{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"","title":"The real reason behind every AI layoff | Maxim Afanasyev, FSI Head for APAC and Japan, Google Cloud","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/63400f546507f800111f7a3d/1779932371418-6f19cf81-25df-42bc-8867-8107adb76757.jpeg?height=200","description":"

Maxim Afanasyev has been coding large language models for over 20 years, long before the AI boom. He's an applied AI expert who's worked at McKinsey building AI transformations and now leads AI strategy at Alphabet.


Maxim and Anthony discuss why 95% of AI applications don't deliver bottom-line impact, the global shortage of applied AI experts (only 200 mature professionals worldwide), and why banks are solving yesterday's problems instead of tomorrow's value propositions.


They cover why LLMs became persuasion machines by learning human biases, how customers are now smarter than banks because of AI agents, and why agentic payments will force financial institutions to rethink their entire business model. Maxim also explains why cutting 20% of your workforce signals that your CEO failed at transformation.


Timestamps

00:00 Intro

01:08 Coding LLMs 20 Years Ago in a Soviet-Era AI Lab

06:02 How RLHF Turned Neural Networks into Machines for Persuasion

10:48 From AI Winter to AI Hype: What Actually Changed

13:40 Why 95% of AI Applications Fail to Deliver Bottom-Line Impact

20:22 The 200 Applied AI Experts Problem

25:27 How to Nurture Applied AI Talent Through Experimentation

32:52 Why Bank Chatbots Create Dead Weight Loss

42:48 How AI-Smart Customers Are Breaking Legacy Banking Models

52:08 Asia's Lead in AI Adoption & Why Banks Must Rethink Value

01:04:14 Agentic Payments, Trust & the Renaissance of Physical Experience

01:16:25 Agents, Regulation & the Future of Human-AI Interaction

01:25:00 AI Deepfakes, Recruitment Fraud & the URL-to-IRL Shift

01:35:04 Why CEOs Who Cut Jobs for AI Are Really Admitting Failure


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