The Gold Dollars

Welcome back,

I was talking recently with a large enterprise customer about how to think about the value of SeerAI. They had a useful framework: blue dollars are the costs you take out of the business, lower infrastructure spend, fewer manual processes, less duplicated work and greater operational efficiency. Green dollars are the measurable economic value you create through better decisions, higher productivity, improved customer outcomes or new revenue.

I couldn't help myself. I suggested there was a third category: gold dollars.

Gold dollars are the value of answering questions you didn't know you could ask. They're harder to put into a traditional ROI model, but my co-founders and I have been thinking for years that they may also be where the biggest opportunity lies.

For most of computing history, answers were expensive. If you wanted to understand why something happened across a large enterprise, somebody had to find the data, get access to it, move it, clean it, reconcile it and analyze it. A seemingly simple question could turn into weeks of work.

So we adapted by rationing our questions. We became very good at asking questions we knew how to answer, usually because the data had already been assembled for that purpose. We built dashboards and reports around those questions and became very efficient at looking at them. But somewhere along the way, we may also have trained some of the curiosity out of ourselves.

The Julia Child Moment

There may be a useful analogy in what Julia Child did for cooking in America.

When Mastering the Art of French Cooking appeared in 1961, it wasn't simply a collection of recipes. It opened up a world that had previously seemed inaccessible to millions of American women. Julia didn't invent French cooking or its ingredients. She made them accessible. She showed people how the ingredients worked together, provided a recipe to get started and gave them enough confidence to begin experimenting on their own.

That unleashed something much bigger than better dinners. For many women whose lives were still constrained by the expectations of the 1950s and early 1960s, cooking became a place to learn, experiment, create and develop confidence. Julia gave people permission to try things they had assumed belonged to experts.

There is an analogy here for what is happening with data today.

For decades, working with data has largely belonged to specialists. Most people inside an organization consume the finished dish. Someone else decides which ingredients to collect, how to combine them and, therefore, what questions can reasonably be asked.

Imagine instead working with enterprise data as if you were walking into a kitchen with nearly limitless ingredients. Your company's data is there, but so is public data: weather, satellite imagery, demographics, mobility, economic activity and thousands of other sources. AI can help you understand what ingredients exist, suggest which ones might matter and do much of the work required to combine them.

Now the interesting question is no longer simply, "What does the dashboard tell me?" It becomes, "I wonder what would happen if…"

Like cooking, you shouldn't need to know in advance whether an experiment will work. You try something, see what happens, add another ingredient and perhaps discover that two things nobody thought to put together tell you something entirely new. Some combinations will be useless. That's fine. What matters is that the cost of trying is collapsing.

The Recipe Matters Too

This analogy actually runs surprisingly close to how we think about the technology at SeerAI.

Having limitless ingredients dumped on the kitchen counter isn't particularly useful. You need some understanding of what they are and how they relate to one another. In our platform, that role is played in part by knowledge graph technology.

A knowledge graph can express relationships as simple triples: this thing, has this relationship, to that thing. A facility is located in a county. A shipment travels through a port. A storm affected a region. An asset belongs to a network.

At sufficient scale, those relationships encode an ontology: an understanding of the important things in a particular world and how they fit together. I think of that ontology as something like the recipe. It doesn't dictate the meal you have to make. It gives you enough structure to understand the ingredients and start combining them intelligently.

That distinction matters. The point of the recipe isn't to limit what you can cook. It's to make experimentation possible.

AI makes this considerably more powerful because the person asking the question increasingly doesn't need to understand triples, graph databases, schemas or where every dataset lives. They can start with the question.

Curiosity Has Been Expensive

Maybe people haven't become less curious after all. Maybe we've simply spent decades teaching them that curiosity at work is expensive.

Ask an unusual question inside a large enterprise and you may accidentally create a six-week data project. Eventually, people learn not to ask unless they're fairly certain the answer will justify the effort. That is rational behavior, but over time it creates organizations that become very good at answering the questions they already know to ask.

AI is changing the economics of that behavior, but AI alone isn't enough. A brilliant model that can't reach or understand the right ingredients is still standing in an empty kitchen.

That's a big part of what we've been trying to solve at SeerAI: making enormous amounts of distributed data accessible and understandable without requiring someone to assemble everything into one place first. The technical architecture matters, but the more interesting outcome may be what happens to people when that friction disappears.

Give people access to the ingredients and enough of a recipe to understand how they might fit together. Give them tools that make experimentation cheap. Then let them start asking questions.

For decades, the scarce resources were computing, data and expertise. Those constraints are rapidly falling away, and we may discover that the next scarce resource is something much more human: curiosity.

That's where I think we'll find the gold dollars.

Want to See What This Looks Like in Practice?

Book a demo or reach us directly at contact@seer.ai