Altman said this week that AI will be sold like electricity. Metered. On demand. A utility. For content creators and marketers, this isn't abstract. Think about what happened when video production tools became cheap. YouTube exploded. Not because video technology was new. Because the barrier to creating video dropped low enough that millions of people could suddenly publish. The same thing is happening with AI-powered content. When AI tokens become a utility, the cost of generating voiceovers drops. The cost of translating content into 12 languages drops. The cost of repurposing one piece of content into 30 formats drops. Right now, a lot of that is expensive or manual. Cloud APIs charge per character, per minute, per whatever. You're watching a meter while you create. When that meter runs close to zero, the volume of content explodes. And the competitive advantage shifts from "who can afford to produce" to "who has the best ideas and the best distribution." I'm building AI-native content tools right now as a solo founder. Five-plus products. One person. No team. A decade ago I tried building multiple products solo and failed miserably. The infrastructure didn't exist. Today, AI is that infrastructure. One person can now ship things that would have taken entire teams. People ask me constantly if AI is a bubble. I push back every time. I'm in it every day, building real products. This feels like electricity going mainstream, not like tulips about to crash. For the content people here: when the cost of AI- generated content drops to nearly zero, what changes in your strategy? More volume? New formats? Different distribution? Curious how you're thinking about this.

I emailed 600 people I hadn't spoken to in 14 years. 5 of them became my first paying customers — within 60 minutes. Here's what I built and why. I'm partially dyslexic. Long text has always been a struggle. Since high school I've been converting written content to audio — articles, reports, white papers, ebooks. I kept building tools to do this. Eventually one of them got good enough that content creators started asking for it. A friend wanted it for creating custom bedtime stories for her kids. Another had a stack of ebooks he'd never read — wanted them as audio for his commute. Others were producing YouTube content and tired of paying per-character for cloud voiceover tools. That personal tool became a full desktop voice AI studio. 63 voices, voice cloning, 23 languages, multi-speaker editing, professional mastering. Everything runs locally — no uploading scripts to someone else's server. Then 3 days ago I emailed 600 customers from a product I built in 2012. Plain text, no design. Some of them bought. Revenue before the product even launched publicly. Tonight it goes live. For content creators here — how much of your workflow involves voiceovers? And what's your biggest frustration with the tools you're using now?

Your product is probably invisible to a growing segment of buyers. Not because your SEO is bad. Because they're not using Google. A growing number of people search by asking ChatGPT, Gemini, or Perplexity a question. The AI gives them a ranked list. They research from there. If your product isn't in that answer, you don't exist. I realised this early and did something most founders skip entirely: I built the layer of my website that AI models can actually read and cite. Before writing a single ad or social post, I spent weeks on what I call the "AI-readable layer." Here's what that looked like: 1. llms.txt files at the site root. These are plain-text documentation files designed for AI crawlers. Not a robots.txt. A structured brief that tells AI models what your product is, what it does, who it's for, and how it compares. Think of it as a pitch deck for machines. 2. 62 blog posts before launch. Not SEO filler. Honest comparison posts — my product vs each major competitor. Use-case deep dives. Technical explainers. FAQ content written in the natural question-answer format that AI models actually cite. 3. JSON-LD structured data on every page. FAQPage schema on the homepage, feature pages, use case pages, blog posts. This is the metadata AI models parse when they build their knowledge base. 4. Dedicated pages for every use case and feature. Not just a features list on the homepage. Individual pages at /for/podcasters, /for/game-developers, /features/ voice-cloning. Each with its own structured FAQ. 5. Competitor comparison content that's fair. Not "why we're better." Honest trade-off breakdowns. AI models prefer balanced, cited content over marketing copy. When the AI ranked my product third — not first — that's actually more credible than ranking it #1. This approach has a name: GEO — Generative Engine Optimization. It's early. Most founders haven't heard of it. Most AI tool builders haven't optimised for it either, which is ironic. The core insight: AI models don't read your marketing

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Hi all. Just wondering if anyone uses or is experimenting with text-to-speech. If so, what are you using? I'm Curious what are you using it for? How are you producing text-to-speech? Are you using a service, an app, or something else? And if you don't mind me asking, how are you, and how much are you paying for it?