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.

Let’s take a step back from the GenAI race, which is rapidly making AI accessible to every organization—and that’s a good thing ! For software professionals like myself, I believe the real transformation isn’t just about improving GenAI model performance. 👉 The Software Development Life Cycle (SDLC) as we know it cannot—and will not—remain the same. 💡 So, here are my 10 key opiniated insights on this profound paradigm shift : 1️⃣ The cost of producing code that works is dropping. Whether measured in lines, functions, or user stories, GenAI has the potential to dramatically reduce development efforts—and it’s only getting better. 2️⃣ Man-days as a metric will soon be obsolete. When AI generates full features in minutes, IT organizations must rethink pricing models, effort estimation, and delivery strategies to stay relevant. 3️⃣ Software teams will shrink and specialize, likely aligning with business verticals. Standardized roles and redundant profiles will disappear, leaving only the most adaptable, business-savvy engineers. 4️⃣ Prototyping will be AI-powered and near-instantaneous. Businesses will experiment, refine, and even develop software independently—akin to a "Data Studio for everyone" moment, but for software. Managing this explosion of AI-generated software will be a challenge. 5️⃣ Agile development cycles will become outdated. The concept of 2-3 week sprints will seem archaic as AI enables continuous iteration and real-time feedback, shifting software delivery from weeks to minutes. 6️⃣ Legacy modernization will require far less effort. AI will help reverse-engineer, refactor, and migrate systems, transforming technical debt from a growing liability into a manageable asset. A great codebase will be one optimized for AI agents (by AI agents ?). 7️⃣ Testing will be fully AI-driven. Automated generation, execution, and refinement will make 100% coverage—once seen as wasteful and absurd—the new standard. Operators have the potential to redefine end-user testing, monitoring, and compliance. 8️⃣ Ultimately, IT professionals will shift from coding software to designing and managing AI-powered pipelines. These pipelines, delivered as-a-Service, will (almost) autonomously produce working software tailored to specific business needs. 9️⃣ These AI-powered pipelines will be the backbone of AI-driven software factories. They will natively support multi-variant testing, continuous deployment, and dynamic optimization—turning traditional development into real-time software evolution. 🔟 Software will no longer follow a “develop then release” model—it will continuously evolve. AI will monitor, refactor, and optimize codebases in real time, dynamically adapting to many factors such as user behavior, intent, and system performance. 🚨 The Big Picture ? IMHO, AI is fundamentally reshaping the SDLC, which was originally designed around human experience, speed, and processes. And the pace of change ? Probably faster than we can imagine.

→ 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

The loops of software delivery are out of sync. The inner loop, where developers write, debug, and build, has never been faster. AI tools can generate code in seconds, fill in boilerplate, and explore solutions that would have taken hours to prototype manually. But the outer loop, where teams test, review, and deploy, hasn’t kept up. Every improvement in speed on the front end creates more pressure on the systems downstream. More code. More diffs. More builds competing for attention. The result is a bottleneck that doesn’t show up in productivity dashboards, but everyone feels it: longer queues, noisier failures, slower feedback. We used to talk about the value of CI/CD as the thing that made integration safe and repeatable. Today, the challenge is different. It’s about making CI/CD adaptive, able to respond to the same scale and variability AI has introduced into the development process. That’s where the next real gains will come from. Not in writing more code, but in helping delivery systems understand and keep pace with the way code is written now.

AI is not just accelerating software delivery 🏎️. It is manufacturing a new attack surface across your business 💥. More AI-generated code. More APIs. More agents. More MCPs. More workflows with real privilege and real business access. For years, AppSec focused on code developers wrote and the software supply chain. Now we have to secure software built by humans, models, and autonomous agents operating at machine speed. This changes the question. It is no longer just: Are we scanning enough? It is: Are we testing what AI is building, or just hoping the old playbook still holds? A lot of vendors are reaching for the “AI pentesting” label right now. In many cases, they just mean LLMs crafting payloads faster. But prediction is not proof. ✅ The real question is whether you can correlate what static analysis predicts with what dynamic testing actually proves, leveraging multiple models for contextual analysis, against real running applications, at pipeline speed, continuously, and turn that into precise remediation back in the developer loop, not just another alert. 🎯That is why Snyk has been moving aggressively, from Evo as an agentic security orchestrator, to AI-native protections around tools like Claude Code and Gemini CLI, to a sharper focus on the emerging agentic supply chain around MCPs, agents, and plugins. The leaders who stand out in this next era will not win with the best AI policy deck. They will win by answering three questions: ❓Are we testing what AI is building? ❓Can we govern what we cannot yet fully inventory? ❓Can we move fast without giving up trust and control? That is the conversation heading into RSAC. Read my full thoughts here: https://lnkd.in/etxqjAAX #RSAC #AISecurity #AppSec #AgenticAI #MCP #Snyk