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.

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.

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.

Is AI capable of managing a successful business and directing human employees? A recent experiment challenges this question by giving an AI system its own storefront, three employees, and a $100,000 budget to test its abilities in real-world conditions.

The Model Context Protocol (MCP), an open standard connecting AI agents with global software, has received its biggest update since its launch by Anthropic twenty months ago. Now under the Linux Foundation's Agentic AI Foundation (AAIF), this update revolutionizes MCP into a fully stateless architecture, improves security by enhancing its authentication processes, and introduces a formal 12-month deprecation policy. This transformation enables enterprises to deploy AI agents at scale without the previous challenges of session persistence, allowing seamless operation behind load balancers using modern cloud infrastructure tools like Kubernetes.

Key changes include removing the dependency on persistent sessions, thereby enabling better fault tolerance and scalability, and extending protocol capabilities with new features such as interactive server-rendered interfaces (MCP Apps) and durable long-running asynchronous tasks (MCP Tasks). These improvements allow AI agents to move beyond simple text responses, supporting richer user interfaces and robust handling of complex operations.

Security is fortified by aligning the protocol with OAuth 2.0 and OpenID Connect standards to prevent mix-up attacks, and with enterprise-managed authorization to ensure corporate identity governance. The update is supported by a diverse consortium of technology giants like Microsoft, Google, Amazon, and OpenAI, emphasizing its neutrality and broad industry backing.

Alongside technical enhancements, the introduction of a clear deprecation timeline assures enterprises of protocol stability. MCP is rapidly adopted, evidenced by 250 million weekly SDK downloads, positioning it as a foundational technology for agentic AI workflows in enterprise environments. The new release marks a significant milestone as MCP evolves from an experimental framework into an enterprise-ready standard capable of supporting the next generation of intelligent agents worldwide.

Chinese AI startup Moonshot AI has published the full weights of its largest open AI model to date, Kimi K3, a powerful 2.8 trillion-parameter system with advanced features like a one million-token context window. While the model is openly accessible for download and use, enterprises must carefully review the unique Kimi K3 license, which includes commercial restrictions not found in typical open-source licenses. Specifically, companies generating over $20 million annually and providing “Model as a Service” to third parties must negotiate a commercial agreement with Moonshot AI, while large-scale commercial deployments require prominent attribution of Kimi K3 in their products. The license offers a broad grant of rights for internal and non-commercial uses, making it suitable for many enterprises that want to self-host advanced AI capabilities. Developers have reacted positively to the release, praising the accompanying infrastructure and technical reports, though they note the licensing limits and the operational challenges of running such a large model. This release exemplifies the evolving landscape where “open weights” do not always equate to fully open-source software, ushering in complex legal considerations for commercial use. Enterprises should assess their intended use cases carefully to ensure compliance with commercial terms while leveraging Kimi K3’s state-of-the-art AI performance.

At some point, a customer will inquire whether your business is SOC 2 compliant. Here’s a straightforward guide to what SOC 2 compliance means and why it might be important for your SaaS company.

Compensation decisions go beyond mere data figures. When employees bring AI-generated salary benchmarks to the table, leaders must carefully balance these insights with human factors to maintain trust, nurture a positive culture, and retain talent.

Open-source AI raises significant debate, with economic interests fueling differing opinions in the tech world. Welcome to this week’s TechScape, where we explore the split within the tech community about AI’s future. We'll also review the European Union’s recent decision to penalize Google for anti-competitive practices and update you on numerous lawsuits targeting social media platforms over their impact on youth addiction.

Apple has reached an extraordinary milestone as only the second company ever to hit a $5 trillion valuation. This surge comes amid a broad sell-off in AI and semiconductor stocks, with investors shifting focus away from AI spending. Apple’s shares climbed to a peak of $342.89, giving the company a market cap of $5.04 trillion before settling near the $5 trillion mark. This rally has been fueled by strong demand for Apple products and its choice to stay out of the AI investment frenzy.