As AI technology rapidly advances, many companies are adopting AI, but they face various challenges in the implementation process. Today, we'll discuss the major issues AI companies are experiencing. We'll focus on three main problems: the surge in server operating costs, the increase in data center construction costs, and the difficulty in securing specialized talent.

Problem 1: Surge in Server Operating Costs

As the AI market grows, the demand for high-performance cloud services capable of quickly processing large volumes of data is increasing. In particular, the server costs required to operate large language models (LLMs) are astronomical. According to ARK Invest's analysis, ChatGPT's daily operating cost is estimated to be around $700,000, amounting to $255.5 million annually. As the number of users increases and large-scale AI services are provided, these costs continue to rise. This can be a significant burden for companies considering adopting AI technology, especially for small and medium-sized enterprises or startups, potentially becoming a barrier to entry for AI technology adoption. This cost issue is closely related to the increase in data center construction costs, which we'll examine next.

Problem 2: Increase in Data Center Construction Costs

As the demand for GPUs required for AI model training and inference surges, data center construction costs are also significantly increasing. The price of NVIDIA's AI-specialized GPU, the A100, is about $10,000-$15,000 per unit, and large-scale AI model training requires hundreds or thousands of these GPUs. Google invested $1 billion in January to build a data center in the UK, while Microsoft and OpenAI plan to invest $100 billion by 2028, and AWS plans to invest $150 billion in data center construction over the next 15 years. This shows the massive initial investment costs that AI companies must bear to maintain competitiveness. These large-scale investments accelerate the development of AI technology but also risk creating a 'winner-takes-all' market where only a few large corporations can participate. This situation, along with the problem of securing specialized talent that we'll examine next, could exacerbate the imbalance in the AI industry.

Problem 3: Difficulty in Securing Specialized Talent

While the demand for AI specialists is increasing, the shortage of talent in the AI field is severe. According to LinkedIn's 2023 report, hiring for AI and machine learning experts increased by an average of 74% over the past five years. However, the supply falls short of this demand. The Stanford AI Index Report 2023 states that there are only about 2,000 AI Ph.D. graduates annually in the United States, which is far from sufficient to meet the demand. In terms of salary, Glassdoor's 2023 data reports that the average annual salary for AI engineers in the US is $134,798, which is a significant burden for small businesses and startups. This talent shortage can slow down the pace of AI technology development and widen the technology gap between companies. Additionally, excessive reliance on high-level talent can hinder the popularization and universal application of AI technology. This, combined with the aforementioned server operation and data center construction cost issues, could limit the development of the AI industry.

Conclusion

We've examined three problems faced by AI companies. These issues are significant obstacles to the development and proliferation of AI technology. On-device AI is gaining attention as a solution to these problems, but there are still limitations with existing approaches.

ZETIC.MLange, developed by ZETIC.ai, is an innovative solution that can fundamentally solve these problems. Through ZETIC.MLange, companies can eliminate or significantly reduce server costs and data center construction costs, while effectively implementing advanced AI technology. How can ZETIC.MLange solve these problems? We'll continue to discuss major issues in the AI industry along with detailed explanations about ZETIC.MLange in the future.