Cost of AGI Delusion:Chasing Superintelligence US Falling Behind in Real AI Race

Original Article ↗ Hacker News Discussion ↗

Article reception and core claim

  • Several commenters find the piece verbose and light on specifics, especially on why AGI-focused work would hurt “practical” AI or why US startups can’t deliver applied value.
  • Others note the article’s real function is policy advocacy: justify billions in US government spending on AI literacy, procurement, and research infrastructure by framing a “we’re falling behind China” narrative.

US vs China: AGI obsession vs applied AI and adoption

  • One recurring argument: many US startups and researchers are ideologically fixated on AGI/superintelligence, while Chinese firms prioritize concrete, monetizable applications and industry integration.
  • Supporters of this view point to China’s “AI Plus” initiative and aggressive deployment of robots/automation, contrasting it with US hype and under-adoption.
  • Skeptics respond that the article’s actual evidence of the US “falling behind” is thin and mostly about adoption targets, not clear capability gaps.

Talent, education, and culture

  • Long subthread argues US CS education has been “watered down”: less hardware, OS, DSP, and systems; graduates lack low-level and HPC skills needed to integrate AI with real-world hardware.
  • Others blame management and incentives more than curriculum: non-technical or business-driven leadership, intolerance of dissent, adtech/FAANG and finance drawing talent into narrow, non-deep-tech roles.
  • Discussion of cultural differences: e.g., Israeli engineers seen as more willing to argue from both technical and business angles; US ICs described as more passive and “artist-like” than engineering-oriented.
  • Counterpoint: the dominance of US tech companies by market cap suggests the industry is not simply “lazy,” though critics say this reflects capital flows, not engineering health.

Actual AI deployment: robotics, self-driving, and LLMs

  • Multiple comments stress that practical AI (self-driving, robotics, industrial automation) is inherently slow and messy: impressive demos but few robust, scalable deployments.
  • Some argue current LLMs have produced a massive coding productivity leap; others report fragile behavior (e.g., repeated syntax errors in Flutter/Dart).
  • Several note China’s strength in robotics and its open release of efficient models (e.g., Qwen), which Western firms can freely build on—echoing past crypto export-control dynamics.

AGI, politics, and social problems

  • Many claim most serious problems (climate, inequality, pandemics) are political and social, not technological; AGI won’t fix governance, and may worsen corporate power.
  • Others counter that making better tech (e.g., cheap renewables) is often easier and ultimately more effective than trying to “fix politics” directly.
  • Debate extends to whether AGI could or should govern humans, and whether truly autonomous AGI would remain aligned with any state’s ideology (US or Chinese).