How AI is Changing Software Delivery

I recently sat down with Erran Berger, VP of Product Engineering at LinkedIn, to discuss a question that’s on every developer’s mind: How is AI actually changing the way we build software? We’re moving past the "AI will write all the code" hype and into a much more interesting reality. The role of the software engineer isn't disappearing; it’s being elevated. 🤌 TL;DR from the conversation: 1/ Systems Thinking > Syntax: As AI handles more of the boilerplate, the value of an engineer shifts toward orchestration and high-level architecture. 2/ The "Human Editor": AI can generate solutions, but human judgment remains the final (and most critical) line of defense for security, ethics, and performance. 3/ Solving Technical Debt: One of the most exciting use cases Erran shared was using AI to refactor legacy systems—turning a months-long headache into a manageable project. 4/ New Must-Have Skills: If you aren't already looking into RAG, LLMOps, and Vector Databases, now is the time to start. The goal isn't just to write code faster; it's to make engineering "joyful" again by removing the friction and focusing on pure problem-solving. Watch the full episode here: https://lnkd.in/gEJb4jdz Thank you, LinkedIn team for inviting me over, for this incredibly insightful conversation 🫶

I was convinced AI would transform how we build software. I did not expect it to happen so fast. Over the past year, through conversations with leaders like Thomas Dohmke, startups in the AI software development space, working with the Anthropic team, and observing our own builders at Doctolib, one thing has become clear to me. AI is changing how we think about building software like nothing before. Specs turn into working prototypes instantly. Design systems and architecture principles are continuously reinforced by the tooling itself. Writing production-ready code from scratch is no longer our bottleneck. Tests are generated automatically to validate intent. Complex refactoring is handled by autonomous agents. And this is accelerating. As Ethan Mollick once said: "The AI we use today is the worst AI we will ever use.” Better models enable more capable agent fleets and higher autonomy, which in turn drive even better models As tech builders, our day-to-day job is changing… We don’t focus as much on manual implementation, writing boilerplate, or debugging line by line. Instead, we design the systems and scaffolding that allow AI to do reliable work. We orchestrate agents with the right intents, we validate AI-generated architectures, and we define strict quality guardrails. ….but the outcome doesn’t change: creating better technologies for our users. This is a strong opportunity for all tech companies to innovate faster, but for us even more so in view of the specificities of healthcare and the quality of our technologies and teams. 🔹 AI will help us create more value for our health professionals and anyone managing their health. 🔹 AI will help us tackle all user feedback, bugs and incidents in minutes. 🔹 AI will make us launch more specialties and more countries faster. At Doctolib, we're going all-in on this transformation. Dozens of specialized agents deployed. Our engineering leaders are driving this change, committing code 5x more frequently than a year ago. Teams already deliver significantly more value to patients and health professionals. If you want to join that revolution and contribute to reinventing the daily life of health professionals and improving health for everyone, we welcome all builders. It's only the beginning.

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀. 𝗜𝘁 𝘄𝗶𝗹𝗹 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘆𝗼𝘂𝗿 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. Most “AI in engineering” conversations are stuck on speed. That’s the trap. The real story is technical deflation: 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 is getting 𝗱𝗿𝗮𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗰𝗵𝗲𝗮𝗽𝗲𝗿 to produce. The winners won’t be the teams who type faster, they’ll be the teams who redesign the production system. Dan Shapiro’s 5-level framing makes it obvious why: 0: 𝗠𝗮𝗻𝘂𝗮𝗹: AI as occasional helper. You still own everything. 1: 𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝗻: delegate bounded tasks (tests, docs, small edits). 2: 𝗔𝗜 𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲: pairing flow. Big boost… and a seductive plateau. 3: 𝗔𝗴𝗲𝗻𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿: AI generates lots of code; your life becomes diffs. Most teams stall here. 4: 𝗦𝗽𝗲𝗰 & 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 write specs, negotiate them, build reusable workflows, let agents run, then validate. 5: 𝗗𝗮𝗿𝗸 𝗳𝗮𝗰𝘁𝗼𝗿𝘆: spec -> software with minimal human involvement. My take: the next competitive moat isn’t code quality, it’s spec quality + verification. In a deflationary world: - Specs become the new source code - Tests become the new management layer - Review becomes a product function, not an engineering chore If I were leading an org right now, I’d measure one thing relentlessly: How long from "clear spec" -> "validated working software"? This is basically 𝗰𝘆𝗯𝗲𝗿𝗻𝗲𝘁𝗶𝗰𝘀 applied to software delivery: Tight loops, clear signals, and automatic correction, so the system improves every run, not every quarter. #AI #SoftwareEngineering #Leadership #DeveloperExperience #CyberneticDelivery

Over the last year, most conversations about AI in software have focused on speed. - Faster coding. - Faster testing. - Faster releases. But speed is no longer the most interesting question. Recently, I experienced something that made this clear. Using an AI native development environment, I took an idea from concept to a deployable app in a very short span of time. The system handled code generation, testing, configuration, and deployment readiness as a single continuous loop. My role was not writing code line by line, but specifying intent, validating outcomes, and iterating on behavior. That moment forced a deeper question. If software can now build, test, deploy, and correct itself in real time, what happens to the operating rules that have governed software delivery for the last two decades? Agile, Kanban, velocity, story points, QA gates. All of these assumed human bounded execution. This article explores what changes when that assumption breaks. - Why AI is moving from a tool to an operating layer - Why validated change, not code, becomes the unit of delivery - What this means for professionals, services firms, and delivery models - Why alignment, not acceleration, becomes the next source of advantage I believe we are entering a phase where the SDLC itself is being rewritten. Not incrementally, but structurally. The full article is below.

𝗖𝗼𝗱𝗲 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸. 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗶𝘀. For years, software delivery was constrained by engineering effort. Writing, testing, integrating, and deploying code shaped how teams planned, estimated, and delivered. That assumption is breaking. I’ve written a new post: 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗜𝘀𝗻’𝘁 𝗖𝗼𝗱𝗲 AI is compressing implementation effort so dramatically that building is no longer the slowest part of delivery. Tasks that once took days can now be prototyped in hours. Iteration is cheaper. Exploration is faster. Yet many organisations are not seeing the leap in outcomes they expected. The reason is simple. The constraint has moved. Across teams adopting AI seriously, the real bottlenecks now sit upstream. Unclear problem definition, slow decision-making, fragmented ownership, delayed feedback, and weak architectural alignment are what slow delivery down. When code becomes easy to produce, deciding what code should exist becomes the hard part. This shift has a knock-on effect. Faster execution increases the cost of poor decisions. Teams can build the wrong thing just as quickly as the right thing. Speed amplifies both value and waste. That is why productivity gains often feel uneven. Engineering moves faster, yet the wider system struggles to keep up. Teams can implement faster than stakeholders can decide, and test faster than feedback can arrive. The constraint is no longer doing. It is knowing. High-performing organisations are responding by redesigning how decisions are made. They clarify intent before execution, shorten decision loops, align ownership, and measure outcomes rather than activity. The advantage is no longer build speed. It is decision quality. Link: https://lnkd.in/e2Jgq6nr If coding is no longer the constraint in your organisation, what is? #AI #SoftwareDelivery #Leadership #CIO #CTO #TechnologyLeadership #BusinessAgility #DigitalTransformation

#AI and #SDLC - What's changing and what #startups can build . Artificial Intelligence (AI) is fundamentally reshaping the Software Development Lifecycle (SDLC), moving it from a human-intensive craft to an AI-augmented process. What are the groundbreaking opportunities? 1. UI/UX Design: From Manual to Curated Creativity 🎨 Today's design workflows, whether starting from scratch or working within existing systems, are riddled with inefficiencies like manual inspiration gathering and tedious design-to-code handoffs. How AI is changing it: AI models can now generate context-aware mockups from feature briefs and brand guidelines, turning designers into curators who review and customize AI-generated options. For implementation, AI can generate production-grade frontend code, allowing engineers to shift from writing boilerplate to reviewing and refining. Startup Opportunities: • AI Designer Assistant: Think of this as a "junior designer" embedded in an organization. It combines a structured component library with an agentic workflow engine to instantly generate mockups aligned with a brand's design system. This is less about inventing new styles and more about automating execution.      • Frontend Execution Agent: This agentic AI system acts like a junior front-end engineer, transforming finalized Figma designs into clean, semantic production-ready code. • Zero-Code App Builder: For non-technical users like small business owners or HR managers, AI can collapse complex app creation into natural language. Imagine telling an AI, "I want a mobile app where customers can book appointments," and it handles the UI, frontend, backend, data, and deployment. This is about delivering outcomes, not just clean code. 2. System Design: Automating the Blueprint 🏗️ System design is critical, yet often a bottleneck, relying on scarce senior talent and informal tribal knowledge. How AI is changing it: AI can ingest vast architectural designs, trade-offs, and best practices to recommend patterns, surface trade-offs, and auto-generate system diagrams and starter code. Startup Opportunities: • System Design Thinker: An AI copilot that acts as a reasoning assistant, helping engineers explore design options, explain pros and cons, and suggest optimal designs based on benchmarks and historical company decisions. This is fundamentally creative work. • System Design Executor: An agentic solution that automates the translation of high-level designs into diagrams, documentation, boilerplate code, and cloud infrastructure templates. This is largely mechanical execution. 3. Code Writing: From Manual Coding to AI-Guided Assembly ✍️ Developers spend 60-70% of their time on repetitive "grunt work". AI models like GPT-4 can now not only read and write code but also reason about it. How AI is changing it: AI can translate natural language into functional code, explain codebases, suggest fixes, refactor modules, and auto-generate documentation.

Software delivery teams were originally designed around one big constraint: writing code was expensive. So teams specialized. Frontend owned the UI. Backend-owned APIs. DevOps managed pipelines. QA handled testing. But that constraint is rapidly changing. AI-assisted development is making code generation much faster. In many teams today, engineers can produce 3–5× more code than they could a few years ago. The challenge is no longer writing code - it’s understanding systems, validating outputs, and deciding what should be built next. This shift is already changing how teams work. Smaller full-stack squads are becoming more common. Internal developer platforms are being treated like products with their own roadmaps. And developer experience metrics are now being used to measure engineering productivity. Another interesting change: when teams generate more code with AI, the delivery pipeline must handle more of the testing, validation, and quality checks automatically. In other words, the bottleneck in software delivery is moving from code generation to system validation. The engineering teams that adapt their structure to this shift will move faster - not because they write more code, but because their systems are designed to handle the new scale of development. #AI #SoftwareEngineering #DevOps #DeveloperExperience #PlatformEngineering #FutureOfWork

For decades, software engineering was built on predictability. You wrote code, ran it with the same inputs, and got the same outputs every single time. But AI is changing that fundamental assumption. As more teams integrate Large Language Models (LLMs) into products, I’ve noticed that many of the engineering principles we’ve relied on for years are evolving. Here are three major shifts that stand out to me: 🎯 1. Engineering for Unpredictability Traditional software is deterministic. AI isn’t. Ask an LLM the same question twice, and you might get two different responses. That means our role is no longer just writing business logic—we’re building guardrails. Validation layers, evaluation frameworks, fallbacks, and monitoring are becoming just as important as the AI model itself. 📈 2. Rethinking Performance Users expect software to feel instant. AI introduces a new challenge: reasoning takes time. The solution isn’t always a faster model. Often, it’s better product design—streaming responses, intelligent caching, background processing, or choosing the right model for the task instead of the most powerful one. 🛠️ 3. Prompts Are Becoming Production Assets A small prompt change can significantly impact behavior. Prompts are no longer “just text.” They need version control, testing, reviews, and monitoring—just like application code. In many AI systems, prompt engineering is becoming a core part of the software development lifecycle. My biggest takeaway? AI isn’t replacing software engineers. It’s changing the problems we solve. We’re moving from building systems that follow instructions perfectly to building systems that can guide, evaluate, and manage intelligence responsibly. How is your team handling the unpredictability of AI in production? I’d love to hear your experiences in the comments. #AI #LLM #SoftwareEngineering #GenerativeAI #MachineLearning #Tech

Delivery Model Struggle ... Wake-Up Call for the Industry 2025 is becoming a defining year for the tech services sector. The traditional delivery model — built on scaling human capital — is under structural pressure. For decades, firms grew by expanding global delivery teams. But generative AI is changing the economics of software and service delivery — fast. Big Tech is leading the shift: - Microsoft: 30% of code is now AI-generated (Satya Nadella) - Meta: AI will outperform most engineers within 12–18 months (Mark Zuckerberg) - Google: DeepMind is automating DevOps and optimizing software stacks - Amazon: AI is powering cloud infrastructure automation at AWS - IBM: WatsonX is embedded across delivery to reduce manual work These firms are not just integrating AI — they’re re-architecting their delivery models around it. Accenture’s recent 9% stock decline is a signal, not an outlier. It reflects the growing pressure on legacy, labor-intensive models — and the urgency to adapt. This moment isn’t about any single company. It’s a call to action for the entire sector: Move to an AI-first, agile, and outcome-driven delivery model — or risk falling behind. ** For us as leaders: The future of delivery won’t be built on scale. It will be built on intelligence **