Rise of Decentralized Computing in AI and ML: What It Means for Investors
Tags: spheron, ai, machinelearning, web3
Published: 2024-01-18 15:38:12

---
title: Rise of Decentralized Computing in AI and ML: What It Means for Investors
published: true
date: 2024-01-16 04:30:48 UTC
tags: spheron, AI, ML, web3
canonical_url: https://blog.spheron.network/rise-of-decentralized-computing-in-ai-and-ml-what-it-means-for-investors
---

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xbjkhh26173miah325j0.png)

[Artificial Intelligence (AI)](https://en.wikipedia.org/wiki/Artificial_intelligence) is the future of innovation, enabling machines to execute tasks that previously required human intellect, such as learning, reasoning, problem-solving, perception, and natural language processing. AI's ultimate goal is to create intelligent systems that can analyze massive amounts of data, identify patterns, and make decisions autonomously, ultimately improving human productivity, efficiency, and quality of life. As technology advances, AI plays a crucial role in solving global challenges.

One significant breakthrough in AI is [ChatGPT](https://chat.openai.com/), a sophisticated language model capable of revolutionizing content creation and consumption. **However, there are hurdles to overcome, such as the substantial computing power required for generative AI. Tapping into the vast number of underutilized GPUs owned by consumers worldwide through a decentralized peer-to-peer network could provide the necessary resources to support the scaling of true intelligence.**

As an investor, You should seek out technologies potentially transforming industries, and [decentralized computing](https://en.wikipedia.org/wiki/Decentralized_computing#:~:text=Decentralized%20computing%20is%20the%20allocation,from%20a%20remote%20centralized%20location.) in AI and Machine Learning (ML) offers enormous promise.

## Underlying Trends Drive Investment Potential in Decentralized Computing for AI and ML

There are several trends underlying the investment case for decentralized computing in AI and Machine Learning (ML)

### 1. Geopolitical Tensions Threaten Global Semiconductor Supply Chain

The production of semiconductors relies on a multifaceted array of factors, including mechanical, physical, chemical, logistical, and commercial processes. Despite efforts by US policymakers to address supply chain security by investing in fabrication capacity, geopolitical tensions continue to threaten the industry's long-term stability. As semiconductors play a critical role in modern technology, Taiwan's dominance in the semiconductor foundry market has become a focal point in the ongoing great power rivalry between the US and China.

Taiwan's control over 63% of the semiconductor foundry market makes it a crucial player in the global supply chain. Moreover, its foundries, such as [TSMC](https://en.wikipedia.org/wiki/TSMC), produce most of the world's most advanced chips used in [92% of high-tech machines](https://www.wired.com/story/i-saw-the-face-of-god-in-a-tsmc-factory/). Concentrating this critical industry in a single region creates a vulnerability that could have far-reaching consequences for the global economy. The persistent threat of military conflict across the Taiwan Strait only exacerbates these concerns, highlighting the need for diversification and risk mitigation strategies to ensure the resilience of the semiconductor supply chain.

### 2. Significant AI and ML Investment

Over the past decade, AI and ML funding has accounted for approximately [10% of global venture capital](https://news.crunchbase.com/ai-robotics/ai-venture-cybersecurity-health-care-series-launch/) dollars, totaling more than **$257 billion from 2013 to 2022**. This momentum is expected to continue, with companies projected to spend over [$500 billion on AI](https://techcrunch.com/2022/12/15/despite-the-market-slowdown-companies-and-vcs-continue-to-invest-in-ai/) solutions in 2023.

### 3. GPU Compute Requirements Skyrocket

Most of the world's leading supercomputers rely on GPUs to fuel their computing needs, specifically in applications like deep learning, machine learning, analytics, financial modeling, manufacturing, construction, and business process optimization. However, as ML models grow more sophisticated, training them has become increasingly resource-intensive, with compute requirements doubling approximately every 3-6 months in recent years. This breakneck pace outstrips the rate of improvement predicted by [Moore's Law](https://sites.dartmouth.edu/dujs/2013/05/29/keeping-up-with-moores-law/), making it imperative to develop more efficient models. Consequently, there is a pressing need for additional GPU resources.

![](https://lh7-us.googleusercontent.com/ItyF6nosz-8stPptR_XaeTJq4BuzLMxpZ1U-8HcAkSXP7jIbKhjapzSzIfmzaBvFW-xI716qYXTMKPQ7_c6eEq9f-_ftuBXvm0tvBHm5YS0auJ73eXgGM5WXEdm8OfSV3RVxcsjciZyz6Ahjdlon4ug)

Unfortunately, current infrastructure is struggling to keep up with this surging demand. Cloud providers face significant challenges in meeting the growing demand for GPU resources due to high initial investment costs, rapidly depreciating hardware values and restrictions on purchasing consumer-grade hardware. Establishing a large GPU data center is a difficult and costly endeavor, made even more so by the current global semiconductor chip shortage.

![](https://lh7-us.googleusercontent.com/KlWVeqVFHQY7YTfBGj4Md-BPBG8jZZrNZL0dW7q8p1JATpNE5QR6ZitXo1VaoioloxVLgluVmZeIDsPa6BkuIYLQx0FBiW5Alb1LdGi5r2yTrfC3dWCZIuP2DcGNTFmWwkyr2xl__KZy4kXyb_t6eaI)

### 4. Unleashing the Potential of Untapped Consumer GPUs in Cloud Computing

**The high cost of renting GPUs from major cloud providers such as AWS, GCP, and Azure has limited accessibility to GPU-based computation despite growing market demand.** The root cause lies in Nvidia's modified [end-user license agreement](https://www.nvidia.com/content/DriverDownloads/licence.php?lang=us&type=GeForce), which restricts consumer graphics cards (e.g., 2080 or 3090ti) from being used by cloud providers, forcing them to purchase more expensive enterprise GPUs (Tesla or Quadro) designed for data centers.

![](https://lh7-us.googleusercontent.com/x7nlgEL884cZb0mJcY-TzZrIH3Fae3mBz5JUwN6lt6VZjwiOqoydJkabVZLpojJzDZI4a9ZOHWA8RKwMdUmlXjLsmapkcH6L7IFOnHiE6DksF4LKLZbA10uO4WuSz_0ynkpJP-vz6EymbSXsjlqruaM)

This shift has created a significant price disparity between consumer and enterprise GPUs, with enterprise-grade cards being 10-15 times more expensive while offering only 20-25% greater computational power. In contrast, consumer GPUs deliver approximately 5 times better performance per dollar compared to their enterprise counterparts.

**This discrepancy presents an attractive value arbitrage opportunity, as hundreds of millions of untapped consumer-grade GPUs are available. By harnessing this potential through decentralized compute, it becomes possible to significantly reduce the cost of GPU-based computation, making it more accessible to a broader range of users and industries.**

### 5. Rapid Technological Advancements

It's worth noting that countries like China are quickly advancing in AI and ML, positioning themselves as leaders in technology. This is due to the substantial investment in domestic company's research and development and the Chinese diaspora's significant role in national development. These developments signify a significant shift in the global technological landscape, with China emerging as a major player in artificial intelligence.

### 6. New Equilibrium in Global Power Landscape

The [BRICS nations](https://indiaai.gov.in/news/brics-creates-an-ai-research-group), consisting of Brazil, Russia, India, China, and South Africa, have been working on a new strategy that could majorly impact the global financial sector. While the strategy is still in its early stages, it can potentially bring about a significant shift in the power dynamics of the global financial landscape. This shift could have implications for the direction of investment in artificial intelligence (AI) and machine learning (ML). If successful, the strategy could redefine longstanding norms and bring a new equilibrium to the financial sector, ultimately leading to a more balanced and fair global economy.

## Conclusion

In conclusion, the intersection of AI, ML, and decentralized computing represents a promising investment opportunity. The rapid growth of AI and ML, combined with the skyrocketing demand for GPU compute resources and the underutilization of consumer-grade GPUs, creates a compelling case for decentralized computing. By leveraging decentralized computing, we can tap into the vast pool of underutilized consumer GPUs, reducing the cost of GPU-based computation and expanding accessibility to a broader range of users and industries. As investors, You must stay ahead of the curve and recognize the potential of decentralized computing in AI and ML, positioning ourselves at the forefront of this exciting and transformative field.