Some AI influencers worry that the EU AI Act's uncertain regulations could disrupt their profitable ventures. Meanwhile, others are embracing the situation by integrating AI transparency as a core part of their creative approach.

The Trump administration disclosed its AI cybersecurity framework details to leading AI labs like OpenAI and Anthropic on Tuesday. However, this framework remains undisclosed to the general public for now.

Most discussions around AI-ready leadership emphasize the technology—what to rebuild and which tools to adopt. However, the CEO's true role lies beyond technology; it is centered on people transformation, starting with their own readiness to lead this change. At Star, we call this process “organizational recomposition,” which involves taking existing talent and skills, breaking them down, and reassembling the company into a more capable organization without necessarily growing in size. This is done by upskilling employees, shifting roles into new areas made possible by AI, and fostering new ways of working.

Importantly, it also involves identifying and removing those unwilling to embrace the AI transformation. It’s not simply a technology project—it’s a people-focused decision that rests on the CEO’s shoulders.

Two primary challenges arise: company culture and the pace of change. Resistance often stems from fear of altering the company’s core culture, which is the foundation of its identity for customers, stakeholders, and employees. Successful transformation uses the existing culture as a vehicle for change, evolving rather than replacing it.

Speed is another critical factor. The journey isn’t about rushing AI adoption blindly but pacing it strategically, learning from past transformations like cloud computing. Leaders must balance moving fast enough to remain competitive with avoiding costly mistakes and runaway expenses, such as uncontrolled AI usage costs.

Ultimately, the CEO must make the challenging decisions regarding culture and strategic direction. AI transformation is a marathon, not a sprint, and success means becoming a more capable company with the people you already have, even if the company’s size remains the same. This is the real win worth striving for.

Michael Schreibmann is CEO and cofounder of Star.

The AI Office of Ireland has expanded its board with seven new members, including notable figures such as distinguished AI researcher Alan Smeaton and Alessandra Sala, AI director at Shutterstock.

Instacart is challenging the conventional approach by asking if much of the engineering work today should actually be handled by machines. At VB Transform 2026, CTO Anirban Kundu explained that development teams often spend time on repetitive, high-volume tasks that AI agents could manage, freeing humans to focus on areas requiring judgment and intent. Remarkably, Instacart’s engineers now rarely read code directly—97% of the time they work through AI systems.

AI handles most code generation and boilerplate tasks, especially for projects updated weekly. This shift means tech debt is no longer a primary concern; inactive code can simply be dropped and rebuilt as needed. However, about 3% of the work, including legacy and compliance tasks, still demands human oversight. Instacart is addressing these by modularizing their systems through their project Atoms, moving from monolithic to RPC-driven architectures.

Evaluation processes are evolving too. Instead of traditional code reviews, the focus is now on an intent-based model where developers query AI with specific goals. Instacart runs thousands of automatic evaluations monthly with near-perfect accuracy.

Additionally, their AI-powered site reliability engineering system, trained on Instacart’s historic incidents, significantly improves detection and resolution of production issues. For example, their internal AI agent Blueberry identified a subtle production hiccup faster than the human team could, showcasing how AI can overcome typical human intuition limits.

Looking forward, engineers’ roles will center on guiding AI systems, designing evaluations, managing experiments, and handling edge cases. Domain expertise is also being democratized across teams through shared code definitions, moving away from siloed ownership of code changes.

Volta, an AI cloud start-up, has signed a major deal to supply Anthropic with computing capacity for six years, significantly enhancing its market valuation to $2.4 billion. This strategic partnership, reported by Bloomberg, marks a significant milestone for the company and highlights its growing influence in the AI cloud sector.

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.

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.

Snowflake has launched Cortex AI Gateway, a centralized platform aimed at regulating how AI agents—including those developed by competitors like Anthropic's Claude Code and Cursor—interact with enterprise data, tools, and models. The gateway features integrations with various identity security providers such as 1Password, Aembit, Linx Security, SailPoint, and Saviynt, creating a collective trust framework for autonomous agents. This move positions Snowflake not just as a data host but as a governance layer controlling AI agent permissions and activities.

The initiative addresses a critical issue: traditional enterprise security models, designed for human actors, are inadequate for AI agents operating at machine speed and across multiple systems. Cortex AI Gateway enforces continuous trust verification for AI agents through dual attribution—logging both agent identity and the human who authorized a task—ensuring task-scoped access and full auditability.

The platform also tackles runaway AI costs by providing IT and finance teams with visibility into AI consumption, attributing spending to specific agents or teams, and enforcing spending limits. This functionality is bolstered by Snowflake’s acquisition of Natoma, which brought advanced identity, policy, and audit features into the ecosystem.

By uniting competing identity vendors under a shared framework, Snowflake aims to prevent new AI silos from forming and foster interoperability. Analyst forecasts predict a trillion-dollar market opportunity in AI agent governance, placing identity verification and control at the heart of enterprise AI strategy. Cortex AI Gateway currently enters public preview, with its partner integrations in private preview, marking a significant step in managing the future of AI agents in business securely and efficiently.