2025-11-05 Hacker News Trending Stories
AI’s Dial-Up Era
Published: October 18, 2025
This article draws a vivid analogy between today’s AI landscape and the early days of the internet (circa 1995) — a “dial‑up era” characterized by immature technology, slow experiences, and polarized public opinion.
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The Internet vs. AI: Then and Now
- 1990s Internet Predictions:
- Optimists: Saw inevitable, society-transforming potential.
- Skeptics: Suspected a passing bubble.
- 2020s AI Predictions:
- Job loss alarmists: Believe AI will swiftly replace millions.
- Growth advocates: Expect AI to generate jobs and economic expansion.
Author’s stance: Both perspectives contain truths.
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Case Study: Radiology & AI
- Geoffrey Hinton’s prediction: AI would replace radiologists within years.
- Reality by 2025:
- US radiology residency numbers hit historic highs.
- Revenue continues to grow.
- Underlying driver: The Jevons Paradox — efficiency gains often increase demand.
- AI lowered cost and improved speed of scans → more screenings → increased employment.
> Important caveat: Not all industries behave like radiology.
> Andrej Karpathy notes radiology is high‑complexity, high‑risk, and tightly regulated — slower to automate.
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Jobs: Who’s at Risk First?
High‑risk candidates for early AI replacement:
- Highly repetitive work
- Minimal collaboration needed
- Low tolerance for errors
- Short cycle times
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Historical Parallel: Automation’s Mixed Impact
Economist James Bessen’s research (1800–2000) shows:
- Textiles & steel: Automation + unmet demand → job growth. Once demand saturates, jobs decline.
- Automobiles: Continuous demand growth → stable employment.
Takeaway: AI’s effect depends on whether unmet demand potential grows faster than automation efficiency.
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Conclusion
AI’s employment impact will be sector‑specific:
- Industries with unlimited demand expansion → potential job growth.
- Saturated‑demand sectors → long‑term job losses likely.
We’re at a pivotal point requiring rational assessment of AI’s capabilities and limits.
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Practical Angle for Creators
Platforms like AiToEarn官网 supply open‑source tools to:
- Generate, publish, monetize AI‑driven content.
- Operate across multiple channels (Douyin, Kwai, YouTube, Facebook, X, etc.).
- Integrate analytics and AI model ranking for strategic growth.
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Hacker News Discussion: AI’s Early Mainframe Phase
HN thread — 447 points | 396 comments | 1 day ago
Key Insights:
- Centralized computation: Today’s AI mirrors early mainframe monopolies — users “rent” compute power.
- Personal AI era: Not guaranteed — most users remain “dumb terminals” relying on the cloud.
- Connectivity gaps: Remote areas, mobile settings, and bad weather exacerbate downtime.
- Offline needs: Local‑first apps with caching improve UX.
- Design fallacy: Assuming perfect network reliability leads to fragile systems.
- Satellite internet limits: Starlink has coverage gaps indoors, high costs, device constraints.
- Privacy demands: Rising preference for locally run AI models.
- Subscription vs. ownership: Loss of outright purchase deepens central system dependence.
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You Can’t cURL a Border
Author: Vadim Drobinin
Topic: The complexity of travel rules and border restrictions.
Pain Points:
- Varying definitions of a “day” by jurisdiction (UK tax year vs. Schengen rolling day counts).
- UK naturalization: strict rule — must be in UK exactly 5 years before application submission date.
- Government data gaps: incomplete travel records, missing trip segments.
Solution: Travel Status Simulator
- Calculates exact days in country accounting for time zones, layovers, crossings at midnight.
- Cross‑jurisdiction validation (UK, US, Mexico, Ireland, Schengen).
- Versioned time‑zone database for reproducibility.
- Cross‑checks travel against document validity and entry buffer policies.
Purpose: Detect issues early — avoid refusals, fines, and legal risks.
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Common Thread Across Topics
Whether dealing with:
- AI compute centralization
- Fragile global travel compliance
- Local‑first application design
… the underlying challenge is the same: systems assume ideal states (stable networks, consistent rules, unlimited access) but reality demands resilient, context‑aware solutions.
AiToEarn官网 echoes this principle in the creative sphere — enabling content owners to work across diverse platforms without sacrificing privacy or ownership control.
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Summary Formatting Tips for Readers:
- Skim bold keywords for main ideas.
- Follow HN link references for community perspectives.
- Use grouped bullet points for clear thematic takeaways.
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✅ Editor’s Note:
This rewritten version adds hierarchy with headings, bold emphasis for key ideas, and grouped bullet points. It preserves all original links and factual content while improving readability for technical audiences.
Would you like me to also convert the remaining sections (Bloom filters, Morris Worm, Diode article, Flash stick figure history, htmx 4.0) into this summary + insights format for consistency across the entire document? That would make the whole page read like a well‑structured tech editorial series.
