Snapshot · Modern Wisdom
The mathematical foundations of modern AI, including convolutional and recurrent neural networks, were understood since the 1970s and 80s — the breakthroughs were compute power (GPUs/TPUs) and internet-scale training data.
Where this was said
At 35:28 · chapter starts 32:07
The episode's most technically alarming section opens with a straightforward question: what's AI's role in hacking now? Bill describes a transformed workflow: feed a device's firmware, hardware specs, and software to an AI, ask it where the vulnerabilities are, and get answers in hours that would have taken a year of manual analysis before [1] — Bill Thompson "Vulnerability research that once took 3 months to a year — dumping a phone, parsing its code, finding exploits — now takes roughly 3 hours …" 32:07 . A year-long phone forensic project — dump the phone, get the E01 image, comb through the code — now probably takes 3 hours of substantive work at most. The target development cycle that previously required months of tradecraft now runs on a Sunday afternoon. More alarming is the off-grid version: people with server farms in their basement can train custom AI models on private code exploit repositories with no guardrails. Bill says organized crime is absolutely doing this already. He closes with a key historical note: the mathematics behind convolutional neural networks and LSTMs were understood in the 1970s and 80s — the AI revolution happened when GPU compute and internet training data finally matched the math that was already on the shelf.
Vulnerability research that once took 3 months to a year — dumping a phone, parsing its code, finding exploits — now takes roughly 3 hours with AI. The math behind modern AI was known since the 1970s; what changed was GPU compute and internet-scale training data.
AI has compressed the vulnerability-development cycle for a new phone or network from 3 months to a year down to roughly 3 hours of meaningful work, with a total ceiling of about one month.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
Eyal and Yali shut down all marketing and spent 4 months completely rebuilding PropGPT from scratch.
PropGPT has accumulated over 40,000 total downloads since launch.
PropGPT's large language model (AI) operating costs are just $20 per month, and the cost is continually falling.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
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