The AI industry is sending mixed signals, with markets turning red while teams report real productivity gains. In this session I explore why we are living in a split reality, where individuals move faster with these tools but the wider economy is ambivalent. We once assumed juniors would get the biggest lift from AI, yet the newer agentic tools seem to reward senior workers who know how to structure problems and judge output. 

In this podcast, I look at the evidence behind that shift and explain how these gains collide with the slow grind of organisational processes.

I cover: 

(00:00) AI productivity: A split reality 

(00:31) Decoding the stock market drop 

(02:53) Unpacking three years of AI productivity data 

(06:09) Does AI help junior or senior developers more? 

(09:54) The surprising group benefitting from AI 

(11:45) Why is there a productivity gap? 

(13:08) Most companies need a process overhaul 

(14:33) Anthropic's alarming discovery 

(16:45) So, are we moving quickly enough? 

(17:29) The counterintuitive truth about AI productivity 

Where to find me:

- Exponential View newsletter: https://www.exponentialview.co/ 

- Website: https://www.azeemazhar.com/ - 

- LinkedIn: https://www.linkedin.com/in/azhar 

- Twitter/X: https://x.com/azeem 

Production by supermix.io and EPIIPLUS1 

Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Junior roles in AI-exposed fields are disappearing fast. 

The obvious culprit is AI rapidly automating entry-level jobs. And yet, this isn't quite right. What is driving the drop is managers’ expectations about what AI will do, not the work that it's already replacing. 

I discussed this with Ben Zweig of Revelio Labs, which builds global workforce data from millions of individual profiles to track hiring, separations and job flows. Their data shows how expectation and uncertainty are reshaping the market.

Together, we explored the future of work and shared practical advice for new grads. 

We covered: 

(01:15) What's happening in the labor market? 

(05:27) The inherent complexity of the labor market 

(06:24) How Revelio Labs captures labor market data 

(08:39) "The Canary in the Coal Mine" 

(11:52) Who does AI exposure harm the most? 

(13:01) How AI anticipation is harming the job market 

(15:15) Testing the expectation mismatch hypothesis 

(17:30) Could AI be creating more jobs? 

(20:44) Breaking down jobs into smaller tasks 

(27:33) Why large companies struggle to reorganize 

(30:35) Focus on creating adaptive, flexible roles 

(36:03) Managing AI's increasing capability 

(39:11) What entry-level workers need to do 

Where to find me: 

- Exponential View newsletter: https://www.exponentialview.co/ 

- Website: https://www.azeemazhar.com/ 

- LinkedIn: https://www.linkedin.com/in/azhar 

- Twitter/X: https://x.com/azeem 

Where to find Ben: 

- https://www.linkedin.com/in/ben-zweig/ 

- Twitter/X: https://x.com/BJZweig 

- Revelio Labs: https://www.reveliolabs.com/ 

Production by supermix.io and EPIIPLUS1 

Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The AI boom isn’t just about chatbots.

In this video, I explain why cloud companies and chipmakers are exploding in value: we’re moving into an economy where computation becomes a fundamental input – like steel, electricity or oil.

If that’s true, our demand for compute could approach infinity.

I also break down new data from Wharton’s 2025 AI Adoption Report, which shows how AI agents and automated workflows are already spreading through major U.S. companies: https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/

Timestamps:

(00:00) The economic shift to computation (00:40) The surprising Cloud business boom (02:52) Is the hardware industry growth a bubble? (03:18) What is computing, really? (04:31) Our insatiable appetite for computing (09:15) Our economic dependence on computation (10:54) The rise of agentic workforces (13:05) What does infinite demand actually mean? (15:23) The future of compute demand

Where to find me:

• Exponential View newsletter: https://www.exponentialview.co/
• Website: https://www.azeemazhar.com/
• LinkedIn: https://www.linkedin.com/in/azhar
• Twitter/X: https://x.com/azeem

Production by supermix.io and EPIIPLUS1

Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I speak with Jordan Schneider, creator of Chinatalk, to explore the new phase of US–China competition. Both countries are using trade policy, export controls and industrial strategy to shift the balance of global power. Yet, their economies remain tightly bound. 

We cover: 

(01:34) The US and China’s decoupling 

(07:28) Why attempts to control China backfired 

(08:51) Understanding the Oct. 9th rare Earth rules 

(11:27) The modern iteration of Chinese communism 

(14:23) Is decoupling a strategy to avoid weaponization? 

(16:12) US leadership might be shooting from the hip 

(19:22) Are system changes inherently messy? 

(21:27) “Vibe-based” sovereignty 

(26:03) AI incumbents aren’t entrenched—yet 

(29:07) Why China remains focused on AI deployment 

(32:45) The different versions of tech-accelerationism 

(33:37) How will societies withstand rapid change? 

(36:54) What the West can learn from China 

(40:10) Where China is most misunderstood 

(43:14) Imagining an improved US-China relationship 

Where to find Jordan:

• Substack: https://substack.com/@chinatalk
• YouTube: @ChinaTalkMedia‬
• Linkedin: / jorschneider
• X: https://x.com/jordanschnyc

Where to find me:

• Substack: https://www.exponentialview.co/
• Website: https://www.azeemazhar.com/
• LinkedIn:/ azhar
• X: https://x.com/azeem

Produced by EPIIPLUS1 Ltd and supermix.io 

Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Azeem Azhar sat down with Matthew Prince, co-founder & CEO of Cloudflare. Matthew is a rare operator with the vantage point to answer a simple question: if agents do the reading, who gets paid? 

This conversation is a practical map of how AI “answer engines” upend the web’s traffic-funded model – and what could replace it.

Chapters: 

• (00:46) The currency of the web is dying 
• (06:08) Google's inflection point 
• (10:08) Why a broken business model might save the internet 
• (14:44) The incentivization of ragebait 
• (20:38) Content scarcity as a solution 
• (24:35) What could a new content business model look like? 
• (28:51) The challenge of pricing information 
• (29:31) How Cloudflare thinks about the creator economy 
• (32:06) Should smaller companies pay less? 
• (34:24) Can markets solve this without Congress? 
• (39:11) How does the agentic web affect content? 
• (43:40) A rare chance to redesign the internet 

Produced by EPIIPLUS1 Ltd and supermix.io 

Production and research: Chantal Smith, Hannah Petrovic, Nathan Warren and Marija Gavrilov.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I spoke with Dan Wang, author of “Breakneck: China’s Quest to Engineer the Future”, shortlisted for the FT & Schroders Business Book of the Year.

Dan is one of the most astute observers of China’s technological and industrial development, and his annual letters from Beijing have long been required reading for those seeking to understand the country’s evolving role in the world.

We unpacked a bold thesis: China is not merely a competitor in AI and tech, but is re-imagining its entire state apparatus as an engineering state - in contrast to the more “lawyerly” institutions of the US and UK.

If you’re interested in AI, energy or geopolitics, this conversation is for you.

We covered: 

(00:47) Why China is an engineering state

(03:40) China’s pro-engineering disposition

(06:08) The role of market competition in China

(08:07) Living through Zero COVID

(11:35) What political science terms get wrong

(12:58) Characteristics of a lawyerly society

(15:23) What Americans misunderstand about China

(21:54) Has China produced essential tech?

(23:50) The AI divide: China vs. US

(27:45) Differences in energy production

(32:07) The inherent value of process knowledge

(38:34) Is the US developing pro-engineering policies?

(44:23) What does it take for countries to compete?

Where to find me:

• Substack: https://www.exponentialview.co/
• Website: https://www.azeemazhar.com/
• LinkedIn: https://www.linkedin.com/in/azhar
• Twitter/X: https://x.com/azeem

Where to find Dan:

• Website: https://danwang.co/
• LinkedIn: https://www.linkedin.com/in/danwang15/
• Twitter/X: https://x.com/danwwang

Production by supermix.io and EPIIPLUS1 Ltd, including Chantal Smith, Marija Gavrilov, Nathan Warren and Hannah Petrovic.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Is AI a bubble? In this episode, I unpack a new five-gauge framework for understanding the biggest question in tech. 

Drawing on lessons from past manias – railways, telecoms, the dot-com boom – and grounding our analysis in fresh data, we examine economic strain, revenue growth, valuations, and the quality of capital fueling AI’s ascent. This is our effort to cut through hype and fatalism to provide a clear dashboard: where today’s AI build-out looks like a genuine boom, and where early warning signs of bubble dynamics may be emerging. 

Whether you’re an investor, policymaker, or executive, this framework offers a disciplined way to navigate the noise.

Jump to the best parts:

• (00:29) Echoes of the past 
• (01:31) The 5 gauge framework 
• (01:54) Gauge #1: Investment intensity
• (03:45) Gauge #2: Monetization level 
• (04:48) Gauge #3: Revenue trajectory 
• (06:23) Gauge #4: Valuation level
• (07:24) Gauge #5: Quality of capital 
• (10:10) Overall assessment

Produced by EPIIPLUS1 and Supermix. 

Thanks to my team: Nathan Warren, Hannah Petrovic, Chantal Smith & Marija Gavrilov

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

GPT-5 was the most advanced AI when it was released, but most people were disappointed. Why? 

In this episode, I unpack the two key paradoxes that shape how we judge new technology: shifting goalposts and negative space.

Timestamps: 

(0:00) The reaction to GPT-5 

(0:40) First paradox 

(2:55) Second paradox 

(5:29) Why this matters 

Dig deeper: https://www.exponentialview.co/p/the-paradox-of-gpt-5  

Production by supermix.io and EPIIPLUS1 Ltd, including Chantal Smith, Marija Gavrilov, Nathan Warren and Hannah Petrovic.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Can AI stocks beat Big Tech? In this episode, I discuss OpenAI and its decision to expand a secondary share sale that lets insiders sell about $10.3 billion of stock at roughly a $500 billion valuation. Although skeptical at first, the calculations reveal there is a path for OpenAI to deliver outsized returns.

I cover:

(0:00) The $500B question 

(01:11) Why the Nasdaq Index is the benchmark 

(03:35) Inside the OpenAI-Microsoft deal 

(05:50) The bull case: OpenAI’s trillion-dollar path 

(09:33) The AI market explosion 

(12:39) The bear case: Competition and constraints 

(17:13) Exploring the models of tomorrow 

(20:58) The disruption premium 

(23:21) Where will OpenAI’s revenue come from? 

(29:14) The final verdict

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is the single most important paper to come out in tech in recent weeks. Erik Brynjolfsson, Bharat Chandar and Ruyu Chen investigated whether generative AI is leading to job losses in roles most exposed to AI – and how these effects differ by age and the way AI is used. In this episode, I break down these results and their implications. 

I covered: 

(01:17) Key finding 

(03:32) What’s going on here? 

(06:13) A canary in the coal mine? 

(8:21) The dataset studied and why it matters 

(10:34) The sectors impacted and why it matters 

(12:37) Why don't firms just reduce salaries? 

(14:34) Historical parallels with electricity 

(17:20) How leadership impacts job losses 

(20:46) Implications for policy, education, equity 

(24:53) Outro 

Where to find me: 

- Substack: https://www.exponentialview.co/ 

- Website: https://www.azeemazhar.com/ 

- LinkedIn: https://www.linkedin.com/in/azhar?originalSubdomain=uk 

- Twitter/X: https://x.com/azeem 

----

Production by supermix.io and EPIIPLUS1

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.