Here's how open-source models such as DeepSeek's are shaking up AI https://t.co/HKVZolQjTu ...

Here's how open-source models such as DeepSeek's are shaking up AI https://t.co/e9wnezT7kx ...

Core Insights - The future of "open source" is facing unprecedented challenges as enterprise-level LLM API spending has doubled from $350 million to $840 million in the past six months, indicating a shift towards closed-source models that are establishing a performance moat in the billion-dollar market [1][4][9] - The report highlights that despite the cost advantages of open-source models, performance gaps and deployment complexities are hindering their expansion in the enterprise market [2][14] - The rise of Anthropic, which has surpassed OpenAI with a 32% market share, reflects a preference for performance over price among enterprise users [2][9] Group 1: Market Dynamics - The adoption rate of open-source models in the enterprise market is stabilizing, lagging behind closed-source models by 9 to 12 months in performance [2][14] - Developers prioritize performance over cost, with 66% upgrading models within their existing provider rather than switching vendors [20][23] - The shift in AI spending is moving from model training to inference, with 74% of developers in startups indicating that most of their workloads are now inference-driven [27] Group 2: Competitive Landscape - Code generation has emerged as the first killer application of AI, with Claude capturing 42% of the market share compared to OpenAI's 21% [13] - The competitive landscape is reshaped as enterprises increasingly favor high-performance closed-source models, leading to a decline in the market share of OpenAI from 50% to 25% over two years [9][12] - The introduction of models like Claude Sonnet 3.5 and 3.7 has accelerated Anthropic's momentum, showcasing the importance of performance in model selection [12][13] Group 3: Future Trends - The report suggests that 2025 will be the "year of agents," where large models evolve from simple Q&A machines to more complex problem-solving assistants through tool integration and multi-turn interactions [2][13] - The use of reinforcement learning with verifiers (RLVR) is identified as a new pathway for expanding intelligence, particularly effective in areas like coding [2][13] - The market is expected to continue evolving rapidly, driven by new model releases and advancements in foundational model capabilities [31]

Model Performance & Capabilities - ZAI's GLM 4.5% model rivals top closed-source models in reasoning, coding, and agentic capabilities [1] - GLM 4.5% demonstrates advanced problem-solving by successfully simulating and solving Rubik's cubes up to 10x10 [2][3][4][21] - The model can solve the Tower of Hanoi puzzle with up to 10 discs, showcasing its reasoning abilities [5][6][7][24][25] - GLM 4.5% exhibits strong coding skills, creating interactive simulations like Lego building, a 3D solar system, and games like Flappy Bird [8][9][21][22] - Benchmarks show GLM 4.5% outperforming other models in agentic tasks and achieving competitive scores in reasoning and coding [17][18][19] Model Architecture & Variants - GLM 4.5% comes in two versions: a larger 355 billion parameter model with 32 billion active parameters, and a smaller "air" version with 106 billion total parameters and 12 billion active parameters [15] - Both models are hybrid reasoning models, capable of both reasoning and non-reasoning tasks [16] Open Source Landscape - China is at the forefront of open-source AI model development with models like GLM 4.5%, Kimmy K2, and Quen 3 [1][15] - Kimmy K2 is comparable in quality to GLM 4.5% but is 250% larger [20] Tools & Resources - HubSpot offers a free AI decoded guide covering AI models, prompts, and tools [12][13][14]

Model Performance & Technology - The company's models have consistently ranked among the top tier in the last two years [1] - The company possesses state-of-the-art capabilities in specific domains such as transcription and optical character recognition [1] - The company's models are optimized for local deployment, including on laptops [1] - The company is a leading actor in open-source models within the US and Europe [1] - The company adopts a full-stack approach to AI deployment, differentiating it from typical model providers [2] Enterprise Solutions & Partnerships - Enterprises often struggle to realize the P&L impact of general AI [2] - Delivering value to enterprises requires more than just models, including tooling for agent deployment, workflow orchestration, and observability [2][3] - The partnership with ENT data aims to combine technology, expertise, and talent to meet P&L expectations with AI [3]