Patrick Moorhead Insights: Overinvest in Data to Scale AI
Few people have had a front-row seat to more technological revolutions than Patrick Moorhead. As founder, CEO, and chief analyst at Moor Insights & Strategy, he’s spent decades tracking the intersection of hardware, software, and business transformation.
In this episode of The AI Forecast, host Paul Muller sits down with Patrick for a wide-ranging conversation on the evolution of AI—from lessons learned during the dot-com era to the rise of hybrid multi-cloud fabrics and the future of human-machine collaboration.
Here are the key takeaways from the conversation.
Comparing the Dot-Com Era to Today’s AI Moment
Paul: I was listening to Scott Galloway and Ed Olson’s podcast a few days ago, and they were likening the level of exuberance and frankly, even the level of dealmaking we’re seeing in the marketplace going on at the moment in AI to the dot-com era. And we all know how that ended—the internet won, but it didn't get there via straight line. How does today's AI moment compare to past waves of innovation that you've seen?
Patrick: I am more comfortable about this than dot-com. When I was part of dot-com, it was, oh my gosh, I’m losing 35 bucks a bag on dog food and VCs are triple investing in the same businesses. It was like putting multiple chips on a craps table and it was pretty clear that that wasn’t going to work.
I like to call it the law of “if thens”—and the law of if thens said, okay, I’m building all this dark fiber and all this capability. If I can get a service that distributes video over the internet… If I can have a PC connected to a DSL or cable modem… If I have gaming that is not multiplayer yet… then yeah, we can fill up these pipes. That’s too many “if thens.”
Today, if I have a web browser, what I can do is absolutely amazing. All the agents that I have running are through a web browser. Now, don't get me wrong, enterprise AI is challenging, but it’s already making a difference. You can see it touches more than the internet did. This is touching healthcare. This is touching consumers. This is touching entertainment. This is touching every form of personal productivity. It’s touching code development. Google said that they're doing 20% of all their code based on AI. That is absolutely mind blowing.
Garbage In, Garbage Out—Still True in 2025
Paul: What are some of the best practices you’ve learned as someone who works with data all the time?
Patrick: The most important thing I learned, I learned in 1984 in my first computer class—and that was garbage in, garbage out. And it has never changed.
If you look at GenAI today, it’s amplified. Your data has to be that much better to get a good decision. If you have the right workload and the right model, the biggest impediment to enterprise AI success is having the right data. I think it’s one of the reasons that Cloudera is such an important company.
Paul: What are you seeing happen in the C-suite and at the boardroom table when it comes to recognizing and addressing the challenges of data quality and fragmentation?
Patrick: The successful companies really do have a proper data management strategy—bringing multivariate data in multiple formats, making sure it’s clean, tagged correctly, and secure. We’ve been talking about having a data management strategy for decades, and this time, it matters.
The Hybrid Future: Why Optionality Wins
Paul: The idea of being able to get to all your data everywhere all the time is going to be critical to connecting the dots. Because let's face it, when you're in the boardroom as an executive, the whole point of getting all those various functions together in one room was to try and assemble all the various experiences and data points that you had across the business to create a cohesive business decision. So, I suppose this hybrid notion—that you’re going to have to be able to get to data no matter where it is—you were talking about this 10 years ago.
Patrick: I was the analyst who was the cloud denier, saying that hybrid was going to be the way to go. Enterprises want optionality. There are things where they want to leave the driving to someone else, and there are some things they want to control.
Even a 15-year-old company has an Oracle database, an SAP implementation, a mixture of on-prem, public cloud, enterprise SaaS, and now sovereign cloud. You must be able to work across all of those.
If you try to copy all of your data into one giant place—it’s impossible. The cost to copy and assemble it all is a complete and utter failure. That’s why I came up with this idea that the future is going to be about hybrid multi-cloud fabrics—whether it’s security, data, compute, or automation.
You want to choose vendors that operate in every modality. Otherwise, you’ll be playing whack-a-mole until the cows come home.
Paul: For boards preparing for AI at scale—what’s your advice?
Patrick: Overinvest in data. Spend more money than you think you need. Don’t do it alone—find a partner that has hybrid multi-cloud fabrics. If you find a partner that’s cloud-only or on-prem-only, you’ve failed.
Catch the full conversation with Patrick Moorhead on The AI Forecast on Spotify, Apple Podcasts, and YouTube.
