Most organizations are still implementing AI as point solutions, dropping new technology into existing workflows to do the same work, just slightly better. The real value lies in system solutions that completely transform how organizations operate. Avi Goldfarb, economist and co-author of Prediction Machines, joins Robb and Josh to explain why AI adoption follows predictable economic principles and why internal resistance, not technology limitations, is the primary barrier to transformation.

This conversation, recorded back in 2023, reminds us that most organizations continue to struggle with the same issues surrounding systemic change in 2026. Goldfarb's core argument: AI is fundamentally cheap prediction. Just as the internet made search and copying cheap, AI makes prediction cheap. When something becomes a commodity, the complements, the things that work alongside it, become more valuable. This includes compute power (benefiting Microsoft, Amazon, Google), unique data, and crucially, human judgment.

The problem? System solutions require organizational transformation. They create winners and losers inside companies. When AI enables insurance companies to shift from pricing risk (the domain of powerful underwriters) to reducing risk (requiring marketing and behavior change expertise), the power structure fractures. Vested interests resist.

Departments see their importance diminished. For leaders evaluating AI investments, the question isn't whether to adopt AI, it's whether you're willing to pursue system transformation and confront the organizational disruption that creates real value.

Chapters

00:00 - Intro: Avi Goldfarb on AI as “cheap prediction”

01:37 - Have LLMs changed the prediction framework?

03:36 - Do we need “new economics” for generative AI?

04:15 - What got cheaper on the internet: search, copying, communication

05:07 - What becomes more valuable as prediction gets cheap? (complements)

05:49 - OneReach.ai sponsor: runtime for AI agents (GSX)

06:46 - AI adoption inside companies: invest in people + workflows

08:13 - Unintended consequences: jobs, bias, discrimination

09:47 - The bigger question: new value creation (not just replacement)

10:33 - Upskilling: writing and opportunity expansion for millions

12:30 - "No more excuses”: using ChatGPT for clearer communication

14:50 - Social media déjà vu: noise, polarization, participation

17:04 - Intermediaries changed: self-publishing, music, podcasting

19:06 - AI commoditization: $600 models + implications for OpenAI

22:36 - Where the money is: compute, data, and complements (not predictions)---------- 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

Book a free demo:https://onereach.ai/book-a-demo/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e4&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

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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

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#AI

#AgenticAI

#AIAgents

#DigitalTransformation

#AIReadiness

#AIDeployment

#AISoftware

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#AIProjects

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#CanonicalKnowledge

#DataGovernance

#SourceOfTruth

#AIArchitecture

#DeterministicAI