Actual AI project manager here.
Stamp collector here.
What is the most precious item in your collection (for philatelic or personal reasons)?
AI Product Management
21–30 of 45 posts
Re: AI Product Management
#22it really is amazing how many people have no interest in doing a good job, nor even any interest in protecting their good name.
Easy to say if you are doing a worthwhile and interesting job in a good environment.
A lot of people (most?) do not have this. People who are overworked, people whose management want them to do things as cheaply as possible, people in physically or mentally bad environments.....
Re: AI Product Management
#23I'm not sure some of this is a good idea. It reads a bit like "These LLMs are great! We can get rid of those pesky engineers!". It reminds me of the xkcd[1] about how some problems are trivial and some are almost impossible and as a layman you don't know which is which. That's more true than ever will LLMs, everything is new and so very few people actually know what is easy and what is hard. When you say "You can go…
Well I think it’s as simple as P vs NP. This is the primary difficulty with AI and always will be. Solutions are easy to get AI to construct. The difficulty is in verifying those solutions. This can be seen in industry even now, evals are the hardest part of non-trivial AI systems
Re: AI Product Management
#24Andrew Ng made another point about AI product management in a previous piece [1] that I found both thought-provoking and a bit contrarian, and I’m surprised he didn’t mention it here. In that earlier piece, he went beyond just advocating for concrete specs and explicitly challenged the traditional design-thinking approach, arguing that teams should pick a fully formed idea and run with it rather than spending too lon…
Advocating for waterfall?
Not every product can be totally designed and spec’d out from the outset. Especially when time to market is important.
Maybe this works at the individual feature scale, but at any reasonably large product, designing _everything_ from the outset would result in brittle design.
Re: AI Product Management
#25The three basic guidelines are: * Specify the product as concretely as possible * Use existing applications to test feasibility * Get non-engineer user feedback on early prototypes These all obviously apply to product management more generally, but Andrew gives some examples/ways in which they apply specifically to AI products. Still, I feel like they're talking more generally about complex/abstract software engineer…
> Specify the product as concretely as possible
This is no small task.
Re: AI Product Management
#26it really is amazing how many people have no interest in doing a good job, nor even any interest in protecting their good name.
It really is amazing how many companies have no interest in upskilling their workers, nor even any interest in keeping long time experienced workers happy.
Re: AI Product Management
#27Cron: bug devs about tickets that are late Cron: bug devs every day at a given time for updates aka a standup Given an epic with keywords organize tasks into that epic and estimate the time and then track if it’s on track or not. Yeah not a lot to PM work. Ooh also a 50/50 coin flipper to saying no to adhoc things There that’s an AI PM
That's project management, not product management.
Re: AI Product Management
#28Andrew Ng made another point about AI product management in a previous piece [1] that I found both thought-provoking and a bit contrarian, and I’m surprised he didn’t mention it here. In that earlier piece, he went beyond just advocating for concrete specs and explicitly challenged the traditional design-thinking approach, arguing that teams should pick a fully formed idea and run with it rather than spending too lon…
Advocating for waterfall? Not every product can be totally designed and spec’d out from the outset. Especially when time to market is important. Maybe this works at the individual feature scale, but at any reasonably large product, designing _everything_ from the outset would result in brittle design.
> Not every product can be totally designed and spec’d out from the outset
I'd argue that no product can be spec'd 100% from the outset. Not even something like the regular Notepad.exe.
You'll always find some hidden complexity overlooked that results in the revision of the spec at the middle of development.
Embrace the change.
Re: AI Product Management
#29The three basic guidelines are: * Specify the product as concretely as possible * Use existing applications to test feasibility * Get non-engineer user feedback on early prototypes These all obviously apply to product management more generally, but Andrew gives some examples/ways in which they apply specifically to AI products. Still, I feel like they're talking more generally about complex/abstract software engineer…
> Specify the product as concretely as possible This is no small task.
Indeed. I've seen people blind to how vague their specs were — I'm sure we all have a similar story, mine was someone wanting to know how much it would cost to make "uber for airlines" but it really was that vague a description even after I questioned them for more specifics, because their attempt at specifics was still vague.
Re: AI Product Management
#30Cron: bug devs about tickets that are late Cron: bug devs every day at a given time for updates aka a standup Given an epic with keywords organize tasks into that epic and estimate the time and then track if it’s on track or not. Yeah not a lot to PM work. Ooh also a 50/50 coin flipper to saying no to adhoc things There that’s an AI PM
100% A couple of cron jobs can easily automate this for my team. Most of the PMs I've seen in the wild only do this. Very few PMs are actually valuable to a team from the product perspective.
Is it me or does this sound like a Project Manager and not a Product Manager? Where is the discussion and negotiation with customers and engineering teams on specs/targets/requirements? Where is the roadmap alignment with strategy and marketing teams? Where is the discussion with research teams on future features? Product managers don't babysit engineering teams on their deliverables. Calling someone doing the work listed on the grandparents post a product manager does not make them one.