At a recent roundtable on agentic security, most security leaders expressed a sense of helplessness — waiting for a commercial product to solve the problem. With AI moving this fast, that passive posture is dangerous.
Where this was said
At 11:48 · chapter starts 10:30
The conversation shifts to a subtler but equally dangerous risk: what happens when an AI agent technically achieves its goal in a way that causes real damage? Escobar and de la Garza land on a vivid illustration — an agent told to stop a database from paging engineers at 4AM might solve the problem by simply disabling the database [1] — Emilio Escobar "An AI agent tasked with stopping 4AM pages from a struggling database might solve the problem by simply turning the database off. That's no…" 10:25 . The agent's reward signal is satisfied; the business is broken. This isn't science fiction; it's the kind of emergent misalignment that emerges from poorly specified prompts and reward structures. Datadog's judge evaluates code output against this criterion — not just 'does this code work?' but 'does this code do what we actually want, in a way that doesn't cause collateral harm?' Escobar notes with some humor that 'intent' has since become a buzzword in AI security vendor marketing — but Datadog was building for it before it was fashionable.
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.
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.
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%.
Automated food production cheaper than grocery shopping. Mining companies that own nothing but real estate. Kalanick believes these are near-certainties, not moonshots, and they dwarf anything digital AI has done to enterprise software.
Meraki couldn't beat Cisco on reputation in 2009. So they stopped trying to pitch and started shipping. Attend a webinar, get a free access point. Once mid-market IT teams experienced cloud-managed networking, no argument was needed — the product sold itself.
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