Arden here—I lead on the 80k website and am not on the one-on-one team, but thought I could field this one. This is a big question!
We have several different programmes, which face different bottlenecks. I’ll just list a few here, but it might be helpful to check out our most recent two-year review for more thoughts – especially the “current challenges” sections for each programme (though that’s from some months ago).
Some current bottlenecks:
More writing and research capacity to further improve our online career advice and keep it up to date.
Better web analytics – we have trouble getting good data on what different groups of users like most and what works best in marketing, so aren’t able to iterate and scale as decisively as we’d like.
More great advisors to add our one-on-one team, so we can do more calls – in fact, we’re hiring for this right now!
There are uncertainties about the world that create strategic uncertainties for the organisation as a whole—e.g. what we should expect to happen with TAI and when. These affect the content of our careers advice as well as overall things like ‘which audiences should the different programmes focus on?’ (For example, in the AI timelines case, if we were confident in very short timelines it’d suggest focusing on older audiences, all else equal).
We’re also a growing, mid-sized org, so have to spend more time on processes and coordination than we used to which takes time. Though we’re making good progress here (e.g. we’re training up a new set of “middle managers” to scale our programmes).
Tracking and evaluating our impact – to know what’s working well and where to invest less – is always challenging, as impacts on people’s careers are hard to find out about, often take years, and sometimes difficult to evaluate. This means our feedback loops aren’t as strong as would be ideal for making plans and evolving our strategy.
I think there are themes around time/capacity, feedback loops, and empirical uncertainties, some of which are a matter of spending more research time, some of which are harder to make progress on.
What are the biggest bottlenecks and/or inefficiencies that impedes 80K from having more impact?
Arden here—I lead on the 80k website and am not on the one-on-one team, but thought I could field this one. This is a big question!
We have several different programmes, which face different bottlenecks. I’ll just list a few here, but it might be helpful to check out our most recent two-year review for more thoughts – especially the “current challenges” sections for each programme (though that’s from some months ago).
Some current bottlenecks:
More writing and research capacity to further improve our online career advice and keep it up to date.
Better web analytics – we have trouble getting good data on what different groups of users like most and what works best in marketing, so aren’t able to iterate and scale as decisively as we’d like.
More great advisors to add our one-on-one team, so we can do more calls – in fact, we’re hiring for this right now!
There are uncertainties about the world that create strategic uncertainties for the organisation as a whole—e.g. what we should expect to happen with TAI and when. These affect the content of our careers advice as well as overall things like ‘which audiences should the different programmes focus on?’ (For example, in the AI timelines case, if we were confident in very short timelines it’d suggest focusing on older audiences, all else equal).
We’re also a growing, mid-sized org, so have to spend more time on processes and coordination than we used to which takes time. Though we’re making good progress here (e.g. we’re training up a new set of “middle managers” to scale our programmes).
Tracking and evaluating our impact – to know what’s working well and where to invest less – is always challenging, as impacts on people’s careers are hard to find out about, often take years, and sometimes difficult to evaluate. This means our feedback loops aren’t as strong as would be ideal for making plans and evolving our strategy.
I think there are themes around time/capacity, feedback loops, and empirical uncertainties, some of which are a matter of spending more research time, some of which are harder to make progress on.