407: Daniel Chait, CEO at Greenhouse: Inside the Doom Loop Where AI Screens AI-Written Resumes

Send us Fan MailTodd Barr is the CEO of Axonis and has spent his career in high growth software infrastructure, living through the shift from Unix to Linux, the rise of DevOps, and the blockchain era before landing in AI. Axonis was built by technical founders with 25 years each in large scale infrastructure across government, defense and intelligence, where sharing the underlying data was never an option. Recorded live from the floor of Ai4 2026, this lightning round explores why you do not have to centralize your data to start doing AI, and what it takes to make a decision you can defend later.Todd and host Dan Turchin dig into what an auditable decision actually records, how governance can live at the data node instead of the application layer, the distance between collecting sensor data and acting on it, and why Todd believes decisions themselves are about to become a category of data.What You'll LearnWhy centralizing your data is not a prerequisite for AI, and how a federated architecture holds up when nodes go offline and come backWhat an auditable decision captures: the data used, the model and its version, the reasoning, and the person who attested to itHow attribute based access control decides what each operator or agent is allowed to see, enforced at the data itselfThe sensor to decision gap, and how a flood management team went from raw readings they could not interpret to acting on them in real timeWhy collecting data and acting on it are two different problems, and what opens up once that distance closesWhat becomes possible once decisions are stored as data, from the cost of each decision to who in the organization decides better🎙️ Part of our Ai4 2026 compilation series. Listen to the full compilation here: https://www.buzzsprout.com/520474/episodes/19816277  ResourcesSubscribe to the AI & The Future of Work NewsletterConnect with Todd on LinkedIn

Send us Fan MailDaniel Chait is the co-founder and CEO of Greenhouse, the industry-leading hiring software platform designed to help enterprise organizations build fair, structured, and effective recruiting processes. He has been a technology entrepreneur in New York for more than 20 years.He started Greenhouse about 15 years ago on a simple conviction: companies that follow structured hiring practices get better results, and job seekers deserve a process that is fair and transparent. Today he is leading the company through a reinvention for the AI era, including the acquisition of Ezra AI Labs to bring transparent, conversational AI tools to job seekers and hiring teams alike. In this episode, Daniel draws on two decades of building companies where every hire lands directly on the bottom line to ask what candidates and employers both need answered: can AI make hiring more human when it is already making it feel less so?In this conversation, we discuss:Why candidates and employers using AI at the same time is creating a doom loop that shuts real humans out.Why AI-led interviews worry so many job seekers, and what it takes for a conversation with an AI to feel fair.Why using AI to automatically reject most applicants is tempting, and why Greenhouse said no to itWhy AI-polished resumes are making every candidate look the same, and how hiring can move beyond the resume.How AI can replicate human bias at light speed, and why Daniel says no vendor should ask you to take their word for it.What Daniel tells anxious workers, from new grads to senior leaders, about disruption, opportunity, and reinventing a career with AI.Explore the Conversation0:00 Welcome Back and This Week's AI Fun Fact4:48 Meet Daniel Chait, Co-Founder and CEO of Greenhouse7:55 From a Soulless Consulting Job to Founding a Company10:43 Reinventing the Applicant Tracking System15:23 How AI Has Already Reshaped Hiring and Job Seeking21:11 What the Candid AI Interview Report Reveals About Trust24:25 Why Auto-Rejecting Candidates With AI Backfires26:08 Sorting Through the Flood of AI-Generated Applications30:46 Confronting Bias in AI-Powered Hiring Tools36:46 Reframing AI Anxiety Into Opportunity40:11 Time Capsule: Advice to His Younger Self42:14 Where to Find Greenhouse and Closing ThoughtsResourcesSubscribe to the AI & The Future of Work NewsletterConnect with Daniel on LinkedInAI fun fact articleOn How Paolo Ardoino, CEO of Tether, is exploring whether decentralized AI can fix banking, crypto, and the future of finance 

Send us Fan MailAndrew Rabinovich is the former CTO of Upwork and has spent 25 years in machine learning and computer vision, long before AI was fashionable. After a PhD in computer vision at UCSD, he worked at Google. He then spent five years at Magic Leap building mixed reality devices before founding Headroom, a video conferencing company that Upwork acquired. Recorded live from the floor of Ai4 2026 in August, this lightning round explores what happens when you actually measure how much of a real job an AI agent can finish.Andrew and host Dan Turchin dig into Upwork's shift from a matching marketplace to a work delivery platform, the meta agent that breaks a job into atomic units of work, how the platform proves a freelancer is human, and more. What You'll LearnHow Upwork is evolving from a matchmaking marketplace into a work operating system, and how its meta agent decomposes a single job into atomic units of workWhat Upwork's AI productivity index revealed when agents were tested against real jobs already completed by human freelancersWhy every agent on the platform is paired with a human expert, and how that pairing changes both the speed and the quality of the resultHow proof of humanness works in practice, from identity verification to AI video interviews, and what happens when the system gets it wrongWhy agents may soon become the demand side of the marketplace, calling human experts in real time to verify what they cannot🎙️ Part of our Ai4 2026 compilation series. Listen to the full compilation here: https://www.buzzsprout.com/520474/episodes/19816277 ResourcesSubscribe to the AI & The Future of Work NewsletterConnect with Andrew on LinkedIn

Send us Fan MailMahe Bayireddi is the CEO and co-founder of Phenom, the global HR technology platform he launched in 2011 with the mission to help a billion people find the right job. Under his leadership, Phenom has raised over $160 million, achieved unicorn status, and consistently ranked among the Inc. 5000 fastest-growing private companies. A serial entrepreneur with a deep technical foundation, Mahe previously co-founded SnipSnap and Bijol Health. He holds BE and MS degrees in computer science.In this episode, Mahe sits down with Dan Turchin to unpack why "AI is taking your job" is the wrong framing, and why the real question is where you sit, what you do, and how deeply you understand the business around you.In this conversation, we discuss:Why replacing jobs and replacing the job you'll have tomorrow are two very different things, and why irreplaceability comes down to understanding the business, not just being intelligent.Why raw machine intelligence falls short in enterprise HR, and why true workforce alignment requires AI systems built on deep business understanding.How culture, structure, and strategy form a company's "organizational DNA," and why most companies fail at execution, not strategy.How a five-dimensional rubric of talent zones, industries, roles, locations, and culture creates hyperpersonalized workflows instead of one-size-fits-all automation.Why frontline workers and specialized knowledge practitioners experience AI as an amplifier of capability rather than a displacement force.What enduring over 100 venture capital rejections teaches founders about developing "cockroach" resilience and leading through persistent industry skepticism.Explore the Conversation00:00 Intro & AI Fun Fact04:27 Meet Mahe Bayireddi, Phenom CEO06:36 AI Is Your Ally, Not Enemy09:29 What Hiring Got Wrong10:43 Culture as Organizational DNA12:26 Why Agent Interviews Won't Scale16:47 Bias and AI Responsibility19:00 Fraud Agents by Region24:23 Frontline vs. Desk-Based AI28:53 A Billion People, Right Jobs31:24 Lessons From the Founder Journey33:01 100 Rejections, One Cockroach35:39 Where to Find Mahe and PhenomResources:Subscribe to the AI & The Future of Work NewsletterConnect with Mahe on LinkedInAI fun fact articleOn using AI to make hiring more fair and eliminate human bias

Send us Fan MailWalk the floor at an event like Ai4 and most of the conversation is about what machines can now do on their own. The question that kept surfacing in our conversations was quieter, and harder: where do humans belong when machines can think?In this special compilation episode of AI and the Future of Work, we bring you three conversations recorded live at Ai4 2026 in Las Vegas. This is the first episode of our four-part Ai4 series.A talent marketplace, a data infrastructure company, and a 164 year old life insurer. Three businesses that could not be more different, and three leaders who arrived at the same answer from opposite directions. What connects them is not caution about automation. All three are automating serious amounts of work. What they share is that the requirement was built in on purpose, because the outcome depends on someone being answerable for it.Featured GuestsAndrew Rabinovich, former Upwork Chief Technology Officer. Full conversation: https://www.buzzsprout.com/520474/episodes/19855791Todd Barr, CEO at Axonis. Full conversation coming Thursday, October 1, 2026Kartik Sakthivel, Chief Information Officer at John Hancock. Full conversation coming Thursday, October 8, 2026Over the next three weeks we will release one full conversation every Thursday. As each episode goes live, we will add the link here.What You'll LearnWhat happens when you measure how much of a real job an agent can actually finish, and why that number should change what you let it do unsupervisedThe one line in hiring that should never be crossed, no matter how capable the technology becomesWhere human agency actually lives once the work itself is handed off, and why how it gets done may matter less than we assumeWhy collecting data and acting on it are two different problems, and what opens up once that distance closesThe question worth asking before any AI project starts, and why the answer is sometimes something far simpler than AIWhat to say to someone watching a machine learn to do the thing that made them valuable, and where the time that comes back should goInspired by something you heard in this episode?Share your favorite insight about where people belong in the systems we are building, and tag us on social.And don't forget to subscribe to AI and the Future of Work for more conversations with the leaders shaping the future of work.Other special episodes:SE 26: Designing AI for Accountability and Harmony with Leaders from Honeycomb, FieldAI, Archetype AI, and Zoom (Live from HumanX 2026 Special Episode)SE 27: AI and the Next Generation: What We Can Learn from Them (International Youth Day Special Episode)SE 19: AI and Education: How Artificial Intelligence Is Transforming Learning (International Day of Education Special Episode)Lessons from Four Unicorn CEOs Disrupting Massive Markets with AI (Special Episode)Ethical AI in Hiring: How to Stay Compliant While Building a Fairer Future of Work (HR Day Special Episode)AI and Safety: How Responsible Tech Leaders Build Trustworthy Systems (National Safety Month Special)Lessons from Leaders: How AI Is Redefining Work and the Human Experience (Labor Day Special Episode) 

Send us Fan MailNick Mehta is a serial entrepreneur, executive leader, and the former longtime CEO of Gainsight, where he helped define and scale the multi-billion-dollar category of modern Customer Success. Widely revered across the technology ecosystem as a beloved mentor and a CEO's role model, Nick pairs sharp business instincts with an infectious, human-first leadership style and an insatiable curiosity for philosophy, mathematics, and cognitive science. In this highly anticipated annual conversation, Nick sits down with Dan for an unfiltered, cathartic deep dive into the psychological toll of the AI era. In this conversation, we discuss:Why the transition from passive software tools to autonomous agents triggers acute "brain fry," and the psychological friction that occurs when machines begin directing human managers.How the anthropological framework of BANI (Brittle, Anxious, Non-linear, and Incomprehensible) explains workplace anxiety, and why the traditional fear of missing out is shifting into the cognitive strain of managing agents that demand constant input.What Richard Dawkins' dialogue with Claude reveals about the debate between functionalists and biological essentialists, and why Nick remains skeptical of claims surrounding synthetic consciousness.How Douglas Hofstadter's concept of "strange loops" and self-referential observation offers a powerful mental model for the future of recursive machine intelligence.Why pursuing automation purely for commercial efficiency creates massive personal and societal opportunity costs, and how tech leaders can weigh business optimization against life-altering scientific breakthroughs.What a tech executive's front-row seat as a parent to college seniors reveals about the job market crisis facing new graduates in business and computer science.EXPLORE THE CONVERSATION00:00 Welcome to Episode 404: Dan and Nick's Annual Public Therapy Session04:34 When AI Shifts from Passive Tool to Demanding Manager08:23 The BANI Framework: Decoding the Brittle, Anxious, and Fragmented Mind11:05 Yelling at ChatGPT: What Our Reactions to LLM Mistakes Reveal About Us14:25 The Consciousness Debate: Functionalism, Qualia, and Richard Dawkins' Experiment19:22 Self-Referential Loops and the Nature of the Self23:33 Mimicry Versus Learning in AI and Humans29:35 The Efficiency Trap: Why Automating for Automation's Sake Loses the Plot32:30 The Opportunity Cost of Tech: The Societal and Ethical Implications of AI Progress37:12 The Role of Philosophy in Understanding AI and Consciousness40:35 What Our Kids Are Facing: The Lived Reality of the 2026 Job Market44:37 Radical Humility: Why Great Leaders Must Abandon the Visionary Facade47:26 But What If We're Wrong? Viewing Today's Historical Phase Shift from the FutureResources:Subscribe to the AI & The Future of Work NewsletterConnect with Nick Mehta on LinkedInBooks referenced in this episode:Gödel, Escher, Bach: An Eternal Golden Braid by Douglas HofstadterThe Beginning of Infinity: Explanations That Transform the World by David DeutschBut What If We're Wrong? Thinking About the Present as If It Were the Past by Chuck KlostermanPlayer Piano by Kurt Vonnegut

Send us Fan MailIlan Peleg is the Co-Founder and CEO of Lightrun, the autonomous remediation platform built to debug and fix software problems before they ever reach a customer. Lightrun is one of the fastest growing companies in the AI observability space, having raised $110 million from elite venture firms including Accel, Insight Partners, and Sorenson Capital. Its technology now runs inside mission critical systems at Salesforce, Citibank, and the New York Stock Exchange.Before founding Lightrun, Ilan spent years as a software developer, watching code succeed in testing and then fail in ways no one could predict once it met real users at scale. That frustration became the founding thesis for a company built around a bold idea: self healing software.In this episode, Ilan draws on his experience building line level, real time telemetry for some of the world's most demanding production environments to make the case that AI reliability depends on grounded evidence, not bigger models, and that the future belongs to practitioners who know how to work with it, not around it.In this conversation, we discuss:How AI agents shift software debugging from reacting to outages to preventing them before they ever reach productionWhy site reliability engineers are being asked to rethink their entire relationship with automation and manual firefightingThe difference between AI workflow automation startups and the ones that will actually survive the next platform shiftWhat actually happens when an AI agent doesn't have the grounded evidence it needs to complete a task correctlyWhy a founder chose to keep his AI observability platform read only, even when customers asked for more autonomyThe architectural bet behind real time, line level telemetry that makes self-healing software possible at enterprise scaleExplore the Conversation00:00 Intro and Fun Fact04:21 Introducing Ilan Peleg04:52 The Lightrun Origin Story08:32 Software in an AI Era10:38 Tracing Code in Real-Time18:08 Shifting Left Observability20:19 Scaling Self-Healing Telemetry25:39 Why Lightrun Stays Read-Only29:23 Can AI Labs Replace It32:26 SREs in an AI World35:15 People Over Product36:32 Early Days in Herzliya39:37 Closing and ConnectingResources:Subscribe to the AI & The Future of Work NewsletterConnect with Ilan Peleg on LinkedInAI fun fact article: Precision Proactivity: Measuring Cognitive Load in Real World AI Assisted Work On How Meredith Broussard, NYU professor and featured expert in Coded Bias, is confronting the social implications of AI 

Send us Fan MailNancy Wang is the Chief Technology Officer at 1Password, where she leads the global engineering team securing the digital safety and identities of millions of prosumers, families, and enterprise workloads. An expert in building highly scalable systems and data protection platforms, she holds seven patents in cloud infrastructure and serves on the boards of early-stage cybersecurity leaders, including Observo AI, which was recently acquired by SentinelOne to power the data intelligence layer for its SIEM products. Prior to joining 1Password, she became the youngest person ever promoted to Director and General Manager at AWS, at age 31, leading AWS Data Protection to serve 98% of the Fortune 500 and building a multi-billion dollar business within five years. She also helped build cloud and data protection products at Rubrik, held engineering roles at Google and in the U.S. intelligence community, and now brings a rare combination of deep infrastructure expertise and security-first product thinking to the age of AI agents. In this episode, Nancy sits down with Dan to explore the massive security responsibilities of the agentic era.In this conversation, we discuss:Why the transition to autonomous AI agents doesn't require brand-new credentials, and how familiar principles of human and machine identity extend to the agentic era.How a proprietary confidential computing architecture built on specialized enclaves protects sensitive user data, even in the event of a catastrophic central breach.Why 1Password actively chose not to release a public Model Context Protocol (MCP) server despite intense market pressure.How a specialized DevOps agent trained on human incident response can learn to navigate real-time outages, runbooks, and cloud traffic monitoring.Why AI is compressing the cost of execution in software, and why the real career advantage now shifts toward taste, judgment, systems thinking, and deep fluency with AI tools.Why prioritizing potential and tenacity over traditional resumes is the key to building diverse engineering teams, and how sponsorship accelerates the rise of high-slope talent   Explore the Conversation00:00 Intro and AI Fun Fact: EvilToken and the Rise of AI-Driven Phishing03:48 Introducing Nancy Wang, CTO at 1Password and AWIT Founder09:07 Secure by Design: Inside the Origin Story and Expansion of 1Password13:32 The Business of Trust: Why 1Password Is in the Security and Relationship Game15:31 Zero-Knowledge Architecture: What Actually Happens if 1Password Faces a Breach?24:06 The Phishing-Resistant Future: Biometrics, Passkeys, and Agent Identity28:17 Inside the Machine: DevOps Agents and Real-Time Incident Response31:22 Rejecting the MCP Server: Why AI Safety Trumps Instant Velocity34:28 The T-Shaped Engineer: Why AI Compresses Execution and Elevates Human Taste39:18 Be a High Sloper: Sponsoring the Next Generation of Tech Leaders Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Nancy Wang on LinkedInAI fun fact article: Hackers use AI to bypass passwords in large-scale phishing attacks Other episodes mentioned on the show:Episode 167 with Amr Awadallah, CEO of Vectara and co-founder of Cloudera, on the tech behind AI search

Send us Fan MailRuss Fradin is the co-founder and CEO of Larridin, the enterprise AI measurement platform that raised $17 million in seed funding led by Andreessen Horowitz, with Bloomberg Beta and Gradient Ventures. Larridin sells to CIOs and CFOs, and answers a question most companies still cannot answer for themselves: which AI tools are being used, by whom, at what level of proficiency, and where they are actually driving productivity.A veteran Silicon Valley entrepreneur with three decades of experience leading high-growth technology companies, Russ previously co-founded and served as CEO of Dynamic Signal (now Firstup), Adify, and SocialShield, and was the first employee at Flycast, one of the internet’s pioneering ad networks.In this episode, Russ sits down with Dan to share the brutal and beautiful realities of building companies in the Bay Area. Tune in to hear his seasoned perspective on why successful AI integration requires ancient change management principles rather than new software licensing, the operational gap between power users and passive users, and why the most at-risk professionals in the AI era are not the ones you think.In this conversation, we discuss:Why a third party measurement company shows up every time money floods into a category, and what that pattern predicts for enterprise AI.What actually happens after you deploy AI to 3,000 employees, and why the gap that opens up is a change management problem, not a technology one.Why revenue growth cannot tell you whether to renew a multimillion dollar AI contract, and why the vendor's own numbers will not either.What the stark division between organizational power users and casual users reveals about the actual bottleneck in modern workplacesWhy token-spend leaderboards can accelerate adoption, and why they become unsustainable once employees learn how to game them.What the reality of task automation means for the future of knowledge work, and why the threat to specific white-collar roles is often misunderstood.Explore the Conversation00:00 Intro and AI Fun Fact: Beyond Job Replacement, How AI Can Improve Work04:22 Introducing Russ Fradin, Co-Founder and CEO of Larridin06:27 Startup Humility: Why Early Failure Is Essential for Founders09:45 Startup Alchemy: The Absurdity and Ambition of Building a Company11:51 The Larridin Vision: Building the Unbiased Measurement Layer for Enterprise AI18:03 Beyond AI Security: Measuring Usage, Spend, Productivity, and Workflows20:33 AI ROI Beyond Revenue: Measuring Productivity, Renewal Decisions, and Change Management27:02 Blazing Trails: How Facebook Speedran Adoption with Token Leaderboards34:59 What We Measure Shapes AI’s Impact on Growth, Costs, and Jobs39:41 AI Replacement Theory: Why Tasks Change, but Most Knowledge Jobs Endure46:57 Where to Connect with Russ Fradin and Learn More About LarridinResourcesSubscribe to the AI & The Future of Work NewsletterConnect with Russ on LinkedIn Learn more about Larridin at https://larridin.com/AI fun fact article: Labor Market Impacts of AI: A New Measure and Early Evidence by AnthropicOn Making AI Smarter Without Harming Humans

Send us Fan MailEvery August 12, the world observes International Youth Day, and this past July 15 marked World Youth Skills Day. Both are good moments to remember what we owe the people entering the workforce: paths, tools, and advice that inspire them, not warnings that scare them.In this special August compilation episode of AI and the Future of Work, we bring back four former guests whose conversations point to the same idea: AI is not a threat. It is an opportunity, and the people starting their careers right now hold an unfair advantage. They have not spent years absorbing policies and habits that are already obsolete, so they get to begin from what is possible instead of unlearning what used to be true.From building AI into daily work, to leadership teams learning from their newest employees, to understanding what becomes more valuable when AI makes creation easier, this episode explores how the next generation is approaching work differently.The conversations point to a broader shift: as AI takes on more of the repetitive work and makes production cheaper, human judgment, creativity, curiosity, taste, intention, and the ability to identify the right problems become more important.Featured GuestsKourtney Cross, Business Analyst at Leidos and ServiceNow Rise Up graduate. Listen to the full conversation here: [https://www.buzzsprout.com/520474/episodes/18741521] Andrea Iorio, Keynote Speaker and Author of Between You and AI, formerly Chief Digital Officer at L'Oreal Brazil. Listen to the full conversation here: [https://www.buzzsprout.com/520474/episodes/18662632] Jake Saper, General Partner at Emergence Capital. Listen to the full conversation here: [https://www.buzzsprout.com/520474/episodes/18982636] Matt Britton, CEO of Suzy and Author of Generation AI. Listen to the full conversation here: [https://www.buzzsprout.com/520474/episodes/18792172] What You'll LearnHow an analyst early in his career built AI into his daily work, and why he went looking for it before anyone handed it to himWhat it takes to make your work visible when you are just starting outHow reverse mentoring puts the newest employees in the room to teach the leadership team what it is missingWhat a workday looks like once these tools handle the repetitive parts, and how to reinvest the time that comes backWhy AI can democratize creation while making taste, intention, trust, curation, and point of view more valuable Why "which tool should I use" is the least interesting question, and what rises in value once memorizing information stops being the pointInspired by something you heard in this episode?Share your favorite insight about learning, curiosity, or the ideas shaping how work gets done, and tag us on social.And don't forget to subscribe to AI and the Future of Work for more conversations with the leaders shaping the future of work.Other special episodes:SE 19: AI and Education: How Artificial Intelligence Is Transforming Learning (International Day of Education Special Episode)Lessons from Four Unicorn CEOs Disrupting Massive Markets with AI (Special Episode)Artificial General Intelligence: Can Machines Really Think Like Us? (Special Episode)Ethical AI in Hiring: How to Stay Compliant While Building a Fairer Future of Work (HR Day Special Episode)AI and the Law: How AI Will Change Legal Careers (Special Episode)AI and Safety: How Responsible Tech Leaders Build Trustworthy Systems (National Safety Month Special)Lessons from Leaders: How AI Is Redefining Work and the Human Experience (Labor Day Special Episode)