It may surprise HN to learn, but most companies don't have top-tier technical talent.
Consequently, how do you do {cutting edge thing board and investors are demanding company do} with the people you have/can afford? Use an offering that decreases the necessary skill level by providing powerful prebuilt components.
From this perspective, LLM integration sounds like a perfect fit. It's something every company is being asked to have a plan on, but one few are technically staffed to execute on their own.
But this is still the "language of hype". LLM integration to do what? I'm not saying they're not doing anything useful, just that this is not a way to describe either a productivity-enhancing technology (feature) or an actual value proposition (benefit) in a meaningful way.
What the end deliverable of the plan is doesn't matter: effectively it's the company demonstrating it can "do AI," so that if it becomes existentially necessary, the company's ability to execute is already known.
Absent demonstration, hell, the company might not even be retaining useful historical data. (E.g. theft data at a major US retailer)
I'm not even saying it's unmitigatedly a negative thing. When the question "What are we going to actually do with XML?" came up, many had good ideas, and useful tech investments started getting past boards that wouldn't otherwise have. -- So there was a lot of "collateral goodness".
But people also started ripping out databases and purpose-built DSLs from existing applications just so they could replace them with something XML-based.
As a way of making tech investments, it's weak. If it's 1999 and I'm asked to invest in a tech company that makes a damned good XML parser and nothing else, I'm well advised to not invest. And if I'm a CTO in a non-tech business asked to start replacing database servers with XML files, I'm well advised to not do it.
It depends who you're selling to. If customers are asking for a way to deal with LLMs, then providing that is a perfectly fine value proposition. The "do what" is going to depend on the customer.
Running your own Spark, especially on prem, is a lot of work. Most companies would prefer to just provide their data and let someone else handle the query engine.
The parent is right however that Databricks has a feature store (tokenization) but it's not simple to set up and just getting content in and out of it is a major pain point right now.
> “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs
I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).