DeepSeek Engineer: Open Source Isn't About Competition, It's About Bringing AGI Sooner

In the race for artificial intelligence, the debate between open source and closed source has long been a hot topic in the industry. Recently, a DeepSeek engineer shared his views on open-source models on Bilibili, offering an answer that seems simple yet thought-provoking: open source is about bringing AGI sooner.

The Core Logic of Open Source: The Opponent Is Time, Not Other Companies

The engineer stated plainly that the reason DeepSeek^Ad chose to open-source its models is very simple—to make AGI arrive sooner. He posed a key hypothetical: if you build technical barriers through innovation, you can indeed block competitors from catching up in the short term and gain a brief lead. But if you widen the time horizon, while such technical barriers block other companies, could they also be blocking the arrival of AGI?

In his view, in the AI race, the real opponent is not other domestic large-model companies, nor overseas large-model companies, but time. Regardless of which model company's achievements they are, everyone is working together to rapidly advance AGI. This perspective shifts competition from a "zero-sum game" to "collective acceleration," with open source becoming a means to accelerate the collective process.

AGI (Artificial General Intelligence) refers to AI systems that possess general cognitive abilities comparable to or surpassing humans, capable of understanding, learning, reasoning, and adapting across a wide range of task domains rather than being limited to a specific task. It stands in contrast to "Narrow AI," such as specialized speech recognition or recommendation systems. AGI remains a theoretical goal, with no universally accepted path to implementation or definition standard, and the industry is deeply divided on its timeline. Precisely because achieving AGI requires long-term breakthroughs across multiple dimensions—algorithms, compute, data, and engineering—it is difficult for a single institution to complete all innovation independently, which is the underlying logic behind why open-source strategies are believed to accelerate its arrival.

The Double-Edged Sword of Technical Barriers

The engineer further explained that while technical barriers protect one's own advantages, they may also slow the progress of the entire field. If every company builds high walls, the flow of knowledge is obstructed, more wheels get reinvented, and the arrival of AGI may actually be delayed.

He asked in return: could such technical barriers also be blocking the arrival of AGI? This question points directly to the deeper motivation behind the open-source strategy—not abandoning competitiveness, but choosing a faster collective path. Open-source code and models allow more researchers to iterate quickly based on existing results, avoiding the inefficiency of starting from scratch.

From DeepSeek's practice, open source has indeed attracted a large number of developers and researchers to participate in ecosystem building, forming a technical feedback loop. This model may weaken exclusive advantages in the short term, but in the long run it accelerates technological evolution.

Technical barriers typically refer to the competitive moats companies build through patents, proprietary data, algorithmic secrets, or engineering accumulation to prevent competitors from imitating or catching up. In the AI large-model field, barriers may take the form of not disclosing pre-trained weights, keeping training recipes confidential, or keeping inference optimization techniques closed-source. The positive role of barriers is to secure the expected return on R&D investment and incentivize companies to keep investing; the negative effect is the isolation of knowledge, leading to duplicated experiments and wasted compute across the industry. The essence of the open-source versus closed-source debate is finding a balance between "incentivizing innovation" and "accelerating diffusion."

Redefining the Industry Competitive Landscape

The traditional view holds that competition among large-model companies is a fierce battle for market share. But this engineer's view challenges that perception: if all companies are working toward AGI, then every company's breakthrough is progress for the entire industry.

By defining the competitor as "time," he implies that the industry needs more efficient collaboration rather than internal friction. In this framework, open source is not an act of charity but a strategic choice—by sharing foundational technology, more smart people can be brought to bear on solving key problems, thereby shortening the cycle to achieve AGI.

Implications for the Open-Source Ecosystem

Although the DeepSeek engineer's sharing was brief, it touches on the core value of the open-source movement. In AI, open-source models lower the barrier to research, allowing academic institutions and small and medium-sized enterprises to participate in frontier exploration. The diversity brought by such broad participation often gives rise to unexpected innovation.

Of course, open source also faces challenges such as sustainability and commercial returns. But this engineer's perspective reminds us that when evaluating open-source strategies, we should not look only at short-term competitive gains and losses, but also at their impact on the long-term development of the entire field. When the opponent is time, every piece of shared code can become a force that accelerates the arrival of AGI.

The open-source model ecosystem typically includes the public release of model weights, inference code, training scripts, and supporting toolchains, often licensed under permissive or restricted licenses (such as Apache 2.0, MIT, or custom community licenses). Typical examples include Meta's Llama series, Mistral, and DeepSeek. The sustainability of the open-source ecosystem depends on multi-party collaboration including community contributions, commercial support, and compute sponsorship, and its challenge lies in maintaining long-term investment without harming commercial interests. Extensive downstream fine-tuning and evaluation feedback can in turn feed back into foundation model iteration, forming a closed loop of "release—use—feedback—improvement."

One-line Summary

A DeepSeek engineer believes open source aims to accelerate AGI's arrival, and the real opponent is time, not peers.

Paragraph Summary

The article centers on a DeepSeek engineer's sharing about open-source models, noting that the core motivation for open source is to bring AGI sooner. The engineer argues that technical barriers can protect a lead in the short term but may hinder knowledge flow and lead to reinventing the wheel in the long run, actually delaying progress across the field. Therefore, the real opponent in the AI race is not other large-model companies but time. Open source promotes collaboration and knowledge sharing, enabling more researchers to iterate quickly on existing results and form a technical feedback loop. The article also redefines the industry competitive landscape, arguing for a shift from zero-sum games to collective acceleration, and discusses the implications and sustainability challenges of open source for the ecosystem.