As the technology industry is continuously reshaping by artificial intelligence, experts speculate on how machines can remain relevant in environments where most of the tasks are heavily lifted.
However, according to Google Cloud's CTO Will Grannis, AI-enabled support doesn't mean not just grasping traditional knowledge, but building on it.
In recent discussions with Business insider, Grannis emphasized that key principles of computer science are virtually unworkable. They are more essential than ever.
Don't throw away the basics
Despite the rapid expansion of AI tools such as Copilot and Codex, Grannis argues that basic computer science capabilities are essential. “We still need to understand how computers work, how data stores work,” he said. These fundamentals provide the frameworks needed to design effective and useful systems, even if the way people interact with technology changes.
Grannis motivates job seekers to “leave on education” and highlights that traditional computer science degrees and coding bootcamps still hold their value. AI may be codifying several coding tasks, but the facts behind the reasons and methods of code behaviour make a great engineer stand out.
Modern tools require modern thinking
Nevertheless, keeping the beginning does not mean ignoring change. Grannis revealed that being competitive in today's high-tech requires temperament and preparation to discover beyond the average prospectus. He proposes learning modern tools, experimenting with new systems, and integrating AI into workflows and roadmap.
In upcoming hackathons, Grannis is using AI to not only write code, but also to recreate, polish and revolutionize it, driving his international team to become obsessed with what he calls “atmosphere coding.” He believes this reflects the future of development: the confusion of rudimentary knowledge and modern flexibility.
Welcome to the age of context engineering
Grannis visualizes the rapid engineering and the next frontier of what he called “context engineering.” This means knowing the entire network that needs to function efficiently and successfully, from the data consumed by AI systems to networked devices and platforms.
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“We're moving from the application layer to a more holistic view,” Grannis said. As AI systems evolve into multi-agent platforms, experts and experts need to understand the larger architecture they are building internally. It's not enough to distinguish between good prompt writing methods. Developers also need to create a context in which AI performs optimally.
Grannis emphasizes the basic message. Nittigritti is not outdated. They are the catalysts and stepping stones for the next surge in innovation. Simply put, you will grasp the past, but be willing to prepare for building the future.
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