Core Insights - Straker Limited has renewed and expanded its strategic partnership with IBM for an additional three years, effective January 1, 2026, with a contract value of approximately NZ$28 million (US$16.1 million) over the initial term [2][3]. Agreement Details - The renewed agreement allows IBM to extend the contract for an additional year beyond the initial three years and is based on customer usage, which may lead to revenue fluctuations [2][3]. - The agreement maintains core terms from the previous contract but emphasizes deploying Straker's AI-driven solutions across IBM's global operations, where 10,000 users are already utilizing Straker's AI-driven Slack translation application [4]. Expanded Strategic Partnership - Straker is now recognized as part of the IBM Ecosystem Partner network, with the collaboration managed primarily through IBM Japan, enhancing Straker's integration within IBM's technology ecosystem [5]. - A significant focus of the partnership includes the joint development of customized small language models using IBM's watsonx AI technology and Straker's Tiri platform, which has shown promising early results [6][7]. CEO Commentary - The CEO of Straker highlighted that the renewal and expansion of the partnership with IBM validate the company's strategy and provide a strong foundation for future growth, emphasizing the transformation of translation services and broader enterprise AI opportunities [8].

RT Sam Hogan 🇺🇸 (@0xSamHogan)I'm excited to announce @inference_net's $11.8M Series Seed funding round, led by @multicoincap & @a16zcrypto CSX, with participation from @topology_vc, @fdotinc, and an incredible group of angels.The next wave of AI adoption will be driven by companies building AI natively into their products at scale.As scaling laws continue to demand larger models and more compute, margins become thin, and operating at scale becomes untenable.We're taking a different approach -- training task ...

Market Trends - Corporate demand for small language models is projected to grow twice as fast as it is for large models [1] - The growth of small language models is starting from a much lower base [1]