HIVE
HIVE brings autonomy to existing industrial machines across logistics, production, construction and infrastructure.
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Podcast Episode
🎙️ Deep Dive
Proxima Fusion, HIVE, Whispp — record fusion round, autonomous machines and reconstructed voice — Daily News July 7, 2026
Listen to the episodeAbout HIVE
HIVE is a physical AI company founded in Norway in 2022 and now headquartered in London. The company builds a “silicon brain” for industrial machines, aiming to make existing fleets of excavators, wheel loaders, forklifts and other assets autonomous and coordinated. In July 2026 it announced a €13.1M / $15M round led by SuperSeed to accelerate product development, AI/robotics hiring and commercial deployments.
The Story
HIVE was born in Norway from a team close to industrial operations, aiming to build autonomy in harsh environments: construction sites, tunnels, snowfields, ports, warehouses and production sites.
How HIVE works
Business Model
B2B physical AI for industrial, logistics, construction, infrastructure and manufacturing operators.
Revenue Model
Not disclosed; likely based on retrofit hardware, software, deployment and machine-hour usage.
Products & Services
Key Products
- HIVE Silicon Brain
- Industrial machine intelligence platform
- HIVE AI Module
Core Use Cases
- Autonomous wheel loaders
- Autonomous forklifts
- Warehouse-to-yard operations
- Construction operations
- Road maintenance
- Industrial production
- Remote machine supervision
Market & Clients
Key Customers
Industrial operators in logistics, construction, infrastructure, production and heavy machinery; deployments include Yara, Veidekke, Presis Vegdrift and Volvo Maskin collaborations.
Geographic Presence
How HIVE competes
Competitors
Competitive Advantages
- Retrofit on existing machines
- Shared intelligence across machine types
- Real-world deployments in Scandinavia
- Reinforcement loop across machine hours
How HIVE grows
Growth Strategy
Accelerate platform development, hire AI/robotics talent, scale commercial deployments and expand to the US.
Distribution Model
Direct B2B deployments with industrial operators and partnerships with machine OEMs and site operators.
Moat (Defensibility)
Machine-hour data, retrofit know-how, deployment footprint, customer-specific operating data and cross-fleet learning loops.
Regulatory Context
Relevant to workplace safety, machine safety, autonomous operation, industrial liability and site-specific approvals.
Key Risks
- Operational safety requirements
- Hardware integration complexity
- Customer adoption in conservative industrial environments
- Reliability in harsh conditions
- Regulatory and liability issues around autonomous machines
Strategic Insights
- Physical AI can scale faster when retrofitted onto existing machines rather than requiring new fleets.
- Every deployed machine hour can become operational data to improve the next fleet.
- Commercial value is not autonomy itself, but productive-hour cost and operator safety.
Funding & Investors
Funding Rounds
- 2026 - Pre-Series A / Seed investment: €13.1M / $15M
Lead: SuperSeed
Key Investors
Founding Team
Founders
Key Executives
- Christoffer Jørgensvaag - Co-founder & CEO
Key Metrics
Lessons from HIVE
- In the physical world, the product must adapt to real operations, not the other way around.
- Retrofit can reduce adoption friction compared with full asset replacement.
- Credibility comes from deployments in harsh environments, not just demos.
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