2025 promises to be a pivotal moment in the evolution of software development, driven by the relentless advancement of artificial intelligence (AI). As AI continues to permeate every facet of our lives, its impact on the software development landscape is undeniable. From automating mundane tasks to revolutionizing the creative process, AI is poised to reshape the way we build and deploy software.
DevPro Journal recently spoke with Bhavani Vangala, VP of Engineering at Onymos, who identified five key trends that will significantly influence software development in 2025. By understanding these trends, developers can harness the power of AI to streamline workflows, enhance productivity, and create more innovative and sophisticated applications.
Skepticism Around Current AI Deployments Will Foster More Thoughtful and Scalable Future AI Development
Vangala says AI has become a crucial topic for executives and technology leaders over the past two years, especially following the surge of generative AI tools and models in late 2022 and early 2023. However, he adds that there is growing skepticism about whether some current AI deployments are truly production-ready solutions or just conceptual and document-level implementations driven by hype—especially in the healthcare, finance, legal, and manufacturing industries. “In 2025, enterprises will increasingly evaluate their end-customer needs to accurately gauge AI’s potential impact, assess the relevance of new AI trends for their businesses, and determine whether their development efforts will yield a return on investment,” he explains. “This evaluation process will lead to more enterprises focusing their AI efforts on delivering tangible and scalable AI that will solve a particular need or challenge for their customers and businesses.”
The Next Wave of Generative AI Model Training Will Focus on Improving Inference and Reasoning
“The next wave of generative AI model training in 2025 is set to be transformative, focusing on enhancing reasoning and inference capabilities to make AI responses more intuitive and aligned with human thought processes,” says Vangala. “OpenAI’s recent release of models like o1-preview models this shift, demonstrating significant improvements in inference and reasoning abilities. These advancements enable AI to process and respond to prompts with increased coherence and contextual awareness, marking a promising step forward.”
Current generative AI models often require detailed prompts and follow-up questions to provide accurate responses, especially for complex or nuanced inquiries, he adds. “This reliance on precise inputs can make interactions feel less fluid, as users must know how to phrase questions specifically to achieve accurate responses. Ongoing training efforts over the next year will aim to make AI’s reasoning processes more natural and adaptable, enabling models to grasp context dynamically, consider implicit details, and apply logical steps similar to human thought patterns. This progression will allow models to not only answer questions but also infer intent and address nuanced needs effectively.”
Generative AI Will Continue to Improve, Evolving Into Key Collaborative Partner
“While models like o1-preview represent substantial progress, the AI field is moving toward even greater strides in making models true collaborative partners,” explains Vangala. “This involves continuous improvements to help models better emulate the way humans approach complex, layered decision-making. Such progress isn’t just about faster responses—it’s about creating a depth of understanding that lets AI interact in a way that feels smooth, helpful, and highly attuned to user intent. However, human oversight remains vital, especially in sensitive fields like healthcare, finance, and law, where AI should support rather than replace decision-making.
“In 2025, generative AI will continue to serve as an assistant, providing experts with fast, refined insights they can validate and adapt as needed. This evolution in model training positions AI as an increasingly trusted partner in decision-making and problem-solving, delivering insights with a depth and accuracy close to human judgment while respecting the complexity and nuances of each unique domain. By embracing AI as a collaborative tool rather than an autonomous replacement in the year – and years to come – industries can strike a productive balance, leveraging AI’s strengths to enhance human expertise and drive meaningful, responsible advancements.”
AI Will Not Take Away the Roles of Software Developers or Architects
Vangala says that while some industries may still be exploring AI solutions at a conceptual level, it’s clear that generative AI tools, especially large language models (LLMs), can improve productivity by summarizing vast amounts of data in minutes.
“This capability allows professionals to draw insights and make informed decisions more rapidly,” he adds. “However, AI will not replace critical roles like software developers and architects in 2025 – or even in the years to come. Generative AI tools can indeed produce code that developers and enterprises may leverage to accelerate software development.
“While this efficiency can appeal to enterprises looking to cut costs and speed up project timelines, AI-generated code requires careful review. The code these tools produce comes from existing text and data shared online, which are contributed for specific purposes or products. Therefore, it cannot be directly integrated by simple copying and pasting.
“Software developers and architects must thoroughly review, test, and adapt this code to fit specific software requirements and ensure it’s robust and maintainable over the long term. This means that software developers and architects remain essential to the software lifecycle. They bring the expertise necessary to adapt code to unique applications and to handle the ongoing evolution of software, making their roles crucial for successful development and deployment. As a result, AI is a tool that aids productivity but does not replace the need for experienced professionals in the development field. This blend of AI-driven efficiency and human oversight is particularly relevant across healthcare, finance, legal, retail, and manufacturing industries, where AI serves as an enabler rather than a replacement.”
Small Language Models Will Gain Popularity for Specific Domains
“In 2025, growing concerns about cost, infrastructure, and privacy will fuel the development and utilization of small language models [SLMs] across multiple sectors, such as healthcare, law, government, and finance, explains Vangala. “While large language models can be beneficial in certain situations, they often necessitate specialized infrastructure and cost, making them less accessible to many companies, especially startups.
“Furthermore, significant privacy and security concerns exist around the data used in training LLMs. This concern is particularly high in healthcare, where safeguarding user (or patient) information is crucial, and model customization is preferred. Just think about it this way: would you be comfortable integrating an LLM based on accurate and misleading data from the web in a tool that assesses cancer risk? By utilizing SLMs, organizations in high-stakes sectors can develop tailored models based on domain-specific and accurate data while ensuring privacy, security, cost-effectiveness, and efficiency.”
