It's definitely a resume number I calculated as a junior engineer. Feel free to give feedback on my math.

It is based on $125/hr and it assumes review time is inversely proportional to number of review hours.

Then time to merge can be modelled as

T_total = T_fixed + T_review

where fixed time is stuff like CI. For the sake of this T_fixed = T_review i.e. 50% of time is spent in review. (If 100% of time is spent in review it's more like $800k so I'm being optimistic)

T_review is proportional to 1/(review hours).

We know the T_total has been reduced by 23.4% in an A/B test, roughly, due to this AI tool, so I calculate how much equivalent human reviewer time would've been needed to get the same result under the above assumptions. This creates the following system of equations:

T_total_new = T_fixed + T_review_new

T_total_new = T_total * (1 - r)

where r = 23.4%. This simplifies to:

T_review_new = T_review - r * T_total

since T_review / T_review_new = capacity_new / capacity_old (because inverse proportionality assumption). Call this capacity ratio `d`. Then d simplifies to:

d = 1/(1 - r/(T_review/T_total))

T_review/T_total is % of total review time spent on PR, so we call that `a` and get the expression:

d = 1 / (1 - r/a)

Then at 50% of total time spent on review a=0.5 and r = 0.234 as stated. Then capacity ratio is calculated at:

d ≈ 1.8797

Likewise, we have like 40 reviewers devoting 20% of a 40 hr workweek giving us 320 hours. Multiply by original d and get roughly 281.504 hours of additional time or $31588/week which over 52 weeks is little over $1.8 million/year.

Ofc I think we cost more than $125 once you consider health insurance and all that, likewise our reviewers are probably not doing 20% of their time consistently, but all of those would make my dollar value higher.

The most optimistic assumption I made is 50% of time spent on review.