Kindly brief us about yourself and your professional journey as the Founding Partner at Qubika.

My Qubika journey began in 2005, as one of the founders of Moove It, a leading technology services provider with multiple locations in the US and South America. We experienced rapid growth, and in 2023 we made the decision to merge with December Labs, with the support of a New York based investment fund, Recognize.

Qubika is the result of this merger and our aim is to create a next generation service provider, with data and AI at the heart of what we do. For the past several years, we’ve been focusing on building out our data and AI capabilities via our dedicated studio – and we have a track record of helping well-known businesses implement machine learning into their processes and products.

As a Founding Partner at Qubika, how is your company leveraging AI to enhance the software development lifecycle, and what benefits have you observed so far?

We’re using AI tools to both speed up, and increase the quality of, software development. We’re running pilots with numerous commercially available tools, such as Github Copilot, Amazon Code Whisperer, Open AI, and Meta AI. We’re seeing the market of available AI tools grow incredibly quickly, but for the time being we’ve decided to focus on these four. Some of these tools, such as Github Copilot, have the advantage of being able to be embedded within the IDEs we use the most.

Interestingly, the greatest benefits we’re seeing are among our mid-level and senior development people. They are achieving a 30%-50% increase in productivity with the use of these tools. In particular, the tools are helping with the building of base artifacts to save time.

In what specific ways can AI technologies improve the efficiency and productivity of software development teams, and how can they contribute to faster product releases?

AI technologies can have a significant impact on improving the efficiency and productivity of software development teams. While we have already started benefiting and realizing its potential, I believe we have just scratched the surface of all that it can do. Some specific ways AI contributes to these improvements are: automated code generation, test automation – including bug detection and even bug fixing, code documentation, and code refactoring to name a few. Meanwhile, for the front-end UX, AI is helping to streamline design workflows, such as by automatically optimizing design elements. I am sure I am leaving many other areas out that we are not thinking of, or not yet focusing on, at this time.

Some individuals express concerns about AI replacing human software engineers. How do you view the balance between AI automation and human expertise in the software engineering process?

We’re at the early stages of the AI revolution, and we do not see AI replacing human software engineers any time soon. Rather, we see engineers leveraging the power of AI more and more – right now, in helping to review code and speed up typical development tasks. Over time, we’ll see human work and AI collaborate more closely, where we’ll see bots essentially acting as peer developers within a team.

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