Let's be real. For the last few years, the AI world has felt like the wild west. Everyone's got a theory, everyone's got a hot take, but we've all been wondering: what are people really doing with these incredible tools every single day? Well, the guessing game is officially OVER. OpenAI and Harvard just dropped the largest study EVER on how people are using ChatGPT. We're talking a deep-dive into 1.5 MILLION conversations to see what's really going on behind the curtain. And let me tell you, the findings are not just interesting—they are a massive green light for every single person in this community who is hungry to build their future. This isn't just data; it's a blueprint showing you what's possible. Let's break it down. 1. THE AI REVOLUTION IS FOR EVERYONE (SERIOUSLY!) Remember when people thought AI was just for coders and tech bros in Silicon Valley? Those days are gone. The study found that the early gender gap has basically vanished. As of this year, over half of new users have typically feminine names. This is HUGE! It proves that AI is becoming a truly democratic tool, accessible to everyone, regardless of their background. Even better? The fastest growth is happening in low- and middle-income countries. This isn't just a tool for the rich; it's a global engine for unlocking human potential. This fires me up because it confirms what I’ve been shouting from the rooftops: access to AI should be a basic right. It's the ultimate leveler. 2. IT'S NOT JUST FOR BUILDING ROBOTS—IT'S FOR YOUR TO-DO LIST! So, what are people actually typing into that little box? Are they all building the next Skynet? Not even close. Three-quarters of all conversations are about getting everyday tasks done. People are using it for practical guidance, getting information, and writing. Think of it less like a super-genius coder and more like the world's most helpful advisor, ready to help you plan your week, draft an email, or just figure out what to make for dinner. The study breaks it down into three simple categories:

Grant Lee founder of Gamma just shared this, hope this helps!! we grew from zero to $50M ARR in <2 years, profitably i've never publicly shared our tactics before it's cost us over $5M to learn what I'm about to share 800-word long post on every growth hack that printed money for us I'll cover: 1. Influencer Marketing 2. Performance Marketing vs Brand 3. User Testing 4. Dogfooding 0-10M was nearly 100% word of mouth and organic content 10-50M was still >50% word of mouth but influencer, affiliate, and referral made up the other half But the foundation we set from 0-$10M is what allowed us to blaze past $50M 1. Influencer Marketing 101: 90% of your reach comes from <10% of content that goes viral. Your job: go broad with influencers and spend enough to find that 10% of content and formats that work. Virality is no accident - test and discover hooks, visuals, formats for each platform, understand why they work, and replicate 100x across your influencer roster. Most startups get three things wrong: 1 - Too small budget 2 - Overly selective on creators and messaging 3 - Give up too early Start with $10-20k/month, commit to 6 months minimum. This lets you experiment with many micro influencers and lets you test many concepts. - List creator personas with audiences that care about your product. Be exhaustive. Influencer outreach is time-consuming. Work with a freelancer or agency if you don't have a team. DM me for recommendations. - Offer base + viral bonus. For TikTok, have them start new accounts. New accounts can get as many views as established ones. But on new accounts creators are unburdened by their brand and can try any format. - Test across all platforms (TikTok, IG, X, LinkedIn work for most B2B/prosumer apps). - Track specific creators and hooks that outperform, not just channels. - Add 'How did you hear about us' to onboarding. Calculate which channels drive leads vs just views. - Compile every asset, video or hook that works into a guide. Once you have 20-30 winning formats, hire your top creators as consultants to train all other influencers on what angles go viral for you and why.

Been thinking about something lately and wanted to get your thoughts. Does anyone else feel like their LLM apps are super hit-or-miss? One minute it works like magic, the next it completely face-plants on a simple task. It's kinda frustrating when you're trying to build something reliable. I stumbled onto this idea called MCP, which is basically just a fancy way of talking about how your AI uses the "tools" you give it. Think about an AI assistant that can book flights. The MCP is the set of steps it takes: finding the right airline website, putting in the dates correctly, selecting the seats, etc. The problem is, most of us are just kind of guessing if it's working right. We run a few manual tests, it looks okay, and we push it out into the world, just hoping for the best. But what if you could actually get a score for how well your AI performs those steps? Like a report card that tells you, "Hey, your AI is great at finding dates, but it messes up the arguments for the seat selection tool 50% of the time." That's where this DeepEval thing comes in. It lets you test that stuff automatically. Being able to actually measure this means you can pinpoint exactly where your app is weak and fix it. Your app stops being a gamble and starts being dependable. And honestly, in a world where everyone is launching an AI app, the one that actually works consistently is the one that's going to win. People will trust it more, use it more, and recommend it. That's how you get a real competitive edge and make your project profitable. Anyway, I was reading their getting started guide and it got my wheels turning. The link is here if you wanna check it out: https://deepeval.com/docs/getting-started-mcp I'm curious, how are you all handling this right now? Are you just testing things by hand or do you have a system for it? More helpful posts from Ai Titus --> https://www.skool.com/@aititus?g=ai-automation-society&fl=posts