Elevate Your AIQ

Bob Pulver talks with Dan Chait, CEO and co-founder of Greenhouse, about how technology, especially AI, is reshaping the hiring landscape — for better and worse. Dan shares Greenhouse’s origin story and the company’s mission to help every organization become great at hiring through structured, data-driven, and fair processes. Together, they explore the “AI doom loop” of automated applications and AI-written job descriptions, the tension between efficiency and authenticity, and how innovations like Real Talent and Dream Job aim to bring trust, fairness, and humanity back into hiring. The conversation also touches on identity verification, prompt injection risks, AI ethics, and the evolving skills that will define the workforce of the future.

Keywords

AI hiring, structured hiring, recruiting technology, Greenhouse, Real Talent, Dream Job, hiring fairness, candidate experience, identity verification, deepfakes, AI doom loop, prompt injection, job seeker experience, future of work, skills-based hiring, authenticity in hiring, mission-driven leadership, HR tech

Takeaways

AI can enhance hiring but must not replace human connection and judgment.

The “AI doom loop” is eroding trust between employers and candidates.

Real Talent helps companies identify legitimate, high-intent applicants.

Dream Job empowers real people to rise above automated applications.

Employers should be transparent about how AI is used in hiring decisions if they want to build trust while improving their employer brand.

The résumé’s role is fading as new ways of showcasing skills emerge.

The future of hiring belongs to organizations that unite data, empathy, and trust.

Quotes

“Our mission is to help every company be great at hiring — and that means putting structure and fairness at the center.”

“We’re caught in an AI doom loop where both sides are using automation to outsmart the other — and no one’s winning.”

“You can’t automate authenticity. The human element is what stands out most in a world full of AI slop.”

“We can do anything, but we can’t do everything. So we focus on what matters most: helping people connect in meaningful ways.”

“It’s not about banning AI — it’s about setting clear expectations for how to use it responsibly.”

“The death of the résumé has been predicted for decades, but maybe this is finally the time.”

Chapters

00:00 – Welcome and introduction

00:44 – Greenhouse origin story and mission

02:50 – Lessons from Dan’s early career and the importance of structured hiring

06:00 – Hiring for skills and potential over pedigree

08:20 – How structured interviews and scorecards create fairness and better data

11:00 – Balancing mission and business success at Greenhouse

13:40 – Introducing Real Talent and solving the “AI doom loop”

16:50 – Detecting fraud, misrepresentation, and risk in job applications

18:45 – Partnership with Clear for verified identities

20:00 – Digital credentialing and transparency in hiring

22:30 – The “AI vs. AI” challenge: automation on both sides of the hiring equation

25:00 – Dream Job: Human intent meets AI efficiency

27:50 – The candidate experience crisis and how to fix it

30:20 – Why resumes and job descriptions are losing meaning

32:00 – Bringing humanity back to hiring in an AI-dominated world

34:30 – The future of the HR tech ecosystem and partnerships

40:00 – Agentic AI and the next frontier of recruiting technology

43:00 – The death of the résumé and what replaces it

47:00 – Skills, AI literacy, and the next generation of workers

52:00 – Setting clear expectations for AI use in hiring

55:00 – Personal AI use: augmenting human connection

56:00 – Closing thoughts and reflections

Dan Chait: https://www.linkedin.com/in/dhchait

Greenhouse: https://greenhouse.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Agi Garaba, Chief People Officer at UiPath, about the organization’s evolution from robotic process automation (RPA) to agentic AI and how that has impacted people, processes, and culture. Agi shares how HR can lead with a human-centric lens during AI transformation, the importance of AI literacy, and the practical steps UiPath is taking to balance innovation with responsible governance. This conversation blends strategic foresight with pragmatic execution and offers a roadmap for any leader navigating AI-enabled change.

Keywords

UiPath, agentic AI, automation, digital workers, RPA, HR technology, AI governance, AI literacy, talent acquisition, responsible AI, workforce transformation, human-centric design, reskilling, change management, future of work, CHRO, culture shift, AI readiness

Takeaways

UiPath’s transition from RPA to agentic automation marks a broader shift in how digital and human workers collaborate.

HR has a central role in driving culture, trust, and adoption around emerging AI tools.

A grassroots approach to agent development—crowdsourcing over 500 ideas from employees—ensures relevance and engagement.

AI governance must evolve with technology; dedicated roles and frameworks are key to managing bias, access, and compliance.

Building AI literacy across the organization—through tiered training and internal tooling—helps democratize innovation.

Recruiting is transforming, but human relationships remain critical, especially in engaging passive candidates and senior-level talent.

Not every task should be automated—some skills, like creative writing or candidate engagement, lose value when over-automated.

Over-automation can create long-term talent gaps; junior roles are vital for succession and cultural continuity.

Quotes

03:00 – From RPA to Agentic Automation

05:00 – HR at the Crossroads of Tech and Culture

07:15 – Org Design with Digital Coworkers

10:30 – Building Trust in Agentic Systems

13:40 – Responsible AI in HR Contexts

17:00 – Prioritizing and Tracking Agent Development

19:00 – Building AI Literacy Across the Organization

22:30 – From Vision to Execution: Pilots and Production

24:10 – Cross-functional Use Cases and Orchestration

26:45 – Governance, Compliance, and Continuous Oversight

30:00 – Redefining Human Skills in the Age of AI

33:00 – Knowing When Not to Automate

35:40 – Long-term Impacts on Junior Roles and Succession

38:45 – Strategic Workforce Planning and Digital Labor

41:00 – Agents in Recruiting: Limits and Opportunities

44:00 – Maintaining Human Relationships in Talent Acquisition 48:00 – Executive Search, Talent Advisors, and the Future of Recruiting

51:30 – Agi’s Personal Use and Reflections on GenAI

54:00 – Balancing Utility, Trust, and Critical Thinking

55:30 – Closing Thoughts and Wrap-up

Agi Garaba: https://www.linkedin.com/in/agnesgaraba

UiPath: https://uipath.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

In this compelling episode, Bob speaks with Lisa Yokana, a pioneering educator and global consultant, about how AI is reshaping the education landscape. Lisa shares her journey from traditional art and architecture teacher to building an experiential design lab, STEAM program, and social entrepreneurship course. Bob and Lisa explore how AI can serve as a catalyst for changing not just what we teach, but how we teach and why. With a focus on student agency, lifelong learning, and the shifting expectations of the future workforce, Lisa offers practical insights and inspiration for educators, parents, and community leaders looking to bring relevance, equity, and innovation into the classroom.

Keywords

AI in education, student agency, maker-centered learning, design thinking, STEAM, lifelong learning, workforce readiness, future of education, educational disruption, personalized learning, human skills, ethical AI, K-12 innovation

Takeaways

AI is a disruptor that can serve as a catalyst for rethinking teaching and learning.

Student agency—not content mastery—is the core skill for future-ready learners.

Traditional education systems are misaligned with the skills needed for the future workforce.

Hands-on, project-based learning nurtures creativity, empathy, and real-world problem solving.

Educators must experiment, fail forward, and reimagine their roles.

Community support is critical for educational transformation.

Ethics, responsible use, and digital literacy must be part of AI education, and must start early.

AI levels the playing field for diverse learners but must be designed and used thoughtfully.

Quotes

“I never ask for permission. I just ask for forgiveness—and sometimes not even that.”

“The big question is: what content is truly important for students to learn—and what can they master on their own?”

“Agency is the kernel. If students have it, they can be resilient, adaptive, and self-directed.”

“We want to create curious, empathetic humans who know they can change the world.”

“AI doesn’t live a life—it can’t replace the embodied experience of being human.”

“Schools need community conversations, not mandates, to adopt AI responsibly and equitably.”

Chapters

00:00 – Lisa Yokana’s background and the early signs of educational misalignment

02:35 – Leaving the classroom to consult globally on innovation and mindset

03:25 – Reframing education: Skills vs. content

06:20 – Nurturing student agency and tackling big problems

09:01 – The disconnect between education and workforce needs

12:56 – How Lisa gained support and built the Scarsdale Design Lab

17:29 – Parent engagement and community buy-in

20:59 – Integrating AI in meaningful, ethical ways

24:06 – Educator mindsets and reframing pedagogy around AI

27:26 – AI use starts younger than we think

29:24 – Rethinking college in the age of AI

35:33 – Global patterns in AI adoption across education systems

39:20 – Addressing neurodiverse needs and accessibility

42:24 – Broadening community engagement and “thinking out loud”

43:38 – Responsible AI use and responsible design

49:11 – Big Tech’s role and thoughtful AI adoption in schools

53:03 – Final advice for parents, educators, and students

Lisa Yokana: https://www.linkedin.com/in/lisa-yokana-81787ba

Next World Learning Lab: https://nextworldlearninglab.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver welcomes Ben Zweig, CEO of Revelio Labs and labor economist, for a deep dive into the evolving world of workforce analytics. Drawing from their overlapping experiences at IBM, Bob and Ben explore how the early days of cognitive computing sparked a journey toward greater transparency in labor market data. Ben explains how Revelio Labs is building a “Bloomberg Terminal” for workforce insights—grounded in publicly available data and powered by sophisticated taxonomies of occupations, tasks, and skills. Together, they examine the importance of job architecture, the promise and pitfalls of AI in workforce analytics, and the complexities of measuring contingent and freelance labor. Ben also shares a preview of his upcoming book, Job Architecture, and how LLMs are being used to redefine how organizations model and respond to changes in work itself.

Keywords

Revelio Labs, Ben Zweig, labor market data, job architecture, workforce analytics, strategic workforce planning, AI in HR, cognitive computing, IBM, labor economics, generative AI, skills-based hiring, public labor statistics, contingent workforce, gig economy, talent intelligence

Takeaways

Revelio Labs aims to recreate company-level workforce insights using publicly available employment data, similar to how Bloomberg transformed financial markets.

Job architecture is built on three distinct but interrelated taxonomies: occupations, tasks, and skills.

Many orgs think of skills as the building blocks of jobs, rather than attributes of people—a conceptual misstep that limits strategic planning.

Gen AI is being used to score the automation vulnerability of tasks, enabling better insights into how work is changing.

Strategic workforce planning is often misnamed—what most companies do is operational, not truly strategic.

Contingent and freelance labor remains a blind spot in many traditional labor statistics and HR systems.

The ability to adjust for data bias, reporting lags, and incomplete workforce signals is critical for creating trustworthy insights.

Revelio’s Public Labor Statistics offers an independent source of macro labor data, complementing BLS and ADP methodologies.

Quotes

“Skills are attributes of people. Tasks are the building blocks of jobs.”

“What’s exciting is that these are hard problems with big upside—unlike finance, where most of the low-hanging fruit is gone.”

“We’re asking LLMs to tell us what they’re good at—and how confident they are in that judgment.”

“Most organizations don’t need to pay $1M to build a taxonomy anymore. They just need the right approach and the right data.”

“There’s no reason we shouldn’t be repurposing labor market insights to help individuals, not just institutions.”

Chapters

00:00 — Intro and HR Tech reflections

02:08 — Ben’s background in economics and IBM analytics

06:43 — Why labor market data lags behind capital markets

09:22 — Building a flexible, bias-adjusted analytics stack

14:19 — Empathy for job seekers and candidate friction

16:10 — Why job discovery is fundamentally an information problem

19:53 — Unpacking job architecture: occupations, tasks, and skills

24:28 — Scoring AI’s impact on tasks, not skills

28:39 — Summarization vs. hallucination in generative AI

38:45 — Introducing RPLS: Revelio Public Labor Statistics

45:40 — The challenge of tracking freelance and contingent work

51:58 — Dealing with ghost data and workforce ambiguity

53:35 — Real-life uses of AI and Ben’s curiosity mindset

54:42 — Closing thoughts

Ben Zweig: https://www.linkedin.com/in/ben-zweig

Revelio Labs: https://reveliolabs.com

Job Architecture (pre-order): https://www.amazon.com/Job-Architecture-Building-Workforce-Intelligence/dp/1394369069/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Emily Scace, Senior Legal Editor at Brightmine, about the intersection of AI, employment discrimination, and the evolving legal landscape. Emily shares insights on how federal, state, and global regulations are addressing bias in AI-driven hiring processes, the responsibilities employers and vendors face, and high-profile lawsuits shaping the conversation. They also discuss candidate experience, transparency, and the role of AI in pay equity and workforce fairness.

Keywords

AI hiring, employment discrimination, bias audits, compliance, workplace fairness, age discrimination, Title VII, DEI backlash, Workday lawsuit, SiriusXM lawsuit, EU AI Act, risk mitigation, HR technology, candidate experience

Takeaways

Employment discrimination laws apply at every stage of the talent lifecycle, from recruiting to termination.

States like New York, Colorado, and California are setting the pace with new AI-focused compliance requirements.

Employers face challenges managing a patchwork of state, federal, and international AI regulations.

Recent lawsuits (Workday, SiriusXM) highlight risks of bias and disparate impact in AI-powered hiring.

Candidate experience remains a critical yet often overlooked factor in mitigating both reputational and legal risk.

Employers must balance the promise of AI with the responsibility to ensure fairness, accessibility, and transparency.

Pay equity and transparency represent promising use cases where AI can drive positive change.

Quotes

“Discrimination can happen at any stage of the employment process.”

“Some state laws go as far as requiring employers to proactively audit their AI tools for bias.”

“Employers can’t just outsource their hiring funnel and blindly take the recommendations of AI.”

“Class actions often succeed where individual discrimination claims struggle — they reveal systemic patterns.”

“Even if candidates don’t get the job, a little touch of humanity goes a long way in making them feel respected.”

“AI has real potential to help employers get to the root causes of pay inequity and model solutions.”

Chapters

00:00 – Welcome and Introduction

00:36 – Emily’s background and role at Brightmine

02:38 – Overview of employment discrimination laws

05:27 – AI and compliance with existing legal frameworks

07:20 – California’s October regulations and employer liability

09:54 – Employer challenges with multi-state and global compliance

11:26 – Proactive vs reactive approaches to AI bias

13:06 – EU AI Act and global alignment strategies

15:37 – High-risk AI use cases in employment decisions

18:34 – DEI backlash and its impact on discrimination law

20:59 – Age discrimination and the Workday lawsuit

27:34 – Data, inference, and bias in AI hiring tools

31:25 – Candidate experience and black-box hiring systems

33:33 – Bias in interviews and the human role in hiring

37:43 – Transparency and feedback for candidates

42:44 – AI sourcing tools and recruiter responsibility

47:52 – Risks of misusing public AI tools in hiring

50:12 – The SiriusXM lawsuit and early legal developments

54:08 – Candidate engagement and communication gaps

59:19 – Emily’s views on AI tools and positive use cases

Emily Scace: https://www.linkedin.com/in/emily-scace

Brightmine: https://brightmine.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob speaks with Brandon Roberts, VP of Global People Product, Analytics, and AI at ServiceNow. Brandon shares how ServiceNow is navigating AI transformation from within its HR organization, balancing internal experimentation with client-informed innovation. They dive deep into responsible AI practices, strategic reskilling, and cross-functional collaboration, while unpacking key frameworks. Brandon also offers a preview of forthcoming research on the future impact of agentic AI on the workforce and shares actionable insights for HR and business leaders on how to lead with confidence, empathy, and clarity in a rapidly evolving landscape.

Keywords

Responsible AI, Agentic AI, HR transformation, AI Playbook, AI readiness, AI literacy, reskilling, upskilling, internal mobility, ServiceNow, people analytics, AI enablement, human-centric, HR-IT collaboration, future of work, AI governance, workforce planning

Takeaways

ServiceNow’s HR team is leading internal AI adoption while helping shape product development through real-world use and feedback.

The AI Playbook for HR Leaders provides a practical framework that blends vision with tactical execution.

Responsible AI isn’t just a compliance exercise—it's a continuous process requiring monitoring, iteration, and cross-functional governance.

ServiceNow’s AI Control Tower centralizes use case tracking, governance status, adoption metrics, and value realization.

The AI Heat Map approach helps identify which tasks are most ripe for AI augmentation and where reskilling efforts should focus.

Strategic reskilling efforts, like transitioning HR operations roles into people partner roles, show how AI can enable—not replace—human potential.

HR-IT collaboration is essential to enabling governance, product experimentation, and sustained transformation.

Upcoming research from ServiceNow estimates 8 million U.S. roles will be transformed by agentic AI in the next five years.

Quotes

“This is a human transformation, not just a tech transformation.”

“Responsible AI isn’t finished at launch—it needs to be continuously monitored.”

“We call it the AI Heat Map—breaking down roles into tasks to see where AI can really help.”

“Strategic workforce planning needs to evolve into strategic work planning.”

“If AI doubles productivity, it should also unlock opportunities—not eliminate people.”

“We want employees to feel safe using AI and know we’re committed to reskilling, not replacing them.”

Chapters

00:00 – Intro and Brandon’s background

02:00 – Brandon’s unique role in HR and product feedback loops

03:20 – Internal vs. customer-led innovation

04:24 – AI solution inventory and governance

07:18 – AI readiness, literacy, and cultural change

10:00 – Role-based skill development

12:00 – Embedding Responsible AI across the enterprise

14:36 – Balancing innovation with ethical oversight

17:50 – HR and IT collaboration at ServiceNow

20:45 – Agentic AI and workforce planning

23:47 – Case study: reskilling HR ops into people partners

29:03 – Why internal talent is often overlooked

33:21 – The evolving value of analytics in the AI era

36:58 – Importance of data quality and governance

40:32 – How AI will transform every role and industry

46:03 – Banking and reinvesting AI-driven time savings

48:27 – How ServiceNow filters and prioritizes AI ideas

49:18 – Teaser: upcoming research on agentic AI’s impact

51:06 – Personal AI tools and what’s exciting (or scary)

54:04 – Final thoughts and call to action

Brandon Roberts: https://www.linkedin.com/in/brandon-roberts-50796ba

AI Playbook for HR Leaders: https://www.servicenow.com/content/dam/servicenow-assets/public/en-us/doc-type/resource-center/ebook/eb-hr-role-in-ai-transformation.pdf

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

What’s Your AIQ?⁠ Assessment interest form

Bob sits down with Cole Napper, VP of Research, Innovation & Talent Insights at Lightcast, to unpack the complex and rapidly evolving world of people analytics. From his eclectic career across industries to his recent book release and his co-hosting role on the very popular people analytics podcast, Directionally Correct, Cole shares practical insights and hard-earned wisdom on topics like AI readiness, org network analysis, and the intersection of data, influence, and leadership. Bob and Cole explore the paradoxes of the HR tech ecosystem, the stubborn persistence of unsolved problems, and why storytelling with data is really about persuasion. Cole also gets candid about the ethical responsibilities facing those who wield data, and why the future of workforce planning demands a complete rethink of how we study work itself.

Keywords

people analytics, talent intelligence, workforce planning, organizational network analysis, Lightcast, HR tech, Gen AI, quality of hire, job analysis, data storytelling, ethical AI, talent metrics, innovation, influence and persuasion, data infrastructure, Directionally Correct podcast

Takeaways

People analytics is only valuable when it influences decisions.

Evolution of HR tech is moving from digitization to “value-first” intelligence.

Effective storytelling with data is about persuasion and influence, not charts.

Despite its maturity, organizational network analysis (ONA) remains underutilized.

Most companies are underinvesting in data infrastructure, even as they chase AI initiatives.

A flexible framework for measuring quality of hire is more useful than a rigid definition.

Job analysis is having a renaissance as AI demands a deeper understanding of work.

Ethics in people analytics isn't just about governance — it's about virtue and trust.

Quotes

“People analytics that doesn't influence decision-making is just overhead.”

“We’re still digitizing HR — we haven’t even started to optimize it.”

“Smart people assume their conclusions are self-evident, but that’s not how decisions are made.”

“We need storytelling with data, but what we really need is persuasion with data.”

“AI’s biggest challenge in HR isn’t capability — it’s data infrastructure and context.”

“There’s no one watching the watchmen — ethics starts with the person in the seat.”

“The study of work isn’t sexy, but it’s suddenly essential again.”

Chapters

00:02 - Welcome and Intro to Cole Napper

00:55 - Cole’s Career Journey

03:29 - Patterns Across Industries and the Illusion of Uniqueness

06:51 - Community, Knowledge Sharing, and Power of Consortiums

08:57 - Why Smart People Still Struggle to Influence with Data

11:33 - From HR Tech to People Analytics: Digitization vs. Value Creation

13:51 - Data vs. Self-Interest: Why Decisions Get Blocked

15:49 - Untapped Potential of Org Network Analysis

18:54 - Use Cases: Building Teams, Referrals, and AI-Enhanced Sourcing

25:17 - Cole’s Book: Why Now, and What It’s About

28:13 - Shifting from Cost Center to Profit Center in People Analytics

32:22 - People Analytics Leading AI Adoption in HR

35:31 - Probabilistic Thinking, Determinism, and Predictive Pitfalls

36:55 - Measuring Quality of Hire: Frameworks vs. Definitions

40:41 - AI Assistants, Prescriptive Insights, and Reinforcement Learning

44:26 - Data Infrastructure as the Real AI Unlock

48:25 - Strategic Work Planning in an AI-Enabled World

52:25 - Who Will Watch the Watchmen? Ethics and Virtue in Analytics

55:28 - Predictions vs. Deductions and Parting Thoughts

Cole Napper: https://www.linkedin.com/in/colenapper

Directionally Correct: https://wrkdefined.com/podcast/directionally-correct

"People Analytics": https://www.colenapper.com/book

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

What’s Your AIQ?⁠ Assessment interest form

In this wide-ranging and thought-provoking conversation, Bob Pulver sits down with Steve Levy — recruiting veteran, technologist, and self-proclaimed “truth-teller” — to explore how talent, technology, and transformation intersect in today’s world of work. From the early days of expert systems and green-screen mainframes to the complexities of generative AI, Steve brings a rare blend of historical context, critical thinking, and humor. Together, they tackle topics like the ethics of candidate AI, bias in hiring platforms, skills-based hiring, the need for AI literacy, and why every recruiter needs to be more curious — and more human. Steve also shares lessons from his decades as a lifeguard at Jones Beach, and how that role shaped his instincts for protecting and empowering people — a theme that carries through everything he does in talent acquisition.

Keywords

AI in recruiting, expert systems, generative AI, candidate experience, skills-based hiring, talent ethics, AI literacy, job applications, bias in hiring, strategic workforce planning, Jones Beach lifeguard, recruiting tech, AI governance, human-centered design, talent intelligence, responsible AI

Takeaways

AI isn't new — it's just louder now: Steve recalls early experiences with AI-like systems in the 1980s and draws parallels to today’s hype and fear cycles.

Recruiters need more curiosity, less fear: Avoiding AI won’t make it go away — recruiters must engage, experiment, and understand where AI fits.

The real problem? Poor inputs: Most job descriptions and resumes are terrible — AI can’t solve for that without better human collaboration.

Bias goes both ways: If employers can use AI to screen resumes, candidates can use it to write them — the key is transparency and integrity.

Quality of hire starts with better intake: Steve emphasizes the importance of understanding real business problems, not just scanning for keywords.

Candidate AI vs Employer AI: The current debate needs to move past gut reactions and toward practical, equitable frameworks.

We need new roles and metrics: From TA ethicists to agentic governance leads, the future workforce demands new capabilities.

Recruiting is about inclusion, not gatekeeping: Steve’s philosophy centers on humanizing the process and finding reasons to say “yes.”

Quotes

“If you can't audit it, don't automate it.”

“The real challenge is working to include someone rather than exclude them.”

“We're seeing artificial stupidity — not artificial intelligence.”

“Being afraid of the ocean because of sharks is like avoiding AI because of hallucinations. You’ve got to get in the water.”

“You can fight this, or you can plan for it. That’s it.”

“Most people don't write good resumes. Most recruiters don't write good job descriptions. AI's not going to save us from that.”

Chapters

00:00 – Opening & Reconnecting with Steve Levy

03:01 – Recruiting Before Computers & the Rise of Expert Systems

08:12 – What AI Is (and Isn’t): Fear, Hype & Progress

13:17 – Strategic TA in an Agentic Era

21:07 – AI Literacy, Education & Workforce Readiness

28:11 – Candidates Using AI vs. Employers Using AI

36:45 – Problems with Job Descriptions, Resumes & Gatekeeping

45:24 – Ethics, Transparency & Legal Implications in Hiring AI

54:10 – Talent Intelligence & Strategic Workforce Planning

1:05:33 – The SiriusXM Lawsuit & Candidate Frustration

1:15:57 – Lifeguard Lessons for the AI Age

1:20:12 – Final Thoughts on What Comes Next

Steve Levy: https://www.linkedin.com/in/levyrecruits

Steve’s Blog: https://recruitinginferno.com/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

What’s Your AIQ?⁠ Assessment interest form

In this lively and thought-provoking episode of Elevate Your AIQ, Bob Pulver reconnects with former collaborator and pioneering technologist Marshall Kirkpatrick. From their early work intersecting social data and influence to Marshall's latest AI-driven workflows, the conversation explores how human insight and machine intelligence are converging. Marshall shares real-world examples of using synthetic personas, market monitoring systems, and creative prompting strategies to uncover early signals, amplify strategic decisions, and reimagine everything from talent acquisition to environmental policy tracking. It's a conversation that navigates the emergence of machine learning for social insights to the frontier of AI innovation.

Keywords

AI-powered market monitoring, synthetic personas, talent acquisition, influencer marketing, social analytics, Claude, Perplexity, scenario planning, digital twins, quality of hire, Obsidian, strategic planning, generative AI, Delphi method, social capital

Takeaways

Marshall’s Journey: Marshall has spent his career identifying experts and building tools to surface valuable insights from social data.

Synthetic Personas in Action: Using tools like Claude to create synthetic expert panels that evaluate documents, surface perspectives, and even challenge his own thinking.

AI-Augmented Talent Scenarios: AI to simulate team compositions, evaluate candidates’ social behaviors, and even model potential collaboration outcomes.

Monitoring the Market with AI: Building systems that detect early signals in markets — including environmental policy — using a mix of RSS, generative AI, and good old-fashioned curiosity.

Digital Twins and Ownership: Exploring who owns the knowledge embedded in a “digital twin” of an employee — and how organizations might leverage them responsibly.

Strategic Planning Reimagined: Using AI to model outcomes based on actions and strategies offers new ways to engage in scenario planning — not just in workforce contexts, but in grantmaking and innovation networks.

Counterargument Workflows: Marshall shares his custom-built browser tool that generates counterarguments to online content using ChatGPT, promoting critical thinking and cognitive diversity.

Quotes

“I try to eat my own dog food — or drink my own champagne — when it comes to market monitoring.”

“There’s gold in that data. We just have to figure out how to mine it responsibly and effectively.”

“Synthetic personas are fast, cheap, and good enough to get the conversation started.”

“What’s the strategy, what’s the output — and what’s the outcome? That’s where AI can help us model the messy middle.”

“You can’t just look at someone’s codebase or resume — you need context, behavior, and communication patterns.”

“I built a ‘counterargument bookmarklet’ to challenge the assumptions in what I’m reading online.”
Chapters

00:00 – Welcome & Reconnection: Marshall’s Background and Journey

03:12 – AI Systems for Market Monitoring and Early Signal Detection

10:58 – The Evolution of Social Analytics and Social Capital

16:39 – Talent Acquisition, AI, and the Value of Social Footprints

24:57 – Scenario Planning with Synthetic Personas

32:05 – Driving Innovation through Grant Monitoring and Project Pairing

40:41 – From Digital Twins to Ethical Implications of AI in the Workforce

50:15 – Counterargument Workflows and Critical Thinking with AI

58:21 – Closing Thoughts: Responsible AI, Community, and the Road Ahead

Marshall Kirkpatrick: https://www.linkedin.com/in/marshallkirkpatrick

Earth Catalyst: https://www.earthcatalyst.co/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

What’s Your AIQ?⁠ Assessment interest form

Bob sits down with Colette Mason, a tech veteran with 40 years of experience in computing and a deep understanding of human behavior through her work in coaching and neuro-linguistic programming. Together, they explore the hype and reality around AI adoption, automation myths, and why “responsible by design” is more than just a catchphrase. Colette shares her perspectives on human-centric design, AI literacy, and how to keep authenticity intact in an AI-powered world. With warmth, humor, and real-world wisdom, this conversation brings clarity to an often-confusing landscape—and reminds us that technology should augment rather than replace what only humans can and should do.

Keywords

AI literacy, human-centric design, responsible AI, automation, digital assistants, content generation, neuro-linguistic programming, human-AI collaboration, ethical AI, digital tools, Colette Mason, trusted AI

Takeaways

AI ≠ Automation: Many tasks called "AI" are really just workflow automation. It's important to distinguish between the two.

Human-Centered Design Matters: AI tools should reflect human needs, limitations, and behaviors, especially when used in sensitive areas like hiring.

The Hype Is Real—and Misleading: Over-promising on AI capabilities can hurt trust and morale. Colette urges a more grounded, realistic view.

Use AI Where It Helps, Not Where It Hurts: Delegate the boring stuff, but don’t let AI speak in your voice without oversight.

Authenticity Still Wins: Whether it's writing, speaking, or building a personal brand, being transparent about AI involvement builds trust.

Responsible Use Is Everyone’s Job: From solo entrepreneurs to large enterprises, we all have a role in building and using trustworthy AI.

Design for Real People: Most users aren’t tech-savvy. Tools need to be intuitive, safe, and aware of different user needs—including neurodiversity.

Top Quotes

“I model people’s brains because I’m a hypnotherapist—and that’s actually a superpower in tech.”

“There’s a lot of AI that isn’t really AI. It’s just automation with lipstick.”

“The system has to read the room—it can’t just say ‘you didn’t give me all the info, mate.’”

“Regular people need AI that helps them make it to their kids’ school play—not impress YouTube bros.”

“Don’t replace yourself with AI. Do less, but make it more you.”

“We’re not in the early innings—we’re still in warmups when it comes to AI literacy.”

Chapters

00:00 – Intro and Colette's Background

02:00 – AI Hype vs. Reality: What’s Really Happening

06:00 – Automation ≠ AI: Breaking the Misconceptions

10:30 – Building Human-Centered Tools and Workflows

17:00 – Responsible AI and “Designing for Safety”

24:00 – Fairness in Hiring and Interviewing with AI

30:00 – The Quality of AI-Generated Content

38:00 – Being Transparent About AI Use

44:00 – Ethics, Reputation, and the Court of Public Opinion

50:00 – Global Perspectives on AI Regulation

54:30 – Favorite Tools and Real-World Applications

01:00:00 – The Future of Personality in AI Models

01:03:30 – Closing Thoughts

Colette Mason: https://www.linkedin.com/in/colettemason

Clever Clogs AI: https://www.cleverclogsai.com/

Ditch Rework, Build Teamwork: https://www.amazon.com/Ditch-Rework-Build-Teamwork-Principles-ebook/dp/B0FBL4C6ZP

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

Substack: ⁠https://elevateyouraiq.substack.com⁠

What’s Your AIQ?⁠ Assessment interest form