Cancer care still forces patients and doctors to guess! Learn how functional precision medicine is replacing that uncertainty by testing cancer drugs before treatment even begins.

In this episode of An Hour of Innovation podcast, host Vit Lyoshin speaks with Jim Foote, co-founder and CEO of First Ascent Biomedical, an innovator who is challenging one of the most uncomfortable truths in modern medicine: many cancer treatments are chosen without knowing if they will actually work.

First Ascent Biomedical is a company focused on transforming personalized cancer treatment through functional precision medicine and data-driven decision support.

In this conversation, they explore how functional precision medicine differs from traditional precision medicine and why testing drugs on patients’ live tumor cells changes everything. Jim explains how AI, robotics, and large-scale drug testing help doctors move from trial-and-error to a true test-and-treat approach. The discussion also covers the risks of ineffective or harmful treatments, the economic cost of cancer care, and what must change for this model to become part of standard oncology practice.

Jim Foote is a former technology executive turned healthcare innovator whose work is deeply shaped by personal loss and firsthand experience with cancer care. He is best known for advancing functional precision medicine by combining genomics, live-cell drug testing, and AI-driven analysis to guide treatment decisions. His perspective matters because it connects real clinical outcomes with the technology needed to give doctors and patients clearer, faster, and more humane options.

Takeaways

* Cancer treatment still relies heavily on trial-and-error, even with modern medical technology.

* Two biologically different patients often receive the same cancer treatment based on population averages.

* Precision medicine based on DNA and RNA sequencing still cannot confirm if a drug will work before it’s given.

* Functional precision medicine tests drugs directly on a patient’s live tumor cells before treatment begins.

* Some FDA-approved cancer drugs can be completely ineffective or even make a patient’s cancer worse.

* Testing drugs outside the body can prevent patients from being exposed to harmful or useless treatments.

* AI and robotics enable hundreds of drug tests to be completed in days instead of weeks or months.

* In a published study, 83% of refractory cancer patients did better when treatment was guided by this approach.

* Knowing which drugs won’t work is just as important as knowing which ones will.

* Personalized, test-and-treat cancer care has the potential to improve outcomes while reducing overall healthcare costs.

Timestamps

00:00 Introduction

02:46 The Core Problem in Modern Cancer Care

04:16 Functional Precision Medicine Explained

06:42 How AI, Robotics, and Data Are Changing Cancer Treatment

10:01 How Cancer Drugs Are Tested Before Treatment

13:20 Personalized, Patient-Centric Cancer Care

18:22 Cost, Access, and the Economics of Cancer Treatment

22:19 The Future of Cancer Care and Patient Empowerment

25:21 Real Patient Outcomes and Success Stories

26:50 Why Functional Precision Medicine Is the Future

31:18 Predicting, Detecting, and Preventing Cancer Earlier

34:27 Where to Learn More About Functional Precision Medicine

36:12 Transforming Healthcare Beyond Trial-and-Error

37:27 Regulations, FDA Pathways, and Scaling Innovation

40:09 Why Cancer Is Affecting Younger Patients

41:17 Innovation Q&A

Support This Podcast

* To support our work, please check out our sponsors and get discounts: https://www.anhourofinnovation.com/sponsors/

Connect with Jim

* Website: https://firstascentbiomedical.com/

* LinkedIn: https://www.linkedin.com/in/jim-foote/

* TEDx Talk: https://www.youtube.com/watch?v=CqLCgNxUhVc

Connect with Vit

LinkedIn: https://www.linkedin.com/in/vit-lyoshin/

X: https://x.com/vitlyoshin

Website: https://vitlyoshin.com

Podcast: https://www.anhourofinnovation.com/

Music education is quietly undergoing a massive shift, and most people haven’t noticed yet.

AI tutors are no longer just tools; they’re starting to shape how musicians learn, practice, and improve. But here’s the real question: where does human creativity and mentorship still matter in an AI-driven world?

In this episode of An Hour of Innovation podcast, host Vit Lyoshin sits down with John von Seggern, a longtime musician, educator, and founder of Futureproof Music School, to unpack what’s actually changing, and what isn’t, in the future of music education. John has spent over a decade designing online music education programs and now works at the intersection of AI, creativity, and human mentorship.

In this conversation, they explore how AI is personalizing music education in ways traditional schools struggle to scale. John explains how AI tutors can analyze music, guide students through complex production workflows, and surface the one or two things that matter most at each stage of learning. They also dig into why AI still falls short in mastery, taste, and creative judgment, and why human mentors remain essential. They discuss the hybrid model of AI tutors and human teachers, the future of music production learning, and what this shift means for creators trying to stay relevant in a fast-changing industry.

John von Seggern is a musician, producer, educator, and music technologist who has worked with film composers and contributed sound design to Pixar’s WALL·E. He previously helped lead and design one of the world’s most respected electronic music programs before founding Futureproof Music School, where he’s building AI-powered, personalized music education systems. His work matters because it goes beyond hype, offering a practical, grounded view of how AI can support creativity without replacing the human elements that make music meaningful.

Takeaways

* AI tutors are most effective when they surface only one or two actionable fixes, not long reports that overwhelm learners.

* Music education improves dramatically when AI can analyze your actual work (like mixes), not just answer theoretical questions.

* The biggest limitation of AI in music is that elite, professional knowledge is often undocumented, so models can’t learn it.

* Human mentors remain essential at advanced levels because taste, judgment, and creative intuition can’t be automated.

* Personalized learning paths outperform one-size-fits-all programs, especially in creative and technical fields like music production.

* Generative AI tools are fun, but most professionals prefer AI that assists the process, not tools that generate finished music.

* AI acts best as an intelligence amplifier, helping creators move faster rather than replacing their role.

* The future of music education isn’t AI-only, but a hybrid model where AI accelerates learning, and humans guide mastery.

Timestamps

00:00 Introduction

03:02 How AI Is Transforming Music Education

07:50 Why AI + Human Mentorship Works Better Than Music Schools

11:43 Why Music Education Curricula Must Evolve Faster

15:04 How AI Personalizes Music Learning for Every Student

19:38 Building an AI-Powered Education Business

24:22 What Students Really Say About AI Music Education

26:20 Electronic Music vs Learning Traditional Instruments

27:58 The Future of AI in Music and Creative Industries

30:28 Why Artists Still Matter in AI-Generated Art

32:21 Who Owns Music Created With AI?

36:50 How Creators Can Survive and Thrive Using AI

42:24 Innovation Q&A

Support This Podcast

* To support our work, please check out our sponsors and get discounts: https://www.anhourofinnovation.com/sponsors/

Connect with John

* Website: https://futureproofmusicschool.com/

* LinkedIn: https://www.linkedin.com/in/johnvon/

Connect with Vit

* LinkedIn: https://www.linkedin.com/in/vit-lyoshin/

* X: https://x.com/vitlyoshin

* Website: https://vitlyoshin.com/contact/

* Podcast: https://www.anhourofinnovation.com/

Most companies have no idea how risky and expensive their AI systems truly are until a single mistake turns into millions in unexpected costs.

In this episode of An Hour of Innovation podcast, host Vit Lyoshin explores the truth about AI safety, enterprise-scale LLMs, and the unseen risks that organizations must fix before it’s too late.

Vit is joined by Dorian Selz, co-founder and CEO of Squirro, an enterprise AI company trusted by global banks, central banks, and highly regulated industries. His experience gives him a rare inside look at the operational, financial, and security challenges that most companies overlook.

They dive into the hidden costs of AI, why RAG has become essential for accuracy and cost-efficiency, and how a single architectural mistake can lead to a $4 million monthly LLM bill. They discuss why enterprises underestimate AI risk, how guardrails and observability protect data, and why regulated environments demand extreme trust and auditability. Dorian explains the gap between perceived vs. actual AI safety, how insurance companies will shape future AI governance, and why vibe coding creates dangerous long-term technical debt. Whether you’re deploying AI in an enterprise or building products on top of LLMs.

Dorian Selz is a veteran entrepreneur, known for building secure, compliant, and enterprise-grade AI systems used in finance, healthcare, and other regulated sectors. He specializes in AI safety, RAG architecture, knowledge retrieval, and auditability at scale, capabilities that are increasingly critical as AI enters mission-critical operations. His work sits at the intersection of innovation and regulation, making him one of the most important voices in enterprise AI today.

Takeaways

* Most enterprises dramatically overestimate their AI security readiness.

* A single architectural mistake with LLMs can create a $4M-per-month operational cost.

* RAG is essential because enterprises only need to expose relevant snippets, not entire documents, to an LLM.

* Trust in regulated industries takes years to build and can be lost instantly.

* Real AI safety requires end-to-end observability, not just disclaimers or “verify before use” warnings.

* Insurance companies will soon force AI safety by refusing coverage without documented guardrails.

* AI liability remains unresolved: Should the model provider, the user, or the enterprise be responsible?

* Vibe coding creates massive future technical debt because AI-generated code is often unreadable or unmaintainable.

Timestamps

00:00 Introduction to Enterprise AI Risks

02:23 Why AI Needs Guardrails for Safety

05:26 AI Challenges in Regulated Industries

11:57 AI Safety: Perception vs. Real Security

15:29 Risk Management & Insurance in AI

21:35 AI Liability: Who’s Actually Responsible?

25:08 Should AI Have Its Own Regulatory Agency?

32:44 How RAG (Retrieval-Augmented Generation) Works

40:02 Future Security Threats in AI Systems

42:32 The Hidden Dangers of Vibe Coding

48:34 Startup Strategy for Regulated AI Markets

50:38 Innovation Q&A Questions

Support This Podcast

* To support our work, please check out our sponsors and get discounts: https://www.anhourofinnovation.com/sponsors/

Connect with Dorian

* Website: https://squirro.com/

* LinkedIn: https://www.linkedin.com/in/dselz/

* X: https://x.com/dselz

Connect with Vit

* Substuck: https://substack.com/@vitlyoshin

* LinkedIn: https://www.linkedin.com/in/vit-lyoshin/

* X: https://x.com/vitlyoshin

* Website: https://vitlyoshin.com/contact/

* Podcast: https://www.anhourofinnovation.com/