Until recently, the artificial intelligence (AI) ‘arms race’ seemed like an all-American affair. OpenAI’s ChatGPT led the charge, with Google’s Gemini and Meta’s LLaMA models not far behind. American dominance in AI was assumed to be an inevitability, and an extension of Silicon Valley’s long-standing supremacy. Now, DeepSeek, the homegrown Chinese model has sent tremors through the AI industry, despite its baggage of state-backed propaganda.
DeepSeek’s emergence should have been unremarkable: yet another large language model (LLM), another iteration in a rapidly evolving space. But it has out-optimized its American competitors by achieving comparable - if not better - results through an optimized co-design of algorithms, frameworks and hardware. Since it is not just a matter of fewer parameters but also the algorithms operating on them more efficiently.
This has thrown a wrench into Meta’s grand AI ambitions. Meta, along with OpenAI and Google, have built their models on the assumption that more parameters mean better performance. Training these behemoth models requires staggering computational resources, and American tech firms have been quick to justify their exorbitant costs. But DeepSeek has shown otherwise. It has outperformed GPT-4o and Claude 3.5 Sonnet - the two US flagship models - on a series of standard and open-ended benchmarks.
Unlike its closed-source competitors, DeepSeek has open-sourced its model, allowing smaller players to adapt it without relying on subscription services of OpenAI or Anthropic. Small but clever modifications like the use of rotary embeddings and group relative policy optimization (GRPO) - a reinforcement learning paradigm - have led it to achieve impressive results without the usual computational bloat.
This has made American AI firms uneasy. Perplexity, a relatively small startup in the U.S., had to rely on post-training methods rather than foundational model training because it simply lacked the resources. Aravind Srinivas, CEO of Perplexity, has publicly noted how DeepSeek’s cost-effectiveness exposed flaws in the current American approach. Whereas OpenAI and Google charge sky-high fees for API access, DeepSeek offers a pricing structure - around $0.34 per 1,000 tokens - that undercuts them significantly.
For years, the prevailing assumption in AI research was that Western firms, with their access to the best talent and most powerful hardware, would remain untouchable. Yet, DeepSeek has shown that even modest improvements in model efficiency can disrupt the market. And now, other countries are taking notice.
American tech firms and policymakers alike have been quick to point out its ties to the Chinese government, warning of potential security risks and propaganda concerns. These concerns are not unfounded. AI models trained in authoritarian regimes inevitably reflect the biases of their environment, and DeepSeek is no exception. But to dismiss its technical achievements outright would be myopic. The reality is that DeepSeek’s advancements are not confined to China. Its innovations in model optimization can be repurposed by anyone. The backlash also smacks of a certain American hubris. Silicon Valley has long viewed itself as the sole architect of the AI revolution. When OpenAI and Google release new models, the conversation revolves around their transformative potential. When China does the same, the narrative shifts to fears of espionage and state control. It is in the interests of American firms to bash DeepSeek not just for geopolitical reasons, but because it threatens their bottom line.
While DeepSeek is unlikely to dethrone OpenAI or Google anytime soon, and its government ties will always make it a controversial player in global AI development, its existence has nonetheless forced a reckoning in Silicon Valley. It has shown that more efficient AI is possible and that cost need not be a barrier to entry. For the first time in a long while, Silicon Valley is feeling just a little bit jealous.(The author is a U.S.-based data scientist)
Comments