The state of your AI transformation.
A report on how a company really uses AI in engineering. Five questions, 25 measurements, scored per team from the work itself. Built for boards, investors and technical due diligence.
The cover and the first page are open to everyone. The rest takes a work email.
Trusted by engineering teams
no new people
116 needed before AI · 1.9× per developer
- This is what the board sees.
- Both numbers are an average of four very different teams.
- So you open one team and ask why.
- And this is where the work actually is.
This is what the board sees.
60 engineers now deliver what 116 would have before AI, for $124,880 a month. Three numbers say whether that works: capacity added, $2,230 for each added engineer, and 48.6% of it on the roadmap. All correct. None of them says what to do next.
Both numbers are an average of four very different teams.
The Platform team pays $1,740 a month for each added engineer. The Data & ML team pays $3,506. Nobody decided this, and one company-wide program will not fix it.
So you open one team and ask why.
Good at everything except one thing: nobody tells them what to build. So the fastest team in the company keeps building things nobody asked for. Capacity and the scores are measured separately: this is what sits behind the number, not what caused it.
0 = not started · 5 = fully there
And this is where the work actually is.
When writing code gets cheap, the hard part is knowing what to build. For the Checkout team, the row holding it down is objective context.
- Ticket clarity1.8
- Epic decomposition1.4
- Roadmap coverage2.2
- Objective context1.2
NextWrite down why each objective exists, not only what it delivers
- Spec readiness1.6
This is what the board sees.
60 engineers now deliver what 116 would have before AI, for $124,880 a month. Three numbers say whether that works: capacity added, $2,230 for each added engineer, and 48.6% of it on the roadmap. All correct. None of them says what to do next.
no new people
116 needed before AI · 1.9× per developer
Both numbers are an average of four very different teams.
The Platform team pays $1,740 a month for each added engineer. The Data & ML team pays $3,506. Nobody decided this, and one company-wide program will not fix it.
- Platform team2.4×$1,740 each18 people, delivering like 43
- Payments team1.8×$2,050 each14 people, delivering like 25
- Checkout team1.7×$2,480 each16 people, delivering like 27
- Data & ML team1.8×$3,506 each12 people, delivering like 21
So you open one team and ask why.
Good at everything except one thing: nobody tells them what to build. So the fastest team in the company keeps building things nobody asked for. Capacity and the scores are measured separately: this is what sits behind the number, not what caused it.
0 = not started · 5 = fully there
- Capacity
- 1.7×
- AI spend
- $27,280
- Cost per engineer
- $2,480
And this is where the work actually is.
When writing code gets cheap, the hard part is knowing what to build. For the Checkout team, the row holding it down is objective context.
- Ticket clarity1.8
- Epic decomposition1.4
- Roadmap coverage2.2
- Objective context1.2
NextWrite down why each objective exists, not only what it delivers
- Spec readiness1.6
Objective context at 1.2 is the row holding this down. The other four are between 1.4 and 2.2.
What a board actually receives.
Eight pages, built from the work itself. See exactly what it covers, then decide whether you want one about your own company.
The state of your AI transformation.
Five questions, 25 measurements, scored per team from the work itself, never from a survey.
- Capacity
- +56none hired
- Cost
- $2,230each
- Roadmap
- 48.6%of the work
60 engineers · 4 teams · 27 repos
Last 30 days
An invented company, so you can see the shape of the answer before you send us a single repository. 2 pages are open to everyone; the rest takes a work email.
Check any number yourself.
25 measurements across 4 teams. Read a row for one team, a column for what is weak everywhere, and open any cell for the evidence behind the score and the next step that raises it.
Five bars in every cell · click a cell to open it
No number here is an average. Each question scores as its weakest measurement, and the overall score comes from how much each team improves, reported as capacity, cost and roadmap alignment. The evidence behind all 25 measurements is in the method page of the example report.
A finished transformation looks like capacity you did not hire.
Every question here moves one number: how much one engineer delivers. It is measured the same way for everyone, so a company can be ranked against the ones below, whether its repositories are public or private.
What 100 engineers at each organization now deliver, on their public repositories. The company in this report is measured the same way, and ranked with them.
- #1OpenAI17.3× since Apr 2025+1,631per 100 real
- #2Microsoft4.2× since Apr 2025+316per 100 real
- #3Cloudflare3.4× since Apr 2025+241per 100 real
- #4Vercel3.1× since Apr 2025+210per 100 real
- #5Google3.1× since Apr 2025+205per 100 real
- #6Meta2.5× since Apr 2025+147per 100 real
- #7This report’s company1.9× · 60 engineers+93per 100 real
- #8UiPath1.2× since Apr 2025+18per 100 real
Same period, same measurement, 15× between top and bottom. The median company reached 3.10×. The difference is not talent, and it is not budget. Your own company is placed on this scale from your own repositories, public or private.
The same measurement on your own repositories, priced per engineer added, is AI ROI. What that capacity costs, and whether anyone asked for it, is token spend intelligence.
What a buyer actually asks.
The same 25 measurements, read by the person writing the cheque.
- 45%from one team
Is the gain real, or is it a few people?
Platform team carries 45% of the added capacity with 18 of the 60 engineers. A strong result and a concentrated one. The report shows the split per team and per repository, before you price it.
- 51.4%off the roadmap
What happens if the AI budget is cut?
$124,880 a month is buying 56 engineers of capacity, and 51.4% of the work it funds is not on the roadmap. The report separates the spend that is working from the spend nobody asked for.
- +27already on payroll
What is the upside still inside the company?
Every team at Platform team’s rate would be 143 engineers of capacity instead of 116. That is 27 engineers you are already paying for and not yet getting.
Everyone says they use AI.
Almost nobody can show it.
We build this for your company, or for one you are buying. Read-only setup, under an hour, built from history you already have.