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. 

Episode highlights:

[00:00:00] Joe’s businesses and revenue breakdown
[00:00:45] Five ways to make money with AI
[00:00:54] Selling AI headshots as a done-for-you service
[00:02:06] Delivering with VAs and prompts
[00:03:36] Getting customers via LinkedIn polls and ads
[00:06:00] Teaching AI while learning it yourself
[00:08:24] Selling ideas before creating the product
[00:09:27] Building a course entirely with AI
[00:14:15] Selling AI-generated infographics to franchises
[00:17:24] Using AI to build landing pages and funnels
[00:22:21] ChatGPT as a co-founder and therapist
[00:25:30] Scaling an agency without adding employees
[00:30:00] Monetizing AI education and communities
[00:34:03] Building basic software and prompt generators
[00:40:03] Creating MVPs without developers
[00:45:27] Focusing ideas into one scalable product
[00:49:03] Rebuilding after COVID, divorce, and burnout

In this episode, Andrew Warner sits down with Joe Apfelbaum, founder of Ajax Union and EvyAI, to break down five practical ways to make money using AI right now — without needing to code, raise money, or build complex software.

Joe walks through real examples from his own businesses, including AI-powered services, courses, and lightweight software tools that generate revenue fast. More importantly, he explains why these models work: people want outcomes, not software — and AI lets you deliver those outcomes with tiny teams and massive leverage.

This is a raw, tactical conversation about turning AI into income, rebuilding after setbacks, and designing businesses that scale without adding people.

Episode highlights:

[00:00:00] The vision: media customized to one person
[00:02:15] Why revenue isn’t the point — yet
[00:03:18] Seeing early personalization at Spotify
[00:06:00] Why kids’ content felt broken
[00:07:48] Making the child the hero of the story
[00:08:42] The hardest problem: image consistency
[00:11:24] Why scaling AI products is nothing like demos
[00:14:06] Personalized media won’t replace broadcast — it adds new behavior
[00:16:21] Why parents are the buyer, not the consumer
[00:20:51] Bedtime as a repeatable ritual
[00:23:42] Why Dream Stories is a service, not a novelty product
[00:28:12] Distribution is the real bottleneck
[00:32:15] Why repeat purchases beat subscriptions
[00:39:00] From “pull” products to “push” experiences
[00:45:00] Context and memory as the real moat
[00:50:06] Learning directly from customers
[00:54:09] Synthetic data and AI-generated avatars
[00:59:06] Automating PR and support with AI

In this episode, Andrew Warner talks with Ricardo, founder of Dream Stories, a company using AI to create fully personalized children’s books where each child becomes the hero of their own story.

Ricardo shares how a simple idea — making a better bedtime story for his own son — turned into a scalable business with tens of thousands of unique characters created. But more importantly, he lays out a bold vision: a future where movies, TV shows, books, and media are customized for a single person, not the masses.

They dive deep into what it actually takes to build a consumer AI company beyond demos and hype — from image consistency problems and synthetic data, to distribution, paid acquisition, and turning one-time novelty purchases into repeat behavior.

This is a rare, honest look at where AI-generated media is headed — and what founders should really be building right now.

Episode highlights:

[00:00:00] Businesses built entirely on Zapier
[00:01:30] The roofer-turned-automation-agency story
[00:03:54] What AI enables that wasn’t possible before
[00:07:12] OpenAI Agents vs. Zapier workflows
[00:11:15] Connecting AI agents to real business tools
[00:13:03] Building a meeting-prep agent live
[00:18:00] Why AI is great at building workflows, not just running them
[00:23:06] Zapier customers, revenue, and bootstrapping discipline
[00:28:57] AI-powered lead qualification in real time
[00:33:18] Automation agencies and speed-to-lead economics
[00:40:03] Why Zapier is positioned to last
[00:42:45] Using AI as a neutral leadership coach
[00:47:06] AI tools Wade personally uses

In this episode, Andrew Warner sits down with Wade Foster, co-founder and CEO of Zapier, to explore how AI agents, automation, and workflows are reshaping how modern businesses operate — from solo founders to companies doing hundreds of millions in revenue.

Wade shares real examples of people who’ve gone from running local service businesses to launching automation agencies powered almost entirely by Zapier. Together, they break down how AI changes what workflows can do, why agents and automations are complementary (not competitors), and how founders can turn speed-to-lead, personalization, and internal tooling into real revenue.

You’ll see a live walkthrough of building AI agents inside Zapier — including meeting prep, lead qualification, and internal coaching — all without writing code.

👉 Join us: https://thenextnewthing.ai/

👉 Team member feedback Zap: https://l.thenextnewthing.ai/r/Pdja7P

🎧 Highlights:
[00:00:00] Humans doing the work of AI — before AI existed
[00:01:12] Why accounting is mostly about language, not numbers
[00:02:33] Shadowing bookkeepers to find automation opportunities
[00:06:00] Manual work Quanta knew software had to replace
[00:07:30] Why building on top of legacy systems wasn’t enough
[00:08:24] Rebuilding the ledger from the ground up
[00:10:12] Continuous reconciliation vs. monthly closes
[00:11:24] From Affirm to founding Quanta
[00:13:30] Why delayed financials are useless for startups
[00:16:03] Validating willingness to pay before building
[00:17:42] Using humans for the “last mile” while automating the rest
[00:20:15] Solving trust and data-ownership concerns
[00:22:48] Why most QuickBooks challengers failed
[00:26:33] Saying no to customers to protect quality
[00:33:36] Why AI makes real-time margins mandatory
[00:36:45] Raising $15M Series A ($20M total)
[00:37:21] Prism: asking your financials questions in plain English

In this episode, Andrew Warner interviews Helen Hastings, founder of Quanta, an AI-powered accounting platform built for modern software companies.

Before AI could reliably understand financial data, Helen and her team had humans doing what AI does today — reading receipts, interpreting memos, categorizing transactions, and reconciling books by hand. That hands-on approach helped her uncover where automation really mattered, leading to a ground-up rebuild of accounting software that works in near real time.

Helen shares how Quanta replaces legacy systems by owning the data end-to-end, combining clean ledgers, continuous reconciliation, and AI-powered analysis — and why this approach helped the company raise $15M in Series A funding (over $20M total) and land nearly 100 customers so far.

 🎧 Highlights:
[00:00:00] From Makerpad to Factory — Ben Tossell’s journey
[00:01:33] Life after acquisition and redefining work
[00:05:06] Why AI might make things harder, not easier
[00:07:30] No-code lessons and the illusion of simplicity
[00:10:42] From teaching no-code to debugging workflows
[00:12:00] Learning to code with AI as your translator
[00:15:09] Curiosity as the new technical skill
[00:16:21] Building a “source of truth” AI system inside Factory
[00:23:42] How Ben uses AI to search across code, docs, and tickets
[00:27:36] Teaching AI to follow his workflow
[00:30:27] Getting comfortable with the command line
[00:33:45] The first time AI made him feel like a real builder
[00:35:42] Makerpad’s growth, Slack community, and hiring from within
[00:41:24] Newsletter growth hacks and lessons from Ben’s Bites
[00:46:03] Selling Makerpad and rediscovering purpose
[00:49:39] Investing through Ben’s Bites Fund
[00:50:33] Returning to his roots: teaching, learning, and building again
[00:53:18] The one-person billion-dollar company — myth or movement? 

In this episode, Andrew Warner talks with Ben Tossell, creator of Makerpad — the #1 community for no-code builders, which he later sold to Zapier. Now at Factory, Ben is helping developers build with AI instead of code — and rethinking what “technical” even means.

Ben opens up about the post-acquisition burnout that came after his sale, why he avoided starting another company, and how AI has reignited his creativity. Together, they explore what it means to go from no-code to “AI-native,” and why the dream of one-person billion-dollar companies might be closer than it sounds.

👉 Join us: https://thenextnewthing.ai/

🎧 Highlights:
[00:00:00] From freelance writer to $10M ARR founder
[00:03:36] How Pepper scaled from a marketplace to an AI-powered content engine
[00:05:06] The hybrid model: humans and AI creating together
[00:07:12] Building “Nimbus” — Pepper’s internal AI platform
[00:08:24] Re-optimizing thousands of old pages automatically
[00:13:03] Why FAQs and freshness signals help you rank in AI results
[00:15:00] GEO: Generative Engine Optimization explained
[00:16:12] Tracking brand mentions across ChatGPT, Claude, Gemini, and Perplexity
[00:18:00] Using AI to generate videos, voices, and creative assets
[00:25:12] Scaling creative testing with 30,000+ AI-made ad banners
[00:27:18] How smaller creators can apply these lessons today
[00:30:27] Reddit, LinkedIn, and UGC as new AI search signals
[00:33:00] Cold-emailing OpenAI’s Greg Brockman and getting access to GPT-3
[00:34:21] Building PepperType.ai and learning from early AI adoption
[00:35:30] Using AI personally to optimize meetings and calendar time

In this episode, Andrew Warner interviews Anirudh Singla, founder and CEO of Pepper, a company that uses AI and human expertise to produce hundreds of thousands of pieces of content for enterprise brands.

Anirudh shares how he went from writing on Upwork to building a platform now doing over $10M ARR, powered by a blend of automation, creativity, and data. He reveals how Pepper uses AI agents to write, edit, and even refresh old content — and why the next big wave isn’t SEO, it’s GEO: Generative Engine Optimization.

Andrew shares the AI tools that real startup founders are using every day — not hype, but the ones that actually help them work smarter.
 From AI note-takers that surface your blind spots to automations that coach your team after meetings, these are the tools that top entrepreneurs rely on.

🔗 Tools mentioned:

• Granola App: https://www.granola.ai/

• Claude Console: https://console.anthropic.com/

• TextBlaze: https://chromewebstore.google.com/detail/text-blaze-templates-and/idgadaccgipmpannjkmfddolnnhmeklj?hl=en-US

• Wade’s Zap: https://agents.zapier.com/copy/f17868bc-cc23-433a-b211-af402f47e1b4

• Garry’s Script Prompt: https://l.thenextnewthing.ai/r/OD14YA

• Alex Lieberman's EOS GPT: https://l.thenextnewthing.ai/r/5BB4do

Which of these are you already using — or planning to try next?

 🎧 Highlights:
[00:00:00] Intro – “Don’t start with SEO. Start with AEO.”
[00:00:36] Why this is the right time to focus on answer engine optimization
[00:01:30] Case study: Webflow drives 8% of signups from LLMs
[00:03:00] Onsite vs. offsite optimization — Reddit and YouTube matter most
[00:06:18] Why Help Center content outperforms traditional SEO pages
[00:13:30] Why AEO is ideal for small startups without big budgets
[00:16:39] “SEO is not dead” — and why Google’s share of search stays stable
[00:19:39] Myths: LLM.txt, robots.txt, and how misinformation spreads
[00:23:06] The scientific method for testing what actually works
[00:25:57] Affiliates and citations — how paid mentions impact LLM rankings
[00:27:27] How to find high-value questions to target
[00:31:12] 60+ AEO tools — and the new “content scoring” era
[00:35:06] How to build an AEO agency (and what services to offer)
[00:39:36] Marketing Graphite — why thought leadership still wins
[00:43:00] The AEO roadmap: what to do first, step by step
[00:47:00] The MasterClass SEO story
[00:48:18] Fires, gardens, and creative thinking in constraint 

SEO is changing—and the next frontier is AEO: Answer Engine Optimization.

In this episode, Andrew Warner sits down with Ethan Smith, founder of Graphite, to break down how brands are already winning visibility on ChatGPT, Perplexity, and Claude — before their competitors even realize what’s happening.

Ethan explains why SEO isn’t dead, but answer engines are the next big channel — and how to optimize your site, videos, and community presence so AI models actually cite your brand in their answers.