--- title: The New AI Superpowers: Focus and Followthrough | daily.dev description: AI tools promise massive productivity gains, but many developers fall into the trap of starting dozens of side projects instead of finishing fewer things well.... canonical: https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The New AI Superpowers: Focus and Followthrough | daily.dev og:description: AI tools promise massive productivity gains, but many developers fall into the trap of starting dozens of side projects instead of finishing fewer things well.... og:url: https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi og:image: https://api.daily.dev/og/posts/DCODnv0mi.png og:image:alt: The New AI Superpowers: Focus and Followthrough 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 New AI Superpowers: Focus and Followthrough **[Hacker News](https://daily.dev/sources/hn)** · 5 min read · 1 upvotes · 0 comments ## Summary AI tools promise massive productivity gains, but many developers fall into the trap of starting dozens of side projects instead of finishing fewer things well. The author shares a personal experience of burnout caused not by overwork in the traditional sense, but by an explosion of 'make-work' enabled by AI efficiency. The key insight: use AI to go deeper on fewer things that matter (vertical growth) rather than starting more projects (horizontal expansion). Drawing on Greg McKeown's Essentialism and the 'partial vs. total eclipse' analogy, the argument is that AI frees us to finally pay the price of that last 1% of quality — but only if we stay ruthlessly focused and follow through. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Community take How the wider developer community reacted, aggregated from 1 discussion and 79 comments across hackernews (as of 2026-07-29). **TL;DR:** Commenters largely relate to the problem of AI-enabled project sprawl and implementation fatigue, but are skeptical of the 2-100x productivity claims and debate whether AI is genuinely helping or just lowering the bar for shipping half-baked work. **Sentiment:** 25% positive · 45% mixed · 30% skeptical **The case for** - AI is genuinely useful for eliminating tedious, low-value tasks (config, containers, boilerplate), freeing developers to focus on actual coding. - Reduced friction to explore side projects and 'what if' ideas that would otherwise never get built. - Managing a backlog of 99%-complete projects may be easier to prioritize than a backlog of 0%-complete ones. **The pushback** - The 2-100x productivity claim is disputed — more code generated is not the same as more value delivered. - AI lowers the bar for proof-of-concept work, causing a flood of low-effort, incompatible copies of the same thing within organizations. - Teams brute-forcing with LLM tokens accumulate tech debt and lose understanding of their own systems, hitting a wall on complex problems. - AI can undermine the signals management uses to assess team competence, rewarding flashy demos over real engineering depth. - The 'last 1%' needed to actually ship polished software remains hard and exposes how much of the AI-generated 90% is 'hacks on top of hacks.' - LLM-assisted development can trigger existential questions about one's role and competence, potentially contributing to burnout in a different way. **By community** - hackernews (mixed): Commenters are split between genuine enthusiasm for AI as a productivity aid on tedious tasks and sharp skepticism about inflated productivity claims, organizational dysfunction from AI-enabled project sprawl, and the quality of AI-generated code near production. **Hottest debate:** Whether AI-generated speed and proof-of-concept demos represent real productivity gains or just lower the bar for shipping low-quality, half-finished work. **Open questions** - How should organizations update their signals for team competence and execution quality in an AI-assisted world? - Is there a reliable way to distinguish which tasks AI can genuinely accelerate versus where human understanding is irreplaceable? - Does AI-assisted development systematically erode developers' deep understanding of their own systems over time? **Highlights** > AI completely broke some of the signals management used as proxies for team competence. In the past, teams that moved quickly and had a proof of concept to show management were valued for the speed of their execution. Usually a team that could execute quickly had some competent developers who could also quickly execute on the challenges of the implementation when it scaled up to real world problems. Now anyone with enough LLM tokens can move quickly and have a pretty proof of concept in front of management in days or maybe hours. There's a land grab to get things in front of management so you can stake your claim on something. The implementation details are assumed to be solvable by enough Claude sessions and ChatGPT prompts. Where this falls apart is that the real difficult problems don't scale well with LLM brute force techniques. You need people who understand the problem and know how to drive the LLM agents around properly, as well as when to set them aside and go look at the code and data themselves. The teams who try to brute force everything with LLM tokens can get a little farther every few months, but for complex problems I've seen enough variations of teams hitting a wall of complexity and tech debt with a team that doesn't know much about how their system works because they've just been asking agents to solve every problem as it comes up. With each iteration it takes a little longer for the agents to come to a solution and the system becomes a little more brittle. Even with our current mega-context-window LLMs you can reach a point where every other change to your big ball of code breaks something else, which requires another agent session to diagnose and fix, which might then break another thing down the line. The refactors to try to clean things up end up breaking even more. Having a zillion tests starts to backfire when the team can't tell which tests are testing valid results and which are baking in incorrect results. Maybe there's an end game where agents get so good that none of this matters, but I think it's more likely that we're going to have to build up better intuitions about what good execution looks like. The old signals of moving fast are meaningly and potentially even negative now. > — [Aurornis on hackernews · 1 comments](https://news.ycombinator.com/item?id=49065530) > This seems very related to a trend I’m seeing as my company goes all in on AI: everyone thinks that every problem is “a couple hours” with AI now, and they all want zero external dependencies because they can move faster alone. As a result, we’re now in an even worse “yet-another-…” age where everyone has built approximately the same (but somehow incompatible) versions of all the same beginner-level software, and (ironically) while they want no external dependencies they’re also pushing for org-level mandates to require everyone else to use their solution. Meanwhile, no one wants to do the slow/bottleneck part that cant easily be automated or scaled; they just throw an “agent” at it and call it done—but there’s nothing _there_. You can trust the agent on easy tasks and you can’t trust it on hard ones, but you can’t tell which ones are easy or hard. Improvements in foundation model tech move thresholds of the problem but can’t eliminate it. Long story short, I think we’re in a phase where the organizational value function is lagging behind the tech. A “proof of concept” used to be correlated with “proof of work” and some amount of domain understanding, but I think now what we need is a focus on “proof of understanding” or else you’re probably just wasting tokens on a baby version of the problem. A decent proxy right now is that if you have zero external dependencies then your solution is probably a toy. > — [cgearhart on hackernews · 4 comments](https://news.ycombinator.com/item?id=49061112) > > So logically, if AI can help us complete tasks 2-100x faster than before, we should be UNDERWORKED and experiencing ZERO burnout, right? Got to admit, I stopped reading here. In my experience, burnout isn't related to the amount of work, but to your confidence in being able to do your job and if you find your job meaningful (like, does it even matter?). I'm probably doing twice as much work now compared to the period just before I burned out, but I'm also nowhere near a burnout. Part of that is general experience, but it's also because I feel like I know what I'm doing (so, no imposter syndrome) and because I've chosen my job based on what I find meaningful to do (is it ethical? does it make a difference in society? is it interesting?). I understand why LLM assisted development can lead to burnout. You now have a machine that "does most of the work for you". It's a gateway to questions like "how good are you really at your job?" and "anyone could push these buttons, couldn't they?" and "is this what the rest of my life should be?". It probably also reduces how interesting or fulfilling the job is for many people. You've been reduced to a cog in the machine, and you might be questioning your ability to be anything but. > — [Skinney on hackernews · 1 comments](https://news.ycombinator.com/item?id=49065312) > I will never believe the premise of 100x boost from AI, why are we still pushing this narative? Yes, AI is amazing in small, constrained, focused pieces of code but the code is nowhere fit for production and the last 10% needed to ship shows that the 90% that's already been done by AI is absolutely trash. Hacks on top of hacks. Is no one trying to ship polished things to customers anymore, will each of us have a hacky, bugged version of the same thing with different quirks? -- My first ever software project even before I worked as a swe had less bugs and frictions than all recent projects where AI was used. Some people like mitchellh seem to know what they are doing (I have not taken a look at ghostty's codebase as it's in zig) so I sometimes get the feeling I'm holding it wrong but in the end everyone around me seems to have similar problems with shipping. > — [taosx on hackernews · 1 comments](https://news.ycombinator.com/item?id=49063667) > It's wild we're seeing people rediscover the same solution to the same problems we've had forever: being precious about your darlings. Code generators reduced the friction for implementing every shitty idea someone has ever had, but because developers seem to have never experienced throwing shitty ideas away (because if it was written in code it _has_ to be important right?) they treat everything coming out of the Codegen as a precious idea that eventually builds up to clutter one's brain leading to Implementation Fatigue the author experienced. The solution? Go through that same process but focus on one idea at a time and make it good. It does make me wonder if the people experiencing this had hobbies outside of software engineering (let's say, anything creative) they'd be able to spot the signs earlier. > — [jalev on hackernews · 1 comments](https://news.ycombinator.com/item?id=49059447) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49057877) · 58 points · 79 comments ## Similar posts on daily.dev - [Hard-Won Lessons From a Year of Using AI](https://daily.dev/posts/hard-won-lessons-from-a-year-of-using-ai-qqlkacn9o) · Atomic Spin · 1 upvotes · 0 comments - [Quit trying to keep up with every new AI tool and keep building](https://daily.dev/posts/quit-trying-to-keep-up-with-every-new-ai-tool-and-keep-building-hzpuzonyn) · Dynatrace · 5 upvotes · 5 comments - [AI made me more productive. Why am I busier than ever?](https://daily.dev/posts/ai-made-me-more-productive-why-am-i-busier-than-ever--qbgids67s) · LeadDev · 1 upvotes · 1 comments --- Tags: [#ai-assisted-development](https://daily.dev/tags/ai-assisted-development) [View this post on daily.dev](https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi) ```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 New AI Superpowers: Focus and Followthrough","url":"https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi"},"datePublished":"2026-07-26T17:28:48.638Z","dateModified":"2026-07-29T14:05:31.900Z","description":"AI tools promise massive productivity gains, but many developers fall into the trap of starting dozens of side projects instead of finishing fewer things well....","image":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","thumbnailUrl":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","isAccessibleForFree":true,"articleSection":"Hacker News","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hacker News","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn","url":"https://daily.dev/sources/hn"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/the-new-ai-superpowers-focus-and-followthrough-dcodnv0mi","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-assisted-development","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hacker News","item":"https://daily.dev/sources/hn"},{"@type":"ListItem","position":3,"name":"The New AI Superpowers: Focus and Followthrough"}]} ```