Every major energy source, every storage technology, and the optimal power solution for every use case — from residential solar to commercial fusion — mapped across current, near, and far timelines.
Cost: Low to medium
Efficiency:
High daylight conversion
Use Case: Homes and
communities
Cost: Medium capex, low opex
Efficiency:
High mixed portfolio
Use Case: Metro baseload
and peak
Cost: Medium-high
Efficiency:
Process dependent
Use Case: Steel, cement,
chemicals
Cost: Declining
Efficiency:
Excellent drivetrain
Use Case: Personal
transport fleets
Cost: High initial, stable long-term
Efficiency:
24/7 firm power
Use Case: AI and cloud
infrastructure
Cost: Medium to high logistics
Efficiency:
Site dependent
Use Case: Off-grid industry and
defense
OTEC exploits the 20°C+ temperature difference between warm surface water and cold deep ocean water to drive a heat engine — producing 24/7 baseload power with zero fuel input. Hawaii demonstrated a 100kW pilot in 2015. A full-scale 100MW OTEC plant would supply power to 100,000 homes continuously. Theoretical global potential exceeds 30,000 GW — rivalling all current global electricity generation. Japan, India, and French Polynesia are advancing national OTEC programmes.
OCEAN THERMAL ENERGY CORP · MAKAI OCEAN ENGINEERING · DCNS GROUP · IHI CORP · NEMO PROJECT (EU)The 60+ sources above are the ones humanity already knows. This space is for the ones we don't. As local AI grows strong enough to reason across every field at once — physics, biology, materials, chemistry — it can connect them in ways no single expert would, and propose genuinely new ways to make energy. Below is a working AI hypothesis; generate a fresh one any time.
Every natural setting holds several untapped energy gradients at once — heat, salinity, pressure, chemical potential, even light — and today we harvest each in isolation, throwing away the rest. The hypothesis: chain them in one closed cascade so the low-grade waste of each stage becomes the input to the next. Imagine a coastal column where warm surface water first drives a heat engine (like OTEC), its cooled outflow then meets freshwater to release osmotic power, the pressure drop next spins a micro-turbine, and finally engineered microbes feed on the residual nutrients to trickle-charge a bio-cell. No single step is new — but stacking every gradient in one exergy-optimised loop could lift total site efficiency far beyond any one method alone. How it would work: an AI continuously models each local gradient and re-routes flow in real time to keep every stage at its thermodynamic sweet spot — something too complex for humans to tune by hand, but natural for a system that can hold the whole picture at once.