What changed that made the historical datapoints so much higher? E.g. you now think that there were >100 technical AIS researchers in 2016, whereas in 2022 you thought that there had been <50 technical AIS researchers in 2016.
I notice that the historical data revisions are consistently upward. This looks consistent with a model like: in each year x, you notice some “new” people that should be in your dataset, but you also notice that they’ve been working on TAIS-related stuff for many years by that point. If we take that model literally, and calibrate it to past revisions, it suggests that you’re probably undercounting right now by a factor of 50-100%. Does that sound plausible to you?
One possible reason is that I’ve included more organizations in this updated post and this would raise many estimates.
Another reason is that in the old post, I used a linear model that assumed that an organization started with 1 FTE when founded and linearly increased until the current number (example: an organization has 10 FTEs in 2025 and was founded in 2015. Assume 1 FTE in 2015, 2 FTEs in 2016 … 10 in 2025).
The new model is simpler and just assumes the current number for all years (e.g. 10 in 2015 and 10 in 2025) so it’s estimates for earlier years are higher than the previous model. See my response to Daniel above.
What changed that made the historical datapoints so much higher? E.g. you now think that there were >100 technical AIS researchers in 2016, whereas in 2022 you thought that there had been <50 technical AIS researchers in 2016.
I notice that the historical data revisions are consistently upward. This looks consistent with a model like: in each year x, you notice some “new” people that should be in your dataset, but you also notice that they’ve been working on TAIS-related stuff for many years by that point. If we take that model literally, and calibrate it to past revisions, it suggests that you’re probably undercounting right now by a factor of 50-100%. Does that sound plausible to you?
Thanks for assembling this dataset!
Good observation, thanks for sharing.
One possible reason is that I’ve included more organizations in this updated post and this would raise many estimates.
Another reason is that in the old post, I used a linear model that assumed that an organization started with 1 FTE when founded and linearly increased until the current number (example: an organization has 10 FTEs in 2025 and was founded in 2015. Assume 1 FTE in 2015, 2 FTEs in 2016 … 10 in 2025).
The new model is simpler and just assumes the current number for all years (e.g. 10 in 2015 and 10 in 2025) so it’s estimates for earlier years are higher than the previous model. See my response to Daniel above.