🎮 The Next Input — Issue #065

⚡ The Briefing — 60 sec

• OpenAI launches Sora app (its TikTok competitor) alongside Sora-2. ChatGPT meets Twitter. Who will be more cringe—Grok or Sora?

• Hollywood turns on AI “actress” Tilly Norwood. Guess I was actually right about AI taking jobs 😬.

• MIT weighs in on generative AI’s climate impact. At The Next Input, we care about the environment. Hoping AI hits energy neutrality within 15–20 years.

🛠️ The Playbook — Green AI Ops Dashboard

Mission Build a live sustainability dashboard for AI workloads—tracking compute, emissions, offsets, and efficiency benchmarks.
Difficulty Advanced | Build time 3–5 hours (pilot)
ROI Ops teams save ≈ 8–12 h/month manually auditing costs, while execs get visibility into both financial and environmental metrics.

0) Why This Matters

As AI workloads scale (think Sora-level video models), their energy draw rivals small nations. Companies will soon face regulatory + reputational pressure to prove sustainable AI. A Green AI Ops Dashboard bridges the gap between compute spend and climate accountability.

1) Architecture

2) Workflow

1. Collect

   • Cron job hits cloud APIs daily: usage by job, GPU type, runtime.

2. Normalize

   • Map GPU type → average kWh draw (Nvidia A100, H100, TPU v5, etc.).

3. Translate

   • CodeCarbon API converts usage → CO2 equivalent.

4. Store

   • Supabase DB logs each run {model, user, job_id, cost, emissions}.

5. Summarize

   • LLM prompt: “Benchmark this workload vs. industry avg. Suggest 1 optimization.”

6. Dashboard

   • Charts: emissions by model, cost per token, offset progress.

7. Alert

   • Slack: “⚠️ Emissions exceeded weekly budget by 25% (job_id: #382).”

3) Prompts

Benchmark Prompt

SYSTEM: You are a sustainability analyst.
INPUT: {GPU_hours, emissions, model_type, cost}
TASK:
- Compare emissions per token vs industry benchmarks
- Suggest 1 optimization (smaller model, quantization, batch size)
Return JSON: {benchmark, recommendation, expected_savings}

4) Guardrails

• Accuracy – Use hardware-specific energy profiles.

• Offsets – Track purchases but show them separately from raw emissions.

• Privacy – Only log workload metadata, not raw inputs/outputs.

• Compliance – Store historical logs for audit readiness.

5) Pilot Rollout — 2 Hours

1. Install CodeCarbon agent on 1 GPU job.

2. Log emissions for 7 days into Google Sheets.

3. Build Looker dashboard with emissions vs cost.

4. Share Slack digest with ops team.

6) Metrics

• CO2e per 1M tokens generated.

• $ cost per ton of CO2e.

• % workloads optimized vs baseline.

• Offset ratio (emissions vs credits purchased).

🎯 The Arsenal — Tools & Prompts

Summarise emissions logs into:
- Total CO2e (tons)
- Top 3 heaviest jobs
- Suggested optimizations
Output in markdown for Slack.

💡 Free Office Hours

Want a Green AI Ops dashboard for your org?
Book a free 15-minute Office Hours slot—no sales pitch, just workflows solved.

→ Grab a slot: https://calendly.com/aaron-cylentis/the-next-input-office-hours

🕹️ Game Over

Track one AI workload’s emissions today—tomorrow your ops team will thank you (and so will the planet 🌏).
Share your win; you could headline Issue #066.

— Aaron
Automating the boring. Amplifying the brilliant.

Forwarded this? Subscribe here