OpenAI Blog
Mar 4, 2025
LaunchDarkly's approach to AI-powered product management
A conversation with Claire Vo, Chief Product Officer of LaunchDarkly, about the changing role of product managers, her anti-to-do list, and building AI-native teams.
AI-Native Product Management: A Vision
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arXiv AI
Jun 29
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.
arXiv AI
Jun 16
Do we have the knowledge we need? Rethinking human-AI decision-making in corporations
arXiv:2606. 15575v1 Announce Type: new Abstract: Organizational knowledge is fragmented across a variety of software systems, tacit expertise, and manual documents that have traditionally been designed for human consumption.
OpenAI Blog
Dec 9, 2024
Put AI to work for your product team
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arXiv AI
Aug 11
Innovating with Generative AI: A Human Bottleneck Framework
arXiv:2608. 07504v1 Announce Type: cross Abstract: We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process.
arXiv AI
Sep 28
Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development
The paper examines how a large embedded systems company is transitioning to an AI‑first organization, focusing on the role of autonomous AI agents in software engineering. Through a mixed‑method study involving 40 workshop participants—scrum masters, architects, managers, and product owners—the authors identify expected impacts on team structure, required competencies, organizational strategies, and developer roles. The study concludes with a concrete roadmap and discusses implications for federated AI team formation, human‑in‑the‑loop practices, and sustainable AI adoption in embedded software engineering.
arXiv AI
Oct 26
Can AI Agents Help Robotic Engineers? Lessons from Two Focus Days in Industry
The paper reports a case study on using AI agents to assist engineers in programming industrial robots. The agent did not increase productivity overall, except for generating highly repetitive code, but its effectiveness improved as engineers learned to use it and the agent was adapted to the domain. The study highlights that AI agents are not plug‑and‑play; successful adoption requires training, a learning curve, and domain‑specific configuration.
arXiv AI
Jun 6
The Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming
arXiv:2606. 05178v1 Announce Type: cross Abstract: As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build.
arXiv AI
Oct 5
Designing the Future of User Feedback for Generative AI
arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and featur...
OpenAI Blog
Mar 21, 2024
Embedding AI into developer software
JetBrains uses OpenAI’s API to build its fastest-growing product ever.
arXiv AI
Aug 20
Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
The paper introduces a production-ready framework that connects e‑commerce search and CRM systems via AI‑powered Product Research Agents. These agents detect users with exploratory purchase intent, perform multi‑agent research using behavioral data, external knowledge, and catalog information, and then send personalized product recommendations through WhatsApp. In a 23‑day deployment, the system sent about 15,000 notifications, achieving higher click‑through rates than standard campaigns and generating downstream purchases and GMV gains.
arXiv Machine Learning
Sep 11
The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
The paper discusses recursive self‑improvement (RSI) for AI, describing how systems can use experience and feedback to make lasting enhancements to both their abilities and their future improvement processes. It introduces the Headroom‑Closed Index (HCI) to expose limitations in current large language models and outlines a development roadmap for RSI, progressing from autonomy in execution to full recursive meta‑improvement. The authors analyze RSI in various contexts such as scientific discovery, embodied intelligence, and software engineering, noting differing requirements and development speeds, and they connect RSI research to practical industry applications while highlighting key challenges to achieving genuine RSI.