I Built a Causal AI System -Part 3
A lot of people have asked me why an aerospace company is suddenly in the AI arena. The short answer is that we're trying to build manufacturing in low-Earth orbit, and getting there requires solving both a materials problem and a financing problem. We've solved the first one. The AI work is how we solve the second — and it turns out our approach to AI is the same posture we took with materials. Five years ago our Fiber Dynamics division figured out how to weave carbon fiber in three dimensions in a way nobody else had managed. The conventional approach is to weave carbon fiber into sheets and stack them — but stacks delaminate when you machine them, so you can't mill or drill traditional carbon fiber the way you can mill aluminum. That's been the ceiling on the material's use in load-bearing aerospace structures for decades. We developed a proprietary technology called the bi-axial heddle that creates weaving sheds in two directions simultaneously, which lets us produce solid blocks of woven carbon fiber. Cubic meter scale on a single machine. Machinable like aluminum. The result is a load-bearing material that's 45 percent the weight of aluminum and stronger than steel. CompositesWorld covered it in 2020 — search "Novo Navis 3D woven machinable carbon fiber" if you want the third-party write-up. The Air Force awarded us a Phase 1 SBIR to develop it for military applications. That's the materials side. It exists. It works. It's patented. On AI — the part I want to spend the most time on. What we learned solving the carbon fiber problem is that the hard engineering challenge is rarely the individual components. It's the assembly. Carbon fiber yarn existed before us. Looms existed before us. Heddles existed before us. What didn't exist was the geometry that let those components produce a structurally sound, machinable block. The breakthrough wasn't a new ingredient — it was a way of putting the existing ingredients together that nobody else had figured out. That's our view on AI, too. Most AI companies right now are selling enterprises a destination — "we'll get you to Tahiti, we'll automate your operations, we'll transform your business" — and then delivering a flight crew. Just the crew. They forgot to deliver the airplane. The engines. The avionics. The radar. The cabin pressurization. All the things that actually make the trip to Tahiti possible. The flight crew is the LLM. It's competent at its specific job. ChatGPT, Claude, Gemini — these are extraordinary tools when you give them a clear instruction and ask for one thing. But an LLM by itself doesn't automate an enterprise any more than three pilots in a room automate a flight. You need the rest of the airplane. The rest of the airplane is the causal logic that decides what to ask. The data plumbing that gets the right inputs into the right model. The verification layer that catches when the model is hallucinating. The action layer that translates a model's output into a system actually doing something. The monitoring layer that catches drift. The orchestration layer that knows which model is appropriate for which decision. None of these are exotic — most of them exist as individual components. But almost no AI company is assembling them into something that actually flies. Novo Navis is trying to be the Boeing of this. Not in the sense of building one big monolithic thing — but in the sense of being the assembler. We don't throw the baby out with the bathwater. We use LLMs where LLMs are good. We use generative AI where generative AI is good. But we surround those components with causal AI systems that map the actual mechanisms underneath an operation — what drives what, what's correlated versus what's causal, what changes the outcome versus what just moves with it — and we wire the whole thing together into something that actually delivers automation across an enterprise, not just a clever chat interface. That's the philosophical bet. AI is going to be assembled by companies like us, not by the labs that build the individual components. The labs are extraordinary at what they do — they're the equivalent of GE making turbofan engines. But somebody has to put the engines on the airframe, design the wing, run the wiring, certify the system, and actually fly it to Tahiti. Why this matters for what we're building. We use this assembled AI for one specific purpose right now: identifying operationally distressed companies whose performance is fixable, and engaging with them on sweat-equity terms. We do the analysis. We do the operational work. They give us equity. Over time, that equity becomes the capital base for the LEO manufacturing program. One of our tools is called Pivotyr. It identifies a distressed company's underutilized assets and structures micro-businesses around them to offset costs and restore the operating metric that defines the business. If anyone had asked us, we could have walked Spirit Airlines through this. Spirit's binding metric is cost per available seat mile — they're an ultra-low-cost carrier and that number is their entire identity. The Pivotyr answer would have been an Airbnb-type business renting underutilized hangar space, plus several other micro-revenue streams running in parallel, all engineered to absorb cost and protect the seat-mile number. The model counterintuitively creates jobs. It doesn't strip a company — it restructures around what the company already owns. Pivotyr is one tool. There are several others, all built on the same assembled-AI framework. The reason this works as a financing path is that operational consulting plus equity is an under-tapped category. Pure consulting firms don't take equity. Pure investors don't do operational work. We do both, because the analysis and the execution are the same skill set when you have causal AI doing the underlying reasoning and assembled AI doing the execution. Each engagement that lands gives us a stake in a real operating business. The cash flow plus the equity compounds. Over time, that capital base funds the orbital program. Why people should care about the orbital piece. Most space infrastructure today is built on the ground, launched at huge cost, and operated remotely. We can't iterate. We can't repair. We can't expand without launching more mass. If we want a durable presence in space — for telecommunications, for science, for energy, for any of the things people are talking about — we need infrastructure that's built up there. That means a hub. The hub doesn't exist. Someone has to build the first one. The materials we developed are exactly the kind of structural primitive you'd want for orbital construction. That's the materials thesis closing the loop with the financing thesis closing the loop with the AI thesis. One more thing because it'll come up. Our propulsion side, which we haven't discussed publicly much, involves work at the attosecond scale. In 2023 the Nobel Prize in physics was awarded for attosecond science. We're interested in what becomes possible when you can redistribute energy at that scale. That's a separate conversation. Happy to take questions. Roast the thesis if you want. I'm posting this on a Saturday because I'm curious what holes people find in it. —Eric Johnston, Novo Navis