The AI Report Podcast

By Liam LawsonBusiness Entrepreneurship Technology

The AI Report Podcast episodes

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/
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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/
Partner Links
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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
July 23Formal Verification, AI Hallucinations, and Mathematical Truth | Tudor Achim, Co-Founder, Harmonic
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
What "truth" means in mathematics versus science, and why logical reasoning is really just a simple form of math

Why the proof of Fermat's Last Theorem had a hidden flaw for two years, even after being announced

Why hallucinations are actually necessary for AI reasoning, and what separates a good hallucination from a bad one

How Harmonic uses Lean and formal verification to make Aristotle's math proofs checkable step by step, like reviewing code

Why Tudor doesn't think any AI will ever be trusted to fully verify its own output

Why Harmonic gives away the Aristotle API for free right now, and where the business model is headed

The "phase transition" Tudor believes is happening in math for the first time in 4,000 years: from English proofs to machine-verified code

Why open-sourcing formal math matters more to Harmonic than keeping a competitive edge

Harmonic's five-year goal: contributing to solving a Millennium Prize Problem by 2028

A debate on whether the U.S. education system actually teaches critical thinking, and what AI should (and shouldn't) change about how kids learn

What Harmonic actually looks for when hiring, and why résumés carry almost no signal anymore

Tudor's take on inequality, taxation, and what a healthy AI-driven economy could look like
Episode Timestamps
00:00 Intro and welcome
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/

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1 hr 10 min
July 16The Reliability Problem Holding AI Back | Dan Klein, CTO, Scaled Cognition
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:
What a language model actually does at its core: next token prediction

Why LLMs are plausibility engines, not truth engines

The difference between a hallucination and a lie

Why AI mistakes have no warning signs the way bad translations or sketchy websites do

How RLHF can train models to be sycophantic instead of accurate

The "package delivery" thought experiment: when reward signals diverge from truth

Why bolting reliability onto LLMs after the fact doesn't work

How Scaled Cognition architects models around verified actions instead of raw text generation

Why bigger models aren't automatically better models

The difference between disruptive technology and scaled technology

Why startups, not incumbents, tend to drive technical breakthroughs

What metacognition is and why today's AI systems don't have it

Why Dan believes reliability is the next major frontier in AI
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/

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1 hr 39 min
July 09How Human Data Shapes Every AI Model | Enzo Blindow, VP of Data & AI, Prolific
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:
Why data volume is a solved problem and quality is everything now

How RLHF actually shaped early versions of ChatGPT

Why AI models lean too heavily into stereotypes

The asymmetry and hidden bias baked into internet-sourced training data

Prolific's ICLR research on commercial pressure in AI models

Who's responsible when AI models cause harm: labs vs. data providers

Synthetic data's ceiling, and why humans still have to validate it

What actually defines "taste" and why it's nearly impossible to model

The risk of AI flattening nuance and marginalized perspectives

Why human data is one of the most defensible moats in AI

Enzo's own definition of what "data" really means
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
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1 hr 1 min
July 02How to Successfully Roll Out AI Across Your Organization | Scott Likens, Global Chief AI Engineer, PwC
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:
Why most organizations are approaching AI the wrong way

The "frozen middle" slowing down enterprise AI adoption

How PwC is scaling AI across a global workforce

Why AI is different from every technology wave before it

The future of software engineering in the age of AI

Which industries are moving fastest with AI adoption

Why AI won't just replace jobs, it will reshape them

The role education must play in an AI-powered future

China's AI strategy versus the United States

Why curiosity may become the most important skill of the next decade
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
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51 min
June 25 The Internet Is Becoming More Centralized. Here's Why It Matters | Ajit Varma, Firefox
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/
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59 min
June 18AI Will Make Everyone Creative | Ian Wang, Adobe Express
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:
Why Adobe believes everyone is creative

The mission behind Adobe Express and "Creativity for All"

How AI is helping people communicate ideas more effectively

Why most people still use AI in fragmented workflows

The "aha moment" Adobe hopes users experience with AI

What makes Adobe Express different from other creation tools

Why enterprise AI needs context, governance, and brand knowledge

Adobe's vision for a fully conversational creative workflow

How AI can eliminate the blank canvas problem

Why the future of work will combine human creativity with AI assistance

The importance of balancing conversation, editing, and human control

How Ian combines design, technology, and business thinking
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/

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27 min

About The AI Report Podcast

From the publisher's feed

The AI Report podcast is a weekly AI podcast for business leaders that looks at artificial intelligence through a human lens. Understanding AI means understanding the people behind it: their motivations, their values, and the choices that shape how AI shows up at work.