Project ideas from Hacker News discussions.

OpenAI resets spending expectations, from $1.4T to $600B

📝 Discussion Summary (Click to expand)

Three dominant threads in the discussion

Theme What the commenters are saying Representative quotes
1. Questioning the realism of OpenAI’s revenue & spending promises Users doubt that the company can deliver the projected $280 bn revenue or the $600 bn cap‑ex, seeing the numbers as hype or “hand‑wavy” commitments. “Must be nice to pull numbers out of one’s ass with zero consequence.” – paxys
“I can’t believe that they’ll have $280 bn in revenue by 2030.” – tyre
2. Infrastructure & cost feasibility concerns Commenters point out that the scale of compute, energy, and hardware required is far beyond current supply chains and that the projections ignore real‑world constraints. “These numbers were always out of line with basic infrastructure constraints.” – carefree‑bob
“If they didn’t appropriately account for risk that the expectation would not pan out, well, that’s on them.” – dragonwriter
3. Broader economic & societal impact The conversation turns to what a 2/3 job‑loss scenario means for consumers, the role of UBI, and how wealth concentration might shape the future. “If we wipe out 2/3 of jobs with AI, who is going to be buying all the stuff?” – ryandvm
“UBI is a more of a convenient trick we use to suppress the part of our conscious that tells us ‘wiping out 2/3 of American jobs is Bad’.” – kylehotchkiss

These three themes capture the main concerns—credibility of projections, practical feasibility, and the societal consequences—of the discussion.


🚀 Project Ideas

Generating project ideas…

SpendSight: AI Infrastructure Spend Tracker

Summary

  • Provides real‑time visibility into AI companies’ capex/opex commitments versus actual spend.
  • Enables investors, CFOs, and product leaders to spot over‑commitments, forecast future costs, and mitigate risk.
  • Core value: transparency and realistic budgeting for the AI boom.

Details

Key Value
Target Audience AI companies, investors, CFOs, board members
Core Feature Dashboard of commitments vs actual spend, alerts, scenario modeling, API integration with cloud providers
Tech Stack Python/Django, PostgreSQL, Grafana, REST APIs, cloud cost APIs (AWS, GCP, Azure)
Difficulty Medium
Monetization Revenue‑ready: subscription per company (e.g., $2k/month)

Notes

  • HN commenters say “I too have reset my spending expectations down from $1.4T.” and “OpenAI is projecting that its total revenue for 2030 will be more than $280 billion.”
  • The tool would let them verify those numbers and see where commitments are being cut.
  • Sparks discussion on “How do you cut a commitment >50%?” and the real cost of AI infrastructure.

CodeGen Scaffold

Summary

  • AI‑powered scaffolding for software teams that auto‑generates code, tests, and CI/CD pipelines.
  • Reduces friction in adopting LLMs and ensures quality through automated QA and linting.
  • Core value: faster, safer AI integration into production codebases.

Details

Key Value
Target Audience Software engineering teams, devops, product managers
Core Feature AI‑driven code templates, test generation, linting, auto‑merge suggestions, Slack/GitHub integration
Tech Stack Node.js, React, OpenAI API, GitHub Actions, Docker
Difficulty Medium
Monetization Revenue‑ready: freemium with paid add‑ons (e.g., $50/month per team)

Notes

  • Commenters note “The tools are good! The main bottleneck right now is better scaffolding so that they can be thoroughly adopted.”
  • “I typed into our slack channel as a note. Someone typed @cursor and moments later the feature was implemented.”
  • Encourages discussion on “How to QA AI‑generated code” and best practices for AI‑augmented workflows.

AI Impact Simulator

Summary

  • Interactive simulation platform that models the economic, social, and environmental impact of large‑scale AI deployment.
  • Allows users to tweak parameters (job displacement %, energy cost, UBI levels) and see projected outcomes.
  • Core value: informed policy decisions and personal understanding of AI’s macro‑effects.

Details

Key Value
Target Audience Policymakers, economists, researchers, curious individuals
Core Feature Scenario engine, data dashboards, interactive charts, exportable reports
Tech Stack Python/Flask, Pandas, D3.js, PostgreSQL, public datasets (BLS, EIA, IPCC)
Difficulty High
Monetization Hobby (open source) or revenue‑ready: consulting & custom reports

Notes

  • HN users express concern: “Unemployed people aren’t much of a demographic, and you can’t just say UBI because that doesn’t make sense.”
  • “What if 2/3 of jobs are displaced?” and “I think TSMC laughed them out of the room when they announced the original numbers.”
  • Provides a concrete tool for debating “What if AI replaces 66 % of jobs?” and the feasibility of UBI or other social safety nets.

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