Background
On January 20, 2025, Stargate was launched. Hosted by President Trump, alongside Larry Ellison from Oracle, Masayoshi Son, a prominent investor from Japan, and Sam Altman from OpenAI, a $500 billion investment was initiated to advance AI infrastructure. “…Together, these world-leading technology giants are announcing the formation of Stargate, a new American 
company. Put that name down in your books because I think you’re going to hear a lot about it,” said Trump. (YouTube)
In response, DeepSeek, a small firm from China 
, released a groundbreaking open-source model, directly competing with U.S.-based models. CNBC reported, “A little-known AI lab out of China has ignited panic throughout Silicon Valley after releasing AI models that can outperform America’s best despite being built more cheaply and with less powerful chips.
DeepSeek, as the lab is called, unveiled a free, open-source large-language model in late December that it says took only two months and less than $6 million to build. The new developments have raised alarms on whether America’s global lead in artificial intelligence is shrinking and called into question big tech’s massive spend on building AI models and data centers.
In a set of third-party benchmark tests, DeepSeek’s model outperformed Meta’s Llama 3.1, OpenAI’s GPT-4o, and Anthropic’s Claude Sonnet 3.5 in accuracy, ranging from complex problem-solving to math and coding.” (CNBC)
According to Reuters, global investors dumped tech stocks on Monday amid concerns that the emergence of a low-cost Chinese AI model threatened the dominance of AI leaders like Nvidia. The chipmaker’s market value evaporated by $593 billion, marking a record one-day loss for any company on Wall Street.
Is this the burst of the AI bubble? I will reflect on that in the conclusion. For now, let’s delve deeper into five emerging conflicts that the DeepSeek/Stargate war exhibits:
Five Conflicts Exhibited in the DeepSeek/StarGate War
1. China vs. USA
The AI competition between China and the U.S. highlights a significant geopolitical struggle where technological advancements are becoming the cornerstone of global influence. Both nations aim to dominate AI innovation, leading to an escalating race with far-reaching economic and national security implications.
2. Personal Data vs. Shared Data
The conflict between personal data privacy and the benefits of shared data underpins the ethical dilemmas of AI development. While shared data drives innovation and model efficiency, safeguarding individual privacy remains a critical challenge in creating responsible AI systems.
3. Open Source vs. Closed Source
Open-source AI models offer accessibility and community-driven progress, contrasting with closed-source systems that prioritize proprietary advancements and monetization. This divide raises questions about fairness, transparency, and the democratization of technology.
Personally, I think that open source is more of a distribution model, and there are always strong players who control or take over it (see, for example, the 2024 WordPress controversy—The Verge).
4. Training Costs vs. Ongoing Costs
High upfront training costs for AI models often contrast with the relatively lower ongoing operational expenses. Balancing these expenditures is critical for sustainable AI innovation and for enabling smaller players to compete in the industry.
5. Foundational Models vs. User Applications
The tension between developing foundational AI models and focusing on user-specific applications underscores the need to align broad capabilities with practical, real-world use cases. Striking this balance is essential for maximizing both scalability and usability.
Conclusion: The Power of the Bubble
Once again, we observe a pattern similar to previous instances, such as the rise of trains in Europe during the 1840s (Wikipedia) and the advent of the Internet around 2000 (Wikipedia). Techno-economic bubbles lead to a significant influx of money into the market, resulting in considerable waste.
However, this influx of funds ultimately gives rise to entirely new economies. When strong, competitive players emerge, we all benefit from better, more cost-effective solutions.
