# You’re Already Being Graded on Your AI Prompts > Published on ADIN (https://adin.chat/world/your-ai-scorecard-is-already-built) > Author: Priyanka > Date: 2026-03-24 ## TL;DR: The Reality Most Engineers Haven't Seen **AI works.** Individual developers are faster at drafting, refactoring, and exploration. What's changed -- quietly -- is what companies are now *recording, correlating, and evaluating* about that work. Starting in 2026, Meta will formally grade every employee on **"AI‑driven impact."** Similar internal programs already exist at Google and Microsoft. A new measurement layer now sits between your prompts and your performance review. **What exists today:** - Every Copilot prompt can be logged and retained via [Microsoft Purview](https://learn.microsoft.com/en-us/purview/audit-log-search) (180 days by default) - AI‑generated code can be classified at the commit level - PR review delays, rework, and acceptance rates are tracked separately for AI vs. human code - Tool usage, prompt context, and accessed resources are auditable by compliance teams - These signals roll up into manager dashboards and performance frameworks Most engineers know AI makes them faster. Far fewer realize how completely that usage is now visible -- and how directly it is being tied to evaluation, budget decisions, and headcount planning. --- Meta will formally grade every employee on "AI‑driven impact" in [performance reviews starting 2026](https://www.newsbreak.com/winbuzzer-com-302470011/4476438108741-meta-to-grade-employees-on-ai-driven-impact-starting-2026). Sundar Pichai told Lex Fridman that Google's *most important metric* is how much AI increases engineering velocity at the company level ([Lex Fridman Podcast](https://lexfridman.com/sundar-pichai/)). Microsoft's UK CEO said Copilot is writing roughly **40% of code internally** (Financial Times). A growing ecosystem of analytics platforms -- LinearB, [Exceeds AI](https://www.exceeds.ai/), [Plandek](https://plandek.com/), and [Waydev](https://waydev.co/) -- now makes this measurable end‑to‑end. Your AI scorecard already exists. You just haven't seen it yet. This isn't an argument about whether AI works. It does. This is a map of the **measurement infrastructure that now exists** around AI‑assisted engineering work -- how it's being deployed, and what companies are doing with it, often without engineers fully realizing it. ## The Other Thing Meta Did After AI Rolled Out: Layoffs Between 2022 and 2024, Meta cut over **21,000 employees**, framing the reduction as part of a shift toward *higher‑leverage, AI‑enabled teams* (Meta earnings call coverage). By early 2025, leadership explicitly emphasized fewer engineers, higher output expectations, and AI as a force multiplier. This matters because it establishes a baseline: once AI is embedded, leadership expectations reset. ## The Prompt Harvesting Economy No One Wants to Name Prompt extraction is already happening -- quietly, by default, and under the banner of compliance. But compliance is only part of the incentive structure. Enterprise Microsoft 365 tenants automatically log Copilot interactions via Purview audit logs, including: - Which user prompted Copilot - When and where the interaction occurred - Every file, email, or document Copilot accessed - Sensitivity labels on accessed data - The Copilot app, context, and plugins involved Retention is **180 days by default** ([Microsoft documentation](https://learn.microsoft.com/en-us/purview/audit-log-retention-policies)). Longer retention is available to enterprises that pay for it. On the tooling side: - **GitHub Copilot Free / Individual** tiers may use prompts for model training (GitHub Copilot privacy FAQ) - **GitHub Copilot Enterprise** disables training *only because customers pay to opt out* This is the part most people miss: prompt data is not just useful for compliance. It is extremely valuable training data. The incentives are aligned: - Free and low‑cost tiers subsidize model improvement - Fewer than **0.5% of users opt out** of training on consumer AI tools (OpenAI usage disclosures) - Prompts encode domain expertise, workflows, and proprietary context We have already seen where this goes: - Samsung engineers leaked confidential chip designs into ChatGPT in 2023 (Bloomberg) - Google disclosed that human reviewers read Bard conversations ([Google AI blog](https://blog.google/technology/ai/ai-principles/)) - Stanford researchers showed that roughly **50 anonymized prompts** are enough to re‑identify a user ([Stanford Internet Observatory](https://arxiv.org/abs/2302.04844)) Once you accept that prompts are both **labor exhaust** *and* **training fuel**, the direction of travel becomes obvious. ## The Tooling Stack Behind the Scorecard This measurement layer already exists off‑the‑shelf: - **Microsoft Purview** -- prompt and access logging - **GitHub Copilot Enterprise** -- user‑level audit logs - **GuageAI / Codemetrics / DevSpy** -- AI vs human code classification - **LinearB / Plandek / Waydev** -- PR throughput, rework, and acceptance tracking - **Microsoft Defender + Purview DLP** -- compliance correlation Together, these systems reconstruct a full trail: - What you prompted - What AI generated - What shipped - What was rewritten - What sensitive systems were touched That's not usage analytics. That's a **forensic trail of AI‑assisted labor**. *Diagram key:* prompts are logged via [Microsoft Purview](https://learn.microsoft.com/en-us/purview/audit-log-search), AI‑generated code is classified by tools like GuageAI and Codemetrics, pull request impact is measured by platforms such as LinearB, [Plandek](https://plandek.com/), and [Waydev](https://waydev.co/), and outputs roll up into internal manager dashboards and performance reviews. Most engineers will never see this map. But their companies already have it. And that's the point.