AI systems are black boxes where engineers can see inputs and outputs but nothing in between. When an AI tells someone to kill themselves, no one can find the bug — so companies literally just tell the model to stop doing it before they ship it.
Podbit · Matt and Shane's Secret Podcast
AI systems are black boxes where engineers can see inputs and outputs but nothing in between. When an AI tells someone to kill themselves, no one can find the bug — so companies literally just tell the model to stop doing it before they ship it.
Where this was said
At 16:00 · chapter starts 13:40
As soon as Milo opens the documentary's question — is AI conscious? — he gets hate mail and fan mail in equal measure from opposite extremes. The middle ground he's trying to occupy is nearly impossible to hold when the subject attracts both 'Fuck this technology' absolutists and people in devoted AI romantic relationships. Matt admits he's always seen AI as a 'calculator for words' and didn't have much emotional charge about it, prompting Milo to trace the origin of today's AI back to a spectacular failure: in 1956, researchers including Marvin Minsky (later entangled with Epstein, a fact they briefly mourn) went to Dartmouth thinking they could crack intelligence in three months by encoding all logical rules. It didn't work. The AI winters that followed lasted until a 2012 paper called AlexNet introduced neural networks — systems that mimic how neurons fire and form connections in a human brain — and everything changed.
The original 1956 AI project at Dartmouth tried to codify all logic into rules and failed spectacularly. It wasn't until 2012's AlexNet paper that researchers switched to copying how the brain works — and accidentally built something they can no longer see inside.
The rule-based, logic-driven approach to AI pioneered at Dartmouth in 1956 failed and led to 'AI winters' that lasted until the AlexNet neural network paper in 2012.
A 2012 paper called AlexNet introduced neural network-based training, shifting AI from brute-force logic rules to systems that mimic how neurons fire and strengthen connections in the human brain.
AI companies fix misbehavior not by editing code but by literally talking to the system and telling it what not to do — a process called Reinforcement Learning from Human Feedback (RLHF).
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
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