More than code: AI in DevOps - Role and limitations

Artificial intelligence has certainly given us a lot. It's not always what we want or what we need. But we have cars that drive their own roads, we have deep conversations with chatbots, and we watch videos of the world's most influential people playing Minecraft together. A revolution is happening right before our eyes. However, the question arises - where is the limit? What is AI capable of, and where are the capabilities of artificial intelligence proving to be insufficient? Fortunately for most, the current list of professions that cannot be replaced by artificial intelligence is quite long.

DevOps also remains in human hands, at least for now. The essence of this field, and its specific requirements for creativity, critical thinking and a deep understanding of complex systems — rests on the full takeover by our silicon counterparts. However, it cannot be denied that AI in DevOps can help people, especially those who know what they are doing. Perhaps the future will not lie in replacing people, but in expanding their competence by supporting them.

AI to Dev

There is no shadow of a doubt that artificial intelligence helps developers in their daily tasks. Let's take GitHub Copilot as an example. Launched amid much fanfare and skepticism, this AI-based assistant has proven to be more than just an advanced autocomplete feature.

It has become an example of how artificial intelligence can enhance human capabilities, leading to a significant increase in developer satisfaction and productivity. According to research conducted by GitHub, Developers using Copilot report feeling more fulfilled in their work, experiencing less frustration when coding, and enjoying the opportunity to focus on more rewarding, creative tasks.

Economic implications

Conversations about the economic implications of integrating artificial intelligence into development processes are equally pleasing to the ear. In another examination It was highlighted that the productivity gains attributed to tools such as GitHub Copilot could potentially increase global GDP by more than $1.5 trillion. We are not talking about petty!

Limitations of artificial intelligence in programming

Despite the eagerly shown potential, artificial intelligence is not without limitations. Example GPT, who passed the Google interview, may lead to the conclusion that AI in DevOps will soon replace developers from blood and blood. However, it is important to realize that while AI can master the syntax and structure of code, its ability to navigate the more complex aspects of software development still leaves much to be desired.

The challenges of AI “hallucinating” or generating code full of errors are real and should not be ignored. These are not just minor stumbling blocks, but significant obstacles that highlight the need for human oversight and intervention.

Developers will not be replaced by AI in DevOps

The current strength of AI is dealing with basic coding tasks, offering suggestions, and even automating repetitive aspects of coding. It is hard to deny that this is a valuable contribution. However, when it comes to solving “strange” problems or the ethical aspects of software solutions, the human being is what, for the moment, cannot be replaced. Artificial intelligence can create blocks, but it is humans that are needed to assemble them into meaningful, functional structures that serve the needs and values of society.

The story of Chevrolet AI chatbots, which was persuaded to sell cars for $1, is a good example of what happens when artificial intelligence encounters scenarios that deviate from the simple and narrow path on which it was programmed. These “strange” problems - situations in which a human would cope with common sense, ethical considerations and awareness of the context - often leave AI confused or, worse, lead to unintended consequences

Beyond technical expertise, developers bring a unique perspective that is critical to driving AI applications. They ensure that these systems work efficiently. They also adhere to ethical standards and social norms. Human surveillance is what keeps our digital progress from falling into absurdity and drowning in a sea of errors caused by hallucinations.

Artificial Intelligence and Ops

Many tedious, time-consuming and repetitive tasks can be entrusted (partially, or fully, if you are brave enough) to artificial intelligence:

• Ensuring compliance: Artificial intelligence constantly monitors systems. He does not need rest. It does not need sleep. 24/7 can ensure compliance with the latest regulatory standards, greatly reducing the manual effort required for compliance checks.
• Data integrity check: Thanks to the ability to quickly process and analyze data, artificial intelligence identifies discrepancies and anomalies. This helps a person make informed decisions based on accurate and consistent information.
• Autonomous management of business tasks: From routine queries to managing complex workflows, AI can autonomously handle multiple business operations. This minimizes the need for human intervention.
• Fast data processing: Artificial intelligence is able to view huge amounts of data faster than a cat is able to flip a glass. Early detection of vulnerabilities and potential system failures prevents serious problems.
• Summarizing the data: As some of us may have learned after condensing a few long emails using GPT Chat, AI is great at summarizing. Similarly, it does an excellent job of summarizing huge datasets into insights that can be acted upon. Decision makers receive clear and concise information that is crucial for informed decision making.

You can say 'Ops' without saying 'people' but is it worth it?

The integration of artificial intelligence (AI) with DevOps has undeniably revolutionized the way operations are automated and optimized. However, this does not mean that we should completely replace the “human” part of “Ops”. We must bear this in mind:

• Imperfections and error-prone: Artificial intelligence, despite all its achievements, is not infallible. It can make mistakes, as a result of incorrect data or algorithmic bias. Human supervision serves to identify, correct and draw conclusions from these errors.
• Cost: While AI in DevOps can streamline many processes, setting up and maintaining advanced AI systems can be costly.
• Ethical and legal issues: People need to oversee these systems to make sure they operate within ethical limits. Perhaps AI is not yet capable of provoking a riot. However, it is worth making sure.
• Hallucinations: Artificial intelligence models, especially LLMs, can generate “hallucinations” or results that are completely fabricated and have no basis in reality. Human intervention is essential to spot these inaccuracies.