# EP 41: The Reward Signal: The Missing Ingredient in Every AI System You’ve Built

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Text version: https://stenobird.com/podcast/data-science-with-sam-4345646/ep-41-the-reward-signal-the-missing-ingredient-in-every-ai-system-you-ve-built.md
Podcast: [Data Science With Sam](https://stenobird.com/podcast/data-science-with-sam-4345646)
Published: 2026-05-26T10:00:00+00:00
Episode link: https://rss.com/podcasts/data-science-with-sam/3006407
Audio file: https://content.rss.com/episodes/395137/3006407/data-science-with-sam/2026_07_20_13_47_17_e5b84db3-faaa-453f-bd34-54e584e8e465.mp3
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JSON: https://stenobird.com/v1/public/podcasts/data-science-with-sam-4345646/episodes/ep-41-the-reward-signal-the-missing-ingredient-in-every-ai-system-you-ve-built
Duration seconds: 2684

## Resource

74% of organizations hope to grow revenue through AI. Only 20% are actually doing it. That gap isn't a technology gap — it's a design gap. And today's guest has a name for what's missing: the reward signal. Alexander Liss is a Data and AI Scientist based in Denver, Colorado, with a 30-year career across analytics, strategy, data science, machine learning, and AI. He's built systems that solve established problems in novel ways, and the long-term problem on his radar is ensuring AI tools provide responsible augmentation of human ability. His research includes Attention Fine Tuning (AFT) - a method for training language models without human annotation labels - and the Experience Orchestrator, a control theory-based governance framework for multi-agent AI. IN THIS EPISODE: ▪ Why 95% of AI pilots fail - MIT research shows businesses bolt AI onto existing processes without tying it to real outcomes ▪ The biology analogy: hunger isn't a goal, it's a continuous feedback signal - and the same principle should govern how AI systems behave ▪ ServiceNow dynamics blindness: LLMs are stateless - they can't consider cumulative impact, and you can't prompt-engineer your way out of that architecture problem ▪ Contextual bandits in marketing: how a reward signal anchored to real conversions creates a self-learning personalisation system that adapts in real time ▪ Knowledge graphs and agent memory: why RAG retrieves answers while a reward-signal system asks what the user needs to do differently ▪ Attention Fine Tuning (AFT): a three-component reward signal (coverage, focus, repeat penalty) that trained a T5-large model to outperform a supervised fine-tuning baseline by 9% — with better multi-turn recall, and no human labels ▪ The Experience Orchestrator: aerospace control theory applied…

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