Parallels Between AI and Big Data Industries
We're back at it again, llama shaving in the world of AI, and today we want to look at something fascinating: the striking similarities between the current AI industry and the early days of the Big Data industry. It's kind of funny, actually. If you remember Hadoop, you'll know exactly what I mean. The same infrastructure we built back in the day—just with AI slapped on top now.
Hadoop was the Big Data revolution—a game-changer for its time. And, yeah, there's something generational about this. Gen Xers got their big houses in the Valley thanks to Big Data. Now, millennials are making their mark with generative AI. But guess what? The challenges are pretty much the same—just wearing a different outfit. We're redoing the same work—building the infrastructure, making all the same mistakes—because, apparently, it doesn't feel like much of that experience got passed on. Thanks, Gen X, for keeping those secrets during your midlife crisis.
Evolution of Compute Frameworks
In the early days of Hadoop, things were pretty straightforward: you had large batch processes running across clusters. This was the "screwing around" phase—people experimenting with data, mostly for themselves. It wasn't a collaborative thing—not yet. People were just trying to see what they could do with huge datasets that couldn't fit on a single node. And then, slowly, the tech matured. We got to a point where it wasn't just about individuals messing around with large datasets. It was about teams collaborating and figuring out how to do things together. And that's where things got interesting.
Around 2010, we started seeing a shift—especially with the work coming out of Berkeley AMP Lab. They wanted to integrate not just the algorithms and the machines but the people, too. That's where Spark came into play. Unlike Hadoop, which kept things memory-based and node-oriented, Spark thought of the entire cluster as one gigantic computer—a shared state—rather than just a bunch of individual nodes.
It's funny—when you look at AI today, we see a similar progression. There's been an explosion of models, and everyone is using essentially the same ones. But just like in the early Hadoop days, there's a struggle with how we think about the infrastructure. Right now, infrastructure is mainly in the cloud, and everyone wants you to scale up quickly and then get out. It's a mindset that's a little too much like Hadoop—where the focus was on getting on and off the cluster as quickly as possible. But with Spark, and the introduction of things like Flink, people started seeing the value in treating the cluster as a unified resource—thinking bigger than just individual nodes.
Bridging the Gap Between Academia and Industry
One of the coolest parts of the Big Data revolution is that some of its biggest advances came from academia. AMP Lab wasn't a big enterprise lab—it was university-driven. And yet, they managed to bring together researchers, students, and industry to build something that fundamentally changed how we handled data. Everyone had a seat at the table - actually, "P" in the "AMP" stands for People, and that was one crucial part of the whole equasion. Academia had leverage back then. Imagine that—professors and students actually being listened to.
Now, fast forward to today with AI. Things are a bit different. There's a gap between academia and industry. A lot of what's coming out of universities is siloed, and by the time it reaches industry, the collaboration aspect is pretty much lost. The influence of big corporations and VCs has tipped the balance, and academia isn't at the forefront the way it once was. Gotta love the sound of VCs telling professors what to do. Nothing like turning groundbreaking research into a race for the next IPO.
Yet, some things remain consistent. There’s always this tension between academia and engineering. Academics want to publish papers and move on, while engineers want to get down and dirty, take things apart, and make them better. At our company, we've seen this first-hand, trying to bridge that gap between the academic pursuit of AI and the need to actually engineer something that can work at scale. It’s tough, but exciting, because we need people from diverse backgrounds to make the next generation of AI a reality—those who understand compilers, distributed systems, and even hardware. Strange bedfellows, indeed. Who knew physicists and compiler nerds could find common ground?
Lessons from Early Silicon Valley
We can also learn a lot from the early days of Silicon Valley—not just from Hadoop versus Spark and Flink, but from the entire mindset of that era. There’s one lesson that really stands out: never trust vendors. Back in the day, Sun Microsystems was the big thing. They had everything—labs, printing presses, the parties to die for. But at the end of the day, they were still a vendor, and that’s a lesson that’s more relevant than ever today. Vendors will promise you the moon and then sell you a ticket to Mars.
Today, it's easy to build a small lab with a few nodes—heck, you can even add AI accelerators to those nodes and do some pretty cool stuff on your own. The point is to get your hands dirty, experiment, and build it yourself rather than rely on a vendor who might disappear or get bought out tomorrow (looking at you, Oracle). Early Silicon Valley was about people coming together to create something—an environment that fostered innovation through collaboration, trial, and error. We’re in a similar place now with AI, where the possibilities are endless, and nothing is set in stone. It’s all in flux, and that’s what makes it so damn exciting.
As we dive further into building out the infrastructure for AI, it’s not just about learning from Hadoop, Spark, and Flink. It’s also about remembering the spirit of early Silicon Valley—that rebellious, creative energy where anything seemed possible, and everything was up for reinvention. So go ahead, build your own version of the future, and for the love of god, don’t let some vendor tell you otherwise.