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DeepSeek Expands Open-Source AI Strategy With New Code Release

Chinese AI startup DeepSeek is doubling down on open-source innovation, announcing plans to publicly release five new code repositories next week. In a post on social media platform X, the company described the move as “small but sincere progress” toward greater transparency in AI development.

“These humble building blocks in our online service have been documented, deployed, and battle-tested in production,” the company stated.

DeepSeek made waves last month when it unveiled its open-source R1 reasoning model, a system that rivaled Western AI models in performance but was developed at a fraction of the cost. Unlike many AI firms in China and the U.S. that guard their proprietary models, DeepSeek has positioned itself as a leader in open-source AI.

The company’s elusive founder, Liang Wenfeng, reinforced this philosophy in a rare interview last July, emphasizing that commercialization was not DeepSeek’s primary focus. Instead, he framed open-source development as a cultural movement with strategic advantages.

“Having others follow your innovation gives a great sense of accomplishment,” Liang said. “In fact, open source is more of a cultural behavior than a commercial one, and contributing to it earns us respect.”

The newly released repositories will provide infrastructure support for DeepSeek’s existing open-source models, enhancing their capabilities and accessibility. This follows the company’s Tuesday launch of Native Sparse Attention (NSA), a new algorithm designed to optimize long-context training and inference.

DeepSeek’s influence is growing rapidly. Since last month, its user base has surged, making it China’s most popular chatbot service. As of January 11, the platform had 22.2 million daily active users, surpassing Douban’s 16.95 million, according to Aicpb.com, a Chinese analytics site.

With its latest commitment to transparency and collaboration, DeepSeek continues to challenge the AI industry’s dominant closed-source model, reshaping the future of artificial intelligence on a global scale.

OpenAI Releases GDPval Benchmark To Gauge AI Performance Against Human Experts

New Benchmark Sheds Light on AI’s Capabilities

OpenAI has unveiled GDPval, a new benchmark designed to evaluate its AI models against human professionals across a broad spectrum of industries. This initiative represents a critical step in understanding how far today’s AI is from matching or surpassing the work quality of experts in sectors such as healthcare, finance, manufacturing, and government.

Methodology and Industry Scope

The GDPval benchmark focuses on nine major industries contributing to America’s gross domestic product and tests AI performance in 44 distinct occupations—from software engineering to nursing and journalism. In its initial version, GDPval-v0, industry professionals compared reports generated by AI models with those produced by their human counterparts. For instance, investment bankers were tasked with evaluating competitor landscape analyses for the last-mile delivery industry, ensuring that the assessment reflects real-world complexity.

Comparative Performance: AI Advances and Limitations

Results indicate promising progress; OpenAI’s GPT-5-high, an enhanced iteration of its flagship model, achieved a win rate of 40.6% when compared head-to-head with industry veterans. More notably, Anthropic’s Claude Opus 4.1 reached nearly 49% on similar criteria. However, OpenAI acknowledges that these models are not yet positioned to replace human labor entirely, as the current iteration of GDPval covers a narrow slice of actual job responsibilities.

Expert Insights and Future Directions

In a discussion with TechCrunch, OpenAI’s chief economist, Dr. Aaron Chatterji, noted that the benchmark’s favorable outcomes suggest professionals may soon delegate routine tasks to AI. This, he argued, will free up valuable time for focusing on higher-impact work. Industry observer Tejal Patwardhan also expressed optimism, emphasizing the significant performance leap from GPT-4’s 13.7% score to nearly triple that figure with GPT-5.

Benchmarking And The Road To Comprehensive AI Evaluation

While GDPval represents an early milestone, it aligns with a broader effort among Silicon Valley titans to create robust testing frameworks, such as AIME 2025 and GPQA Diamond, that better quantify AI proficiency for real-world applications. OpenAI plans to expand GDPval to encapsulate more industries and interactive workflows, aiming to bolster its claims about AI’s growing economic value.

As the benchmark evolves, GDPval could play an instrumental role in the ongoing debate around artificial general intelligence, highlighting the potential and limitations of AI models poised to reshape the modern workforce.

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