Thank you. Both questions are what I'm wondering right now.

I'm pursuing of a strategy of:

* Doubling down on domain knowledge. Even if coding becomes completely automated, there will always be value in knowing some particular corner of an industry very well. For me, that's insurance. This includes knowing how classical ML techniques can be applied in my industry (LLMs are not replacing xgboost any time soon).

* Keeping tabs on bleeding edge LLM capabilities, just to have a sense of what's possible. However, staying out of highly technical LLM implementation details like CUDA, inference, batching, etc. Perhaps fine tuning will be valuable but probably I would focus on understanding "fine tuning as a service" offerings rather than rolling my own.

* Investing in knowledge of infrastructure/orchestration of LLM services in a way that doesn't tie me to any particular problem space. It's hard to predict right now what kinds of new products LLMs might enable. But I think there will be value in being able to translate business problems into a high level architecture for an LLM-enabled app, even if that just means wiring together existing service offerings.

Maybe, but for every haystack that an LLM can search, RAG could search an even larger one. I don't think RAG is a killer app but I think tools that help LLMs find the right context will continue to be useful.

> Doubling down on domain knowledge

I'm a dev, but this is my main strategy right now. I am digging deep into my corner of fintech and hoping that will stave off some of the disruption I see coming for tech. Personally, I don't see why an AI couldn't also become a domain expert in my niche, but it should buy me some time and potentially align me with some powerful stakeholders (portfolio managers just need to convince the big money that a human touch is good and I will still be business).

Thanks for your input

Question: in what ways does insurance domain knowledge set you apart?

I understand certain industries benefit from Engineers with domain experience (e.g. Healthtech companies needing HIPAA knowledge).

I'm curious as to what particular skills insurance companies need from Engineers?

* Rating (a deep, classical, tabular ML problem) is still catching up to SOTA techniques in many insurance companies + uneven regulatory environment makes it tricky to navigate.

* Marketing is another classical ML problem, the goal is to optimize ad/lead broker spend/conversion funnel.

* CS of course has huge opportunity with LLMs.

* Automated claim fraud detection is a very difficult problem amenable to LLMs/MM DL models. Lots of "pivot to AI" going on here.

And there are others of course. The main thing is that insurance companies have limits on product innovation - policy terms are in many cases mandated by law, so the main differentiator is operational efficiency and exellency. Solutions that move the needle on big revenue/cost levers are very valuable and can be applied widely.

Can you elaborate on this?

As to whether that rebrand itself is successful, I don't know. It feels like the DS role is undergoing a maturation where different skillsets are being cleaved into different roles. I was always a more natural fit for the Ops side, I don't have the academic creds for a research-oriented position. But I'm not sure how much of ops means "LLM ops".

same. future looks very uncertain. on one hand, the new figure of "ai-assisted x" where x can range from developer to copywriter to a whealth of other primarily creative job will replace the non ai assisted variants due of the tangible increase in productivity

on the other hand the reduction in these cost possibly means 5x less capital requirement to build a startup, whose primary cost today is personell, at least in their early stages.

wether will that increase in company creation be enough to raise the demand for personell to a level where the society is stable I don't really know.

the primary risk I see is not from career prospet but from the massive capital investment that is required to be in the club of model providers, these will be the new elite, with unparallel power over economies and entire states. if you think the robber baron period was harsh, wait until three companies are ingrained in every aspect of your life from bureoucracy to healthcare and having to have to argue with a self moderated model instead of a bureaucrat, and if that's not enough imagine that maybe you have a uncommon or suggestive name and the model won't believe you no matter what proof you might give it. imagine these model being part one way or another in the production of entartainment, generating a monoculture that is extremely prude and milquetoast. or in general the model being wrong about anything really, you cannot reason with them.

my concern is that there's a lot that should be happening right now at a legislative level, not in term of stifling innovation, but let's say putting certain role outside of the sole control of AI, which it isn't, and if we wait to be reactive about it, it's gonna end up like it did for self drivin: blood on the road and I cannot have my car acting as a taxi and earning for me during off hours because for safety reason only the tech owner can profit from it, yeah, right.

those panning for gold (app devs, startups etc) may or may not find it. remains to be seen and i remain skeptical.