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Cover image for TECH TRENDS (2026)
Aman Verma
Aman Verma

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TECH TRENDS (2026)

• Future feels overengineered, expensive, and unfinished

  • AI bosses, robot partners, smart devices with ads
  • Startup ideas = old ideas + “AI / Quantum”

• Software jobs

  • 2023 hiring boom is gone, fewer openings
  • Still ~15% growth forecast till 2034
  • AI tools won’t replace engineers in 2026
  • New role emerging: "code janitors" fixing AI-written code
  • H1B visa changes make cheap foreign hiring harder

• AI bubble status

  • AI hype not over yet, but LLMs have plateaued
  • GPT-5 disappointed, no true intelligence leap
  • Many white-collar jobs at risk (managers, designers, analysts)
  • AI companies still private → IPO wave will signal peak
  • Possible major IPOs in 2026 (OpenAI, Anthropic, SpaceX)

• Robots & hardware

  • Humanoid robots (Neo, Tesla Optimus, Figure) are gaining hype
  • Tech is still weak, but funding is huge
  • 2026 may see robots entering factories

• Wearables & XR

  • Past AI wearables failed (Rabbit, Humane Pin)
  • New attempts coming (OpenAI + Jony Ive)
  • VR/AR is still niche and mostly unprofitable
  • Apple Vision Pro flop, cheaper version rumored

• Chips & infrastructure

  • Biggest winners: Nvidia, ARM, TSMC
  • AI demand = massive GPU + electricity needs
  • Intel was saved by the US government, “too big to fail.”

• Energy & nuclear

  • Data centers running out of power
  • Nuclear energy revival possible
  • Small modular reactors for data centers are emerging

• Quantum computing

  • Breakthroughs in 2025–26
  • First real-world useful quantum algorithms
  • Could dwarf the AI boom
  • US vs China race intensifying

• Government tech

  • Digital IDs and CBDCs expanding in Europe & UK
  • Privacy concerns are increasing
  • Smartphones becoming state-controlled gateways

• JavaScript ecosystem

  • Node.js improves, native TypeScript support
  • Deno adds built-in bundler
  • Bun.js is gaining popularity (fast + built-ins)
  • React is still dominant but improving
  • New frameworks emerging (e.g., Ripple)

• Skill advice

  • Understanding AI fundamentals > hype
  • Strong engineering skills still matter
  • Learning how AI actually works is key

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