> Your local plumber is going to want a funny action movie trailer slash plumbing advertisement to advertise their services. They wouldn't have even been in the market before.

And why would your local plumber hire someone to produce this funny action trailer (which I'm not convinced would actually help them from an advertising perspective), when they can simply have an AI produce that action funny action trailer without hiring anyone? Assuming models improve sufficiently that will become trivially possible.

> Independent filmmakers will be making their own Miyazaki and Spielberg epics that cater to the most niche of audiences - no more mass market Marvel that has to satisfy everybody, you're going to see fictional fantasy biopic reimaginings of Grace Hopper fighting the vampire Nazis.

Well, first of all, if the audience is "the most niche of audiences", then I'm not sure how that's going to lead to a sustainable career. And again -- if I want to see my niche historical fantasy interests come to life in a movie about Grace Hopper fighting vampire Nazis, why will I need a filmmaker to create this for me when I can simply prompt an AI myself? "Give me a fun action movie that incorporates famous computer scientists fighting Nazis. Make it 1.5 hours long, and give it a comedic tone."

I think you're fundamentally overvaluing what humans will be able to provide in an era where creating content is very cheap and very easy.

> "We're profitable on inference. If we didn't pay for training, we'd be a very profitable company."

That's OpenAI (though I'd be curious if that statement holds for subscriptions as opposed to API use). What about the downstream companies that use OpenAI models? I'm not sure the picture is as rosy for them.

Microsoft claims that they have an AI setup that outperforms human doctors on diagnosis tasks: https://microsoft.ai/new/the-path-to-medical-superintelligen...

"MAI-DxO boosted the diagnostic performance of every model we tested. The best performing setup was MAI-DxO paired with OpenAI’s o3, which correctly solved 85.5% of the NEJM benchmark cases. For comparison, we also evaluated 21 practicing physicians from the US and UK, each with 5-20 years of clinical experience. On the same tasks, these experts achieved a mean accuracy of 20% across completed cases."

Of course, AI "doctors" can't do physical examinations and the best performing models cost thousands to run per case. This is also a test of diagnosis, not of treatment.

Waar heb je het over? "Welgelegen Buitenrust Nooitgedacht Rustenburg" is volkomen cromulent Engels.

For what it's worth, I do use AI for language learning, though I'm not sure it's the best idea. Primarily for helping translate German news articles into English and making vocabulary flashcards; it's usually clear when the AI has lost the plot and I can correct the translation by hand. Of course, if issues were more subtle then I probably wouldn't catch them ...

> the iPhone was only "in charge" for a brief year or two, and then Android ate its lunch in terms of marketshare.

This is true worldwide, but there are significant regions where iOS quite handily beats Android (such as the US, Japan, and even some parts of Europe).

Oh man, I played the shit out of the Lone Wolf books when I was a kid in the eighties and nineties. My brother had the first couple of them, and I still have his old books somewhere with his notes. I should find them for nostalgia's sake (my brother having long since passed).

> Character stats, names, details about players - those are inputs, and structured ones at that.

Some details about players are structured and can be easily stored and referenced. Some aren't. Consider a character who, through emergent gameplay, develops a slight bias against kobolds; who's going to pick up on that and store it in a database (and at what point)? What if a player extemporaneously gives a monologue about their grief at losing a parent? Will the entire story be stored? Will it be processed into structured chunks to be referenced later? Will the LLM just shove "lost a father" into a database?

Given current limitations I don't see how you design a system that won't forget important details, particularly across many sessions.

> who's going to pick up on that and store it in a database (and at what point)

LLM might, if prompted to look at it, or if there was a defining moment that could invoke such change. It won't pick on a very subtle change, but then most people reading a story wouldn't either - this is more of the kind of stuff fans read into a story when trying to patch potential continuity issues.

> What if a player extemporaneously gives a monologue about their grief at losing a parent? Will the entire story be stored? Will it be processed into structured chunks to be referenced later? Will the LLM just shove "lost a father" into a database?

The scale depends on the design, but I'd say yes, shoving "lost a father" into a database so it pops up in context is a good first step, the next step would be to ensure the entry looks more like "mentioned they continue to grieve after loss of their father <time ago>", followed by a single-sentence summary of their monologue.

Personally, I had some degree of success with configuring LLM (Claude 3.5 Sonnet) for advising on some personal topics across multiple conversations - the system prompt contains notes in <user_background> and <user_goals> tag-delimited blocks, and instructions to monitor the conversation for important information relevant to those notes, and, if found, to adjust those notes accordingly (achieved by having it emit updates in another magic tag, and me manually apply them to the system prompt).

> Given current limitations I don't see how you design a system that won't forget important details, particularly across many sessions.

It's not possible. Fortunately, it's not needed. Humans forget important details all the time, too - but this is fine, because in storytelling, the audience is only aware of the paths you took, not of the countless other possibilities you missed or decided not to take. Same with LLMs (and larger systems using LLMs as components) - as long as they keep track of some details, and don't miss the biggest, most important ones, they'll do the job just fine.

(And if they miss some trivia you actually care about, I can imagine a system in which you could ask about it, and it'll do what the writers and fans always do - retcon the story on the fly.)

You sound like a fun guy.

We're not allowed to record meetings at work, with some exceptions such as trainings. I'm not at all sure why, but I believe it has to do with merger plans. We're not working on anything particularly shady, to my knowledge.

Notes are often taken but not always. Depends on the people in the meeting.

I suspect you're overstating the degree to which an LLM might be unsuitable for some types of work. For example, I'm a data scientist who works primarily in the field of sales forecasting. I've found that LLMs are quite poor at this task, frequently providing answers that are inappropriate, misleading, or simply not a good fit for the data we're working with. In general I've found very limited use in engaging LLMs in discussion about my work.

I don't think I'm calling myself a super special snowflake here. These models are just ... bad at sales forecasting.

LLMs aren't entirely useless for me. I'll use ChatGPT to generate code to make plots. That's helpful.

I would never recommend an LLM for sales forecasting. It's just the wrong tool for that job.

You seem to readily agree that my use case is inappropriate for LLMs, but not ToucanLoucan?

Zero LLMs have been trained on doing sales forecasting to my knowledge (and it isn't the right use regardless). In contrast, many LLMs have been trained on enormous quantities of code, coding languages and platforms and uses. Billions and billions of lines of code covering just about every sort of project. Millions of projects.

If someone says "Well my software dev project is too unique and novel and they are therefore of no value to me, but I understand it works for those simple folk with their simple needs", there is an overwhelming probability they are...misinformed.

Would it help if I said most “normal folk” applications of LLMs are a waste of time and money too then? Because I’m also absolutely a believer that a huge bubble burst is coming for OpenAI and company.