The AI Why with Liam Lawson

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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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/

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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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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:

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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/

Partner Links:

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