top. This new approach to addressing the compute shortage in the field of artificial intelligence is gaining attention and is seen as a potential game-changer.
The developments in generative AI have had a profound impact on various industries. Language models are being used to develop legal strategies in court cases, image diffusion models are augmenting the workflows of major entertainment studios, and computer vision advancements have brought fleets of self-driving cars on roads en masse. However, the primary bottleneck to scaling these systems is access to compute resources.
Wait times and hourly rates for spot instances of Nvidia GPUs have consistently trended upward, and chip production capacity simply cannot keep up with the demand. The ongoing shortage of graphics cards is attributed to a perfect storm of materials constraints, supply chain disruptions, surging demand, geopolitical tensions, and the long production cycles inherent in fabricating complex GPUs. Additionally, key materials like advanced silicon, specialized substrates for PCBs, and memory chips are all facing shortages due to supply and demand imbalances.
Datacenters, which are crucial for AI workloads, require significant upfront capital expenditure in the form of land, electricity, and enterprise-grade hardware. However, setting up and operating new data centers often rely on external financing, and the rates are high while capital is tight. Furthermore, as AI models increase in size and complexity, the price per unit of computational performance halves every thirty months, while AI-specific compute requirements double every six months. This exponential growth in demand is expected to outpace supply by orders of magnitude.
To address this compute shortage problem, a new form of crypto network called Decentralized Physical Infrastructure Networks (DePINs) is emerging as a solution. It is estimated that there are billions of freely available consumer GPUs globally, along with millions of datacenter GPUs deployed worldwide outside of major cloud providers. Consumer-grade GPUs often have comparable computational throughput to enterprise-grade cards.
Historically, it has been challenging to incentivize or coordinate these disparate GPUs into usable clusters. However, specialized AI-focused DePINs, such as Render Network and IO net, have solved this problem. They offer incentives to latent GPU operators to contribute their resources to a shared network in exchange for rewards. Additionally, they create a decentralized networking layer that represents these disparate GPUs as clusters that AI developers can utilize.
These decentralized compute marketplaces now offer hundreds of thousands of compute resources of varying types, creating a new avenue to distribute AI workloads across a previously unavailable cohort of qualified hardware. In addition to creating net new GPU supply, DePIN networks are often significantly cheaper, up to 50% cheaper than traditional cloud providers. This cost advantage is achieved by outsourcing GPU coordination and overhead to the blockchain, eliminating employee expenses, hardware maintenance, and datacenter overhead.
The emergence of DePINs represents a tectonic shift in innovation that has the potential to impact practically every business overnight. As every Fortune company is currently figuring out their AI strategy, the demand for compute resources is expected to skyrocket. Compute has become the new oil, and GPUs are the currency of AI. DePINs offer a solution to the compute shortage problem by tapping into the vast supply of unutilized consumer and datacenter GPUs, providing a decentralized and cost-effective alternative to traditional cloud providers.
While we are still in the early stages of the AI renaissance, the potential of DePINs to augment and displace workforces, drive productivity, and fundamentally reshape how businesses operate is immense. With the ability to distribute AI workloads across a previously untapped resource pool, DePINs have the potential to democratize access to compute resources and accelerate the pace of AI innovation.