Ground Level AI

Welcome to the first episode of the Ground Level AI Podcast! I’m excited to offer up thoughtful conversations with AI leaders with a strong focus on what happens when AI meets the real world. From AI infrastructure and cybersecurity to enterprise adoption, policy, geopolitics and the workforce, we'll explore where the technology collides with reality. My first guest is Joshua Saxe, cofounder and CTO of Abundant Security and the former engineering lead for Llama security at Meta.

Yesterday, Meta announced a massive expansion to its planned Hyperion AI data center in northeast Louisiana. What was first revealed as a $10 billion project, and then a $27 billion project, has now ballooned to $50 billion and 5 gigawatts — equivalent to the output of several nuclear reactors or enough to power several million homes.

To watch Silicon Valley react on X to last week’s raft of new AI models from OpenAI, Meta and Xai was to witness awe, joy and an unrelenting sense of LFG (let’s f***ing go). “This week in AI was genuinely unhinged... what a week to be building,” said one poster.

Cybersecurity experts have warned for years that AI would eventually automate cyberattacks. This week, threat researchers at Sysdig found that this prediction is quickly becoming reality. In a blog post, the researchers detailed what they say is the first documented case of AI agent-run ransomware: an end-to-end extortion campaign, dubbed JadePuffer, that Sysdig said was orchestrated by an LLM and had no human in the loop.

Today’s frontier AI models—ChatGPT, Claude, Gemini—owe much of their success to the public internet. For years, AI companies feasted on billions of web pages collected through archives like Common Crawl and massive datasets such as The Pile. Then, of course, came reinforcement learning from human feedback, and other post-training techniques, that rely on human-generated data designed to teach AI models how to reason, use tools, and complete real-world work.

Open-weight AI models — which allow developers to download, customize, and run the model on their own infrastructure — were back in the spotlight last week. Most recently, Palantir CEO Alex Karp said enterprise customers are increasingly concerned about giving AI companies access to their data and that some U.S. government customers have switched from closed AI models to open ones.

A quick, light anecdote on this eve of the July 4th holiday weekend šŸŽ†: As someone who reports on AI all day, every day, I usually feel like Iā€™m on top of whatā€™s going on in the field. Iā€™m terminally online. I know all (ok, most) of the memes. I constantly chat with sources. Itā€™s tough to catch me with news I havenā€™t heard about. But yesterday morning? Color me red-hot surprised.

A quick, light anecdote on this eve of the July 4th holiday weekend šŸŽ†: As someone who reports on AI all day, every day, I usually feel like Iā€™m on top of whatā€™s going on in the field. Iā€™m terminally online. I know all (ok, most) of the memes. I constantly chat with sources. Itā€™s tough to catch me with news I havenā€™t heard about. But yesterday morning? Color me red-hot surprised.

(l-r) Andy Konwinski, Nathan Lambert, Bryan Catanzaro, Percy Liang and Ion Stoica The debate over “open” AI has entered a new phase. For years, advocates have argued that openly available frontier AI models — whose underlying parameters ("weights") are released so researchers and developers can study, modify, and build on them —accelerated research, innovation, and competition.