The Continued Trajectory of Idiocy in the Tech Industry

Original Article ↗ Hacker News Discussion ↗

Recurring hype cycles in tech — from “Big Data” and blockchain to today’s generative AI — are prompting skepticism about whether industry leaders are solving real problems or just chasing buzzwords and investor money. Commenters contrast largely speculative fads like NFTs and Web3 with AI systems that already change workflows (e.g., code assistance, Q&A, accessibility, medical imaging), while also flagging serious concerns about reliability, ethics, and enshittification driven by ad and data-collection incentives. Many see genuine breakthroughs in machine learning but argue that grift, overblown marketing, and sloppy terminology make it harder to separate durable innovation from the next bubble.

Blockchain / Crypto vs Real-World Impact

  • Some argue blockchain/NFT hype was largely online and didn’t translate into significant real-world adoption.
  • Others counter with concrete examples: major banks ran serious blockchain initiatives, El Salvador’s Bitcoin experiment, Bitcoin ATMs in rural areas, and widespread exposure via financial media.
  • Perceived “real” crypto use cases: buying drugs, ransomware payments, and speculative investment; little evidence in the thread of mainstream non-criminal utility.
  • General sentiment: blockchain was heavily driven by grifters, vendors, and buzzword-chasing executives.

Nature and Impact of AI Hype

  • Many posters see a clear difference between AI/ML and blockchain: AI is viewed as a long-running academic field that’s delivering real results (LLMs, computer vision, protein folding, robotics).
  • Others think generative AI is overhyped, especially around content creation and AGI claims, and lump current “AI” marketing in with past bubbles.
  • Comparison to earlier cycles: web, smartphones, SaaS, cloud seen as hypes that left lasting value; question is where AI will land on that spectrum.

Practical Uses and Limitations of LLMs

  • Reported useful applications:
    • Translation, documentation Q&A, RAG over large wikis, semantic search.
    • Programming help, CLI examples, debugging, general “how do I…?” questions.
    • Accessibility (speech interfaces, help for blind users), radiology support, research, robotics/vision tasks.
  • Some users say AI assistants have significantly changed their workflows and largely replaced basic web search.
  • Others report frequent hallucinations and time-wasting failures (wrong queries, made‑up frameworks/APIs), leading them to revert to traditional search or manual work.
  • Several stress the need to distinguish LLMs from broader ML used in self-driving and robotics.

Ethical and Social Concerns Around AI

  • One camp sees “zero ethical concerns” in training data.
  • Another lists issues: scraped books (Books3), social media and YouTube data without consent, GitHub code regardless of license; calls these ethically problematic.
  • There is also discomfort with forced, opt‑out deployment of AI features (OS-level assistants, “AI Overviews,” AI buttons in products).

Patterns of Tech Hype and Grift

  • Recurrent theme: tech cycles are driven by grifters, VCs, marketing, and credulous management; useful innovation and bullshit coexist.
  • Some posters think critics are simply threatened by new tech; others say skepticism is rational given enshittification and past bubbles.

Other Topics

  • Brief debate over software patents as defense vs patent trolls.
  • VR/AR cited as another hype cycle; views differ on its long-term value.
  • Some mention potential non-crypto uses for blockchains (e.g., direct democracy, data integrity), but acknowledge these are drowned out by grift.