DeepSeek's Year-Long Fight Against "Overworking": Why Standing Firm Turned Out to Be the Winning Strategy

When everyone in Silicon Valley is frantically competing, one lab is going against the grain. Exactly one year ago today (January 20, 2025), DeepSeek R1 was launched, breaking the industry’s high barrier myth with an extremely efficient and low-cost approach. Looking back, this was not just a model release but a redefinition of the entire AI competition rules. Even more interestingly, when this “laggard” was supposed to be forgotten, the world was changed because of it.

Why DeepSeek Went Against the Grain in Silicon Valley’s “Competition”

Over the past year, the keyword for global AI has been one word: competition.

OpenAI raised funds aggressively, Microsoft invested heavily, Anthropic’s valuation soared, and even Elon Musk couldn’t resist capital’s allure, raising $20 billion for xAI. All giants are driven by capital, monetizing for profit. But in this race where everyone is eager to cash out, DeepSeek chose a path of “zero external funding.”

What confidence do they have?

DeepSeek’s parent company, Fantasia Quant, achieved an extraordinary 53% return last year, with profits exceeding $700 million (about 5 billion RMB). Liang Wenfeng used this old money to fund the new dream of “DeepSeek AGI.” This model grants DeepSeek extreme control over money—no meddling investors, no corporate illnesses, only technical responsibility.

Compared to labs with massive funding, DeepSeek’s advantage is clear. Many funded labs fall into vanity and internal strife—like Thinking Machine Lab, which recently faced frequent employee departures, or Meta AI with its various scandals. DeepSeek doesn’t need to rush to release all-in-one apps for good financial reports, nor does it chase multimodal trends just to compete.

Among competitors eager to claim all-in-one, multimodal, and AI search capabilities, DeepSeek maintains a minimal 51.7MB installation package, avoids hype and aggressive promotion, and focuses on refining inference models with each iteration. On the surface, this seems like “falling behind.” But in reality, DeepSeek’s models are still the first choice for many platforms.

When the World Is “Competing,” DeepSeek Competes with Efficiency

The key is that DeepSeek has rewritten the rules of the AI race.

If a year ago the competition was about who had more GPUs or larger models, DeepSeek’s emergence has turned it into a contest of efficiency and cost-effectiveness. OpenAI and its internal teams recently admitted that the release of R1 caused a “great shock” to the AI competition, even describing it as a “seismic shock.”

According to ICIS intelligence, DeepSeek has completely broken the “compute power determinism.” It demonstrated that even with chip restrictions and extremely limited costs, models comparable to top US systems could be trained. This directly shifted the global AI race from “building the smartest model” to “who can make models more efficient, cheaper, and easier to deploy.”

Microsoft’s recent “2025 Global AI Popularization Report” listed DeepSeek’s rise as “one of the most unexpected developments.” The report revealed that DeepSeek not only dominates the domestic market (89% market share) but also has taken root in regions like Africa, Belarus, and Cuba—areas difficult for US tech giants to reach. In Africa, DeepSeek’s usage is 2 to 4 times higher than elsewhere. Its free and open-source strategy eliminates expensive subscriptions and credit card barriers.

Microsoft also acknowledged that the next billion AI users may not come from traditional tech hubs but from regions covered by DeepSeek.

Europe: From Passive Adoption to Active Innovation

DeepSeek’s influence has crossed the globe.

Europe has long passively used US AI, with its own models like Mistral but without much impact. DeepSeek’s success has shown Europeans a new path: if resource-limited Chinese labs can do it, why can’t Europe?

According to Wired, Europe’s tech community is launching a “European DeepSeek” competition. Many European developers are creating open-source large models, with SOOFI explicitly stating, “We will become Europe’s DeepSeek.” DeepSeek’s impact has also heightened Europe’s anxiety over “AI sovereignty”—they realize over-reliance on US closed-source models is risky, and the efficient, open-source approach exemplified by DeepSeek is a model they need.

Hugging Face, the world’s largest open-source AI community, reflected on the impact of R1’s first year, emphasizing “Chinese AI has truly risen.” They see R1 as a watershed, proving that even with limited compute, open-source can achieve technological breakthroughs. To them, R1’s real value lies in lowering barriers:

Technically: Open inference details enable high-level capabilities to be reused.
Practically: Permissive licenses (MIT) facilitate rapid commercial integration.
Psychologically: It builds confidence that Chinese AI can lead rather than follow.

Today, in 2026, China’s open-source models dominate global downloads. Major domestic companies and startups fully embrace open source, and many foreign new models are built upon Chinese open-source foundations.

V4: The Second Chapter of Anti-“Competition” Strategy

If R1 was DeepSeek’s demonstration to the AI industry, will the upcoming V4 be another counterintuitive move?

Recent technical leaks and official code reveal several key signals:

New Model “MODEL1” Leaked

In DeepSeek’s official GitHub repository, a new model code named “MODEL1” was unexpectedly exposed. The code structure shows MODEL1 as a separate branch alongside V32 (DeepSeek-V3.2). This indicates MODEL1 does not share parameters or architecture with V3 series but is a completely new, independent technical path.

The code shows it adopts a completely different KV cache layout and introduces new mechanisms for sparsity handling. Memory optimizations in FP8 decoding suggest better inference efficiency and VRAM usage.

Industry speculation is that two recent influential papers from DeepSeek—on optimized residual connections (“mHC”) and the AI memory module (“Engram”)—are likely integrated into MODEL1’s architecture.

Core Capabilities: Code Generation and Long Contexts

In today’s homogeneous general dialogue models, V4 chooses a more hardcore breakthrough: productivity-level coding abilities. Insiders say V4’s code generation and handling capabilities have already surpassed Anthropic’s Claude and OpenAI’s GPT series in internal tests.

More importantly, V4 aims to solve a major pain point in current coding AI: handling “ultra-long code prompts.” This means V4 is no longer just an assistant for writing scripts but aims to understand complex software projects and process large codebases. To achieve this, V4 has improved training processes to prevent performance degradation when handling massive data patterns.

Key Technology: Engram

Beyond the V4 model itself, a recent paper by DeepSeek and Peking University reveals their secret weapon for continuous breakthroughs under limited compute—an innovative technique called “Engram” (imprint/conditional memory).

While competitors are frantically stacking H100 GPUs, DeepSeek has taken an unusual route. Engram allows models to efficiently access basic information without repeatedly consuming compute. The saved compute is then used for more complex high-level reasoning.

Researchers say this technology can bypass VRAM limits and support aggressive parameter expansion. Amid rising GPU resource constraints, DeepSeek’s breakthrough indicates they are not solely relying on hardware stacking.

Possible Release Around Spring Festival

There are reports that DeepSeek plans to release its new flagship model V4 around the Spring Festival. Last year, R1 was also launched at this time, igniting global attention during the holiday. This timing avoids the usual release congestion in Western tech circles and leverages the holiday period for user curiosity.

Counterintuitive Is the New Normal

DeepSeek’s evolution over the past year is fundamentally about solving industry-wide problems with counterintuitive approaches.

It earned 5 billion USD last year, enough to train thousands of DeepSeek R1s, yet it hasn’t blindly competed in compute or GPUs, nor announced plans to go public or raise funds. Instead, it is exploring how to replace expensive HBM memory with cheaper memory modules. Over the past year, it has almost completely abandoned the traffic war for all-in-one models, focusing instead on refining inference models amid a landscape of monthly and weekly updates from other model makers.

These choices seem “wrong” in the short term. No funding means less resource competition with OpenAI. Not building multimodal all-in-one apps means missing out on user engagement through images and videos. The law of scale hasn’t failed—how to produce the strongest models without stacking more compute?

But in the long run, these “wrong” choices may be paving the way for V4 and future phases. That is DeepSeek’s core: while others compete over resources, it competes over efficiency; while others chase commercialization, it pushes technological limits.

When the industry asks “Why not compete harder,” DeepSeek proves with its one-year results that sometimes, the smartest move is to compete less.