May 29, 2024
2024 AI Computing, Navigating Demand Challenges
AI

2024 AI Computing: Navigating Demand Challenges

In the past year, the rise of user-friendly generative artificial intelligence (AI) tools has led to widespread adoption by consumers, transforming how technology is integrated into daily life. From basic inquiries to managing complex work tasks, technology’s role is expanding.

The data supports this trend, with prominent AI platforms experiencing a significant surge in usage. As of January 2024, OpenAI’s widely-used AI chatbot ChatGPT boasts approximately 180.5 million monthly users.

Within this swiftly evolving landscape of generative AI, the demand for computational power to support these technologies is reaching unprecedented levels. Coping with the intricacies of managing this surge in computational requirements is presenting challenges for businesses, prompting industry experts to seek practical solutions.

Doug Petkanis, CEO and co-founder of video infrastructure network Livepeer, provided insights into the escalating demand for AI computing power in 2024 and strategies to cope with the growing requirements for running this technology.

Computing power is fundamental to AI development and deployment, influencing the speed and efficiency of AI models. According to Petkanis, substantial computing power is typically required during the three stages of the AI lifecycle: training, fine-tuning, and inference, where the model produces outputs or predictions based on inputs (prompts).

The quest for quicker responses, however, clashes with the economic reality of high costs. Petkanis explained, “More computing power generally correlates with faster responses. But there’s always a balance between user experience (speed) and the project’s economic viability (cost).”

Some estimates place OpenAI’s daily operating costs at $700,000, further intensified by a scarcity of GPUs suitable for AI computing. Petkanis emphasized the critical role played by crypto-networks in addressing the substantial costs of computing, underscoring the emerging synergy between DePINs and AI.

“Thankfully, the same GPUs that are already running in crypto-coordinated DePIN networks performing tasks like video transcoding and 3D rendering, are well-suited for AI.”

While computing power becomes a pressing concern for technology developers, Petkanis highlighted that consumers are often unaware of the underlying infrastructure’s intricacies. He noted, “Most people don’t give much thought to infrastructure: where the power comes from, how the internet works, the cost or carbon footprint of a Google search. They want utilities to work on demand, every time.”

“Consumers won’t notice issues with computing power until the costs get passed on in the form of an increased quantity of ads, decreased speed/quality of responses, or rising subscription costs.”

This sentiment extends to AI computing power, as users prioritize speed and relevance over considerations such as energy consumption or computing costs.

Looking at broader implications, Petkanis expressed apprehensions about the potential monopolization of scaled AI platforms. Similar to the emergence of “Big Tech” during the internet’s early days, he highlighted the race for dominance among mega-companies in the AI era.

“These handful of big tech companies already own large proprietary data sets farmed from customer data to train models, they have spent billions training these models…”

To counter this concentration of power, Petkanis emphasized the importance of the open-source AI movement, citing examples like the DePIN infrastructure. This approach aims to ensure accessibility and mitigate risks associated with centralization.

“Countries should be supporting these movements to ensure this wave of innovation is accessible to all and benefits a worldwide population.”

As the AI boom continues, Petkanis and other industry leaders foresee a “whole new set” of economic, environmental, and social considerations that will shape the future of this transformative technology.

Image by Freepik

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