Invisible Machines podcast by UX Magazine

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