HYPERBŌLA

From the plow to the steam engine to the silicon chip, every major technological revolution has begun with a surge of investment, a chorus of skeptics, and a few visionaries willing to bet on the future. Today, artificial intelligence stands at that same precipice. Critics warn of an “AI bubble,” citing sky-high valuations and speculative capital. But history suggests that what looks like froth may be the necessary fuel for transformation. The agricultural revolution reshaped human settlement and labor. The industrial revolution redefined production and global trade. The internet age rewired communication and commerce. Each began with immense capital outlays, unproven technologies, and a cadre of relentless minds solving age-specific problems. AI is no different. As Goldman Sachs economist Joseph Briggs notes , the billions being spent on AI infrastructure are justified by the “enormous economic value promised by generative AI,” with potential to generate up to $8 trillion in ...

Earlier this year, I had the opportunity to join a team tasked with migrating a large language model (LLM) and fine-tuning it on a virtualized public cloud. I won’t name the provider, but while the model sufficed for the MVP, performance bottlenecks and unexpected costs quickly surfaced. That experience led us to explore virtualized GPU-as-a-Service (GPUaaS). Initially, it felt like overkill—but even a modest increase in compute capacity yielded a noticeable performance boost. It made me question how cloud infrastructure will evolve to support advanced AI applications, especially for enterprises operating at massive scale. Traditional public cloud infrastructure—still the default for scalability and elasticity—may no longer meet the demands of modern AI workloads. The rise of LLMs, generative AI, and real-time training and inference has exposed critical limitations in public cloud environments, particularly around GPU availability, cost efficiency, and latency. While GPUs are ...

In the early 2000s, economists like Jim O’Neill of Goldman Sachs sparked global attention by predicting that China would surpass the United States as the world’s largest economy by 2030. That forecast, once considered ambitious, is steadily materializing. In 2024, China’s GDP reached approximately $18.7 trillion — an astronomical rise from just $2 trillion in 2004. The International Monetary Fund expects China to grow at more than 3% annually throughout the decade , while JPMorgan Chase forecasts nearly 5% growth per year , reinforcing China’s trajectory toward the top of the global economic hierarchy. China’s ascent over the past two decades has been driven primarily by its dominance in manufacturing and infrastructure development. But today, a new force is propelling its economy forward: artificial intelligence. With AI increasingly embedded in industrial productivity, national strategy, and sector-wide efficiencies, it is poised to become China’s next growth engine. Goldma...

In 2025, an estimated $320 billion will be invested in the AI sector, with a significant portion allocated to infrastructure. This level of investment is unprecedented. The last time such a massive amount of capital flowed into a single sector was at the peak of the dot-com boom in 2000, when most of the investment went toward software startups rather than infrastructure. Unlike the dot-com boom—when dial-up connections were the norm—the AI revolution requires immense computing power to process and transmit vast amounts of data. While AI's software advancements dominate headlines, the vital infrastructure powering it—data centers and interconnects—often goes unnoticed. NVIDIA is the most frequently mentioned company in AI infrastructure-related news, largely due to its dominant GPU market share. Data centers may soon become the new global symbols of power. The US leads the world in the number of data centers (~45%), followed by Western Europe and China. Given China’...