Robert Hawker’s Post

For senior engineers, it's imperative to learn how to use AI to help with code generation to speed up your dev cycle. StackOverflow -> AI is similar to the jump of books to using the internet for research. For junior engineers, learning the fundamentals and understanding whats going on within the systems you work on is going to be what makes you stand out. Be able to answer questions about what's going on underneath the hood. While everyone is now able to crank out code, knowing the what, why, and how is what will make you, and the organizations you work with successful.

The engineering hiring equation has changed. For years, the math was simple: hire junior engineers at $50-100K, accept the learning curve and training time from your seniors, then finally scale your team. AI tools aren't democratizing engineering expertise, they're amplifying the gap between junior and senior talent. Here's what an AI-enabled senior engineer actually looks like: They're using an agent (Cursor, Claude Code, something running on a home-lab, pick your poison) to generate code 'n' times faster, but more importantly, they're reviewing and immediately spotting the subtle bugs in that generated code. They're using AI to explore architectural options in minutes, but they know which tradeoffs matter for YOUR specific constraints. They're letting AI write tests while focusing on the test patterns and strategy that actually catches the critical issues rather than giving you pretty green check-marks. Junior engineers with the same tools? They're still learning to ask the right questions, but they don't have the scars to know when the AI is confidently wrong. They haven't built the mental models to evaluate whether that "working" code will become technical debt in six months. This is why fractional senior engineers are becoming the smarter play. You get the years of battle-tested judgment, now operating at peak velocity, for a fraction of the financial and time cost of building a junior team. The AI handles the grunt work. The senior handles the hard parts: architecture decisions, code review, mentoring your team, and most importantly knowing what NOT to build. The industry shift isn't AI replacing engineers. It's AI making experienced judgment exponentially more valuable, and with organizations like us, more accessible.

As a Principal Engineer working with production systems, I’ve been noticing a growing and slightly worrying pattern. “AI expertise” has become a checkbox on almost every resume. In many cases, it simply means someone learned how to prompt a model to generate code. In real production environments, that’s not leverage — it’s risk. A junior engineer with an AI copilot is still a junior. They may ship syntax faster, but they often lack the architectural context to understand why the code exists, what constraints it must satisfy, and what it will break six months later. This is why, when evaluating engineers for applied AI work, seniority comes first. I look for people who have: owned real systems in production, made architectural trade-offs under pressure, carried responsibility for reliability, cost, and long-term maintainability. Before testing AI-assisted workflows, I want to see that they can reason about systems, write clean code without crutches, and explain their decisions clearly. Only on top of that foundation does AI become a multiplier — not a liability. At that point, the same engineer can reliably deliver 2–5× the output without accelerating technical debt. If you skip that base layer of senior engineering skill, you’re not hiring a partner. You’re just compressing the timeline to your next refactor. Curious how others are separating genuine senior architects from “AI-powered juniors” in practice.

Junior engineers are more immediately valuable than they used to be. The problem is that this is only true for organizations that understand what they are doing in the moment we are in. If you are one of those organizations, we are headed back to the team structures described in The Mythical Man Month. It's workflow, it's removal of bottlenecks in real-time, it's context delivered in vivo to a process with a heartbeat measured in minutes. Having only senior engineers is not only a massive waste, it is sub optimal. Senior engineers with downstream Juniors is the future. A ration of 1:3 - 1:5 seems optimal, to pick some numbers. Juniors work their way up the stream by understanding the mission, the context, the architecture, and finally the code. This is not rocket science. It's industrial mechanics. If you are a junior engineer, non of what is going on is an indictment of you and your value. You are getting a lesson early that is usually learned late: most leadership has absolutely no idea what they are doing. Unfortunately this reality is currently frontloaded and a lot of people are getting hurt, also at industrial scale. But the market is a cruel beast and it is going to absolutely savage organizations that focused on Cutting Costs rather than Increasing Value. This is going to happen this year. Juniors, you are coming onboard at an amazing time and your future is going to be amazing. Stay engaged, follow the people who speak to your beliefs. Build mental models, talk about them with excitement, validate them, throw out what's wrong, start again. I love working with Juniors. I love beginner's mind energy. You are needed. You are loved. You will prevail. This is the year. peace (not written by AI)

𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗮𝗿𝗲𝗲𝗿 𝗽𝗮𝘁𝗵. 𝗧𝗵𝗲 𝗺𝗶𝗱𝗱𝗹𝗲 𝗶𝘀 𝗱𝗶𝘀𝗮𝗽𝗽𝗲𝗮𝗿𝗶𝗻𝗴 - 𝗳𝗮𝘀𝘁. Senior engineers are using AI for architecture, design, and system-level decisions. Their leverage is increasing. Junior engineers are using AI to generate code faster. Their output is increasing. Productivity is rising at both ends. Here’s the concern. The layer where engineers internalize systems, not just produce code is thinning. 𝘈𝘐 𝘦𝘹𝘱𝘢𝘯𝘥𝘴 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯 𝘤𝘢𝘱𝘢𝘤𝘪𝘵𝘺. 𝘉𝘶𝘵 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦. 𝘈𝘴 𝘢𝘣𝘴𝘵𝘳𝘢𝘤𝘵𝘪𝘰𝘯 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘦𝘴, 𝘥𝘪𝘳𝘦𝘤𝘵 𝘦𝘹𝘱𝘰𝘴𝘶𝘳𝘦 𝘵𝘰 𝘴𝘺𝘴𝘵𝘦𝘮 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳 𝘥𝘦𝘤𝘳𝘦𝘢𝘴𝘦𝘴. If output scales faster than understanding, engineering depth will not keep up. AI will improve engineering velocity. The leadership challenge is how we deliberately strengthen this middle layer, so depth grows alongside speed.

Engineering team structures are changing quickly. For a long time, the model was pretty straightforward: a pyramid. Big graduate intakes at the bottom, lots of juniors coming through, structured training and mentorship, steady internal progression. Some people rise fast, some move on, but the system keeps replenishing itself. That’s how most strong engineering organisations were built. But I’m seeing a real shift now. Away from the pyramid… towards something more like a diamond, maybe even a Christmas tree. There’s very little junior hiring, smaller grad intakes, and a heavier focus on mid-level and senior engineers. Pair that with strong AI tooling, and suddenly those experienced hires become output multipliers. Fewer people, higher output, faster iteration. From a business perspective, it makes sense: budgets are tighter; teams are under pressure to deliver; AI is compressing what one capable engineer can produce; hiring managers want someone who’s ready today, not in two years’ time. But it’s worrying. Because if the bottom of the pyramid disappears, where do future seniors come from? Entry-level opportunities absolutely still exist. But the competition is becoming brutal, the bar is much higher, and the funnel is narrowing. As a parent, that part does make me uneasy. I’ve spoken to a lot of engineering leaders recently, many with kids going through university, and there’s a shared sense of sadness. The traditional on-ramp into the industry is eroding. To be clear, this isn’t just about AI. It’s economics, uncertainty, and a more cautious approach to hiring. But the outcome is the same: the shape of teams is changing. And the people at the very start of their careers are the ones who notice it most.

Software Engineering ≠ Writing Code Software engineers have been misused for decades. We turned "engineering" into "typing code" — translating human intent into machine language, line by line. That's not engineering. That's bricklaying. Look at every other engineering discipline. Civil, mechanical, aerospace — they all share the same DNA: rigorous design, disciplined processes, methodical testing, prototype iteration, thorough requirements gathering, and meticulous documentation. When a bridge gets built, the engineer doesn't lay the bricks. They design it. They inspect it. They test it. They find the defects and correct them. The Sound Transit light rail crossing Lake Washington is a perfect example — the first train on a floating bridge in the world. Contractors built it. Engineers found quality defects in the concrete track supports that had to be demolished and rebuilt. The CEO said it himself: even when you contract with outside companies, you still have to monitor them to make sure everything's delivered as designed. That's the discipline. Design. Inspect. Correct. Software engineering was always supposed to work the same way. The "engineering" in Software Engineering means pattern recognition, system design, creative problem-solving, and architectural thinking. Not semicolons and syntax. Here's what's changed: our contractors are now machines. Some operating at PhD-level capability. The question for the future isn't "who can write the most code" — it's who can direct these machines to solve real problems. Think about what that means for a new college graduate today. They're walking into the workforce with access to the collective intelligence of the world's best researchers, on demand. Yes, it's probabilistic — but some of the sharpest minds on the planet are working to make it more deterministic every day. A 22-year-old with the right architectural thinking and creative problem-solving skills now has the leverage to build what used to require a 200-person engineering org. Innovation is cool again. Let's build some new empires. What are you building with this leverage? #SoftwareEngineering #AIEngineering #BuildWithAI #Innovation #FutureOfWork #SystemDesign #TechLeadership

I had a brief meeting with a VP of Engineering this morning who dropped the corporate filter on a topic that every tech leader is privately terrified of “AI has made my Senior and Principal Engineers 2x faster, but it’s making my Junior Engineers invisible. The overlords are starting to question why we even have them in the team” I was going to write about something else today, but this comment this morning made me pivot completely as I just had to share it (and, of course, my opinion!) Here is the problem as I see it If AI now handles the majority of the “grunt work” - I‘m thinking things like the boilerplate, the basic testing, even some of the more simple features - how do Juniors ever really learn the fundamentals? Historically, these “boring” things were the training ground. It’s where they got in the trenches and earnt their stripes before becoming a senior engineer By automating the entry-level work, we are (whether by accident or intentionally) cutting off the talent pipeline We are creating “human debt” Something that we will be paying for in the future if we aren’t paying for it now Eventually your Seniors, your Principals, your Tech Leads etc - they’ll all move on. They probably aren’t retiring with you. Which means that there will be nobody ready to step up who actually understands how your system works under the hood I’m of the opinion that the most successful CTO’s won;t be the ones who are, right now, buying more AI seats They’ll be the ones that are redesigning their mentorship programmes to ensure that the Juniors aren’t just prompting but are *actually learning* Engineering Leaders - Are we “solving” today’s delivery and budget goals by sacrificing our 2028 talent pipeline? Or is the “Junior Developer” title officially just a thing of the past?

The New Definition of “Senior Engineer” in 2026 Being a senior engineer used to mean: 🔹 Writing complex code 🔹 Mastering frameworks 🔹 Understanding patterns In 2026, it means something bigger. Today’s senior engineer is someone who can: 1) Navigate ambiguity When requirements are half-formed and pressure is high, they don’t freeze, they clarify. 2) Think in systems, not functions They don’t just write code, they anticipate how that code ripples across products, teams, and users. 3) Leverage AI, but not rely on it They know when AI suggestions are helpful and when they need human judgment. 4) Communicate with empathy They can explain trade-offs to PMs, negotiate scope with stakeholders, and coach juniors without condescension. 5) Own outcomes, not just tickets Shipping on time is good. Shipping something that still works six months later is better. Senior engineering in 2026 isn’t about lines of code. It’s about leadership, foresight, and lasting impact. What’s one skill you think every future senior engineer must have? #SoftwareEngineering #CareerGrowth #EngineeringCulture #TechLeadership #FutureOfWork #AIinDev #SystemDesign

☁️ 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗼𝘄 𝗪𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗦𝗲𝗻𝗶𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 I recently took a 90‑minute timed coding assessment, and it made me reflect on how we assess senior engineering talent ? Timed tests do measure depth — but only a specific kind: algorithmic depth under pressure. Useful, yes. But senior engineering requires a much broader range of thinking. Real impact comes from: • Architectural judgment • Handling ambiguity • Making thoughtful trade‑offs • Building systems that last • Staying calm under pressure • Elevating the team around you These dimensions of depth don’t fit neatly into a fixed timer ⏱️ Today’s tools, including AI, can help us deliver working solutions faster than ever. That’s a strength we should embrace. But it also shifts the focus: speed is no longer the best indicator of senior engineering capability. Thoughtful design, clarity of reasoning, and long‑term thinking matter far more. In the past few years, I’ve interviewed candidates whose resumes weren’t flashy, yet their thinking and problem‑solving were exceptional. It reminded me that interviewing carries real responsibility. A rigid process can easily filter out the right person — and that’s a loss for both sides. Maybe it’s time to balance algorithmic testing with evaluations that reflect real‑world engineering: how someone thinks, learns, collaborates, and approaches complex problems. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗿𝗲𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗲𝗽𝘁𝗵 𝗶𝗻 𝘀𝗲𝗻𝗶𝗼𝗿 𝗵𝗶𝗿𝗲𝘀? 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆. #EngineeringLeadership #SeniorEngineers #TechHiring #SoftwareEngineering #EngineeringCulture #TechLeadership #FranceTech