rafiki6

What are the most successful avenues? I'm located in Toronto so not finding the in-person meetings to be too useful.

Being a SWE or MLE in my case, it seems like we're highly valued as executors on well understood problems.

I'm wondering if there is a way to become a part of something ground breaking like neurobiological research instead?

I appreciate the hype, I get it, productivity booster yada yada. I just don't have the energy to keep up with it. I got a Masters and got into ML thinking we had time. My domains were mostly about NLP, even though I never enjoyed it much. For most applications businesses have, GPT4 is sufficient. It can behave as a general purpose classifier and even recommendation engine. It's just too big.

I feel like my options are to try a pivot towards non-generative CV or IoT/Forecasting/tabular data, but even those have mostly been commoditized.

I feel like your only shot of doing interesting work in software is to get into a group working on a foundational problem that's high risk.

Otherwise I think this field is on a slow death march. I don't think we will recover this bubble and achieve a ridiculous job market ever again. Companies are all getting wise to what GPT4 can do, and GPT5 is coming within the year.

Honestly progress is great, but humans are getting left behind. That's fine I guess, I just want to be able to plan for it and adjust.

Is anyone else just done?

The modern 'tech' industry (i.e. companies that primarily sell software based services), has grown and thrived in a sub 7% fed fund rate environment (which we are fast approaching and will likely need to surpass to fight inflation).

Presumably this worked because many tech based businesses were seen as massive high risk bets that were unproven. VCs were the primary source of funds and many relied heavily on leverage upstream.

Without low rates the VC based funding model will need to change, and companies may need to seek funding from more traditional sources where revenue matters much more upfront. This might mean premature optimization on business models leading to more moderate growth and less of a focus on hypergrowth and global scale.

Does this mean the hypergrowth era is over?

I'll start. For me, I think the most impactful thing I've ever built was an internal application for a FX trading desk that eventually went on to handle billions in daily trades.

It didn't use any fancy frameworks, just plain old CRUD on Java.