Hmm compared to film/entertainment yes, but from the perspective of an individual developer worker, your alternatives are not just in film/entertainment
Top professionals whose comp is tied to performance didn't work 40hr 9-5s in the first place - but their comp is tied to performance, so when they have 10x the output they are compensated accordingly
Roles that come with a 40hr work week were already decoupled from performance, if AI made those workers 10x more productive they will rarely see the fruits of their productivity
On an individual level it seems like the correct move is to either move to a role that rewards output or organize and get equity comp as part of everyone's package
Do you know people who have gotten 10x raises due to increased output since AI came out? The one group I can think of is workers at AI companies but that seems more like a gold rush situation than anything
AI companies have reaped the most benefit out of AI, so naturally most 10xers come from people who have equity in those companies that grow really fast
I don't think the average person has even close to a 10x output increase due to AI.
How can one move horizontally and then vertically? I have been thinking about getting into a more technical position. I work as a data engineer but essentially just a data modeller while manager and staff engineer took all the fun jobs -- it is even very hard to know what they are working on, so it is impossible to even ask for certain tickets.
And now with AI coming out in hot, and companies only hiring seniors, I found it very hard to move horizontally. It is not like I can't take a pay cut, but people simply won't hire someone who takes some time to learn the rope.
I might as well figure out how to increase my Charisma to 18 and sleep with someone at the top /s
I think equity compensation should be normalized (or ideally allow employees to choose the % of their compensation is equity vs. cash) so every employee can partake in the upside of the company.
Check out the methodology section at the bottom -- we are trying to better convey this information.
1. These numbers are based on percentiles, which inherently can't be saturated. Most benchmarks operate on something like 0-100% of correct answers, so it's natural to make that assumption when you see our numbers. Perhaps we should divide by 100. We create a modified score based on percentiles against other agents, which rebalances every time we add new entries. So when a new frontier model comes out, all of the existing entries get downweighted if the new model outperforms them. And MiMo V2.5 Pro is a much stronger model than people realize.
2. Agents write code to play most of these games (accounting for ~80% of the combined bench score). There is increasing evidence that nearly identical patterns of weights emerge in different models, trained on different mediums and using different algorithms. Pattern matching and extrapolation don't care if the scenario is a 3D "game" environment or a Salesforce "work RL" environment. Examples of drawing distant connections in different domains can reward similar circuitry.
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