Plug in to LeadDev  ## Metadata - Author: [[Harry Guinness]] - Full Title: Plug in to LeadDev - Category: #articles - Document Note: --- tags: - #ai_coding_tools - #engineering_management - #open_source_advocacy Author: Harry Guinness Source: LeadDev Reference: https://leaddev.com/process/how-ai-changes-engineering-management date created: 2025-08-22 00:00 --- # Title: How AI Changes Engineering Management ## Table of Contents: - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#-notes) - [📖 References](#-references) - [🗣️ Paraphrase](#-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical validity](#-logical-validity) - [🔍 DARVO Analysis](#-darvo-analysis) - [🔍 SEO Evaluation](#-seo-evaluation) - [🎯 Conclusion](#-conclusion) ### References: - GitHub survey of 500 US-based developers on AI coding tools usage. - Communications of the ACM article by Matt Welsh on “the end of programming.” - Interviews and perspectives: Piyush Tripathi (Square), Hywel Carver (Skillerwhale), Oguz Acar (King’s College London). ## 📝 Summary: The article argues that AI-powered coding assistants (e.g., ChatGPT, GitHub Copilot) are already reshaping software development and will increasingly redefine engineering management. In the short term, teams can gain 2–3x productivity with proper training and realistic expectations. In the long term, experts like Matt Welsh foresee the end of traditional programming, with humans in supervisory roles over trained systems. Managers must simultaneously exploit near-term gains, manage risk (quality, compliance, data), and prepare their organizations for a shift in engineers’ responsibilities toward problem definition, architecture, and governance. Key lines: - “Generative AI-powered programming tools are here – and they’re already effective.” (≈10%) - “A survey … found that 92% of developers are now using AI coding tools at work.” (≈18%) - “The conventional idea of ‘writing a program’ is headed for extinction.” (≈45%) - “Managers will need to review … significantly larger quantity of code, not all of it directly written by humans.” (≈82%) (Percentages indicate approximate positions within the article.) ## 📖 Main Content: The piece opens by asserting that AI coding tools are already competent at meaningful tasks: writing code (simple and complex), refactoring across languages, generating docs, and explaining unknown code. It frames a pragmatic managerial question: these tools won’t fully replace juniors now, but what should managers do today? Near-term reality: - Usage is widespread and largely bottom-up. A GitHub survey reports 92% of developers already use AI coding tools, crediting them with improved quality, speed, and fewer incidents. Tripathi (Square) reports 2–3x productivity among engineers who lean into AI and highlights the importance of speed and flexibility in adopting AI to streamline workflows (≈18–27%). Risk and enablement: - AI is “very experimental.” Managers must temper expectations, invest in training, and anticipate resistance. Change management matters: introduce AI in ways that affirm people’s value. Legal and compliance risks loom, especially with confidential inputs and future regulation (EU AI Act) (≈27–36%). Long-term disruption: - Welsh predicts “the end of programming,” shifting from coding to training systems, driven by economics: dramatic cost reductions compared to human labor, even if estimates are off by orders of magnitude (≈36–55%). Carver counters that cheaper code will raise expectations; developers may remain numerous but take on different roles with higher output expectations (≈45–55%). Role evolution: - Engineers increasingly focus on architecture, problem formulation, performance, safety, monitoring, and quality—senior responsibilities that may become the core of the role (≈63–73%). Acar emphasizes problem framing and decision-making as critical skills. Carver notes the new difficulty of reviewing AI-generated code when you’re not hands-on daily (≈72–82%). Managerial imperatives: - Prepare for a surge in code volume and incorporate rigorous review, governance, and compliance. Strategically position between extreme views—from full AI replacement to denial—by being informed, experimental, and cautious (≈82–91%). Regardless of the timeline, managers will continue to define problems, assure quality, prioritize, and collaborate—while increasingly managing AI systems alongside humans (≈91–100%). ### 🗒️ Notes - Effectiveness of AI tools (≈9–18%) - Code generation for trivial/complex tasks. - Refactoring and language translation. - Documentation and code explanation. - Adoption landscape (≈18–27%) - 92% of developers use AI tools at work. - Reported benefits: quality, speed, fewer incidents. - Tripathi: 2–3x productivity with willing learners. - Manager’s near-term opportunity (≈18–27%) - Move quickly; be flexible in integrating tools. - Identify workflow simplifications and codify patterns. - Risks and constraints (≈27–36%) - AI is experimental; bugs and inconsistency. - Training is required; don’t assume organic upskilling. - Anticipate resistance; maintain psychological safety. - Legal/compliance risks now and under future regulation (EU AI Act). - Long-term scenarios (≈36–55%) - Welsh: from coding to training systems; supervisory human role. - Economic rationale: massive cost differential. - Even modest savings disrupt staffing models. - Impact on roles (≈55–73%) - Junior developers most exposed (boilerplate, integration work). - Developers may shift to higher-level responsibilities. - Expectations rise: more features, faster, with oversight skills. - Skills for the AI era (≈63–82%) - Architecture, safety, performance, monitoring, quality assurance. - Problem formulation and reframing; constraints definition (Acar). - Code review capabilities without constant hands-on coding (Carver). - Preparation and governance (≈82–100%) - Expect volume surge in code requiring review. - Strengthen regulatory and data handling practices. - Balanced stance: neither apocalyptic nor dismissive. - Core management persists: define problems, prioritize, cross-org collaboration, manage humans and AI. ## 📖 References: - GitHub survey on AI coding tool adoption and benefits. - Matt Welsh, Communications of the ACM: argument for “the end of programming.” - Interviews: Piyush Tripathi (Square), Hywel Carver (Skillerwhale), Oguz Acar (King’s College London). ## 🗣️ Paraphrase: 1) Original: “If the current generation of AI-powered tools are already making engineers two or three times as effective, what are they going to be capable of in five years?” (≈45%) Paraphrase: If today’s AI assistants already multiply engineer productivity two- or threefold, their capabilities in the next five years could upend how we build software. 2) Original: “In a widely-discussed Communications of the ACM article, Welsh argues that the ‘the conventional idea of ‘writing a program’ is headed for extinction,’ and that complex applications would be trained rather than programmed, relegating humans ‘to, at best, a supervisory role.’” (≈45–50%) Paraphrase: Welsh contends that classical programming is waning; instead of coding complex systems, we’ll train them—leaving people primarily in oversight and direction. ## 📰 Newsletter: Learning web development just got a turbo boost thanks to AI copilots. This week, we’re unpacking how tools like ChatGPT and GitHub Copilot are reshaping coding—from writing boilerplate to translating between languages and explaining legacy code. The big takeaway: teams that embrace AI thoughtfully are seeing real gains in speed and quality, but it takes training, good prompts, and solid review practices. What’s new: - Productivity pops: Many developers report 2–3x efficiency with AI assistants. - Safer, faster shipping: AI can help reduce incidents when paired with strong tests and code r - Summary: LeadDev.com discusses the rise of generative AI-powered programming tools, such as ChatGPT and GitHub Copilot, that can write usable code, refactor, or convert code between languages, create documentation, and explain the function of unknown code. While not yet able to replace junior developers, these tools are already being used in the real world, with 92% of developers surveyed by GitHub reporting they use AI coding tools at work. Engineering managers need to explore and understand these tools, but also have realistic expectations of what they can do and anticipate pushback. In the long term, large language models could bring about the end of programming as we know it, but for now, engineering managers need to prepare for a time where they need to review and ensure the quality of a significantly larger quantity of code, not all of it directly written by humans. - URL: https://leaddev.com/process/how-ai-changes-engineering-management You have **0** further articles remaining this month. [Join LeadDev.com for free](https://leaddev.com/register?s=dw) to read unlimited articles. The rise of competent AI coding assistants could shift the role of software engineering managers considerably. Are you ready for that change? 2023 Conferences & Events Events  ##### 2023 Calendar - Join us in San Francisco and Berlin this year Get unparalleled access to industry leaders. Learn from diverse voices in tech. Develop your skills as an engineering leader. Generative AI-powered programming tools are here – [and they’re already effective](https://leaddev.com/tech/7-generative-ai-productivity-hacks-developers). Tools [like ChatGPT and GitHub Copilot](https://leaddev.com/tech/11-generative-ai-programming-tools-developers) can write usable code to solve both trivial and complex problems, refactor or convert code between languages, create documentation, or explain the function of unknown code. While these tools aren’t able to replace a junior developer yet, is it only a matter of time? And what can engineering managers do to stay ahead of things? #### The genie is out of the bottle In a sense, it’s already too late to get ahead of AI. The current generation of tools are already being used in the real world, [whether managers want people to or not](https://www.businessinsider.com/chatgpt-secret-productivity-work-ai-technology-ban-employees-coworkers-job-2023-8?r=US&IR=T). [A survey conducted for GitHub](https://github.blog/2023-06-13-survey-reveals-ais-impact-on-the-developer-experience/) found that 92% of developers are now using AI coding tools at work. According to the 500 US-based developers surveyed, “AI coding tools can help them meet existing performance standards with improved code quality, faster outputs, and fewer production-level incidents.” Piyush Tripathi, a lead engineer at fintech company Square, is one of these developers. His team uses ChatGPT, another GPT-based tool called Auto-GPT that he likens to “ChatGPT on steroids”, and GitHub Copilot to perform a variety of programming tasks on a daily basis. “I’ve seen engineers who were willing to learn to use AI get twice – or maybe three times – as productive,” he says. This creates an immediate window of opportunity for managers that are prepared to embrace these tools right away. “Everyone’s talking about AI and ChatGPT, and trying to include them in their products or use them,” he says. It just depends on “how fast you are and how flexible you are in using these AI tools to simplify your workflow.” #### Understanding the risks While Tripathi feels engineering managers need to understand and explore AI tools, they need to have realistic expectations of what they can actually do too. “AI is very experimental,” says Tripathi. “I’ve seen teams where people had high expectations and thought it was going to be a game changer, and then realized it’s buggy and has lots of issues.” He thinks that in the right environment, where it’s okay if things don’t work 100% of the time, the current crop of AI tools can make people more productive. However, managers can’t simply expect engineers to “just magically learn everything”, Tripathi says. They will need to be trained to make the most of AI if managers want them to be more productive. They should also anticipate some pushback. “People are going to oppose it, so introduce it in a way that tells people they are important,” he says. Then there are the [legal ramifications of inputting confidential data into these models](https://leaddev.com/tech/6-biggest-generative-ai-risks-developers), and using the model’s suggestions in production. To complicate matters further, it’s not just existing laws that engineering managers need to consider, but also future legislation and compliance. Loose data handling decisions made now could have serious implications in a few years time, especially if the European Union takes a hardline stance on enforcing [its proposed AI Act](https://artificialintelligenceact.eu). #### The end of programming as we know it? In the longer term, things are likely to shift radically. If the current generation of AI-powered tools are already making engineers two or three times as effective, what are they going to be capable of in five years? Matt Welsh, chief architect at AI startup Fixie.ai and a former professor of computer science at Harvard, believes that large language models (LLMs) will bring about “the end of programming.” [In a widely-discussed Communications of the ACM](https://cacm.acm.org/magazines/2023/1/267976-the-end-of-programming/fulltext) article, Welsh argues that the “the conventional idea of ‘writing a program’ is headed for extinction,” and that complex applications would be trained rather than programmed, relegating humans “to, at best, a supervisory role.” Over Zoom, Welsh explained that it’s as much an economic argument as anything. “I did the math!” he says. “If you take a Silicon Valley engineer, one engineer is paid around $1,200 dollars a day if you consider their salary and benefits and how many working days there are in a year. Imagine you had an AI that could do their job. The amount of money you pay for the same output is something like $0.12 cent a day, so you have like a 10,000X reduction in cost.” Welsh thinks that even if he’s wildly off in his estimate and it’s only a 10X or even 2X cost saving, it’s still a compelling argument for “massive numbers of jobs being eliminated, because AI can ultimately do this work that we’ve been expecting highly paid, highly skilled, very expensive humans to do.” Welsh thinks that junior developers are likely to be the most impacted. “If 30% of software projects that are done by humans today could be done by AI in the future, then that’s going to disproportionately impact the people that are doing more mundane things”, like integration and writing boilerplate code. Even for the highly skilled workers that do stick around, their jobs are likely to change as a result. Hywel Carver, CEO of team coaching service [Skillerwhale](https://www.skillerwhale.com) believes that if the cost of writing code becomes cheaper, companies will just expect engineers to deliver more code and build out features faster. “Development is something you always want more of,” he says. “I think we will probably end up with a similar number of developers, doing quite different roles, but our expectations from them will massively increase.” In either case, it’s clear that the role of an engineer will have to adjust as generative AI tools become more prevalent. “We’re seeing a lot of hand wringing around this right now because people don’t really know what it means to be a software engineer in this world of AI systems being extremely capable,” Welsh says. #### Moving up the stack Hands-on-keyboard coding is of course a big part of software engineering, but it’s not the whole job. Architecture design, solving complex problems, debugging, planning, and collaborating across the entire organization are all important parts too, and LLMs won’t necessarily obviate any of them. “Literacy around the content and operation of the structure of programs is going to be extremely important, even if I’m not the one writing it,” explains Welsh. “How do you instruct models to interface to software systems? How do you ensure safety? How do you ensure good performance? How do you monitor and measure quality?” These have always been part of the job of software engineering, but typically for those later in their careers who have been able to move up the stack to tackle more complex problems. If this becomes the entire job, that has a massive potential impact on the profession as a whole. Oguz Acar, a professor of marketing and innovation at King’s College, London, similarly thinks that the role of engineers will shift to [defining problems and making decisions](https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future). To make good use of AI going forward, he explained, “we want to get better at formulating problems and identifying their root causes… How do we frame it? How do we reframe it? How do we think about the constraints of it?” Identifying what problems need to be solved, breaking them down into manageable chunks, and choosing what resources are most appropriate for tackling each one sounds a lot like the job of an engineering manager. In the age of AI, does this mean every engineer will need to learn these skills to be effective? “One of the things that becomes hard is having skills that are strong enough to do a good review and to really understand what an AI has produced when you yourself are no longer writing code,” Carver says. #### Preparing for change It’s impossible to predict exactly how this will all play out, but either way, software development is going to undergo a dramatic shift in the next few years. In the short term, it seems likely that coding productivity will explode, whether that is because improvements to the current generation of tools, or new tools that allow smaller numbers of engineers to oversee LLM-powered coding programs. Engineering managers will need to prepare for a time where they need to review and ensure the quality of a significantly larger quantity of code, not all of it directly written by humans. With that they will need to pay special attention to any potential regulatory, compliance, and data handling issues. Sloppy AI practices could quickly become major legal headaches in the future. After that, though, it’s really anyone’s guess. At one end of the spectrum you have Welsh, who foresees the end of programming as we know it, with AI programs trained to solve most coding problems. At the other end there are engineers who could certainly be accused of burying their heads in the sand and hoping that generative AI is [the next Web3](https://www.reddit.com/r/programming/comments/111lu0p/is_web3_dead/). While somewhere in the middle is Carver – managers who are informed enough to prepare for this change, while unprepared to rip up the current playbook just yet. Either way, engineering managers will still need to bring traditional skills to a new set of challenges: defining problems, ensuring software works as it should, prioritizing product decisions, and working with other departments. The big question really is how much they will be managing humans – and how much they will be managing AI., Plug in to LeadDev, Harry Guinness, 01h7z5b6mmwe8adq3a116gncc0.