The Impact Of Emerging Technologies On Practical Engineering

A friend texted me this weekend for some advice. They said their CEO just sent this over and asked how to reply. "If AI tools can really turn junior or average engineers into 10x contributors, why aren’t we doing more? Why does this still feel optional?" I wasn’t surprised. This is the number one question I hear from engineering leaders.  So here’s how I broke it down: 1. Reality today Across teams using tools like Copilot, Cursor, Amazon Q, Gemini Code Assist, Sourcegraph, and others, we’re seeing measurable productivity gains. Studies and data from the Jellyfish platform consistently show: - 30-50% faster throughput for engineers who engage deeply with these tools - Significant reductions in boilerplate work and time-to-implementation - But only one-third of engineers are using them consistently (monthly) It’s a significant gain, but it’s certainly not 10x. And while those gains do happen, they're just not indicative of what you can realistically expect in the common case. We usually see those outsized speedups under the following conditions: - Greenfield teams writing entirely new code - AI-native workflows with heavy automation - Engineers who are deeply enabled, trained, and incentivized to adapt Most orgs today are still operating in legacy environments, where coding new features is a minority share of engineering time, and maintenance, integration and iteration dominate. AI is powerful, but it hasn’t replaced the full software development lifecycle – or at least not yet. 2. What to tell your CEO We shouldn’t push back on the ambition. Instead, we should match it with clarity and action. If your CEO is asking for 10x productivity, answer with the following: “I want that too. Let’s lean in together,” then: - Implement clear telemetry on current AI usage and outcomes - Build a plan to increase engagement and enablement – training, guidance, and accountability - Create a case for investing in deeper experimentation with emerging tools and workflows - Stay tuned in to the state of the art – surface, dig in, investigate and understand the circumstances that made outsized gains possible - And, if needed, ensure there is a willingness to make team and process changes to meet the moment AI in engineering is no longer optional. But we’re still in the very early innings. Just because 10x gains aren’t a reality across the board today doesn’t mean they won’t ever be. Things are moving quickly and as we move out of the IDE to take a more agentic approach, productivity gains will only increase. Success requires more than licenses. It demands leadership, enablement, and serious cultural investment. This is a transformation. Let’s treat it like one.

In a world chock full of #AGI talk, amazing vibe coding demos and worries around AI impacting jobs, it's hard to know what's really happening. Thankfully Nicholas Arcolano, Ph.D. + Jellyfish have system level data - Not Surveys - of the impact that #GenAI coding tools are having on developer productivity. Highlights below. 🚀 AI’s Real Impact on Engineering: Insights from 2M+ PRs Engineering leaders: The AI revolution in software development is no longer theoretical—it’s here, and the numbers are staggering. Jellyfish’s latest analysis of over 2 million PRs (July 2024–June 2025) reveals how AI is fundamentally transforming engineering velocity, quality, and team dynamics. 📈 AI Adoption Is Surging AI-powered PRs have skyrocketed from just 14% in June 2024 to 51% in May 2025. The ratio of “high-AI” to “no-AI” PRs has more than tripled in less than a year, now at 1.6-to-1. ⚡ Cycle Times Are Shrinking PRs using AI are now 1.16x faster (Q2 2025) than non-AI PRs, up from 1.11x in Q3 2024. Average PR cycle time dropped from 95.5 to 83.8 hours—a 13.7 hour savings per PR, split between faster coding (8.6 hours) and review (5.1 hours). High-AI PRs are now 16% faster than non-AI PRs, up from 11% last year. 👩💻 Seniority Gaps Are Closing Senior engineers initially saw the biggest AI productivity gains, but junior engineers have caught up—now, PRs with AI are about 1.2x faster for all experience levels. 🛡️ Code Quality Remains Steady No increase in bugs: The percentage of bug-related PRs remains steady (8–9%) regardless of AI adoption level. AI is accelerating delivery without sacrificing quality. 📊 AI Lifts All Boats AI’s impact isn’t limited to growth or roadmap work—all areas (growth, KTLO, support) are seeing gains as AI adoption spreads. Bottom line:  AI has moved from hype to delivering real, measurable impact for engineering teams—speeding up cycle times, empowering all engineers, and increasing output across the board, all without compromising quality. (Full post with data in comments) #EngineeringLeadership #AIDriven #SoftwareEngineering #JellyfishAI #DevVelocity

𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐇𝐢𝐬𝐭𝐨𝐫𝐲: 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧. As history consistently teaches us, every major production boom – from the Gutenberg Press to the Ford Assembly Line – inevitably brings forth new challenges alongside its transformative opportunities. Based on research from Georgian, in partnership with FirstMark (a benchmarking report on how AI is being adopted by R&D and GTM teams surveying 308 R&D leaders), my FirstMark colleague David Waltcher and I discuss in this episode how we're now witnessing this very phenomenon unfold with the 𝐀𝐈 𝐜𝐨𝐝𝐢𝐧𝐠 𝐰𝐚𝐯𝐞, which is rapidly redefining engineering at an unprecedented pace. In just 24 months, companies like Cursor, Windsurf, Replit, Lovable and products like GitHub Copilot are seeing explosive growth, with millions of developers already leveraging AI to write code. We're already seeing 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐠𝐚𝐢𝐧𝐬: * 30-50% faster throughput in engineering processes. * 12% increase in PR merges. * 17% more time on roadmap vs. maintenance. However, the current AI coding revolution is no different from past industrial shifts in that it 𝐚𝐥𝐬𝐨 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐬 𝐧𝐞𝐰 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐢𝐞𝐬. We're now navigating: * Increased 𝐝𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 time and rising 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬. * Performance issues and 𝐨𝐯𝐞𝐫𝐰𝐡𝐞𝐥𝐦𝐞𝐝 𝐐𝐀 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬. * 𝐀 𝐟𝐚𝐬𝐜𝐢𝐧𝐚𝐭𝐢𝐧𝐠 𝐬𝐡𝐢𝐟𝐭 where experienced engineers are becoming professional code reviewers. This parallel with past industrial shifts presents immense opportunities for CTOs, engineers, founders, and investors: * 𝐂𝐓𝐎𝐬 face critical decisions on talent, architecture, and security, much like leaders during previous technological paradigm shifts. * 𝐇𝐢𝐫𝐢𝐧𝐠 𝐢𝐬 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠, now valuing great editors, reviewers, and prompt engineers – a new kind of "specialist" emerging from a new era of production. * 𝐍𝐞𝐰 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 are emerging in automated security, reasoning-based QA, and agentic code checks, mirroring the new industries that sprung up to solve problems created by past revolutions. The future of engineering is rapidly evolving. What historical parallels do you see in today's AI transformation, and how are you preparing for what's next? #AI #Engineering #FutureOfWork #TechInnovation #CTO #SoftwareDevelopment #FirstMark https://lnkd.in/e5sGaaH7