I passionately hate hype, especially the AI hype

What “hype” means and whether it’s inherently bad

• Some see hype as pure manipulation: a substitute for objective evaluation, used to push tech (cloud, AI, blockchain) into orgs regardless of fit, cost, or risk.
• Others argue hype is just the noisy exploratory phase of real innovation: lots of people trying lots of ideas, most failing, but some becoming foundational.
• Several note there’s now “anti‑hype hype”: railing against trendy tech is itself a way to signal sophistication without engaging specifics.

Is AI/LLM tech revolutionary or overblown?

• Pro‑AI commenters claim LLMs are “S‑tier” advances, comparable (or close) to PCs, the web, or smartphones: fast mass adoption, broad applicability, and major productivity gains (especially for coding and knowledge work).
• Skeptics argue that putting LLMs on that list now is premature; true revolutions are obvious only after they withstand time and become infrastructural.
• A recurring demand from doubters: concrete present‑day examples of businesses or workflows that are dramatically better than non‑AI competitors, not just projections about future models.

Capabilities, reliability, and appropriate use

• Supporters report big speedups in everyday tasks (writing, debugging, searching, explaining complex topics), describing LLMs as qualitatively different from search because they synthesize and adapt responses.
• Critics emphasize hallucinations, basic reasoning errors, and non‑determinism, arguing that tools you must constantly double‑check are unsuitable for many business processes.
• Some frame the divide as: people willing to accept probabilistic, fallible tools vs those who expect computers to be reliably correct.
• There’s consensus that LLMs work best where answers are hard to derive but easy to verify, or where they’re one component in a filtered/checkable pipeline.

Hype vs reality in industry and economy

• Multiple comments describe companies chasing AI “because we must” without clear problems to solve, echoing earlier VR, blockchain, and cloud migrations.
• AI is also seen as convenient cover for layoffs, hiring freezes, and rent‑extraction by large vendors, with little demonstrated net productivity so far.
• Environmental and resource costs (energy, water, datacenters) are raised as a serious downside given uncertain societal payoff.

Context from past hype cycles

• Comparisons are drawn to the internet, mobile, cloud, blockchain, VR, and “metaverse” booms.
• Cloud and smartphones are broadly acknowledged as real wins that were also heavily hyped; blockchain and NFTs are cited as mostly hype.
• Databases, GPUs, broadband, and other low‑glamour tech are held up as examples of under‑hyped but hugely impactful advances.