AI-Curious with Jeff Wilser

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:
Reach out directly at [email protected]

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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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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