I keep seeing companies call every new product an AI breakthrough, but most of what I’ve tested feels like clever branding more than real innovation. After comparing tools, ads, and actual results, I’m struggling to tell what’s genuinely useful and what’s just marketing hype. I need help figuring out how others separate real AI progress from exaggerated AI claims so I can make better decisions.
You’re seeing the market clearly.
Most “AI” products fall into 3 buckets.
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Old feature, new label.
Autocomplete, search, templates, OCR, chatbots. Same core product, now with “AI” on the landing page. -
Wrapper on top of an existing model.
A lot of startups are a thin UI over GPT, Claude, or an image model. That does not mean zero value. It does mean the “breakthrough” claim is often marketing. -
Real workflow gain.
This is the part worth tracking. If a tool saves you time, cuts labor, or improves output quality in repeat tests, it matters.
A simple filter helps.
Ask these 5 things.
What task did it improve?
By how much?
Compared to what baseline?
How often does it fail?
What part is new tech vs good UX?
Example. AI meeting notes are useful. They save 15 to 30 minutes per meeting for some teams. “AI toothbrush” is branding nonsense. Same word, totaly different value.
The hype cycle is normal. Cloud did this. Crypto did this. “Smart” products did this.
If you want to judge tools, ignore ads. Run a small test on your own work. Time saved. Error rate. Cost. Setup pain. If the numbers are weak, it’s marketing. If the numbers hold up, keep it. That’s prety much the only clean way to sort signal from noise.
Yeah, a lot of it is a marketing campaign, but I’d push back on the idea that this means there’s no real shift happening. The hype is fake in a lot of cases. The capability jump is not.
What’s throwing people off is that the actual breakthrough happened lower in the stack than the ads make it sound. The big change is not “this app has AI now.” It’s that language, image, and coding models got cheap enough and usable enough that every company rushed to bolt them onto whatever they already sell. So the revolution is real, but the product layer is full of slop. Both things can be true at once.
I mostly judge this stuff by substitution, not novelty. If a tool can replace part of a contractor, intern, support rep, researcher, or junior dev workflow, even imperfectly, that’s meaningful. If it just adds a sparkly button to summarize things nobody needed summarized, yeah, that’s brochure bait.
Where I slightly disagree with @yozora is this: repeatable ROI is important, but consumers often spot the fake stuff even earlier by asking who carries the risk when it messes up. If the answer is still “you do,” then the product probably isn’t as revolutionary as the pitch says. Real breakthroughs usually absorb complexity. Fake ones offload it to the user and call it “assistive.”
Also, the dead giveaway is when the company can explain the model but not the outcome. Tons of AI pages are like, “multi-modal intelligence, agentic automation, proprietary orchestration layer blah blah.” Cool. What does it actually stop me from doing manually on Tuesday at 2:17 pm?
So yeah, trust your own testing, but also watch behavior. Are companies charging more because it creates value, or because “AI” lets them repackage old software at a premium? That tells you alot. Right now we’re in the gold rush part, not the settled-industry part. Tons of shovel sellers, not many actual railroads.
I think the phrase “marketing campaign” is directionally right, but maybe too generous. A lot of this is just feature inflation.
The pattern I keep seeing is simple:
- Existing software hits maturity.
- Growth slows.
- Company adds “AI.”
- Pricing jumps 20 to 80 percent.
- User still does QA, cleanup, and exception handling.
That last part matters more than the demo. If the human still has to babysit the output, the product is not transforming work. It is rearranging it.
I slightly disagree with @yozora on one point. Replacement is not always the best test. A tool can fail as a substitute and still be useful as an accelerator. The real question is whether it improves throughput without creating a verification tax so big that the gain disappears.
My rough filter:
- Good AI reduces decision fatigue
- Bad AI creates review fatigue
- Good AI shortens boring work
- Bad AI adds another layer to manage
- Good AI gets quieter over time
- Bad AI keeps asking to be noticed
That’s why so many launches feel fake. They are selling possibility, not reliability.
Pros for ': can improve readability if used carefully, can make dense content easier to scan, can help frame comparisons clearly.
Cons for ': easy to overuse, can turn vague fast, can make weak products sound clearer than they really are.
So yes, there is real technical progress. But at the product level, we are still in the era of AI garnish. Not every dashboard needs parsley on it.