OpenAI is now at the center of a cloud war between Amazon and Microsoft. For the first time ever, Sam Alman's company is teaming up with Amazon Web Services in a 38 billion dollar deal that makes AWS the newest hyperscaler to power chat GBT. The move comes just a week after Microsoft lost its right of first refusal on new compute deals, really clearing the way for OpenAI to sign with Azure's biggest rival.Under the agreement, OpenAI gets instant access to hundreds of thousands of NVIDIA GPUs on AWS. It's al ...

Group 1 - Alphabet's full-year capital expenditure (capex) guidance is between $91 billion and $93 billion, aligning closely with Microsoft and Amazon's spending in the cloud sector [1] - The company's backlog number stands at $155 billion, indicating strong future revenue potential [2] - Monthly active users for the Gemini app are projected at 600 to 650 million, compared to OpenAI's 800 million [2] Group 2 - Search and other revenue, a key segment for the company, reached $56.57 billion, surpassing estimates of $55.1 billion [3] - YouTube revenue for Q3 was reported at $10.26 billion, exceeding the expected $10.01 billion [4] - Cloud revenue grew by 34% year-over-year, totaling $15.16 billion, as the company aims to improve its market position [4]

Responses API Overview - OpenAI introduced the Responses API to evolve beyond the Chat Completions API, addressing design limitations and enabling new functionalities for building agentic applications [1] - The Responses API combines the simplicity of chat completions with the ability to perform more agentic tasks, simplifying workflows like tool use, code execution, and state management [1] - The core of the Responses API is an agentic loop, allowing multiple actions within a single API request, unlike Chat Completions which only allows one model sample per request [2] - The Responses API uses "items" for everything, including messages, function calls, and MCP calls, making coding easier compared to Chat Completions where function calling was bolted onto messages [2] - The Responses API is purpose-built for reasoning models, preserving reasoning from request to request, boosting tool calling performance by 5% in primary tool calling eval tobench [2] - The Responses API facilitates multimodal workflows, making it easier to work with images and other multimodal content, including support for context stuffing with files like PDFs [2] - Streaming is rethought in the Responses API, emitting a finite number of strongly typed events, simplifying development compared to Chat Completions' object deltas [2] - Long multi-turn rollouts with the Responses API are 20% faster and less expensive due to the ability to rehydrate context from request to request, preserving the chain of thought [2] Agent Platform and Tools - OpenAI is changing deployment with its agent platform, centering on the Responses API and Agents SDK for building embeddable, customizable UIs [3] - Agent Builder and Chatkit, built on the Responses API, make it easy to build workflows into applications with minimal effort [3] - The Responses API is at the core of the improvement flywheel, enabling distillation and reinforcement fine-tuning using stateful data, along with tools like web search and file search [3]

AI Hype and Adoption - The industry believes the AI hype surrounding companies like Nvidia is not overdone [2] - A study indicated that 95% of AI projects are not deriving value, raising concerns about overhyping AI [3] - The slow pace of AI implementation in businesses, particularly in enterprise AI, is a challenge [4] - Consumer applications like Chat GPT and Google Gemini are being adopted more readily than enterprise AI solutions [5] Software as a Service (SaaS) and AI - The industry suggests that SaaS companies like Salesforce and ServiceNow may face challenges if they don't adapt to support AI models [6] - The software industry needs to be rebuilt to integrate AI effectively, changing how users interact with software [7][8] Semiconductor Market and Competition - Broadcom is highlighted as a company to watch in the AI chip space [8] - Major hyperscalers are building their own chips, potentially challenging Nvidia's margins [9] - Hyperscalers' chip strategy will bifurcate, with external sales relying on Nvidia (over 90% until the end of the decade) and internal use shifting to custom chips [12] - Meta is reportedly building 6,000 rack-scale systems with its own silicon to power its data centers [14] Nvidia's Position and Future Outlook - Nvidia is currently the backbone of the AI infrastructure boom [8] - The industry anticipates Nvidia will maintain a strong position for at least two more years [13] - The AI chip market is projected to exceed $580 billion by 2029 [14]