In 2025, the artificial intelligence (AI) industry is grappling with a severe resource bottleneck, particularly in the realm of hardware. This challenge has not only hindered current AI developments but also sparked a critical reevaluation of the existing AI infrastructure. The rapid growth of generative AI has led to an unprecedented demand for hardware, most notably for graphics processing units (GPUs). However, several events and market changes have exacerbated this dependence, making it increasingly unsustainable. In January 2023, an earthquake struck Taiwan Semiconductor Manufacturing Company (TSMC), a major supplier of GPUs, causing significant disruptions in global GPU production. This event, combined with the astronomical price hikes of Nvidia's H100 GPU—ranging from $30,000 to $40,000, ten times its production cost—highlighted the volatility and fragility of the market. Training highly complex models, such as Groq's Grok 3, which boasts 1.8 trillion parameters, requires an astronomical 10²⁴ floating-point operations and 100,000 GPUs. The inference costs for such models can reach up to $1,000 per query, and the energy consumption of data centers has surpassed that of small countries. These statistics underscore a critical issue: traditional GPU-based AI hardware is no longer sufficient to meet the demands of future AI growth. The increasing scale and complexity of generative AI models are driving up hardware and energy costs, creating a significant barrier to development. Grok 3, developed by Groq, exemplifies this extreme resource demand. While the model aims to deliver unprecedented AI performance through its massive parameter count and computational power, its lofty training and inference costs have raised significant concerns within the industry. industry experts are increasingly vocal about the unsustainability of current hardware architectures and are exploring alternatives. One promising solution is neuromorphic chips, which emulate the structure of the human brain, potentially offering lower power consumption and higher efficiency. Groq, a pioneering company in this field, is actively researching neuromorphic technology to address the resource bottleneck. The company's efforts are seen as a crucial reference point for future AI hardware advancements. As the AI industry wrestles with these challenges, TSMC stands out as a key player continuing to reap substantial benefits from the AI revolution. Despite the setbacks and escalating costs, TSMC's first-quarter performance in 2025 was remarkable. The company shipped 3.26 million wafers, a 7.6% increase year over year, but its revenue surged by 35.3% to $25.53 billion. More impressively, net profits grew by 53% to $10.97 billion, representing 43% of total revenue—10 percentage points higher than a decade ago. TSMC's profitability is driven by its dominant position in advanced manufacturing and packaging technologies, as well as the surging demand for high-performance chips in AI applications. The average selling price of a 12-inch wafer is nearly $8,000, double what it was five years ago. Even with the benefits of Moore's Law, which has historically reduced the cost per unit of functionality, TSMC continues to benefit significantly, particularly from producing Nvidia's data center GPUs, which generate higher margins compared to other chip manufacturing businesses. To meet future market needs, TSMC has ambitious capital expenditure plans. The company's 2025 capital spending is projected to be between $38 billion and $42 billion, a 34.4% increase from the $29.8 billion in 2024. A majority of this expenditure, around 70%, will go toward advanced process equipment, while 10% to 20% will be allocated for photomask manufacturing, packaging, and testing. The remaining portion will be invested in specialized technologies. Additionally, TSMC is expanding its manufacturing capacity in Arizona, where the first factory began producing 4-nanometer (N4) chips in the fourth quarter of 2024, with output comparable to its Taiwanese facilities. The second factory, which focuses on 3-nanometer (N3) chips, is ramping up production, and plans are underway for four more factories that will produce 2-nanometer (N2), 1.6-nanometer (A16), and other advanced chips, with about 30% of 2-nanometer capacity slated for Arizona. TSMC's technological advancements are also contributing to the solution. The company anticipates that 2-nanometer chips will begin mass production in the second half of 2025 at its facilities in Hsinchu and Kaohsiung, Taiwan. These chips are expected to offer a 10% to 15% performance increase or a 20% to 30% power reduction compared to 3-nanometer chips. The A16 process, set to enter production in the second half of 2026, will further enhance performance by 8% to 10% or reduce power consumption by 15% to 20% over the improved N2P process. The demand for AI chips has also driven improvements in packaging technology. TSMC's CoWoS technology, which connects high-bandwidth memory (HBM) and high-performance computing engines, has seen "crazy" demand, far exceeding the company's initial expectations. TSMC is working to double CoWoS production capacity, aiming to balance supply and demand by 2026. In the revenue breakdown, TSMC's High-Performance Computing (HPC) segment achieved $15.1 billion in the first quarter of 2025, a 73.5% year-over-year increase. Within this segment, sales of AI training and inference chips reached approximately $6 billion, constituting 40% of HPC revenue and 23.5% of total revenue. Looking ahead, TSMC expects AI accelerators to dominate its revenue streams, potentially accounting for half of the company's income in the coming years. TSMC CEO, C.C. Wei, expressed optimism during the earnings call, noting that the company expects AI-related chip revenue to double in 2025. Based on their models, TSMC's AI chip revenue was $13.1 billion in 2024 and is projected to reach $27.6 billion in 2025, a 2.1-fold increase. The first quarter of 2025 already saw $6 billion in AI chip sales. Industry insiders view TSMC's leadership in AI chip manufacturing as a significant advantage. As the world's largest semiconductor foundry, TSMC combines cutting-edge technology with strong customer trust and strategic global expansion. This unique position ensures the company's continued growth and profitability amidst the evolving AI landscape. In conclusion, the AI industry in 2025 is under immense pressure due to resource and cost constraints, particularly the unsustainable reliance on traditional GPU-based hardware. However, the emergence of neuromorphic chips and other innovative technologies offers promising solutions. TSMC, with its robust manufacturing capabilities and forward-looking investments, is well-positioned to capitalize on the rising demand for advanced AI chips, ensuring its leadership and prosperity in the field.