. He also calls on AI startups to stop obsessing over the models, and start building for real users.

If you’re wondering how AI models actually work, what responsible AI looks like in practice, or whether AGI will be our greatest asset or deepest threat, this one’s for you.

Time Stamps

02:14 – Oscar’s Role at Google & Why Responsible AI Matters

06:04 – Hack of the Week: Using Gemini (and Oscar’s Honest Review)

10:07 – How LLMs Really Work (and Why It’s Just Predicting the Next Word)

14:21 – From Transformers to Emergent Behaviours: What Surprised Engineers

21:29 – AI Leaderboards, Model Size & the Case for Smaller Systems

28:40 – Decoder vs Encoder Models: What You Actually Need to Know

33:15 – How RAG & Embeddings Power Real-World AI Products

36:41 – What Is Responsible AI? And Why You Can’t Bolt It On Later

39:17 – Building Responsibly on a Budget: Oscar’s Advice to Founders

43:46 – Product Confession: Why Google Assistant Let Him Down

45:05 – AGI: Net Positive or Existential Risk?

Resources

👨‍💻 Oscar Wahltinez on LinkedIn – https://www.linkedin.com/in/owahltinez/

📚 Google’s Responsible Generative AI Toolkit – https://ai.google.dev/responsible

🚀 Google AI First Accelerator – https://startup.google.com/programs/accelerator/ai-first/australia/

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