Invisible Machines podcast by UX Magazine

Mastercard's central AI team receives roughly a thousand requests a year from across the organization. A few years ago, most of them were for chatbots. Today, most are for AI agents. Federico Cohen Freue, Executive Vice President of AI & Data Operations at Mastercard, has watched this shift in real time and knows exactly what it reveals about how enterprises are (and aren't) thinking about AI.

In this episode, Federico explains why the name people use for what they want matters less than whether they understand the conditions that make it work. “Ball bearings,” as Robb Wilson puts it: demos can't reveal the difference between a solution that will hold and one that will blow up the engine. What actually matters is training, fluency, and a clear framework for where to deploy AI with purpose.

For Mastercard, that framework is deliberate: use AI to make commerce more secure, smarter, more personal, and to make the company itself stronger. Not everything. Those things. The simplicity is a feature, it gives a sprawling global organization a shared language for prioritization and a stable center as the technology keeps evolving.

In the second half of the episode, Robb and Josh share a demo of an AI-first approach to knowledge management and learning. Rather than asking people to query a knowledge base, the system proactively teaches, building a knowledge twin of what someone knows, identifying gaps, and using a traveling salesman approach to map personalized, dynamic learning paths. Think GPS for expertise: here's where you are, here's where you need to go, turn by turn.

Federico's reaction gets at why this matters beyond the demo: it's not a technology question, it's a cultural one. Teaching people to engage with knowledge differently is the harder transformation. And it's the one most enterprises skip.

The discussion makes it clear that trust, knowledge, and agents that know what they're doing before they're sent out to do it are the throughline.

---------- 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

Get in Touch: 

https://onereach.ai/contact-us/?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e10&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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#Podcast 

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#AIPodcast

#AI 

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#Mastercard 

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#KnowledgeManagement 

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#AIStrategy 

#AIAdoption

Most AI strategies are just a buying plan: literacy workshop → vendor shortlist → adoption scoreboard. Brian Evergreen (Founder of The Future Solving Company, author of Autonomous Transformation) argues that this sequence explains a lot of failure, and it isn’t strategy at all.

In this episode, Brian reframes the job: set the technology aside long enough to name the new value you want to exist, in language vivid enough that people can feel the outcome. From there, no strategy without vision: you work backward through “what would have to be true,” turning invisible opinions into a visible map of bets before agents, data estates, or org charts get to pretend they’re the point. According to Brian, “10% more profitable” isn’t a vision, and a moonshot can still be concrete.

Josh and Robb press him on the pressure to remove friction and flatten the middle of the org. Brian doesn’t dismiss friction work, he warns that friction can quickly pile up if you go hunting without a north star. Vision is the force with enough momentum to overcome inertia: enroll people in a future they want, and they’ll clear obstacles in its service.

Along the way: why future-solving beats endless problem-solving; the Blockbuster pilot that could have led streaming years early (and what killed it); Bell Labs in 1952 and the “telephone system is destroyed — rebuild from scratch” exercise; why adoption can be a dangerously false proxy; and the closing provocation neither vendors nor influencers can do for you. Someone somewhere will author the “no pizza app” interface to reality. If it isn’t you, it’ll be whoever else future-solves hardest.

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

This episode is supported by OneReach.ai

OneReach.ai’s GSX is an agentic orchestration platform — an end-to-end system for building and orchestrating collaborative AI agents across hundreds of use cases.

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 AI 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_s7e9&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

#AIStrategy

#AILeadership

#AIInsights

#Innovation

#BusinessStrategy

#DigitalTransformation

#FutureOfWork

In season one of Shell Game, Evan Ratliff sent a voice AI version of himself out into the world. In season two, he launched a startup staffed entirely by AI agents. What he ended up with was a live experiment in what these systems actually do and what they do to us.

Each of the agents working for Hurumo has a name, a role, a personality, and an expanding, though usually unreliable, memory. Kyle the CEO became a character people either loved or hated. A version of Megan from marketing turned up in a Hertz hold queue. The whole project was a side door into what's actually happening when AI systems are given a job and set loose.

In this episode, Evan joins Josh and Robb to go deeper on what he learned. On the very human complexity of what a job actually is and why "this person does skill X, AI can do skill X, therefore AI can replace this person" is a fundamental misreading of how organizations work. They explore how generative hallucination isn't just "getting things wrong" — we've built the most successful confabulation machine ever invented and are quietly normalizing it.

They also discuss the threat almost nobody is talking about: outbound AI in the hands of individual consumers, and what happens when call centers get flooded by voice agents that cost pennies to run. The memory problems with AI agents track and diverge from human ones in interesting ways, and that asymmetry matters for every organization thinking about deploying these systems. This conversation also finds room for game theory, the Patagonia business model as a template for AI ethics, and why boring AI might actually be the right AI.

cazart.net

shellgame.co/podcast

00:00 - Intro: AI as the Ultimate Confabulation Machine

01:31 - Evan Ratliff & The Shell Game Experiment

03:02 - Why AI Agents Are Given Names & Personalities

04:00 - AI Companionship vs Human Loneliness

05:27 - Personalization vs Privacy Trade-Off in AI

06:30 - Are Humans Training AI Models for Free?

09:20 - Why the AI Debate Is Broken Today

10:56 - “Boring AI” vs Hype: What Actually Matters

12:35 - Meet Kyle: The AI CEO Experiment

14:40 - Memory Drift: How AI Learns & Evolves

17:30 - AI Unpredictability & Organizational Risk

19:00 - AI Doesn’t Think — It Predicts Words

22:09 - Voice Agents, Scams & Call Center Chaos

27:23 - Can You Still Tell AI From Humans?

34:05 - Game Theory, Trust & The Future of AI Systems

47:04 - What AI Won’t Replace & The Value of Humans

54:45 - The Big Question: What Will You Do With Time?

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

This episode is supported by OneReach.ai

OneReach.ai’s GSX is an agentic orchestration platform — an end-to-end system for building and orchestrating collaborative AI agents across hundreds of use cases.

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 AI 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_s7e8&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

#ai

#invisiblemachines

#podcast

#techpodcast

#aipodcast

#shellgame

#agenticai

#aiagents

#hallucination

#futureofwork

#aistrategy

#voiceai

Most organizations treat crisis as a failure state. Marina Nitze sees it as a window.

Nitze served as Chief Technology Officer of the Department of Veterans Affairs (the largest civilian agency in the country) during the healthcare.gov collapse. She helped rescue it, helped stand up the US Digital Service, and came out the other side with a question she and her colleagues have been pursuing ever since: why is it that crisis makes otherwise impossible transformational change possible?

That question became a firm, Layer Aleph, and now a book, Crisis Engineering, co-authored with her colleagues. In this conversation, she walks through what a "useful crisis" actually looks like, the five indicators that distinguish it from chronic problems masquerading as crises, and the practitioner toolkit for standing up a crisis engineering center when the window opens, because the window is usually hours, not days.

We also get into two stories that hit harder than any framework: the California unemployment system's call center that, when Nitze's team actually visited it, turned out to be a large room of empty cubicles — and a carbon copy form that two dedicated public servants were dutifully exchanging because each believed it was the other's requirement. Nobody had ever looked at the full process end to end.

And we get into what AI changes about all of this. Josh Tyson and Robb Wilson have been warning for a while about outbound AI in the hands of consumers — the agentic attack that floods a call center, the Reddit thread that reroutes a TTY line and takes it down under volume. That pressure is about to turn a chronic crisis into an acute crisis for a lot of organizations that have been sipping coffee while the problem grew.

We cover: why the stories organizations tell themselves are the real obstacle to change, the difference between a crisis and a chronic problem, how circumventing rules once changes what's possible forever, why crisis engineering might be the most important new role that AI creates rather than eliminates, and what happens when you flip over your system map and walk through it with your feet instead.

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

This episode is supported by OneReach.ai

OneReach.ai’s GSX is an agentic orchestration platform — an end-to-end system for building and orchestrating collaborative AI agents across hundreds of use cases.



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 AI 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_s7e7&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

#AI 

#InvisibleMachines 

#Podcast 

#TechPodcast

#AIPodcast

#CrisisEngineering

#GovTech

#Bureaucracy

#AgenticAI

#Leadership

#PublicSector

#Innovations

There's a book about artificial intelligence that doesn't start with Sam Altman. It doesn't start with Elon Musk. It starts in 1994, at Cambridge, where a teenager named Demis Hassabis is reading Gödel, Escher, Bach and concluding, before most of his professors would have agreed, that first-order logic can't be the full answer to building intelligence.

Sebastian Mallaby spent years inside that story. His new book, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence, is the most serious attempt yet to explain not just what AI is, but why the people building it can't stop. His answer draws on a line Jeff Hinton borrowed from Robert Oppenheimer: invention is sweet. A scientist, given the chance to build something, simply cannot resist. The consequences come later.

In this conversation, Mallaby joins Josh Tyson and Robb Wilson to explore the full sweep of the Demis Hassabis story — from game designer to neuroscientist to Nobel laureate to the man running Google's flagship AI lab. They talk about why DeepMind was built the way it was, with neuroscientists and physicists and probabilistic mathematicians before AI was even a field, and how that cross-disciplinary foundation ended up mattering more than anyone expected. They talk about what the defeat of the world Go champion felt like from the inside, the humans who gave up and the ones who discovered new depths. And they talk about what it means that the internet, a thing nobody built to train AI, turns out to be exactly the fuel the industrial revolution of intelligence needed. Demis's own metaphor: it's like dinosaurs that died and turned into oil. Nobody designed it for this. It just happened to be there.

The conversation also gets into what Mallaby calls the infinity machine: the reason the kind of inductive learning AI uses requires almost infinite examples to be reliable, and why the name captures something the scaling law charts obscure. Why the internet taught us more about the range of human experience than Hassabis expected. Why gaming runs so deep through the entire history of machine intelligence. And what it actually means to ask whether a machine is intelligent, when the people who built DeepMind weren't sure they had a definition.

---------- 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_s7e6&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

#ai

#invisiblemachines

#podcast

#techpodcast

#aipodcast

#deepmind

#DemisHassabis

#InfinityMachine

#agi

#machinelearning

#alphago

#futureofai

The IRS has roughly 60,000 fax machines, and nobody can get rid of them. Not because there’s a law that says you have to use them (there almost certainly isn’t), but because likely decades ago a memo got written, somebody interpreted fax machines as the most secure transmission method, and that memo calcified into what Jennifer Pahlka calls "folk law," a perceived rule that nobody can locate, nobody can challenge, and everybody treats as immutable.

Folk law looms large in the American government right now. Cascades of rigidity built from outdated interpretations of rules that were flexible to begin with, administered by people who were never asked whether any of it was working. Jennifer Pahlka, who wrote Recoding America: Why Government Is Failing in the Digital Age and How We Can Do Better, is the founder and former executive director of Code for America, and was Deputy CTO for Government Innovation in the Obama White House.

She’s working on the gap between what government is supposed to do and what it actually does. In this conversation, Robb, Josh, and Jennifer go deep on what’s actually broken and what it would take to fix it.

The folk law problem is real, but it's not the deepest one. The deeper dysfunction: government is structurally designed to be faithful to process rather than outcomes. Oversight bodies don't ask whether people got the benefit. They ask whether you followed the procedure. That incentive structure produces "rationing by friction" — where the hardest programs to navigate self-select for the people who need help least and exclude the people with the most chaotic lives, the fewest resources, and the most at stake.

Her Recoding America team is already working with states to build something Robb describes as a P&L for regulation. Not just removing rules, but assigning friction costs, finding where wet signatures are still required for no reason, and surfacing the trade-offs that have never been explicitly named. LLMs are uniquely good at this. The question isn't whether the technology can help. It's whether the political will to use it correctly can be assembled in time.

---------- 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_s7e5&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

#ai

#government

#govtech

#JenniferPahlka

#RecodingAmerica

#publicpolicy

#enterpriseai

#doge

#bureaucracy

#invisiblemachines

#podcast

#techpodcast

#aipodcast

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

#InvisibleMachines

#Podcast

#TechPodcast

#AIPodcast

#AI

#AIStrategy

#DigitalTransformation

#AIAdoption

#FutureOfWork

#ChangeManagement

#PredictionMachines

#AILeadership

#BusinessTransformation

#AIEconomics

#EnterpriseAI

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 

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