DeepSeek is deliberately stepping back from the venture capital playbook that defines most AI startups, choosing instead to prioritize artificial general intelligence (AGI) development over rapid monetization and market dominance. In a rare four-hour investor conference that circulated within the industry, founder Liang Wenfeng outlined a philosophy that stands in stark contrast to competitors racing to capture market share and secure billion-dollar valuations.

Why Does DeepSeek Reject the "Next ByteDance" Narrative?

When DeepSeek launched two years ago, the company had minimal resources, no significant brand recognition, and a small team of ordinary engineers. Rather than leveraging this obscurity as a liability, Wenfeng framed it as a strategic advantage. The founder explained that DeepSeek deliberately avoids the trap of chasing venture capital metrics or positioning itself as the next major tech conglomerate.

"We do not have many other advantages, we do not have any skills, we do not have money, we do not say that our personnel are better than others. When we founded this company two years ago, we didn't have much money, we didn't have many cards, we didn't have much visibility, we had no appeal, we were a very common group," Wenfeng stated.

Liang Wenfeng, Founder of DeepSeek

This restraint, according to Wenfeng, is not a weakness but a deliberate strategy. The founder argued that exercising restraint increases the probability of eventually achieving AGI, a goal he views as far more valuable than capturing incremental market share or competing with established internet platforms.

What Does DeepSeek's "Restraint Strategy" Actually Mean?

DeepSeek's approach involves three core pillars that diverge sharply from typical AI startup behavior. Understanding these principles reveals how the lab plans to navigate the competitive AI landscape without abandoning its long-term vision.

• Open-Source Commitment: DeepSeek treats open-source development not as a concession but as a strategic lever. By releasing models and tools openly, the company believes it increases the likelihood of achieving AGI while avoiding direct competition with larger internet companies.
• Low-Cost Operations: Rather than pursuing expensive compute-intensive approaches, DeepSeek focuses on efficiency and cost optimization, allowing the lab to operate with limited capital while maintaining research momentum.
• AGI-Focused Roadmap: DeepSeek explicitly avoids lucrative but tangential markets like video generation or 3D modeling, concentrating instead on what the founder views as the core path to artificial general intelligence.

Wenfeng emphasized that this restraint is not altruism but calculated strategy. "The matter of AI is too big and too good. We have exercised great restraint, and if we can do so, the final interest will be great," he explained, suggesting that the ultimate commercial value of AGI is so substantial that competing for smaller market segments would be counterproductive.

Wenfeng

How Does DeepSeek Plan to Reach AGI?

DeepSeek's technical roadmap reveals a layered approach to advancing AI capabilities. Rather than pursuing multiple parallel directions, the company has identified what it views as the critical stepping stones toward AGI.

The founder described AI development as a ladder with distinct rungs. Last year's breakthrough was chain-of-thought reasoning, which allowed models to achieve higher levels of intelligence by working through problems step-by-step. This year's focus has shifted to Agent systems, which can perform more complex tasks and operate with greater autonomy. However, Wenfeng noted that Agents must still rely on chain-of-thought reasoning, which in turn depends on foundational language models.

After Agent systems, DeepSeek believes the next critical challenge is continuous learning. Current AI models receive training once and then operate with fixed knowledge. Wenfeng argued that true AGI requires models capable of learning continuously, similar to how humans acquire knowledge throughout their lives. Only after solving continuous learning does DeepSeek expect to approach what the founder calls "singularity," the point at which AI systems can improve themselves and develop better versions of themselves.

"After continuous learning, perhaps we will come to a strange point. The strange thing is, when the model can continue to learn, it can do everything that humans can. It is able to develop its own version, to re-examine it, to develop its next version and to develop better artificial intelligence models," Wenfeng explained.

Liang Wenfeng, Founder of DeepSeek

Why Does DeepSeek Reject Trendy AI Applications?

One of the most striking elements of Wenfeng's presentation was his explicit dismissal of popular AI applications that competitors are racing to develop. Video generation, 3D modeling, and other creative AI tools have become hot markets, with many companies treating them as essential to their AI credentials. DeepSeek takes the opposite view.

Wenfeng stated that video production is commercially attractive but has nothing to do with the path to AGI. "We will not do it because it is a good business, we will do it only because it is something on the smart road map," he said. This disciplined focus reflects DeepSeek's belief that world models and visual intelligence, while eventually important, are not the most critical bottlenecks to AGI at this stage.

Wenfeng

Instead, DeepSeek prioritizes training efficiency and continuous learning mechanisms, viewing these as the foundational problems that must be solved before pursuing more specialized capabilities.

How Does DeepSeek Balance Commercial Viability With AGI Research?

Despite its AGI focus, DeepSeek is not operating as a pure research lab. The company maintains both consumer-facing users and business customers, generating revenue to sustain operations. However, Wenfeng emphasized that this commercialization strategy differs fundamentally from typical venture-backed AI companies.

When DeepSeek suddenly gained a large user base in spring, the founder noted that the company did not aggressively pursue monetization or attempt to extract maximum value from users. Instead, DeepSeek focused on serving users effectively. This approach, Wenfeng suggested, has proven successful historically and aligns with the company's broader philosophy of restraint.

The founder also highlighted an important insight about AI's future trajectory. He noted that the development of AI can be understood as accelerating itself, meaning that AI systems can be used to speed up AI research. This creates a nonlinear acceleration curve rather than a linear progression, potentially compressing the timeline to AGI.

DeepSeek's strategy represents a fundamental departure from the venture capital-driven AI landscape, where startups typically prioritize rapid scaling, market dominance, and exit opportunities. By explicitly rejecting these incentives, DeepSeek is betting that disciplined focus on AGI fundamentals will ultimately prove more valuable than capturing incremental market share in trendy AI applications.