Software engineers at General Motors' autonomous driving division now spend only about 15% of their time coding, with AI agents handling much of the other 85%, including data analysis, problem triage, and testing. According to Rashed Haq, GM’s VP of autonomous vehicles, this shift has led to roughly three times as many merged pull requests, faster releases, and fewer defects. GM’s strategy focused on redesigning the entire engineering workflow around AI agents rather than just adding coding assistants. The company connected these agents to internal tools and massive data sets using custom Model Context Protocol servers and gave them instruction sets called "skills" to perform specific tasks. This approach allows agents to access vehicle telemetry data, run experiments in parallel, and automate bottlenecks across simulation, road testing, and post-deployment monitoring. With engineers overseeing critical points and agent permissions matching user access, GM successfully increased development velocity while maintaining quality. This comprehensive integration of AI into workflows substantially improved productivity and reduced errors, far exceeding initial expectations.
Iceye, the Finnish space technology firm, has raised €300 million from the Scaleup Europe Fund. CEO and co-founder Rafał Modrzewski shared with Bloomberg that the company envisions going public in Europe in the near future. For further details, visit Silicon Republic.
Runway spent weeks tackling a persistent bug where AI-generated avatars drifted off-center in real-time video generation. Instead of fixing it through backend patches, they created a front-end feature to automatically re-center user images, ensuring stable video output. Ryan Phillips, head of enterprise product at Runway ML, shared insights at VB Transform 2026 on how their real-time generative video technology evolved, highlighting the importance of cross-team evaluation processes and innovative development techniques like distillation and adversarial post-training to optimize performance. Runway’s approach emphasizes turning model limitations into user-friendly features, rigorous infrastructure debugging, and embracing “failure hell” to drive breakthroughs. This new mindset allows creatives to shift from designing single assets to creating dynamic worlds generated in real-time by AI models.
Federal health officials have organized private meetings with technology companies, researchers, and lobbyists to encourage greater adoption of clinical artificial intelligence in healthcare settings.
Hilton’s Global CMO, Mark Weinstein, shares insights on how the company leverages creators, artificial intelligence, and customer loyalty programs to adapt to the changing advertising landscape in hospitality.
Robinhood has introduced a new venture fund open to retail investors, allowing them to put money into current and former startups from Y Combinator. This initiative provides broader access to early-stage venture opportunities, although it comes with specific conditions.
Nvidia has introduced an open platform designed to assist automakers in developing self-driving systems, allowing them to leverage existing work instead of building technology from the ground up.
Google announced significant leadership changes involving its DeepMind division. Demis Hassabis, previously CEO of Google DeepMind, will now serve as chair of Google DeepMind and chief scientist at Alphabet, allowing him to concentrate on broader strategic initiatives and the future of artificial general intelligence (AGI). Koray Kavukcuoglu, formerly CTO of DeepMind, takes on the role of senior vice president of DeepMind while continuing as Google's chief AI architect. Jeff Dean, a pioneer at Google and former chief scientist of DeepMind, is departing to launch Discovery Loop, an organization aimed at automating machine learning, science, and engineering, with Google as a founding investor. Despite concerns over delayed releases and talent shifts to competitors like OpenAI and Anthropic, Google CEO Sundar Pichai emphasized ongoing innovation and areas of leadership within AI development.
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The Research
This article draws from two key research streams: a qualitative study on AI-assisted discovery highlighting four pathways AI uses to generate insightful analytical breakthroughs, and an examination of organizations as algorithmic assemblages—shaped by data accessibility, configured capabilities, and agency distribution. The core value of professionals lies in generating insight—a fresh perspective or connection that reshapes understanding. Expert knowledge, while powerful, can limit discovery by framing what is seen and overlooked. AI, particularly agentic AI, moves beyond simple prompting to proactive, configuration-based analysis that sustains deep, systemic insight.
Two Ways of Working With AI
Most users engage AI conversationally—asking, refining, and interpreting responses. Agentic AI requires configuring context, capabilities, and orientation to enable persistent, autonomous analysis. This allows professionals not just to prompt but to direct, orchestrating multiple agents that analyze the same data from different perspectives, producing richer insights.
Four Discovery Approaches
- Use Multiple Lenses: Apply competing frameworks simultaneously to reveal contradictions and new questions.
- Surface Silences: Identify vital organizational issues not named in official discourse by comparing data and conversations.
- Bridge Levels: Trace problems from symptoms to root causes across organizational layers.
- Stress-Test Categories: Compare formal classifications with operational reality to uncover hidden problems.
Skillful Directing
Effective AI direction requires designing systems for discovery, valuing unexpected findings, evaluating patterns as proposals, and tracking analytical decisions. Agentic AI enhances but does not replace professional judgment; the best outcomes come from framing problems to reveal new insights and leveraging AI as a powerful tool for discovery.
Encore AI uses advanced analysis of calls, messages, and CRM data to uncover successful sales strategies, transforming them into actionable playbooks for AI agents to improve customer engagement.
