Podbit · The Tucker Carlson Show
Signal isn't safe. According to Kim Dotcom, the US government has a backdoor into Signal and laughs at everyone who thinks they're protected. There is no communication tool he would recommend — they are all compromised.
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.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
The only way to have privacy is to disconnect from the internet entirely. Every website, every tool, every piece of hardware you use is controlled or monitored by the US government — and that's not paranoia, it's the architecture.
AI is the next phase of the surveillance state. It will be plugged directly into the US government's mass data collection systems and will know everything about every person on Earth. The only defense is to avoid AI and the internet entirely.
The US government hoovers up roughly 10 petabytes of data every day — all communications, everything. That data appetite is driving the massive wave of new data center construction, not AI competition with China.
No sequence of text notes passed between students would ever let one of them nail the saxophone from their first try. The same logic applies to AI: without accumulating real experience into weights, you can't build genuinely capable systems.
Almost no alignment research addresses the hardest version of the problem: keeping an AI safe when its weights are being updated continuously from millions of real-world sessions. This is structurally similar to the human parenting problem — you need to give the system enough foundational values that it improves without going off the rails.
Today there are fewer than 5 prominent AI base models, all trained on similar data, producing eerily homogeneous outputs — classic mode collapse. Continual learning from diverse real-world deployments could finally break this, producing a genuinely diverse ecosystem of AI minds.
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