BERLIN, GERMANY – AUGUST 31: A visitor tries out an HP Specter XT laptop computer with a … [+]
Developers are special. Because software application developers have a particularly busy way of reasoning, rationalizing, and even eating (pardon the pizza joke), they play a highly valued role in the modern era of information management and data science. While the rise of artificial intelligence has been variously described as a kind of death for programmers (the suggestion being that we won’t need them anymore), there is a perhaps more likely reality that sees AI tools supporting, augmenting and extending the functions of developers. .
The question then is, how should software developers build with AI?
Tough toolkit
Despite the cycles of AI hype that drive the media and marketing, there is still no visible sign of automation fatigue; This may be because we’re still working out how to apply ground-level AI at the developer “command line,” even as coders are using increasingly abstract, low-code or no-code tools. The benefits are described as a force multiplier for developers. Why? Because AI coding functions can handle jobs like code refactoring (debugging and cleaning up existing code without changing its current function to make it more efficient), generating new code, translating code from one programming language to another and more.
In this space, AI-powered tools like Cursor.ai are taking the dirty work out of developers and gaining a lot of traction. The technology is essentially a software code editor with features such as auto-completion of AI code and offers an AI chat service for programmers to ask questions about it.
“In this new world of AI-powered development, we believe it is important to embrace change. The era of AI empowerment has begun, but more importantly, it is intersecting with Kubernetes [cloud orchestration technology] as a de facto hybrid multi-cloud infrastructure control plane and the standard for application delivery,” said Randy Bias, vice president and vice president of open source strategy and technology at hybrid multi-cloud company Mirantis.
Bias suggests that by combining AI-powered tools like Cursor.AI for developers and GPTscript (a software tool that allows developers to use natural language as simple sentences using normal human syntax) with AI-enabled Kubernetes management tools , the industry can provide time for new applications dramatically.
Eliminating historical suffering
“We’ll also be able to increase feature velocity, decrease deployment time, remove risk during upgrades, and generally remove much of the historical drudgery that has reduced software engineer effectiveness. Why do the dirty work yourself when you can have computers do it for you? We believe deeply in the intersection of these technologies and what they enable for the modern developer,” said Bias.
Continuing this thought process further, we can suggest that as AI transforms software development, companies face a challenge in terms of how to reliably integrate large language models (and the generative AI functions that provide ) in their applications. This isn’t a question of whether a business “installs some AI”, it’s a more fundamental question related to how well it’s architected.
“Developers are racing to customize generic LLMs with domain-specific content and real-time updates. This make-or-break challenge requires two essential components: sophisticated feed-back generation pipelines for customization and powerful conversational APIs for interaction,” proposed Mark Fussell, co-founder and CEO of Diagrid., the company behind the Dapr (Runtime Application Distribution) open source project that aims to simplify cloud-native application development.
Fussell says that RAG pipelines (all creation and management of augmented return generation functions in the AI universe) require orchestration across multiple stages: data ingestion, processing, embedded generation, and vector storage. Without proper orchestration, teams risk pipeline failures, inconsistent updates, and maintenance woes.
“The conversational API extension is just as critical, managing everything from state interactions to security controls. It’s a shield against vendor lock-in and a gateway to trusted AI interactions,” said Fussell. “Together, these components form the backbone of production-ready AI applications. In today’s AI-driven world, the difference between success and failure often comes down to this fundamental architecture.”
Methodology of the Month
There’s a lot to take in here, mainly because technology platforms are still evolving so quickly and – in many cases – we’re at the intersection of truly cloud-native deployments, now joining AI intelligence and automation. We also have factors like DevOps (for unified developer-operator harmony), FinOps (for cloud cost control), and DevSecOps (where security is at) all vying for attention as methodology of the month.
So, of course, some white noise is inevitable.
In his role as chief evangelist, the Cloud Foundry Foundation—the body behind an industry-standard open source cloud application platform—Ram Iyengar reminds us that computing execution now varies between edge, cloud, fog, bare metal, IoT, quantum and others. He further notes that computing workloads can run as containers, unikernels, WASM binaries, in virtual machines, and in other immutable ways—all in a world where exploiting software supply chain vulnerabilities is at a record level.
“In such a noisy and distracting universe, developers need ways to help them publish an app regardless of target,” Iyengar affirms, in positive terms. “There is no easy way to split an application to run on a single stack. Those with that luxury are considered lucky. Automation is becoming increasingly important. More aspects of development are being controlled through automation frameworks. Instead of charging a developer, teams are finding ways to build platforms that incorporate more of these frameworks. Security and compliance are handled more easily using smart tools. Write once, run anywhere has a whole new meaning. For the next decade, the secret sauce is the developer platform.”
Consumption, Coordination, Creation
While it’s still difficult to determine exactly how software developers will use AI in the near and immediate future at any level, any suggestion that the role itself will be fully automated within the current decade seems a bit premature.
The current focus of the tech industry is more concerned with how developers will be able to consume and coordinate AI-powered services in the creation process. Maybe we should take those baby steps right away before we send all our programmers to the farm.
In basic terms, AI programming tools can fix a lot, but they need to be deployed first.