My company was using token burn as an AI adoption metric, but they’ve started clawing that back in search of better metrics.
After a bunch of people all blew through $2000 of tokens over a weekend building “something”, leadership cooled their heels and is pivoting to trying to use AI more intelligently.
And yet we can still measure productivity by how fast it takes to get a feature deployed and we can measure customer satisfaction… I still don’t understand why that’s insufficient. If adding AI made the line go up, then it is a benefit. If line not go up, why buy AI?
Instead it seems like a lot of companies are just looking for new magic numbers to chase in order to justify their predetermined conclusions.
Instead it seems like a lot of companies are just looking for new magic numbers to chase in order to justify their predetermined conclusions.
That describes politics everywhere. Just people trying to find numbers that they can point to that “show” they are doing a good job, with tons of confirmation bias.
My company was using token burn as an AI adoption metric, but they’ve started clawing that back in search of better metrics.
After a bunch of people all blew through $2000 of tokens over a weekend building “something”, leadership cooled their heels and is pivoting to trying to use AI more intelligently.
And yet we can still measure productivity by how fast it takes to get a feature deployed and we can measure customer satisfaction… I still don’t understand why that’s insufficient. If adding AI made the line go up, then it is a benefit. If line not go up, why buy AI?
Instead it seems like a lot of companies are just looking for new magic numbers to chase in order to justify their predetermined conclusions.
That describes politics everywhere. Just people trying to find numbers that they can point to that “show” they are doing a good job, with tons of confirmation bias.