The Startup Ideas Podcast

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OpenAI DevDay: Dots, Agents & $100B Opportunities Sep 29, 2026

Show notes

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

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$5T opportunity: AI Roll Ups Sep 28, 2026

Show notes

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.
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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/
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Muse AI Connectors: The Next App Store Moment? Sep 24, 2026

Show notes

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

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The Right Way To Write With AI Sep 21, 2026

Show notes

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

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Jev is HERE. How to use it Sep 18, 2026

Show notes

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

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Youtube: https://www.youtube.com/@vogeldev/videos

Instinct AI: The AI Assistant for normal people Sep 15, 2026

Show notes

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

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AI with Remy: https://www.aiwithremy.com/

Building a Software Factory that actually works (Full Course) Sep 14, 2026

Show notes

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.

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You're using GPT-6 Astra WRONG Sep 10, 2026

Show notes

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

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Local AI Clearly Explained Sep 08, 2026

Show notes

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

The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/

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