[poisonfountain]

>I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work

I don't believe that anymore, to be honest. Models are starting to get good at ambiguity. Claude Code now asks me when something is ambiguous. Soon, all meetings will be recorded, transcribed and stored in a well-indexed place for the agents to search when faced with ambiguity (free startup idea here!). If they can ask you now, they'll be able to search for the answers themselves once that's possible. In fact, they already do it now if you have a well-documented Notion/Confluence, it's just that nobody has.

It's probably harder to RL for "identify ambiguity" than RL'ing for performance algorithms, sure, but it's not impossible and it's in the works. It's just a matter of time now.

[momojo]

> Models are starting to get good at ambiguity

That's fair, and something I've observed too. I wish I had written "the rest of us shouldn't freak out and quit software today".

But here's another data point: At the biotech I work for, writing good code has never been the bottleneck. I actually told my boss that a paid Claude vs free subscription wouldn't be that much value because even if it took every piece of code or algorithm we've ever written and 10x-ed the hell out of them, we'd still be bottlenecked by the biology and physics which dictates that we wait 24 days for our histology assay pipeline.

I have a hunch most fields outside of software are this way. And I'm personally not planning to quit anytime soon.

[poisonfountain]

Ok, but you job is clearly not a good sample for a "job most mortals work on".

[DoctorOetker]

Everybody likes to think their job or specialization is the bottleneck.

I think the domain knowledge and scientific knowledge is more often the bottleneck than people like to admit.

Sure it doesn't take a lot of knowledge to repeat or automate what the previous generations did, but such decisions were very far from the optimum considering the actual scientific frontier.

[momojo]

1. I admit my job is not a good sample of 'job most mortals work' 2. I still think 'most mortals' software devs aren't Antirez, and work in industries or fields that still require a human to bridge the ambiguity gap.

[TranquilMarmot]

> Soon, all meetings will be recorded, transcribed and stored in a well-indexed place for the agents to search when faced with ambiguity (free startup idea here!)

We were doing that over at Vowel a few years back, unfortunately it didn't pan out because you're competing directly against Zoom, Google Meet, Microsoft Teams, etc. They are all (slowly) catching up to where we were as a scrappy startup 4 years ago.

It was truly game-changing to have all of your meetings in an easily searchable database. Even as a human.

[dzhiurgis]

Why record when it can build in realtime as meeting is going on.

Slack is kinda there with Salesforce - can do a lot already on Agentforce and in Slackbot, but two aren't integrated just yet and Slackbot doesn't support group chats/channels. One interesting aspect in this will be - who has superiority boss, client, analyst or developer?

[risyachka]

In coding the ambiguity is very, very limited and constrained compared to any non dev job that involves any decision making

[exfalso]

That's.. not even close to being the case. It's literally a series of ambiguous questions and strategic decisions.

Non-ambiguous is like a first semester algorithms class in university.

[antonvs]

There seems to be a category of "coder" which fits the other commenter's description, where someone else makes all the significant decisions and they just write the code. Not coincidentally, that category seems most at risk from AI, because they're basically like a human version of a coding agent.

[mrdomino-]

Tacit knowledge is definitionally not recorded in any of these systems. This proposes to solve the problem of tacit knowledge by getting rid of it. It is not clear to me if that solution is either possible or desirable.

[nl]

The labs are spending hundreds of millions of dollars hiring people doing many fairly random (but economically valuable) tasks to collect this tacit knowledge for RL.

It works really well.

[mrdomino-]

It ceases to become tacit as soon as it is collected.

Maybe this rephrase will help: the proposed solution is to render all knowledge explicit.

[nl]

> It ceases to become tacit as soon as it is collected.

I'm not sure.

It it is collected via preferences then it isn't necessarily something that can be communicated (except in the LLM's latent space).

That still feels tacit to me.

To simplify that argument, the relationship between King and Queen in the Word2Vec latent space can be easily explicitly labelled.

But the relationship between Napoleon and Tsar Alexander I also exists and encodes much of the tacit knowledge about their relationship but isn't as easily labelled (eg, Google AI Mode says "Napoleon I and Tsar Alexander I had a volatile "bromance" that shifted from mutual admiration to deep animosity, acting as a defining conflict of the Napoleonic Wars".)

Word2Vec is a very simple model. In a more complex LLM that deeper knowledge can be queried by asking questions but you can never capture it all. Isn't that what "tacit knowledge" is?

[mrdomino-]

It's a good question, yeah, and a lot of these boundaries get fuzzy when they're looked at closely enough.

It's certainly the case that LLMs already are able to represent and make use of some kinds of apparently still tacit knowledge, and that the scope of that is apparently expanding. I don't question that. I question two things: whether it is always desirable for that scope to expand, and whether it is possible for that scope to ever fully cover what it seeks to cover.

[Yokohiii]

So self chosen total surveillance and transparency so your fav LLM can be better?

[DennisP]

Could always use a local LLM for stuff like that. One of my relatives works for one of the big audit firms and that's what they do.

[Yokohiii]

Sure. Still from what he said, your company wants every communication from you stored somewhere, ready for analysis. I don't think an unfiltered data acquisition is good, my interpretation and decision making is also part of my work. Also meetings may share some personal details that I would never tell on the record.

Full transparency has a cost, and we cannot afford it.

[wuschel]

Unfortunately you can't record meetings in many jurisdictions, including court sessions. Hence we have to rely - for worse, or perhaps even for better - on human driven note taking.

[poisonfountain]

You're downplaying the AI lobby here. They're eating down copyright laws, something that seemed impossible just a couple of years ago. Screwing privacy laws is just the next step.

Also, we are seeing a cultural shift around that as well. Now people bring "AI notetakers" to Zoom calls without even asking for your permission. People are already acting like privacy laws don't exist anymore, it's going to be even easier for the AI lobby to take it down now. Just like piracy normalized copyright infringement, opening the path to the current rulings around "fair training".

[Yokohiii]

Such invasive practices are pretty disgusting. But I don't think it will be pervasive. Once it spreads, AI vendors and abusive companies will be hold accountable. There is also an obvious conflict, the surveillance will likely be very selective. Programmers have to record everything, while middle managers have a choice to sign off everything. Senior management will of course do whatever but have full insight on the data. This will create even more backlash. Of course the social culture will turn stone cold and hostile over night with such installments.

[Yokohiii]

thanks for the downvote anon. its an convenient conversation.

[poisonfountain]

I disagree but it wasn't me who downvoted, just so you know.

[Yokohiii]

Yeah I wasn't accusing you. Was likely that you disagree, I can deal with that.