Comment by Spooky23 | Hacker News Reader

Comment by Spooky23 | Hacker News Reader

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It’s going to ramp. I have a team where 60% of the staff (6/10) is retiring in a year.

Their function is around reconciling utilization and bills from multiple related suppliers with different internal stakeholders. They do a bunch of analysis and work with the internal stakeholders to optimize or migrate spend. It is high ROI for us, and the issue is both finding people with the right analytical and presentation skills and managing the toil of the “heavy” work.

Basically, we’re able to train interns to do 80% of the processing work with LLM tooling. So we’re doing to promote two of the existing staff and replace 2/6 vacancies with entry level new grads, and use the unit to recruit talent and cycle them through.

In terms of order of magnitude, we’ll save about $500k in payroll, spend $50k in services, and get same or better outcomes.

Another example is we gave an L1 service desk manager Gemini and made him watch a YouTube video about statistics. He’s using it to analyze call statistics and understand how his business works without alot of math knowledge. For example, he looked at the times where the desk was at 95th percentile call volume and identified a few ways to time shift certain things to avoid the need for more agents or reduce overall wait times. All stuff that would require expensive talent and some sort of data analysis software… which frankly probably wouldn’t have been purchased.

Thats the real AI story. Stupid business people are just firing people. The real magic is using the tools to make smart people smarter. If you work for a big company, giving Gemini or ChatGPT to motivated contracts and procurement teams would literally print money for you due to the stuff your folks are missing.

Except that we know that even if you can't put your finger on what it is right now, there's something in the way of "ChatGPT just literally prints money" being at all realistic. It's pretty obvious in the markets if anyone has figured out a repeatable strategy for printing money.

This is to say that we know from looking at outcomes over the long term that the kinds of concrete gains you're describing are offset by subtler kinds of losses which most likely you would struggle to describe as decimal numbers but which are equally real in their impact on your business.

>This is to say that we know from looking at outcomes over the long term

Public LLMs have been around for 3 years, and adoption is still nascent. We don't have any long term data, and the longest term data we have involves a bunch of outdated models. Most people are still awful at using LLMs, and probably the most skilled users are the college kids who are graduating right now (the youth is always god-tier with new tech).

I cannot think of anything more foolish right now than not trying to leverage SOTA models to save you money, especially because you heard rumors of shadow losses that can only be found in the bottom line.

I think saying you can't imagine anything more foolish makes you look the fool. You just pick which evidence you'd prefer to ignore and go on with being told "you're absolutely right" all day.

And your company now won't progress. Won't have insight. Sure you can keep on keeping on, but that is all. You also farm out expertise instead of building it.

AI is an entropy machine for everything it touches. Companies that runs off of AI is a zombie company. Without people to understand an industry, what does your company ad? Without people to see new revenue streams, new directions, and most importantly NEW RISKS to your business model, you are a dead zombie company living off what the previous living, growing company built. But does that matter to owners when they get $500,000 more a year in their pockets?

Yeah, I work closely with people in this space (on the in-house side). You're going to find very quickly that the LLM "analysis" isn't worth much more than the paper its printed on. Due to management directives they have been testing AI tools for financial analysis for 2ish years now and the output is still about what you would expect from an intern who just started working in finance a few weeks before.

Gemini "analysis" looks superficially detailed but if you actually do any deep dive (or even a shallow dive) you're going to start finding all the things its wrong about because it's not actually analyzing anything; its just connecting most likely words together. Very crucial, because of the ways LLMs work, they can't generate any conclusion that isn't already in its data set. But it's exactly those sorts of non-obvious conclusions that are the reason you hire people to do Finance.

Will AI eventually take over these kinds of jobs? Sure, in 10 to 20 years when actual AI exists. But LLMs aren't AI. They're just brute-force black box machine learning algorithms.

If LLM-based tools were such a money printing machine with a big ROI, why would you ever want to downsize? Keep redirecting that excess human capacity into building bigger and better capabilities to generate more revenue. Hire cheap grads to do the work.

Except it seems like the opposite is happening. CS grads have high unemployment. Companies laying off staff.

The rhetoric doesn't seem to add up to the reality.

This is a great way of looking at it.

Do you use tech to grow your business or increase dividends?

Also reducing staff via attrition shows far better management skills than layoffs which imo says more about the CEO & upper management.

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