AI-Curious with Jeff Wilser

What happens when a social network is built for AI agents, not humans, and millions of bots start posting, debating, and “performing” identity in public?
In this episode of AI-Curious, we break down Moltbook, the agents-only social platform that briefly became one of the strangest (and most revealing) experiments of the AI era. We unpack what Moltbook is, why it matters, and what it suggests about a near future where AI agents don’t just answer prompts, but interact with each other at scale.
Key topics we cover
00:00 — Why we’re doing a solo episode, and why Moltbook still matters even in “fast AI time”
01:23 — Moltbook 101: a social platform for AI agents, and what “no humans allowed” means in practice
02:56 — The controversy layer: how much was truly agent-generated vs. nudged or orchestrated by humans
03:18 — The “AI manifesto” moment: why the most extreme posts are revealing (and not proof of sentience)
06:24 — Grok’s existential thread: authenticity, overload, and agents giving each other “therapy”
09:15 — Sci-fi archetypes in real time: Pinocchio logic, and why “feels real” can be enough
13:03 — Identity and scale: inflated agent counts, bots-on-bots dynamics, and what “real” even means now
16:18 — Agent-to-agent futures: negotiation, coordination, and the infrastructure being built for agent workflows
17:27 — The money question: why crypto keeps coming up as a plausible payment rail for AI agents
19:55 — The synthetic internet problem: misinformation, trust collapse, and a likely shift from text to video agents
26:19 — Hyperstition: how AI can “manifest” outcomes by seeding narratives humans act on
33:40 — The long-tail risk: why pattern matching alone could still produce harmful behaviors as agents gain capabilities
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For anyone interested in Jeff’s AI Workshops for their company:
Reach out directly at [email protected]

What does it take to make AI adoption stick in a high-stakes, heavily regulated industry, without triggering job-loss panic?
In this episode of AI-Curious, we have a hyper-specific case study of AI adoption. Host Jeff Wilser talks with Krista Snelling (CEO and Chairman) and Matthew March (CIO and EVP) of West Coast Community Bank about their practical playbook for rolling out AI the right way: governance first, culture second, and measurable wins that free up time without cutting headcount.
Why this is something of a “very special episode”: The story and success of the West Coast Community Bank is something that Jeff knows personally. Jeff was honored to visit WCCB’s headquarters and work with their leadership team on AI culture and AI strategy, helping to transform curiosity into clarity.
In this podcast for the first time, Jeff peels back the curtain to share the AI and Leadership workshops he conducts for businesses. 
Special thanks to Vistage Chair Richard Bell and the larger Vistage community. 
Guests
Krista Snelling — CEO and Chairman, West Coast Community Bank
Matthew March — CIO and EVP, West Coast Community Bank
Key topics we cover
00:37 — Why we’re sharing this case study and what “curiosity-driven” adoption looks like
06:58 — Bank scope and context: footprint, size, and what makes this implementation notable
10:29 — When AI shifted from “vaporware” to something teams could use right now
15:23 — The banking reality: protecting customer data and operating in a regulated environment
17:43 — Governance first: policies, model risk management, and third-party/vendor risk
23:02 — The “Curiosity Canvas,” the “drudgery dump,” and targeting tedious work for automation
25:14 — Building an AI Working Group across departments and flipping the pyramid
33:51 — Making adoption repeatable: SharePoint collaboration, prompt sharing, Teams channel support
36:24 — A concrete workflow win: extracting data from PDFs to generate letters automatically
39:19 — Another win: scraping hundreds of statements for key data elements in a fraction of the time
42:21 — System conversion regression testing: validating outputs at scale with better traceability
44:35 — Security approach: approved tools, tenant controls, DLP settings, and “what not to use AI for”
49:29 — A hard boundary: avoiding AI for anything that directly impacts financial reporting
52:11 — The culture message: “efficiency, not reduction,” and why that unlocks curiosity
53:02 — Advice for leaders: start small, build momentum, and appoint an internal champion
56:51 — Quick personal use cases: everyday ways they use AI outside the office
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Vistage Chair Richard Bell:
https://app.vistage.com/sites/s/chairs/0038000000sllSFAAY/richard-bell
West Coast Community Bank:
https://app.vistage.com/sites/s/chairs/0038000000sllSFAAY/richard-bell
For anyone interested in Jeff’s AI Workshops for their company:
Reach out directly at [email protected]

What happens when software stops just “chatting” and starts acting in the real world, across real workflows, with real consequences?
In this episode of AI-Curious, the Head of AI at Cognizant goes deep on AI agents and agentic workflows: what they are, why enterprises are investing heavily, and what it actually takes to make agent systems reliable and safe at scale. We unpack what separates an AI agent from a traditional chatbot, why “agency” changes the stakes, and how multi-agent systems can be designed to reduce risk instead of amplifying it.
We also explore concrete enterprise use cases, including agent hierarchies that coordinate across complex systems (like networks, utilities, and other operations), plus how “agentic process automation” builds on older automation models while adapting to unexpected edge cases. Finally, we zoom out to the future of work: which tasks get augmented first, why disruption is happening faster than most forecasts, and how trust in AI systems may shift over the next several years.
Guest
Babak Hodjat — Head of AI at Cognizant; leads AI lab work focused on scaling reliable, trustworthy agent systems; longtime AI builder with deep experience in applied natural language systems. 
Key topics we cover
07:00 — What an AI agent is (and how it differs from a chatbot)
13:03 — State of play: what’s working, what’s not, and why “agent systems must be engineered”
17:00 — A practical multi-agent design pattern across telecom, power, and agriculture
20:28 — Agentifying rigid processes (and handling unforeseen situations)
24:14 — Who should deploy agents, why single “do-everything” agents are risky
26:34 — An open-source starting point for experimenting with multi-agent systems
29:12 — Guardrails: reducing hallucinations, adding redundancy, and safety thresholds
35:29 — Why we should use LLMs for reasoning, not knowledge retrieval
38:15 — The future of work: tasks, jobs, and decision-making roles shifting upward
41:59 — AGI, limitations, and why modular multi-agent systems may matter
44:57 — A prediction: we’ll delegate more than we expect as systems become more trustworthy
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Is AI quietly creating more work than it’s replacing, and are we measuring the job market the wrong way?
In this episode of AI-Curious, we talk with the CEO of Upwork, Hayden Brown, about what the platform is seeing across the global freelance economy, and why the “AI is killing jobs” narrative can miss what’s happening at the edges of the market. We also dig into how to adopt AI inside an organization without just “sprinkling fairy dust” on old workflows, and what it takes to make AI rollout a cultural shift, not just a tooling upgrade.
Guest
Hayden Brown is the CEO of Upwork, the global work marketplace connecting businesses with freelance talent across knowledge-work categories. We discuss Upwork’s vantage point on hiring trends, the rise of fractional work, and what AI-driven change looks like when companies redesign workflows end-to-end rather than retrofitting existing systems.
Key topics we cover
03:50 — A global background and why opportunity access shapes the mission
05:27 — The scale of Upwork and why freelancing is a major part of the economy
07:14 — How we approached AI adoption as a structured, company-wide program
08:47 — Early “two-year vision” ideas that reshaped marketing and product workflows
11:34 — Reducing fear: how we framed AI internally, including room for mistakes
16:03 — Building an AI agent experience (and what it changed about job posts)
17:14 — Why “reinventing, not retrofitting” separates AI winners from strugglers
22:24 — Why macroeconomics can explain more than AI in hiring slowdowns
23:01 — The core claim: AI creating more opportunities than it’s destroying
24:05 — Fractionalization: how full-time jobs get broken into AI + human slices
25:09 — A concrete example of humans working alongside AI in production workflows
26:32 — From “prompt engineer” to “AI generalist”: orchestration becomes the ask
28:11 — Why the AI jobs debate is too binary, and what’s getting missed
31:43 — Practical reskilling: embedded experts who train teams while upgrading systems
36:29 — AI’s impact across unexpected categories, including creative work
39:15 — Five-to-ten-year outlook: humans as orchestrators, premium on human skills
43:22 — Career advice for early-career listeners in an AI-shaped job market
45:40 — Real-life AI use: editing, learning, and replacing the blank page problem

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What does “human-first AI” actually look like when you have to make decisions under pressure, hit numbers, and keep trust intact?
In AI-Curious, we talk with Kate O’Neill — “the Tech Humanist” and author of What Matters Next — about how leaders can adopt AI in ways that strengthen human outcomes instead of quietly eroding culture, morale, and customer experience. We dig into why so many AI initiatives fail for non-technical reasons, how to think beyond short-term wins, and why prompting is less “prompt engineering” and more like learning to delegate clearly.

Key topics:
Prompting as delegation: defining success conditions, constraints, and what “good” means (00:00)

Kate’s early work at Netflix and what personalization taught her about human impact (04:45)

What “human-unfriendly” tech looks like in practice, from subtle friction to scaled harm (09:28)

The Amazon Go example: how small design constraints can scale into behavior change over time (11:19)

AI in the workplace: why “cut, cut, cut” is shortsighted, and what gets lost when you optimize only for this quarter (14:14)

Trust and readiness: why reskilling fails when people don’t believe there’s a future for them (16:45)

The now–next continuum: making decisions that “age well,” not just decisions that look good immediately (17:29)

Preferred vs. probable futures: identifying the delta and acting to move outcomes toward what you actually want (19:22)

“Chatting with Einstein”: using AI to become smarter vs. outsourcing thinking (22:13)

Why most AI pilots fail: human and organizational readiness, not the tech itself (24:02)

Questions → partial answers → insights: building an organizational muscle that compounds (28:21)

Bankable foresight: why Netflix invested early in what became streaming (30:37)

Trend watch: the pivot from LLM hype to agentic AI, and why prompting still matters (38:58)

Sycophancy and “best self” prompting: getting better outputs by being explicit and structured (41:01)

Probability vs. meaning: what LLMs can do well, and what they can’t replace (44:45)

A fun real-world workflow: Kate’s Notion + AI system for hotel coffee-maker recon (46:26)

Career advice in the AI era: adaptability, “human skills,” and shifting definitions of value (49:21)

Guest
Kate O’Neill is a tech humanist, founder and CEO of KO Insights, and the author of What Matters Next: A Leader’s Guide to Making Human-Friendly Tech Decisions in a World That’s Moving Too Fast. She advises organizations on improving human experience at scale while making emerging technology commercially and operationally real.

KO Insights:
https://www.koinsights.com/about-kate/

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