AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics

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Todd 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 Learn
Why centralizing your data is not a prerequisite for AI, and how a federated architecture holds up when nodes go offline and come back
What an auditable decision captures: the data used, the model and its version, the reasoning, and the person who attested to it
How attribute based access control decides what each operator or agent is allowed to see, enforced at the data itself
The sensor to decision gap, and how a flood management team went from raw readings they could not interpret to acting on them in real time
Why collecting data and acting on it are two different problems, and what opens up once that distance closes
What 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  

Resources
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Connect with Todd on LinkedIn

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Daniel 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 Conversation
0:00 Welcome Back and This Week's AI Fun Fact
4:48 Meet Daniel Chait, Co-Founder and CEO of Greenhouse
7:55 From a Soulless Consulting Job to Founding a Company
10:43 Reinventing the Applicant Tracking System
15:23 How AI Has Already Reshaped Hiring and Job Seeking
21:11 What the Candid AI Interview Report Reveals About Trust
24:25 Why Auto-Rejecting Candidates With AI Backfires
26:08 Sorting Through the Flood of AI-Generated Applications
30:46 Confronting Bias in AI-Powered Hiring Tools
36:46 Reframing AI Anxiety Into Opportunity
40:11 Time Capsule: Advice to His Younger Self
42:14 Where to Find Greenhouse and Closing Thoughts

Resources
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AI fun fact article
On How Paolo Ardoino, CEO of Tether, is exploring whether decentralized AI can fix banking, crypto, and the future of finance

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Andrew 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 Learn
How 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 work
What Upwork's AI productivity index revealed when agents were tested against real jobs already completed by human freelancers
Why every agent on the platform is paired with a human expert, and how that pairing changes both the speed and the quality of the result
How proof of humanness works in practice, from identity verification to AI video interviews, and what happens when the system gets it wrong
Why 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 

Resources
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Connect with Andrew on LinkedIn

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Mahe 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 Conversation
00:00 Intro & AI Fun Fact
04:27 Meet Mahe Bayireddi, Phenom CEO
06:36 AI Is Your Ally, Not Enemy
09:29 What Hiring Got Wrong
10:43 Culture as Organizational DNA
12:26 Why Agent Interviews Won't Scale
16:47 Bias and AI Responsibility
19:00 Fraud Agents by Region
24:23 Frontline vs. Desk-Based AI
28:53 A Billion People, Right Jobs
31:24 Lessons From the Founder Journey
33:01 100 Rejections, One Cockroach
35:39 Where to Find Mahe and Phenom

Resources:
Subscribe to the AI & The Future of Work Newsletter
Connect with Mahe on LinkedIn
AI fun fact article
On using AI to make hiring more fair and eliminate human bias

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Walk 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 Guests
Andrew Rabinovich, former Upwork Chief Technology Officer. Full conversation: https://www.buzzsprout.com/520474/episodes/19855791
Todd Barr, CEO at Axonis. Full conversation coming Thursday, October 1, 2026
Kartik Sakthivel, Chief Information Officer at John Hancock. Full conversation coming Thursday, October 8, 2026
Over 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 Learn
What 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 unsupervised
The one line in hiring that should never be crossed, no matter how capable the technology becomes
Where human agency actually lives once the work itself is handed off, and why how it gets done may matter less than we assume
Why collecting data and acting on it are two different problems, and what opens up once that distance closes
The question worth asking before any AI project starts, and why the answer is sometimes something far simpler than AI
What to say to someone watching a machine learn to do the thing that made them valuable, and where the time that comes back should go

Inspired 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)