Causergy —  Integrating Causal AI

We want to make the systems that are already in place better. Causal AI does three things for operators; one is it has a very good ability to filter out signal versus noise. In SCADA systems and predictive AI Suites, there are a lot of alarms that go off  — on average 83 an hour, which is unmanageable for anyone. 80% of these are just noise. The first thing that Causal AI can do is single out the alarms that actually matter for operators, and which they can discard.

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Then the second layer is the context layer. Causal AI can provide what are the underlying causal factors that make this problem occur.

The third layer is on the decision side — what can I do as an operator to make sure that I can solve this underlying problem? What are the estimated effects, both on the financial and, on the environmental side? 

We are targeting power generation because it was a sector where there has been historically a lot of correlation versus causation problems. That's how decision-making is done in terms of operations. We are able to distinguish that they are correlated, but it doesn't mean correlation equals causation. We can also bring much better decision-making in that sense. We believe that what ChatGPT is now for knowledge work, causal AI will be for decision-making in industrial operations, and we are the first one here to apply it to power plants.

That you need a lot of prior energy experience to be able to innovate in energy. I had no prior experience whatsoever. There's a lot of innovation to be done.

A challenge has been to try to explain causal AI to utility operators that have barely heard even of more basic AI. We were initially focused on how the technology works, and where it comes from, econometrics, and causal relationships. But the more we interacted, we realized that we probably shouldn't talk too much about the underlying technology. It's more about what type of value or outcomes do you actually, as an operator, get from doing this. They care less about the nitty-gritty details of the underlying technology. 

Predictive AI for power generation is $11 billion, per year, but I think that's a flawed market, given the alarm fatigue and that you don't really know why something is happening and what to do about it. I think creating a new market which is more proper causal decision-making in power for utilities would be a trillion dollar market.

So if we improve decision-making, it would have huge impact. The financial and environmental value of each decision can be larger than a few other normal SMEs given the scale of things.

Our long-term vision is to be the causal AI layer for utilities worldwide. That means that we're now starting in U.S. gas generation due to the data availability, but we are able to scale geographically to other parts of the world, we can scale horizontally to other energy sources inside the utilities, and also, vertically, so we can go from power generation to dispatch transmission grid, etc. In 10 years, we're the golden standard for causal AI in utilities, and in 5 years, we have established ourselves as a very dominant player in this market, covering different continents and have expanded beyond gas, so both horizontally, vertically, and, geographically.