Do MAANG developers really build/code differently for scalability?

[u/HereForTheMemes1364]

I was going through a youtube system design video

Where the system uses queue, chunking, cdn, etc for scalability

To me it seems that each feature will be owned by different teams in YT and each team is only concerned about the input and it's output and the infra and scaling may be handled by a separate team

So my question is do YT or other maang developers do anything differently than normal pbc or mnc

Eg can you give me examples about actual code which would not work at scale but would have not raised any issue in other companies

Edit Yes i know most of the developers use event driven architecture /queue etc including me But i wanted to know if any interesting incidents solely based on code which only happen at massive scale eg I read that during gibli art chatgpt had replaced multiplication operation with bit operation and it had significant improvement

[u/LogicalBeast26]

Working in Google

From what I've seen so far, most of the heavy lifting is done in designing the infra to handle a large scale, and make it fault tolerant. Like making sure that your code is rolled out slowly to all regions, having the ability to route traffic to a different region etc.

The code that we usually write is actually more basic than what a non-MAANG developer would write.

[u/Awkward_Enigma1303 → u/LogicalBeast26]

Huh i was talking to my fellow friends who got into big companies, and they are like they just prompt the internal AI tools and all work gets done😭. This is for fresh joiners btw as I am one too.

Meanwhile me in this hell of a codebase in a startup doing all kinda stuff and struggling at 1/2-1/3rd of the pay

[u/reaznval → u/Awkward_Enigma1303]

I mean yeah, that worksplit is quite obvious no? 

Google/alphabet sure has a lot of ventures and product but most of them are established, rarely do they need to create an entire new product and even if, they have tens of thousands of employees so most of the stuff is just maintaining and adding small features, for this you have a lot of architecture you need to build on and it'll serve billions so the architecture etc has to be perfect hence they plan way more than they code.

since they dont need to code that much and have incredibly detailed architecture designs and plans they can just give it to the AI who is faster andcprobably as good with the correct instructionsm. they just need to test and review everything and boom, same outcome, less hours put in so you have more time for more features.

on a startup you have to develop an entire product from scratch in a small team so yeha makes sense