--- title: The compiler was supposed to end programming too | daily.dev description: AI is not ending programming, but it is reshaping where developer time goes, this piece argues using data through 2026. A METR study of 16 open-source... canonical: https://daily.dev/posts/the-compiler-was-supposed-to-end-programming-too-swbrievw6 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The compiler was supposed to end programming too | daily.dev og:description: AI is not ending programming, but it is reshaping where developer time goes, this piece argues using data through 2026. A METR study of 16 open-source... og:url: https://daily.dev/posts/the-compiler-was-supposed-to-end-programming-too-swbrievw6 og:image: https://api.daily.dev/og/posts/swBRIeVw6.png og:image:alt: The compiler was supposed to end programming too og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The compiler was supposed to end programming too **[Hacker News](https://daily.dev/sources/hn)** · 14 min read · 0 upvotes · 0 comments ## Summary AI is not ending programming, but it is reshaping where developer time goes, this piece argues using data through 2026. A METR study of 16 open-source developers found AI tools made them 19% slower despite expecting a 24% speedup. Faros AI data on 22,000 developers showed task completion up 34% in 2026, but median review time grew five times and incident-to-pull-request ratios more than tripled. Stanford researchers found software developer employment for ages 22-25 down nearly 20% since 2022, while Anthropic says over 80% of code merged into its own codebase is now written by Claude. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Did the METR study find AI coding tools actually speed up experienced developers? No. In a randomized controlled trial, 16 experienced open-source developers working on 246 real issues in their own repositories took 19% longer when using AI tools, even though they had expected a 24% speedup beforehand and believed afterward that it had cut their time by 20%. The study used early-2025 AI tools on codebases the developers knew well. _Anyone weighing AI tools against a time budget can track studies like this one on daily.dev before trusting the hype._ ### How has AI coding affected code review workload on software teams? Faros AI telemetry from 22,000 developers in 2026 found task completion per developer up 34%, but median pull request review time grew five times over, 31% more pull requests merged without any review, and the ratio of incidents to pull requests more than tripled. An earlier 2025 Faros study of 10,000 developers found review time up 91% alongside a 98% jump in merged pull requests. _Teams rethinking review processes around AI-generated code can follow data like this on daily.dev._ ### Has AI coding reduced entry-level software developer hiring? Yes, according to Stanford Digital Economy Lab research using ADP payroll data. Employment for software developers aged 22 to 25 fell nearly 20% from its late-2022 peak, and by mid-2026 young workers in AI-exposed jobs were 19% below where they'd be had they kept pace with less-exposed peers, while experienced developers showed no comparable gap. _Developers and managers worried about the junior hiring pipeline can follow this kind of labor data on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 253 comments across hackernews (as of 2026-10-22). **TL;DR:** Commenters largely reject the compiler analogy, arguing LLMs are qualitatively different (non-deterministic, driven by ambiguous natural language) rather than a deterministic abstraction layer, while debate rages over whether AI will replace most white-collar work or just automate rote portions of it. **Sentiment:** 20% positive · 40% mixed · 40% skeptical **The case for** - Some argue current models like Opus produce code cleaner than most professional developers when prompted well. - AI is seen by some as a natural extension of the decades-long trend toward higher-level abstractions in programming. - A few note it frees them from monotonous aspects of coding they didn't enjoy anyway. **The pushback** - Many insist LLM output is non-deterministic and unlike a compiler's precise, reproducible translation. - Several report that without careful oversight, agents produce messy, hard-to-maintain code full of anti-patterns. - Concerns that even partial automation of white-collar jobs could cause severe economic and societal disruption. - Natural language is seen as too ambiguous to serve as a reliable 'source language' compared to formal programming languages. **By community** - hackernews (heated): A long, contentious thread pitting those who see LLMs as fundamentally different from compilers (non-deterministic, ambiguous input) against those who see them as just the next abstraction layer, with sharp disagreement over code quality and job impact. **Hottest debate:** Whether comparing LLMs to compilers is valid at all, given disagreement over determinism, abstraction, and whether natural language can ever function as precise 'source code'. **Open questions** - Will AI's automation of rote work create new higher-value roles the way past technological shifts did, or will there be no room left for humans to add value? - How much of the job displacement will stem from AI directly replacing tasks versus organizations restructuring teams to do more with less? **Highlights** > If you can't see the difference between a compiler and an LLM then that's on you. LLMs are a game changer in a completely different way than compilers. With compilers there is a (roughly, barring optimizations) 1:1 correspondence between input and output. With LLMs you take an idea and it will drive the whole thing from the beginning all the way to the end. This is unlike anything that we've done so far. It's also strange to focus on programming: LLMs are coming for all information related jobs. If your job is somehow described as 'staring at a screen' then that job is on the line, if not sooner, than later. > — [jacquesm on hackernews · 14 comments](https://news.ycombinator.com/item?id=50185834) > I hear this meme of "Opus writes better code than me" repeated a lot. I am absolutely baffled by it. I use all the latest and greatest Anthropic (and OpenAI) models, and interact with them probably on average 10h a day. I experiment with changing effort levels, with different ways of prompting and guardrailing, etc. Giving more instructions, giving less instructions. Doing heavy planning, just giving vague ideas. I've done everything from fully vibe coding massive projects, to using LLMs regularly in my day job and reviewing every single line they produce. Unless you are *really* on top of your agents, they inevitably end up writing an absolute mess. Not only does the code become impossible for humans to reason about very quickly, it actually becomes difficult for agents to reason about it. It turns into a total garbage in = garbage out situation. Have you honestly reviewed a vibe coded project after it has been going for a while, even one written by current SOTA models, and thought "yep, this is great code"? > — [sunaurus on hackernews · 3 comments](https://news.ycombinator.com/item?id=50186196) > This reinforces the comparison to compilers. Modern compilers produce better assembly code than almost every human programmer. Despite that, there are way more programmers than before compilers existed. The job of a programmer is partially to automate away the current job of a programmer, and we've been successfully riding that curve at least since the days of punch cards. > — [timerol on hackernews](https://news.ycombinator.com/item?id=50186552) > Whenever I read these arguments, it seems to me like folks are talking past each other because programmers tend to fall into two camps. Those who spend time thinking about a problem, then writing design notes, specifications, and test plans, and focus on the implementation last. For these people, AI agents primarily act like an extension to their existing toolchain, and thus like the analogy of "AI as the new compiler". The other side are people who don't like writing specifications and such, and rather prefer to sit down figure things out as they start coding. For them, an agent feels radically different from a compiler. In both instances the AI agent is still the same tool, with the same real-world capabilities and limitations. It is not like one camp is blind, they just have very different workflows. It also seems to me like many in the "sit down and code" camp then slowly figure out that doing some planning ahead of time helps them get better results, and they see this as another AI-led paradigm shift, when in reality it is how many other programmers have worked for decades. > — [david-gpu on hackernews](https://news.ycombinator.com/item?id=50186170) > Can we please stop comparing AI with a compiler? A compiler takes a programming language as input float x = 3.1; Has only one translation into assembly. The programming language is well defined (or it should be). If you try to do float x = '3.1'; you will get an error However, the human language is not a programming language. It's open to interpretation "create a variable x and assign it 3.1" is not a well defined programming instruction. Is x a number or a string? If it's a number, what's the precision. Etc. > — [gonzalohm on hackernews · 4 comments](https://news.ycombinator.com/item?id=50185781) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=50185045) · 72 points · 253 comments ## Similar posts on daily.dev - [How AI coding makes developers 56% faster and 19% slower](https://daily.dev/posts/how-ai-coding-makes-developers-56-faster-and-19-slower-t4jx0ry0u) · The New Stack · 0 upvotes · 0 comments - ["2026", AI Users vs The Unemployed.](https://daily.dev/posts/2026-ai-users-vs-the-unemployed--tavhigwcp) · DEV · 41 upvotes · 8 comments - [The State of AI Coding 2025](https://daily.dev/posts/the-state-of-ai-coding-2025-hwdwm1bkn) · Hacker News · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#productivity](https://daily.dev/tags/productivity), [#ai-coding](https://daily.dev/tags/ai-coding), [#code-review](https://daily.dev/tags/code-review) [View this post on daily.dev](https://daily.dev/posts/the-compiler-was-supposed-to-end-programming-too-swbrievw6) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The compiler was supposed to end programming too","url":"https://daily.dev/posts/the-compiler-was-supposed-to-end-programming-too-swbrievw6","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-compiler-was-supposed-to-end-programming-too-swbrievw6"},"datePublished":"2026-10-21T19:10:43.158Z","dateModified":"2026-10-22T10:03:15.749Z","description":"AI is not ending programming, but it is reshaping where developer time goes, this piece argues using data through 2026. 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