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

🚀 AI Is Rewriting the Future of Software Engineering—And Google Just Dropped the Blueprint AI isn’t just “assisting” engineers anymore—it’s co-creating with them. 📌 Google’s latest update on AI in Software Engineering pulls back the curtain on how deeply AI is embedded in its software development lifecycle—from code generation to planning, testing, and even reviews. Some 🔥 highlights: 30%+ of new code at Google is now AI-generated. Engineers are seeing 20–25% productivity gains using AI-powered tools. From internal IDEs to bug triaging systems, AI is quietly revolutionizing how engineering happens at scale. But what sets Google’s approach apart isn’t just the tools—it’s the philosophy: ✅ Select projects with measurable developer impact ✅ Embed AI into “inner-loop” workflows (where devs live day-to-day) ✅ Build feedback loops to constantly improve performance & trust ✅ Share learnings with the broader ecosystem (open papers, DORA reports) One of the most exciting frontiers? Agentic AI 🤖—systems that plan, act, and adapt on behalf of developers. Google's acquisition of Windsurf’s top talent into Google DeepMind signals serious intent here. These tools won’t just autocomplete your functions… they’ll soon handle full-stack code changes, migrations, and dependency resolutions—autonomously. 👨💻 This also means the role of the engineer is evolving. Welcome to the era of the Generative Engineer (GenEng)—where prompts, design thinking, human-AI pair programming, and strategic oversight replace routine code churn. Of course, challenges remain: ⚠️ Ensuring reliability & debugging AI-written code ⚠️ Avoiding misalignment with developer intent ⚠️ Managing trust, governance, and security across codebases But Google’s model—balancing speed with rigor—offers a practical path forward. 💬 So here’s my take: AI won’t replace software engineers. But engineers who embrace AI as a true partner? They’ll be 10x more valuable—because they’ll ship better software, faster, and at scale. If you're in tech leadership, now’s the time to: 🔹 Assess AI-readiness across your dev lifecycle 🔹 Define how productivity and quality will be measured 🔹 Empower teams with the right AI tools, context, and guidance The future of software isn’t about who writes the best code—it’s about who builds the smartest systems to write, verify, and evolve that code over time. 💡 Let’s not just use AI to write software. Let’s use #AI to reinvent how software gets written. #SoftwareEngineering #GenAI #DevOps #EngineeringLeadership #AItools #TechInnovation #AgenticAI #FutureOfWork #GoogleAI #ProductivityBoost #DevX #LLM #GenerativeEngineering 🚀👨💻🤝

→ 𝐓𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐀𝐈 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐧 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 𝐪𝐮𝐢𝐞𝐭𝐥𝐲, 𝐲𝐞𝐭 𝐢𝐭 𝐢𝐬 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐞𝐯𝐞𝐫𝐲 𝐬𝐭𝐞𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞. Most developers and managers focus on coding alone, but the real transformation starts much earlier and continues long after the first line of code is written. 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐦𝐚𝐩 𝐨𝐟 𝐡𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐞𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐞𝐚𝐜𝐡 𝐬𝐭𝐚𝐠𝐞 𝐨𝐟 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: • Requirements Gathering & Analysis AI can analyze stakeholder inputs, previous project data, and user feedback to generate precise requirements. Tools like Jira with AI plugins, Aha!, and Receptive AI help teams avoid ambiguous specs and reduce rework. • Project Planning & Management AI optimizes resource allocation, predicts project timelines, and flags potential risks. Tools like ClickUp AI, Monday.com AI, and Asana AI assist PMs in creating realistic roadmaps and improving team efficiency. • UI/UX Design AI generates design prototypes, predicts user behavior, and suggests improvements based on analytics. Figma with AI plugins, Adobe Firefly, and Uizard help designers create intuitive and data-driven interfaces. • Coding & Development From auto-completing code to generating boilerplate functions, AI accelerates development while reducing errors. Popular tools include GitHub Copilot, Tabnine, and CodeWhisperer. • Quality Assurance & Testing AI-driven testing predicts high-risk areas, auto-generates test cases, and identifies anomalies faster than humans. Tools like Testim, Mabl, and Applitools enhance test accuracy and speed. • Monitoring & Maintenance AI monitors application performance, predicts failures, and recommends fixes proactively. Dynatrace, New Relic, and Moogsoft empower teams to maintain high availability and user satisfaction. The reality is clear: every stage of the software lifecycle is now influenced by intelligent automation. Ignoring AI today could mean falling behind tomorrow. Follow Sandeep Bonagiri for more insights

AI-first engineering is not a tools rollout. It is a new enterprise delivery operating model. The first wave of AI in software delivery focused on individual productivity. Copilots helped developers write code faster. Useful, but incomplete. The next wave is agentic delivery. AI now participates across the full lifecycle: understanding legacy systems, enriching requirements, generating specs, writing code, testing, validating, deploying, observing, and feeding learning back into the system. That shifts the executive question. Not: Which AI coding tool should we buy? But: How do we redesign the engineering organization so humans and agents can safely deliver business outcomes at scale? This is where the real work begins. The bottleneck is no longer only code generation. It is review, validation, architecture, security, product intent, governance, and organizational absorption. If those do not change, AI creates more inventory, not more throughput. The organizations that will win will not be the ones with the most tools. They will be the ones that build the right engineering harness around AI. That means context agents can reason over. Specs that capture business intent clearly. Guardrails embedded into the workflow. Quality and security controls by design. Platform ownership that scales beyond pilots. Metrics tied to business outcomes, not activity. It also means teams redesigned around orchestration, judgment, and accountability. The role of the engineer is not disappearing. It is being redefined. Engineers become orchestrators, reviewers, system designers, and accountable stewards of quality. Product managers become sharper authors of intent. Quality Engineers become designers of verification systems. Architects become continuous guardrail owners. CTOs become operating model architects. AI-first delivery is not about replacing human judgment. It is about raising the leverage of human judgment across the entire delivery system. Orgnizations that treat this as a tool adoption program will see marginal gains. Organizations that treat it as an operating model transformation will change the economics of software delivery. That is the real shift. #AIFirstEngineering #AgenticAI #EnterpriseAI #EngineeringLeadership #SoftwareDelivery #DeveloperExperience #AITransformation Thoughtworks