According to God of Prompt on X, citing @xiangxiang103, DeepSeek V4 is reportedly slated for late April 2026 with a trillion-parameter MoE architecture that activates around 37B parameters at inference, claiming 35x speedup and 40% lower energy use compared with prior baselines; it also touts a 1,000,000-token lossless context window and native multimodal support across text, image, video, and audio (source: X post by God of Prompt referencing @xiangxiang103). According to the same source, the model is said to be trained and inferenced end-to-end on Huawei Ascend 950PR with roughly 85% compute utilization and one-third the deployment cost of Nvidia-based stacks, while reporting inference cost at about 1/70 of GPT-4, implying substantial TCO reduction for high-throughput workloads (source: X post by God of Prompt). As reported by God of Prompt, benchmark claims include AIME 2026 at 99.4%, MMLU at 92.8%, SWE-Bench at 83.7%, and HumanEval at 90% with support for 338 programming languages, alongside a self-developed mHC architecture and Engram memory module that purportedly lowers inference cost (source: X post by God of Prompt). According to the same X thread, the rollout plan includes a web client with Fast and Expert modes, OpenAI-compatible APIs with 5M free tokens for new users, and an intention to open-source model weights with local deployment support, which—if verified—could create new business opportunities in multilingual coding assistants, enterprise RAG at million-token scale, and low-cost multimodal agents for video and audio analytics (source: X post by God of Prompt referencing @xiangxiang103).

According to DeepLearning.AI on X, DeepSeek denied Nvidia and AMD early access to its upcoming DeepSeek-V4 while sharing the model with Huawei, signaling intensifying U.S.–China friction and the limits of export controls on advanced compute competition; as reported by The Batch via DeepLearning.AI, this access strategy could shift enterprise AI partner ecosystems, evaluation pipelines, and hardware–software co-optimization timelines for foundation model deployments. According to DeepLearning.AI, vendors traditionally secure pre-release access to optimize inference kernels, memory layouts, and compilers; restricting Nvidia and AMD may slow CUDA and ROCm tuning for DeepSeek-V4 while Huawei’s Ascend stack could gain a time-to-market edge in localized Chinese deployments. As reported by DeepLearning.AI, enterprises should reassess multi-hardware inference strategies, negotiate model-hosting SLAs tied to specific accelerators, and explore portability layers to mitigate vendor lock-in amid geopolitically driven access asymmetries.

According to Sawyer Merritt, Tesla's Full Self-Driving (FSD) technology achieved the highest score of 90 out of 100 in Hyundai's internal autonomous driving AI assessment, outperforming competitors such as Huawei (70), Mobileye (50), Momenta (50), and Hyundai's own Atria AI (25). This evaluation highlights Tesla FSD's strong performance in supervised autonomous driving scenarios and underlines its market leadership. As reported by Sawyer Merritt, Hyundai's results may influence future business partnerships and procurement strategies in the rapidly evolving autonomous vehicle technology sector.

According to God of Prompt on Twitter, Huawei and Shanghai Jiao Tong University (SJTU) have introduced WorldGrow, an advanced AI system capable of generating infinite, seamless 3D environments from a single seed. Unlike traditional methods that rely on loops or stitching, WorldGrow uses a coarse-to-fine generation approach to maintain logical global layouts while preserving crisp local details. Its integrated 3D inpainting module ensures any missing regions are contextually filled, enabling AI agents to freely explore and interact within these dynamically created spaces. This technology opens up significant business opportunities for game development, virtual reality, autonomous robotics, and digital twin industries by enabling scalable, realistic world-building with minimal manual design. (Source: God of Prompt via Twitter, official paper on arxiv.org/abs/2510.21682, project details at world-grow.github.io)