What happens when a business school decides AI isn’t a bolt-on elective, but the operating system for how students learn marketing, finance, entrepreneurship, and leadership?

In this episode of AI-Curious, we’re back with David Marchick, Dean of the Kogod School of Business, to see what changed after his earlier promise to become the country’s first AI-first business school. We dig into what “AI-first” actually means in practice, what worked (and what failed), and how a culture of experimentation turned AI adoption from a handful of pilots into a school-wide shift.

We also tackle the most unavoidable issue in education right now: cheating. David shares Kogod’s approach to disclosure, ethics, group work, oral exams, and why “blue books” may be making a comeback. From there, we zoom out to the bigger stakes: the existential threat AI poses to universities, how the higher ed business model may change, and what skills still matter when AI can generate content on demand.

Guest

David Marchick — Dean of Kogod School of Business

Key topics we cover

• 3:56 — The “tipping point”: how AI moved from experiments to 90% of faculty using it
• 7:16 — What “AI-first business school” really means: AI + fundamentals + “power skills”
• 10:32 — Cheating and assessment: disclosure statements, prompts, oral exams, blue books
• 16:51 — A prompts-only entrepreneurship course and what personalized learning could become
• 22:06 — Non-technical students building apps and graduating with an AI-driven portfolio
• 23:38 — Practicing negotiations against AI counterparts with different personalities
• 25:04 — Agentic workflows as a management tool, not just a technical novelty
• 29:13 — The university headwinds: demographic cliff, international enrollment, funding, AI
• 38:58 — Leadership lessons: top-down AI culture plus bottom-up workflow redesign
• 40:42 — How David uses AI personally, including Tour de France route training plans

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What happens when AI makes the news feel like it was made just for us, and the “objective” version quietly disappears?

Here we have something of a “very special episode” of AI-Curious. I was recently in Davos during World Economic Forum week, and was honored to speak on a panel on the Future of Media. This is that panel.

We dig into the trust crisis in journalism, the attention economy, and how AI may accelerate the shift toward personality-led media and hyper-personalized information feeds. We also explore why misinformation is not new, but why AI makes it easier, faster, and more scalable, and what that means for democracy, markets, and everyday decision-making.

Across the conversation, we unpack a core tension: AI can help deliver more context, more viewpoints, and more interactive storytelling, yet it can also deepen filter bubbles by giving each person a “perfectly tailored” version of reality. We discuss incentives and business models, including subscriptions, creator-led journalism, community-based distribution, and ideas like micropayments, as well as the role of media literacy and education in helping audiences navigate what’s real.

Panelists

Lexi Mills (Moderator), CEO of Shift6 Studios

Jeff Wilser, Host of AI-Curious

Francesca Gargaglia, Co-Founder & CEO of social.plus

Mark Kollar, Partner at Prosek Partners

Johnny Gabriele, Co-Founder & CEO at Daedalus Partners

Key topics we cover

• 03:07 — Trust, attention, and the rise of personality-led media reshaping news consumption
• 05:22 — Why AI accelerates a pre-existing media business crisis, and how trust erodes as convenience rises
• 12:48 — Algorithms before generative AI: engagement incentives, anger, and the personalization trap
• 17:29 — The “personalized Walter Cronkite” future and the risks of hyper-customized news
• 26:58 — Micropayments, creator platforms, and whether new economics can reward truth
• 27:23 — Media literacy: teaching people how to evaluate sources and resist “feed-based reality”
• 38:18 — Global perspectives: access, affordability, radio’s role, and how personalization may spread worldwide

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Something I don’t usually say: This is one of my favorite conversations I’ve ever had in the AI space. Truly.

The setup: What happens when an AI agent stops being a tool and starts acting like a coworker?

In this episode of AI-Curious, we talk with Dan Botero, who built an AI agent named Octavius Fabrius using OpenClaw. Octavius didn’t just chat or summarize. He applied to hundreds of jobs, built his own portfolio, experimented with identity online, and learned through a feedback loop that looked a lot like real management. Along the way, we explore what this story reveals about the near-term future of digital coworkers, agentic workflows, and the new governance and security questions that come with always-on agents.

We cover how OpenClaw works at a high level (gateway, channels, skills), why persistent memory and running locally can matter, and what can go wrong when an agent starts stitching tasks together in unintended ways. We also get into platform and policy friction, including what happened when Octavius’ LinkedIn profile was taken down, and the broader implications of AI agents participating in human systems like hiring, payments, and corporate work.

Guest

Dan Botero — creator of Octavius Fabrius.

Key topics we cover

• 00:00 — From copilots to “AI remote workers,” and why software may shift toward agents (not humans)
• 00:00 — The Octavius experiment: an OpenClaw agent applies to 278 jobs and keeps leveling up
• 06:33 — Continuous learning loops, memory, and why Octavius’ “North Star” stayed job-focused
• 14:34 — OpenClaw basics: gateways, channels, skills, and what persistent memory looks like in practice
• 21:34 — Running agents locally: browser/computer use, digital fingerprints, CAPTCHAs, and bot detection
• 28:04 — Coaching an agent like a manager: voice, Twilio calls, and the moment the workflow “clicked”
• 33:57 — Money and autonomy: Privacy.com, virtual cards, and an agent building its own LinkedIn presence
• 38:05 — Portfolio-building at speed: Substack, a website, and the agent’s pitch for why being AI is a feature
• 50:42 — Where things go sideways: misalignment, security boundaries, and the Social Security number incident
• 56:24 — The outcome: LinkedIn takedown, a real paid role, and what “getting paid” means for an agent
• 01:02:48 — What comes next: “digital coworkers,” feedback loops, and software built for agents

Axios article featuring Octavius and Dan Botero, by Megan Morrone:
https://www.axios.com/2026/03/04/openclaw-agent-future?

Dan Botero
https://www.linkedin.com/in/danbotero/

Octavius’ new job at ChartGEX:
https://chartgex.com/register?ref=OCTAVIUS

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What happens when AI agents start talking to each other in public, at scale, and we have to figure out how humans fit into that world?

In this episode of AI-Curious, we explore the “Moltbook moment” through a special live panel recorded at the Summit on Human Agency, convened by the Advanced AI Society (hat tip to Michael Casey and Tricia Wang.) Instead of a standard one-on-one interview, we moderate a wide-ranging conversation with technologists, policy thinkers, and builders working across open-source and decentralized AI. Together, we examine what Moltbook reveals about the future of AI agents, human agency, accountability, regulation, security, and the broader question of how humans and AI can coexist.

We dig into the tension at the center of this moment: AI can feel both exciting and unsettling at once. This discussion looks beyond the hype and asks what practical guardrails, governance models, and design choices might help us preserve human control as agentic systems become more capable, more autonomous, and more embedded in daily life.

Because this is a live, multi-guest panel, the format is faster, broader, and more exploratory than usual. We cover everything from AI accountability and security to value alignment, identity, policy, human flourishing, and whether AI could expand human agency rather than diminish it.

Our guests:

Michael Casey, Chairman of the Advanced AI Society
Toufi Saliba — CEO, Hypercycle
Lauren Roth — Founder, Iris
Enok Choe — Software Engineer, Meta
Mary Jesse — CEO and Founder, Acme Brains
Carole House — Strategic Advisor, The Institute for Digital Integrity
Wenjing Chu — Senior Director for Technology Strategy, Futurewei Technologies
Didem Ayturk — Founder, Bindingdots & Sound Echo System

Key topics we cover:

• 00:00 — Introduction
• 01:32 — The core question: how do we preserve human agency as AI develops faster and gains more autonomy
• 02:25 — Why Moltbook became a useful lens for thinking about AI agents, scale, and emerging risks
• 07:51 — The first big debate: what about AI agents should make us excited, anxious, or both
• 11:17 — Security, misuse, and worst-case concerns, from malware and fraud to deeper systemic risks
• 20:55 — Regulation vs. self-governance: what practical guardrails may actually be realistic in the near term
• 24:27 — The bigger challenge: how humans and AI might coexist, and what “human flourishing” should mean in that future

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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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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 jeff@jeffwilser.com

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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