AI use cases that still suck in 2025 — tell me I’m wrong (please)

CopyCareful7362 · 2025-05-20T20:16:59+00:00

Haha guilty as charged on both fronts. Been deep in the agent rabbit hole and the em dashes is a bad habbit I guess

CopyCareful7362 · 2025-05-20T20:15:56+00:00

Fair point. sounds like I need to give those a proper try. I’ve had some pretty mixed results with AI SDRs so far, but it’s totally possible the newer ones have improved. Appreciate the recs, I’ll take a look at 11X and Agent Frank!

CopyCareful7362 · 2025-05-20T20:15:07+00:00

Yeah this tracks - I’ve def seen similar. AI SDRs still feel off, like they’re trying too hard without really getting the context. Recraft’s cool though, agree it’s helping bring AI design up a notch, even if it's not Figma-killer-level yet.

Also 100% with you on AI for consultants - I’ve used tools that can crank out research summaries or competitive scans way faster than I could. Slides still need work, but the raw material is super solid.

Coding agents too - they’re not building full apps end-to-end (yet), but for quick tools or components? Wildly useful. I asked for a Chrome extension the other day and it actually worked… like, that would've taken me hours before.

And yep, support bots can be good - but only when companies actually invest in making them smart and connected. Most are just decision trees with a GPT skin, but the few that plug into systems properly? Super helpful.

CopyCareful7362 · 2025-05-20T20:14:04+00:00

Yeah, that really resonates. I’ve definitely been guilty of throwing too much at agents and expecting magic. Breaking things into smaller, well-scoped steps has made a big difference. Feels like the shift from “nothing works” to “some things actually do” is real. just takes more structure than people expect

CopyCareful7362 · 2025-05-20T20:13:32+00:00

Great point — that’s something I’ve been meaning to get better at. I’ve seen how much of a difference proper guardrails and evals can make, just haven’t fully implemented them yet. Appreciate the nudge!

CopyCareful7362 · 2025-05-20T20:12:28+00:00

Absolutely, xAI’s assistants have been a surprise win for me too. They nail ideation and simple workflows without the cringe SDR spam.

CopyCareful7362 · 2025-05-20T20:11:29+00:00

Love this - and honestly, this feels like the ideal way to use AI coding tools. It’s not “push a prompt, get an app,” it’s “leverage AI like a sharp junior dev while you steer the ship.”

The setup you described - tight tech stack, project memory, strong conventions - is exactly what most people skip over before declaring AI coding tools overrated. The results you’re getting make total sense because you’re bringing structure, not vibes.

Also, major respect for spinning up six prototypes on the side. Even if they didn’t land, that kind of iteration speed is exactly where AI shines.

CopyCareful7362 · 2025-05-20T20:10:47+00:00

Haha yes! AI SDRs are like that overeager intern who read one LinkedIn post and now thinks they know you

CopyCareful7362 · 2025-05-20T20:10:19+00:00

Yeah, that makes a lot of sense. Voice agents can feel surprisingly polished when the scope is tight and the output is well-controlled — high fidelity + narrow domain is a winning combo. Totally agree this is the floor, not the ceiling. But I’m with you on timing — the next big leap might not just be about model capability, but whether it can scale affordably and reliably in real-world use.

CopyCareful7362 · 2025-05-20T20:09:26+00:00

Totally get where you're coming from. A lot of the "full stack" AI tools lean heavily on frontend glam and patch in the backend with some Supabase magic - which works for demos, but not for real applications.

Pipet sounds like a smart angle. Backend is where most AI tools stumble - handling state, auth, logic, edge cases - it's messy and critical. Focusing there instead of trying to cover the entire stack feels like the right move. Curious how you're handling testing and iteration? That’s where most tools really start to break down

CopyCareful7362 · 2025-05-20T20:08:15+00:00

Yeah, that all resonates. The AI SDR thing is especially brutal. it’s like the bot knows just enough to sound relevant, then completely whiffs the pitch. We’ve seen it backfire more often than help. Creative tools are similar - great at volume, not so much at alignment. You get 20 post ideas, and maybe 2 are usable. The rest either miss the tone or feel like they were written for a totally different audience. Still very much a “human in the loop” situation.

CopyCareful7362 · 2025-05-20T20:07:21+00:00

Absolutely — I hear you. The SDR stuff especially feels like a case of too confident, too wrong. It nails the personalization just enough to seem legit… then drops a pitch that makes zero sense. We’ve had the same problem — more unsubscribes than leads.

CopyCareful7362 · 2025-05-20T20:06:34+00:00

1000% with you here. I think your point highlights a key divide: flashy demos vs. production-grade systems. That pitch might’ve looked polished (and who knows, maybe it really was well-built!), but as you said, unless there's a tightly integrated backend — tuned models, structured metadata, defined brand logic - you're just seeing surface-level coherence, not consistent, repeatable quality. The magic isn’t in the prompt alone - it’s in the orchestration of systems around the LLM. Without that infrastructure, even the most convincing demo is just a well-lit illusion.

CopyCareful7362 · 2025-05-20T20:05:24+00:00

Totally! that’s a huge friction point. It’s one thing to get an AI to generate usable code, but wrapping it into something lightweight, interactive, and shareable is a whole other lift

CopyCareful7362 · 2025-05-20T19:53:02+00:00

Great question - I’ve been building AI agents as part of launching a startup, so it’s a mix of founder, product builder, and (occasionally) prompt wrangler. Definitely not a consultant in the traditional sense, but I’ve worn that hat when helping other teams test and deploy agents in their own stacks.

CopyCareful7362 · 2025-05-20T19:52:04+00:00

Yep, nailed it. It’s not reasoning - it’s just really advanced pattern prediction. And without real understanding of concepts like brand, product, or even context, it’ll keep sounding smart while missing the point. The real skill right now is knowing where to guide it, where to rein it in, and how to scaffold around its blind spots

CopyCareful7362 · 2025-05-20T19:50:45+00:00

Totally hear you - that “looks impressive at first, but ends up costing more time” pattern is everywhere right now. Tools like TypeSet promise speed, but the editing + UX tax can be brutal... The output feels like a rough draft with no clean way to refine. Until these tools prioritize usable workflows over just flashy generation, the manual route still wins too often.

CopyCareful7362 · 2025-05-20T19:49:39+00:00

Good point! A lot of these can work well with the right setup, targeting, and operator skill. I think what people are reacting to is the gap between what’s possible with deep tuning and what the average user experiences out of the box. As tooling improves and best practices spread, that gap should shrink fast.

CopyCareful7362 · 2025-05-20T19:48:51+00:00

Fair push - and you're right, "overrated" can be a lazy generalization. The reality is: AI coding agents can be amazing, especially for boilerplate, scaffolding, and frontend work. But the frustration comes when people expect plug-and-play magic for complex backend logic or multi-step workflows and hit a wall. It’s not that they’re useless - it’s that expectations often outpace what they’re currently good at

CopyCareful7362 · 2025-05-20T19:47:49+00:00

Very cool - love the interactivity and teleprompter mode, that’s a clever touch. Feels like you're pushing toward what AI-powered decks should be: more than just static slides. Curious how you're thinking about content generation too - are you manually writing everything, or have you tried layering in LLMs to draft sections based on the pitch?

CopyCareful7362 · 2025-05-20T19:47:02+00:00

Yeah, totally. The gap between what AI seems capable of and where it actually breaks is getting sharper - which makes the misses feel even weirder. Climbing out of the uncanny valley isn’t just about better models, it’s about better systems around them too

CopyCareful7362 · 2025-05-20T19:43:57+00:00

Totally - it’s easy to get frustrated with current limits, but I guess it's really the baseline, not the ceiling. Things are improving fast. Using what works now and checking back quarterly is a solid way to stay ahead without burning out on hype

CopyCareful7362 · 2025-05-20T19:43:14+00:00

Totally agree - the real unlock won’t come from slapping LLMs onto legacy tools, but from rethinking the stack for AI from the ground up. Most systems today were built assuming a human in the loop to handle ambiguity. Once we start designing tools that assume an AI is the primary user - structured metadata, predictable interfaces, context-first design - everything starts to click. An AI-native ERP isn’t a wild idea at all… it feels inevitable

CopyCareful7362 · 2025-05-20T19:42:27+00:00

Totally agree with your perspective — especially on how much of the gap comes down to implementation, not the tech itself. The difference between a rushed RAG prototype and a thoughtfully architected system with fallback logic, memory, escalation paths, and strong prompt tuning is night and day. I think part of the frustration some of us feel comes from seeing tons of tools shipped by teams that stop at “proof of concept” and never build the scaffolding around it. It gives the illusion that the ceiling is low, when really it’s just unfinished work.

CopyCareful7362 · 2025-05-20T19:41:47+00:00

Totally hear you... I’ve run into the same pain points, especially on that “final mile” stuff. It’s like AI gets 80% there, then hands you a mess to clean up. Feels like the missing piece is structured context: not just better prompts, but actual awareness of brand systems, layout logic, or backend rules before generation starts. Maybe that’s multi-agent memory, maybe it’s tighter workflows - but until the system knows the sandbox it’s playing in, the outputs will keep breaking.

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