What Did All That AI Spend Actually Ship?

How Standard Metrics gave its engineering leaders one view of AI spend, team health, and the outcomes that reach production

Engineering teams have gotten very good at consuming tokens. More models. More agents. A bigger invoice every month.

Then a CTO asks the obvious question: what did all that AI spend actually accomplish? Most teams can't answer it. The spend is real. The read on what it produced is not.

Standard Metrics set out to close that gap. The company builds the system of record for venture capital portfolio data. More than 100 VC and growth-equity firms, including Bessemer Venture Partners, General Catalyst, and Lux Capital, use it to track and benchmark across over 9,000 portfolio companies.

AI-assisted development lifted the team's shipping velocity. It also sharpened a question the invoice couldn't answer. On a codebase where LPs and auditors treat the output as ground truth, was that AI spend producing the right outcomes, or just more code?
Standard Metrics brought in Entelligence and changed the way they run Eng teams.

More code, shipped faster, no clear read on its worth

The velocity was easy to feel and impossible to account for. Nothing showed:

• where the AI budget was going

• which projects and engineers were consuming it

• what each task actually cost

• what share of the generated code ever reached production

On a fund-admin platform, shipping more is not the same as shipping the right things. The difference can be a compliance event. Standard Metrics needed AI usage, engineering behavior, and product outcomes in one place.

Agent Insights: from tokens to outcomes

Overview. Agent Insights opens on how coding agents are actually used across the org: the most active agents, model usage, where tokens go, and where teams spend their time. It also flags token wastage, context health, and adoption patterns. For the first time, the AI layer of the codebase is legible instead of buried in a bill.

Budgets. Leaders set a monthly AI budget per project and watch it in real time, not at invoice time. At a glance they see:

• which projects are driving spend

• which engineers are contributing to it

• what each coding session or task costs

• whether a project is under, on, or over budget

Spend becomes something to steer, not something to explain after the fact.

Outcomes. This is the part most tools skip. Agent Insights connects spend back to the work it produced. When a platform team burns twice the tokens in a month, a leader can see whether that bought three shipped features and a pulled-forward launch, or nothing.

Spend is broken out by what it created: new features, maintenance, bug fixes, review, and experiments. From there it drills down by repository, project, team, or individual engineer. Some of that value is defensive, like the high-severity bugs caught in review before merge that never became customer-facing incidents. The AI budget stops being an expense and becomes an investment the team can measure.

Team and individual insights: the human side

AI spend is only half of engineering. The other half is how the team actually works.

Agent Insights answers where the AI budget went. Team Insights answers who did the work. It shows who worked on what, what their contributions actually look like, and how each team is running: engineering behavior, AI consumption, AI output, and which teams are moving fast or stalling.

For a leader, that replaces impressions with facts. Who is carrying which project. Whether a team's rising AI spend is turning into shipped output. Whether a process change actually moved delivery. It is not a scoreboard, no rankings and no commit-counting, and at review time a manager walks in with real contribution and output instead of a gut feel.

How Entelligence brings this to engineering leaders

Put it together and five separate tools become one picture: AI usage, budgets, product outcomes, team dynamics, and individual growth.

That is the real shift for engineering leadership. Not another dashboard to check, but answers to the questions leaders actually get asked. What did the AI spend ship? Where is effort going? Who needs support? What should the team do more of?

Standard Metrics earned its reputation by giving investors one trustworthy source of truth for their portfolios. Entelligence gives its engineering leaders the same thing for how the software gets built, so they can optimize spend, support their teams, and scale on outcomes instead of token counts.