If Your Moat Was Data, What Happens When AI Can Manufacture It?

Welcome to Uphoff on Media. I'm Tony Uphoff. In late 2021, ZoomInfo was worth about $25 billion. Today it's worth $1 billion. The database didn't get worse. The customers didn't vanish. So what the heck broke? My argument is that the data moats have been built in 3 eras. In the first, the moat was the cost of assembling the data. In the second, it was scarce data living inside software customers couldn't easily leave. The third, the agentic era, removes the foundation of both. Agents can now assemble facts for close to nothing, and customers can use those same agents to build the workflow themselves. Synthetic data adds pressure on anyone selling patterns instead of facts. I ran ThomasNet.com through a version of this, and it came down to 2 questions. Can a customer get your data anywhere else? And if they can, what did you build on top of it that they'd keep paying for even if the data were free? So what's the answer in the agentic era? Stop selling access, start delivering results. That changes your services strategy, your channel strategy, and how you architect your product. The post has 2 charts to help. One maps the 3 eras. The other is the data moat test, a 2x2 that shows you which square your business is actually in. And there are 3 questions you can answer in a sentence each. Run them on your own business before an investor does it for you. Okay, let's get to work. This episode is titled, If Your Moat Was Data, What Happens When AI Can Manufacture It? In late 2021, ZoomInfo was roughly $25 billion. Today it's worth about a billion. The database didn't get worse. The customer list didn't disappear. Something else broke, and every B2B data and intelligence business needs to understand what before the market explains it to them the hard way. The company says the moat still holds. ZoomInfo cut its full-year revenue guidance in May, then raised it modestly in August. Net revenue retention has slipped from 90% to 89%. Existing customers are generating 11% less revenue than they were a year ago. The stock has fallen from a peak of— a 2021 peak of $77.35 to under $4. CEO Henry Shuck's response has been direct. In May, his quote was, ZoomInfo data and our go-to-market context is the ultimate competitive advantage. In August, he stated, you cannot token your way to building the data asset that we have built. The first is an argument about where the value sits, data plus context, not data alone. The second is a scarcity claim, and it's the one most exposed to what's coming. Whether either holds is the only question that matters here, and it's not a question the company gets to answer for itself. The market answers it. I've had to answer these questions from the inside, not from a research note. I spent 5 years leading ThomasNet.com, an industrial data and directory business, through the transformation that turned it into a platform Xometry paid roughly $300 million to acquire. Every year of that transformation came down to 2 questions about our own assets. The first was scarcity. Could a customer get this data anywhere else, or was it just expensive for someone else to assemble at the time? If it was only expensive, we had a cost advantage, and cost advantages can erode. The second was functionality. Had we built enough into the workflow tools our customers ran their jobs through that they would keep paying even if the data were free? If so, we had a product, not just a dataset. The answers describe very different businesses. The pitch deck reads the same for all of them. Data moats were built in 3 eras, and the 3rd removes what the first 2 stood on. The first era was aggregation. The value was in assembling what was scattered— a directory of industrial suppliers, a database of companies and contacts, a registry of transactions. It took people time and capital to build, and that cost was the moat. Customers paid because doing it themselves was not a serious option. ThomasNet began here as a directory. The second era was workflow. The data was still scarce or expensive to mine, but now it lived inside software the customer used to do a job. The sales rep's prospecting screen, the buyer's supplier search. Switching meant Retraining a team and rebuilding a process. Finding another source was the easy part. That is the era Thomas Knets' Rebuild into a Platform belonged to and the era ZoomInfo was built for. The 3rd area— era is the agentic era, and it attacks both foundations at once. Agents can now assemble facts from public sources for close to nothing, which erodes the cost of aggregation. Customers can use those same agents to build the workflow layer themselves, which erodes the switching cost of the 2nd era. Schuck says it has started. Customers are building interfaces that combine their own data with Zoom info, though so far only at, quote unquote, the top less than 1% of companies. That is where a pattern shows up first. Agents assemble data and models generate it, and they threaten different businesses. AI reaches a data business by 2 different routes, and the second gets less attention than it should. The first is agentic assembly. An agent researching a prospect can crawl the open web, pull public records, and structure what it finds in seconds. Clay lets a customer run a single lookup across many data providers in sequence, which turns a database into one input among several. Nothing gets fabricated. Real facts are collected cheaply. If the capital needed to assemble and refresh a contact database was the real source of the moat, then the moat was a temporary cost advantage all along. And AI removing that cost exposes what the moat was made of. This is the most direct pressure on contact and firmographic data. The second is synthetic data. Records generated by a model with no real-world source at all. It's gaining steam in training, testing, and modeling. And businesses that sell that kind of data feel it first because a statistical pattern is exactly what a model can reproduce. What a model can't reproduce is a fact about a specific real entity, who owns an asset, what a particular supplier can actually make, what a transaction cleared at. And it needs real data to stay anchored. A 2024 paper in the journal Nature found that indiscriminate training on model-generated content causes irreversible defects in the resulting models in which tails of the original content distribution disappear. The rare cases go first, and rare cases are where a lot of B2B data earns its price. Real-world facts can be assembled cheaply but not fabricated, so their value moves to whoever can verify them and stand behind them. I made the upside of that argument in the post, The Vertical Intelligence Company. Every data business rests on scarcity or functionality, and the agentic era tests both. Scarcity and functionality are the whole test. In the blog post accompanying this podcast on Uphoff on Media on Substack, there is a downloadable framework that you can use as a diagnostic tool in your business. Both can be true at once, and the strongest data businesses run on both. Most don't. Most run on scarcity that was really just cost dressed up as exclusivity for as long as costs stayed high. That's the quadrant ZoomInfo's own numbers suggest the market has placed it in. Borrowed time, contact and firmographic data that steadily gotten easier for a horizontal AI tool to approximate, sold to customers who are already spending less for it. Shuck's answer is a bet that the business can move to the functionality side, that, quote, go-to-market context, end quote, is the workflow layer a customer can't build for themselves. It's a real bet. It hasn't been proven yet, and 89% net revenue retention is the market's current vote on whether it will be. In the agentic era, the moat is the work you deliver. If the data and workflow can both be built cheaply, what's left to build a moat on? The answer is the shift I described in the post from SaaS to services software. Moats in the agentic era will be built by delivering a service to customers through your platform or product that has a material business benefit. Data and software become inputs. The result is what the customer pays for, and someone has to deliver it and stand behind it. I built a business this way. At Pipeline360, we created the demand-as-a-service strategy, a service delivered to customers rather than software they had to license or run. This model helped drive EBITDA from 25% at inception to nearly 40%. Moving from selling access to delivering results changes the go-to-market plan in 3 places, and every vendor with a data asset needs to rethink all 3. The first, services strategy. Services stop being the cost center that supports the software and become part of the product. Foundation Capital's Ashu Garg and Jayagupta put it this way after a year of watching AI-native vendors, quote, in enterprise AI, integration is not a post-sale activity. It is the product surface, end quote. Customers now expect to see the outcome on their own data before they sign. That raises the cost of sale and vendors have to plan for it rather than fight it. Channel strategy. The channel now includes the platforms where the customer's work happens and the partners who can carry delivery along with the license. ZoomInfo is building for that reality. Its GTM.AI layer is designed to plug into platforms like Salesforce Agentforce and HubSpot Breeze, so the data shows up where the customer's agents already work. A vendor that insists the customer come to its interface is competing against one that shows up inside the customer's agent. And product architecture. Design the product so customers can build and manage part of the functionality themselves. That feels backwards to any vendor who spent a decade building lock-in. It's the only way to keep the customers who are going to build anyway. Open the APIs, expose the rules and data as building blocks, and let their agents run on your data and your accountability. The customer who assembles an interface on top of your product is still your customer. The customer who assembles it on top of someone else's, Is not. Run your own business through the test before an investor does it for you. Every founder pitching proprietary data and every investor who's ever funded that pitch should be able to answer 3 questions in 1 sentence each without looking at the pitch deck. Number 1, scarcity. Can a customer get this data anywhere else right now? If not, why not? A real answer is structural, a relationship, a regulatory position, or a physical presence. Number 2, functionality. If a customer could get the raw data elsewhere, what have you built on top of it that they would miss enough to keep paying for? And number 3, result. What measurable business outcome does a customer get from you, and would you stand behind it in the contract? A strong answer to the first 2 puts you in the real moat. The 3rd answer is what carries that position or any other into the agentic era. The moat was always a question. ZoomInfo hasn't lost, not yet. Henry Shuck is making a real argument about where the value in his business actually sits, and it may turn out to be right. But the lesson here isn't about ZoomInfo. If you run a data business, write down your 3 answers before your next board meeting, because the market will rewrite them for you if you don't. If you can't answer the first 2, What you have is inventory. But if you can't answer the third, you're selling access to a customer who is learning to do without it. Data built the moats of the first 2 eras. The work you deliver built the— will build the moat of this one. Hey, that's it for this episode. If you found this useful, please do one thing. Follow the Uphoff on Media podcast on Apple or Spotify and leave a rating. It's the single best way to help another operator find this. I'm Tony Uphoff, back at it next week.