The Developer’s Crystal Ball: Predicting the Impact of GenAI in 3 Years

I work in Developer Enablement and over the past two years, my focus has shifted towards integrating generative AI solutions in software development to accelerate change. When it comes to generative AI, we believe in keeping humans in the loop, but I want to reduce the tedious, mind-numbing tasks, automate more, and make every developer feel like they have superpowers.

Given I am in this mindset, and I spend all my free time experimenting or developing GenAI solutions, I have a vision for how transformational GenAI is going to be for developers. I am going to boldly make my predictions and come back in three years to see how close I was.

Please comment below and let me know your thoughts and predictions.

Application Development

• Accessibility: Client side GenAI accessibility tools will also expand to auto-fill the gaps by companies who have not prioritized universal design such as browsers adding alt tags automatically based on vision models.
• Accelerated Development: UI designers will leverage AI to quickly prototype and iterate on design concepts. Figma will auto-generate entire web apps with database backends, based on your mock-ups.
• AI-Driven Optimization: AI will optimize cloud resource usage, leading to cost savings and improved application performance. However, we will spend far more on GenAI cloud resources.
• Code Review: GenAI will autonomously review pull requests, ensuring adherence to best coding practices, security measures, and documentation standards before merging code. It will also make suggestions that you can quickly approve.
• Cross-platform Development: GenAI will streamline cross-platform app development by automatically adapting code for different environments (e.g., mobile, desktop, web) based on a single codebase.
• Developer Migration: GenAI will automate developer migrations by creating sophisticated prompts that explain exactly what needs to be changed in the codebase. In most cases, we will be able to use prompts to create prompts.
• Developers in the Loop: GenAI will expedite developer workflows but will not replace developers.
• DevOps and Automation: GenAI will automate routine DevOps tasks such as infrastructure provisioning, deployment, and monitoring, increasing efficiency and reducing human error. We will use chatbots to create config as code.
• Enhanced User Experience: GenAI will help generate user interface designs based on user behavior analytics, leading to more intuitive and personalized user experiences.
• MVP Development: GenAI will accelerate MVP processes by generating rapid prototypes as proofs-of-concepts and help determine ROI from your pilots.
• Observability: AI tools will monitor application health in real-time, predicting and preventing potential failures through automated maintenance tasks.
• Performance Optimization: AI tools will identify and prevent performance bottlenecks and memory leaks during the development process, ensuring robust, high-quality applications.
• Refactoring: GenAI will proactively suggest code refactoring opportunities to improve performance and reduce technical debt. It will also be able to test the refactoring to ensure no breaking changes.
• Security: GenAI will be integrated into various aspects of security, covering threat detection and prevention, vulnerability scanning, incident response automation, user authentication, fraud detection, and data privacy. It will also enhance compliance, security awareness training, automated patch management, network security, DevSecOps automation, supply chain security, IAM (identity and access management), and secure software design and modeling.
• Testing: GenAI will revolutionize software testing by automatically generating comprehensive test cases, identifying edge cases, and predicting potential bugs before they reach production.

Developer Documentation

• Citation: LLM hallucinations will only happen due to misconfigurations of temperature or other settings. Many GenAI solutions will cite their sources and allow you to jump directly to the applied context.
• Code Samples: Code samples can be self-generated if your LLM can be augmented with your product source code, either through RAG or fine-tuning.
• Documentation Health: AI Agents will be created to monitor documentation health, including the accuracy and quality of the content. They will auto-update docs when the health score falls below set thresholds.
• Knowledgebase Articles: Knowledge-based articles will be automatically written based on conversations captured when troubleshooting with end-users or questions asked in meetings.
• Just-in-time: Developers will engage with a GenAI agent in their IDE rather than scouring documentation for answers to their problems. They may actually never look at your markdown pages directly.
• Reranking: Reranking of RAG results will be critical in ensuring that the most up-to-date context is provided to users since coding libraries and dependencies are constantly changing.
• Use-Case Guidance: GenAI will be able to help us create use-case guidance across multiple products.
• Vector Indexing: Everyone will want to index almost everything but it will be challenging ensuring proper permissions on index results, and making it easy for users to control their data and how it is accessed as more services access those indexes.

Education, Training, and Growth

• AI-Driven Recommendations: Continuous learning will be embedded into the developer workflow through AI-driven recommendations and just-in-time training modules.
• AI Mentor: Wearable devices will provide feedback on communication skills, public speaking, and overall mentorship.
• Employee Career Paths: GenAI will personalize employee career paths, suggesting tailored learning opportunities, internal job transitions, and accelerate professional growth.
• Learning Experiences: GenAI will personalize developers’ learning experiences, suggesting courses and resources tailored to their skill gaps and career aspirations.

Product & Project Management

• Automate: GenAI will assist in project planning by automating scheduling, resource allocation, and risk assessment, making project management more efficient and proactive.
• Forecast: AI tools will provide real-time analytics and forecasts, enabling project managers to make data-driven decisions and adjust strategies on the fly.
• Predict: AI will predict user needs based on usage data, which will help developers build features aligned with customer expectations and future trends.
• Sentiment: GenAI will analyze customer feedback and sentiment across multiple channels to inform product improvements and strategic decisions.

Miscellaneous

• AI Agents: Almost every job function at a company can benefit from creating AI agents (Assistants, CustomGPTs, Gemini Gems, Copilots, etc.).
• AI Workflows: Tools like zapier and make.com are growing in popularity but eventually complex workflow creation will be as easy as a chat prompt.
• Copilot Assistant: Every internal application with a UI could benefit from a copilot to assist users with tasks or engage in support discussions.
• Enterprise Automation: We will have just as many, if not more, automation in our enterprise versions of GenAI-powered chats as we do in apps like Slack.
• Identity Management: Biometrics on our phones will ensure our identity when we talk or message, similar to the blue checkmark validation we see on social media today. This will reduce people attempting to steal your identity by replicating your voice or creating avatars.

Disclaimer: Please be advised that the views and opinions expressed within this document are solely those of the author and do not necessarily reflect or represent the official stance or position of any organization or employer with which the author may be affiliated.