AI buildouts need $2 trillion in annual revenue to sustain growth, but massive cash shortfall looms
Turbines pretty much sold out for the next half a decade. This is because AI is a very real secular trends and we will consume all that can be built. Cheers 👊🏻🫡 pic.twitter.com/nPuSsJkU7s October 22, 2025
General Electric’s LM6000 and LM2500 series — both derived from the CF6 jet engine family — have quickly become the default choice for AI developers looking to spin up serious power in a hurry. OpenAI’s infrastructure partner, Crusoe Energy, recently ordered 29 LM2500XPRESS units to supply roughly one gigawatt of temporary generation for Stargate, effectively creating a mobile jet-fueled grid inside a West Texas field.
Meanwhile, ProEnergy, which retrofits used CF6-80C2 engines into trailer-mounted 48-megawatt units , confirmed that it has delivered more than 1 gigawatt of its PE6000 systems to just two data center clients. These engines, which were once strapped to Boeing 767s, now spend their lives keeping inference moving.
Siemens Energy said this year that more than 60% of its US gas turbine orders are now linked to AI data centers. In some states, like Ohio and Georgia, regulators are approving multi-gigawatt gas buildouts tied directly to hyperscale footprints. That includes full pipeline builds and multi-phase interconnects designed around private-generation campuses.
But the surge in orders has collided with the cold reality of turbine manufacturing timelines. GE Vernova is currently quoting 2028 or later for new industrial units, while Mitsubishi warns new turbine blocks ordered now may not ship until the 2030s. One developer reportedly paid $25 million just to reserve a future delivery slot.
It’s not difficult to see why aeroderivative turbines are an attractive option. They’re faster to start, faster to ship, and entirely modular. An LM6000 can go from cold start to 50 megawatts of power in under 10 minutes. ProEnergy’s systems, mounted on trailers with integrated switchgear and cooling, can be dropped onsite and up and running in under 30 days. But they burn diesel or methane, and the emissions stack up quickly.
While newer models are shipped with selective catalytic reduction and oxidation systems that scrub nitrous oxide and carbon monoxide, regulators are sceptical. In Tennessee, Musk’s xAI supercomputer project — powered by dozens of methane turbines — has prompted backlash, with community groups filing appeals and the Shelby County Health Department issuing a permit for 15 turbines in July 2025 amid accusations that many more were already in operation without adequate authorization.
In many jurisdictions, mobile power units don’t trigger the same pollution thresholds as permanent plants unless they operate beyond a 12-month “temporary” window. But the current demand for AI compute is anything but temporary, and given how far away potential alternatives like modular nuclear power might be, operators will feasibly run these turbines for years, long enough to exhaust the useful life of their engine cores and, potentially, the public’s patience.
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Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://www.tomshardware.com/tech-industry/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/turbine-shortage-threatens-ai-datacenters-as-wait-times-stretch-into-2030#main
- https://www.tomshardware.com
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Informational only. No financial advice. Do your own research.