For a long time, decentralized compute was viewed as a theoretical concept rather than a practical requirement. Today, that perception is changing.
As AI and DePIN evolve from narratives into production-level infrastructure, a recurring issue has become clear: many Web3 applications still rely on centralized cloud providers to operate. This reliance introduces a critical weakness.
Regardless of how decentralized an application’s smart contracts or governance may be, dependence on centralized cloud services means that execution remains controlled by a small number of providers. This creates risks related to cost, availability, censorship, and long-term sustainability.
Across the DePIN ecosystem, multiple approaches are emerging to address this problem: @AkashNetwork $AKT focuses on decentralized cloud marketplaces, enabling open and competitive access to compute resources. @Render Network $RENDER concentrates on GPU compute, supporting AI and rendering workloads that require large-scale parallel processing. @Fluence $FLT targets general-purpose, permissionless compute, allowing applications to run without reliance on centralized cloud providers or heavy virtual machine architectures.
These projects operate at different layers and use different models, but they share the same direction. What distinguishes the current phase is usage. These networks are being built to support real workloads, including AI inference, data-intensive applications, and open services that require high availability and resistance to censorship.
As AI adoption accelerates, compute is increasingly becoming a bottleneck. Control over compute therefore represents a significant concentration of power. Decentralized compute offers an alternative model: infrastructure that is modular, efficient, and distributed, where execution is not owned or controlled by a single entity.
Applications and interfaces will continue to change. The underlying infrastructure will not. Historically, long-term value tends to accumulate at that foundational layer.
Lately, I’ve noticed the #Web3 infrastructure conversation changing. People aren’t just talking about “scaling” anymore. The focus is shifting to cost, reliability, and who really controls the infrastructure. That’s where #DePIN and decentralized compute start to feel less like theory and more like something practical.
• @Fluence $FLT stands out to me in this shift. The idea of cloudless compute is simple but important. Instead of being locked into one hyperscaler, workloads can run on decentralized resources with more predictable costs. For AI and backend services, that matters. Lock-in is expensive, and many teams are clearly looking for alternatives that don’t break when prices change or access is limited. I see similar signals in a few other projects shaping this narrative: • @Akash Nation $AKT is pushing the open cloud marketplace idea. Providers and users connect directly for CPU and GPU. It challenges the usual cloud pricing model, and that alone makes it worth watching. • @Render Network $RENDER is unlocking distributed GPUs. Turning idle hardware into usable compute feels like a natural response to growing AI demand. • @Filecoin $FIL keeps proving that decentralized storage can work at scale, which is a key layer for any real infrastructure stack.
What connects these projects is not hype, but usefulness. They’re all trying to solve real problems: cost pressure, access, and control. Fluence fits neatly into this bigger picture by focusing on compute that doesn’t depend on centralized clouds. DePIN feels like it’s quietly moving from “interesting idea” to “necessary infrastructure.” That’s the trend I’m watching closely.