Invisible Machines podcast by UX Magazine

Joshua Gans, economist and co-author of Prediction Machines (and holder of the Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto) joins Robb and Josh to reframe how enterprise leaders should think about AI. Rather than chasing the hype around artificial intelligence, Gans argues we should understand AI as an advance in computational statistics that drops the cost of prediction, reduces decision-making friction, and fundamentally reshapes organizational structure.

Many organizations are full of people waiting for phones to ring, managing buffers, absorbing uncertainty. As AI makes prediction cheap, this middle-management friction layer flattens. His new book, The Microeconomics of Artificial Intelligence, examines the ways AI enhances and perhaps enables decision-making, and how that’s poised to affect organizations and industries. The trio discusses the "hidden secret" of AI adoption that the people who choose the systems used to automate work are essentially "selecting their usurper." While AI will eliminate friction and flatten hierarchies, it will supercharge frontline workers rather than replace them.

Forbidding employees from experimenting with AI tools and pushing adoption underground prevents the learning curve needed for proficiency. For leaders navigating AI adoption, this conversation offers a clearer lens: stop thinking about intelligence, start thinking about prediction costs, friction reduction, and the organizational restructuring required to actually capture value. True AI transformation isn't about deploying models, it's about redesigning decision-making architecture across the enterprise.

https://www.joshuagans.com

---------- Support our show by supporting our sponsors!
This episode is supported by OneReach.ai
Forged over a decade of R&D and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale.

Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.
A complete system for accelerating AI adoption - design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents).
- Use any AI models
- Build and deploy intelligent agents fast
- Create guardrails for organizational alignment
- Enterprise-grade security and governance

Chapters
0:00 — Who is Joshua Gans + why “Prediction Machines” still matters
1:34 — AI as prediction (and why that framing wins)
2:45 — The “AI startup” wave + the deep learning shift
3:25 — AI is computational statistics, not magic
4:22 — Why “Artificial Intelligence” is a misleading label
6:02 — Econ lens: what becomes cheaper + abundant
6:43 — Cheaper prediction: fraud → self-driving
7:47 — ChatGPT/LLMs: next-token prediction, new apps
9:16 — LLMs as decision support (info → output)
10:43 — Rules vs decisions (weather app example)
12:45 — Better decisions: error costs + human judgment
13:43 — Airports: “cathedrals to uncertainty”
16:02 — Hospitals: capacity is an information problem
18:07 — Digital twins: avatars, meetings, AI “TA”
22:06 — “Ship then shop”: Amazon, prediction, logistics + lock-in

Request free prototype:
https://onereach.ai/prototype/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e3&utm_content=1

---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere: Amazon — https://bit.ly/4hwX0a5

#InvisibleMachines
#Podcast
#TechPodcast
#AIPodcast
#AI
#ArtificialIntelligence
#PredictionMachines
#EnterpriseAI
#EconomicsOfAI
#DigitalTransformation
#FutureOfWork
#TechInnovation
#DecisionMaking
#BusinessStrategy
#AIStrategy

Before enterprises can deploy AI agents that actually work, they need something most organizations don't have: a single, authoritative source of truth. Joe DosSantos, Workday’s VP of Enterprise Data and Analytics, joins Robb and Josh for a wide-ranging conversation about canonical knowledge, the semantic layer, and why data governance, a concept from the 1990s, has suddenly become essential for AI deployment.

Large language models are predictive engines modeled to anticipate what users probably likely mean. For B2C applications where multiple interpretations are acceptable, this works fine. But enterprises need deterministic truth, not probabilistic guesses. The trio outline a solution in three layers: establishing canonical knowledge, building a semantic layer to translate between human definitions and machine-readable formats like YAML, and using LLMs as an interface to deterministic back-end systems.

For leaders evaluating AI investments, this episode clarifies what actually needs to be built before agents can deliver value: not flashy use cases, but the unglamorous, essential work of data governance and semantic translation.

---------- Support our show by supporting our sponsors!

This episode is supported by OneReach.ai
Forged over a decade of R&D and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale.

Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.

A complete system for accelerating AI adoption — design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents).
- Use any AI models
- Build and deploy intelligent agents fast
- Create guardrails for organizational alignment
- Enterprise-grade security and governance

Request free prototype:
https://onereach.ai/prototype/utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e2&utm_content=1

---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere:

Amazon — https://bit.ly/4hwX0a5

Chapters -
0:00 – Welcome to Invisible Machines
1:28 – Why AI Agents Fail Without a Source of Truth
2:34 – Canonical Knowledge Is More Than Feeding Data to an LLM
3:16 – LLMs Are Good at Language, Not Truth
4:16 – The Convergence of Governance and Generative AI
5:48 – Implicit vs Explicit Knowledge Explained
7:31 – Why Accuracy Breaks Down in AI
8:37 – The Real Launchpad for AI: Get the Facts Right
9:42 – Alignment, Not Intelligence, Is the Hard Problem
10:53 – Semantic Layers: Teaching Machines Meaning
12:38 – LLMs Are Interfaces, Not Systems
14:26 – Routing Questions: Inference vs Deterministic Answers
16:21 – Canonical Knowledge Requires Human Ownership
18:16 – There Is No ROI for Data (It’s the Foundation)
23:59 – From Use Cases to Systems Thinking

Episode Credits:
Robb Wilson - Host
Josh Tyson - Host
Elias Parker - Executive Producer
Vishal Menon - Producer
Maksym Zlydar - Audio/Video Editor
Mykhailo Lytvynov - Audio/Video Editor
Eugen Petruk - Graphic Design
Alla Slesarenko - Copy
Vira Prykhodko - Web Development

#InvisibleMachines
#Podcast
#TechPodcast
#AIPodcast
#AI
#AgenticAI
#AIAgents
#DigitalTransformation
#AIReadiness
#AIDeployment
#AISoftware
#AITransformation
#AIAdoption
#AIProjects
#EnterpriseAI
#CanonicalKnowledge
#DataGovernance
#SourceOfTruth
#AIArchitecture
#DeterministicAI

Ben Goertzel, the researcher who helped popularize the terms "AGI" and “singularity”, as one of the most influential modern champions and systematizers of AGI, returns to Invisible Machines to discuss the decentralization of AI and what's actually missing from today's most advanced systems with Robb Wilson and Josh Tyson.

As enterprises rush to deploy AI agents and LLMs reshape workflows, a critical question emerges: who controls the infrastructure? Goertzel argues that while big tech dominates model development, a tension is building between centralized hegemony and decentralized, open systems — the same dynamic that shaped the internet itself.

In this wide-ranging conversation, Goertzel discusses his current work on Hyperon (the successor to OpenCog) and the ASI Chain, systems designed to enable decentralized AGI development. He explains why the rapid cycles of AI hype and disappointment — the traditional "AI winters and summers" — no longer slow progress the way they once did. The speed of change has accelerated into what he calls a "mathematical singularity," where six-month cycles replace decades-long shifts.

---------- Support our show by supporting our sponsors!

This episode is supported by OneReach.ai
Forged over a decade of R&D and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale.
Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.
A complete system for accelerating AI adoption — design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents).
Use any AI models
Build and deploy intelligent agents fast
Create guardrails for organizational alignment
Enterprise-grade security and governance

Request free prototype:
https://onereach.ai/prototype/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e1&utm_content=1

---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere: Amazon — https://bit.ly/4hwX0a5

#InvisibleMachines
#Podcast
#TechPodcast
#AIPodcast
#AI
#AGI
#ArtificialIntelligence
#AgenticAI
#DecentralizedAI
#AIInfrastructure
#AIAgents
#FutureOfAI
#Singularity

We’re in a moment when organizations are approaching agentic AI backwards, chasing flashy use cases instead of building the scaffolding that makes AI agents actually work at scale. Erika Flowers, who led NASA’s AI Readiness Initiative and has advised Meta, Google, Netflix, and Intuit, joins Robb and Josh for a frank and funny conversation about what's broken in enterprise AI adoption. She dismantles the myth of the "big sexy AI use case" and explains why most AI projects fail before they start. The trio makes the case that we're entering a post-software world, whether organizations are ready or not. Listen and learn why the scaffolding— or agent runtime — matters more than use cases, why organizational gaps kill AI projects, how to move projects from pilot to production, and what "post-software" actually means for enterprises.

Check out Erika’s podcast, “Flower Power Hour”: https://open.spotify.com/show/15BTSl9fWiH3QTmVAYj6Fd
Learn more about Erika at
www.helloerikaflowers.com/

0:09 - NASA AI Readiness Explained | Erica Flowers on Agentic AI & Runtimes
1:48 - Why the “Big Sexy AI Use Case” Is a Lie
2:42 - AI Didn’t Start with ChatGPT: What NASA Has Been Doing for 30 Years
4:24 - Why AI Runtimes Matter More Than Any Single Use Case
5:21 - The Hidden AI Problem: Legacy Data, Silos & Organizational Reality
7:13 - The Boring AI That Actually Works (And Why Enterprises Ignore It)
8:10 - The AI Arms Race Nobody Understands
9:22 - AI Scaffolding Explained: The Metaphor Every Leader Needs to Hear
12:12 - AI Readiness Is Cultural Change, Not Just Technology
14:38 - From Parking Lots to Companies: How Simple AI Agents Quietly Scale
17:01 - Why Most AI Features Feel Useless in Real Products
19:08 - Stop Automating Spreadsheets: Ask AI the Question Instead
25:06 - The Post-Software Era: Why Designers Aren’t Enough Anymore
28:33 - UI Is a Medium: How AI Will Absorb Interfaces Entirely
46:24 - Infinite Content, Human Creativity, and the Future After AI

---------- Support our show by supporting our sponsors!

This episode is supported by OneReach.ai
Forged over a decade of R&D and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale.
Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.
A complete system for accelerating AI adoption - design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents).
Use any AI models
Build and deploy intelligent agents fast
Create guardrails for organizational alignment
Enterprise-grade security and governance
Request free prototype:

https://onereach.ai/prototype/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s6e12&utm_content=1
---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere: Amazon — https://bit.ly/4hwX0a5

#InvisibleMachines
#Podcast
#TechPodcast
#AIPodcast
#AI
#AgenticAI
#AIAgents
#DigitalTransformation
#AIReadiness
#AIDeployment
#AISoftware
#AITransformation
#AIAdoption
#AIProjects
#NASA
#AgentRuntime
#Innovation
#AIUseCase

As 2025 draws to a close, Robb and Josh look back on some of the conversations they had this year both on the podcast and advising major enterprises and government leaders to offer their predictions for agentic AI in 2026. With major disruptive forces like outbound AI in the hands of consumers and agent runtime environments allowing organizations to create scalable infrastructure for AI agents, next year could see seismic changes in the way investors look at companies, and the ways companies look at themselves. Featuring a look at the components of an agent runtime, as well as previews of upcoming episodes with returning guest Ben Goertzel of SingularityNET and Joshua Gans, co-author of Prediction Machines, this episode is required viewing for anyone charged with finding ROI with agentic AI.

00:00 – Introduction to 2026 Agentic AI Predictions
01:12 – Outbound AI Arrives
02:30 – Scaling vs. Inventing AI
04:55 – Ben Goertzel Preview
06:45 – Scrappy Innovation in AI
08:20 – Invisible Work Explained
10:00 – Agents Job-Hunting for You
11:15 – Bottom-Up AI Adoption
13:10 – Layoffs, Knowledge Loss & AI
15:00 – The “Fake AI Expert” Problem
16:25 – Why Runtimes Matter
18:00 – What IDWs Actually Do
20:00 – Canonical Knowledge for Agents
28:20 – Invisible Work Demo
37:10 – Simulation Becomes the Next Frontier
---------- Support our show by supporting our sponsors!

This episode is supported by OneReach.ai
Forged over a decade and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale.

Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.
A complete system for accelerating AI adoption - design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents).
Use any AI models
Build and deploy intelligent agents fast
Create guardrails for organizational alignment
Enterprise-grade security and governance

Request free prototype:
https://onereach.ai/prototype/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s6e11&utm_content=1

---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere: Amazon — https://bit.ly/4hwX0a5

#InvisibleMachines
#Podcast
#TechPodcast
#AIPodcast
#AI
#AgenticAI
#AIAgents
#DigitalTransformation
#AI2026
#2026Predictions
#ArtificialIntelligence
#FutureOfWork
#AITrends
#AIRuntime
#IntelligentDigitalWorkers
#AIInvestment
#EnterprisAI
#AIStrategy
#AIROIStrategy
#AITransformation
#InvisibleMachines
#AIRuntime