Tesla Energy: which dollar moves the P&L

Context

Capital asks ran ahead of the budget. Dozens of initiatives fought for the same dollars with weak ties to profit.

Constraints

• Only $23M was available against 50+ competing requests
• Leaders needed a transparent kill-or-fund rule, not a popularity contest
• Labor, cost, and timing had to live in one decision model

Decisions

• Tie each ask to return and risk in Python and Tableau models
• Fund the highest-return work and stop the rest in public reviews
• Reuse the ranking method later when sequencing AI use cases

Outcome

The funded set delivered $156M in incremental annual profit, with 3.2× Megapack scale, $260M in annualized savings, and $550M in tariff exposure mitigated.

My role

• Owned the Energy investment roadmap and review cadence
• Built the models executives used to compare asks
• Ran the tradeoff meetings that killed low-return work

Team

• Finance partners stress-testing cost and return assumptions
• Site and program leads accountable for execution
• Leadership sponsors who enforced the kill list