AI's Role in Software Engineering

Founders, If your engineering teams haven't yet embraced AI tools like ChatGPT, GitHub Copilot, or AWS Whisper, it's a critical time to reconsider. These tools are transforming the landscape of software development. As a seasoned developer, I’ve been using these AI tools daily. They're not just about coding faster; they're about coding smarter. My typical workflow involves starting with a detailed TODO comment to structure my code. Then, AI takes over, drafting both code and unit tests. I review and refine the AI-generated code, usually finding just a minor issue or two. The rest is efficiently covered by the AI-generated unit tests. This way, I can spend more time designing the software systems than typing the code, and I also enjoy a more holistic view but still keep myself in the coding details. 🚀 This approach has revolutionized my productivity. I've experienced a 10x increase! Complex projects that once needed a team are now manageable solo. I've become proficient in 10+ programming languages overnight, enabling me to pick the best tools for each project without the daunting learning curve. The quality of my work has improved dramatically, and I complete tasks faster and with higher quality. This efficiency gives me more time to learn, experiment, and expand my skill set. ⚠️ A word of caution: If your teams aren’t adopting this pattern, you risk falling behind. In this fast-paced tech race, competitors leveraging AI can move faster, innovate quicker, and deliver superior solutions. AI in software development isn't just the future; it's the present. It's time to embrace these tools and transform how we build, test, and refine our software. Let’s lead the charge in this AI-driven era! #ai #copilot #productivitytips #softwaredevelopment

At Duolingo, we dedicate two hours company-wide every Friday to learning how to use AI to change how we work. What I’ve done with some of that time is shadow software engineers to observe their use of AI coding tools. It’s been very eye-opening. Here are some of the things I’ve learned so far. > One experienced software engineer has challenged himself to not write any code himself at all. “Writing code is 90% solved. LLMs are very, very good at this stuff. What I can do as an engineer is focus on high-level architecture and use my intuition to guide things.” He described AI coding like working with a "super genius intern." He provides occasional hints while leveraging his expertise in high-level architecture, his intuition, and his knowledge of the codebase. > An intern noted that interns and new grads should prioritize learning foundational skills first. Relying heavily on AI for implementation hinders deeper understanding of intricate system interactions. She uses AI more for explanations rather than direct implementation to enhance her learning process.  > Critical thinking is very important. “Vibe coding” is amazing for unlocking prototyping and tool building for non-software engineers. Software engineers still need to apply their skills to guide AI tools. > There’s no single front-runner for AI coding tools. Engineers that are successful in using AI have figured out which tools and which models are good for which task, whether it’s debugging a stack trace, fixing a bug, building a new feature, refactoring, migrating code, understanding a repo, etc. > Tech specs are more important than ever. In fact, good prompting looks a lot like a tech spec. While use of AI coding tools like Cursor and Claude Code have taken off, it’s clear that we’re still very much in the learning phase. For all the note-worthy AI wins, there are also the AI failures that people are less likely to talk about: going down a rabbit hole trying to solve a problem with AI assistance and then having to restart from scratch the old way. We’re not yet in the stage of seeing meaningful productivity gains that translate into faster product iterations. And that’s okay. It takes time to learn new ways to do work, especially when the tools themselves are changing so quickly. #engineering

I spend a lot of my time now speaking to companies about AI strategy. It's exciting but I find it challenging sometimes because while discussing the amazing potential we can't ignore the societal risks. I'll start a dialog with you today with one example involving the state of software coding: A recent research paper (https://lnkd.in/emyyQsZw) examined how AI tools can enhance developer productivity, focusing on the use of GitHub Copilot at ANZ Bank, a large organization employing over 5000 engineers. The study found that GitHub Copilot led to a significant increase in developer productivity and job satisfaction, helping engineers code up to 55% faster on average. Additionally: - 46% of code is now being written with the help of GitHub Copilot across all programming languages, and up to 61% for Java code specifically. - 90% of developers reported completing tasks faster with GitHub Copilot. - 73% said it allowed them to better stay in flow and conserve mental energy. - Up to 75% of developers felt more fulfilled and able to focus on satisfying work. The authors conclude that AI will likely transform software engineering practices and the developer experience in the coming years. This raises the question, will AI continue to be primarily an effective assistant, or will more advanced tools begin to change the nature of what it means to be a software engineer? An example of a more ambitious AI coding tool is Devin from Cognition Labs (https://lnkd.in/ewAgg-We), described as an engineering "buddy" that can build alongside developers or independently complete tasks for review. While still early, this six-month-old company has generated significant interest and is valued at $2 billion dollars. We can also see open-source projects exploring similar ideas, such as the combination of Wasp and Aider (https://lnkd.in/ehz3UkdZ), which aims to provide an AI-driven development workflow. As AI continues to advance, it's interesting to consider how the role of these tools may evolve in software development. Could we see a progression from AI "buddies" to "mentors" or even "managers"? While the trajectory from narrow AI to more general or "Super AI" is still largely theoretical, it's a fascinating area of speculation. Personally, I find these developments both exciting and thought-provoking. The potential for AI to augment and enhance human capabilities in software development is significant. However, it's also important to consider the potential risks and disruptions these advancements could bring. What about you? Are you more apprehensive or excited about the future of AI in software development? What potential benefits or concerns come to mind? #AI #SoftwareEngineering #DeveloperProductivity #GitHubCopilot #Devin #CognitionLabs #WaspAider #NarrowAI #GeneralAI #SuperAI

New development paradigm emerges as engineers learn to harness AI's stochastic nature effectively Software development stands at a critical juncture between technological extremes. While some predict complete AI automation and others dismiss AI-generated code entirely, Zed introduces "Agentic Engineering" as a practical middle path that preserves human expertise while leveraging AI capabilities. This approach recognizes that quality software demands skilled engineers who remain responsible for outcomes. AI provides new leverage by handling repetitive tasks, allowing developers to focus on architecture, taste, and judgment that distinguish exceptional software from merely functional code. The technical challenge involves mastering stochastic tools that behave differently from traditional deterministic development environments. Engineers must develop new skills around directing AI agents effectively, requiring clarity, rigor, and intentional collaboration patterns that didn't exist in purely human workflows. Current development tools weren't designed for real-time AI interaction, creating gaps in feedback loops and code review processes. This demands infrastructure that supports parallel agent conversations while maintaining code quality and clarity standards. Zed addresses this evolution through live expert sessions and collaborative learning, acknowledging that optimal AI integration techniques remain actively developing. The goal isn't replacing craftsmanship but amplifying its impact through thoughtful human-AI collaboration. 🔗https://lnkd.in/eSAsPhQs

I  recently joined the "Decoding GenAI" program for a fascinating discussion with Eugina Jordan and Mariana Saddakni about the transformative effects of Generative AI (GenAI) on software engineering. The episode explored how GenAI is impacting developers across three categories: 1️⃣GenAI Engine and Large Language Model (LLM) Developers: These researchers at companies like OpenAI, Meta (Llama3), and Google (Gemini) are building the core foundations of GenAI technology. 2️⃣AI Application Developers: These engineers leverage tools like the LLMs we mentioned before, TensorFlow, and others to create groundbreaking AI applications in areas like image recognition, recommendation engines, scientific computing, and many more. 3️⃣Software Engineers (Non-AI Focused): This is the largest group and is being significantly impacted by GenAI in different ways than the second group. Their work is evolving as GenAI automates repetitive tasks and introduces innovative approaches to software development. We talked about  the rise of the AI assistant: GenAI-powered tools like Microsoft Github Copilot, Amazon Q, IBM WatsonX, and Tabnine are becoming invaluable allies for software engineers. These AI assistants handle tasks like: ✅ Developing code snippets: They can generate boilerplate code, suggest function calls, and complete code based on context. ✅ Automating tests: They help write unit tests and integration tests, streamlining the testing process. ✅ Debugging: They can help identify and fix bugs in code. ✅ Performing code reviews: They can analyze code for potential issues and suggest improvements. ✅ Creating documentation: They can automatically generate documentation from code, saving developers valuable time. We talked about the challenges and solutions. One of the key takeaways is that this is just the beginning. We can expect even more powerful automation capabilities as GenAI technology continues to develop. Link to the full episode in the comments. I'd love to hear your thoughts on how GenAI is impacting your work. Share your experiences and predictions about the AI-powered future in the comments below! #aiforleaders #ai #artificialintelligence   _______________ ➡️ About Me: I'm Talila Millman a management advisor, keynote speaker, and executive coach. I help CEOs and C-suites create a growth strategy, increase profitability, optimize product portfolios, and create an operating system for excellence. 📘 Get My Book: "The TRIUMPH Framework: 7 Steps to Leading Organizational Transformation" launched as the Top New Release on Organizational Change 🎤 Invite me to Speak at your Event about Leadership, Change Leadership, Innovation, and AI Strategy

Having been in the software industry for about 30 years, I find the recent study predicting that AI assistants will be integral to developers by 2028 both exciting and necessary. This transition towards AI-driven development tools, like GitHub's Copilot, signifies a pivotal shift in our industry. However, those who resist this change risk being left behind. One of the main issues I see today is the overconfidence among many developers who believe they know it all, a contrast to the curiosity and adaptability that characterized devs in the late 90s and early 2000s. Back then, there was a relentless pursuit of knowledge and a willingness to evolve with the technology landscape. Embracing AI tools is about enhancing productivity, but it is also about staying relevant in a rapidly evolving field. These tools can handle repetitive tasks, allowing developers to focus on more complex problems and innovate further. Moreover, they open up new learning opportunities, such as mastering prompt engineering and integrating AI seamlessly into workflows. The future of software development is undeniably intertwined with AI. As we move forward, it's crucial to maintain the same spirit of curiosity and adaptability that has driven our industry for decades. Those who embrace this evolution will not only stay ahead but also drive the next wave of innovation. Read the article: https://lnkd.in/gbdiJeeP #SoftwareDevelopment #AI #Innovation #TechTrends #FutureOfWork

If you spent any time on the internet last week, I am sure you heard of Devin, the AI agent who can write software. This is incredible technology, built on top of capabilities that LLMs unlock. As LLMs get more advanced, the big question has been: how can we move from generating media (text, images, video) to actually having an agent DO things safely and responsibly. A few months ago, Microsoft Research published a really interesting paper that goes deeper into this with an example of its own software engineering agent, AutoDev! AutoDev, like Devin, represents a significant leap forward in software development, acting as an advanced assistant that goes beyond just suggesting code snippets. It's designed to tackle complex tasks autonomously, from editing and managing files to running tests and even handling technical setup tasks. This smart tool can understand and execute a wide array of software engineering processes, making development faster, more efficient, and less prone to errors. Moreover, it ensures the security of your code by operating in a protected environment, allowing developers to set specific permissions for what the tool can do, safeguarding privacy and project integrity. The introduction of tools like Devin and AutoDev offers a glimpse into a future where developers and analysts can delegate tedious tasks to AI agents, focusing instead on creative and problem-solving aspects of their projects. This not only speeds up the development or coding process but also helps democratize it, opening up new possibilities for innovation. At the same time, it also begs the question of how we redesign jobs and workflows that can make the most of this type of tech, while keeping humans in the loop and in the driver's seat (metaphorically... as someone who hates driving, I can't want for self-driving cars to go mainstream!)

AI in Code: Navigating the Future Landscape for Developers One side argues that AI will dominate technical jobs, while another dismisses AI as hype. Truthfully, both perspectives hold water. While the job landscape will inevitably evolve, as it always does, AI technologies are set to not only complement numerous technical roles but also forge entirely new ones we've yet to envision. Some AI services might underdeliver, but AI's vast impact and benefits across industries are undeniable. Currently, 44% of developers are integrating AI tools into their workflow, with another 26% intending to do so in the near future. A significant 55% of budding coders are already harnessing AI tools, positioning them as future mainstream AI users once they transition into full-fledged developer roles. Prominent platforms are already onboard: Stack Overflow has fused generative AI with OverflowAI to aid developers with technical queries. Google introduced Vertex AI for data science brigades and an AI App Builder for swift developer experiences. Not to be left behind, GitHub offers GitHub Copilot, leveraging generative AI to suggest code snippets to developers. I'm particularly eager to witness AI breakthroughs in: ➡ Anomaly detection for robust cybersecurity and predictive maintenance ➡ Quantum AI, promising unparalleled speed and intricate problem-solving ➡ Self-repairing code to autonomously pinpoint and rectify bugs, glitches, and performance hiccups Yet, AI is only the panacea for some developer challenges. AI systems are not invulnerable. They can be prime targets for cyber-attacks, risking breaches of sensitive company data, intellectual property, and user privacy violations. This underscores the imperative for developers and tech leaders to immerse themselves in AI, judiciously evaluate its applications, and discern its immense potential and inherent risks. As Isaac Lyman aptly put it: "The startups poised to ride the crest of the AI tidal wave will be those that recognize generative AI's role: not as mere allure for investors and early adopters, not as a convenient replacement for human ingenuity, and not as an expressway to mission-critical coding. Instead, as a dynamic bridge linking real-world challenges with secure, meticulously crafted technical resolutions." Stay ahead of the technology curve. ➕ Follow me for the latest strategies and insights! #Devops #Observability #Kubernetes #Gitops #Cloudnative #Engineering Props to: Priyanka Vergadia of Google Cloud and Erin Yepis of Stack Overflow.

I'm excited to share my latest article: "How to Be an AI-Powered Solutions Architect: Leveraging AI Tools in Software Engineering" Drawing from my 17 years of experience, I've witnessed the transformative impact of AI on our industry. This article offers an in-depth exploration of: - AI's role in enhancing each phase of the software development lifecycle - Essential AI tools for requirement analysis, development, deployment, and monitoring Whether you're a seasoned Solutions Architect or an emerging architect, this guide provides valuable insights on leveraging AI to optimize your solutions and maintain a competitive edge in the rapidly evolving tech landscape. I invite you to read the full article and discover how to seamlessly integrate AI into your workflow, driving innovation and efficiency in software engineering. #AI #AIinTech #SolutionsArchitecture #SoftwareEngineering #CloudComputing #ArtificialIntelligence #TechInnovation #DevOps #ContinuousImprovement #Copilot #Microsoft365Copilot #GitHubCopilot #Azure #Coding I'm keen to hear about your experiences with AI in software architecture. Please share your insights in the comments – let's foster a collaborative learning environment and push the boundaries of what's possible in our field.