Rogue Startups

Craig Hewitt sits down with Marc Thomas, a consultant who works on lifecycle marketing, to talk about what it feels like to run a business while AI changes the ground under everyone's feet. Both swing between optimism and pessimism from one day to the next. They look at where founders should put their marketing effort, what has happened to SEO and content, whether AI personas are worth building, and what AI might mean for their kids' working lives. Topics discussed Why the constant stream of tech and AI news feels like a fire hose, and why the highs and lows come so fast Outcome-based pricing, where customers pay when they are satisfied with the result, and Marc's experience paying for credits in a tool he tried Trust, switching costs and why reputational risk stalls big enterprise deals more than price does Craig's scale of AI adoption, and where he puts his own business on it The worries founders have as AI improves: customers building their own product, retention and upsell, and finding new customers Acquisition versus getting more from your existing list, with a CRM-for-window-cleaners example Why SEO and content no longer work the way they did, and how AI search favours established brands Organic social, and what happens when you build an AI avatar or AI persona Kids, college, layoffs and what work might look like in the future A book recommendation: Before the Deluge, and the parallels Marc sees between 1920s Berlin and today Key takeaways Marc's view: big enterprise deals depend on a champion inside the organization, and looking after that person matters more than price. His rule of thumb for acquisition: if you are below a million dollars of annual revenue, put your focus on acquisition; beyond that, getting more from your list matters more unless your list is huge and converts very poorly. Craig says SEO and content were a fantastic way to acquire customers and that taking that away is a big deal for the bootstrapped SaaS world. Marc's approach to the AI upheaval is to stay curious, stay open-minded about his own skill set and stay generous in his thinking. Craig is up and down every day but, in his soul, very optimistic about where this ends up. Chapters (00:00:00) - Daily swings between optimism and pessimism(00:00:57) - The PHP vulnerability and information overload(00:02:44) - New product launch: outcome-based pricing(00:04:38) - Pay-for-results tools and trust in vendor switching(00:06:45) - Enterprise champions and reputational risk(00:08:25) - Where are we on the AI adoption scale?(00:09:42) - Exponential problems as AI improves(00:12:28) - Layoffs, job security, and going independent(00:13:58) - Focusing on existing customers vs new acquisition(00:15:13) - Tiny Seed feedback and demand generation struggles(00:16:33) - Absolute vs relative marketing opportunity (window cleaner example)(00:20:49) - SEO decline and the rise of AI search(00:22:56) - What Castos would do differently today: organic social(00:25:20) - Beehiiv vs Mark Lou drama and audience building(00:27:25) - AI avatars and virtual personas experiment(00:32:10) - Parenting, kids' futures, and career advice in the AI era(00:34:21) - Societal change and staying curious amid disruption(00:39:59) - Economy, UBI, and shifting value of work(00:41:47) - Berlin in the 1920s as a historical parallel(00:44:53) - Optimism, mindset, and closing thoughts

In this episode Craig talks with Aaron, who built DocsBot, an AI customer support agent that's almost four years old, about what AI is really doing to SaaS. Aaron shares DocsBot's numbers: slower growth and fewer new customers, but more revenue per customer and lower churn after narrowing in on the right customers. They also get into what it costs to run an AI product, how to choose and switch models, and the AI tools Aaron is excited about next. Topics discussed Growth in the AI era: new customer acquisition is down year over year, but revenue per customer is up 20% from last year Finding the ideal customer: talking to customers, cancellation surveys, and pointing AI agents at all of the product and Stripe data, after reading Jason Cohen's book Hidden Multipliers Signal density: why founders with only a handful of customers struggle to draw conclusions Where customers come from now: organic search, AI search, referrals, conferences like WordCamp, and an agency partner program Why big companies move slowly: and why that makes larger customers stickier Churn to DIY: churned customers citing AI or their own custom solution went from 1.6% to 13%, and most weren't a good fit anyway Pricing around token costs: from bring-your-own-key to included usage to AI credits Evaluating new models: why letting customers edit their system prompt makes evals hard, and a classifier that behaved differently on GPT-5.4 Choosing models: balancing cost, speed and quality instead of reaching for the newest model Local and specialised models: embedding models, Cohere for other languages, and Mistral for PDF extraction Privacy: a tiny model in the chat widget that redacts personal information before it leaves the user's browser What's next: using a new model to re-rank search results at a fraction of the cost Key takeaways The biggest cheat code for finding your ideal customer, in Aaron's words, is to "talk to your customers". Don't pick your ideal customer by revenue alone: DocsBot's biggest enterprise customer uses the product completely differently from almost everyone else. Customers who leave to build their own alternative are often not best-fit customers: too price sensitive, a tiny team, or not enough support tickets. Bring-your-own-key let a fully bootstrapped company launch an AI product without paying for inference for about the first two years. For an AI support agent, speed matters, so the newest model on its highest reasoning setting isn't always the right choice. Model upgrades can cause regressions that evals don't necessarily show. Chapters (00:00:00) - Intro: Is the SaaS apocalypse here?(00:00:59) - Growth metrics and MRR trends over 12 months(00:02:08) - Focusing on ICP to boost revenue per customer(00:03:11) - Churn rates and pricing restructuring(00:04:49) - Talking to customers to define the ideal customer profile(00:07:32) - Signal density and sample size in customer data(00:09:13) - Customer acquisition channels and what's working now(00:10:47) - Events, conferences, and agency partnerships(00:13:05) - Enterprise inertia vs. consumer switching risk(00:15:19) - Enterprise adoption pace and pilot deployment friction(00:17:03) - How AI changes customer support buying behavior(00:19:07) - DIY/AI churn: are customers building their own solutions?(00:20:45) - Price increases and churn reasons breakdown(00:21:55) - Token costs, pricing strategy, and bring-your-own-key history(00:26:06) - Evaluating and switching AI models internally(00:29:24) - Personal model stack and balancing cost, speed, quality(00:34:00) - Local models, embeddings, and specialized extraction tools(00:36:25) - Small/local models and future of specialized AI(00:38:56) - PII redaction with tiny on-device models(00:40:16) - What's exciting next: re-ranking, Gemini, and new paradigms(00:43:19) - Wrap-up and where to find DocSpot

Hiten Shah has built Crazy Egg, KISSmetrics, and a long list of products — and right now he’s shipping a new batch of AI tools while stepping back into Crazy Egg after nearly 20 years. In this conversation we get into what’s actually real in AI right now versus what’s hype. We start with Noats, Hiten’s local-first meeting recorder, and why “the tech wasn’t there” a year ago. That leads into local models: how much hardware you really need, why open-weight models still have a “flavor” from how they’re trained, and why even the frontier labs don’t fully know what a model will be good at until it’s done. Then we get tactical. Hiten explains how he evaluates new AI tools within minutes of launch, why he treats his work like an “AI factory,” and the big idea of the episode: in the past, go-to-market was a wrapper around the product — today, the product is a wrapper around the go-to-market. He walks through how that plays out across his own products (paid ads, freemium, and a niche community play), and then turns it on my business, Castos, to show what the “obvious future” of a commoditized category looks like. Chapters0:00 Noats and local-first meeting notes4:10 What it really takes to run local models6:28 Why model training is still a black box10:22 Where are the US open-weight models?11:55 Local vs. frontier: how Hiten actually works13:18 “X is where everything breaks” — evaluating new AI tools fast15:46 Do you really need to switch tools every week?18:16 Big-company AI vs. startups for personal productivity20:39 How to think about growth for an AI product24:24 The product is now a wrapper around go-to-market26:30 Does every product need a free tier?29:19 Coming back to Crazy Egg after 20 years32:16 What’s exciting — and what keeps Hiten up at night34:47 Finding the “obvious future” of your business (Castos breakdown)38:22 Why “grow my podcast” is the new job to be done40:50 What Hiten’s excited about in AI right now49:17 Wrap-up: pick who you serve LinksNoats: https://noats.aiHiten on X: https://x.com/hnshahHiten’s weekly Friday calls: https://www.hiten.com/Castos: https://castos.com Chapters (00:00:00) - Notes app and local model parity with Granola(00:02:08) - Local models, product motion, and go-to-market teaser(00:04:08) - RAM requirements and practical limits of local compute(00:05:50) - Why training is a black box and model 'flavor'(00:09:25) - Chinese-trained models and the search for US open weights(00:11:14) - Personal workflow for testing new models and Twitter as signal(00:15:42) - Setting up systems to experiment like a factory(00:18:03) - Muse vs startups: trust, security, and Meta's advantage(00:20:32) - Notes go-to-market and distribution strategy(00:24:22) - Product as a wrapper around go-to-market(00:28:29) - Flexibility across enterprise vs consumer go-to-market(00:29:10) - Returning to Crazy Egg and AI-driven efficiency(00:32:09) - Obvious futures: websites that change themselves(00:34:49) - Finding your business's obvious future: Castos case study(00:37:43) - Podcast platforms needing to drive growth, not just hosting(00:40:52) - Excitement about agent tools: Muse, Grokbot, Instinct comparisons(00:47:17) - Differentiation, distribution, and closing thoughts

What does it take to turn an audience into a business—and how do you do it without losing the enjoyment that got you started? This week on Rogue Startups, Craig and Colin compare notes on building creator businesses. Colin shares what he’s exploring after selling his business, including his golf content, sponsorship opportunities, and fractional roles. Craig talks about growing his YouTube channel toward 25,000 subscribers and building a second revenue stream through ads and brand partnerships. We dig into the gap between getting views and building a business: growing an email list, creating relevant lead magnets, nurturing subscribers, and developing your own products. We also explore how to find a clear coaching niche by combining your skills and experience—and why honest feedback from your team can help you focus on the work where you add the most value. Then we get practical about YouTube growth and podcast repurposing. We discuss strong premises, titles, thumbnails, and why the first minute deserves so much attention. We wrap up with how AI tools are changing content production and software businesses, including the opportunities—and uncomfortable questions—that come with automating work you once paid people to do. Fittingly, this episode includes an intro reflecting on using Codex and DaVinci Resolve to edit the conversation itself. Talking points: Building a creator business without turning a hobby into a chore. Monetizing through sponsorships, brand deals, and fractional roles. Why smaller audiences can still be valuable to sponsors. Turning podcast and YouTube audiences into newsletter subscribers. Using lead magnets, segmentation, and your own products to build more predictable revenue. Finding a coaching niche through a distinctive combination of skills. Asking your team where you can contribute most effectively. Giving each piece of content a clear job: views, subscribers, or revenue. Creating stronger YouTube hooks, titles, thumbnails, and opening minutes. Repurposing podcast conversations into standalone videos with custom intros. Using AI to streamline editing, copywriting, and publishing. How changing customer needs could reshape SaaS and service businesses. Subscribe to Rogue Startups wherever you listen, and subscribe on YouTube for future episodes. Follow Craig @thecraighewitt and Colin @colinmcgray on social media. Let us know what you thought of the AI-edited episode—and which practical AI workflows you’d like us to explore next. Get goodies via email: https://craighewitt.kit.com/a7a6c4b5ef

AI agents are getting a lot more capable, but are they actually making work easier yet?  Craig and Colin compare their latest experiments with GrokBot, Hermes, OpenClaw, Claude Code, Codex, and other AI tools, including what works, what breaks, and where agents are genuinely reducing cognitive load.  They dig into open vs. closed systems, shared company agents, local AI, model costs, and the idea of an AI chief of staff that keeps founders focused.  Plus, Colin shares how he's using AI to overhaul a 15-year-old WordPress site, and they explore what the next generation of AI-powered websites and marketing tools might look like. Highlights from Craig and Colin’s conversation: Open vs. closed AI agents for running a business Why Craig chose Hermes as Castos' shared company agent The security challenge of giving AI agents real power Why AI should reduce cognitive load, not create more work How Colin uses AI for CRM, task management, and daily planning Claude Code vs. Codex, Cursor, and other AI development tools Why expensive AI models aren't always the best choice The case for running AI locally or privately How cheap AI access encourages founders to experiment Can an AI chief of staff actually keep you focused? Using AI to overhaul a 15-year-old content site How tiny AI-built tools could replace traditional lead magnets Resources and Links from This Episode: Produced by Castos Productions: https://castos.com/productions/  The Podcast Host: https://www.thepodcasthost.com/  Colin on LinkedIn: linkedin.com/in/colinmcgray?originalSubdomain=uk  Castos Free Tools: castos.com/tools Email me: [email protected]  Find me on Twitter: https://twitter.com/TheCraigHewitt  LinkedIn: https://www.linkedin.com/in/craig-hewitt-78386a66/  Email: [email protected] Chapters (00:00:00) - Intro and topic setup: bots and agents(00:01:23) - Open vs closed agent platforms(00:03:35) - Why choosing Hermes for a shared team agent(00:07:07) - Permissioning and security challenges with Hermes(00:09:08) - Vision for AI teammates across a company(00:10:44) - Personal use: Grokbot vs Claude code workflows(00:12:53) - CRM, task management, and cognitive load reduction(00:16:09) - Mobile workflows, Notion, and GitHub syncing(00:19:06) - App preferences: Claude vs Codex vs Cursor vs Antigravity(00:22:00) - Right-sizing models and cost discipline(00:22:19) - Golf video workflow and downgrading models to save cost(00:25:33) - Local and private AI compute considerations(00:29:35) - Enterprise API costs, subsidies, and sovereignty concerns(00:32:35) - Value of experimentation under usage limits(00:33:56) - Chief of staff model and multi-agent orchestration(00:37:59) - Chief of staff for managing priorities and stale projects(00:41:25) - Old-school business plans teaser(00:42:01) - Renovating lawns4u.com with AI agents(00:45:00) - WordPress vs modern stacks (Astro, Next.js) discussion(00:47:33) - Building tools and lead magnets beyond WordPress(00:50:15) - Wrap up and future topics