Larry Ellison's argument going viral: the model itself isn't the AI edge anymore, the only edge left is data nobody else has. Here's exactly how to apply it — pull data, save data, pick the model trained on yours.
There's a Larry Ellison clip going viral right now. His argument: AI models are all becoming the same because they're trained on the same public internet, so the model itself isn't the competitive edge anymore. The only edge left is having data nobody else has.
Is it self-serving? A little — Oracle sells the systems that store proprietary data. But that doesn't make him wrong. And even though he's talking about giant companies, the same principle applies to a one-person business: if Claude, GPT, and Gemini all read from the same internet, the only thing that makes your AI smarter than your competitor's is the data only you have.
Three ways to build that moat. Pull data the LLMs don't have. Save the data you're already throwing away. Pick the model that's already trained on the data you actually need. Below: the tools, the prompts, and the weekly ritual that makes it compound.
Frontier LLMs are trained on a snapshot of the internet, not the live internet. They can't see what people are saying in your niche today. You can. That's a data edge sitting right there.
The tools:
· Apify — the go-to scraper for X, Reddit, TikTok, Instagram, LinkedIn, customer review sites. Most flexible.
· Firecrawl — turns any URL into LLM-ready Markdown. Best when you need clean data for a Claude Project or RAG setup.
· GummySearch — Reddit-specific audience research, no code. Lowest lift if you want to mine subreddits.
· BrightData — bigger-scale enterprise scraping. Pricier, more compliance-friendly.
The sources worth pulling:
· Customer reviews of your competitors on G2, Trustpilot, Capterra, App Store, Yelp.
· Real-time conversations on X and Reddit in your niche.
· Comment sections under top podcasts and YouTube videos in your space.
· Niche Substack archives and paid newsletters you subscribe to.
· Discord, Skool, and Circle communities you're already in (export the chat history).
Once you've scraped 100+ raw comments / reviews / posts, this prompt synthesizes them into the document that becomes your brand-voice and ICP layer for every future prompt.
Copy this prompt
You are a customer research analyst. I've scraped real customer language from where my target audience complains about my competitors. Below is the raw data. My business: [one-line description] My niche: [be specific] The competitors I scraped: [list] Raw scraped data (paste 100+ items): --- [paste] --- Do all of this: 1. Top 5 jobs-to-be-done. For each, the verbatim customer quote that proves it. 2. Top 5 pains. For each, the verbatim quote that proves it. 3. The 20 phrases customers use most often (the swipe file for my copy). 4. The 3 biggest gaps competitors are leaving (what customers want but aren't getting). 5. A 1-page ICP doc with: demographic, psychographic, JTBD, pains, what success looks like to them. 6. A brand-voice-doc.md ready to load into a Claude Project, with 3 voice attributes, 5 do-says, 5 don't-says, 10 example headlines, 10 example product-description openers. 7. END with a brutal go/kill verdict: based on this data alone, is this market a painkiller or a vitamin? No corporate hedging. Cite the data verbatim. If the patterns are weak, say so.
This is the data you're already creating every day and almost certainly throwing away. It's the highest-leverage version of "proprietary data" because nobody else even has access to it.
What to save, where:
· Customer DMs and comments. Export your IG and TikTok DMs monthly. Pipe Manychat conversations to a Notion DB or Google Sheet.
· Sales calls and team meetings. Auto-record everything with Otter, Fathom, or Granola. Transcripts > recordings (Claude can read transcripts).
· Your best emails. Save the ones that converted, the ones that got a real reply, the ones in your voice at its best. They're your tone library.
· Reviews and testimonials. Stamped.io or Yotpo if you're on Shopify; Trustpilot if you're not. Auto-request after every purchase.
· Voice memos. Whatever you ramble into your phone walking around — that's your raw thinking. Transcribe with Whisper and dump it in a folder.
· Past launches, campaigns, and content. Every brief, every iteration, every result. The actual artifacts and the metrics together.
Where to put it all: a single folder that doubles as a Claude Project, NotebookLM notebook, or Custom GPT knowledge base. The moat isn't the data sitting in storage; it's the data loaded into your AI so every prompt is pre-trained on you.
The weekly ritual
Every Sunday: paste the week's new DMs, calls, and reviews into Claude, ask it to surface the top themes + 3 actions, and add those to your knowledge base. Your AI gets smarter every single week without you doing anything else. That's the compounding move.
Most people pick their AI by brand. Frontier users pick by what the model was trained on. The right model depends entirely on which data layer matters most for your work.
· Meta AI — trained on Instagram and Facebook. The only major LLM with deep access to that data. If you run a brand, do social-first marketing, or analyze creator content, this is a goldmine you're probably ignoring.
· Grok — wired into X. Best for real-time newsjacking, viral trend research, anything where the latest conversation matters today.
· Gemini — deepest Google Search integration. Best for fact-heavy research where you want live web grounding.
· Claude — best at long-context work, code, and honesty. Best when you're loading your own proprietary data and want the model to think carefully about it.
· ChatGPT — biggest plugin ecosystem and the most third-party integrations.
The pattern: use the right model for the right data layer. The same task can produce wildly different answers depending on which model's training set you're tapping.
Everything above, packaged into a one-page stack you can stand up this weekend:
· Scrape data the LLMs can't see: Apify or Firecrawl + GummySearch.
· Save your own data: Otter or Fathom for calls, Stamped.io for reviews, Manychat for DMs, a Notion DB or private GitHub repo as the master knowledge base.
· Load it where you'll use it: Claude Projects for personal work, NotebookLM for research-heavy tasks, ChatGPT Custom GPTs for shared team prompts.
· Synthesize weekly: Claude (Opus 4.8 on high effort) for the Sunday synthesis ritual.
· Multi-model where it counts: Meta AI for social audience research, Grok for X-native real-time intel, Claude/ChatGPT for everything else.
Total cost for a starter setup: roughly $40–$80/month depending on which scrapers you use. The data advantage compounds for the life of your business.
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