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The Bezos Earth Fund’s AI for Climate and Nature Grand Challenge was launched on April 16, 2024—not in 2026. It is a multi-year grant program that could award up to $100 million for artificial-intelligence projects addressing climate change, biodiversity loss, food systems and nature conservation.
The program has since moved beyond its application stage: 24 teams received $50,000 Phase I grants in May 2025, and 15 teams were selected in October 2025 for Phase II awards worth up to $2 million each. Those announcements represent at least $31.2 million in publicly announced funding, not a completed $100 million payout.
What Bezos Earth Fund actually announced
The Bezos Earth Fund announced the AI for Climate and Nature Grand Challenge on April 16, 2024. Its stated commitment was to award up to $100 million in grants over several years.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →That wording matters. The program was not a $100 million check to one company, a venture-capital fund for startups, or an immediate distribution of the entire amount. “Up to” describes the program’s potential ceiling. Funding was designed to be awarded in stages after proposals were evaluated and developed.
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The challenge also was broader than a conventional climate fund. Its scope included biodiversity, ecological monitoring, sustainable proteins, food systems, energy infrastructure and other major problems involving climate change and nature loss.
How the grant program works
The first-round structure had three main parts:
- Phase I seed grants: Up to 30 teams could receive $50,000 each to develop and test an initial concept.
- Innovation Sprint: Selected teams received mentoring, expert support and assistance developing an implementation plan. Technology partners included Amazon Web Services, Google.org, Microsoft Research, Ai2 and Esri.
- Phase II implementation grants: Up to 15 teams could receive as much as $2 million each to advance their projects.
The program’s grant-awards page describes the initial two-phase allocation as up to $31.5 million. The later Phase I and Phase II announcements identified 24 grants worth $1.2 million in total and up to $30 million for 15 Phase II teams.
| Stage | Publicly stated structure | Publicly announced amount |
|---|---|---|
| Overall program | Multi-year grants of up to $100 million | Ceiling, not a distributed total |
| Phase I | 24 grants of $50,000 | $1.2 million |
| Phase II | 15 awards worth up to $2 million each | Up to $30 million |
| Phase I plus Phase II | Based on the two official award announcements | At least $31.2 million publicly announced |
The $2 million figure is a maximum per Phase II team. It should not be interpreted as proof that every recipient received exactly $2 million.
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Applicants had to choose one of four focus areas:
- Sustainable proteins, including ways to improve alternative-protein research and production.
- Biodiversity conservation, such as species monitoring and protection.
- Power-grid optimization, including systems that could help integrate renewable energy.
- Wildcard proposals addressing a significant climate or nature problem outside the other categories.
A proposal could not apply to multiple focus areas at once, although a team could submit separate proposals. The program was therefore not limited to emissions reduction or renewable-energy software. It also covered nature, agriculture, food and conservation applications.
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What the Phase I grants supported
The 24 Phase I recipients, announced in May 2025, received $50,000 each. Their work covered a range of practical environmental applications, including:
- Sustainable-protein research and cultivated-meat development
- Food-waste conversion
- Wildlife monitoring and poaching detection
- Plant identification and biodiversity data
- Illegal-fishing detection
- Electric-grid optimization
The seed-grant stage was intended to help teams validate an idea and prepare for implementation, rather than demonstrate that the projects had already delivered measurable emissions cuts or conservation gains.
See the Earth Fund’s Phase I announcement for the complete recipient list.
What the Phase II teams are building
In October 2025, the Earth Fund announced 15 Phase II teams. Their projects included:
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- Artificial intelligence to optimize electric-vehicle charging and support renewable-grid stability
- Edge AI for poaching detection and biodiversity monitoring
- AI-assisted cultivated-meat production
- Sustainable-protein modeling
- Bird-population tracking
- Computer vision for identifying plants
- Genome analysis for endangered-species conservation
- Edge AI aimed at reducing illegal fishing
- Weather forecasting tools for African farmers
- Coral-reef mapping
- Ocean carbon-removal modeling
- A “rumen digital twin” intended to help reduce livestock methane emissions
The Earth Fund said these teams would test, refine and evaluate their approaches over the following years. The announcement therefore describes projects entering implementation—not completed proof that they have already reduced emissions, protected species or improved grid performance.
The full announcement is available in the Phase II award summary.
Who was eligible to apply?
The first round was not an open application program for individuals or any startup to apply directly as the lead applicant.
Eligible lead entities included:
- Global academic institutions
- U.S.-based organizations recognized as 501(c)(3) nonprofits
Private companies, government entities and non-U.S. nonprofits could participate as contributing partners, subject to the program’s eligibility and sanctions requirements. Individuals could not apply independently as lead applicants, and proposals had to be submitted in English.
The original application window opened in June 2024. Program materials later stated that Phase I submissions were due July 30, 2024, after the deadline had been extended from the original schedule. That application round is closed.
How proposals were judged
The five Phase I criteria were equally weighted:
- Impact: Could the idea create transformative environmental benefits?
- Viability: Was the proposed AI application technically and practically plausible?
- Suitability: Was AI particularly appropriate for solving this problem?
- Scalability: Could the solution be adapted across locations or contexts?
- Societal benefit: Would it create accessible and equitable value while addressing potential harms?
Phase II put greater emphasis on quantifiable outcomes, implementation requirements, resources and risk mitigation. This distinction is important: a promising model or prototype was not enough by itself to secure larger implementation funding.
What “modern AI” means in this challenge
The program’s definition was not limited to generative AI or chatbots. It included advances from roughly the previous five years, such as:
- Deep learning and neural networks
- Computer vision
- Foundational and transformer models
- Self-supervised learning
- Large language models
- Accelerated computing
Potential uses include forecasting weather, identifying species from images, analyzing genomes, detecting illegal activity, optimizing electricity demand and modeling complex scientific systems. In many cases, the relevant AI function is prediction, classification, monitoring or optimization rather than text generation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why AI could help—and why it is not the environmental solution by itself
AI can process large datasets, identify patterns that are difficult to detect manually and generate forecasts or recommendations quickly. Those capabilities may be useful where environmental systems produce vast amounts of satellite, sensor, genomic, weather or wildlife data.
But the relevant question is not simply whether a project uses AI. It is whether AI is necessary for the problem, whether it performs better than realistic alternatives and whether that performance changes an environmental outcome.
Several risks deserve attention:
- AI’s environmental footprint: Training and operating models can consume electricity, water, computing hardware and data-center capacity. A project should be judged by its net environmental benefit, not merely its use of AI.
- Data bias: Models may perform poorly where monitoring data are sparse, local knowledge is excluded or training data come mainly from wealthy countries.
- False precision: A detailed forecast is not necessarily an accurate or actionable one.
- Scaling barriers: A model that works in one watershed, reef, grid or species population may require new sensors, local staff, regulatory approval or community consent elsewhere.
- Privacy and safety: Conservation surveillance can create risks for communities if location data or monitoring systems are misused.
- Funding dependence: A system may become unusable when grant-funded computing, cloud credits or technical support ends.
The program’s FAQ also says applicants had to sign a non-negotiable Grand Challenge Agreement covering confidentiality and intellectual-property provisions. Those terms matter for teams developing potentially commercial technologies and should not be confused with a blanket promise that every resulting tool will be open source.
What would demonstrate success?
Project announcements and grant totals show funding activity, but they do not establish environmental impact. A meaningful evaluation would look for project-level evidence such as:
- Tons of emissions or methane avoided or reduced
- Additional renewable energy integrated into the grid
- Forecast accuracy and actual adoption by farmers
- Conservation area or species populations monitored
- Poaching or illegal-fishing incidents detected
- Cost per hectare, species, community or environmental outcome served
- Compute and energy consumed per unit of environmental benefit
- Continued operation after grant funding ends
For carbon-removal projects, theoretical potential would not be enough. Evidence would also need to address permanence, monitoring and whether the removals are additional to what would otherwise have happened.
The bottom line on the $100 million headline
Bezos Earth Fund launched a substantial, multi-year grant initiative, but the headline number is a maximum commitment rather than money already distributed. The program has awarded $1.2 million in Phase I grants and announced up to $30 million for 15 Phase II teams, bringing publicly announced funding to at least $31.2 million.
The challenge is significant as a funding and coordination experiment because it connects environmental organizations, researchers and AI specialists. Its ultimate value, however, will depend on whether the funded systems produce measurable, durable and equitable environmental improvements—not simply whether they use advanced AI.
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