All writings tagged with "AI"
AI agents are automating software development, but vague requirements lead to incorrect implementations. We must shift from traditional requirements documents and simple prompts to machine-verifiable, executable specifications to ensure accuracy.
The economics of artificial intelligence present a stark paradox: Africa is poised to become one of the world’s largest consumers of AI, yet it risks owning virtually none of the underlying infrastructure, models, or intellectual property. From data to energy, the continent is supplying the raw inputs for the global tech economy while renting the finished intelligence. Without regional compute strategies and sovereign data governance, Africa will transition from a landscape of technological opportunity to one of permanent digital tenancy.
Traditional software left a clear audit trail of hardcoded logic. Stochastic AI agents change this; they dynamically interpret context, choose tools, and execute high-consequence actions. Because prompts alone don't prove intent, engineering teams must shift from tracking execution to auditing runtime judgment—building "decision provenance" to prove exactly *why* an agent acted.
Software engineering has never purely been about syntax extraction. Code is simply the visible artifact: the part people can screenshot, benchmark, demo, and debate on forums.
For the last two decades, many software careers began with CRUD (Create, Read, Update, Delete) applications. A junior developer would receive a ticket to build a user form, add database persistence, create an API endpoint, add validation, and render a table view. They would then repeat this cycle
What actually happened is that the AI showed up on Monday asking where the repository lives, spent Tuesday reading the entire codebase, and by Wednesday was submitting pull requests while openly questioning architectural decisions made by senior engineers. The latest models don't just "write a function."