Ayush Prakash Podcast

Nicholas Nadeau argues that the AI most people use is a bootleg of human expertise: a confident statistical average built from scraped public data, without the judgment, consent, or unpublished knowledge that makes an expert worth consulting.Nicholas is the CTO and Co-Founder of Onix. He holds a PhD in human-robot interaction and previously served as CTO of humanoid-robotics company 1X and data company SmartOne.ai. Onix is building private AI systems grounded in the approved work and judgment of individual experts.In this conversation:■ Whose answer you receive when you ask AI a question ■ Why frontier models blend textbooks, social media, and internet noise ■ What gets lost when AI attempts to imitate a human expert ■ Why an expert's most important knowledge may never have been published ■ How privacy and on-device AI can improve personal usefulness ■ Whether expert AI will replace professionals or increase their capacity ■ What young founders should understand about risk, stamina, and relationships ■ Why smart investors must contribute more than capital ■ How personal AI could eventually connect with robotics ■ The question Nicholas asks himself every day: should Onix exist? CHAPTERS00:00 Whose Answer Is AI Giving You? 04:02 Transactional AI vs. Wicked Problems 06:03 What AI Misses About Expert Judgment 11:51 Dark Data and the Knowledge Experts Never Publish 17:28 How Onix Began 22:42 Advice for Young Founders 28:45 Smart Money and Choosing Investors 32:45 Onix Beyond Health: Robots and Human Expertise 36:59 Will Expert AI Replace Humans? 41:20 Would Nicholas Let His Children Use AI? 46:45 The Question That Keeps Him Up at Night 48:49 What Gives Him Hope 50:45 Where to Find Nicholas and Onix CONNECT WITH NICHOLASWebsite: https://nicholasnadeau.com Onix: https://onix.life LISTEN TO THE PODCASTSpotify: https://open.spotify.com/show/1ILhje5HSua1FEOlTyFAhG Apple Podcasts: https://apple.co/3qXL37W CONNECT WITH AYUSHWebsite: https://ayushprakash.com LinkedIn: https://www.linkedin.com/in/prakash-ayush/ Instagram: https://instagram.com/ayushprakashofficial AI FOR GEN Zhttps://www.amazon.com/dp/0981182135

Renée Sieber explains why schools and public institutions are adopting generative AI before confronting its effects on learning, judgment, privacy, and civic life. Her central warning: when AI substitutes for the struggle involved in thinking and writing, students may become dependent on systems they were never given a meaningful choice to refuse.Renée is an associate professor at McGill University whose work spans AI, natural language processing, public policy, and civic engagement. We discuss what AI literacy should include, why transparency means little when people cannot act on it, how communities can contest unwanted systems, the history of Luddism, and what a genuinely humanizing technology could look like.In this episode:- Why Renée has gained respect for controlled-corpus deep learning- How social justice separates signal from noise in AI policy- Why individual “choice” fails as a frame for structural AI harms- What governments and communities can do when AI becomes embedded- How generative AI can remove productive struggle from education- Why Renée embraces the original meaning of Luddism- Where local, bounded AI systems may serve real human needs- Why Gen Z’s resistance gives her hopeChapters:00:00 What Renée changed her mind about00:46 Where she gets information about AI01:50 Social justice as the signal in AI policy06:31 Why these harms are difficult to discuss09:33 What AI literacy should actually mean14:54 Can communities reject embedded AI?20:28 Rebuilding civic engagement in Canada23:10 What generative AI is doing to students34:38 Why Renée embraces Luddism35:29 What humanizing technology could look like37:26 Thinking, writing, and the value of struggle39:50 The technology Renée would eliminate41:15 What gives her hope43:30 Where to follow Renée’s workConnect with Renée:Website: https://aifortherestofus.ca/McGill profile: https://www.mcgill.ca/geography/sieberPodcast:Spotify: https://open.spotify.com/show/1ILhje5HSua1FEOlTyFAhGApple Podcasts: https://apple.co/3qXL37WConnect with Ayush:Website: https://ayushprakash.comLinkedIn: https://www.linkedin.com/in/prakash-ayush/Instagram: https://instagram.com/ayushprakashofficialBook:AI for Gen Z: https://www.amazon.com/dp/0981182135

Nidhi Hegde explains why giving the public access to chatbots does not make AI democratic when users have no control over how the systems are built, trained, or deployed. Her central argument is that agency, representation, privacy, fairness, and technical robustness must be treated as connected design and governance problems.Nidhi is an associate professor of computing science at the University of Alberta, a Fellow at the Alberta Machine Intelligence Institute, and a Canada CIFAR AI Chair. We discuss what trustworthy AI means technically, the unavoidable trade-offs between privacy, fairness, accuracy, and cost, why broad AI regulation targets the wrong unit, and what public participation in AI decisions could actually require.In this episode:- How model robustness may produce fairer outcomes- What privacy, fairness, robustness, and trust mean in practice- Who currently decides the trade-offs built into AI systems- Why AI governance should focus on products, sectors, and consequences- How the post-ChatGPT boom changed AI research and graduate training- Why access to a chatbot is not the same as democratic control- What agency and representation would add to AI governance- Why government and education are struggling to match AI’s pace- How community-led action could fill the institutional gapChapters:00:00 The one research idea Nidhi would keep01:50 Privacy, fairness, robustness, and trust04:54 The unavoidable trade-offs in trustworthy AI07:54 What Canada gets wrong about AI policy10:19 Why AI governance should regulate products and sectors15:11 How the ChatGPT boom changed AI research22:51 Why Nidhi rejects “democratized AI”24:39 What democratic AI would actually require26:18 Should AI become a national political question?31:34 Building national AI literacy35:32 Why institutions cannot match AI’s pace38:54 Community action when government falls behind41:09 The missing intervention42:07 What gives Nidhi hope43:29 Where to follow Nidhi’s workConnect with Nidhi:Amii profile: https://www.amii.ca/people/nidhi-hegdeUniversity of Alberta: https://apps.ualberta.ca/directory/person/nidhihResearch website: https://sites.google.com/view/nidhihegdePodcast:Spotify: https://open.spotify.com/show/1ILhje5HSua1FEOlTyFAhGApple Podcasts: https://apple.co/3qXL37WConnect with Ayush:Website: https://ayushprakash.comLinkedIn: https://www.linkedin.com/in/prakash-ayush/Instagram: https://instagram.com/ayushprakashofficialBook:AI for Gen Z: https://www.amazon.com/dp/0981182135

Fenwick McKelvey joins the podcast to explain why Canada’s AI problem runs deeper than research funding or regulation. Canada helped sustain the ideas behind modern AI and launched an early national strategy, yet it still struggles to turn that history into independent industry, enforceable public accountability, and a clear position between the United States, China, and the European Union.Fenwick breaks down the difference between regulation, policy, and governance; how hype shapes investment and news coverage; why a small number of companies already make rules for the public; and why strengthening existing institutions may matter more than building another AI regulator. We also discuss ChatGPT in higher education, open models, proactive regulation, political privacy, and the technology he is watching after AI.Fenwick McKelvey is an associate professor in Communication Studies at Concordia University. His research covers digital politics, internet policy, algorithmic media, and AI governance. His book *SimPolitics: America’s Quest to Solve Politics with Computers* was published by the MIT Press in 2026.Chapters:00:00 Why Parliament testimony rarely becomes policy01:28 Regulation, policy, and governance explained04:38 Governing AI when nobody knows its real impact08:04 Should governments regulate AI hype?11:06 Did Canada build AI and lose the industry?14:14 Regulation is not Canada’s innovation problem17:00 Where Canada fits between the US, China, and EU20:31 Fenwick’s AI governance wish list25:01 Is ChatGPT ruining higher education?27:07 How students actually use AI30:10 Can regulation get ahead of technology?32:35 The next disruption after AI: quantum34:44 SimPolitics and where to follow FenwickConnect with Fenwick:Website: https://www.fenwickmckelvey.com/Concordia profile: https://www.concordia.ca/faculty/fenwick-mckelvey.htmlSimPolitics: https://mitpress.mit.edu/9780262053198/simpolitics/Podcast Info:Spotify: https://open.spotify.com/show/1ILhje5HSua1FEOlTyFAhGApple Podcasts: https://apple.co/3qXL37WConnect:Website: https://ayushprakash.comLinkedIn: https://www.linkedin.com/in/prakash-ayush/Instagram: https://instagram.com/ayushprakashofficialBooks:AI for Gen Z: https://www.amazon.com/dp/0981182135

What happens when the AI tools you rely on stop being cheap? Matthew Guzdial argues that becoming dependent on today’s generative AI could leave people and businesses exposed if prices rise or providers disappear.Matthew is an associate professor of Computing Science at the University of Alberta. We discuss AI’s role in game design, why players object to generative AI, and why synthetic data can’t supply missing ground truth in his research. He explains his prediction of an AI bubble and what he thinks could survive it.The conversation also covers VR’s adoption problems, Roblox and child safety, platform accountability, and how to talk to children about screens. We finish with his advice for young people trying to decide what to do with their lives. His market forecasts and parenting observations are his views, not established outcomes or clinical guidance.Chapters:00:00 Introduction: what makes a good game?02:12 AI in games before ChatGPT03:42 Concept art, coding and generative AI05:48 Why players push back against AI08:27 The risk of relying on cheap AI11:41 What could follow an AI bubble?16:10 Small datasets and synthetic data17:28 AI winters and the road to AGI19:08 Escapism, Roblox and child safety21:04 Why VR struggles with adoption23:17 How marketing shapes technology expectations24:53 Should children be banned from games?28:53 Parenting, screens and autonomy34:24 Advice for young people35:47 What gives Matthew hope36:35 Where to follow Matthew and his gamesConnect with Matthew:Website: https://guzdial.com/University profile: https://apps.ualberta.ca/directory/person/guzdialBluesky: https://bsky.app/profile/matthewguz.bsky.socialGoogle Scholar: https://scholar.google.com/citations?user=jKqmTbIAAAAJPodcast Info:Spotify: https://open.spotify.com/show/1ILhje5HSua1FEOlTyFAhGApple Podcasts: https://podcasts.apple.com/ca/podcast/ayush-prakash-podcast/id1557703631Connect:Website: https://ayushprakash.comLinkedIn: https://www.linkedin.com/in/prakash-ayush/Instagram: https://instagram.com/ayushprakashofficialBooks:AI for Gen Z: https://www.amazon.com/dp/0981182135