{"slug": "the-best-software-engineers-are-getting-ridiculously-fast-with-ai", "title": "The Best Software Engineers Are Getting Ridiculously Fast With AI", "summary": "According to a widely shared engineering essay, the most significant advantage of AI for software engineers is not faster code writing but the compression of the learning curve when understanding unfamiliar codebases, enabling engineers to interact with systems conversationally and accelerate context construction. The piece warns that AI explanations can create a false sense of understanding, and emphasizes that real understanding comes from answering probing questions about system behavior and failure modes.", "body_md": "There is a mistake I see engineers making with AI.\n\nThey use it to write code faster.\n\nThat’s useful.\n\nBut it’s not the biggest advantage.\n\nThe real advantage is that AI can dramatically reduce the time between:\n\n**“I don’t understand this.”**\n\nand\n\n**“I understand what’s happening.”**\n\nThat changes engineering.\n\nA lot.\n\nImagine you’re given a 500,000-line codebase you’ve never seen before.\n\nBefore AI, you might spend hours — or days — figuring out:\n\nNow you can start asking questions.\n\n“Show me the lifecycle of a request from the API gateway to the database.”\n\nThen:\n\n“Which components can introduce latency?”\n\nThen:\n\n“Where are retries implemented?”\n\nThen:\n\n“Which services have the highest coupling?”\n\nThen:\n\n“What happens if Redis becomes unavailable?”\n\nYou’re not asking AI to write the application.\n\nYou’re using it to **compress the learning curve.**\n\nBecause engineering isn’t just coding.\n\nA huge amount of engineering time is spent on:\n\n**understanding.**\n\nUnderstanding:\n\nAnd understanding is often the slowest part.\n\nAI can attack that bottleneck directly.\n\nThis sounds backwards.\n\nShouldn’t junior engineers benefit more from AI?\n\nThey absolutely can.\n\nBut experienced engineers often get something much more powerful from the same tools.\n\n**Context.**\n\nA senior engineer sees an unfamiliar codebase and already has mental models.\n\nThey know:\n\n“This pattern usually creates connection pool problems.”\n\nThey know:\n\n“This retry policy looks dangerous.”\n\nThey know:\n\n“This abstraction is probably hiding a consistency problem.”\n\nThey know what questions to ask.\n\nAI gives them speed.\n\nExperience gives them direction.\n\nBefore AI:\n\nYou investigate.\n\nYou search.\n\nYou read.\n\nYou grep.\n\nYou open files.\n\nYou trace calls.\n\nYou write notes.\n\nYou form hypotheses.\n\nWith AI:\n\nYou can still do all of that.\n\nBut now you can interact with the system conversationally.\n\nYou can ask:\n\n“Why does this function exist?”\n\n“Who calls it?”\n\n“What assumptions does it make?”\n\n“What happens if this database call fails?”\n\n“Show me all places where this value can be modified.”\n\nThat is an entirely different interface to software.\n\nAI can make you **feel** like you understand something.\n\nThat’s dangerous.\n\nYou ask:\n\n“Explain this architecture.”\n\nAI gives you a beautiful explanation.\n\nYou read it.\n\nEverything makes sense.\n\nThen production breaks.\n\nAnd you realize:\n\nYou understood the explanation.\n\nYou didn’t understand the system.\n\nThose are different things.\n\nReal understanding comes from being able to answer:\n\nWhy does this exist?\n\nWhat assumptions does it depend on?\n\nWhat happens when those assumptions fail?\n\nWhat would break if I removed it?\n\nHow would I know it was failing?\n\nWhat would I change if traffic increased 10x?\n\nIf you can answer those questions, you’re learning.\n\nIf you can only repeat the AI’s explanation, you’re outsourcing understanding.\n\nThis is becoming one of the biggest differences between engineers.\n\nConsider two prompts.\n\n“Explain this repository.”\n\n“Trace the request lifecycle for/checkout. Identify every network call, database query, retry boundary, cache interaction, and asynchronous event. Then list the three most likely failure points under elevated traffic.”\n\nSame AI.\n\nCompletely different result.\n\nThe second engineer has already started thinking like an engineer.\n\nAI simply accelerates it.\n\nA useful approach is to think in four layers.\n\n“What is happening?”\n\n“What could be wrong?”\n\n“What alternatives exist?”\n\n“Implement the chosen solution.”\n\nMost developers jump directly to Layer 4.\n\nThat’s why they can become extremely fast at producing code without becoming equally good at engineering.\n\nIt’s **context construction**.\n\nThe quality of your answer depends heavily on the quality of the context you provide.\n\nInstead of:\n\n“Why is this slow?”\n\nGive AI:\n\nNow you’re not asking AI to guess.\n\nYou’re giving it a problem it can reason about.\n\nBefore merging an important change, ask:\n\n“Assume this code will run at 10x today’s traffic. Find the first five things that could break.”\n\nThen:\n\n“Review this change specifically for race conditions.”\n\nThen:\n\n“Review it for database consistency.”\n\nThen:\n\n“Review it for failure recovery.”\n\nDifferent questions reveal different problems.\n\nThat’s much more powerful than:\n\n“Review my code.”\n\nThis is one of my favorite uses of AI.\n\nTake a new architecture and ask:\n\n“Simulate a database outage.”\n\nThen:\n\n“Now simulate Redis becoming unavailable.”\n\nThen:\n\n“Now simulate the payment provider timing out for 30 seconds.”\n\nThen:\n\n“Now simulate duplicate message delivery.”\n\nThen:\n\n“Now simulate a deployment that succeeds in one region and fails in another.”\n\nYou’re effectively conducting a tabletop incident exercise.\n\nBefore the real incident happens.\n\nYou can take a technology you’ve never used and ask:\n\n“Teach me Redis from the perspective of someone who already understands PostgreSQL.”\n\nThen:\n\n“What mental models from PostgreSQL will mislead me here?”\n\nThat’s a fantastic question.\n\nBecause advanced learning isn’t just:\n\n“What is this?”\n\nIt’s:\n\n“What assumptions from what I already know will stop working?”\n\nAI is extremely useful for that.\n\nThey’ll be the ones who know what **not** to delegate.\n\nDelegate:\n\nKeep:\n\nThat’s the balance.\n\nIf AI disappeared tomorrow, you should still understand your system.\n\nYou should still know:\n\nAI should make you faster.\n\nIt shouldn’t make you helpless.\n\nThat’s the standard I would use.\n\nDon’t ask:\n\n“How many lines of code did AI generate?”\n\nAsk:\n\n“How much engineering time did AI eliminate?”\n\nMaybe AI saved you three hours of repository exploration.\n\nThat’s valuable.\n\nMaybe it helped identify a production bottleneck in 20 minutes instead of four hours.\n\nThat’s valuable.\n\nMaybe it generated a test suite that caught a race condition.\n\nThat’s extremely valuable.\n\nThe number of generated lines doesn’t matter.\n\n**The avoided engineering effort does.**\n\nSenior engineers spend a lot of time on high-context problems.\n\nAI is getting increasingly good at helping with high-context information processing.\n\nThat combination is powerful.\n\nYou can potentially:\n\n**read more**\n\n**investigate more**\n\n**prototype more**\n\n**test more**\n\n**challenge more**\n\n**learn more**\n\nwithout adding proportional amounts of time.\n\nThat’s leverage.\n\nI think the workflow will increasingly look like this:\n\n**Problem**\n\n↓\n\n**AI-assisted investigation**\n\n↓\n\n**Human judgment**\n\n↓\n\n**AI-assisted exploration**\n\n↓\n\n**Human decision**\n\n↓\n\n**AI-assisted implementation**\n\n↓\n\n**AI-assisted adversarial review**\n\n↓\n\n**Human approval**\n\n↓\n\n**Production**\n\n↓\n\n**AI-assisted monitoring**\n\n↓\n\n**Human ownership**\n\nThat’s very different from:\n\n“AI, write my code.”\n\nIgnoring AI because:\n\n“I’m already a good engineer.”\n\nThat’s exactly why you should learn it.\n\nThe better engineer you are, the more leverage you can get from these tools.\n\nYou already know what matters.\n\nNow you can potentially investigate it much faster.\n\nThis may be the most important takeaway.\n\nIf AI can write 500 lines in a minute, don’t try to beat it by writing 600.\n\nThat’s the wrong competition.\n\nCompete on:\n\n**judgment.**\n\n**architecture.**\n\n**debugging.**\n\n**tradeoffs.**\n\n**ownership.**\n\n**business understanding.**\n\nThen use AI to make those skills faster.\n\nThat’s where the real advantage is.\n\nAI isn’t removing the need for engineering judgment.\n\nIt’s increasing the amount of work one strong engineer can potentially handle.\n\nThe opportunity isn’t:\n\n“Let AI replace my engineering.”\n\nIt’s:\n\n“Let AI multiply my engineering.”\n\nThat’s a much more interesting future.\n\nI built a practical system for senior backend engineers using **Claude Code and Cursor** to investigate, implement, debug, review, and ship faster — without blindly trusting generated code.\n\n[AI for Senior Backend Engineers: Ship Faster Without Breaking Production](https://devrimozcay.gumroad.com/l/rhqqg?utm_source=chatgpt.com)\n\nMore essays on AI, backend engineering, architecture, production systems, and career leverage:\n\nRead the Substack\n\n[The Best Software Engineers Are Getting Ridiculously Fast With AI](https://blog.stackademic.com/the-best-software-engineers-are-getting-ridiculously-fast-with-ai-671fc51d3f45) was originally published in [Stackademic](https://blog.stackademic.com) on Medium, where people are continuing the conversation by highlighting and responding to this story.", "url": "https://wpnews.pro/news/the-best-software-engineers-are-getting-ridiculously-fast-with-ai", "canonical_source": "https://blog.stackademic.com/the-best-software-engineers-are-getting-ridiculously-fast-with-ai-671fc51d3f45?source=rss----d1baaa8417a4---4", "published_at": "2026-08-30 10:52:25+00:00", "updated_at": "2026-08-30 11:22:46.334318+00:00", "lang": "en", 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