Digital Disruption with Geoff Nielson

Are we in an AI bubble or at the beginning of the biggest technological convergence cycle since the Industrial Revolution?

On this episode of Digital Disruption, we’re joined by the CEO of the Future Today Strategy Group and tech futurist Amy Webb.

Amy joins Geoff Nielson to unpack what 2026 really looks like through the lens of artificial intelligence, programmable biology, quantum computing, biological computing, geopolitics, and systems-level change. Amy argues that we’ve officially entered a new convergence cycle, a rare historical moment where AI, biotech, computing architectures, economic systems, and geopolitics collide to create an entirely new reality. This isn’t incremental innovation. It’s structural transformation.

If you’re looking for a conversation grounded in data-backed frameworks to help you navigate disruption, understand convergence cycles, and build real strategic vision in an age of uncertainty, this episode is for you.

Tune in to hear what “creative destruction” truly means for business leaders, how power is shifting between Big Tech, governments, and capital markets, why “future-proofing” is a myth, and why many CEOs are falling short when it comes to long-term strategic foresight.

Amy is recognized as the global authority who transformed the practice of strategic foresight into a rigorous, data-driven discipline. A pioneering quantitative futurist, she established the field’s foundational methodologies that today guide leaders, organizations, and governments in anticipating disruption, shaping the future, and securing long-term growth. Ranked the #4 Most Influential Management Thinker in the World by Thinkers50, Amy is regarded as one of the most important voices on the future of technology, business, and society. Forbes named her “one of the five women changing the world,” and the BBC recognized her among its 100 Women of the Year.

In this video:
00:00 Intro
01:08 Where are we in 2026? A world in technological “Typhoon”
01:49 What is a convergence cycle? (Industrial revolution to internet era)
04:14 Why AI is the foundation of this new era
05:04 Systems-level change vs trend stacking
06:19 From AI winters to generative AI breakthroughs
07:27 Power shifts: OpenAI, Microsoft, Google & Competitive Dynamics
08:31 Winners vs losers in the convergence economy
09:26 Fear, FOMO & leadership paralysis
12:17 Rethinking regulation & geopolitical power shifts
15:40 Why “Future-Proofing” is a myth
17:14 AI capital flood: Is there a bubble?
19:25 Enterprise AI & the 80% workforce threshold
23:27 The attention economy & AI hype
27:28 Where AI actually creates real impact
28:11 Programmable biology & DeepMind’s Evo 2
30:00 Climate solutions, agriculture & synthetic biology
31:59 Dolly the Sheep & why transparency matters
38:19 mRNA, public trust & technology communication
40:15 The polycompute future: AI + quantum + biological computing
41:01 Quantum computing’s business implications
42:20 Brain organoids & biological computers
46:48 Creative destruction: What must die for you to survive?
49:00 Why evolution isn’t enough anymore
51:47 Why leaders avoid dangerous conversations
52:39 Strategic foresight & data-backed vision
56:36 Why AI navel-gazing is procrastination
57:23 Leadership in an age of convergence

Connect with Amy:
LinkedIn: https://www.linkedin.com/in/amywebb/
X: https://x.com/amywebb
Instagram: https://www.instagram.com/amywebbfuturist/

Our links:
Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast
Follow us on YouTube: https://www.youtube.com/@InfoTechRG

Is AI really replacing software engineers, or just changing how they work?

On this episode of Digital Disruption, we’re joined by Bala Muthiah, Director of Engineering at Lyft.

Bala sits down with Geoff to cut through the hype around AI in software development and explain what’s actually changing inside high-performing engineering teams and what that means for the future of work. From vibe coding and AI-powered prototyping to production-ready systems, productivity gains, and the reality behind 10x (or 100x) engineer claims, Bala shares a grounded perspective on why true improvements are closer to 10–20%, not exponential overnight disruption. They discuss engineering leadership in the AI era, bridging skeptics and evangelists, why value creation matters more than lines of code, the importance of customization over out-of-the-box AI, data privacy and governance responsibilities, the growing digital divide, and the critical role of curiosity, culture, and trust in building modern tech teams.

Bala is a technology leader who builds high-performing teams and AI that enhances human connection. Beyond his technical leadership, he serves as a startup advisor and served as an advisory board member at Defy Ventures (nonprofit focused on prison reform), reflecting his belief that community impact and innovation should grow together. He emphasizes that AI with humans in the forefront shapes everything he does. AI. He promotes positive aspects of AI while recognizing that leaders must guide its development responsibly.

In this episode:
00:00 Intro
00:57 Why this is the most pivotal moment in tech
02:21 Bridging the AI dreamers and skeptics
03:18 Productivity vs. value creation
04:44 Vibe Coding: Hype vs. reality
05:27 Democratizing software development
06:09 Prototyping vs. production code
08:17 Will AI reduce the need for engineers?
12:25 What engineers should focus on now
13:59 Curiosity as a core engineering trait
15:34 Why engineers must be close to customers
17:28 Feature slop & intentionality
20:39 Lyft’s real-time AI design workflow (cursor example)
23:32 When AI is (and isn’t) truly real-time
25:18 Custom AI vs. out-of-the-box tools
26:55 Data ethics, privacy & governance in ai
29:04 A framework for sensitive data
31:18 Why leaders must act before regulation
32:38 AI Hype: Utopia vs. doomsday narratives
36:23 Culture as a competitive advantage
38:55 What makes a great engineering leader
40:42 Common mistakes new tech managers make
45:19 From “sell” to “tell”
47:55 Leading hybrid & remote engineering teams
49:46 The 10x (or 100x?) engineer debate
53:29 Advice for young engineers
55:23 The future of work
56:55 Bridging the digital divide
58:51 “Give Before You Take” philosophy

Connect with Bala:
LinkedIn: https://www.linkedin.com/in/balaarjunan/
X: https://x.com/balaarjunan

Our links:
Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast
Follow us on YouTube: https://www.youtube.com/@InfoTechRG

What does it actually take to lead in the age of AI?

On this episode of Digital Disruption, we’re joined by Deborah Liu, former CEO of Ancestry and former VP of Facebook Marketplace at Meta.

Deborah joins Geoff to share a candid, practical look at modern leadership in 2026. Drawing on her experience scaling billion-user platforms and transforming legacy organizations, she explains why “adding AI” isn’t a strategy and what it truly means to build an AI-native company.

They unpack Facebook’s mobile-first pivot and what it teaches about leading through disruption, why adaptability may be the most important executive skill of the next decade, and how CEOs should think about AI governance, security, and enterprise guardrails. Deborah also discusses building with a founder mindset inside large organizations and creating a culture where innovation comes from the bottom up.

This conversation also explores the human side of leadership and why communication makes up 80% of the job.

Deborah was most recently President and CEO of Ancestry, where she brought the legacy company into its next phase of growth. In her prior role at Meta, she turned persistence into a platform. The idea for Facebook Marketplace came to her during her first interview, though it took six years of strategic thinking and tenacious advocacy to build what would become a global marketplace serving over a billion people. She also architected Facebook’s first mobile ad products and payments infrastructure, proving that the most powerful solutions emerge when you connect the right people, ideas, and opportunities. Her 20+ years in tech began with integration work at PayPal and eBay — complex projects that taught her how to see the connections others miss.

In this video:
00:00 Intro
01:00 What Being a CEO Means in 2026
06:30 Rebuilding a legacy company for the AI era
07:20 Facebook’s mobile-first pivot (stock price crisis)
10:45 The power of top-down buy-in
12:00 Big bets vs incremental change
15:00 The “Future Us” decision-making framework
19:35 How to build great products
21:00 Fall in love with the problem, not the solution
22:45 AI: Blessing or curse for product teams?
24:30 AI governance, security & data risks
26:45 Are developers becoming obsolete?
28:30 Why senior engineers are more valuable in AI
30:00 Should CEOs own AI strategy?
34:30 Magic wand dinners & listening as a leader
36:00 Remote work vs in-person culture
40:00 Breaking the CEO archetype
44:00 Failure as a career advantage
47:00 Communication is 80% of leadership
51:00 Why experts often fail as managers
53:30 Building a culture of innovation
58:00 Scaling infrastructure to unlock product velocity
1:10:00 Parenthood & career stalls (the honest truth)
1:15:00 Networking for introverts
1:20:00 Adaptability: The most important skill of the next decade
1:21:30 Closing thoughts

Connect with Deborah:
LinkedIn: https://www.linkedin.com/in/deborahliu/
X: https://x.com/debliu_

Our links:
Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast
Follow us on YouTube: https://www.youtube.com/@InfoTechRG

Is AI actually going to replace developers? Or is the hype getting ahead of reality?
On this episode of Digital Disruption, we’re joined by Sebastian Raschka, AI Research Engineer and author.
Sebastian Raschka sits down with Geoff Nielson to unpack the real state of Large Language Models (LLMs) in 2026. As an LLM research engineer, Sebastian bridges deep technical expertise with practical, real-world AI implementation. In this conversation, he cuts through AI hype to focus on what’s actually achievable with modern LLMs, reasoning models, reinforcement learning, and inference scaling and where the limitations still exist. Sebastian explains why most companies should not build a large language model from scratch, but also why understanding the fundamentals may be one of the most important investments technology leaders can make.
This conversation breaks down:
◼️Why coding is currently the strongest LLM use case
◼️Why “reasoning” models still fail simple tasks like counting letters in “strawberry”
◼️The reality behind Math Olympiad gold-level AI claims
◼️The true cost of training large models (millions in GPU compute)
◼️The privacy risks of uploading proprietary data into APIs
◼️How enterprises should think about fine-tuning vs API-based prompting
◼️Why benchmarks and leaderboards can be misleading

Sebastian Raschka has over a decade of experience in artificial intelligence and machine learning. His work bridges academia and industry, serving as a Senior Engineer at Lightning AI and as a faculty member at the University of Wisconsin–Madison. He is the author of Build a Large Language Model from Scratch and is widely recognized for his practical, code-driven approach to AI education and research. His expertise lies in LLM research, transformer architectures, reinforcement learning, and the development of high-performance AI systems, with a strong focus on real-world implementation.

In this video:
00:00 Intro
01:23 The Rise of “Reasoning” and Thinking Models
03:06 Inference scaling vs training scaling
06:17 What LLMs are actually good (and bad) at
07:09 The “Strawberry” Problem and Reasoning Limits
09:00 Tool use and why LLMs don’t need to count letters
10:20 Math Olympiads & self-refinement techniques
12:01 Why coding is the killer use case
13:28 Does AI make developers obsolete?
18:02 The Reality of 10x developer productivity claims
21:43 Generalist vs specialized models
23:53 Build from scratch vs fine-tune vs API prompting
25:01The true cost of training an LLM
27:33 API customization vs owning your model
29:12 Who should build an LLM from scratch?
33:16 Data requirements & why you need terabytes
34:28 Enterprise data challenges
35:40 Retrieval-Augmented Generation (RAG) explained
46:05 Multi-agent systems & tool calling
49:48 The problem with LLM benchmarks
55:43 Using LLMs as judges
58:00 Biggest misconceptions about LLMs
1:04:19 Reinforcement learning with verifiable rewards
1:06:32 Advice for technology leaders
1:11:48 Escaping AI hype through fundamentals

Connect with Sebastian:
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
X: https://x.com/rasbt
Connect with Sebastian:
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
X: https://x.com/rasbt

Our links:Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcastFollow us on YouTube: https://www.youtube.com/@InfoTechRG

On this episode of Digital Disruption, we’re joined by Josh Browder, founder of DoNotPay.

Josh, also known as the “robot lawyer,” is an entrepreneur and the founder of DoNotPay, often referred to as “the Robin Hood of the Internet.” He created DoNotPay after noticing the disproportionate targeting of elderly and disabled motorists through parking ticket enforcement. Since its launch, DoNotPay has helped UK and New York motorists save an estimated $5 million. Josh has been recognized as one of MIT Technology Review’s “35 Innovators Under 35” and named one of the top legal innovators in America by the Financial Times.

Josh joins Geoff to discuss how AI in law is transforming the legal landscape and driving real-world innovation. From the impact of artificial intelligence on consumer rights, the legal system, and everyday life, this episode explores how AI is being used to help people push back against predatory business practices.

Josh shares how his company, DoNotPay evolved from simple legal templates into sophisticated AI agents capable of negotiating directly with companies and in some cases, even AI vs. AI negotiations with corporate chatbots. The conversation dives into how large organizations profit from consumer friction, dark patterns, and bureaucracy, and how AI can help consumers fight fire with fire.

In this video:
00:00 Intro
01:10 Mission behind DoNotPay
03:00 Why big companies exploit consumer inaction
05:00 Robo Revenge: Fighting spam calls with AI
08:00 From templates to AI chatbots: The evolution of DoNotPay
11:00 AI vs AI: Negotiating bills with corporate chatbots
14:00 Subscription traps, free trials, and dark patterns
16:00 Medical bills, the no surprises act, and AI negotiation
18:45 Property taxes as the next major consumer battleground
20:00 Phone AI agents and real-time negotiation challenges
23:00 Ethical Constraints: Why DoNotPay’s AI never lies
25:00 Passive AI: The future of automatic refunds
27:00 Unclaimed money and forgotten refunds
29:00 Will AI replace lawyers? Legal industry disruption
32:00 AI inside DoNotPay: Running a lean, profitable company
35:00 Training AI models and the new AI job economy
38:00 Bias, truth, and the limits of AI judgment
41:00 Venture capital, scale, and staying independent
44:00 Real stories: Justice, anger, and consumer wins
47:00 Why consumer exploitation isn’t going away
49:00 The future of AI assistants, wearables, and contracts
51:00 Bold predictions: AI, robotics, and human relationships
53:00 Where AI should not be used

Connect with Josh:
LinkedIn: https://www.linkedin.com/in/joshua-browder-b0b573116/
X: https://x.com/Joshuabrowder
Instagram: https://www.instagram.com/joshuabrowder12/

Our links:
Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast
Follow us on YouTube: https://www.youtube.com/@InfoTechRG