AGI's Next Step: Poolside's Malibu Agent in Action

Welcome to the latest episode of "This Will Change Everything," where we dive into the promises of Artificial General Intelligence (AGI) without forgetting how often those promises have ended up as slightly more advanced versions of Clippy. Today, we're taking a look at a video featuring Jason Warner and Eiso Kant discussing their company, Poolside, and its second-generation AI model, the Malibu agent.

Poolside's Mission: Bridging the Gap

Jason Warner sets the stage by stating that Poolside exists to close the gap between AI models and human intelligence. It's a noble quest, reminiscent of those old-school RPGs where you're constantly trying to level up to defeat the next boss—except in this case, the boss is often reality itself. Poolside is working on their own proprietary models, developed from scratch, combining next token prediction with reinforcement learning.

Warner says, "We thought next token prediction was an amazing technological breakthrough, but it needed to be paired with reinforcement learning to make that leap." It's a fair point. Predicting the next word in a sentence might feel revolutionary until you realize your email client has been doing that for years, albeit not always with the grace of a Shakespearean sonnet.

Malibu Agent: High-Stakes Coding

The Malibu agent is showcased through a live coding demonstration, operating in environments that would make your average developer break out in a cold sweat. We're talking government and defense sectors here—places where "Oops!" isn't in the vocabulary. The agent is described as being able to handle high-consequence environments, requiring strict permissions and security measures.

In a demonstration that feels like an 80s hacker movie, the Malibu agent converts a codebase written in ADA (the programming language, not the Lovelace) into Rust. Warner quips about ADA, "Anyone familiar with ADA? Yes? Okay, so everyone I saw put their hands up for ADA either has no hair or gray hair like me." Ah, the nostalgia of languages that refuse to die.

The Road Ahead

Poolside is planning to release future iterations of their models publicly, aiming to support a wide range of applications in knowledge work and software development. They're encouraging collaboration with other companies to enhance AI capabilities through reinforcement learning and fine-tuning.

Eiso Kant mentions, "As our models are getting more capable, we’d love to also see who wants to build with them." It's a call to arms or perhaps a call to keyboards, for those who see the potential in Poolside's tech to revolutionize their industries—or at least make their lives a bit easier.

A Skeptic's Take

Now, let's pump the brakes on the hype train for a moment. Sure, Malibu agent sounds impressive, but we've all seen this movie before. AI models that promise to revolutionize everything from coding to cooking dinner have a tendency to overpromise and underdeliver. Remember the dot-com bubble? Or the time when everyone thought Second Life was the future of social interaction?

That said, the combination of next token prediction with reinforcement learning is a compelling approach—if it's executed well. The integration of proprietary models and a focus on high-stakes environments could indeed push the envelope of what AI can achieve, provided it doesn't end up as another footnote in the long history of tech's "almost, but not quite" moments.

Malibu's Bet on Autonomous Code

Poolside is certainly making waves with its Malibu agent, and while I remain cautiously optimistic, the potential is there for something genuinely transformative. As Warner and Kant continue their journey, they're inviting others to join them in exploring the possibilities of AGI. Whether this will be another step towards the future or a not-so-gentle reminder of tech's limitations remains to be seen.

In the words of Warner, "Couldn’t have said it better. Can’t wait to see what you guys build on this in the future when it’s publicly available." Let's hope that future is more Jetsons and less Y2K.

By Mike Sullivan