Claire and Josh break down the growing push to “pace the frontier” as Dario Amodei and other AI leaders debate how quickly AI should advance and what new safety guardrails could look like.

They also unpack Suno V6’s new licensed approach to AI-generated music and what it could mean for artists, before diving into Bending Spoons’ agreement to acquire Miro and what the deal signals for the next generation of software companies.

Chapters:

00:00 Welcome to Lightwork

0:58 AI Safety Goes Mainstream

04:47 What Does “Pacing the Frontier” Actually Mean?

05:32 Dario Amodei’s Call for a Coordinated AI Slowdown

07:59 Are AI Models Optimizing for Human Intent?

10:48 Microsoft’s Rules for Safer AI

12:30 Who Should Regulate Frontier AI?

16:02 Is AI Safety Just Fear-Mongering?

18:53 Suno Ships the Licensed Models

20:26 How Suno V6 Handles Copyright & Artist Rights

23:43 Bending Spoons Buys Miro

27:17 What Is Bending Spoons’ Bigger Strategy?

29:21 The Future of AI, Music & Software

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Profound co-founder and CEO, James Cadwallader, joins Lightspeed’s Claire Zau following the company’s $180 million Series D at a $1.8 billion valuation.

They discuss how AI agents are changing the way consumers discover brands, Profound’s evolution from AI visibility analytics into an always-on AI marketing teammate, and why enterprise marketers need a unified context layer to create high-quality, on-brand work at scale.

James also breaks down the emerging role of the marketing engineer, Profound’s work to build a comprehensive marketing benchmark, what the new funding could help the company build, and why he believes the future of marketing will be AI-driven and human-supervised.

Timestamps:

0:00 What Does Profound Do and What Is AEO?

3:16 What Does Profound Unlock Today?

4:59 Where Does AI Marketing Still Need Human Judgment?

7:20 What Is a Marketing Engineer and How Will the Role Evolve?

9:27 How Do You Benchmark Marketing?

11:45 What Drove Profound to Raise Its Series D?

13:10 What Could Marketing Look Like in Five Years?

Santi Nestares joins Claire Zau and Sam Eisler to explain why he rebuilt the ERP from scratch. Santi ran a previous company to about a hundred million, then lost nine months to a legacy ERP migration that left him staring at a blank screen while lenders and investors waited on numbers. His argument is that the ERP looks too big to attack directly, so startups go after an adjacent wedge instead, get good at it, and end up dependent on the system they meant to replace. DualEntry went at the hard thing first. Santi calls it the first ERP created after ChatGPT-3.

Sam explains why this was a team Lightspeed wanted to back, and Santi tells the story of a negotiation that ran from a car to JFK and ended with him arguing millions on the phone in front of a TSA agent, then tearing up his ticket and heading back into the city. He also describes the team he put in what they call the basement, kept from talking to the rest of the company, and tasked with collapsing the product onto a single Kanban board where agents draft the work and a human approves it. Along the way, a customer who took one discovery call, went quiet for a month, spent 36 hours in the sandbox and came back ready to sign. A team of close to a hundred that includes a top 150 tennis player and a professional poker player. And a migration Santi ran from his plane over a Starlink propped in the window, books reconciled to the penny before landing.

Timestamps

0:00 Welcome to Lightwork

1:41 What Is DualEntry and What Is an ERP

2:20 Why Build a New ERP

3:50 How AI Made Next-Generation ERP Possible

5:44 Leaving a Successful Company

7:58 Finding the Right Way to Tackle ERP

12:23 Which DualEntry Customer Stories Stand Out?

14:03 How Lightseed and DualEntry First Connected

18:12 How DualEntry Has Evolved Since the Partnership

20:40 Inside Project Helios

24:59 Building Fast-Moving Teams at DualEntry

26:09 What Makes a Great DualEntry Hire

30:02 The Future of AI, Fintech, and Organizations

Samar Abbas, co-founder and CEO of Temporal, joins Claire Zau and Lightspeed partner Anoushka Vaswani to break down a $550 million Series E at a $12.55 billion valuation, seven months after a round that valued the company at $5 billion. Temporal is an open source platform for durable execution, the guarantee that code runs to completion no matter what fails underneath it. Samar explains why that matters more in the agent era. The longer an agent runs and the more tools it touches, the more the chances of failure compound, and the real world is full of cases where a retry means refunding a customer twice.

Anoushka makes the case that reliability and long running execution are the two things every useful agent needs, which puts Temporal in the path of token flow as agents do more work. Samar walks through the subscription workflow that can take “four lines of code” instead of a stack of queues and timers, and the nearly twenty year arc from Simple Workflow at Amazon through Cadence at Uber. They close on Temporal Nexus and what it would take to extend the same guarantees across company boundaries, so one business's agent can reliably call another's.

Chapters:

0:00 Temporal’s $550M Series E and $12.55B Valuation

0:45 What Temporal Does and How It Works

3:00 How Temporal Can Solve a Critical AI Infrastructure Problem

4:16 Where AI Models Fail in Real-World Execution

7:04 How Temporal Changes the Developer Experience

8:43 Temporal’s “Aha Moment” for AI Teams

9:43 Interesting Temporal Use Cases

11:44 From Simple Workflow at Amazon to Cadence at Uber

14:11 Temporal's Partnership with Lightspeed

17:59 How Temporal Will Use the New Funding

19:13 The Future of Agents Communicating With Each Other

22:38 What Will Become Obvious About AI in the Future

Claire Zau and Josh Machiz are joined by Lightspeed partner Bucky Moore for a week about how much control we are handing over to AI. OpenAI released GPT-6 Astra, its most capable model and, by OpenAI's own account, a harder one to monitor. Bucky explains why looping inference compresses the chain of thought that used to make these models readable, and why the near term answer may be tighter permissions and access controls rather than interpretability research.

Then to the agents people are actually running. Bucky has given Instinct his personal email and credit card and let it book flights and hotels, and he argues the ChatGPT moment for personal assistants came from a startup rather than an incumbent. Meta launched Muse the same week. Claire and Josh weigh where they still want a person reviewing the work, and the group gets into what the web does when booking sites cannot tell an agent from a customer. Bloomberg also reported that ByteDance is building a real time world model, and Bucky explains why serving those models is a hard problem of its own. Reactor is a Lightspeed portfolio company. Lightspeed co-led the seed round and led the Series A.

Chapters

0:00 Welcome, Bucky Moore!

1:12 OpenAI's Astra

3:50 The New AI Model’s Architecture

4:40 AI Safety and Guardrails

8:01 Trusting AI Agents

11:09 The Future of AI Models

12:06 The AI Agent Land Grab

17:09 AI, Trust, and Personal Data

20:12 Improving AI Agent Capabilities

22:11 Enterprise AI Assistants and Personal Context

24:05 AI Agents and the Future of the Web

25:50 ByteDance and World Models

28:19 World Models and Consumer Content

30:56 Reactor and World Model Infrastructure

34:13 Real-Time Video Models and Scaling

Mistral has raised €3 billion in a Series D, the largest equity round ever completed by a European tech company, just three years after the Paris AI lab launched. The round brings Samsung and EQT onto the cap table. The money will go toward compute, infrastructure, commercial growth, and international expansion, with Mistral aiming for a gigawatt of European compute by 2030.

Chapters:

0:00 How Did Lightspeed Get Involved With Mistral?

2:55 How Has Mistral Changed Since Its Early Days?

4:30 Where Will the €3B be Invested?

6:05 Why Does Sovereign AI Matter?

7:37 What Role Is Mistral Trying to Play in AI?

10:29 Why Is Mistral Building European Compute?

11:59 How Does Mistral’s Full-Stack Strategy Fit Its Vision?

13:38 What’s Next for Mistral and European AI?

15:25 Could Mistral Transform Physical Industries?

Claire Zau and Josh Machiz are joined by Lightspeed partner Bucky Moore for a week about how much control we are handing over to AI. OpenAI released GPT-6 Astra, its most capable model and, by OpenAI's own account, a harder one to monitor. Bucky explains why looping inference compresses the chain of thought that used to make these models readable, and why the near term answer is tighter permissions and access controls rather than interpretability research.

Then to the agents people are actually running. Bucky has given Instinct his personal email and credit card and let it book flights and hotels, and he argues the ChatGPT moment for personal assistants came from a startup rather than an incumbent. Meta launched Muse the same week. Claire and Josh weigh where they still want a person reviewing the work, and the group gets into what the web does when booking sites cannot tell an agent from a customer. Bloomberg also reported that ByteDance is building a real time world model, and Bucky explains why serving those models is a hard problem of its own. Reactor is a Lightspeed portfolio company. Lightspeed co-led the seed round and led the Series A.

Chapters

0:00 Welcome, Bucky Moore!

1:12 OpenAI's Astra

3:50 The New AI Model’s Architecture

4:40 AI Safety and Guardrails

8:01 Trusting AI Agents

11:09 The Future of AI Models

12:06 The AI Agent Land Grab

17:09 AI, Trust, and Personal Data

20:12 Improving AI Agent Capabilities

22:11 Enterprise AI Assistants and Personal Context

24:05 AI Agents and the Future of the Web

25:50 ByteDance and World Models

28:19 World Models and Consumer Content

30:56 Reactor and World Model Infrastructure

34:13 Real-Time Video Models and Scaling

Tim Cook's fifteen years running Apple ended this week and John Ternus becomes the company's eighth CEO, a hardware engineer who led every iPad, the last five iPhone generations, and AirPods. Claire and Sam argue Apple's decision to sit out the model building race may have been the right one, since it left the company competing on product experience while licensing models from others. Ternus fronts his first iPhone event next Wednesday.

Meta agreed to pay eighteen billion dollars to settle state claims that Instagram and Facebook were designed to addict minors, without admitting wrongdoing, and the product terms are the part that matters. A two hour daily cap across both apps, no feeds or Reels between midnight and six, and only a parent can lift any of it. The same morning Meta ran full page ads asking TikTok and YouTube to adopt the same rules, offering to tighten its own cap to one hour per app if they follow. Then Sam explains why twenty one financial institutions want to issue a dollar stablecoin of their own, and why the banks watched transaction volume move onto rails they do not own. She reads it as a treasury tool before it is a consumer one.

Chapter

0:00 Tim Cook Steps Down As Apple CEO

1:45 Tim Cook's Legacy By The Numbers

4:00 John Ternus: Apple's 8th CEO

5:05 Apple's AI Strategy Explained

10:33 Meta's $18 Billion Teen Safety Settlement

12:29 New Instagram And Facebook Teen Limits

17:52 21 Banks Explore A Joint Stablecoin

18:28 What Is A Stablecoin?

22:33 Why JPMorgan Isn't Joining The Group

Zack Peng, co-founder and CEO of Seel, joins Lightwork to explain why he treated e-commerce returns as an underwriting problem rather than a logistics one. He was a philosophy and physics major who learned to code at Orbital Insight, where he watched algorithms predict events for hedge funds and insurers, and noticed that payment chargebacks drew heavy investment despite representing under 1% of GMV while returns ran 15 to 25%. Seel started by predicting returns from pre-purchase behavior using roughly 200 signals, including one Zack still finds surprising, which is whether a product photo uses a human model.

The conversation gets into the choice to sell the liability rather than the signals, and a go-to-market that is free for brands and paid by shoppers at checkout, where an insurance-backed refund policy sits alongside features like 24/7 support and one-click resale through an agent that lists an item across seven marketplaces. Zack explains why Seel builds agencies rather than agents, arguing that an agency owns the outcome while a product can always blame the user, and why he thinks that accountability gap is what leaves many agentic support tools short of full resolution. He closes on where agentic commerce leaves the shopper, the consumer app Seel built around that, and why he expects a one-person billion dollar brand to stop sounding far-fetched.

Chapters:

0:00 Welcome, Zack Peng!

1:45 What Does Seel Do for E-Commerce?

4:21 How Zack Peng Went From Satellites to E-Commerce

6:07 How Does Seel Predict Returns?

7:25 How Seel’s Return Prediction Model Became Profitable

9:05 What Did Seel Learn From Smaller Brands?

11:17 How Does Seel’s Business Model Work?

14:52 Who Is Accountable When Things Wrong?

17:28 Where Does Seel Draw the Line on AI Operations?

18:21 How Will Agentic Commerce Change E-Commerce?

20:07 How Will AI Change the Consumer Shopping Experience?

22:21 The Future of Seel

24:57 Closing Thoughts

Will Johnson joins Claire Zau and Josh Machiz to explain why he buys health insurance agencies instead of just selling them software. Gyde calls itself the first AI-native insurance, wealth and health brokerage. Will's argument is that broker software did not really automate labor, it only organized data into dashboards, so he believes software alone was never going to produce the unlock he wanted. Will resists the roll up label. He calls it a transformation company. Gyde launched in January with $60 million led by Lightspeed.

Then the conversation turns to open enrollment and to jobs. Will says this could be one of the most difficult fourth quarters brokers have seen. Renewals AI is Gyde's answer, with Gia running the outreach, the needs assessment, and the scheduling while the human broker delivers the advice. On jobs he says not one person has been removed because of AI and that Gyde has been hiring into acquired teams. He also puts the workforce math at six brokers retiring for every one who enters, which by Gyde's own analysis makes the problem capacity rather than replacement. He also describes speaking with more than a hundred agency owners who had already sold their firms, and ninety nine of them being deeply unhappy with how it went.