The Data Land Grab: Why the AI Race Is Really a Real Estate Race
There is so much discussion right now about the AI race, but I think we may be looking at it through too narrow a lens. Yes, companies are racing to build better models, deploy AI faster, secure more compute and figure out how to monetize all of it, but underneath that is something even more fundamental.
We are in the middle of a massive land grab around data. The difference is that the property is not measured in acres or square footage. It is measured in access, infrastructure, compute, storage, ownership, influence and ultimately consumption.
When you look at what the hyperscalers are doing, particularly Microsoft, AWS and Google, among others, they increasingly look less like traditional technology companies and more like some of the largest commercial real estate operators in the world. Their objective is to build the infrastructure, attract as many organizations as possible into that environment, increase consumption once they are there and make that ecosystem increasingly valuable and increasingly difficult to leave.
That is why I keep coming back to the real estate analogy. In traditional commercial real estate, value is driven by location, occupancy, utilization, expansion and the ecosystem that develops around a property. In cloud and AI infrastructure, the economics are not that different. Instead of square footage, we are talking about compute. Instead of tenants, we have workloads. Instead of expanding a lease, customers increase cloud consumption. Instead of amenities designed to keep tenants on the property, technology companies surround customers with marketplaces, AI models, security platforms, data services, partner programs and applications that make the environment more useful the deeper you go. The technology is obviously more sophisticated, but the economic principle is familiar: own the infrastructure, create demand around it and build enough value around the customer that leaving becomes harder than staying.
What makes this so important from a business perspective is that data is no longer simply an IT conversation. It sits at the intersection of AI, security, privacy, compliance, customer experience, go-to-market strategy and, increasingly, corporate ethics. For years we talked about data as an asset, but I think organizations need to start treating it more like property. It has a location. It has an owner. It has a custodian. It has access rights. It has security requirements. It has value depending on who can use it and under what circumstances. It can also become a liability very quickly if the people managing it do not understand the responsibility that comes with it.
That responsibility is going to become increasingly important because the technology is advancing much faster than most organizations' governance around it. We now have the ability to collect, enrich, analyze and infer information at a scale that would have been almost unimaginable a few years ago.
If I put on my go-to-market and CRO lens, today we can identify intent, monitor engagement, understand buying behavior, predict likely outcomes, track changes in organizations and build very sophisticated pictures of customers and prospects. So the question is no longer whether we can do those things. The question is what organizations are doing to ensure that we do them responsibly. That is where I think leadership has to become much more disciplined.
Ethical data use cannot simply sit with the legal team or be reduced to a privacy policy that someone updates once a year. It has to become part of the operating model. When we select cloud providers, data platforms, enrichment tools, AI applications, CRMs or any other technology that touches customer information, we are effectively adding another participant to our data supply chain. Every vendor creates another place where information can move, another set of rules around how it can be stored and used and another dependency that may have commercial consequences later. For a CEO, CRO or CMO, that means the tech stack is no longer just an efficiency decision. It is also a trust decision.
That has direct implications for go-to-market strategy. There is a great deal of noise right now around AI initiatives, automation, intent tools, personalization and how companies are going to become more efficient. Some of that is valuable, and some of it is simply technology searching for a problem. At the end of the day, I think the responsibility of a go-to-market organization is still remarkably straightforward. We need to understand our customers better, communicate with them more intelligently, create value earlier and make it easier for them to buy from us. Data should help us do that. It should not become an excuse to overwhelm people with more outreach, more automation and more irrelevant messaging simply because the tools make that possible.
This is something I have had the opportunity to see very directly at memoryBlue. If you looked at the business five years ago, you might have described it primarily as an outsourced sales development organization. That description today would miss an enormous part of the value because the real opportunity is increasingly in what can be learned from the data being generated across thousands of conversations, campaigns, personas, industries and markets. When you are able to use that information responsibly, you start to see patterns that can materially improve the way a company goes to market. You begin to understand which messages are actually resonating, where an ICP may be wrong, which personas are engaging, where objections are changing and where intent is emerging before it is obvious somewhere else.
That is a very different use of data than simply building a bigger list. The value is not in having more names, more contacts or more records sitting in a database. The value is in understanding what those interactions are telling you. Every campaign should teach you something. Every customer conversation should make the next conversation more relevant. Every objection should help refine your positioning. Every response, including the negative ones, should give you more intelligence about the market. If you are doing that well, then your data becomes part of your competitive advantage because you are constantly learning while everyone else is simply executing.
I also think this changes the way companies need to think about partnerships. We increasingly operate inside ecosystems rather than independently. Whether you are building around Microsoft, AWS, Google, NVIDIA, Snowflake, Databricks, Salesforce or another major platform or vendor, those relationships are becoming more than traditional technology partnerships. They influence where your product lives, how your customers discover you, how data moves through your environment and ultimately how you scale. That means partnership strategy needs to go deeper than asking who can help us sell. We need to understand whose infrastructure we are building on, whose data practices align with ours, where our customers already operate, what dependencies we are creating and whether those relationships make us more valuable or simply more dependent.
The organizations that will win this next phase will not necessarily be the ones with the most data. I actually think that is one of the biggest misconceptions in the market right now. More data does not automatically create more intelligence, and more intelligence does not automatically create better decisions. The advantage will go to the organizations that know how to separate useful signals from noise, apply context to what they are seeing and then act on it in a way that creates value for the customer. That requires technology, but it also requires judgment, governance and leadership.
We are going to have access to more information about customers, prospects, markets and behavior than we have ever had before. AI will continue to accelerate how quickly we can analyze it and how much we can infer from it. The hyperscalers will continue building the infrastructure that allows all of that information to be stored, processed and monetized. The data land grab is already underway.
But I do not think the ultimate winner will simply be the company that owns the most infrastructure or houses the most data. The real competitive advantage will belong to the organizations that earn the right to use it.
That means understanding that trust has economic value. Privacy has economic value. Security has economic value. Responsible data stewardship has economic value. And perhaps most importantly, using data intelligently enough to create a better experience for the customer has enormous economic value.
The technology may change quickly, but that principle will not.
