Spot on. As a Product Owner, I see this daily. Better tools don't mean we check 'done' and go home; they mean we finally have the bandwidth to tackle the high-level problems that were previously 'out of scope.' The ambition of the product roadmap always expands to fill the capacity of the tools. You said it Rob Imbeault, "keep winning!" We aren't building less; we're just building things we couldn't have even imagined five years ago! with Shams Mistry

🚀 How top engineering teams stay fast without breaking things Just watched a powerful workflow breakdown from AutonomyAI on how tasks and PRs are managed at Fei Studio and it’s a masterclass in clarity, speed, and collaboration. Here’s what stood out: 🔹 Tasks are broken into clear, actionable units no ambiguity, no wasted time 🔹 Workflow is transparent (To Do → In Progress → Review → Done) 🔹 PRs are small, focused, and always tied to context 🔹 Code reviews are collaborative, not critical 🔹 AI is used to accelerate execution, not replace thinking The result? ⚡ Faster delivery, better code quality, and stronger team alignment. This is a reminder that great engineering isn’t just about writing code,it’s about building systems that help teams work smarter every day. 👏 Special thanks to Marketing team and entire eam at Autonomy AI for sharing this insight. #SoftwareEngineering #AI #Productivity #DevWorkflow #EngineeringCulture #TechTeams #Innovation #viral #trending

The most unfortunate truth about AI (pt. 1): The value of engineering is going down. Every week I talk to founders, developers, and business owners about building software products, I see a pattern in their response. Everyone wants a working product in 10-15 days. Use AI, ship fast, think later. In this process, the engineering is getting ignored. The architecture decisions, model evaluations, error handling, security, logging are getting ignored because it takes time and thought. These aren't optional features. They're the difference between a demo and a production grade product. Here's the uncomfortable reality: In the era of AI, the bottleneck is not the work, it's the thinking. How something gets designed, built, scaled, and secured. AI speeds up execution. It does not replace judgment and judgement takes time and thought. But founders, business owners are being sold a different story. With AI, everything takes seconds. That infrastructure is someone else's problem. That good enough is good enough. It isn't. At Prognos Labs, we build products with intention, care, and an eye toward scale. We'd rather build few thing that lasts than ten things that break #AI #ArtificialIntelligence #SoftwareEngineering #Engineering #Founders #Startups #Developers #ProductDevelopment

AI has made building products incredibly easy. It has not, however, made thinking any easier. Today, anyone can quickly assemble sophisticated features. AI tools efficiently handle much of the technical development, drastically reducing build time. This speed, however, reveals a deeper challenge for new ventures. The bottleneck has decisively shifted from execution to strategic architecture. Without clearly defining the target customer, their journey, the unique experience, and the core monetization logic, people often just assemble features. They fail to build a coherent, sustainable business. This fundamental gap is exactly where many rapidly built, AI-powered ideas will inevitably falter. Strategic clarity and robust architectural planning now truly outweigh swift component assembly. As you plan your next venture, reflect: Are you merely building features, or are you thoughtfully designing a strong business architecture?

There is a growing assumption that AI will replace engineers. In reality, it is changing what good engineering looks like. Wynand Viljoen, our Principal Strategist, has written a blog exploring why custom development still matters in an AI first world. It looks at where AI genuinely helps, where it introduces hidden risk, and why architectural thinking remains essential for startups building things that need to last. From what we see at The Delta, the teams moving fastest are not removing engineers from the process. They are giving them better tools and clearer problems to solve. The link to the full blog is in the comments.

In 2025, Howie Liu, CEO of Airtable, restructured the entire company around a single question: if we founded this business today with AI as a core capability, how would we operate? The answer required splitting teams into two modes to prevent one team from bottlenecking the other. The teams: 1.) "Fast-thinking" teams experiment rapidly with AI, iterate short cycles, and tolerate mess. 2.) "Slow-thinking" teams building durable infrastructure. Liu's observation about where AI productivity gains actually land is worth thinking about. The advantage does not go to specific roles like engineers, PMs, or designers but to individuals who cross-pollinate across roles and treat AI as a thinking tool rather than a task executor. The traditional PM-engineer-designer split is increasingly artificial. The people seeing the biggest productivity gains are the ones who can prototype, test, and iterate across all three functions. Liu himself started coding and prototyping again, returning to individual contributor work because the tools let him test hypotheses directly instead of waiting for engineering cycles. His acid test for any company considering AI transformation: "If you cannot articulate how you would operate as an AI-native company founded today, you should find a buyer." #AITransformation #Leadership #OrgDesign #ProductManagement #TechStrategy

Great Expectations? Which of these two images better portrays the software delivery life cycle in your company?   Dickens’ masterpiece was written long before hyperscalers were a thing.  Unfortunately the dystopian parody accurately represents how most CTOs deliver technology enablement to business. A dark world of backlog items, delays and overworked engineering.  a16z’s thesis is that next AI unicorns won't be making AI tools, but simply delivering the services is not just for startups.  It is also for CTOs. Of the CTOs I speak with about AI strategy, every single one says they have a strategy.  For most "strategy" is the team's use of AI co-pilots, tooling, and things that deliver more lines of code.   The conversations here are mostly about barriers. The exhausted political capital in the company, the difficulty of changing some of the most entrenched (and expensive) vendor agreements in the company, and the resistance to change from their own teams.  A select few are moving to AI-enabled services.  There is an energy here among these leaders, the ones that recognize the rising tide. There is an insatiable hunger in these companies, to tear up precedent and redefine the relationship between technology and business. In the book Joe Gargery — the humble blacksmith — gets the best line: "Life is made of ever so many partings welded together."  Something to consider for tech leaders navigating painful transitions.

This video perfectly explains what’s happening in AI right now… and it’s uncomfortable. 😬 Everyone says: AI companies with software engineers. But the reality looks like this 👇 Slow preparation. Over engineered steps. Too many tools. Too much complexity. Meanwhile… Lean AI teams are shipping faster, simpler, and smarter. ⚡ The old model: • Large engineering teams • Months of planning • Endless architecture debates • Heavy infrastructure The new model: • Small AI first teams • Prompt driven development • Rapid iteration • Ship in days, not quarters The biggest shift isn’t AI replacing engineers. It’s AI changing how much engineering you actually need. The companies that win will: • Reduce complexity • Move faster • Build lighter • Think AI first, not code first This is the moment where speed beats size. Where clarity beats process. Where builders beat planners. 🚀 Most teams are still preparing the shrimp. A few are already serving the dish. Which one are you? 👀 Follow Anish Anurag for more AI and tech insights. #AI #ArtificialIntelligence #SoftwareEngineering #Startup #Tech #AIFirst #ProductDevelopment #NoCode #Founders #FutureOfWork

There's a new ratio I think every engineering lead should have on their dashboard. Not sprint velocity. Not DORA metrics. Not deployment frequency. The Human-to-Agent Ratio. For every meaningful piece of work your team ships, how much was actually driven by humans vs AI agents? Not "did we use Copilot" but genuinely, where does human judgment begin and end in your workflows? Here's where it gets interesting. Unlike the golden ratio, there's no universal number. It's different for every team, and that's the point. A fintech team in a regulated environment should be human-heavy. A startup shipping internal tooling should probably be agent-first. A platform team can let agents own the toil while humans own the architecture calls. Your ideal ratio depends on your risk surface, how fast you can catch agent errors, how novel your domain is, and honestly, how AI-literate your team actually is right now. Without thinking about this deliberately, most teams drift into one of two traps. Either humans are doing work agents could handle, burning out good engineers on stuff that shouldn't need them. Or agents are running loose in high-stakes areas and nobody has enough context to catch mistakes until it's too late. Both are expensive. Only one shows up fast in your incident log. So the question isn't "are we using AI?" It's "is our human-to-agent ratio something we chose, or something that just happened?" Map your workflows. Find where the ratio is accidental. Then design it. What does yours look like right now? #EngineeringLeadership #AIAgents #FutureOfWork #SoftwareEngineering

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗻𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹, 𝗯𝘂𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗽𝗼𝗼𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. After studying how Anthropic builds production-grade systems, one idea stands out clearly: agents are not the starting point. There are two fundamental approaches: Workflows = The system defines and controls each step Agents = The model dynamically decides the next steps and tool usage This decision directly affects cost, latency, and reliability. A common mistake is jumping straight to autonomous agents without evaluating simpler alternatives. A more effective approach is to start with a single LLM call (enhanced with retrieval), move to structured workflows if needed, and only introduce agents when workflows are no longer sufficient. Agents provide flexibility, but they also increase cost and introduce the risk of compounding errors across multiple steps. The key insight is this: AI systems do not scale by improving prompts alone. They scale through better system design, clear workflows, well-defined tool interfaces, and strong evaluation mechanisms. The real skill today is not building agents. It is knowing when they are actually necessary. For teams building AI products, this single architectural decision can determine whether a system succeeds in production or fails under complexity. #AI #AIAgents #SystemDesign #LLM #Engineering #Startups