MIT Professor & AI Doomer Admits He Was WRONG (Ramesh Raskar)

<p>What happens when millions of people hand their lives to AI agents, and nobody has built the rules of the road?</p><p><br></p><p>This episode is brought to you by AgelessRx, where aging becomes a puzzle that can be solved. The mission is to help you live longer and healthier by adding more life to your years through science-backed solutions and personalized care. Learn more at <a href="http://agelessrx.com/beyond-tomorrow"><u>agelessrx.com/beyond-tomorrow</u></a>.</p><p><br></p><p>MIT Media Lab professor Ramesh Raskar argues the real danger isn&#39;t the AI engine, it&#39;s agents with no governance. His case: decentralize AI so billions of people run their own agents, and build the licences, inspections and audit trails agents need, instead of trusting a few labs to make the engine safe. His P(doom) has fallen from 90% to under 10%.</p><p><br></p><p>The conversation covers what an AI agent actually is, personal agents like Dot, Muse, Instinct and CC, the DRIVE framework for gradually trusting an agent, why there&#39;s no such thing as &quot;zero-shot AGI,&quot; the OpenAI and Hugging Face incident, prompt injection, and the shift from an attention economy to an intention economy. One question runs through it all: can we get the benefits of AI agents without handing control to a handful of companies, or to the agents themselves? Ramesh&#39;s answer: don&#39;t just build better engines, build the roads, the rules and the governance.</p><p><br></p><p>Key Takeaways</p><ul><li><p>An agent is an LLM in a loop with tools: the LLM is the engine, the agent is the car, the internet is the road.</p></li><li><p>Build trust with DRIVE (Discover, Request, Instruct, Venture, Execute), and start with reversible tasks, no credit cards.</p></li><li><p>Decentralized agents shrink the blast radius, which is why Ramesh&#39;s P(doom) has dropped sharply.</p></li><li><p>Cost is the bottleneck: running an agent per user costs far more than serving a social media user, so expect revenue-share deals.</p></li><li><p>Prompt injection is the top risk because LLMs can&#39;t separate commands from content. Agents need licences, inspection stickers and audit trails.</p></li></ul><p><br></p><p><strong>About Ramesh Raskar</strong></p><p>Ramesh Raskar is a professor at the MIT Media Lab, where he works on decentralized AI and the Internet of Agents. His research focuses on how personal AI agents can be built so power stays distributed instead of concentrated in a few companies.</p><p><b