The industry is changing so fast that it’s challenging to keep up, and I receive a lot of inquiries about employment and further education. I&#39;ve also had consultations about university admissions, so I will share some insights based on our current situation and future trends. Firstly, I currently do not recommend computer science as a field of study or pursuing IT engineering jobs at companies like GAFAM. The reason is clear: with the advent of LLMs like ChatGPT, most tasks are becoming automated. The speed and accuracy of these models are rapidly improving, creating a sense that many high-paying IT jobs might be at risk. At present, the hardware aspects of quantum computing are on the rise. Quantum software can be divided into three major categories: annealing, NISQ, and FTQC. While annealing and NISQ are expected to shrink, FTQC is predicted to grow. Those looking to get a head start should focus on FTQC, which includes concepts like phase estimation and amplitude amplification. Annealing deals with solving combinatorial optimization problems using QUBO, and NISQ involves hybrid variational calculations, but jobs in these areas are declining. Hardware development, however, is booming, and there&#39;s a national shortage of talent. If you&#39;re interested in quantum computing hardware development, now is a good time to get involved. Data center-related jobs are also growing, especially with the importance of data centers in generative AI. For example, institutes like AIST are adopting GPUs for quantum computer simulations, so there’s a high demand for data center solutions utilizing GPUs. Managing multiple machines and orchestrating them requires skills beyond traditional CPU+storage+memory systems, as GPUs generate significant heat. Consequently, the knowledge base differs, and the trend is moving towards water cooling management. Data center and server equipment requirements are changing significantly, with increased consultations on heat management for desktop machines. Water cooling systems, heat exchangers, chillers, and other cooling facilities are becoming essential, marking a significant shift from traditional air-cooled systems. Floor load issues related to water cooling also pose challenges that won&#39;t be easily resolved. Machine learning, especially centered around LLMs, continues to grow. Even in quantum computer programming, generative AI can be utilized. As GPU performance improves, data becomes more refined, and models enhance, traditional programming jobs might diminish. Those previously skeptical of these advancements might now be astonished by the rapid performance improvements. With the GPU roadmap indicating continual performance increases, jobs will continue to evolve, with more roles emerging that leverage LLMs. The demand for non-coding engineers, such as prompt engineers, is also rising. Front-end engineering has significantly decreased in our company. Previously, we used JavaScript frameworks for PoCs, but now everything can be done with Python using Gradio, making the need for front-end skills unnecessary. As someone who started as a full-stack developer working with HTML/CSS/JS + PHP and Python, I used to believe that engineers should be proficient in front-end development. However, given the extensive time required for backend Python development, it’s no longer necessary to learn HTML. With PoCs handled entirely by Python Gradio, the demand for front-end development is decreasing. For our regular products, we use React for the front end and Go for the back end. Overall, trends are shifting dramatically, and 2024 is shaping up to be a major turning point. Those who quickly adapt to the next trend will thrive, while others may struggle. Therefore, I recommend early adaptation. We must not fall behind internationally, so I will continue to share information generously!

Quantum computing, machine learning, and data center work on the front lines. Freelancers welcome too.

Yuichiro Minato