AI-Curious with Jeff Wilser

What does it actually look like to use AI tools in the real world, beyond the usual chatbot prompts and hype?
In this episode of AI-Curious, Jeff Wilser shares five AI tools and workflows that are shaping how he works right now, from Claude Code and personalized news briefings to NotebookLM, multi-model prompting, and using AI to write more closely in your own voice. The goal is not to offer a comprehensive list of every AI product on the market, but to show how these tools can be used in practical ways that expand capability, streamline research, and create new workflows.
We explore how vibe coding and AI agents can help non-coders build useful internal tools, why personalized AI news feeds may become increasingly common, and how NotebookLM can synthesize large amounts of information across transcripts, documents, and YouTube videos. We also look at the benefits of using multiple AI models together instead of relying on just one, and why feeding AI much richer context can dramatically improve writing outputs.
Throughout the episode, we return to a core idea: using AI to empower, not eliminate. Rather than treating AI only as a cost-cutting tool, we examine how it can help individuals and businesses do more, think more creatively, and build smarter systems around the work that matters most.

Key topics we cover
3:15 — Claude Code, vibe coding, and why non-coders should be paying attention
6:01 — Building a custom AI-powered conference outreach and research tool
11:05 — “AI to empower, not eliminate” as a guiding philosophy
16:16 — Personalized AI news briefings and the future of customized information
21:58 — How NotebookLM helps synthesize transcripts, documents, and YouTube content
27:04 — Why a “polymodel” approach can be better than relying on one chatbot
31:15 — Using AI to write more closely in your own voice through deeper context
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What happens when AI stops being a productivity tool and starts reshaping the structure of work itself?
In this episode of AI-Curious, we talk with Kelly Monahan, a future of work and AI advisor, about what AI may actually do to the workplace over the next few years, and why the reality is likely to be messier than both the hype and the fear suggest. We dig into the tension between using AI for augmentation versus automation, why so many companies are still struggling to prove ROI, and how AI agents could transform business workflows while also creating major governance, accountability, and implementation challenges.
We also explore what this means for knowledge workers, middle managers, and enterprise leaders trying to adapt in real time. Along the way, we discuss why small businesses may have an advantage over large organizations, how workers can focus on higher-value contributions, and why the future of work may require not just new tools, but a new mindset.

Guest
Kelly Monahan — Future of Work and AI Advisor

Key topics we cover
2:49 — Kelly’s optimistic and pessimistic theses on the future of work
5:15 — Where AI is overhyped, and the disconnect between leaders and workers
6:35 — Why generative AI adds complexity inside organizations
10:05 — What the research says about AI ROI
12:54 — Where AI is delivering real wins today, especially for freelancers and small businesses
16:27 — Advice for leaders and middle managers inside large organizations
18:39 — Why curiosity, learning, and experimentation need to be rewarded
19:02 — AI agents, the hype cycle, and why the excitement may still be justified
22:25 — Why enterprises are struggling to keep pace with the speed of AI change
29:18 — What the future of work may look like over the next 3 to 5 years
30:02 — Why white-collar work could face major disruption
33:37 — The “elevator to skyscraper” analogy for how AI should reshape work
35:08 — Predictions for AI adoption, governance failures, and labor market shifts
39:00 — How Kelly uses AI in her own work and business

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For anyone interested in Jeff’s AI Workshops for their company:
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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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For anyone interested in Jeff’s AI Workshops for their company:
Reach out directly at [email protected]

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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For anyone interested in Jeff’s AI Workshops for their company:
Reach out directly at [email protected]

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
Follow AI-Curious on your favorite podcast platform:
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For anyone interested in Jeff’s AI Workshops for their company:
Reach out directly at [email protected]