Kettle, a reinsurance startup that uses ML modeling on weather, satellite imagery, and other datasets to predict climate change risks, raises a $25M Series A
Ingrid Lunden / TechCrunch : Tweets: @ourkettle , @alitamaseb , @trueventures , @acrewcapital , and @homebrew Tweets: Kettle Reinsurance / @ourkettle : Announcing our Series A. Many 🙏 to @AcrewCapital @trueventures @adaugelli @satyap @homebrew @sacca @anthemis @lowercarbon @LaurenKolodny @thisismattj @DCVC @claydumas Valor, and all our supporters https://techcrunch.com/... Ali Tamaseb / @alitamaseb : Wildfires are ever more frequent & a risk that's become impossible to quantify using conventional statistical methods. @DCVC we're excited to have partnered with @ourkettle a wildfire and climate reinsurance company. https://techcrunch.com/... @trueventures : Congratulations, @ourkettle! We're proud to support you. https://twitter.com/... @acrewcapital : Today, portfolio company @ourkettle, announced their $25MM Series A to reinvent climate risk for insurers. Congrats to @natpmanning, @eze_in_nyc & the entire Kettle team! https://techcrunch.com/... @homebrew : Such an important and exciting company for our climate future Thrilling to work with @ourkettle https://techcrunch.com/...
Context & Ripple Effects
Kettle's $25M Series A lands in the middle of a two-year run of capital going into climate-risk pricing: Descartes Underwriting raised $18.5M in 2020 for climate risk modeling and transfer, Cervest pulled in $30M mid-2021 for AI-based climate intelligence, and Firemaps raised a $5.5M seed for wildfire home hardening just months ago.
What distinguishes Kettle is where it sits in the stack: rather than selling policies or analytics, it applies ML on weather and satellite imagery at the reinsurance layer, where DCVC's Ali Tamaseb argues wildfire frequency has become impossible to quantify with conventional statistical methods. That puts it upstream of consumer-facing players like Kin, whose $63.9M Series C targets homeowners in disaster-prone states.
First-order effects
- Kettle gains the capital to underwrite wildfire and climate risk directly using its own ML models, backed by True Ventures, Acrew Capital, Homebrew, DCVC, Lowercarbon, and Valor.
- Traditional reinsurers pricing wildfire exposure with legacy actuarial methods now compete against an entrant whose core asset is a continuously updated dataset pipeline rather than historical loss tables.
Second-order effects
- A functioning ML-priced reinsurance layer strengthens the downstream market for companies like Kin and Firemaps, since cheaper or more available risk transfer makes insuring and hardening homes in fire-prone regions more viable.
- Other climate-data startups such as Cervest and Descartes Underwriting face a choice between partnering with model-driven reinsurers like Kettle and building competing underwriting capacity themselves.
Third-order effects
- If ML-based pricing proves out at the reinsurance layer, climate risk transfer shifts structurally from periodic actuarial assessment toward continuous satellite-and-sensor-driven modeling, changing who can profitably carry catastrophe exposure.
- Sustained venture funding across every layer — analytics, home hardening, retail insurance, and now reinsurance — points toward a consolidated climate-adaptation stack where capital, not regulation, is currently setting the pace.
The trend: Venture capital is rebuilding the climate insurance value chain around machine-learning risk models, with each 2020–2021 round extending data-native pricing one layer deeper into the stack.