DeepSeek Engineer: Open Source Is About Bringing AGI Sooner

In the field of artificial intelligence, the debate over open source versus closed source has never ceased. A DeepSeek research engineer recently shared on Bilibili his thoughts on the motivation for open source and how he views competition among large model companies. His view is blunt and profound: open source is not about defeating peers, but about outrunning time.

The Core Motivation for Open Source: Accelerating the Arrival of AGI

The engineer admitted frankly that the reason DeepSeek^Ad open-sources the results of its hard research is "very simple"—to push AGI to arrive sooner. In his view, building technical barriers through innovation can indeed block competitors from catching up in the short term and secure a leading advantage, but over a longer time horizon, such barriers may also slow down the entire AGI process while blocking others.

The double-edged sword effect of technical barriers is worth pondering. For an individual company, a barrier is a moat; for the industry, a barrier may be a speed bump. By choosing open source, DeepSeek is essentially giving up short-term exclusive advantage in exchange for the long-term benefit of accelerating progress across the entire field.

AGI (Artificial General Intelligence) is a long-term goal of AI research, referring to intelligent systems capable of reaching or surpassing human-level ability across a wide range of tasks, as opposed to today's "narrow AI" focused on specific tasks (such as speech recognition, image classification, and large language model question answering). AGI is not a strictly defined technical specification, but rather more of a directional concept: it emphasizes cross-domain generalization, autonomous learning, and transferability. Precisely because the goal is grand and the path uncertain, progress toward AGI depends heavily on collaborative exploration by the entire research community, and no single institution can independently complete all foundational breakthroughs. This also explains why "accelerating the arrival of AGI" can become the underlying motivation for some teams to choose open source—open source can broaden participation and increase the frequency of trial and error, thereby shortening the exploration cycle from current large model capabilities to more general intelligence.

Competitive Mindset: The Opponent Is Time, Not Fellow Companies

"In the AI race, I personally believe our opponent is not other domestic large model companies, but time." This statement clarifies the underlying logic behind how DeepSeek views competition—no matter which company achieves results, it is essentially a step forward for humanity toward AGI.

This shift in perspective means that peers are not opponents in a zero-sum game, but participants jointly advancing the same goal. Under the real constraints of relatively limited compute, data, and talent, open source can reduce reinventing the wheel and allow more researchers to stand on existing results and continue moving forward.

Zero-sum game is a concept in game theory that refers to a situation where one party's gain is exactly another party's loss and total payoff is always zero. In business competition, the zero-sum perspective assumes that market share, users, and profits are a fixed pie, and that a peer's success necessarily means one's own failure. However, in large model and AGI R&D, this assumption does not fully hold: improvements in foundation model capabilities, maturation of toolchains, and publication of evaluation methods often have positive externalities and can be reused across the entire industry. By framing competition as "the opponent is time," DeepSeek is essentially redefining peer relationships from a zero-sum game to a positive-sum game—all parties can benefit as they jointly push the technological frontier, and the focus of competition shifts to who can contribute more effective progress to the overall process.

The Dual Value of Open Source

From an engineering perspective, external feedback brought by open source can accelerate model iteration, and community-contributed optimizations, toolchains, and evaluation methods are often more efficient than working in isolation. From an ecosystem perspective, open source lowers the entry barrier for small and medium-sized teams and researchers, allowing more innovation to happen on top of open-source foundations. DeepSeek's practice shows that open source is not an act of charity, but a strategic choice—using the depth of the ecosystem to hedge against the fragility of single-point leadership.

Technical barriers usually refer to the competitive moats companies build through patents, proprietary data, unique algorithms, or engineering experience, making it difficult for competitors to replicate their capabilities in the short term. In the large model field, technical barriers may manifest as high-quality training data, distributed training infrastructure, alignment and evaluation methods, and so on. For the holder, barriers can bring pricing power and a leading window, but for the entire industry, duplicated R&D and closed exploration reduce the efficiency of knowledge flow. Open source is equivalent to proactively lowering some barriers and turning internal results into public knowledge assets, enabling external researchers to continue improving on the same foundation. This strategy does not deny the value of barriers, but weighs their short-term protective effect against the risk that they may slow the industry's overall progress in the long run.

Implications for the Industry

When leading model companies begin to treat "the opponent is time" as a consensus, the logic of large model competition may shift from competing over who is more closed to competing over who can make technology flow faster. Open source is not abandoning competition, but elevating it to a higher dimension: competing over who contributes more to the AGI process. Whether this judgment is correct will ultimately be answered by time.