30/ @pipenetwork has seen healthy traction since its mainnet launch in October 2025.
The network has 33k nodes on mainnet covering 6 continents and has over 10.1M verified work done by nodes in mainnet.
29/ Anyone can turn unused bandwidth to a @pipenetwork node and earn rewards.
Network users pays for bandwidth usage while node operators receive $PIPE tokens as rewards for delivering verified traffic.
28/ @pipenetwork improved on content delivery networks.
- Nodes are closer to end users
- Rewards are transparent and recorded onchain
- Packet deliveries are private and verified
27/ @pipenetwork provides a unified stack.
Pipe delivers content from nearby nodes, provides a decentralized storage and uses the fastest paths for routing.
26/ @UpRockComβs ecosystem thrived in 2025.
Monthly USD equivalent of $UPT burned surged 3.7Γ from January to December 2025, reflecting strong demand growth throughout the year.
25/ @rendernetwork demand grew in 2025.
Average monthly revenue rose 23% to $212k, with $2.5M earned for the year. December revenue grew 7% MoM. Render launched Dispersed, a decentralized on-demand HPC platform.
24/ @nosana_ai completed 2M jobs in 2025.
Monthly jobs stabilized around 200k in 2H25, pushing the annual total past 2M. Average monthly jobs doubled vs 2024.
23/ @ionet demand stabilized in December.
Compute hours held around 1.4M, while real demand remained strong as @vistaralabs and @LeonardoAi scaled AI workloads using io.net.
22/ @ionet bought back $2.9M worth of $IO in 2H25.
Buybacks used a portion of revenue tied to a dynamic burn formula, as io.net shifts toward a utility-driven token model anchored to GPU demand.
21/ @onocoyRTK developed a healthy supply after two years.
By December 2025, deployers contributed 6.7k miner stations. High Value Areas were introduced to drive targeted GNSS deployments.
19/ @NATIXNetwork demand and supply stabilized in 2025.
NATIX added 60k drivers, who mapped 116M kilometers and collected 685M data points. The launch of WorldSeek unlocked new ways to analyze street-level imagery.