余胜海/文 2026年，"十五五"规划大幕拉开，经济高质量转型迈入深水区。一边是超长期特别国债持续加码释放的政策活水，一边是AI技术重构产业生态的浪潮，消费 市场也早已从"追风口、逐热点"转向"重品质、看内核"。多重变量交织下，中小企业的日子过得并不轻松：融资难像块压在胸口的石头，合规要求升级让人 提心吊胆，巨头挤压下订单难拿，劳动力短缺更是常年困扰。有数据为证，2025年中小企业存活率仅62%，也就是说每10家企业里，就有近4家熬不过一 年。其实面对复杂形势，中小企业不用盲目跟风扩张，更该沉下心打磨核心能力，从政策借势、产品扎根、数字提效、融资抗险四个维度精准发力，就能在 风浪中站稳脚跟，实现可持续发展。 一、借势而为：精准承接政策红利，降低经营成本 2026年国家超长期特别国债规模持续扩容，截至2025年底已发行1.3万亿元，较2024年多增3000亿元，这股国家级资金"活水"正精准流向实体经济。但很多 中小企业老板都有同感：政策红利从来不会"自动落袋"，盲目申报只会白费功夫、空耗精力。青岛雪达集团技术质量部长张洪宾就曾吃过信息滞后的 亏，"以前快到申报截止日期才看到政策，只能手忙脚乱补材料，甚至有政策过 ...

Core Insights - Fastino is developing a new AI model architecture designed for miniaturization and specific tasks, contrasting with the trend of large, expensive GPU clusters used by tech giants [1] - The company has raised $17.5 million in seed funding led by Khosla Ventures, bringing its total funding to nearly $25 million [1] - Fastino's models are claimed to be faster, more accurate, and significantly cheaper to train compared to flagship models, while outperforming them in specific tasks [1] Funding and Financials - Fastino's recent funding round was led by Khosla Ventures, known for being the first investor in OpenAI [1] - The company previously raised $7 million in a pre-seed round led by Microsoft's venture arm M12 and Insight Partners [1] Product and Performance - Fastino's models are small enough to be trained on low-end gaming GPUs costing less than $100,000 [1] - Early users have been impressed with the model's performance, which can provide detailed answers in milliseconds [2] - The focus is on creating small models tailored to specific enterprise tasks, such as sensitive data anonymization and document summarization [1][2] Market Position and Competition - The future of enterprise-level generative AI may lie in smaller, more focused language models, a trend that is gaining recognition [3] - Fastino is competing in a crowded enterprise AI market, with other companies like Cohere and Databricks also promoting specialized AI models [2] - The company aims to attract top AI researchers who are not solely focused on building the largest models or beating benchmark tests [3]