{
  "schemaVersion": "1.0",
  "language": "en",
  "id": "economics-sustainability",
  "name": "Economics & Sustainability",
  "description": "Cost-effectiveness, labor, procurement, and computing externalities.",
  "alternateNames": [
    "経済性・持続可能性",
    "ROI",
    "Labor Market",
    "Inference Cost",
    "Data center",
    "Power",
    "Water resources",
    "Sustainability",
    "AI Procurement",
    "Vendor Evaluation",
    "Lock-in",
    "Labor market",
    "Employment",
    "Productivity"
  ],
  "canonicalUrl": "https://catalysphere.co.jp/en/topics/economics-sustainability/",
  "machineReadableUrl": "https://catalysphere.co.jp/en/topics/economics-sustainability/index.json",
  "updatedAt": "2026-10-01",
  "articles": [
    {
      "slug": "ai-use-case-portfolio-prioritization",
      "title": "AI use-case selection depends on reallocating validation capacity, not a fixed ranking",
      "summary": "Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.",
      "quickAnswer": {
        "what": "Select AI use cases as a validation portfolio that improves the next decision, not as a ranking of expected value.",
        "why": "Benefits, harms, testability, reversibility, and shared infrastructure differ, and a single weighted score hides consequential uncertainty.",
        "action": "Give each candidate a minimum evidence stage and withdrawal condition, then reallocate budget to scale, hold, redesign, or stop after each test."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/index.json"
    },
    {
      "slug": "ai-pilot-experiment-design",
      "title": "A Pilot Comparison Design for Moving Generative AI into Production",
      "summary": "Compare the existing and AI-assisted workflows under aligned conditions, including human review and revision time, confirmed quality, and unfinished cases. Use a record to decide deployment, hold, retest, or stop.",
      "quickAnswer": {
        "what": "Limit production deployment to the scope where a comparison with the existing process supports workflow outcomes and quality.",
        "why": "Separate access, actual use, drafting speed, review load, and confirmed outcomes; retain unfinished work and unobserved outcomes in the decision.",
        "action": "Fix assignment, intake, total person-time, quality, follow-up, missingness, and stop authority in a comparison record, and align the measured inputs handed to financial evaluation."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/index.json"
    },
    {
      "slug": "algorithmic-management-worker-agency",
      "title": "Does algorithmic management deprive workers of discretion?",
      "summary": "We organize work design by separating allocation, monitoring, evaluation, and reward by function, and setting minimum standards for autonomy, connectivity pressure, anxiety, and the ability to appeal, in addition to efficiency.",
      "quickAnswer": {
        "what": "Algorithmic management is neither uniformly harmful nor beneficial; it can simultaneously generate resources such as role clarity and demands such as reduced discretion, monitoring, and anxiety.",
        "why": "This is because existing evidence consists largely of surveys, observations, and limited cases, and outcomes vary depending on the management function, rewards, workplace participation, and human review, even for the same technology.",
        "action": "We separate allocation, monitoring, evaluation, and sanctions, set separate minimum standards for performance, autonomy, health, and the effectiveness of appeals, and implement worker participation and discretion."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/algorithmic-management-worker-agency/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithmic-management-worker-agency/index.json"
    },
    {
      "slug": "small-model-routing-economics",
      "title": "Using the strongest model for every request is not always the best investment",
      "summary": "Designing a system that sends all requests to a high-performance model, or one that fixes them to a low-cost model, is not necessarily optimal; drawing from cascade and router research, we consider routing under quality constraints.",
      "quickAnswer": {
        "what": "Rather than deciding on a single model size for the entire company, it may be more rational to choose a low-cost path or a high-performance path for each request.",
        "why": "This is because the difficulty of requests and the strengths and weaknesses of models are heterogeneous, leaving room to reduce expensive calls while maintaining quality.",
        "action": "We measure the internal input's quality floor, misrouting loss, retries, and latency, and verify rules that escalate to the safer side when confidence is low or the request is high-impact, out of scope, or unverifiable."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/small-model-routing-economics/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/small-model-routing-economics/index.json"
    },
    {
      "slug": "generative-ai-energy-water",
      "title": "The power and water footprint of generative AI cannot be reduced to a per-question figure",
      "summary": "Define measurement boundaries for power and water that include usage, processing volume, model, region, time, and equipment, rather than relying on the impression value of a single prompt, and reflect these in procurement and operations.",
      "quickAnswer": {
        "what": "The IEA's 2026 update presents a central forecast that global data center power consumption will approximately double from 485 TWh in 2025 to 950 TWh by 2030.[1]",
        "why": "The U.S. 2030 estimate is 649 TWh for the base case, accounting for 11.8% of total power, and water cannot be managed with a single conversion value because it varies by model, equipment, region, and time.[2] [3]",
        "action": "Compare processing volumes and alternatives on a use-case basis, obtain the measurement scope, region, period, and estimation method for power and water from the provider, and evaluate them in the same meeting as quality and cost."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/generative-ai-energy-water/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-energy-water/index.json"
    },
    {
      "slug": "ai-vendor-evaluation-exit",
      "title": "AI vendor selection must test the ability to resume operations after exit",
      "summary": "We organize vendor evaluations that test data, model dependency, contracts, portability, and stoppage/alternatives before implementation, rather than selecting based solely on accuracy demos.",
      "quickAnswer": {
        "what": "In AI vendor evaluations, selection criteria should include not only current quality but also visibility of components, change notifications, data return, migration to alternative models, and deletion after termination. [1] [2]",
        "why": "NIST organizes generative AI risks into 12 groups, explicitly listing third-party component dependency, decommissioning, fallback, and contract terms as management targets. [2]",
        "action": "Before the PoC contract, conduct an exit exercise by actually exporting sample data, re-running it with alternatives, and measuring recovery time, quality differences, and lost functionality. [3]"
      },
      "updatedAt": "2026-07-24",
      "url": "https://catalysphere.co.jp/en/articles/ai-vendor-evaluation-exit/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-vendor-evaluation-exit/index.json"
    },
    {
      "slug": "generative-ai-labor-market-effects",
      "title": "Will generative AI change employment, or will it restructure work first?",
      "summary": "We analyze where changes emerge by dividing the impact of generative AI into task time, wages and working hours of existing employees, freelance demand, and youth hiring.",
      "quickAnswer": {
        "what": "The labor impact of generative AI may not manifest as a simultaneous reduction in employment, but rather first in the allocation of work, outsourcing demand, and youth hiring.",
        "why": "This is because task time reduction and corporate employment, wages, and hiring follow different adjustment pathways and timelines.",
        "action": "We continuously monitor not only headcount but also hiring, mobility, time allocation by job, and new supervisory tasks."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/generative-ai-labor-market-effects/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-labor-market-effects/index.json"
    },
    {
      "slug": "ai-coding-productivity-context",
      "title": "When does AI coding accelerate development, and when does it slow it down?",
      "summary": "Average effects of AI coding are not enough for investment decisions. Research from laboratories, enterprises, and mature open-source projects reveals the work, personnel, and measurement conditions that determine whether development speeds up.",
      "quickAnswer": {
        "what": "While AI coding may accelerate tasks with clear specifications that are easy to verify, it can slow down maintenance work requiring deep contextual understanding.",
        "why": "This is because the burden of explanation, waiting, verification, review, and correction varies by task, not just the generation time of the output.",
        "action": "We categorize work by verifiability and contextual depth, comparing the period from initiation to post-merge rework with and without AI assistance."
      },
      "updatedAt": "2026-07-24",
      "url": "https://catalysphere.co.jp/en/articles/ai-coding-productivity-context/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-coding-productivity-context/index.json"
    },
    {
      "slug": "generative-ai-roi-design",
      "title": "The ROI of Generative AI Is Determined by Pre-Implementation Design",
      "summary": "Do not equate productivity gains demonstrated by research directly with business value. We propose designing ROI by linking time, quality, accuracy, and usage costs at the business process level.",
      "quickAnswer": {
        "what": "The ROI of generative AI should be judged by business outcomes, including quality, rework, and total cost, not just time savings.",
        "why": "Because the effectiveness of the same AI can reverse depending on task difficulty, user proficiency, and verification processes.",
        "action": "Fix baseline metrics and criteria for continuation or termination at the business process level before implementation, and begin with small comparative validations."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/generative-ai-roi-design/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/generative-ai-roi-design/index.json"
    }
  ]
}