In response to the challenges posed by artificial intelligence tools like ChatGPT, an increasing number of educators are adopting oral exams as a method to assess students’ learning. This shift aims to ensure that evaluations reflect genuine understanding rather than reliance on AI. For instance, at the University of Wyoming, a recent final oral examination provided a setting where instructors could directly engage with students, allowing for a more nuanced assessment of their knowledge. This trend highlights a growing acknowledgment among educators of the need to adapt traditional assessment methods in the face of evolving technology, as they seek to maintain academic integrity and rigor in their classrooms.
The New York Times looks at how the ongoing artificial intelligence boom has prompted major tech companies to adopt innovative financing strategies to mitigate risk. Companies like Meta, Microsoft, and Google are leveraging short-term leasing agreements for computing power to avoid the financial burden of long-term commitments. For instance, Microsoft signed a series of deals worth over $60 billion with various data center providers, allowing it to quickly scale its AI infrastructure while keeping costs categorized as operating expenses rather than capital investments. This approach not only reduces immediate financial exposure but also shifts potential risks onto smaller companies eager to participate in the AI market, raising concerns about the long-term sustainability of such arrangements.
Why do we care?
What stands out here is how different sectors are responding to AI pressure by changing systems rather than tools.
In education, assessments are being redesigned to focus on explanation and understanding instead of written output. In infrastructure, companies are restructuring financial commitments to preserve flexibility while still scaling aggressively.
These approaches reflect a broader pattern: when technology advances faster than governance, successful organizations modify incentives, processes, and constraints instead of trying to suppress the technology itself.
For customers, this means AI adoption increasingly comes with workflow redesign, new validation steps, and different risk models. For IT service providers, the value lies in helping organizations rethink how work is measured, verified, and financed in an AI-enabled environment.
The lesson is not about AI features. It’s about system design under uncertainty.