The rapid evolution of artificial intelligence (AI) is reshaping the technological landscape, presenting challenges and opportunities for developers. As AI becomes more integrated into everyday processes, developers must adapt their roles beyond traditional coding to meet the demands of an AI-driven future. The question looms: will going into 2025 be the year developers are left behind, or will they embrace this new frontier?
Looking toward the future, a significant trend is the increasing incorporation of AI, particularly generative AI (GenAI), into the software development lifecycle. Global development teams are moving from experimentation to fully embedding AI into their workflows, resulting in significant productivity gains. Market data suggest that AI can increase developer efficiency by as much as 200%. This acceleration underscores the importance of developers shifting from code-focused tasks to higher-order responsibilities that leverage AI.
Organizations are either outsourcing AI capabilities or building them in-house in the enterprise space, according to my research study, the AI in the Enterprise – Decision Maker IQ Survey Dashboard, Dec 2023. Integrating AI into routine tasks enables developers to focus on more complex and creative challenges, creating a more dynamic and innovative environment.
The Rise of Platform Engineering
One critical development supporting the AI shift is the rise of platform engineering, which is transforming how organizations manage the software development lifecycle (SDLC). Platform engineering consolidates tools and workflows, offering developers enhanced capabilities for building, testing, and deploying applications. By codifying processes and establishing strong internal support systems, platform engineering allows developers to concentrate on delivering value to end users rather than getting bogged down by operational tasks.
Self-service developer portals, such as Spotify’s Backstage (now part of the Cloud Native Computing Foundation), further democratize AI. These portals streamline access to AI tools, enabling developers to experiment and easily deploy models. This evolution marks a critical step in making AI more accessible to a broader audience of developers.
AI’s impact also extends to IT resource allocation. Executives are realizing that AI can unlock the creative potential of development teams, encouraging a strategic realignment of roles. This shift positions developers as architects of innovation rather than mere coders, enabling organizations to stay competitive in an AI-driven world.
The Infrastructure Challenge
The rapid advancement of AI applications is putting unprecedented pressure on existing IT infrastructure. As AI models, particularly deep learning and generative models, grow in size and complexity, their computational and storage needs increase exponentially. For instance, OpenAI’s GPT-3, with 175 billion parameters, required more than 350 GB of storage and vast computational resources for training. The computing power necessary for training advanced AI models has been growing by a factor of 10 every year since 2012, far outpacing Moore’s Law.
In fact, a report from McKinsey indicates that over 50% of companies find their existing infrastructure inadequate for the processing demands of advanced AI. This bottleneck can delay AI deployment and significantly impact the overall return on investment (ROI) for AI initiatives. As AI workloads often require GPUs, TPUs, and high-performance computing (HPC) clusters, companies must invest in scalable, modernized infrastructure capable of handling this intense processing demand.
Cloud infrastructure providers, such as AWS, Microsoft Azure, and Google Cloud, have stepped in to offer AI-optimized infrastructure, such as dedicated machine learning instances and serverless AI tools. However, relying solely on the cloud can lead to cost management issues. A hybrid approach, where companies invest in on-premises solutions coupled with cloud services, is gaining traction as a balanced strategy for managing the computing needs of AI systems. According to a 2023 Gartner survey, 45% of enterprises are adopting hybrid cloud strategies to optimize costs while scaling AI infrastructure.
Additionally, the energy consumption of AI models is becoming a critical concern. The carbon footprint of training a single large-scale model has been estimated to be equivalent to the lifetime emissions of five cars, as reported by the University of Massachusetts Amherst. To address this, organizations are exploring energy-efficient hardware and sustainable data centers. For example, Nvidia’s AI chips, such as the A100, are designed to handle more calculations with less energy, optimizing both performance and sustainability.
This increasing infrastructure demand means developers will need to collaborate closely with infrastructure specialists. Together, they can ensure that AI systems are optimized for performance, cost-efficiency, and sustainability while still delivering the required computational power.
Democratizing AI with Low-Code and No-Code Approaches
The rise of low-code and no-code platforms is reshaping the AI development landscape by enabling a broader range of professionals to build AI-driven solutions without deep technical expertise. According to Forrester, the global low-code platform market is projected to reach $21.2 billion by 2024, growing at a rate of 40% annually. These platforms are accelerating the AI adoption curve by lowering the barrier to entry for AI development, especially within enterprises that lack a surplus of highly specialized developers.
A survey conducted by Gartner revealed that by 2025, 70% of new AI applications will be developed using low-code or no-code technologies, with a significant portion of that being driven by business users or “citizen developers.” These platforms allow business experts to create AI models by leveraging pre-built components and drag-and-drop interfaces, streamlining workflows and reducing development time.
Companies are already seeing success with this approach. For example, General Electric has used no-code platforms to deploy predictive maintenance models across their operations, cutting development time by 60% and saving millions in operational costs. Similarly, platforms like Microsoft Power Platform and Appian enable organizations to implement AI-driven applications quickly, empowering business units to solve problems without heavy reliance on IT departments.
Organizations can democratize innovation by opening AI development to a wider audience, and encouraging employees across departments to contribute to AI projects. This is expected to spur a surge in AI-driven initiatives, particularly in areas like automation, customer service, and business intelligence, where AI can immediately impact.
Regulatory and Ethical Considerations
Regulatory scrutiny intensifies as AI becomes more ingrained in business operations and consumer-facing applications. The complexity of AI models, especially those that utilize personal data, has raised significant ethical and legal questions, prompting governments worldwide to consider regulatory frameworks.
The ongoing OpenAI investigation in Europe, which scrutinized potential GDPR violations in how AI models process personal data, is one example of the growing concern around data privacy. Italy’s temporary ban on ChatGPT in 2023 underscored the importance of compliance with data protection laws, and a problem now echoed across other regions. According to the European Data Protection Board, a task force has established guidelines for using AI models like ChatGPT to balance innovation with legal and ethical standards.
Regulatory bodies worldwide are rolling out AI governance frameworks in response to these concerns. The European Union’s proposed AI Act, expected to take effect by 2025, seeks to classify AI systems based on their risk level, imposing stricter regulations on high-risk applications, such as those used in healthcare, finance, and law enforcement. Similarly, the National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework in the United States, providing guidelines for developing trustworthy AI systems.
AI developers and companies must take these evolving regulatory landscapes seriously. Failure to adhere to regulations can result in hefty fines, reputational damage, and the rollback of AI projects. As a result, organizations are investing in AI ethics boards and compliance teams to ensure their AI technologies align with ethical guidelines and legal requirements.
The focus on responsible AI development is leading to the creation of AI auditing tools designed to provide transparency into how AI models make decisions. These tools allow developers to ensure that their AI systems are fair, transparent, and free from biases that could harm marginalized groups. According to a 2023 PwC survey, 60% of companies now embed ethical AI principles into their development processes, indicating a clear shift toward responsible innovation.
As the regulatory landscape evolves, developers must focus on technical advancement and consider the broader societal impact of their AI applications. Collaborating with legal, ethical, and compliance experts will become increasingly essential for organizations looking to harness AI’s full potential while mitigating risks.
The Future of Development in the AI Era
The rise of platform engineering, the adoption of self-service AI tools, and the strategic realignment of developer roles signify a fundamental shift in how developers work. As GenAI continues to become integrated into everyday tasks, developers will take on new responsibilities, from overseeing AI systems to solving intricate problems and balancing innovation with ethical considerations.
The AI revolution has transformed developers from traditional coders to key innovators driving the future of technology. Those who embrace AI and adapt to these evolving roles will stay relevant and play a pivotal role in shaping the future of the digital landscape. Developers’ evolving role is a testament to their creativity, adaptability, and contribution to the ongoing transformation of the technology industry.
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