On AIBiz, hosted by Sayan, Dr. Craig Kaplan breaks down what AI safety really means—beyond movie-style panic—and why the biggest risks aren’t always the loudest ones. You’ll hear how AI evolved from rule-based systems to machine learning, deep learning, and now agents that can take actions in the real world.

This episode is for anyone feeling overwhelmed by rapid AI change—founders, professionals, and everyday users who want clarity without hype. You’ll leave with a practical way to think about alignment, why “guardrails after the fact” struggle, and how collective intelligence could offer a safer route to superintelligence.

00:06:57 Meet Craig Kaplan and why AI safety is “controversial”

00:12:12 The 3 eras of AI: symbolic → machine learning → LLMs

00:14:05 Why computing power changed everything (and why NVIDIA matters)

00:17:52 What people misunderstand about AI safety: short-term vs long-term risk

00:19:15 Jailbreaks, “Whack-a-Mole,” and why guardrails don’t scale

00:22:59 Bias in training data: how history leaks into modern systems

00:26:29 AI agents and the next step: collective intelligence for safer superintelligence

Dr. Craig Kaplan is an AI researcher focused on AI safety and superintelligence design, with decades of experience spanning symbolic AI, machine learning, and modern LLM-era systems. He also built Predict Wall Street, applying collective intelligence to market prediction at scale.

Think of AI safety in time horizons: immediate harms vs low-probability, extreme-impact risks.

“Test-and-patch” safety can become Whack-a-Mole—new loopholes emerge as fast as fixes.

Jailbreaks show why intent-based prompts can bypass restrictions unless safety is built deeper.

Training data can embed historical bias; “more data” isn’t always better without careful curation.

Agents change the game by adding autonomy + tools, so oversight becomes more critical.

Your online behavior matters—AI learns values from what people collectively create and share.

https://www.superintelligence.com/ 

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