**[U.S. AI Startups Race to Develop Low-Cost Alternatives to Chinese AI Models, But Face Funding Challenges]** BlockBeats News, August 2: According to a report by *The Wall Street Journal*, as Chinese open-weight models like Kimi, Qwen, and DeepSeek approach the performance of top U.S. models at lower costs, concerns are growing in Silicon Valley and Washington that Chinese models may compress the profit margins of American AI companies in the long term. U.S. startups such as Arcee AI, Reflection AI, and Poolside are racing to develop domestic alternatives to meet user demand for low-cost, downloadable, and customizable models. However, American open-weight model companies are facing difficulties in securing funding. Some investors question whether free open models can generate stable revenue and worry that the technology might undermine the value of their investments in OpenAI and Anthropic. In the first quarter of 2026, AI startups raised a total of $255.5 billion, with nearly two-thirds coming from three major funding rounds by OpenAI, Anthropic, and xAI. Arcee AI, operating with limited funds, utilized 2,048 NVIDIA Blackwell B300 chips to complete 33 days of pre-training and launched Trinity Large with a budget of approximately $20 million. While the model is smaller than top-tier models and lags behind OpenAI and Anthropic in several benchmark tests, the company plans to develop larger models through a new round of funding. NVIDIA has become a key supporter of the U.S. open AI ecosystem, not only developing the Nemotron series of models but also investing in Reflection AI, Poolside, and Thinking Machines Lab. Industry insiders note that the U.S. open-weight ecosystem remains relatively small, while Chinese models continue to hold an overall advantage. [Original Link]