AI is reshaping the tech landscape, but a big question remains: is this just another platform shift, or something closer to electricity or computing in scale and impact? Some industries may be transformed. Others may barely feel it. Tech giants are racing to reorient their strategies, yet most people still struggle to find an everyday use case. That tension tells us something important about where we actually are.

In this episode, technology analyst and former a16z partner Benedict Evans joins General Partner Erik Torenberg to break down what is real, what is hype, and how much history can guide us. They explore bottlenecks in compute, the surprising products that still do not exist, and how companies like Google, Meta, Apple, Amazon, and OpenAI are positioning themselves.

Finally, they look ahead at what would need to happen for AI to one day be considered even more transformative than the internet.

Timestamps: 

0:00 – Introduction 
0:17 – Defining AI and Platform Shifts
1:50 – Patterns in Technology Adoption
6:04 – AI: Hype, Bubbles, and Uncertainty
13:25 – Winners, Losers, and Industry Impact
19:00 – AI Adoption: Use Cases and Bottlenecks
24:00 – Comparisons to Past Tech Waves
32:00 – The Role of Products and Workflows
40:00 – Consumer vs. Enterprise AI
46:00 – Competitive Landscape: Tech Giants & Startups
51:00 – Open Questions & The Future of AI

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Jonathan Swanson has built two rare successes: Thumbtack, the home-services marketplace, and Athena, the fast-growing platform that pairs ambitious people with world-class personal assistants. Today he runs a 4,000-person company, invests on the side, and raises four kids — all by designing his life around leverage.

a16z General Partner, Erik Torenberg, sits down with Jonathan to unpack what that actually looks like. They discuss how elite assistant culture shaped his philosophy, why delegation is a skill most founders never truly learn, and how the combination of humans and AI is redefining personal productivity. Jonathan explains why he believes ambition grows with leverage, not the other way around, and breaks down how he delegates everything from scheduling to search processes to entire life systems.

They also get into the future of work, the rise of machine-generated delegation, the expanding role of chiefs of staff, and how founders can design their time around the few things that matter most. It’s a conversation about work, life, and the systems that allow people to operate at scale.

Timecodes

0:00 – Introduction

1:52 – The power of delegation: from the White House to Thumbtack

03:13 – Human vs. AI assistants: the future of delegation

05:30 – Levels of delegation: from tasks to algorithms

07:31 – Principles of effective delegation

08:50 – Delegation & productivity hacks

10:46 – The future: machine-generated delegation

12:36 – Global talent & leveraging international teams

13:33 – Assistants and financial leverage

14:45 – Company culture across borders

16:18 – Assistants as accountability partners

17:52 – Coaching, feedback, and the human element

19:30 – Goal setting, time management, and prioritization

23:07 – Frameworks for founders: time, energy, and meetings

26:06 – The efficient path vs. the effect path

28:19 – Executive hiring: principles and pitfalls

30:19 – Reference check signals 

33:09 – Principles for company transparency

36:55 – Cofounder relationships & company building

39:19 – Chief of staff vs. executive assistant

40:06 – Learning from high-performers: Lonsdale, Elon, Thiel, etc.

47:10 – Building your universe: org structures and talent networks

52:33 – Managing founder psychology & staying in the game

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.

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Originally published on the a16z Infra podcast. We're resurfacing it here for our main feed audience.

AI coding is already actively changing how software gets built.

a16z Infra Partners Yoko Li and Guido Appenzeller break down how "agents with environments" are changing the dev loop; why repos and PRs may need new abstractions; and where ROI is showing up first. We also cover token economics for engineering teams, the emerging agent toolbox, and founder opportunities when you treat agents as users, not just tools.

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Naveen Rao is cofounder and CEO of Unconventional AI, an AI chip startup building analog computing systems designed specifically for intelligence. Previously, Naveen led AI at Databricks and founded two successful companies: Mosaic (cloud computing) and Nervana (AI accelerators, acquired by Intel). 

In this episode, a16z’s Matt Bornstein sits down with Naveen at NeurIPS to discuss why 80 years of digital computing may be the wrong substrate for AI, how the brain runs on 20 watts while data centers consume 4% of the US energy grid, the physics of causality and what it might mean for AGI, and why now is the moment to take this unconventional bet.

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Fei-Fei Li is a Stanford professor, co-director of Stanford Institute for Human-Centered Artificial Intelligence, and co-founder of World Labs. She created ImageNet, the dataset that sparked the deep learning revolution. 

Justin Johnson is her former PhD student, ex-professor at Michigan, ex-Meta researcher, and now co-founder of World Labs.

Together, they just launched Marble—the first model that generates explorable 3D worlds from text or images.

In this episode Fei-Fei and Justin explore why spatial intelligence is fundamentally different from language, what's missing from current world models (hint: physics), and the architectural insight that transformers are actually set models, not sequence models.

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Recently, a16z General Partner Anish Acharya joined Ollie Forsyth on NEW ECONOMIES. They talked about why consumer tech is surging again, how AI is enabling 100M-user products at unprecedented speed, and what founders need to understand heading into 2026 — from distribution shifts to founder mindset to the mechanics behind the fastest product cycle in tech history.

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a16z General Partners David Haber, Alex Rampell, and Erik Torenberg discuss why 19 out of 20 AI startups building the same thing will die - and why the survivor might charge $20,000 for what used to cost $20.

They expose the "janitorial services paradox" (why the most boring software is most defensible), explain why OpenAI won't compete with your orthodontic clinic software despite having 800 million weekly users, and reveal how non-lawyers are building the most successful legal AI companies. Plus: the brutal truth about why momentum isn't a moat, but without it, you're already dead.

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A16Z co-founder Ben Horowitz joins Shaan Puri and Sam Parr on My First Million to talk about how to be a great leader.

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Russ Fradin sold his first company for $300M. He’s back in the arena with Larridin, helping companies measure just how successful their AI actually is.

In this episode, Russ sits down with a16z General Partner Alex Rampell to reveal why the measurement infrastructure that unlocked internet advertising's trillion-dollar boom is exactly what's missing from AI, why your most productive employees are hiding their AI usage from management, and the uncomfortable truth that companies desperately buying AI tools have no idea whether anyone's actually using them. 

The same playbook that built comScore into a billion-dollar measurement empire now determines which AI companies survive the coming shakeout.

Timecodes: 

0:00 — Introduction 

2:15 — Early Career, Ad Tech, and Web 1.0

3:09 — Attribution Problems in Ad Tech & AI

4:30 — Building Measurement Infrastructure

6:49 — Software Eating Labor: Productivity Shifts

8:51 — The Challenge of Measuring AI ROI

14:54 — The Productivity Baseline Problem

18:46 — Defining and Measuring Productivity

21:27 — Goodhart’s Law & the Pitfalls of Metrics

22:41 — The Harvey Example: Usage vs. Value

25:18 — Surveys vs. Behavioral Data

28:38 — Interdepartmental Responsiveness & Real-World Metrics

31:00 — Enterprise AI Adoption: What the Data Shows

33:59 — Employee Anxiety & Training Gaps

38:31 — The Nexus Product & Safe AI Usage

42:08 — The Future of Work: Job Loss or Job Creation?

44:40 — The Competitive Advantage of AI

53:45 — The Product Marketing Problem in AI

55:00 — The Importance of Specific Use Cases

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In this episode, a16z GP Martin Casado sits down with Sherwin Wu, Head of Engineering for the OpenAI Platform, to break down how OpenAI organizes its platform across models, pricing, and infrastructure, and how it is shifting from a single general-purpose model to a portfolio of specialized systems, custom fine-tuning options, and node-based agent workflows.

They get into why developers tend to stick with a trusted model family, what builds that trust, and why the industry moved past the idea of one model that can do everything. Sherwin also explains the evolution from prompt engineering to context design and how companies use OpenAI’s fine-tuning and RFT APIs to shape model behavior with their own data.

Highlights from the conversation include: 

• How OpenAI balances a horizontal API platform with vertical products like ChatGPT
• The evolution from Codex to the Composer model
• Why usage-based pricing works and where outcome-based pricing breaks
• What the Harmonic Labs and Rockset acquisitions added to OpenAI’s agent work
• Why the new agent builder is deterministic, node based, and not free roaming

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Ben Horowitz reveals why the US already lost the AI culture war to China—and it wasn't the technology that failed. While Biden's team played Manhattan Project with closed models, Chinese developers quietly captured the open-source heartbeat of global AI through DeepSeek, now running inside every major US company and university lab. The kicker: Google and OpenAI employ so many Chinese nationals that keeping secrets was always a delusion, but the policy locked American innovation behind walls while handing cultural dominance to Beijing's weights—the encoded values that will shape how billions of devices interpret everything from Tiananmen Square to free speech.

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