Navigators

Fireflies gives away unlimited meeting recordings and transcriptions. Krish Ramineni (Co-Founder & CEO of Fireflies) says that was the plan from day one: commoditize yourself before someone else does it for you.In this episode of Navigators, Paul sits down with Krish to talk about what happens to software when models drive most of the interaction. Krish thinks most SaaS will be given away for free and that companies will charge for the agentic work instead. If you don't become agentic, you become a data source or a connector inside someone else's model, and all the value goes to them. He explains why you can't vibe code a CRM, why seat-based pricing breaks when companies run with fewer people, and why Salesforce had to decide whether to go headless.They also cover how Fireflies hit unicorn status on $19M raised, how open weight models made the free tier possible, and why Fireflies routes across seven to 10 model providers.00:00 Intro 01:48 Years of failed products before the tech caught up 04:25 Why meetings were the data nobody could capture 07:21 Running Fireflies on Fireflies with 130 people 08:41 The support agent that handles more tickets than any human 10:37 Why Fireflies won't train on customer data 11:55 Open weight models and an unlimited free tier 13:19 Why app companies need a model router 16:05 You can't vibe code a CRM 17:32 SaaS goes free, agentic work gets paid 19:47 Become infrastructure or become a connector 23:20 A unicorn on $19M raised 25:17 The second act: email, Slack, and a context graphBrowserbase gives AI agents a browser to operate in. Navigators is our series of conversations with the people building the agentic future.

Harrison Chase (Co-Founder & CEO of LangChain) has rebuilt his product stack four times since 2022, chasing models that kept changing underneath him. In this episode of Navigators, Paul sits down with him to talk about what LangChain got right, and the one piece of infrastructure it built years too late.Harrison admits the team underestimated how massive the demand for raw inference would become, and how big general-purpose coding agents would get. He walks through the shift from LangChain to LangGraph to DeepAgents, why sub-agents rarely work the way people expect, and why LangChain now runs its own hiring, content, and coding work through internal agents exposed in Slack.Topics covered:The gateway LangChain should have built a year earlierWhy agent capability converging on human capability changes the tools debateCode mode: why agents writing code beats agents using human-style toolsRunning GTM, content, and coding agents entirely inside SlackWhy sub-agents need to own whole tasks, not small piecesThe agent identity problem: delegated access vs. service accountsWhy LangChain's own AI slop problem got bad enough to need a Slack emojiNavigators is a series by Browserbase where we sit down with AI leaders navigating the future of what's happening in AI today.Host: Paul Klein (@pk_iv), founder, BrowserbaseGuest: Harrison Chase (@hwchase17), co-founder and CEO, LangChainBrowserbase: https://browserbase.com LangChain: https://langchain.com

Immad Akhund (Co-Founder & CEO of Mercury) uses what he calls the cosmic cube to decide what Mercury builds next: one axis for customer type, one for product, one for compliance and infrastructure. In this episode of Navigators, Paul sits down with him to talk about why that space is functionally infinite, and why trying to expand into all of it at once is how companies lose.Immad also explains the two-year mistake of waiting to launch a corporate credit card, why he treats every new product like a fresh seed-stage startup inside Mercury, and why talent, not capital, is the real limit on how many bets the company can run at once.CHAPTERS:00:00 Why 1-on-1s stopped working once he was managing execs07:34 Agentic cards: letting AI spend on your behalf11:09 Why fintech is three times harder than software13:50 Chewing glass: getting bank-charter ready16:08 Reaching the edge of knowledge in six months28:26 Killing 1-on-1s and what Jensen Huang has to do with it36:39 The "cosmic cube": how Mercury picks its next bet---Navigators is a series by Browserbase where we sit down with AI leaders navigating the future of what's happening in AI today.Host: Paul Klein (@pk_iv), founder, BrowserbaseGuest: Immad Akhund (@immad), co-founder and CEO, MercuryBrowserbase: https://browserbase.com Mercury: https://mercury.com 

Shawn Wang, aka Swyx, has spent the last several years narrating the AI boom through his podcast Latent Space and the AI Engineer conference. In this episode of Navigators, Paul sits down with him to talk about how far agents have actually come, and why most of the economy hasn't caught up yet.Swyx points to OpenAI's own research showing that roughly 80% of US GDP could be automated by agents today. The holdup isn't the models, he argues, it's everything downstream: cost, reliability, and a hardware shortage severe enough that people are scavenging memory out of old MacBooks to keep up with demand. He also unpacks the split between "agent labs" chasing domain specificity and "model labs" chasing AGI, and where he thinks the next wave of AI begins.Topics covered:Why 80% of GDP could be automated by agents right nowThe real reason adoption is lagging behind capabilityAgent labs vs model labs: Cursor, Sierra, and domain-specific betsPre-training, mid-training, and post-training, explained simplyThe chip and memory shortage nobody's talking aboutWhy "diffusion is cope" divided the AI industryThe self-driving car tradeoff coming for agentsFollow Navigators:https://x.com/pk_iv https://x.com/browserbase Follow Swyx:https://x.com/swyx Latent Space podcast:https://www.latent.space 

This data turned AI marketing founder, Kashish Gupta, thinks the reason AI hasn't taken over marketing the way it's taken over coding comes down to one word: taste. In this episode of Navigators, Paul talks with him about what it actually takes to teach a model a brand's aesthetic, and why most companies trying to do this are being lazy about it.He explains why marketing lacks the verifiable feedback loop that made AI adoption easy in customer support, and how his team spent over a year building the context layer needed to translate a creative director's unspoken instincts into something a model can act on. He also talks about the subagent whose only job is finding ideas a brand's competitors haven't tried yet, and why he believes the next leap in AI marketing depends less on smarter agents and better prompts, and more on models that generate images and video without inventing details that were never there.Topics covered:Why marketing lags coding: no verifiable loop, and taste is subjectiveTurning a creative director's unspoken instincts into something a model can queryThe subagent built to find whitespace competitors haven't touchedWhy enterprise customers, not SMBs, were the only ones who could adopt thisGoing from access to insights to content: how one problem led to the nextWhere the next inflection point comes from: instruction-following and image generationHow coding agents changed the day-to-day of running the company itselfNavigators is a series from Browserbase where we speak with AI leaders navigating the future of building with AI.Guest: Kashish Gupta, co-founder and co-CEO of HightouchHost: Paul Klein IV, founder and CEO of BrowserbaseHightouch: https://hightouch.com Browserbase: https://browserbase.com