The Lobster Talks Podcast by Lobster Capital

They applied to YC with 90 minutes on the clock—and got in. Then they pivoted into the most operationally gnarly corner of fintech: global payroll. Avi Konduru (Shor) breaks down how AI agents + stablecoins can vertically rebuild EOR, cut costs by an order of magnitude, and expand the market beyond today’s incumbents.

In this episode, we go deep on YC as an ambition amplifier, pivot mechanics under real pressure, price vs. TAM strategy, and why launch videos (done right) are still YC’s most underrated distribution hack.

You’ll learn:

• How a 90-minute YC application (and one-take demo) still cleared the bar
• The precise wedge: vertically owning entities + automating back office with AI
• Why “someone else’s margin is your opportunity” actually maps to EOR
• Pricing strategy: undercut to expand TAM vs. match to maximize margin
• How YC Launch video distribution compresses customer discovery into days
• The pitfalls: agent reliability, compliance debt, and scaling beyond the batch

Chapters

00:00 The Last-Minute Application Rush

00:42 Welcome to Lobster Talks

01:07 Introducing Shor: Reinventing Global Payroll

02:38 The YC Experience: A Rollercoaster Journey

05:02 The Pivot: From Stablecoin Infra to EOR

10:29 Bootstrapping Challenges and Lessons Learned

18:00 The Unexpected Turn: Applying to YC Again

23:27 Competing with Deel and Rippling

27:07 Understanding Reseller Margins and Fees

27:27 Deel's Automation and Disruption in Entity Management

27:46 Setting Up Entities in High-Traffic Countries

28:49 Challenges and Regulatory Issues in Global Payroll

30:06 AI Agents Revolutionizing EOR Operations

32:33 Pricing Strategy and Market Expansion

40:11 The Power of Launch Videos in YC

44:07 The Role of Influencers in Marketing

48:07 Future Challenges and Customer Acquisition

51:31 Conclusion and Final Thoughts

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

We finally found a voice assistant that actually ships work. Not a demo, not a hype reel—April closes the loop on email and calendar while you’re driving, lifting, or walking to your next meeting.

In this YC-insider conversation, Neha (co-founder of April) breaks down how a narrow, vertical agent can outperform “do-everything” assistants, why dogfooding—not retention dashboards—built their product moat, and what a screen-lite future means for founders and operators. We also cover YC batch dynamics in a crowded voice category, Demo Day strategy, and the roadmap to a true “voice OS.”

What you’ll learn:

- Why narrowing scope (email + calendar first) beats generalist agents for real outcomes

- The dogfood standard: building to a founder’s own bar, then scaling

- How YC treats multiple “competing” companies—and why that can help you ship faster

- Demo Day tactics: being live, iterating weekly, and selling the founder, not the fantasy

- Voice vs. screens: trust, closed-loop execution, and the path to screenless workflows

- April’s roadmap: LinkedIn, WhatsApp, Slack, Notion—and verticalizing for sales & investors

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Chapters

00:00 Introduction to April: The AI Voice Assistant

00:24 Welcome to Lobster Talks: Meet Neha from April

01:12 Diving into April's Features and Use Cases

02:46 The Journey of Building April

03:58 Challenges and Successes in the Voice AI Space

05:36 The Future of Voice AI and Investor Insights

07:37 YC Experience and Investor Reactions

08:47 The Competitive Landscape and Collaboration

12:25 The Role of YC and the Voice AI Market

20:08 The Vision for a Screenless Future

23:06 Preparing for YC Demo Day

25:38 The Importance of Execution in Business

26:16 Advice for Startups: Ship Quickly and Get Feedback

26:50 Future Integrations and Features

28:37 Challenges and Strategies During YC Journey

32:31 Maintaining Momentum Post-YC

33:20 Mental Resilience and Personal Well-being

38:10 Exciting Future Plans for April

40:17 Predictions and Insights on AI Assistants

41:09 Lessons from Zoho and Book Recommendations

43:23 Conclusion and Final Thoughts

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@lobstercap

A Netflix storage engineer walks into YC and ships an “infinite, shareable disk” on top of S3—30× faster and up to 90% cheaper—then dials GTM for the AI era. This is the file system’s comeback story.

Today I sit down with Hunter Leath (ARL) to unpack how a decade inside AWS + Netflix revealed a gap the hyperscalers won’t close: developers want storage that feels local, scales like S3, and doesn’t nuke the budget. We get into: YC as confidence engine, moving a family to SF, rebuilding for speed, why AWS won’t copy this, and why the file system—not object storage—becomes AI’s universal interface.

You’ll learn

-Why the clouds won’t ship a product that cannibalizes billions in revenue

-The architecture that makes ARL 30× faster and up to 90% cheaper

-How to catch customers exactly when new AI workloads start (the real ICP)

-Post-batch velocity: how SF energy kills the YC slump

-Why “serverless everything” needs a serverless disk to persist state

-The contrarian bet: the file system is the future data interface for AI

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Chapters

00:00 The Risk of Leaving Big Tech

00:33 Introducing Hunter Leh and Aril

01:32 Hunter's Journey from AWS to Netflix

02:47 The Birth of Aril

06:55 Challenges and Insights from YC

07:26 The Solo Founder Experience

19:39 Building and Launching Aril

21:01 Go-to-Market Strategy and Customer Acquisition

24:04 The AI Industry's Growing Demand

24:49 Fundraising Journey and Investor Insights

26:35 AWS and Market Dynamics

29:17 Innovations in Data Storage

36:23 Maintaining Momentum Post-YC

38:27 Future Predictions in Data Infrastructure

42:22 Contrarian Views on AI and Data Storage

44:20 Conclusion and Contact Information

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

YC just changed the rules—and the market is catching up. We break down why seed is the power position, how CVCs are reshaping Series A, and what YC’s new Early Decision really means for founders and investors.

In this episode, we cover Lobster Capital updates (first Series A, first DPI), how we decide follow-ons from an insider vantage point, the rise of “seed-strapping,” Coinbase Ventures x YC’s RFS on Fintech 3.0, and why stablecoins + AI agents may be the next real on-chain wedge. We also unpack YC’s Early Decision—who it actually benefits—and what to expect heading into the next Demo Day.

You’ll learn:

Why seed has asymmetric leverage (and why top YC teams don’t optimize for dollars)

How we evaluate follow-ons: revenue quality, NRR, churn, hiring, and real signal vs noise

CVCs at Series A: when specialization beats the “Tier-1” logo

“Seed-strapping”: profitability at seed, and why some teams skip A entirely

Coinbase Ventures x YC’s Fintech 3.0 RFS and the stablecoin/AI-agent stack

YC Early Decision: who it helps (hardware/bio) and how YC captures talent earlier

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Chapters

00:00 Introduction to Investing Insights

00:23 Welcome to Lobster Talks

00:45 Lobster Capital Updates

03:16 Series A Graduation Rates

04:08 Fund Strategy and Follow-Ons

05:38 YC Companies and Profitability

10:33 Fundraising and Strategic Alliances

17:28 Crypto and FinTech 3.0

25:23 Global Currency Dynamics

26:20 The Rise of Stablecoins

27:27 AI and Crypto Synergy

32:03 Speculative Trading and Meme Coins

38:42 YC's Early Decision Program

44:01 The Future of YC and Startup Ecosystem

51:04 Conclusion and Upcoming Content

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

A robotaxi playbook… for dirt. Flywheel AI is turning excavators into remotely operated, camera-first machines — collecting the data to make them autonomous next.

In this YC-insider episode, we unpack Flywheel AI’s “Waymo for excavators” strategy: retrofit any machine in hours, deliver value with tele-op now, and use that profitably collected data to train autonomy later. We get into labor shortages, safety economics (OSHA penalties), competitor traps (drive-by-wire only), and how to actually do hardware at YC in 90 days without getting stuck in pilot hell.

You’ll learn

Why construction’s bottleneck is skilled operators — and how tele-op removes it

The dangerous blind-spot reality on sites and the true cost of safety incidents

Flywheel’s retrofit + single-screen UX that works on any excavator brand/size

The autonomy roadmap: camera-only stack, data flywheel, edge-case capture

How to win data rights on site (be the only retrofit, own the dataset)

The YC hardware playbook: sell first, build last; parallelize to kill lead-time

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Chapters:

00:00 From Sandbox to Real Excavator: The Journey Begins

00:20 Introducing Flywheel AI: Revolutionizing Excavators

01:49 The Labor Shortage Crisis in Construction

03:51 The Dangers of Operating Excavators

05:58 Teleoperation: Enhancing Safety and Efficiency

11:28 The Path to Autonomous Excavators

16:34 Competing in the Autonomous Excavator Market

22:26 Demo Day: Bringing an Excavator to YC

24:37 Returning the Excavator

24:47 Demo Day Setup and Reactions

26:04 Autonomy and Data Training

26:38 Joining YC and Initial Thoughts

28:03 YC's Impact on Hardware Startups

29:18 Building and Iterating Hardware

33:13 Advice for Hardware Startups

34:49 Final Thoughts and Reflections

45:03 Accelerating Iteration Cycles

48:38 Conclusion and Contact Information

A “cute idea” until it wasn’t: RealRoots walked into YC as an overlooked consumer play and walked out with $9.4M ARR and an oversubscribed round.

Summary: In this YC-insider episode, Dorothy Li (RealRoots) breaks down how AI-powered friendship matchmaking turned into real traction across 80+ cities. We unpack the demo day shock, the stigma shift (friendship ≈ dating 10 years ago), and the manual-to-AI playbook that de-risks consumer. We also cover investor blind spots, GTM math (cold DMs → paid), and why cofounder fit is a “you’ll know in 10 seconds” decision.

You’ll learn:

-Why “consumer is back” at YC—and how RealRoots rode a stigma shift

-The manual-first, AI-next method that actually finds PMF

-How 2–3k cold DMs converted to 11% paid ($20) and seeded the funnel

-The marketplace + AI stack behind curated IRL events at scale

-Investor pattern errors: scar tissue vs. behavior/tech inflections

-Co-founder tactics, hiring posture, and sustainable pace vs. 9-9-6

Chapters:

00:00 Introduction to RealRoots and Demo Day Success

00:48 Meet Dorothy Li: Founder of RealRoots

01:32 The Problem of Loneliness and Finding Community

02:43 How RealRoots Uses AI to Build Friendships

04:02 Expansion and Success of RealRoots

08:07 The YC Experience and Its Impact

17:39 Overcoming Stigma and Building for the Future

29:27 Investor Hesitations and Scar Tissue

30:12 Challenges in the Friendship App Space

30:59 The Concept of 'Targets' in Startups

32:04 The Importance of Consumer Behavior and Technology Changes

33:04 Co-Founder Story: Meeting Through RealRoots

36:16 Manual Efforts in Early Startup Stages

38:49 The Impact of a Co-Founder

39:45 Work-Life Balance in Startups

44:18 YC's Focus on Younger Founders

48:21 Validating Your Startup Idea

55:09 Customer Acquisition Strategies

56:30 Conclusion and Final Thoughts

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

A YC founder turns a manual, low-IQ grind into an AI agent that finds creators, negotiates terms, and scales UGC—sometimes a little too far. What starts as a viral local-model demo becomes Stormy AI’s end-to-end engine for influencer marketing.

Fresh off YC Demo Day, Robert Lukoszko (Stormy AI) breaks down the pivot, the fundraising blitz, and how agencies are replacing hours of scrolling with autonomous outreach. We get into model-proof moats, why micro-creators beat celebrity accounts, and the coming wave of AI-generated influencers.

You’ll learn:

How a YC pivot formed around a founder’s own pain (and real demand)

The playbook: sourcing, outreach, negotiation, and QA with AI agents

Why “every better model makes us stronger” is the right moat test

Micro vs. macro creators: what actually converts in 2025

The next act: AI-native UGC, personalization, and brand-owned AI faces

Tactical Demo Day lessons: energy + social proof = signed checks

Chapters:

00:00 Introduction and Cold Open

00:13 Welcome to Lobster Talks

00:37 Demo Day Insights

03:05 The Journey of Stormy AI

04:39 From Viral Demos to YC Acceptance

07:35 Pivoting to Stormy AI

09:41 Automating Influencer Marketing

18:39 The Future of Influencer Marketing

19:50 AI and UGC: The Future of Influencer Marketing

20:11 The Rise of AI Influencers

20:41 AI-Generated Content vs. Human Content

21:05 The Makeup Industry and AI Influencers

22:26 The Shift to Micro-Influencers

23:23 Emerging Platforms and the Decline of Meta

25:22 The Future of AI in Content Creation

28:58 Preparing for an AI-Driven Future

31:46 Building a Moat in the AI Industry

34:54 The Importance of Vision and Customer Interaction

38:16 Stormy AI: Current and Future Plans

39:20 Conclusion and Call to Action

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

AI won’t live in chat. The future is headless agents doing real work — and calling humans only when it matters. Dexter Horthy, cofounder of HumanLayer, explains how agent-driven software actually ships.

In this fast, tactical deep-dive, we unpack HumanLayer’s origin story (from failed data tools to paid customers in a week), why frameworks lag real production apps, and the workflow that lets AI agents ship in complex codebases. We cover research-plan-implement loops, context engineering, team process, and how “specs become the new code.”

You’ll learn

- How HumanLayer emerged from a SQL “janitor” agent that needed human approvals

- The YC grind as a solo founder and closing first revenue in a week

- Why horizontal AI dev tools are hard — and how top 1% teams actually build

- Context engineering 101: research → plan → implement, and why it beats vibe coding

- How to review plans, not code, to scale quality across a team

Where headless agents win first — and why culture, not models, is the bottleneck.

Chapters

00:00 Introduction and Cold Open

00:21 Welcome to Lobster Talks

00:47 Guest Introduction and Background

01:12 Early Startup Journey

01:35 Building the AI Agent

02:18 Challenges and Pivots

03:52 Solo Founder Experience

05:28 The Importance of Data Tools

11:20 YC Experience and Revenue

13:59 Building for the 1% vs. 99%

23:50 Exploring New Ideas

25:18 Exploring Cloud Code SDK

25:34 Building Experiments with Claude

26:05 Challenges and Learnings

26:49 Insights from AI Engineering Talks

28:22 The Future of Coding with AI

30:22 Context Engineering and Workflow

32:19 Product Development and Customization

38:22 Scaling AI in Teams

48:10 Exciting Future Prospects

50:42 Conclusion and Farewell

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Show Notes & Resources:

• 14min Youtube video on wielding coding agents: https://hlyr.dev/ace

• Blog post version - https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/ace-fca.md

• Sign up for codelayer beta: https://humanlayer.dev/code

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

Founders closing rounds before lunch. Investors making handshake commitments in the hallway. A startup with $9M+ ARR and another with $15M ARR lighting up the room. YC Demo Day isn’t a show; it’s a marketplace where speed and execution decide everything.

In this fast, founder-first debrief, we break down what actually happened at the latest YC Demo Day: the subtle format changes (that matter), why the one-minute pitch is only the opener, and how deals really get done. We cover the batch’s AI/devtools tilt, the contrarian bets in defense and hardware, and why early traction remains the single best predictor at seed. We also unpack portfolio construction, conversion-rate dynamics inside YC, and what support looks like after the cameras stop.

You’ll learn:

- The real Demo Day mechanics: tranches, chat apps, long breaks for dealmaking

- Why some hot rounds are already full and what to do about it

- How to win YC deals: first-meeting decisions, 24–48h timelines, and prep work

- The $9M+ ARR investment we made—and why traction beats narrative

- Why we’re now backing deeptech/hardware (missiles vs drones, autonomous excavators)

- Portfolio strategy: aiming for the fund returner, not spray-and-pray

Chapters:

00:00 Introduction and Demo Day Overview

00:15 Behind the Scenes of Demo Day

01:03 Changes and Improvements at YC Demo Day

03:11 Investor Insights and Strategies

04:23 Engaging with YC Startups

06:35 The Importance of Early Traction

09:49 Demo Day Pitch Dynamics

13:42 Fundraising Conversations and Strategies

18:07 Lobster Capital's Investment Approach

21:54 Portfolio Construction and Future Prospects

27:02 AI and Dev Tools: A Crowded Space

27:39 Challenges in Identifying Winners

28:48 Customer Acquisition: The Key to Success

29:57 The Importance of Traction

33:32 Investing in Deep Tech and Hardware

39:24 Evaluating Flywheel's Potential

45:46 Supporting Startups Post-Demo Day

50:09 Looking Ahead: The Never-Ending Cycle

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

A surfer duct-tapes a phone to his board… and ends up saving enterprise couriers 32 seconds per stop. The last meter of delivery — not the last mile — is where the money is.

In this Lobster Talks episode, Ingo Boegemann, co-founder/CEO of truemetrics, breaks down how sensor fusion + mission intelligence turn messy building entrances, courtyards, and wrong pins into precise, repeatable delivery actions. We go deep on Europe vs. US GTM, GDPR constraints (and why the US may unlock even more value), landing whales like GLS, and the unscalable POC that unlocked scale.

You’ll learn

• Why “a generic geocode is just the starting point” — and how to map entrances that actually work
• The POC → pilot → rollout playbook (and why Truemetrics charges for POCs)
• How to integrate via SDK without slowing ops — and show value before engineering lifts a finger
• Europe vs. US: privacy ceilings, data linking, and why boots-on-the-ground still wins enterprise sales
• The real driver bottleneck: pressure, compliance, and turning best drivers’ tacit knowledge into software
• The long game: building a data moat for autonomous last-meter delivery

Chapters

00:00 Introduction to Last Meter Delivery

01:07 Meet Ingo Boegemann: The Journey to Truemetrics

02:15 From Surfboards to Sensor Fusion

05:04 Challenges and Realizations in Delivery Solutions

06:47 European vs. US Market Dynamics

11:42 The Path to Scaling in North America

19:02 Innovative Solutions for Delivery Logistics

21:37 How Truemetrics Technology Works

26:36 Magnetic Field Intensity and Machine Learning Models

27:29 Challenges in Courier Data Integration

29:52 Sales Process and Proof of Concept

32:08 Logistics Industry Vulnerabilities

35:42 Future of Autonomous Deliveries

38:30 Data-Driven Delivery Solutions

48:31 Closing Remarks and Contact Information

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson