Get your creative juices flowing with The Startup Ideas Podcast. Published twice a week, we bring you free startup ideas to inspire your next venture. Hosted by Greg Isenberg, CEO of Late Checkout and former advisor to Reddit and TikTok. Subscribe so you don't miss out.
For more startup ideas, we created a database of 30+ startup ideas you can take at https://gregisenberg.com/30startupideas
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Données mises à jour le 02/10/2026
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In this episode, I talk with Vas from Varick about what it takes to put AI to work inside a real company. Vas makes the case that AI pays off through process reengineering, and he walks me through the exact method his forward deployed engineers (FDEs) use: interviews, process mining, step sorting, and agents built inside existing systems of record. We go through real engagements, including a $5B public software company and an accounts payable overhaul that cut the cost per invoice from $31 to $6. By the end, you get a clear picture of the FDE role, the business opportunity behind AI roll-ups, and a five-day plan to start on your own.
Links Mentioned:
FDE Presentation: https://startup-ideas-pod.link/FDE-slides
Vas’s Article: https://startup-ideas-pod.link/vas-fde
Timestamps
00:00 – Intro
01:31 – Sponsor: Google
03:58 – FDE Overview
05:02 – AI Roll-Ups and Process Reengineering
08:04 – The Personal Systems Analogy
09:55 – Understanding a company’s process (step-by-step)
14:11 – Case Study: $5B Software Company
18:11 – 4 Buckets for Every Step
19:04 – Build Inside Systems of Record
21:36 – Case Study: PE Portfolio
23:43 – Selling to C-Suite Executives
27:07 – Process of Mapping Five NetSuite Companies
28:39 – Example: Accounts Payable Process Map
32:54 – Case Study: 60-Person Accounting Firm
34:51 – When to Use Code, Agents, or Humans
36:00 – Choosing AI Models
38:16 – Sidekick vs Background Agents
40:29 – The 3 Skills of a Top FDE
42:25 – Why FDEs Earn So Much
45:26 – Five-Day Starter Plan
47:39 – On-Premise Hardware Demand
48:36 – OpenAI Private Intelligence
50:20 – The Full Playbook
52:01 – Closing Thoughts
Key Points
AI pays off when you re-engineer the process first, then build agents into it.
Map the real process with interviews, system-of-record mining, and existing docs.
Sort every step into four buckets: delete, plain code, agent, or human decision.
Build agents inside the tools clients already use, like Salesforce, NetSuite, and Slack.
Sell the outcome each buyer cares about, and prove it with before-and-after KPIs.
Top FDEs combine domain knowledge, production engineering, AI judgment, and strong communication.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND VAS ON SOCIAL
Varick Agents: https://www.varickagents.com/#hero-section
X/Twitter: https://x.com/vasuman
AI Forward Deployed Engineers: https://learn.varickagents.com/fde-in-30-days
OpenAI DevDay: Dots, Agents & $100B Opportunities
mardi 29 septembre 2026 • Durée 18:56
I watched almost 60 minutes of Sam Altman on stage at OpenAI Dev Day 2026. Out of 20-plus launches, I pick the three or four that I think can make people billions of dollars in aggregate. I break down Dots, OpenAI's personal agent platform, plus the Decisions API, the Agents API with computer use, and Sign in with ChatGPT. I also share my four-step framework for building in this world and two business ideas I hope someone takes. If you want to build a business and make money around AI, this episode is for you.
Timestamps
00:00 – Intro
01:48 – Dots, the Personal Agent Platform
03:29 – OpenAI Doubles Down on Plugins
05:02 – Decisions API
06:43 – Agents API With Computer Use
08:01 – Sign In With ChatGPT
12:36 – Where should you build?
13:46 – Idea: Real-World Work APIs
15:04 – Idea: Analytics for Agent Discovery
17:05 –Closing Thoughts
Key Points
Dots gives plugin builders a front door to 1.2 billion weekly active users.
OpenAI is doubling down on plugins to make ChatGPT the app ecosystem for AI.
Sign in with ChatGPT lets users bring their current plan, so a free core app with paid upsells now works.
Workflows too niche for OpenAI to build become viable businesses.
The strongest businesses in this world own the trigger, the action, and the feedback.
Two open opportunities: real-world work APIs and an analytics layer for agent discovery
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
$5T opportunity: AI Roll Ups
lundi 28 septembre 2026 • Durée 29:49
In this solo episode, I break down the $5 trillion wave of small businesses set to change hands as their owners retire, and why AI agents make this wave a real opening for solo founders. I walk through how Thrive Holdings and General Catalyst buy accounting firms, property managers, and support centers, then run AI agents inside them to lift margins. Then I show how I'd run a one-person holding company: the folder structure, the agent files, the human approval rule, and my average week. I close with the strongest arguments against AI roll-ups, including the one I take most seriously.
Timestamps
00:00 – Intro
01:52 – Why the $5 Trillion Shift Is Happening Now
04:52 – Examples: Thrive Holdings& General Catalyst
08:38 – The Fund Playbook
10:07 – The Small-Deal Gap
10:51 – The One-Person Holdco
14:23 – The Folder Structure
16:47 – The Agent Pipeline
18:38 – Inside a Reviewer Agent File
19:41 – My Week Running the Holdco
21:49 – How to Land the First Business
22:24 – Arguments Against AI Roll-Ups
27:43 – Closing Thoughts
Key Points
About a million small businesses, part of a $5 trillion shift, are set to sell by 2035 as owners retire (McKinsey).
AI agents now handle the work these firms run on: data entry, document chasing, status updates, and first drafts.
The big funds chase bigger deals, which leaves the small firms open for solo founders and small teams.
A one-person holdco runs on shared agents and rules, plus a GM with real upside at each business.
A person approves all agent work before it reaches a client.
The corrections log, turned into rules every week, becomes the most valuable asset in the holdco.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
Muse AI Connectors: The Next App Store Moment?
jeudi 24 septembre 2026 • Durée 26:15
In this solo episode, I break down the huge business opportunity because Meta has opened Muse, its personal AI agent, to developers. You can now submit a connector, which lets Muse use your service when someone asks it for help, and I think this could be the app store moment for AI. I explain how a connector works, share four startup ideas you can build on one, and cover how to get customers beyond Meta's directory. I also show how I'd build a first version with a coding agent like Claude Code or Codex, and what I'd test before submitting it to Meta for review.
Timestamps
00:00 – Intro
01:35 – The App Store Parallel
04:55 – How a Muse Connector Works
07:29 – Startup Idea 1: Lead Gen for Business Suppliers
09:28 – Startup Idea 2: Home Repair Dispatch
11:07 – Startup Idea 3: Paddle Match and Court Finder
12:47 – Startup Idea 4: Family Dinner Planning
14:10 – How to pick an idea?
14:56 – How People Find Your Connector
15:32 – Growth Idea 1: Partner With Creators
16:17 – Growth Idea 2: Product-Led Sharing
17:46 – Growth Idea 3: Connected Marketplaces and Directory Placement
19:34 – Building the First Version
21:38 – Custom Connectors and Meta Approval
23:56 – Where to Start This Week
25:31 – Closing thoughts
Muse Connector Prompt: https://startup-ideas-pod.link/muse-connector-prompt
Key Points
Meta has opened Muse to developers, and a connector lets Muse use your service when someone asks it for help.
I look closely at the step in a request where someone needs a business that can deliver and money changes hands.
With a small budget, I'd start with lead generation, because I can show a customer a sample before writing much software.
I plan distribution around channels I can reach, such as creators, product-led sharing, and connected marketplaces.
A coding agent can build the first version, and I still inspect the results and test the awkward requests myself.
To start this week, I'd talk to one type of customer about the last time they dealt with the task.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
The Right Way To Write With AI
lundi 21 septembre 2026 • Durée 01:09:34
Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP
In this episode I speak with Nicholas Cole about the real value of everything you write on the internet. Cole has 15 years of experience as a nonfiction writer and ghostwriter, and he runs the SaaS platform Typeshare. He gives me a simple model with three tiers of content: commodity, personality, and original. He also explains the digital brain, which he calls your personal language model, and he shows how AI repeats your approved language at scale. By the end, you know how to judge the short, medium, and long-term value of a piece before you write it.
Start Writing Online In 30 Days: https://startup-ideas-pod.link/ship30
Timestamps
00:00:00 – Intro
00:03:36 – POV is the Moat
00:05:40 – Language As Open Source
00:11:24 – Value Is Relative To The Reader
00:14:26 – Approved Language
00:17:43 – The Value of AI in Writing
00:22:09 – Commodity Ideas
00:24:23 – How To Set Up The System
00:27:58 – Ownership IS Association
00:33:56 – Build your Personal Data Set
00:39:53 – Branded Content vs Founder-Led Content
00:45:17 – What is Original Content?
00:46:35 – Writing Versus Short-Form Video
00:48:58 – Three Types of Hooks
00:49:50 – Timely Content vs Timeless Content
00:53:27 – Voice is 3 dialed settings
00:56:32 – Finding your Voice
01:00:49 – The Company Brain As The Moat
01:07:30 – Closing Thoughts
Key Points
A point of view creates the moat, because copycats must wait for your next idea.
Content sits in three tiers: commodity, personality, and original. Each tier adds different leverage.
Ownership equals association. Volume builds it, and personality details make it strong.
Your life story is the unmade data set, so AI learns it only from your own writing.
Every piece sits on a spectrum from timely to timeless, so match your expectations to the type.
Humans do the thinking and the writing. Robots do the repeating and the remixing.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND COLE ON SOCIAL
X: https://x.com/Nicolascole77
Youtube: https://www.youtube.com/@nicolascole77
Ship 30 for 30: https://startup-ideas-pod.link/ship30
Jev is HERE. How to use it
vendredi 18 septembre 2026 • Durée 28:24
In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus an output schema and returns a probability for each choice in about 200 milliseconds. He demos Jev sorting 1,700 emails for 18 cents total, then covers lead scoring, support routing, video clipping, and browser control. I push him on the startup angle: find a business with an expensive queue of incoming information and put Jev at the front of it. You leave with a clear mental model, real use cases, and a simple way to try it today.
Links Mentioned:
Jev/Typeface AI: https://typesafe.ai
AI Gateway: https://vercel.com/ai-gateway
Timestamps
00:00 – Intro
02:27 – What Jev Is and Why It Matters
04:32 – Email Triage Demo
07:19 – Jev as an AI Decision Maker
15:46 – How to Use Jev in a Business
20:48 – Startup Idea: Local Services Matching and Instant Quotes
22:51 – Use Case 1: Bitcoin Signal Test and Limits
24:03 – Use Case 2: Auto-Clipping Long Videos
25:27 – Use Case 3: Browser Control: Flight Pick in 7.1 Seconds
26:18 – How to Get Access
27:25 – Closing Thoughts
Key Points
Jev is a classifier: an input and an output schema go in, and a probability for each choice comes out.
Ryan's demo scores 1,700 emails for 18 cents total.
Each Jev query takes about 200 milliseconds, whatever the input and output structure.
Use Jev at any point where a business makes fast, repeatable decisions on incoming data.
Keep Jev in an advisory role, and save frontier models for high-intelligence tasks like trading.
Instant access runs through the Vercel Gateway, and a waitlist covers direct access.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND RYAN ON SOCIAL
X: https://x.com/ryanvogel
Youtube: https://www.youtube.com/@vogeldev/videos
Instinct AI: The AI Assistant for normal people
mardi 15 septembre 2026 • Durée 27:30
I sit down with Remy to go through Instinct, the new invite-only personal agent that runs inside iMessage. Remy shares his raw chat history on screen: a haircut booking in Copenhagen, a restaurant reservation, a Bali visa on arrival, and an Emirates Skywards sign-up. We cover the parts that impress us, the points where the agent hits a wall, and the privacy questions that stay open. By the end of this episode you understand what Instinct does today, and you get fresh ideas for personal agents in general.
Timestamps
00:00 – Intro
02:08 – Instinct Pros
11:18 – Instinct Cons
13:16 – Simple Onboarding
15:19 – Tools and Connectors
17:43 – Example 1: Booking a Haircut in Copenhagen
20:24 – Example 2: Restaurant Booking and Calendar Entry
23:03 – Example 3: Bali Visa on Arrival and Emirates Skywards
26:00 – Closing Thoughts
Key Points
Instinct hides the agent complexity behind a phone number and iMessage, so a first-time user starts in seconds.
Remy gets it to book a haircut, hold a restaurant table, file a Bali visa on arrival, and open an Emirates Skywards account.
A spend-limited virtual card keeps the blast radius small when the agent pays for things.
The agent stalls when a task needs a phone app or an Indonesian checkout page.
Users report that Instinct keeps copies of email after they disconnect Google, so treat privacy as an open risk.
The trusted person network lets one Instinct talk to another, which builds network effects into the agentic era.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND REMY ON SOCIAL
X: https://x.com/remy_gaskell
Youtube: https://www.youtube.com/@aiwithremy
AI with Remy: https://www.aiwithremy.com/
Building a Software Factory that actually works (Full Course)
lundi 14 septembre 2026 • Durée 31:29
Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP
I welcome Ras Mic back to the pod to explain the phrase "software factory." Mic shares his screen and walks through the exact system that he runs today. His factory has four steps: isolate, build, prove, and ship. He keeps the whole system in five or six markdown files, so it works with any model and any harness. By the end of this episode, you can boot up your own factory, run many agents in parallel, and trust the code that comes back.
Create your own Software Factory: https://startup-ideas-pod.link/ras-software-factory
Timestamps
00:00 – Intro
02:17 – Software Factory Definition
03:44 – Why the Software Factory Matters
05:23 – Step 1: Isolate With Git Work Trees
11:34 – Step 2: Build With the Code Structure Skill
14:48 – Step 3: Prove With Evidence-Driven Testing
22:25 – Step 4: Ship With Grep Loop and Greptile
26:52 – The Physical Factory Analogy
29:21 – A Software Factory Is Markdown Files
30:02 – Closing Thoughts
Key Points
A software factory is a workflow of skills and domain knowledge, so it runs with any model and any harness.
Isolate: every feature starts in a fresh git work tree branched from origin main, so each agent keeps its own station.
Build: a code structure skill makes the agent write service layer code that a human developer can read.
Prove: the agent records a before state and an after state as video, screenshots, or numbers.
Ship: Greptile scores the PR, and the agent loops back to build until it earns five out of five.
Mic runs up to 15 features in parallel and reviews the visual proof instead of the raw code.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND MIC ON SOCIAL
X/Twitter: https://x.com/Rasmic
Youtube: https://www.youtube.com/@rasmic
You're using GPT-6 Astra WRONG
jeudi 10 septembre 2026 • Durée 22:52
I talk with Ras Mic about GPT-6 Astra. We skip the game demos and the 3D toys, and we focus on use cases to earn money or improve products. I share 9 Astra prompts that I posted publicly, and Greg Brockman reposted. Ras then shows his hardware project: he moved from a speaker idea to a parts list, a Blender layout, and merged code in about 30 minutes. The takeaway is simple: use this model for the ideas that felt too large for you last year.
Timestamps
00:00 – Intro
01:53 – Astra Overview
04:14 – 9 Astra Prompts
11:48 – Jarvis Speaker Idea
16:21 – Think Bigger with Astra
18:29 – Vibe Coding to Vibe Manufacturing
21:16 – Closing Thoughts
Key Points
Astra costs more per task, and it uses fewer steps, so the value per dollar stays high.
A performance audit moved one of Ras’s apps from 800 ms to 20–30 ms.
A security audit on his live payments app found real risks in production.
Ras went from a speaker idea to a $561 parts order and a merged pull request in about 30 minutes.
Ras’s point: intelligence keeps climbing, and bravery stays flat. Ask for bigger things.
The shift that vibe coding brought to software now reaches physical products.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND MIC ON SOCIAL
X/Twitter: https://x.com/Rasmic
Youtube: https://www.youtube.com/@rasmic
Local AI Clearly Explained
mardi 8 septembre 2026 • Durée 38:46
I run this episode solo. I explain local AI in plain terms: the model runs on hardware I control, and a cloud model runs somewhere else. I map the four pieces of the local AI landscape — the model, the warehouse, the software, and the workflow — and I define the words that beginners meet first: parameters, tokens, context window, quantization, and GGUF. I walk through the Google open model stack (Gemma 4, Google AI Edge, LiteRT-LM, AI Edge Gallery), compare the other open model families, and show three ways to run a model today. I close with a first workflow you can copy and three startup ideas that use local AI as the wedge.
And a special thank you to Google for supporting the podcast.
Timestamps
00:00 – Intro
01:35 – The Open Model the Landscape
03:09 – Vocab Decoder
06:48 – Google Gemma Clearly Explained
10:29 – Other Open Model Families
14:20 – Path 1: Run Gemma in LM Studio
18:17 – Path 2: Ollama
20:15 – Path 3: Google AI Edge
21:07 – Hardware Cheat Sheet
21:52 – First Workflow to Build
22:47 – Workflows Before Fine-Tuning
25:06 – Local vs Cloud vs Hybrid Eval
26:33 – Framework for Local AI Startup Ideas
27:22 – Startup Idea 1: Home Health QA Reviewer
29:24 – Startup Idea 2: Offline Field Report Copilot
32:10 – Startup Idea 3: Pre-Send Reviewer for Professional Services
34:47 – Build Your Local AI Lab
37:55 – Closing Thoughts
Key Points
Ask whether the model is good enough for the job, and the business opportunities become clear.
Local AI has four pieces: the model, the warehouse (Hugging Face), the software (LM Studio or Ollama), and the workflow you build around them.
Gemma 4 E4B is my practical starting point; E2B fits phones and older machines.
Hybrid architecture wins: local does the private first pass, cloud does the heavy reasoning, and a human approves anything important.
Start with one repeated workflow — one folder, one model, one output — and run it 10 times.
I see a 24-month window to build local-AI-native software for verticals that still run early-2000s tools.
The #1 tool to find startup ideas/trends - https://www.ideabrowser.com
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
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