I. Introduction: The Broken Promise of AI Utopia
Look, I remember the hype like it was yesterday – or, hell, like it was last week, because the tech bros haven't stopped yapping about it. Back in the early days of all this AI frenzy, around 2022 or so, they painted this picture of a coding paradise where machines would swoop in like superheroes, building entire apps faster, better, more robust, all while being dirt cheap and never once complaining about overtime or coffee breaks. "Human devs? Obsolete!" they'd crow from their TED stages and Twitter threads (sorry, X threads now). It was this shiny promise that had venture capitalists drooling and CEOs sharpening their layoff lists. Imagine: millions in profits, zero payroll, and code that just... works, eternally.
For folks like me, grinding away as a web dev, it sounded equal parts terrifying and tantalizing. Terrifying because, duh, who wants their job turned into a relic? Tantalizing because, come on, if AI could handle the grunt work, I'd have more time for the fun stuff – like architecting wild user experiences or finally diving deeper into a new framework or maybe a new language or game dev. But years have ticked by since those bold declarations, and here we are in October 2025, and guess what? That utopia? It's a mirage. AI hasn't replaced a single dev worth their salt. If anything, it's made it crystal clear how irreplaceable we pros are. Turns out, what we got wasn't a tireless genius army. It was an overly expensive autocomplete on steroids – faster at spitting out suggestions, sure, but about as clairvoyant as a Magic 8-Ball when it comes to actually knowing what the hell you need.
Don't get me wrong; I'm not here to dunk on AI entirely. I've been knee-deep in it for my front-end work, poking at tools like GitHub Copilot, Cursor, and whatever flavor of the month Claude or GPT is serving up. And yeah, there are moments where it feels like a game-changer. But mostly? It's a reminder that the emperor's got no clothes – or at least, no pants that fit right. The promises were wild: AI would democratize coding, let anyone build anything, crush inefficiencies. Reality? It's amplified the need for sharp humans who can steer the ship, because left to its own devices, this thing veers into the ditch more often than not. I don't write React much anymore – these days it's all Svelte and SvelteKit with TypeScript for me, chasing that lightweight reactivity without the bundle bloat. But even in my Svelte world, the story's the same.
Let me back this up with what's actually happening out there. Reports from 2025 are unanimous: AI's boosting productivity, sure – devs using it crank out code 20-50% faster on rote tasks – but it's augmentation, not automation. No one's workforce got swapped out wholesale; if anything, companies that bet big on "AI-only" teams are scrambling to hire back talent because the bots can't handle the nuance. One study even showed that AI-assisted devs outperform non-users by a mile, but that's because humans are doing the heavy lifting – prompting, debugging, integrating. The replacement dream? Busted.
So, why the disconnect? Why did they sell us this bill of goods? Part of it is the grift – endless funding rounds need a narrative, and "AI will eat the world" sells tickets. But for us in the trenches, it's personal. I dove in expecting a co-pilot; I got a backseat driver who occasionally grabs the wheel and swerves into traffic. In this piece, I'm gonna rant through my own rollercoaster: the highs (few and fleeting), the crashes (plenty), and why, despite the hype hangover, AI's still worth keeping in the toolbox – just not as the whole damn shed. Buckle up; if you've ever copy-pasted an AI-generated snippet only to spend hours untangling it, you're gonna nod along. And trust me, in Svelte land, those untanglings hit different – more on that soon.
II. Where AI Shines (But Only Just): Boilerplate and the Familiar
Alright, let's start with the good stuff – because yeah, there is some. I'm not a total cynic; AI has carved out a niche in my workflow, and it's mostly the boring, brain-draining bits that used to make me want to hurl my laptop out the window. Think repetitive front-end boilerplate: slapping together a new component in SvelteKit that match your existing design, writing and designing an enhanced form, or churning out those endless Tailwind classes that all blur together after the tenth one. I know what it should do, I know how it should look – I just don't always feel like typing it out by hand. That's where AI steps in like... well, not even a lazy intern, because most juniors I've mentored are sharper and way more useful on day one. At best, it's a fancier TypeScript autocomplete, guessing the next prop or import with a bit more flair.
For instance, last week I was knee-deep in a SvelteKit app, and I needed a basic timer feature for a countdown in a productivity dashboard. Normally? I'd hit up the Svelte docs or MDN, skim a few examples, and hack it together with $state for the countdown variable and $effect for the interval logic. With AI? I prompt something like: "Write a Svelte 5 countdown timer using runes: count down from 10 minutes, handle pause/resume, fire a callback on zero. TypeScript, keep it clean and SSR-safe." Boom – it spits out solid code in seconds. Sometimes, it even surprises me in a good way. Like, I'd forgotten to think about accessibility – adding ARIA live regions or focus management – and there it is, tucked in like it read my mind. Or I'd planned a simple setInterval, but it suggests a more efficient $effect with cleanup, something I'd have Googled or added later after noticing. Those moments? They feel electric. Like, "Holy crap, this tool is actually earning its keep – at least for the stuff I already know."
It's the same for anything in my wheelhouse. Querying for how to integrate a third-party lib I half-remember, like wiring up a Svelte action for a drag-and-drop zone? AI pulls it up faster than Google, often with a tailored snippet that slots right into my +page.svelte, it handles the tedium so I can focus on the architecture. These days I don't mostly even copy and paste from the internet as we have AI in our code editors and they help make the fixes faster and the mistakes even faster, so yeah, we hardly go online to prompt, we just prompt from our editors. And yeah, it's saved me trips to the search bar. No more endless scrolling through half-baked forum posts from 2019. In a world where deadlines are assassins, that efficiency bump is no joke.
But here's the rub: this "shine" only glows when the task is familiar turf. I rely on AI for front-end stuff I know cold because I can spot the wins (or the duds) a mile away. If it nails it, great – copy-paste, test, ship. If it hallucinates a deprecated API? I catch it quick. It's like having a script kiddie who's memorized the basics but panics on anything custom. And let's talk web dev specifics, because that's where the cracks show early. You'd think AI would be primed to crush this space – after all, it gobbled up mountains of public code from GitHub to train on. But thanks to that scraping frenzy (without explicit permission, mind you – ethically murky at best), most LLMs are biased toward whatever was hot when the datasets froze. Enter React: the golden child. Its popularity exploded the repo counts, so AI can churn out React hooks and components like it's 2020 all over again. Solid, functional stuff, even.
That stuck around for a good while, making tools feel tailor-made for the masses. Hell, even no-code platforms jumped on the bandwagon. Take Lovable – that AI-driven builder everyone's hyping for "apps in minutes." It primarily spits out React/TypeScript code, which is fine if that's your jam. But try asking for Svelte? Crickets. Solid.js? Mostly no. Vue gets some love through component libs like shadcn/ui, but it's spotty – not seamless like React. Angular? Eh, maybe a hacky port, but I steer clear anyway. The point? As web tech evolves – and boy, does it, with signals and runes shaking things up in Svelte 5 – AI's stuck regurgitating yesterday's patterns. Those "stolen" (sorry scraped) snippets? Half are outdated, tied to frameworks we're ditching for lighter, faster alternatives. So while it shines on the familiar, it's dimming fast on the frontier. Devs who actually build real things? We're still the ones bridging the gap, tweaking AI's output to fit modern stacks like SvelteKit. It's helpful, sure – but it's no revolution. More like a turbocharged copy-paste from a slightly smarter clipboard, and in Svelte's case, one that occasionally invents syntax that flat-out doesn't exist.
III. The Unpredictability Trap: From Spaghetti to Bottlenecks
Okay, enough patting on the back. If AI were just a reliable boilerplate bot, I'd be singing its praises from the rooftops. But no – the real gut-punch is how damn unpredictable it is. One minute it's your best buddy, the next it's that friend who shows up hammered and rearranges your furniture. And this isn't some edge-case quirk; it's baked in, a core flaw that turns "quick win" into "why me?" every time you dare to dream bigger. In SvelteKit, where infinite loops are rare, AI manages to summon them like it's auditioning for a horror flick.
Take small tasks, the ones that should be slam-dunks. I decide to add a simple feature – say, a reactive search filter in a Svelte component. I know the basics from the docs: $state for the query, $derived for filtered results, maybe a debounce action. Prompt AI: "Implement a Svelte 5 search filter with debounced input, TypeScript, and derived store for results from an array prop." Should be straightforward, right? Sometimes, yeah – it delivers clean, performant code with proper cleanup and even a loading state I didn't ask for. Feels like magic. Other times? It barfs up spaghetti: nested $effects that make your eyes bleed, or worse, it mixes Svelte 4 syntax like old-school reactive declarations into a runes setup, breaking everything. Why? Who knows – could be "contamination" from its training soup, where some half-baked tutorial from 2024 bled into the mix. Or lack of context: I forgot to specify "Svelte 5 only, no legacy," so it shoehorns in version 4 patterns that clash. Or just plain bad luck – LLMs aren't deterministic like your code should be. Same prompt, same model, same codebase? You might get elegant runes one run, leaky subscriptions the next. It's the temperature setting, the sampling roulette; outputs vary like weather in spring.
This roulette wheel spins hardest on bigger tasks, where the "sometimes" turns into "seldom." I've tried handing off medium-sized features – like a full multi-step form with validation and server actions in SvelteKit – and watched it introduce bottlenecks that'd make a senior dev weep. Async race conditions in $effect? Check. Unoptimized re-renders from improper rune usage (how did it manage that?). Double-check. And the hallucinations? Oh man, the code that seems to have no lint errors and even works in dev—but I notice the issues right away, like when I was setting up a CI pipeline for GitHub on my vaultnote app. AI finally gave something without lint errors, but it bombed on the actual GitHub runner because of its workflow triggers. (I even wrote an article about that.) Worse, it loves dropping $effect where it shouldn't – like in a simple state update that triggers an infinite loop, subscribing to its own changes in a framework designed to avoid that mess. It's not that AI falls apart on epics; it's that it teeters everywhere. Unpredictable in the micro, chaotic in the macro. You can't build on sand like that – every integration becomes a debug marathon, chasing ghosts in the reactivity graph.
And fixing it? Back to square one: Svelte docs, Discord channels, or elbow grease. If the AI's suggestion flops, I don't rage-quit (much). I just pivot like pre-AI days – hunt for a tool, trace the error stack, iterate. At best, if it's boilerplate-y, I feed it the docs: "Using this SvelteKit load function API, implement server-side pagination with context from my existing store." It might nail the addition, but full app? Full feature? Nope. It's like giving a map to a tourist – they'll get to the corner, but navigating the whole city? Lost. This unreliability isn't just annoying; it's a productivity vampire. You spend as much time vetting as creating, turning a "tool" into a timesink. Non-determinism is the villain here – code's supposed to be reproducible, bite-sized bricks for empires. AI? It's jazz improv: thrilling sporadically, frustratingly off-key the rest. And in Svelte's elegant world, those off-notes echo louder.
IV. The Big Fail: No Full Apps, Libraries, or "From Scratch" Wins
If the unpredictability is the daily grind annoyance, the big fail is the soul-crusher: AI's utter inability to go solo on anything meaty. I went in wide-eyed, thinking, "Okay, self, you've never built a full Svelte library? No sweat – AI's got the blueprints." Ha. Spoiler: nope. I prompted for a simple utility lib—what I got? A Frankenstein of half-baked exports. The code it gave never even worked enough to manage – it was so broken that now it's just added to my list of things to build by actually learning it the proper way, no shortcuts.
This isn't a one-off flub. I've tried bootstrapping full apps from scratch or an MCP with zero prior knowledge on my end– just vibes and prompts. No way, just broken codes and even more broken dreams. I'm still going to have to build that MCP myself when I actually carve out time. It's like asking a sketch artist to build a house: cute drawings, zero livable square footage. AI excels at snippets because that's what it trained on – isolated functions from repos. But a cohesive package? That needs vision: versioning, docs, benchmarks. Stuff that screams "human oversight." It often coughs up non-existent syntax, forcing me to rewrite half of it anyway.
And oh boy, don't even get me started on the security side – or lack of it. AI's blind spots here are a nightmare waiting to happen. It rarely thinks about basics like input sanitization, auth guards, or even just not hardcoding secrets. Take the Tea app, that women-only dating thing that went viral earlier this year – built heavy on AI-generated code, and bam, a massive breach exposed over 72,000 user photos, IDs, and even 1.1 million private messages because of unsecured endpoints and zero validation. Hackers had a field day; it was like leaving the front door wide open. Or look at McDonald's AI hiring bot from Paradox.ai – exposed millions of applicants' data due to sloppy security flaws in the auto-generated backend. And that's just the tip: AI tools have sparked a huge boom in env var leaks on GitHub, with devs copy-pasting snippets that dump API keys right into public repos, no .env checks or anything. Cyber sec folks are quietly smiling at all these over-exposed vulnerable apps popping up, while hackers and bad actors are rubbing their hands – easier pickings than ever, with AI handing them blueprints full of holes. It's not just buggy; it's risky, and without human eyes on security from the start, you're building sandcastles in a storm.
No-code darlings like Lovable amplify this. They're pitched as "AI builds your app," but peek under the hood: primarily React/TypeScript generation, with limited support for Vue or Solid via add-ons – Svelte and Angular? Not in the cards yet. It's guardrails galore because without them, you'd end up with unmaintainable mush. Devs still build the real things, guiding AI like a puppeteer. I've yet to birth something substantial with nothing but prompts and prayers. It's not what I expected – not the "build from ignorance" freedom they hyped. Instead, it's a crutch for the cognoscenti, worthless without your domain know-how.
V. StackOverflow's "Death" and the AI Illusion
Speaking of illusions, let's hit the cultural fallout: Stack Overflow's slow-mo funeral. I used to live on that site – quick Q&A for the un-Googleable, war stories from devs who'd bled on the same bug. Now? It's a ghost town. Question volume's cratered 64% year-over-year, and yeah, AI's the culprit. Newbies (and lazy vets) think, "Why post when ChatGPT'll solve it?" Turns out, for the low-hanging fruit – "Convert this React snippet to Svelte?" – AI zaps the need, slashing back-and-forth. Saves time, sure; 84% of devs are leaning on it for basics. But here's the irony: SO ain't dead, it's just hibernating. What's left? The thorny stuff AI chokes on – niche integrations, prod-scale weirdness like SvelteKit adapter quirks. And get this: 35% of surviving traffic is now "How do I fix this AI-generated bug?" Meta as hell.
This ties into the bigger scam: companies and execs chasing the AI dream to gut workforces. Remember those US firms boasting "We'll replace everyone with bots"? Fast-forward: crickets. They pivoted back to open-source gems or paid tools – human-built, every one. AI couldn't touch the creativity or context. It's dying because we mistook it for omniscience, not realizing SO's magic was crowdsourced grit for the gaps no search fills. I predict a phoenix rise: as AI plateaus, we'll flock back for the real talk. Edge cases don't autocomplete; they need debate.
VI. The Reality Check: Treat AI Like a Tool, Not a Savior
So, tough love time: Stop treating AI like the messiah of code. It's a tool – potent for the right nail, useless swung wildly. Scope it tight: tasks you can verify, like docs-fed additions or remembered patterns. Don't offload full features; use it for lazy wins – boilerplate I skip, quick "how-to" queries. Research? Handy for breadth, but verify everything: that "lib" it suggests? Probably vaporware. Technique feasible? Cross-check sources.
"Devs, wielding it surgically: right problem, right dose. It'll stick like the computer mouse or autocomplete – ubiquitous, un-hyped. Hammer for nails, not miracles. And in my SvelteKit setups, it's barely scratching the surface of what a solid TS setup already does out of the box.
VII. Conclusion: Expensive Autocomplete in a Hammer-Seeking World
Whew, that was a rant and a half. If you made it this far congrats. Bottom line: AI's delivered an expensive autocomplete – suggestions on tap, but no solo symphony. Great for portions, features, boilerplate drudgery; lousy for apps, libs, or unknowns. It's not bad, wrong, or useless – far from it. Just not the ultimate coder, drafter, researcher. And let's not even talk about the cost – I could build a whole app for the price of adding one feature with some of these models (cough, Claude). Overhype met reality, and we blinked first.
Optimistic close: It'll evolve, sure. But winners? Pros who guide it, not grovel to it. Experiment, but own the direction. Me? I'll keep prompting for that timer feature, and building empires the old(ish)-fashioned way – like that shadcn-svelte MCP I'll finally tackle when the stars align. Devs forever – augmented, not automated. So, fellow Devs, what's your AI horror story? Drop it below – maybe we can crowdsource a prompt that actually sticks better than these bots' wild guesses.
Top comments (1)
Brilliantly funny - written like no "AI" ever could, at least not currently, or in the foreseeable future :-)
P.S. I was again gonna say (I risk repeating myself): can this post PLEASE be featured in dev.to's "Top 7 posts of the week"? But I realize that this article is probably a little bit too divisive/polemic for that - dev.to are now "partnering" with Google's AI, and they surely don't wanna offend them :P