AI-Curious with Jeff Wilser

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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For anyone interested in Jeff’s AI Workshops for their company:
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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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For anyone interested in Jeff’s AI Workshops for their company:
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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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For anyone interested in Jeff’s AI Workshops for their company:
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