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by toprerules 138 days ago
Same situation as when an engineer can't figure something out, they translate the problem into human terms for a product person, and the product person makes a high level decision that allows working around the problem.
1 comments

Uh that's not what engineers do; do you not have any software development experience, or rather any outside of vibe coding? That would explain your perspective. (for context I am 15+ yr experience former FAANG dev)

I don't meant this to sound inflammatory or anything; it's just that the idea that when a developer encounters a difficult bug they would go ask for help from the product manager of all people is so incredibly outlandish and unrealistic, I can't imagine anyone would think this would happen unless they've never actually worked as a developer.

As a product owner I ask you to make a button that when I click auto installs an extension without user confirmation.
Staff engineer (also at FAANG), so yes, I have at least comparable experience. I'm not trying to summarize every level of SWE in a few sentences. The point is that AI's infallibility is no different than human infallibility. You may fire a human for a mistake, but it won't solve the business problems they may have created, so I believe the accountability argument is bogus. You can hold the next layer up accountable. The new models are startling good at direction setting, technical to product translation, and providing leadership guidance on technical matters and providing multiple routes for roadblocks.

We're starting to see engineers running into bugs and roadblocks feed input into AI and not only root causing the problem, but suggesting and implementing the fix and taking it into review.

Surely at some point in your career as a SWE at FAANG you had to "dive deep" as they say and learn something that wasn't part of your "training data" to solve a problem?
I would have said the same thing a year or two ago, but AI is capable of doing deep dives. It can selectively clone and read dependencies outside of its data set. It can use tool calls to read documentation. It can log into machines and insert probes. It may not be better than everyone, but it's good enough and continuing to improve such that I believe subject matter expertise counts for much less.
I'm not saying that AI can't figure out how to handle bugs (it absolutely can; in fact even a decade ago at AWS there was primitive "AI" that essentially mapped failure codes to a known issues list, and it would not take much to allow an agent to perform some automation). I'm saying there will be situations the AI can't handle, and it's really absurd that you think a product owner will be able to solve deeply technical issues.

You can't product manage away something like "there's an undocumented bug in MariaDB which causes database corruption with spatial indexes" or "there's a regression in jemalloc which is causing Tomcat to memory leak when we upgrade to java 8". Both of which are real things I had to dive deep and discover in my career.

There are definitely issues in human software engineering which reach some combination of the following end states:

1. The team is unable to figure it out

2. The team is able to figure it out but a responsible third-party dependency is unable to fix it

3. The team throws in the towel and works around the issue

At the end of the day it always comes down to money: how much more money do we throw at trying to diagnose or fix this versus working around or living with it? And is that determination not exactly the role of a product manager?

I don't see why this would ipso facto be different with AI

For clarity I come at this with a superposition of skepticism at AI's ultimate capabilities along with recognition of the sometimes frightening depth encountered in those capabilities and speed with which they are advancing

I suppose the net result would be a skepticism of any confident predictions of where this all ends up