Digital Disruption with Geoff Nielson

Predictive AI often gets ignored, yet it may hold the real key to business value in the current tech landscape.

Machine learning expert Eric Siegel, author of The AI Playbook and Predictive Analytics, joins the Digital Disruption podcast to explain why organizations should invest at least as much in predictive AI as they do in generative AI. He breaks down how predictive AI can improve real-world operations, why so many AI pilots fail to reach deployment, and where the promise of autonomous AI agents encounters reliability challenges. While generative AI captures the headlines, Eric argues that more dependable returns often come from systems that predict outcomes and improve decision-making.

The conversation explores the differences between predictive and generative AI, why meaningful business value comes from deployment rather than impressive demos, and the limitations of fully autonomous AI agents and artificial general intelligence. Tune in to learn how predictive AI could make generative AI more reliable, why business leaders and data scientists need to work together, and whether AI will really cause a job apocalypse.

Like and follow for more conversations on AI, technology, innovation, the future of work, and digital transformation.

00:00 Why businesses may be investing in the wrong AI
00:47 Predictive AI vs. generative AI
03:01 How large language models fit into predictive AI
06:59 What kind of AI is solving real-world problems?
08:06 The two “wows” of AI: Impressive demos vs. deployment
09:28 Build your AI strategy with Info-Tech
10:37 Why predictive AI is still delivering business value
12:32 Why most AI pilots fail to reach production
16:25 AI hype and mismanaged expectations
19:31 AI agents, AGI, and the promise of autonomy
20:44 Can predictive AI make generative AI more reliable?
23:57 Why full AI autonomy remains unrealistic
26:59 Large language models vs. human intelligence
29:21 The limits and diminishing returns of AI models
31:35 What businesses are getting wrong about AI
35:35 Bridging the gap between technology and business leaders
37:18 Why leaders need a semi-technical understanding of AI
38:59 Three questions every predictive AI project must answer
42:22 Will AI replace developers and other technical roles?
49:29 Who should own AI strategy and accountability?
53:41 Is AI creating the productivity gains we were promised?
55:49 Could future technology overcome today’s AI limitations?
59:21 How organizations can unlock more value from AI
1:01:43 Eric Siegel’s six-step AI deployment framework
1:04:42 Why AI is a business improvement project, not a tech install
1:06:02 What’s new in The AI Playbook
1:09:30 Eric’s advice for business and technology leaders
1:10:46 About Info-Tech

Connect with Eric:
LinkedIn: https://www.linkedin.com/in/predictiveanalytics/
X: https://x.com/predictanalytic?lang=en
IG: https://www.instagram.com/eric_siegel/

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

The robot workforce is already here. AI-powered humanoid robots are entering factories and warehouses to address a growing labor gap, but are they filling jobs or replacing the people who do them?

Peggy Johnson, CEO of Agility Robotics, joins us to talk about how Digit is moving from robotics demonstrations into real workplaces. She explains why humanoid robots could become essential to manufacturing and logistics, how AI is accelerating their capabilities, and why safety remains the biggest barrier to widespread adoption.

From physical AI and labor shortages to robot fleet managers and the future of work, this episode looks beyond the fear of robot overlords to ask: how will humans and robots work together in the years ahead?

Subscribe for more conversations on AI, technology, innovation, the future of work, and digital transformation.

00:00 Why humanoid robots may be essential
00:47 Peggy Johnson, CEO of Agility Robotics
01:41 Are humanoid robots creating a dystopian future?
03:43 How workers respond to robots in the workplace
05:42 Why build robots in the human form?
07:27 Will humanoid robots replace human jobs?
11:45 Why reshoring manufacturing will require robotics
14:12 Info-Tech AI strategy
14:42 What humanoid robots could do by 2032
17:35 The biggest barrier to adoption: Robot safety
19:17 Why robots in the home are still years away
22:13 How big and powerful is agility robotics’ Digit?
24:44 Can humanoid robot accidents be prevented?
28:29 Competing with Elon Musk and Tesla Optimus
30:56 What separates deployment from a robotics demo
33:37 How companies deploy humanoid robots
37:01 Which industries are adopting robotics?
39:24 Scaling production to meet demand
41:08 How AI is accelerating humanoid robotics
45:36 How digit makes decisions on the factory floor
48:52 Hardware and software upgrades for robots
50:57 What comes after agility robotics’ Digit?
53:22 Will robots eventually do everything humans can?
55:58 The future of physical AI and automation
58:13 Peggy Johnson’s advice for business and tech leaders
59:32 About Info-Tech

Connect with Peggy:
LinkedIn: https://www.linkedin.com/in/peggyljohnson/
X: https://x.com/agilityrobotics
IG: https://www.instagram.com/agility_robotics/

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 is PDoom, and why are the people building artificial intelligence willing to risk human extinction?
Kevin Roose, a New York Times technology columnist, Hard Fork co-host, and author of The AGI Chronicles, shares what he learned from interviewing more than 150 people inside the world’s leading AI companies.
Kevin joins us on Digital Disruption to talk about what artificial general intelligence really means and how fear, ego, rivalry, and mistrust are accelerating the race between OpenAI, Anthropic, and Google DeepMind. He also explains why AI leaders compare this moment to the Manhattan Project: if they don’t build AGI first, someone more dangerous might.
Kevin also shares why he estimates a 10% chance of AI-caused extinction and discusses the private fears of AI researchers, government regulation, autonomous AI agents, and whether the AI boom is a bubble or the beginning of a profound transformation.
This episode moves beyond the hype to ask an important question: If advanced AI is inevitable, how much control do we still have over what happens next?
Subscribe and follow for more conversations about AI, technology, innovation, and digital transformation.

In this episode:
00:00 Why AI builders fear human extinction
00:43 Inside the race to build AGI
01:24 What does AGI actually mean?
05:16 AI safety warnings or marketing hype?
07:27 The rivalries driving the AI race
08:44 Building a boardroom-ready AI strategy
09:51 The personal feuds behind AGI
10:58 AI’s Manhattan Project mindset
12:27 Is AI the new atomic bomb?
14:58 Can advanced AI be contained?
16:28 AI regulation and the global arms race
17:35 What is P(doom)?
19:54 What will force governments to act?
21:29 What AI researchers say in private
23:08 Why the public backlash is growing
26:15 Why Kevin Roose changed his mind about AI
29:42 Is advanced AI inevitable?
32:28 AGI and the point of no return
34:38 The race to automate AI research
36:23 AI and gradual human disempowerment
40:10 What happens when AI masters your skills?
42:18 Will AI liberate us or replace us?
44:18 The AI claims Kevin does not believe
46:21 Is the AI boom a financial bubble?
50:20 AI labs and economic concentration
55:08 Inside the AGI chronicles
57:07 How should people prepare for AI?
58:29 About Info-Tech

#ai #futureofwork #technologypodcast #aibubble #bigtech #artificialintelligence #technology #datacenter #agi #anthropic #futureofai

Connect with Kevin:
LinkedIn: https://www.linkedin.com/in/kevin-roose/
X: https://x.com/kevinroose
IG: https://www.instagram.com/kevinroose/

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

Why have the United States and China pulled so far ahead in the global technology race? Mehran Gul, author of The New Geography of Innovation, argues that the answer goes far beyond individual companies or breakthrough technologies.

In this episode, we sit down with Mehran to discuss how successful innovation ecosystems are actually built, why Silicon Valley remains so difficult to replicate, and how China rapidly emerged as a global technology superpower. We also talk about why Europe and India have struggled to produce technology giants at the same scale, and how talent, capital, universities, government, and entrepreneurship shape long-term competitiveness.

Mehran also breaks down the extraordinary concentration of money flowing into AI companies like OpenAI, Anthropic, and Nvidia, whether today’s AI valuations are sustainable, and why a major market correction could still happen without ending America’s technological dominance.

From AI investment and venture capital to Silicon Valley, China, Europe, India, and the future of global innovation, this episode looks beyond the AI hype to ask a much bigger question: who will actually shape the next era of technology?

Subscribe and follow for more conversations with the world’s leading thinkers on AI, technology, innovation, and digital transformation.

In this episode:
00:00 The AI bubble and America’s technology lead
01:47 The new geography of innovation
05:30 How China became a technology superpower
09:13 Can other countries replicate China’s success?
13:17 What does “innovation” actually mean?
16:21 Are tech valuations measuring real innovation?
21:03 Does geography determine who wins in technology?
25:01 Will the U.S. and China dominate the next decade?
29:31 Why Europe’s push for tech sovereignty is struggling
33:05 Can government policy actually create innovation?
37:24 PayPal vs. Skype: How innovation creates more innovation
39:47 Is America’s technology dominance a zero-sum game?
43:52 Who is the world’s third-largest technology power?
45:07 Why “indispensability” may matter more than tech sovereignty
47:00 What This global tech race means for business leaders
48:32 Why AI investment is becoming more concentrated
51:29 Are we betting too much on a few tech leaders?
53:35 What Japan’s collapse can teach us about U.S. tech
56:13 Could the AI bubble actually burst?
59:22 Could China overtake the U.S. in Technology?
1:03:29 When government helps and hurts innovation
1:05:51 Predictions for the next era of global technology
1:07:50 Do We need to redefine innovation?
1:10:02 Where business leaders should look for innovation
1:14:02 Why venture capital isn’t actually global
1:18:00 Final thoughts
1:19:41 About Info-Tech Research Group

Connect with Mehran:
LinkedIn: https://www.linkedin.com/in/mehrangul?originalSubdomain=ch
X: https://x.com/mehrangul_

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

Are we in an AI bubble? Veteran Silicon Valley investor Bill Reichert says yes, but that doesn’t mean the AI revolution is slowing down.

In this episode we sit down with Bill Reichert, General Partner at Pegasus Ventures, to discuss the unprecedented investment pouring into AI, why many AI startups will fail, and where the next wave of innovation could emerge. This conversation highlights everything from data centers and quantum computing to China’s growing technology ecosystem and what businesses need to do to innovate and compete.

Bill also shares his “Yellow Jacket Paradox,” raising a bigger question about the future of AI: If biological intelligence can accomplish so much with so little energy, could a fundamentally new AI architecture eventually disrupt today’s LLMs, data centers, and AI leaders?

Subscribe and follow for more conversations with the world’s leading thinkers on AI, technology, innovation, and digital transformation.

In this episode:
00:00 The AI bubble is here
01:02 Why this tech cycle is different
03:30 Is AI growth sustainable?
04:18 Why most AI startups will fail
06:11 What separates AI winners from the hype
09:08 The “unfair advantage” in AI
10:59 Where is the next big AI opportunity?
13:16 Investing beyond generative AI
18:13 Why AI is becoming a commodity
19:05 The data center and energy opportunity
20:17 How AI is transforming every industry
22:23 Is AI changing venture capital?
26:13 Does more investment create better technology?
29:01 China, the US, and the global innovation race
34:28 The race for quantum computing
36:51 Is China really just copying the west?
40:06 Why big companies struggle to innovate
45:54 Startups as corporate innovation labs
47:29 How to spot an overhyped tech vendor
49:13 Why companies need to test before they invest
51:04 Why innovation pilots fail
52:49 Escaping “pilot hell”
54:54 Will the pace of innovation ever slow down?
57:30 How AI changes the future of work
59:48 Does AI mean we’ll have to work harder?
01:03:06 The leadership mistake that causes burnout
01:06:08 Control vs. unlimited growth
01:07:12 The “Yellow Jacket Paradox”
01:09:29 Why today’s AI architecture may be wrong
01:11:13 Will today’s data centers become obsolete?
01:13:38 The price of innovation
01:14:08 Closing thoughts

Connect with Bill:
LinkedIn: https://www.linkedin.com/in/billreichert/

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