DeepSeek: The “Chinese” AI Making Waves with Surprising Open-Source Models
1. Geopolitical Tensions and an Unexpected Challenger
Predictions have circulated that, by 2025, geopolitical risks—especially around democracy vs. authoritarianism—might overshadow existential concerns about AI itself. DeepSeek seems to exemplify this shift. At first glance, DeepSeek looked like another obscure AI startup in China. Observers noted it produced models of moderate quality, certainly not at Google or OpenAI levels. Recently, however, the company made an astonishing leap forward—launching models that not only rival leading AI labs but also come as open-source releases.
DeepSeek-R1, introduced on January 20, 2025, is a new “reasoning” model similar to OpenAI’s o1 (released December 5, 2024). The gap between a well-funded U.S. lab and DeepSeek’s Chinese team now appears to be just one month—a surprisingly short interval. Even those who had predicted China’s accelerated AI progress did not expect it to come this quickly. It is also notable that OpenAI keeps certain projects under wraps, while DeepSeek has openly shared its R1 model with the world.
2. Breaking Down DeepSeek-R1 vs. OpenAI o1
DeepSeek published side-by-side comparisons of R1 against OpenAI’s o1 on multiple benchmarks, including GPQA Diamond (science-oriented), SWE-bench Verified (software engineering tests), Codeforces (coding), and AIME (math). The results show that R1 often matches o1. Where differences do appear, they are usually small. By contrast, older high-end models like GPT-4o and Claude 3.5 lag by large margins on several tasks when measured against these “reasoning” models. 
DeepSeek also released a total of eight open-source models, not just R1. Among them is R1-Zero, a variant without human-labeled, post-training data. Additionally, six smaller models (based on Qwen or Llama) were fine-tuned using “distilled” data from R1. Distillation is a process where a powerful model teaches a weaker model via synthetic datasets. Surprisingly, these distilled smaller models perform much better than they originally did—some even surpass GPT-4o on certain tasks. According to DeepSeek, they achieved this at only 5–10% the cost of developing an equivalent OpenAI-level model. How exactly they managed such efficiency remains a mystery, prompting questions about whether they discovered a new training strategy or possibly mirrored an approach akin to Meta’s open-source tactics.
3. The Notable Difference: R1 vs. R1-Zero
DeepSeek’s technical report outlines two distinct models:
- R1: This version performed standard pre-training on massive web data, then underwent supervised fine-tuning (SFT) with high-quality human examples. Afterward, it received a reinforcement learning (RL) boost, plus extra “reasoning at inference time” that OpenAI popularized with o1-preview.
- R1-Zero: This model skipped the supervised fine-tuning step, instead jumping straight from pre-training to RL. It learned to “reason” without the curated human-labeled data. AlphaGo Zero at DeepMind famously used a similar concept for the game of Go, discovering top-tier strategies by self-play rather than studying human moves.
So far, R1-Zero is slightly weaker than R1. It also has less readable outputs, sometimes mixing languages or using unconventional symbols. Nonetheless, it suggests that, for certain tasks, advanced reasoning may be possible without extensive human guidance—a feat with intriguing parallels to DeepMind’s earlier breakthroughs in board games.
4. Could AI Evolve Beyond Human-Like Reasoning?
One key question arises: Will future “Zero” models become progressively more alien as they improve? In the case of AlphaGo Zero, the AI found novel ways to play Go that humans never considered. A next-generation AI agent working from first principles might do something similar in math, science, or coding—skipping human “bad habits” and inventing a logic system entirely its own. Critics and scientists alike wonder if that could render its thought processes incomprehensible to us, even if its results are correct.
Additionally, there is speculation that OpenAI hides certain intermediate steps in its chain of thought partly to maintain a competitive edge, but also to prevent humans from seeing AI’s increasingly inscrutable internal reasoning. DeepSeek’s openness, by contrast, invites the world to witness these phenomena firsthand—raising concerns that some behaviors might become more cryptic over time.
5. The Core Secrets: Distillation, RL, and Base Model Quality
DeepSeek’s documentation suggests three primary factors for success:
- Strong Base Models: R1 and R1-Zero are based on DeepSeek-V3, a large pre-trained model. Building and improving such a “backbone” is expensive but remains essential.
- Distillation: R1 can “teach” smaller Qwen or Llama variants through synthetic data, dramatically raising their performance without new post-training.
- Reinforcement Learning: R1-Zero shows that an RL phase can strengthen reasoning skills even if one omits the typical human-labeled fine-tuning stage.
In a graphical overview, DeepSeek confirms that better base models still push forward the upper limits of intelligence, while distillation efficiently elevates smaller models to a new floor of capability.
6. Geopolitical Undercurrents and Future Possibilities
DeepSeek’s groundbreaking release echoes China’s broader ambition to rival or surpass the United States in AI development. Analysts had cautioned that a window of American dominance might be shrinking, yet few expected the door to close so fast. OpenAI CEO Sam Altman once stated that “democratic visions for AI must prevail over authoritarian ones,” warning that America’s lead was not guaranteed. With DeepSeek publicly releasing R1—and presumably more advanced versions in the pipeline—the lines between open-source collaboration and geopolitical competition have never been more blurred.
Several open questions remain:
- Is China orchestrating a strategic move to overshadow American AI labs or simply capitalizing on the slipstream?
- Will DeepSeek keep releasing open-source models or eventually go private?
- What secret cost-saving methods allow DeepSeek to operate at 5–10% of OpenAI’s expenditures?
Meanwhile, advanced versions from OpenAI (the upcoming o3) and Google (Gemini 2.0 updates) promise even fiercer competition in 2025 and beyond. Yet DeepSeek’s approach—coupled with a detailed technical report—delivers transparency rarely seen among leading AI labs, ironically reanimating the debate: Who is truly “open” now?
For many observers, DeepSeek’s journey from a relatively unknown name to a prime competitor in high-level reasoning models may sound like a sci-fi plot twist. The fact that these models are open-source opens the door for faster global research and potential leaps in AI capabilities—while also heightening questions about regulation, policy, and ideological influence.
However it plays out, the DeepSeek phenomenon underscores that the race toward artificial general intelligence (AGI) is expanding beyond a small cluster of Western companies. The message to U.S. labs and policymakers: wake up, because this field no longer belongs to just one side of the world.
