The AI Report

By Liam LawsonTechnology

The AI Report episodes

October 01How EY Is Moving Companies From 18 Months of AI Experiments to Real ROI | Mike Flynn, Principal
In this episode, Mike Flynn joins Liam to break down how companies are moving from AI experimentation to measurable business value. Mike has spent more than 20 years in consulting, including nearly a decade at EY, nine years at PwC, and now leads the technology sector for EY's consulting business.
They discuss why businesses need to redesign workflows around AI instead of simply adding AI to existing processes, how EY's "Design for Zero" approach works, and why AI agents are changing the economics of software.
Mike also explains the real costs behind AI agents, what enterprises want from AI vendors, how AI is reshaping consulting, and why EY is investing in forward-deployed engineering.
Key Topics Covered
Moving from AI experiments to measurable ROI

EY's "Design for Zero" approach to AI-first workflows

Why per-seat software budgets break with AI agents

The real cost of an AI agent task

What enterprises want from AI vendors

How AI is changing consulting and forward-deployed engineering
Episode Timestamps
00:00 - Introduction and Mike's path from the Air Force to EY and PwC
01:58 - What Mike's role at EY looks like today
03:57 - Why point solutions create "trapped work"
04:29 - From AI experiments to end-to-end transformation
07:47 - Where AI-first redesign is working today
10:00 - Why AI is breaking the per-seat software model
13:01 - The real costs behind running an AI agent
16:00 - Measuring AI spend versus business value
18:28 - How much work will AI actually take over?
20:52 - Why companies need more granular AI cost controls
25:53 - Building automated control loops for AI
31:21 - Experimenting with enterprise AI tools at scale
36:02 - Why enterprise AI adoption moves slower than personal AI
39:37 - CIOs shift from adoption to process redesign
41:29 - What enterprises want from AI vendors
45:24 - Consulting versus AI deployment at EY
46:36 - Why AI may create more consulting work, not less
49:00 - EY's forward-deployed engineering strategy
52:16 - The two qualities Mike looks for when hiring
55:33 - Why Mike does what he does
Where to find Mike:
LinkedIn - https://www.linkedin.com/in/michaelbernardflynn/
EY - https://www.ey.com/
Partner Links
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1 hr
September 24How to Build AI Systems People Can Actually Trust | Cillian Kieran, CEO & Founder, Ethyca
In this episode, Cillian Kieran, CEO and founder of Ethyca, joins Liam to talk about why data privacy and AI governance need to be treated as engineering problems, not just legal and compliance challenges.
Cillian explains how a consulting project for Heineken ahead of the GDPR pushed him to rethink privacy from an engineer's perspective and eventually build Ethyca. He breaks down how the company's product suite works, why Fides has become a widely adopted open-source privacy standard, and what changes when AI agents are given read and write access to tools like Stripe, QuickBooks or a CRM.
They also get into what foundation model providers may be missing on governance, why AI needs a harness that directs its capabilities without slowing it down, the responsibility engineers have when building AI systems, and why he thinks much of what we call AI is still statistical math wrapped in a marketing label.
Later, Cillian and Liam discuss where AI startups may consolidate, Yann LeCun's work on world models, the human impact of increasingly agent-driven work, and how growing up around art shaped Cillian's view of software as a creative pursuit.
Key Topics Covered
Why privacy and AI governance are engineering problems

How Fides became a widely adopted open-source privacy standard

The risk of giving AI agents read and write access to business systems

Why AI needs a harness that directs its capabilities without slowing it down

What foundation model providers may be missing on governance

Why engineers, not just users, carry responsibility for AI safety

Why Cillian calls AI "statistical math" with a marketing label

World models, startup consolidation and the future of AI infrastructure
Episode Timestamps
00:00 - Introduction and welcome
00:58 - From a physics dropout to a data consultancy for Fortune 500 brands
03:17 - The Six Problems of Privacy and becoming the world's most trusted technology company
07:20 - Inside the product suite: Fides, Helios, Janus, Lethe and Astralis
11:29 - How Fides became a widely adopted open-source privacy standard
14:20 - What Cillian believes foundation model providers are missing on governance
16:08 - Why AI needs a harness, not just guardrails
18:19 - Ethyca's business model and the rise in demand for consulting
23:07 - Why Ethyca went after enterprise customers first
24:50 - Testing Grok's agent tools
27:34 - MCP proliferation, OpenClaw and the risk of "permissive access"
31:11 - The seatbelt analogy for building safe AI systems
34:28 - Why AI governance isn't getting the coverage it deserves
36:26 - AI as "statistical math" and Ted Chiang's take on the label
38:43 - Foundation model economics and the future of AI startups
39:49 - Yann LeCun, world models and where Cillian would place his next bet
50:12 - Zen and the Art of Motorcycle Maintenance: classical versus romantic thinking
56:05 - Why Cillian still does what he does
01:03:34 - Where to find Cillian and closing thoughts
Where to find Cillian:
LinkedIn - https://www.linkedin.com/in/cilliankieran
Ethyca - https://ethyca.com
Partner Links
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1 hr 7 min
September 17Inside Block's Bet on Multiplayer AI | Brad Axen, Head of AI Capabilities
Brad Axen, Block's Head of AI Capabilities and the original author of Goose, joins Liam to talk about what actually makes AI useful at work. They cover how Block went from building an early open-source AI agent to MoneyBot, ManagerBot, BuilderBot and Buzz, and why Brad thinks the hardest problems now are memory, access and interface, not just model intelligence.
Brad also explains why AI memory should belong to the business rather than a single bot, what ants can teach us about shared memory systems, why "meat proxy" is becoming a new office problem, and how Buzz is testing a multiplayer model where humans and AI agents work in the same space.
Key Topics Covered
Goose, Block's open-source AI agent, and the Agentic AI Foundation

Agents vs. harnesses vs. interfaces

MoneyBot, ManagerBot and BuilderBot

Why AI memory should belong to the business, not the bot

Stigmergy and ants as a model for shared memory

Buzz and Block's "multiplayer" approach to AI at work

"Meat proxy" and the new office busywork AI can create

Why the bottleneck is shifting from writing code to deciding what to build

How AI is changing hiring, interviews and day-to-day work

The human cost of spending all day working with AI
Episode Timestamps
00:00 Intro
00:07 What Block actually is
02:40 From CERN to Block
05:19 Building Goose and taking it open source
06:29 Agents vs. harnesses vs. interfaces
10:28 MoneyBot, ManagerBot and BuilderBot
15:56 Memory, access and learning over time
22:53 What ants can teach us about AI memory
26:48 Two versions of where AI could go
28:26 Buzz and the idea of multiplayer AI
29:56 "Meat proxy": the new office problem
35:09 The Buzz case study and a 50% productivity jump
37:06 The new bottleneck now that AI can write the code
49:11 Rebuilding institutional knowledge after team restructuring
52:00 How AI is changing hiring and interviews
57:35 The loneliness of working with AI all day
59:13 Why Brad does what he does
Connect with Brad on LinkedIn:
https://www.linkedin.com/in/bradleyaxen/
Partner Links
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1 hr 5 min
September 10The Agent Economy, AI Identity, and the New Rules of Business | Loni Stark, VP Strategy & Product, Adobe
In this episode, Loni Stark, VP of Strategy and Product at Adobe, joins Liam to talk about what happens when a 25 year tech career runs alongside a full creative practice in painting, sculpture and writing, and what that split brain teaches her about building for the AI era. Loni explains why she thinks brands may already be invisible, or worse, misrepresented, inside AI answers, why every company needs to start treating AI as a new kind of audience, and how she is running her own home AI lab, complete with a self built server and a personal agent that has now run continuously for over 150 days, to understand what actually gives an AI agent an identity.
Along the way, Loni and Liam get into her Harvard Extension School research into "orphan values," the personal values people can't express in any of their current life roles, and what happens to that alignment as AI reshapes the roles themselves. She also breaks down how she balances Adobe's biggest enterprise bets, including Experience Manager, Commerce, Brand Concierge and LLM Optimizer, against the need to experiment without limits in her own time.
Key Topics Covered
Why Loni keeps a full art practice, painting, sculpture and writing, alongside her tech career

Growing up with parents who didn't understand the arts, and using creativity as a form of rebellion

Whether humans are innately creative, and why AI makes protecting your own voice more important

Why Loni has stayed at Adobe for 25 years, and how she thinks about "growing the aquarium"

Building a personal AI server at home instead of a garden, and what that setup actually involves

Swapping the underlying model and the agent harness to test what gives an AI agent a persistent identity

Her Harvard Extension School research into "orphan values" and how AI is reshaping the roles we express them through

The shift from human mediated to AI mediated experiences, and why that changes what "traffic" even means

Why being invisible to AI isn't the worst case, being misrepresented by it is

How brands should start preparing their content and catalogs to be "agent ready"

The placebo effect of working with agents, and how that belief shapes performance and creativity

How Loni balances limitless experimentation with the governance enterprise AI actually requires

Why she does what she does: an insatiable need to grow, create and become more than she currently is
Episode Timestamps
00:00 - Introduction and welcome
00:05 - Balancing a full art practice with a 25 year tech career
05:38 - Why she's stayed at Adobe for 25 years
08:30 - AI as the biggest creativity enabler she's seen
12:50 - Inside her home AI lab: hardware, memory, and swapping the agent harness
18:11 - Studying psychology at Harvard, and what "orphan values" mean
26:28 - The shift to AI mediated business, and why invisible isn't the worst case
31:46 - How brands become "agent ready" for the AI agent economy
36:23 - The placebo effect of working with AI agents
37:35 - Balancing limitless experimentation with enterprise governance
47:05 - Why Loni does what she does
49:14 - Where to find Loni and closing thoughts
Loni's Socials:
LinkedIn - https://www.linkedin.com/in/lonistark/
Loni’s Art Gallery: https://atelierstark.com/work/

Partner Links
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54 min
September 03Why This Financial Firm Built Its Own AI Tools Instead of Going Off the Shelf | Braden Warwick, Financial Planning Product Architect, PWL Capital
In this episode, Braden Warwick, Financial Planning Product Architect at PWL Capital, breaks down why so much of the financial advice sold at big banks is a sales pitch dressed up as a plan, and what a real financial plan actually requires. Braden traded a PhD in aerospace engineering for a career rebuilding how Canadians plan their money, and he brings that same engineering mindset to financial planning: define your objectives, map your constraints, then solve for the outcome that actually improves your life.
Braden also walks Liam through the AI infrastructure PWL has built in house, from a proprietary data lake to an AI powered meeting note tool and planning summaries, and explains why they chose to build their own tools instead of buying off the shelf software. They get into Monte Carlo simulations, why financial planning is really about the distribution of outcomes rather than one predicted path, and what a financial planning engagement might look like in 2031.
Key Topics Covered
How Braden went from a PhD in aerospace acoustics to building financial planning tools at PWL Capital

Why PWL's advisors are paid for advice, not for selling products, and how that changes the plan you get

The six areas of a real financial plan: investing, cash flow, tax, insurance, retirement, and estate

Treating a financial plan like an engineering problem: objectives, variables, and constraints

Why Monte Carlo simulations model financial planning as a distribution of outcomes, not one fixed path

What forms of uncertainty most financial software still misses, from real estate values to life expectancy

Why PWL built its own AI meeting note tool and data lake instead of buying an off the shelf solution

How AI is helping PWL's advisors scale personalized, evidence based financial plans

PWL's acquisition by One Digital and what it changed, and did not change, about how Braden works

What a financial planning engagement could look like by 2031
Episode Timestamps
00:00 - Introduction
00:40 - From aerospace engineering to financial planning
03:54 - Why PWL approaches financial advice differently
07:31 - The six areas of a real financial plan
11:48 - Financial planning as an engineering problem
17:56 - The psychology behind financial planning
23:14 - Objectives, constraints, and uncertainty
28:10 - How Monte Carlo simulations work
33:21 - What financial planning software still misses
39:11 - Building financial planning tools at PWL
44:16 - Inside PWL's financial planning system
51:38 - How AI is changing the advisor workflow
57:20 - Why PWL built its own AI tools and data infrastructure
1:03:41 - What changed after the OneDigital acquisition
1:06:34 - The future of financial planning
1:11:47 - Why Braden does what he does
Braden's Socials:
LinkedIn - https://www.linkedin.com/in/braden-warwick-a40b48a3/
Resources Mentioned:
Braden’s article, The Optimal Financial Plan - https://pwlcapital.com/the-optimal-financial-plan/
Partner Links
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1 hr 16 min
August 27Inside Shopify's Plan for Agentic Commerce and AI Shoppers | Andrew McNamara, VP of Applied ML, Shopify
In this episode, Andrew McNamara, VP of Applied ML at Shopify, returns to unpack how much has changed in agentic commerce since his last episode. Andrew and Liam dig into why agents are becoming "the new front door to commerce," why orders coming to Shopify stores from AI are up 13x, and what's actually happening inside Shopify's personalized shopping agent in the Shop app.
They also get into the Universal Commerce Protocol (UCP) and why AI commerce is growing 9x faster than social commerce did at the same stage, how Sidekick's architecture and app extensions work, and SimGym, Shopify's system for training AI shoppers to A/B test store changes before they ever reach a real customer.
Key Topics Covered
How shopping is shifting from stores and desktops toward agents as "the new front door to commerce"

Why orders coming to Shopify stores from AI are up 13x, and why catalog-powered AI search converts twice as well as general AI search

Inside Shop app's personalized shopping agent, and how it learns different shopping personas (like shopping for a pet versus a child)

Why customers are shifting from keyword searches to natural language queries, and the higher conversion rates that come with it

Why Shopify keeps shopping data personalized to the individual user rather than training it into a larger internal model

What the Universal Commerce Protocol (UCP) is, and why AI commerce is growing 9x faster than social commerce and 3x faster than mobile did at the same stage

The story of Shopify's CEO giving his own Hermes agent a budget so it can send him gifts in the mail

Sidekick's app extensions, and how partners like Klaviyo and Loop plugged in at launch

Campaign Autopilot's "auto research loop," and its parallels to reinforcement learning

SimGym, and how Shopify trains AI shoppers to A/B test store changes before running them on real customers

Why Sidekick runs on Anthropic's Sonnet model hosted on Google Cloud, and why that choice is model agnostic

Andrew's own habit of shopping by taking pictures throughout the week and searching by image through UCP-connected agents
Episode Timestamps:
00:00 - Introduction and welcome
00:29 - What's changed in AI and shopping since their last conversation
01:47 - Agents becoming "the new front door to commerce"
04:16 - Inside Shop app's personalized shopping agent
07:32 - Why data stays personalized to each shopper instead of training a larger model
11:53 - What the Universal Commerce Protocol (UCP) is, and orders from AI up 13x
14:58 - Merchant tooling for tracking AI-driven traffic and conversions
15:55 - The story of Tobi's Hermes agent sending him gifts in the mail
20:48 - Andrew's own habit of shopping by taking pictures and searching by image
26:59 - Sidekick's app extensions and partner integrations
33:02 - Inside Sidekick's architecture: the Sonnet model and knowledge base
35:18 - Campaign Autopilot's auto research loop
38:58 - SimGym: training AI shoppers to test store changes
42:23 - What's next for Shopify's agentic commerce features
44:17 - Where to find Andrew
Andrew's Socials:
Twitter (X) - https://x.com/DrewCH
LinkedIn: https://www.linkedin.com/in/andrewmcnamara1/
Partner Links
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46 min
August 20What It Takes to Build a Smart Shopping Cart Used by Millions | David McIntosh, Chief Connected Stores Officer, Instacart
David McIntosh, Chief Connected Stores Officer at Instacart, joins Liam to explain why the company is betting on smart shopping carts instead of rewiring stores with ceiling cameras. David walks through the $350 million acquisition of Caper, how Instacart is now live in more than 100 cities with thousands of connected carts, and why the screen on the cart, not the checkout speed, turned out to be the real driver of sales lift for retailers.
David also gets into the surprisingly hard engineering problems behind a smart cart, like figuring out whether a basket is actually empty, fusing camera and scale data in real time, and building recommendations that know exactly where a shopper is standing in the store. He and Liam talk about who owns all that shopping data, what agentic AI looks like when it moves from chat into the aisle with tools like Cart Assistant, and why grocery budgets and meal planning are becoming one of the most requested AI features in the store.
Key Topics Covered
Why David left Tenor, the GIF search engine used by billions, to build Instacart's Connected Store business

The strategic bet behind unifying online and in-store grocery shopping

Why Instacart acquired Caper for $350 million instead of building smart carts in-house

The reason Instacart chose carts over ceiling cameras for in-store AI

How a simple running total and real-time coupons drive measurable sales lift

The NVIDIA Jetson hardware and multimodal sensor fusion that let the cart "see" what's in the basket

The strange edge cases in physical AI, like why "is this cart empty" is a genuinely hard question

Who owns retailer and shopper data, and how it's used to improve recommendations

Cart Assistant: how Instacart lets customers shop inside ChatGPT and directly on retailer websites

Using agentic AI to fix store operations like out-of-stock items and supplier issues

How budget-conscious meal planning became one of the most requested AI features in the store

David's answer to Liam's closing question: why he does what he does
Episode Timestamps
00:00 - Introduction and welcome
00:14 - David's path from Tenor to Instacart's Connected Store
02:01 - The bigger bet behind bringing online and in-store shopping together
05:29 - Entering the smart cart market and acquiring Caper
08:07 - Caper's scale today: 100+ cities and millions of daily sensor inputs
10:28 - How the smart cart actually drives sales lift
12:49 - Why Instacart bet on carts instead of ceiling cameras
17:33 - The unglamorous detail that makes or breaks adoption: charging
19:26 - What makes the experience sticky enough to keep customers coming back
24:41 - Inside the hardware: NVIDIA Jetson and multimodal sensor fusion
28:43 - The strange edge case behind a seemingly simple question
35:14 - Who owns the shopping data, and how retailers use it
37:30 - Agentic shopping: Cart Assistant and buying inside ChatGPT
42:16 - Using agentic AI to fix store operations, not just shopping
46:17 - Why David does what he does
Connect with David on LinkedIn:
LinkedIn: https://www.linkedin.com/in/mcintoshdavid/
Partner Links
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53 min
August 13AI Agents Should Never Touch the Public Internet | Zachary Smith, Co-Founder & CEO, Datum
In this episode, Zachary Smith, CEO and co-founder of Datum and previously the founder of Packet (acquired by Equinix for $335M) and Voxel (acquired for $35M), joins Liam to explain why the internet is about to undergo its biggest transformation since the cloud. As AI agents, vibe coding, and thousands of new applications flood the web, Zac believes the open internet model we've relied on for decades is breaking down.
Zac argues that every person, every company, and eventually every AI agent will need its own private network. He explains why the future internet may look more like the Visa network than today's public web, how digital sovereignty and geopolitics are reshaping infrastructure, and why developers are increasingly relying on dozens of cloud services rather than just the hyperscalers.
The conversation also dives into Zac's unlikely journey from Juilliard-trained musician to building and exiting two infrastructure companies, the emotional toll of entrepreneurship, and why he keeps coming back to startups despite already having financial freedom.
Key Topics Covered
Zach's journey from Juilliard and classical music to building infrastructure companies

Building Voxel and selling the company for $35M

Starting Packet and its $335M acquisition by Equinix

Why AI agents are creating a security problem for the internet

Why every person and company may eventually need a private network

The difference between the public internet and private internet

Why the future internet could resemble the Visa network

Digital sovereignty, geopolitics, and the splintering of the internet

Why developers increasingly rely on dozens of cloud providers

How AI is turning millions of people into software developers

APIs, MCP, and the next phase of application architecture

Why Zach believes AI agents should only talk to approved systems

Open source, network effects, and Datum's long-term vision

The emotional side of entrepreneurship and why community matters more than money
Episode Timestamps
00:00 Introduction and welcome
00:06 Zach's background: from Juilliard and classical bass to startups
02:44 Building Voxel and the early cloud era
08:44 Starting Packet, raising capital, and the Equinix acquisition
15:28 Why taking time off helped him dream again
18:02 What Datum does and the idea of a network cloud
19:38 Three forces changing the internet
20:41 Hyperscalers explained: Amazon, Google, and Microsoft
24:52 Why new cloud providers are emerging
27:16 Digital sovereignty and the fragmentation of the internet
32:03 Public internet vs. private internet
32:54 Inside the physical "meet me rooms" that connect the internet
39:49 How internet routing actually works
45:56 Why developers use so many cloud providers
48:10 APIs, MCP, and AI agents
51:07 Why the future internet may resemble the Visa network
54:23 Who Datum's customers are, and why Datum is open source
1:03:07 AI agents and the next generation of software
1:07:50 Why Zach keeps building companies, and why he does what he does
Connect with Zac:
LinkedIn: https://www.linkedin.com/in/zsmith/
Website: https://www.datum.net/

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1 hr 14 min
August 06Inside the AI Hiring Pipeline: Interns, Apprentices, and Full-Time Coworkers | Vinay Gidwaney & Mike Sullivan, OneDigital
Vinay Gidwaney is Chief Product Officer and Mike Sullivan is Co-Founder and Chief Growth Officer 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
Why treating AI adoption like rolling out a new CRM guarantees failure

The failed early bet on automating RFPs, and why augmenting human thinking won instead

The stat that proves AI adoption is personal: a manager's own AI use doubles their team's usage

Building an AI "disruption calculator" overnight, and the 1,800-job number it produced

OneDigital's three-part AI framework: coworkers, builders, and agents

How OneDigital literally hires its AI: job descriptions, interns, apprenticeships, and performance improvement plans

Meet Ben, the AI coworker fielding daily conversations with 1,600 benefits consultants

Why they stay LLM-agnostic and separate the intelligence layer from the model and the harness

The real risk behind AI cost metering: losing access to the intelligence your company now depends on

A claim from Lemonade's CEO that AI agents beat humans on customer empathy

Irreducible vs. reducible skills: how to decide what AI should do and what humans should keep

The case for "faces, not headcount" and OneDigital's internal "humanity test"

What's actually inside their upcoming book, Workforce Intelligence
Episode Timestamps
00:00 Introduction
00:41 The thesis: AI transformation is about talent and leadership, not technology
02:44 Why treating AI adoption like a CRM rollout fails
04:29 Pitching VCs a "Workday for AI agents," and why it flopped
07:19 Why automating tasks failed, and augmenting human thinking won
11:20 The stat that changed everything: manager AI usage doubles team usage
15:04 Mike's personal epiphany and the "Claude" nickname from his family
18:10 Building an overnight "disruption calculator" with Claude and Replit
19:56 The result: a model showing 1,800 of 6,000 jobs at risk if nothing changes
21:11 Coworkers, builders, and agents: OneDigital's three-part AI framework
24:02 The AI hiring pipeline: job descriptions, interns, and apprenticeships
27:39 Meet Ben: the AI coworker now used by 1,600 benefits consultants daily
31:10 What's actually deployed: an LLM-agnostic stack with a separate intelligence layer
35:38 The metering and access risk: the Fable/White House security scare
39:56 Measuring "workforce intelligence": blending human and AI capability
50:07 Irreducible vs. reducible skills, and the Lemonade insurance AI-empathy example
53:38 The reskilling problem, and why human judgment stays irreplaceable
56:30 "Faces vs. headcount" and the company's internal "humanity test"
1:07:08 The origin of their book, Workforce Intelligence (out August 25th)
1:11:10 The risk of "renting back" your own intelligence after cutting your best people
Learn more about OneDigital and the book Workforce Intelligence: https://www.onedigital.com/
Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikesullivanatdigital/
Connect with Vinay on LinkedIn: https://www.linkedin.com/in/gidwaney/
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1 hr 21 min
July 30How Your Voice Reveals Emotion, Deception, and Intent | Rana Gujral, former CEO, Behavioral Signals
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
Why Behavioral Signals bet on voice as a behavioral signal instead of a language signal

The 75 to 100 dimensions of emotion, intent, and cognitive state hidden in a voice

How deepfake detection works when a synthetic voice is good enough to fool a mother

Surprising commercial uses of voice AI in marketing, call centers, and fraud detection

The science of compatibility: why some conversations click and others feel like effort

What 8 years of studying voice changed about how Rana communicates

The unconscious vocal tells that give away hesitation and suppressed stress

Why the EU banned emotion AI in workplaces, and where Rana draws his own ethical line

The idea behind Rana's book, The AI Instinct, and why AGI is the wrong question to ask

Artificial General Experience (AGE): the missing piece between intelligence and judgment

Where augmentation ends and replacement begins, from GPS to Neuralink

The geopolitical inequality of who gets access to the most capable AI tools
Episode Timestamps
00:00 - Introduction
00:09 - The contrarian bet: voice as a behavioral signal, not a language signal
02:51 - Mapping 75 to 100 dimensions of emotion, intent, and cognitive state
06:07 - Commercial uses beyond law enforcement: marketing, call centers, fraud detection
10:35 - The science of compatibility and conversational entrainment
13:35 - Beyond voice: body language, physiology, and why voice is the primary channel
16:42 - How 8 years of studying voice changed Rana's own communication
19:32 - The unconscious vocal tells everyone gives off
23:40 - Pitch compression: the real signature of suppressed stress
23:53 - The ethics of emotion AI: EU AI Act, modulation vs manipulation
27:08 - Why Rana wrote The AI Instinct
30:04 - Artificial General Experience (AGE) and what LLMs are missing
33:59 - How lived experience dynamically updates memory and meaning
38:04 - Why the goal isn't to build machines that are more human
40:50 - Augmentation vs replacement: where the line gets drawn
43:55 - Purpose, meaning, and the risk of frictionless cognition
51:23 - What we should be teaching the next generation
56:01 - The geopolitical inequality of AI access
59:11 - What Rana hopes readers take from The AI Instinct
1:02:32 - Where to find Rana and the book

Rana's website: https://ranagujral.com/
Rana’s LinkedIn: https://www.linkedin.com/in/ranagujral/

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1 hr 4 min

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