The Next New Thing

Presented by Zapier
https://zapier.com/
Episode Highlights / Timestamps
00:00 $7M ARR as a solo founder
01:21 Profit, margins, and team size
02:51 Josh’s path from Uber to Wave
05:24 Choosing ideas in the early AI days
06:18 Why summarization felt like the killer app
08:15 Competing with Otter, Fireflies, and others
10:21 Recording real-world audio vs meeting bots
12:18 Spending more on AI to improve quality
13:39 Knowing you’re onto something from user emotion
15:09 Why Wave stayed general instead of vertical
16:12 Learning to build with ChatGPT
18:00 How Wave’s architecture evolved
19:39 Using Claude Code day-to-day
21:00 AI agents analyzing analytics and logs
25:21 The tools behind Wave (Cursor, Twilio, Adapt)
27:27 Building instead of buying SaaS tools
30:00 Using AI to ship features faster
32:06 Why Zapier matters for data portability
34:03 The future of cheap, abundant software
36:09 Running Wave like a corner store, not a startup
40:12 Growth goals without VC pressure
42:18 How Wave gets customers today
49:03 Why SEO side projects didn’t convert
50:24 “If you’re good, things might work out”
54:45 Revenue breakdown and take-home profit
What does it look like when a single founder builds a profitable AI company — alone — and quietly grows it to millions in revenue?
In this episode of The Next New Thing, Andrew Warner sits down with Josh Mohrer, creator of Wave AI, to unpack how he built a $7M ARR AI business with no full-time team — and how modern AI tools fundamentally changed what’s possible for solo founders.
Josh previously helped scale Uber in its early days, but Wave AI is a very different story. It’s a one-person, profitable SaaS built around a deceptively simple idea: record real-world conversations, transcribe them, and generate high-quality summaries people actually trust. No hype. No venture capital. No big team.

Presented by Zapier
https://zapier.com/
Episode Highlights / Timestamps
00:00 Why every email should be personalized
00:18 Ryan’s background and what Untangle does
00:45 Rethinking traditional email drips
01:12 Customizing emails based on user situations
01:39 A real example that led to a signup
02:06 Daily automated marketing insights via email
03:00 Doing things that don’t scale with AI
04:03 Walking through the AI email system
05:06 Using lead magnets and contextual data
06:09 Enriching leads and storing user context
06:45 Hourly cron jobs and email scheduling
07:39 Feeding context into the LLM correctly
08:15 Preventing hallucinated features
08:24 Sending emails with Resend
09:18 Measuring clicks instead of opens
10:12 Layering engagement-based follow-ups
10:39 Long-term personalized nurture loops
12:00 Turning marketing emails into real value
13:03 Building vertical-specific AI agents
14:15 Using Zapier and modern automations
16:12 Building systems with AI coding agents
18:27 Running multiple AI agents at once
21:27 Deciding what to build in a world of “free code”
24:09 Daily AI-generated growth recommendations
27:45 Using AI to generate and validate ideas
31:03 Increasing insight frequency, not brilliance
34:21 Why personalized email is a massive opportunity
34:48 Final takeaways
Why isn’t every email completely customized for the person receiving it — especially now that AI can do it for us?
In this episode of The Next New Thing, Andrew Warner sits down with Ryan Carson, a three-time founder currently building Untangle, to walk through a very practical, very real AI system he uses every day to grow his business.
Ryan has spent over 25 years building startups, but while setting up a “standard” email drip for Untangle, he stopped and asked a simple question: why are we still sending the same emails to completely different people? Instead of writing dozens of templates, he built an AI-powered workflow that generates fully personalized emails — based on each user’s situation, behavior, and engagement — and adapts over time.

Presented by Zapier

Episode Highlights / Timestamps
[00:00] Why Pat decided to build his own video platform after YouTube strikes
 [02:06] Rebuilding a YouTube-style site in just a few hours with Claude Code
 [07:30] Designing the video experience before worrying about features
 [14:06] Using modern frameworks without writing code
 [23:06] Adding video streaming with third-party APIs instead of building from scratch
 [34:03] Letting AI debug and test the app automatically
 [42:00] Deploying the app live with one command
 [48:18] Why your website should be the hub, not social platforms
In this episode of The Next New Thing, Andrew Warner talks with Pat Walls, founder of Starter Story, about how he used AI coding tools to quickly rebuild a version of YouTube after his channel was hit with content strikes.
Pat walks through how he used Claude Code to design, build, debug, and deploy a working video platform in real time — without writing traditional code. Along the way, he explains why founders should treat social platforms as distribution, not infrastructure, and how owning your audience and your software changes how you think about risk, growth, and leverage.
If you’ve ever wondered how far AI can really take you in building real products, this episode shows exactly what’s possible today.

Presented by Zapier
Episode Highlights / Timestamps
[00:00] Building AI software for companies, not just selling tools
[00:36] Crossing $2M in annual revenue
[01:12] A real-world AI document automation example
[02:15] Why hourly pricing breaks in AI services
[03:36] Using consulting to learn before building products
[06:09] Landing the first customers through relationships
[09:18] Founder-led sales and networking strategies
[10:39] Hosting events to build credibility and deal flow
[17:06] Why most AI pilots fail in production
[23:06] How Press W positions itself as an AI engineering firm
[27:09] Why “AI transformation” stopped working as a pitch
[36:36] Inside Press W’s AI-native operating system
In this episode of The Next New Thing, Andrew Warner sits down with Tarun Thummala, founder of PressW, to break down how his team builds custom AI systems for real businesses — and why services, not SaaS, were the right starting point.
Tarun runs an AI engineering firm that designs and ships production-grade AI applications for companies in regulated industries like finance, healthcare, and legal. Instead of selling vague “AI transformation,” his team focuses on concrete workflows: document processing, internal tools, sales ops, and systems that actually ship and get used.

Presented by Zapier
https://zapier.com/
Episode Highlights / Timestamps
[00:00] A broker replaces himself with an AI voice agent
[00:45] Early pricing and first customers
[01:30] The reality of cold calling expired listings
[04:21] Why off-the-shelf AI voice tools weren’t good enough
[05:15] First AI-booked listing appointment
[08:15] Launching without a website using Meta lead forms
[12:27] Using Zapier to glue the system together
[14:51] Why this model works beyond real estate
[16:12] Fine-tuning models for sales conversations
[19:12] Shutting down a profitable agency to build SaaS
[22:12] Founder roles and co-founder fit
[30:00] What AI coding tools really do (and don’t) replace
[32:42] Breaking down the early revenue
[35:24] Naming the company and what comes next
 What happens when someone is so fed up with cold calling that they build an AI to do it for them — and it actually works?
In this episode of  The Next New Thing, Andrew Warner sits down with Yevgeniy Matsay and Aidan Richards, co-founders of Rezora. They share how a frustrating real-estate sales job turned into an AI voice-agent business that generated real revenue — and why they ultimately shut down a profitable agency model to build scalable software instead.
Yevgeniy started as a real estate agent, spending entire days cold calling expired listings. When early AI voice agents emerged, he decided to build one tailored specifically for sales conversations. It landed listing appointments almost immediately. Instead of keeping it to himself, he sold it as a service to other brokers, validating demand fast — but also running into the limits of manual setup and constant customization.
From there, the conversation digs into how they:
Proved demand with a scrappy agency-style rollout
Used tools like Zapier and voice AI to stitch together a working system before SaaS existed
Learned why “just prompting” breaks down for sales calls
Transitioned from custom workflows to a self-serve product built on fine-tuned language models
Thought about scalability, founder roles, and when to pause revenue to build the right thing
This is a grounded, technical, and honest look at turning AI automations into a real business — including the tradeoffs, the hard parts, and what actually works in practice.