The enormous size of the oil and gas market drives adoption of synthetic fuel production

Originally posted at the Terraform blog June 16 2026 . One of the underrated joys of hardware tech development is having the opportunity to tell and retell the story until it gradually becomes less mysterious. To me, what Terraform is doing was as obvious as breathing five years ago. To others, even long standing employees and investors, some of the finer points are not yet as obvious as they should be. When I stumble on one of these areas, it provides an opportunity to write a blog about it. How does new technology enter the market? New technology costs more. Adoption at scale requires two strategies: A beachhead market willing to pay enough to make early production profitable, and economies of scale that drive down costs as adoption increases, with cost falling at least as fast as the beachhead is exhausted. Think of Tesla’s strategy around electric cars. First, the Roadster, an overpriced toy to mature the tech at a smaller and cheaper scale. Then the Model S and X, premium cars that addressed a much larger market. Finally, the 3 and Y for the mass market. This pattern even continues with the piloting of cybercab tech through the cybertruck, e.g. the 48 V bus and large aluminum castings. What are economies of scale? Bigger things are cheaper, everyone knows this! But hang on a second. Foxconn makes phones cheap by making millions of them. TSMC makes chips cheap by making millions of them. Hanwha Ocean makes container ships cheap by making them enormous. Wärtsilä makes marine Diesels cheap by making them enormous. Is it strictly correct to label both of these economies of scale? One gigantic thing vs trillions of tiny things? I think this is fine, provided one understands what is actually delivering improved value in this operation. The world does not need one gigantic chip or a trillion tiny container ships. What delivers value is the repetition and ultimately automation of numerous atomic processes. For ships and chips the square-cube law also comes into play – I will leave the unification of this observation as an exercise for the reader. I will note that scale doesn’t automatically make things cheaper. There are also profligacies of scale, with examples too numerous and obvious to even mention. Why does repetition make things cheap? What is the learning rate? The learning rate, also called Wright’s Law , is a phenomenological description of a real world process. Manufacturing engineers optimizing US production of warplanes in WW2 noticed that cost dropped by a fixed percentage, typically about 15%, per cumulative doubling of production. In the context of WW2 plants churning out thousands of airplanes, this became a powerful driver in US over-production of war materiel, hastening the end of the war. This curve, for B-29 production, shows a late time acceleration of the learning rate, which has been observed in a few other industries, typically when scale of production finally breaks through whatever the previous limiting factor was. For example, solar photovoltaic module production turned a corner in 2009 when scale finally justified dedicated silicon processes rather than using leftovers from chip manufacturing. Since then, the solar learning curve has continued to steepen, reaching 48% last year! Currently, global production doubles roughly every 24 months, so the price of modules falls by nearly a factor of two in that time. In contrast, the lithium ion battery learning curve is a more modest 23%, similar to Boeing’s B-29 plant. As a result the price halves roughly every three years, even though manufacturing is doubling at a faster pace than solar, doubling every year for most of the years since 2018. Sea water reverse osmosis learning rate (15%). Why is it that learning rates vary so much? To first order, the more complicated the product, the lower the learning rate. Solar modules contain no moving parts, relatively few materials, have a fungible supply chain, an efficient market for production tooling, and so there’s a broad attack surface for process engineers to suck out cost and increase production rate. SWRO, in contrast, involves lots of parts, lots of moving parts, corrosive, fouling chemistry, high pressures, and rather finicky membranes. Still, 15% is far better than zero, and the most advanced plants churn out fresh water for just 40c per cubic meter! So what does Terraform’s learning rate need to be? There’s an aspect of Terraform Industries’ market expansion that I always thought was extremely obvious, but recently an investor pushed back on it so I actually did the math. It turns out not to be super obvious, but after this post it will be, so strap in! Question Okay, fine, your beach head market is premium chemically pure methane with higher revenue, but what does your learning rate have to be to ensure that your cost of goods sold (COGS) drops fast enough that as you expand you can still sell profitably, even as your beachhead premiums go away? That is, we believe you can produce chemically pure methane and sell it profitably, but that market is worth only a few hundred million dollars per year globally, and that’s just not enough to justify venture returns. The global oil and gas industry is worth over eight trillion dollars annually, but your current cost is nowhere near competitive, so what’s the plan? Economies of scale? Answer The oil and gas market is big. You just won’t believe how vastly, hugely, mind-bogglingly big it is. I mean, you may think, say, Google, earns a lot of money, but that’s just peanuts to oil and gas. You might spend 15 minutes reading this post. In that time, the oil and gas industry turned over $250m. The way to think about this is to consider the size of the incremental market expansion bought by a trivial cost improvement, say 1%. Short run oil demand is highly inelastic, but even so, a 1% reduction in price will increase newly addressable demand by about 0.08%, or $7b/year. $7b on top of Apple’s iPhone sales would be a 12% sales bump – enormous! In the oil industry, that’s Tuesday evening. Short run elasticity isn’t the right measure, though. Below are two charts demonstrating methane’s long run addressable market elasticity is 2.6, while methanol, the oil precursor, is 5.0. That means that when Terraform decreases at-scale production cost by 1%, the methane market expands by 2.6% ($41b), while the methanol market expands by 5% ($115b). It is difficult to imagine a more inviting addressable market! Because we have 24 doublings of production before saturation, even a 10% learning rate cuts cost faster than the beachhead premium decays. We never sell at a loss on the way down. Indeed, if we can compound our production skill with a learning rate of more than 12%, our margins will strictly increase as we scale out. 24 doublings is a lot of runway to push our production costs down by the factor of four required to unconditionally undercut drilling in any market, no questions asked. That’s the intuition, let’s make it rigorous. Here is our current estimate for the global methane market ladder. Currently we can produce methane for under $30/MCF – an achievement in itself! The red curve shows a 10% learning rate. 10% is nothing spectacular – it’s less than half the improvement seen in B-29 production, during war time, in the 1940s, on a plane whose size and complexity was so unprecedented that the flight test program took the lives of the test pilot Edmund T. Allen and 31 other people. This curve also assumes that the bulk Henry Hub market remains artificially depressed by natural gas co-production during fracking for oil products. Pure play gas fracking projects typically turn a profit at about $9/MCF, which would necessitate a learning rate of only about 6%, which is even more ludicrously unambitious. To be blunt, we have designed the Terraformer from Day 1 to be scalable in production and as simple as possible. We will exceed a 10% learning rate! Natural gas is about a fifth of the oil and gas market. The remainder can be addressed with methanol and its derivatives, so we’ve accelerated our progress on methanol production and expect to begin direct-to-consumer sales soon! The methanol ladder is more complex, showing higher purity premium product tiers (green) and methanol-to-X downstream products such as gasoline, aviation fuel, plastics, etc (purple). A mere 6% learning rate easily clears this ladder retaining at least 25% margins. A 20% learning rate will result in accelerating margins and substantial additional induced demand and economic growth. Of course, if we doubled production every six months for a decade it is possible that we could temporarily outrun the solar learning rate before becoming its primary driver, but this would be an excellent outcome for everyone. Further implications of these facts? While mass scale deployment of Terraformers is capital intensive, it is readily financeable with quantifiable risk and solid returns above 20% IRR, and at times far higher. You can explore various configurations at terraform-simulator.com . The Amazon fulfillment center build out might be a good parallel for this process. The business will expand as quickly as possible while retaining positive EBITDA less re-investment in growth. Once we get the ball rolling, expansion and strategy is straightforward. Just try not to be drowned by the nearly infinite money gusher. What is the smallest possible catalytic spark? What is the key to unlocking growth? Terraform is in the process of deploying our first ever full scale Terraformer at our Muroc test site in Rosamond, California. We expect this development unit to have positive unit economics. The pilot will actually generate value, no $1b leap of faith required. This is critically important to avoiding the so-called “Valley of Death”. From there, we scale up production by bringing up new sites. Each site generates net revenue. We expect company level profitability to occur with between 15 and 25 Terraformers operational and will size our second development site well in excess of this. Join us! Terraform is hiring. Check out open roles at terraformindustries.com .