According to Andrew Ng (@AndrewYNg), the increasing adoption of agentic coding in AI-assisted software development has made automated software testing more vital than ever. Agentic testing, where AI systems generate and run tests, is especially effective for infrastructure components, resulting in more stable platforms and fewer downstream bugs (source: deeplearning.ai/the-batch/issue-319/). Ng notes that while coding agents boost productivity, they also introduce new types of errors, including subtle infrastructure bugs and even security loopholes. AI-driven methodologies such as Test Driven Development (TDD) benefit from automation, reducing the manual burden on developers and enhancing reliability. Business opportunities lie in automating rigorous back-end and infrastructure testing, as deep-stack bugs are costly and hard to trace. Companies focusing on agentic testing solutions can address a high-value pain point in the AI software development lifecycle.

According to Sam Altman on Twitter, OpenAI has released GPT-5-Codex, a specialized version of GPT-5 focused on agentic coding. This new model is designed to be significantly faster and smarter than its predecessors, with enhanced capabilities that facilitate more autonomous and efficient code generation. For businesses and developers, GPT-5-Codex opens up new opportunities to automate complex software development tasks, accelerate product cycles, and lower operational costs. The introduction of agentic coding features positions GPT-5-Codex as a potentially transformative tool for AI-driven software engineering, supporting the growing demand for scalable, intelligent automation in enterprise and startup environments (source: Sam Altman, @sama).

According to OpenAI (@OpenAI), GPT-5-Codex has been released as a specialized version of GPT-5, targeting agentic coding tasks for developers. This AI model is further optimized for code generation, review, and automation, and is now integrated across multiple platforms, including Codex CLI, IDE extensions, web interfaces, mobile devices, and GitHub code review workflows (Source: OpenAI, Sep 15, 2025). This release offers practical benefits for enterprises and developers by streamlining software development, automating code reviews, and enhancing productivity through multi-platform support. The move highlights OpenAI's focus on expanding AI-powered developer tools and sets a new standard in AI-assisted coding, opening new business opportunities in workflow automation and developer productivity solutions.

According to DeepLearning.AI, Andrew Ng recapped their recent Buildathon event where developers leveraged AI-assisted agentic coding to create functional applications within hours, demonstrating how rapid engineering practices are revolutionizing software development productivity (source: @DeepLearningAI, Aug 21, 2025). This trend highlights increasing business opportunities for companies adopting AI-driven software engineering tools, enabling faster product iteration and reducing time-to-market. Additionally, the event underscores how AI coding assistants are becoming essential in competitive software markets (source: @DeepLearningAI).

According to @andrewmayne, @markchen90, and @nickaturley on the OpenAI Podcast Episode 2, the discussion focused on the practical process behind ChatGPT's development, revealing how OpenAI approaches product design and iteration to ensure robust AI deployment (source: @andrewmayne via Twitter, June 2024). The episode also explored future AI business opportunities in agentic coding—where models autonomously write and optimize code—and in multimodal assistants that combine text, image, and audio capabilities, signaling strategic trends for enterprise adoption and next-generation AI services (source: OpenAI Podcast Episode 2).