What will it take to scale long-duration grid batteries (4-100 hours)?
Long-duration batteries can store renewable power for hours or even days, which a grid built on wind and solar needs to stay reliable. The leading designs use very different materials and factories. This report sets out the opportunity, then shows why the real test is turning low-cost, abundant materials into reliable systems at volume.
4
companies compared
6
material inputs
6
chokepoints
3
risk axes
The opportunity
What reaching volume would mean
Long-duration storage is what a grid running on wind and solar needs to stay reliable. Here is the opportunity across the market, the product, and the factory ramp.
Market
85-140 TWh by 2040
Reaching a net-zero grid could require 85 to 140 TWh of long-duration storage worldwide by 2040, a multi-trillion-dollar build-out.
Performance
100h at ~1/10 the cost
These batteries can store energy for up to four days at a time, something today's standard batteries cannot do affordably.
Timing
Factories ramping now
All four companies in this comparison are mid-way through building their first automated factories. The ones that industrialize first will set the cost curve for the sector.
Climate
Long-duration storage lets a grid rely on wind and solar continuously, displacing fossil-fired peaker and baseload plants.
Explore models on KoiThe bottom line
The active materials are low-cost and abundant, so the real test is industrialization: building first-of-kind factories and automated lines. Even Eos lost $44M in a quarter of record output.
Detailed findings
The full company comparison
Get the 6-input supply-chain map, all 4 company profiles, and the risk matrix across 3 axes, with the agents' reasoning and comparative takeaways.
See the reasoning behind each rating
See the agents' evidence-backed reasoning and comparative takeaways. Access every comparison with a single request.
Implications
What this means for you
The same findings, read for your decision.
Allocators
Spreading capital across the whole space
The opportunity is large, and execution decides who captures it: materials are not the differentiator here. Favor the companies furthest up the manufacturing learning curve, and remember that several still post losses while they scale.
Investors
Backing one company
Follow the factory, not the chemistry: yield, line uptime, and the path to positive gross margin. Eos's $44M quarterly gross loss on record revenue shows how far volume can run ahead of profitability.
Policymakers
Shaping incentives and supply security
The opportunity is a domestic manufacturing base, not scarce minerals, apart from bromine and vanadium, which are geographically concentrated. Support for first-of-kind factories and qualified electrolyte supply matters most.
Operators
Building or buying the technology
For multi-day storage, weigh the land and equipment each design needs. Form's iron-air needs large footprints, while flow systems add tanks and pumps. Match the design to your site, duration, and grid connection.
Turn this into a strategy
Rho's team works with stakeholders across climate finance, policy, and operations to turn this intelligence into action.
How this was made
Adversarially validated by AI agents
Six agents research, challenge, and refine every claim. Weak claims get removed before you see them.
Workflow orchestrator
Evidence validation cycle
Generate
Sources into findings
Critique
Claims under challenge
Refine
Evidence into signal
Agent
01
Scout
Maps the company and picks the product line that matters.
Agent
02
Researcher
Traces critical materials and supply chains from primary sources.
Agent
03
Critic
Adversarially challenges every key claim.
Agent
04
Refiner
Removes anything the Critic disproved. Never invents.
Agent
05
Risk Analyst
Scores forward supply risks and scale-up failure modes.
Agent
06
Comparison Critic
Withdraws competitor comparisons that fail consistency checks.
Disclaimer: experimental alpha
Every finding on this page was generated by Koi's multi-agent AI research engine from publicly available sources. This analysis draws on public information only. The companies named were not consulted for it, and none has reviewed or endorsed it.
AI-generated research can contain errors, omissions, or outdated information. Severity ratings reflect agent judgment based on cited public evidence, not professional due diligence. Absence of a rating is not absence of risk. Nothing on this page is investment advice.
Are you a company named here? Request a review or correction.