The Future of Engineering: Obsession vs Average

3 weeks of hiring there’s one clear conclusion: It’s a weird time to be hiring a tech team. AI has pulled the rug out: if everyone had (partial) self driving cars, how do you pick good drivers? Do you measure them on “good” when we didn’t have driver assistance? Or “good” because they aren’t afraid to push the buttons? How much weight do you give skills of the “past”?  How much do you index on how the landscape may shape in the next 6-12 months? A core tenet of engineering is responsibility: Good engineers are accountable even when things are out of their control but leveraging vibe / AI coding inherently conflicts this. For the first time, good engineers are being asked to sacrifice more of what they used to control. More of what made them capable. Because AI isn’t just writing code, it’s making decisions along the way - from how it writes and importantly: what it didn’t write. In our hiring journey - we used a two speed technical interview: “offline/no Ai mode” + Ferrari “use all your favourite tools” mode. I met applicants from various levels - and a variety of approaches: - One candidate outwardly said “look! The coding agent codes better than me!” Another could code faster than back and forths with cursor agent could - One candidate showed us how she’d scaffolded the codebase with context in 2 languages, bridging two geographies (solving something I hadn’t considered with the current coding wave - language barrier transcendence). What did this amount to? - The successful candidates held high levels of responsibility and accountability even if they used AI coding (they found ways to retain that stance) - They proved it through: solid foundational understanding coupled with jack of all trades was a big draw card - They are an early adopter and could expose tradeoffs and limitations - And most importantly: They are good humans that are just as driven to help parents/carers and kids as we are. So: How are you going about your hiring decisions? What are you favoring for your company’s challenges? P.s. if you want more detail on how we ran our processes (or our serious Cap game) - shoot a comment or DM

unpopular opinion: if your best skill is prompting ai, you're already obsolete. great post by Jeff Quach about hiring in the ai era that perfectly captures why "human WITH ai" isn't going away. the challenge: if everyone has ai coding tools, how do you measure capability? the answer isn't in the tools. it's in the accountability. the post described candidates who "held high levels of responsibility and accountability even when using ai coding." this is exactly what i've been saying. ai doesn't eliminate the need for skilled humans. it raises the bar for what "skilled" means. the best candidates aren't the ones who code fastest with ai. they're the ones who understand: - when to trust the ai - when to override it - what the ai didn't write (and why that matters) - how to stay accountable for output they didn't directly create one candidate literally said "my coding agent works better than me!" - but didn't get hired. another scaffolded a codebase in two languages to bridge geographic teams - solving a problem the interviewer hadn't even considered. guess which one demonstrated human+ai mastery? the companies winning at ai adoption aren't looking for "good with ai tools." they're looking for "maintains engineering excellence while leveraging ai tools." massive difference. this is why throwing ai at problems without understanding human capability is such a dangerous game. credit to Jeff for running a thoughtful two-speed interview process. this is how you actually find ai-native talent. I'll keep that in mind!

How Do I Retain Great Engineers? Following up from my last post on how to find them, what's just as important and even more challenging is creating an environment engineers enjoy and thrive in. Engineers are not coding machines. If you treat them as a utility, they will always go to the highest bidder. Here are three methods I have learned along the way that have resonated with our team members: 1. Offer Autonomy As important as it is to rigorously define project specifications and execute without feature drift, it is important to create an environment where an engineer's primary constraint is client/user satisfaction. When you don't allow engineers to investigate new tools/patterns, or assign them to mundane, factory-like work, they may quickly become unmotivated. Additionally, many engineers do not find their ideal working hours to be from 9a to 5p. Letting your engineers tell you when they are going to work, and allowing them the flexibility to go overtime or "under-time" is crucial for them to work at their best capacity. Engineers love businesses that understand this and you will quickly find great talent coming directly to you if they hear this is the culture you are building. 2. Stay Focused Engineers are easy to exhaust if you split their attention. Too many times I have seen leaders ask engineers to straddle multiple projects at the same time, only to burn them out and achieve mediocre results across the board. Don't assume if your business isn't a startup, or your use case is "un-sexy" that a focused engineer can't feel a sense of passion or ownership around the application. Focusing engineers on solving a specific problem for a user/client creates an ownership mentality which will drive creative, customer obsessed solutions that accelerate your application's ROI and encourage top-talent retention. 3. Collaboratively Define & Reiterate Core Principles Every Tuesday, our team comes together in an all-hands meeting to review and refine our company’s core engineering principles. These principles cover expectations for ourselves, our clients, and our software development lifecycle. Everyone has a voice and we actively debate and discuss our thoughts on prompts like “how do you unstuck?” or “what’s the difference between a POC and a MVP?”. In just 45 minutes a week, we accomplish several crucial goals: knowledge transfer, mission alignment, full team buy in, and exercising communication skills company wide. These methods aren’t the only ways we retain talent, but I can’t imagine any of our engineers sticking around long without them. If you're looking for a partner who brings deep understanding of how to successfully launch AI solutions, don't hesitate to reach out! Website: https://www.rosenblatt.ai Book a Meeting: https://lnkd.in/gXEUzZq3

⚠️ Stop trying to hire "10x engineers." That entire concept is broken. It celebrates the "lone genius"—the brilliant coder who also happens to be a knowledge-hoarding, team-blocking bottleneck. The math has changed. 1 engineer writing code 10x faster = a 10x gain. 1 engineer who unblocks 5 teammates (making them 2x better) = a 10x gain. In the AI era, the first skill is being commoditized. The second one is priceless. This is the "Force Multiplier." This is the engineer who understands that their job isn't just to write code, but to increase the velocity of the entire team. Their "hard skills" aren't just algorithms. They are: Mastering the (ego-free) Pull Request Writing documentation that prevents future meetings Translating technical debt into business risk These are the most valuable, real-world skills in 2025. We wrote the playbook on how to find, hire, and build them. The full article is in the first comment. #TechTalent #EngineeringLeadership #FutureOfWork #TeamCulture #TechX

Agree with everything below, and here are few thoughts from my end. There's a dangerous narrative emerging: that AI allows you to immediately swap senior engineering talent for cheaper alternatives to "fund innovation." This isn't just bad strategy; it's a foundational error that paves the way for failure. It reveals a fundamental misunderstanding of how to build great software and resilient teams. Here’s how to embrace AI without sabotaging your future. 1. Find Your Real Navigators Innovation isn't about buzzwords. It's about substance. To find the people who can actually lead your AI transformation, look for two things: Masters of Context: Who on your team writes the clearest, most detailed documentation and project specs? This person understands how to break down complex problems and provide the precise context AI needs to function. They are your natural prompt engineers and system architects. Elite Learners: An overall level of curiosity and learning abilities; every hard skill can be learned, but ability to learn as a skill is an extremely expensive and rare skill to find on a market. So even if a chosen person isn't hands-on yet with an innovation you'd like to adopt, the speed with which they are able to overcome an initial learning curve is what brings their price up significantly. 2. Win the War, Then Count the Spoils You don't restructure your army in the middle of a campaign based on the promise of a new weapon. You use the weapon, prove its effectiveness, and THEN adapt your strategy. Adopting AI is a complex mission that adds new risks. If you apply outdated, Taylorist cost-cutting logic, you'll extinguish the very culture of psychological safety and synergy required to survive the journey. Prove the gains first. Then, and only then, earn the right to optimize. Peace 🤘

Are software engineers still innovating, or just maintaining? A recent survey shows that many feel bogged down by endless maintenance tasks. Find out how they really feel about their roles, and what forward-thinking leaders are doing to help them get back to meaningful, creative work. #SoftwareDevelopment #SoftwareEngineers #AI

📊 We surveyed 1,200 engineers and technology leaders to better understand the state of the developer experience today. Not shockingly, 93% of engineers reported finding writing code and building new features to be rewarding. But what's discouraging is they're spending only around 16% of their week on this type of work. A few more key findings: *The energy drain: only 1/3 of engineers strongly agree they spend most of their time on energizing, innovative work. *Undeniable impact of automation and AI: 89% of engineers are saving at least 3 hours each week with AI. *Excessive workloads: 35% of engineers point to an excessive workload and burnout as major obstacles to a positive work experience *Tool sprawl challenges: nearly 9 in 10 engineers say switching between tools disrupts their workflow *Retention risks: 66% of technology leaders worry about retaining top engineering talent Read the full report on the blog 👇 https://lnkd.in/eDD78_VQ

Today's passing thought: Many software engineering jobs are moving back closer to company headquarters, which is shrinking their talent pool options geographically again. From what I've seen job hunting over the past several months, this shift is real. Positions that used to be remote are increasingly listed as in-office or hybrid only. Many of the remote positions I see are ones that are continuously reposted. More than 50% of the positions I saw when I started my search are still up and being reposted weekly or monthly. At the same time, there's another trend pulling in a different direction: AI adoption is creating a divide within engineering teams. Whether it's for coding assistance or business applications, organizations and engineers are split into two camps. Some are fully embracing the technology and see it as the future, while others are skeptical about its value and resist the change entirely. As someone who's all-in on AI and has been talking with engineering leaders during interviews, I can tell you there's no consensus on strategy. One of my primary questions during interviews has been to ask about a company's general AI position and or strategy. The responses I get are all over the map. Some say "if you're interested in AI, this is probably not the position for you" while others want "AI forward engineers with 2 years experience building LLM backed applications." There is a big gap between those two positions that in my mind are clinging to traditional software engineering hiring strategies. From my perspective, to not have an active AI strategy is not good nor is saying "we only want people with experience with these specific tools". Generative AI, whether for coding or delivering business value through automation or novel products, is not about code or tools. It is about learning quickly and focusing on application of this new-ish technology in ways that deliver measurable business value. Both coding and AI applications require a different mindset and the time to experiment. "We don't use AI" and "we want people who already have experience" both miss what I am speculating is the right balance for a digital strategy that will have legs over the next few years. My point here, if there is one, is that I do not envy the position the position that the hiring managers are in right now. A list of must-know technologies and desire for engineers who want to work outside of their lane via AI empowerment makes for a near impossible candidate technical evaluation process :) Strange days for sure...

Tech engineers are losing opportunities to innovate as they spend more time on maintenance work. With agentic AI, companies can reduce the time spent on routine tasks and enable engineers to focus their skills and expertise on more creative, high-impact work. Here’s how. #SoftwareDevelopment #SoftwareEngineers #AI

The Next 5 Years of Software Engineering I’ve been writing software for 30 years. It’s faster now. Easier. But not always better. Code is still fragile — full of bugs, hacks, and vulnerabilities. With AI writing much of the boilerplate, the role of the engineer is shifting. What will matter? • Architecture over syntax → AI can generate code, but it can’t reason about tradeoffs, failure modes, or long-term maintainability. • Outcomes over output → Companies don’t pay for lines of code. They pay for working, reliable, and secure systems. • Curation over creation → The bottleneck is no longer writing, but validating, governing, and integrating. • Hybrid skills → Engineers who combine technical depth with product, compliance, and communication will thrive. • Renaissance mindset → We are not just coders anymore. We are system thinkers, orchestrators, and stewards of trust. The question isn’t “Will AI replace us?” but rather: Are we ready to lead in an AI-powered world? Maybe this is a renaissance. Maybe it’s a reset. Either way, the future belongs to those who can think beyond code.