We've heard (and written, here at VentureBeat) lots about the generative AI race between the U.S. and China, as those have been the countries with the groups most active in fielding new models (with a shoutout to Cohere in Canada and Mistral in France). But now a Korean startup is making waves: last week, the firm known as Motif Technologies released Motif-2-12.7B-Reasoning, another small parameter open-weight model that boasts impressive benchmark scores, quickly becoming the most performant model from that country according to independent benchmarking lab Artificial Analysis (beating even regular GPT-5.1 from U.S. leader OpenAI). But more importantly for enterprise AI teams, the company has published a white paper on arxiv.org with a concrete, reproducible training recipe that expos [...]
Baseten, the AI infrastructure company recently valued at $2.15 billion, is making its most significant product pivot yet: a full-scale push into model training that could reshape how enterprises wean [...]
Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]
Mistral AI on Monday launched Forge, an enterprise model training platform that allows organizations to build, customize, and continuously improve AI models using their own proprietary data — a move [...]
Microsoft on Tuesday released Phi-4-reasoning-vision-15B, a compact open-weight multimodal AI model that the company says matches or exceeds the performance of systems many times its size — while co [...]
In the race to deploy generative AI for coding, the fastest tools are not winning enterprise deals. A new VentureBeat analysis, combining a comprehensive survey of 86 engineering teams with our own ha [...]
The prevailing assumption in AI development has been straightforward: larger models trained on more data produce better results. Nvidia's latest release directly challenges that size assumption â [...]
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]
Most AI products in financial services are, if you strip away the marketing, large language models bolted onto data feeds. They retrieve information. They do not understand it. And when the stakes inv [...]
Researchers at Nvidia have developed a new technique that flips the script on how large language models (LLMs) learn to reason. The method, called reinforcement learning pre-training (RLP), integrates [...]