Vinay Gidwaney is Chief Product Officer and Mike Sullivan is Co-Founder and CEO of OneDigital, a 6,000-person, PE-backed benefits, HR, and wealth consultancy serving roughly 100,000 employers. Their contrarian bet: AI transformation has almost nothing to do with technology and everything to do with treating AI as talent. Instead of automating tasks, OneDigital built an internal hiring pipeline for AI, complete with job descriptions, an intern-to-apprentice-to-full-time promotion path, and performance improvement plans, and used it to avoid the layoffs most "AI transformation" playbooks assume are inevitable.

Liam sits down with both of them to unpack the night Mike built a "disruption calculator" that showed OneDigital was on track to cut 1,800 of its 6,000 jobs, and how that all-nighter became the catalyst for a different strategy. They get into Ben, the AI coworker now handling daily conversations with 1,600 benefit consultants, why Vinay cites a claim from Lemonade's CEO that AI agents scored higher on customer empathy than human call center staff, the risk of companies "renting back" their own intelligence after gutting their workforce, and the thinking behind their upcoming book, Workforce Intelligence, releasing August 25th.

Key Topics Covered

Episode Timestamps

Learn more about OneDigital and the book Workforce Intelligence: https://www.onedigital.com/

Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikesullivanatdigital/

Partner Links

Learn more about your ad choices. Visit megaphone.fm/adchoices

Rana Gujral is the former CEO of Behavioral Signals and the author of the upcoming book The AI Instinct. During his time leading Behavioral Signals, Rana has led a company built on a contrarian bet: that the words in a conversation are the least interesting part of it, and that the real signal, intent, trust, stress, deception, lives in how something is said rather than what is said. His team has mapped roughly 75 to 100 behavioral dimensions in the human voice, work that now powers everything from deepfake detection for government agencies to a matching system that pairs call center customers with the agents they are most likely to have a natural, flowing conversation with.

Liam and Rana dig into the unconscious vocal tells we all give off, why pitch compression, not raised volume, is the real signature of suppressed stress, and how studying voice for eight years changed the way Rana himself talks and listens. They also get into the ethics of emotion AI, including why the EU has banned it from workplaces, and the central idea behind Rana's book: that large language models are missing an entire axis called experience. Rana introduces his concept of Artificial General Experience, or AGE, and makes the case that the real fork in the road for AI isn't intelligence versus replacement, it's whether these systems make us more ourselves or less.

Key Topics Covered

Episode Timestamps

Rana's website: https://ranagujral.com/

Partner Links

Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Tudor Achim, Co-Founder and CEO of Harmonic, the AI lab behind Aristotle, a mathematical reasoning system that won gold at the International Math Olympiad, makes the case that AI hallucinations aren't the problem with today's models. The real problem is that nobody can verify whether a hallucination is right or wrong. Tudor explains why his team bakes formal, computer-checkable verification (using a language called Lean) directly into how Aristotle reasons, so instead of trusting an AI's word, you can mathematically prove it's correct.

Liam and Tudor go deep on what "truth" actually means in mathematics versus the real world, why Andrew Wiles's famous proof of Fermat's Last Theorem had a two-year hidden flaw, and why Tudor believes math is in the middle of its first fundamental shift in 4,000 years, moving from proofs written in English to proofs written in verifiable code. They also get into a spirited debate about the U.S. education system, what Harmonic actually looks for when hiring (hint: it's not the résumé), and why Tudor thinks AI will never be trusted to grade its own homework.

Key Topics Covered

Episode Timestamps

00:33 What is truth, and is logic just math?

07:59 Why the company is called harmonic.fun

08:48 The real problem with LLMs and truth

12:57 How formal verification works inside Aristotle

16:43 Who else is building in this space

19:48 Can AI ever be verified with 100% accuracy?

23:10 Why AI can't fully verify its own answers

26:46 Open-source math vs a venture-backed business

29:39 The five-year goal: a Millennium Prize Problem by 2028

31:51 Has Harmonic's vision changed?

34:28 AI, formal verification and the future of education

44:34 Debating the US education system

45:09 How Harmonic hires and what they look for

50:21 Testing for trust and honesty in interviews

54:14 Harmonic's biggest challenge right now

58:19 The future of working with AI

59:55 Family, kids and optimism about the future

1:01:38 Inequality, capitalism and AI's role in the economy

1:04:19 Why Tudor does what he does

1:05:19 Where to find Tudor

Connect with Tudor on LinkedIn: https://www.linkedin.com/in/tudorachim/

Partner Links

Learn more about your ad choices. Visit megaphone.fm/adchoices

Every answer an AI gives you sounds equally confident, whether it's true or completely made up. That's not a bug. It's how the technology was built.

Dan Klein is CTO and co-founder of Scaled Cognition, and a professor of computer science at UC Berkeley. In this conversation with Liam, Dan breaks down what a language model actually is, why it was never designed to know the truth in the first place, and why today's AI systems have no "smells," the subtle warning signs humans usually rely on to tell good information from bad.

They get into why reinforcement learning from human feedback quietly trains models to tell people what they want to hear, how that can tip into outright deception, and why Dan believes reliability, not raw intelligence, is the biggest unsolved problem in AI today.

Key Topics Covered:

Episode Timestamps:

00:00 Intro

00:15 What a language model actually is

06:31 From well-formed sentences to general knowledge

08:27 Why LLMs are plausibility engines, not truth engines

12:06 How Perplexity approaches verifiable answers

12:40 Dan's background and Scaled Cognition's mission

15:16 The two anti-patterns companies use to control LLMs today

21:16 How Scaled Cognition architects models differently

23:28 Does every client need a custom-trained model?

29:12 Why prompting alone can't guarantee reliability

30:55 Modularity, contracts, and building reliable systems

34:40 Why trust and digital literacy matter beyond the enterprise

39:12 Code smells and why AI mistakes have no warning signs

41:14 Are AI companies incentivized to tell the truth?

42:55 How reinforcement learning actually works

44:35 The package delivery thought experiment

48:44 Why models are trained to be sycophantic

51:01 Where this incentive is mechanically baked into the model

53:43 Does responsibility fall back on humans?

58:10 Just be more reliable than a human, not perfectly true

1:02:59 The last major technique shift in AI

1:10:55 Why frontier labs keep scaling despite the risk of disruption

1:17:15 The future of hyper-specialized models vs. one broad model

1:19:47 Is there anything uniquely human AI can't replicate?

1:25:45 Wearing three hats: professor, researcher, and CTO

1:29:47 Why Dan does what he does

Connect with Dan on LinkedIn:
https://www.linkedin.com/in/dan-klein/

Partner Links

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH

Learn more about your ad choices. Visit megaphone.fm/adchoices

The volume problem in AI is solved. Now it's all about data quality, and who gets to define it.

Enzo Blindow is VP of Data & AI at Prolific, a platform that connects hundreds of thousands of people worldwide to the frontier labs and enterprises training and evaluating AI models. In this conversation with Liam, Enzo breaks down what actually goes into building high-quality training data, why models lean too hard into stereotypes, and the research Prolific published showing how easily AI can be nudged toward commercially motivated, and sometimes harmful, suggestions.

They discuss why synthetic data hits a ceiling that only human data can break through, how a single mistranslated instruction can quietly corrupt an entire dataset, and why "good taste" might be one of the hardest things for AI to ever replicate.

Key Topics Covered:

Episode Timestamps:

00:00 Intro

00:21 What Prolific actually does

02:48 MCP vs. API vs. CLI access

04:19 How frontier labs started working with Prolific

06:40 Data volume vs. quality, and the role of RLHF

10:58 Who Prolific's biggest customers are

13:12 Why labs choose Prolific over other data vendors

16:13 Fact vs. opinion in AI training

19:02 Stereotypes and bias in AI models

21:15 Prolific's ICLR research on commercial pressure

23:36 Who's responsible: labs, governments, or data companies

27:22 How Prolific's data collection actually works

31:59 Synthetic data vs. human data

36:04 What defines "taste" in AI-generated content

39:33 Good taste vs. bad taste, and the risk of AI regression to the mean

42:36 Why Enzo joined Prolific

45:56 Blind spots most people have about training data

47:22 The "SaaSpocalypse" and data as a business moat

51:38 How Enzo visualizes "data" in his own mind

54:22 Why Enzo does what he does

57:16 Where to find Enzo and Prolific

Connect with Enzo on LinkedIn:

https://www.linkedin.com/in/enzoblindow/

Partner Links

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH

Learn more about your ad choices. Visit megaphone.fm/adchoices

AI isn't replacing jobs. It's changing the way work gets done.

Scott Likens, Global Chief AI Engineer at PwC, spends his days helping organizations navigate one of the biggest technological shifts in history. In this conversation with Arturo Ferreira, Scott shares what he's seeing inside some of the world's largest companies as they race to adopt AI, transform workflows, and prepare for a future that's arriving faster than most people expect.

They discuss why many AI projects fail, the "frozen middle" preventing organizations from scaling AI, how education needs to evolve, and why the biggest challenge isn't the technology itself; it's helping people adapt to it.

Key Topics Covered:

Episode Timestamps:

00:00 Intro and the story behind Scott's LinkedIn profile

03:20 What a Chief AI Engineer actually does

06:15 Why AI is different from previous technology revolutions

10:00 The "frozen middle" problem inside organizations

15:25 Why AI adoption is more about people than technology

18:45 PwC's partnership with Anthropic and enterprise AI

22:00 Which industries are moving fastest with AI

27:00 AI, jobs, and workforce transformation

33:00 Why education needs to change

35:30 China, the U.S., and the global AI race

40:00 The questions CEOs are asking about AI today

44:00 Why most AI projects fail

47:30 Advice for leaders trying to scale AI

50:00 Books, learning, and final thoughts

Connect with Scott on LinkedIn:

https://www.linkedin.com/in/scottlikens/

Connect with Arturo on LinkedIn:

https://www.linkedin.com/in/arturoferreira/

Partner Links

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH

Learn more about your ad choices. Visit megaphone.fm/adchoices

Most people don't think about their browser. Ajit Varma thinks they should.

As Head of Firefox, Ajit sits at the intersection of AI, privacy, open-source software and the future of the internet. In this conversation with Liam Lawson, he explains why browser competition matters more than ever, how AI is changing the way we interact with the web, and why user choice could become one of the most important issues of the next decade.

Key Topics Covered:

- Why Firefox believes the future of AI should be built on open standards

- How AI is changing browsers and the way people access information online

- Why browser competition matters more than most people realize

- The hidden risks of relying on a single AI model or platform

- How Firefox approaches privacy differently from Chrome and Safari

- Why most users choose convenience over customization

- The role open source software plays in preserving an open internet

- Why AI could create more builders, creators and entrepreneurs

- Ajit's vision for a future where AI works for humanity, not just corporations

Episode Timestamps:

00:00 Intro and the mission behind Firefox

02:09 Browser engines and why they matter

04:48 AI, browsers and the future of the web

05:58 Why Firefox takes a different approach to AI

11:15 User choice, AI models and customization

16:40 Why Ajit joined Mozilla

21:13 AI competition, consolidation and the future of tech

25:07 Does open source have a branding problem?

28:07 Privacy, customization and Firefox users

34:26 Product design, simplicity and consumer behavior

39:40 How Ajit uses AI personally

41:53 AI, entrepreneurship and the future of work

53:07 Why do you do what you do?

Connect with Ajit on LinkedIn: https://www.linkedin.com/in/ajitvarma/

Partner Links:

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH

Learn more about your ad choices. Visit megaphone.fm/adchoices

Most people don't think of themselves as creative. Ian Wang thinks that's about to change.

As VP & Head of Product for Adobe Express, Ian sits at the intersection of creativity, business strategy, product development, and AI. In this conversation with Liam Lawson, he explains why everyone is creative, how AI can help people communicate ideas more effectively, and why the future of creative work will be more conversational, accessible, and collaborative than ever before.

Ian also shares Adobe's vision for a fully conversational creation experience, why enterprise AI requires more than just powerful models, and how Adobe is building tools that help millions of people move from idea to execution faster.

Key Topics Covered:

Episode Timestamps:

00:00 Ian's journey from designer to Adobe executive

02:00 Combining creativity, business, and technology

05:45 What "Creativity for All" actually means

07:44 What Adobe Express is and who it's for

10:00 The AI "aha moment" Adobe is building toward

12:15 Why context matters more than models

14:00 Serving everyone from consumers to Fortune 500 companies

16:05 Adobe's upcoming conversational AI experience

18:00 Why the future isn't prompt-only workflows

19:40 The skills that matter most in an AI-powered world

22:10 Why Ian loves building creative products

Connect with Ian on LinkedIn: https://www.linkedin.com/in/ianwang/

Partner Links:

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: www.theaireport.ai/leaders-launch-guide

Learn more about your ad choices. Visit megaphone.fm/adchoices

Anthropic just released its newest model, Fable 5.* OpenAI has officially taken another step toward public markets. And some of the biggest competitors in AI are now relying on the same infrastructure behind the scenes.

This week, Anthropic launches Fable 5 and introduces built-in safeguards for advanced use cases*, OpenAI confidentially files paperwork for a future IPO, Apple unveils its biggest Siri upgrade in years at WWDC, Google signs a massive compute agreement with SpaceX to support Gemini, and Meta expands its AI infrastructure footprint through a new partnership in India.

If you are a founder, operator or executive trying to keep up with AI, this is your weekly five minute briefing every Tuesday.

Stories Covered This Week:

*Update: Since recording, Anthropic has paused broader public access to Claude Fable 5 and Mythos 5. Stay tuned for next week's episode for the full update.

Episode Timestamps:

00:00 Intro

00:16 Anthropic launches Fable 5

01:49 OpenAI moves toward public markets

02:43 Apple's biggest Siri update in years

04:00 Google signs major AI infrastructure deal

05:01 Meta expands AI infrastructure in India

05:59 Outro

Partner Links:

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH

Learn more about your ad choices. Visit megaphone.fm/adchoices

Everyone thinks the future of work means spending more time interacting with AI. Aaron Mitchell Finegold believes the opposite.

As Head of Product Marketing at Adobe, Aaron sits at the intersection of enterprise AI, agentic workflows, brand intelligence and the future of how work gets done. In this conversation with Liam Lawson, he explains why most companies are approaching AI adoption incorrectly, what an agentic content supply chain actually looks like, and why trust, judgment and human relationships may become even more valuable in an AI-first world.

Stories Covered This Week:

Episode Timestamps:

00:00 Intro and Aaron's background

03:01 Hands-on experience building with AI

04:41 The biggest misconception about generative AI

05:38 What an agentic content supply chain looks like

07:30 When should AI agents replace humans?

08:40 How Adobe's Brand Intelligence works

10:44 Where AI starts and human creativity ends

12:09 Aaron's vision for the future of work

13:41 What trust means for humans and AI agents

16:34 Reliability, AI systems and enterprise adoption

19:34 The personality traits that will thrive in the AI era

21:38 Why do you do what you do?

Connect with Aaron on LinkedIn: https://www.linkedin.com/in/finegold/

Partner Links

Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass

Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe

Join the community: www.theaireport.ai/leaders-launch-guide

Learn more about your ad choices. Visit megaphone.fm/adchoices