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Carbon Robotics

Category: AI in Agriculture

An AI-powered agricultural robotics company that develops high-precision laser weeding systems and autonomous tractor solutions to eliminate weeds without chemicals or manual labor. Carbon Robotics was founded in 2018. The company is led by Paul Mikesell. Based in Seattle, USA. Team size: 100-250. Total funding raised: $157.0M. Latest round: Series D. Key investors include ["BOND","NVentures (NVIDIA)","Anthos Capital","Sozo Ventures","Fuse","Ignition Partners","Revolution","Voyager Capital"].

Founded
2018
Headquarters
Seattle, USA
Team size
100-250
Total funding
$157.0M

Value proposition

Reduces weed control costs by up to 80%, increases crop yields by eliminating soil disturbance and chemical usage, and solves agricultural labor shortages through 24/7 autonomous operation.

Products and solutions

["LaserWeeder™ (Flagship pull-behind robot with 30 high-powered CO2 lasers)","Carbon AutoTractor™ (Retrofit autonomy kit for existing tractors with remote supervision)","LaserThinning (High-speed precision crop thinning capability)","Remote Operations Control Center (ROCC) (Real-time remote monitoring and intervention service)","Carbon Ops Center (Data analytics and field performance dashboard)"]

Unique value

The first commercialized system to use thermal energy from high-powered CO2 lasers to kill weeds at the meristem with sub-millimeter precision, guided by millisecond-latency AI.

Target customer

Large-scale commercial farmers, specialty crop growers (e.g., onions, carrots, leafy greens), and organic farming operations typically managing 500+ acres.

Industries served

["Agriculture (AgTech)","Robotics","Artificial Intelligence","Sustainability & Regenerative Farming"]

Technology advantage

Combines deep learning models trained on millions of plant images with high-speed edge computing to identify and zap weeds in real-time, day or night, while offering a 'supervised autonomy' model that allows remote human intervention to ensure 100% uptime.

How they differentiate

Utilizes high-powered thermal CO2 lasers for weed elimination rather than mechanical blades or chemicals, allowing for sub-millimeter precision without soil disturbance or residual herbicide use.

Main competitors

["FarmWise","Stout Industrial Technology","Verdant Robotics"]

Key partnerships

["NVIDIA (NVentures) - Technology and investment partner","John Deere - Compatibility partner for AutoTractor retrofit kits","Regional Dealerships - Partnerships with RDO Equipment, Torgerson’s, and others for sales and field support"]

Notable customers

["Grimmway Farms","Carzalia Valley Produce","Mercer Ranches","Bowles Farming Company","Triangle Farms"]

Major milestones

["Commercial launch of the LaserWeeder in 2021","Named to the CNBC Disruptor 50 list in 2025","Unveiled the 'AutoTractor' autonomous platform in early 2025","Successful expansion into the European and Australian agricultural markets in 2024","Closed $70M Series D funding round led by BOND in October 2024"]

Growth metrics

Reported 3x year-over-year revenue growth in 2023; LaserWeeder has successfully eliminated over 10 billion weeds across thousands of acres.

Market positioning

Premium technology leader in the autonomous AgTech space, targeting large-scale commercial and organic specialty crop growers.

Geographic focus

Primary operations in North America (USA and Canada) with active expansion into Europe and Australia.

Patents and IP

Holds at least 8 patents related to autonomous weed control and laser-based agricultural systems (e.g., US Patent 11,337,419).

About Paul Mikesell

Paul Mikesell is a serial entrepreneur with a distinguished track record in scaling deep-tech companies. He co-founded Isilon Systems (distributed storage), which went public and was later acquired by EMC for $2.25 billion in 2010. He also co-founded Clustrix (distributed database), acquired by MariaDB in 2018. Prior to Carbon Robotics, he served as Director of Infrastructure Engineering at Uber, where he established the Seattle engineering office and led initiatives in deep learning and computer vision.

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