I'm also a skeptic on AI replacing many human jobs anytime soon. It's mostly going to assist, accelerate or amplify humans in completing work better or faster. That's the typical historical technology cycle where better tech makes work more efficient. Eventually that does allow the same work to be done with less people, like a better IP telephony system enabling a 90 person call center to handle the same call volume that previously required 100 people. But designing, manufacturing, selling, installing and supporting the new IP phone system also creates at least 10 new jobs.

So far the only significant human replacement I'm seeing AI enable is in low-end, entry level work. For example, fulfilling "gig work" for Fiverr like spending an hour or two whipping up a relatively low-quality graphic logo or other basic design work for $20. This is largely done at home by entry-level graphic design students in second-world locales like the Philippines or rural India. A good graphical AI can (and is) taking some of this work from the humans doing it. Although it's not even a big impact yet, primarily because for non-technical customers, the Fiverr workflow can still be easier or more comfortable than figuring out which AI tool to use and how to get what they really want from it.

The point is that this Fiverr piece-meal gig work is the lowest paying, least desirable work in graphic design. No one doing it wants to still be doing it a year or two from now. It's the Mcdonald's counter of their industry. They all aspire to higher skill, higher paying design jobs. They're only doing Fiverr gig work because they don't yet have a degree, enough resume credits or decent portfolio examples. Much like steam-powered bulldozers and pile drivers displaced pick axe swinging humans digging railroad tunnels in the 1800s, the new technology is displacing some of the least-desirable, lowest-paying jobs first. I don't yet see any clear reason this well-established 200+ year trend will be fundamentally different this time. And history is littered with those who predicted "but this time it'll be different."

I've read the scenarios which predict that AI will eventually be able to fundamentally and repeatedly self-improve autonomously, at scale and without limit. I do think AI will continue to improve but, like many others, I find the "self-improve" step to be a huge and unevidenced leap of faith. So, I don't think it's likely, for reasons I won't enumerate here because domain experts far smarter than I am have already written extensively about them.

Not really. It could also mean their company's effective headcount is much greater than its nominal one.

Apple has fewer employees than Google (164k < 183k).

what kind of employees does Docusign employ? surely Digital Documents dont require physical onsite distribution centers and labor

The US is not a signatory to the International Criminal Court so you won't see Musk on trial there.

The closest Elon ever came to anything Hague-worthy is allowing Starlink to be used in Ukrainian attacks on Russian civilian infrastructure. I don't think the Hague would be interested in anything like that. And if his life is worthless, then what would you say about your own? Nonetheless, I commend you on your complete lack of hinges. /s

> Even compared to just a few years back, people seem more often to struggle to carry a thought, and resort much more quickly to emotional belligerence.

We're living in extremely uncertain times, with multiple global crises taking place at the same time, each of which could develop into a turning point for humankind.

At the same time, predatory algorithms do whatever it takes to make people addicted to media, while mental health care remains inaccessible for many.

I feel like throwing a tantrum almost every single day.

But context windows for LLMs include all the “long term memory” things you’re excluding from humans

I don't think we shift the goalposts for AGI. I'm not getting the sense that people are redefining what AGI is when a new model is released. I'm getting the sense that some people are thinking like me when a new model is released: we got a better transformer, and a more useful model trained on more or better data, but we didn't get closer to AGI. And people are saying this not because they've pushed out what AGI really means, they're saying this because the models still have the same basic use cases, the same flaws and the same limitations. They're just better at what they already do. Also, the better these models get at what they already do, the more starkly they contrast with human capabilities, for better or worse.

> Chess is not exactly a simple logic task.

Compare to what a software engineer is able to do, it is very much a simple logic task. Or the average person having a non-trivial job. Or a beehive organizing its existence, from its amino acids up to hive organization. All those things are magnitudes harder than chess.

> I remember being extremely surprised when I could ask GPT3 to rotate a 3d model of a car in it's head and ask it about what I would see when sitting inside, or which doors would refuse to open because they're in contact with the ground.

It's not reasoning its way there. Somebody asked something similar some time in the corpus and that corpus also contained the answers. That's why it can answer. After a quite small number of moves, the chess board it unique and you can't fake it. You need to think ahead. A task which computers are traditionally very good at. Even trained chess players are. That LLMs are not goes to show that they are very far from AGI.

This misses the point. LLMs will do things like move a knight by a single square as if it were a pawn. Chess is an extremely well understood game, and the rules about how things move is almost certainly well-represented in the training data.

These models cannot even make legal chess moves. That’s incredibly basic logic, and it shows how LLMs are still completely incapable of reasoning or understanding. Many kinds of task are never going to be possible for LLMs unless that changes. Programming is one of those tasks.

> I'm not sure why people are expecting a language model to be great at chess.

Because the conversation is about AGI, and how far away we are from AGI.

press X to doubt

o3-mini wasn't even the second place for non-STEM tasks, and in today's announcement they don't even publish benchmarks for those. What's impressive about Gemini 2.5 pro (and was also really impressive with R1) is how good the model is for a very broad range of tasks, not just benchmaxing on AIME.

I don't know how anyone could look at any of this and say ponderously: it's basically the same as Nov 2022 ChatGPT. Thus strategically they're pivoting to social to become too big to fail.

I appreciate the info and I have a question:

Why would anyone use a social network run by Sam Altman? No offense but his reputation is chaotic neutral to say the least.

Social networks require a ton of momentum to get going.

BlueSky already ate all the momentum that X lost.

Not surprising. Add comments to sora.com and you've got a social network.

chatGPT should be built into my iMessage threads with friends. @chatGPT "Is there an evening train on Thursdays from Brussels to Berlin?" Something a friend and I were discussing but we had to exit out of iMessage and use GPT then back to iMessage.

For UX The GPT info in the thread would be collapsed by default and both users have the discretion to click to expand the info.

They should launch o999 and count backwards for each release till they hit oagi

Have any of the models been deprecated? It seems like a deprecation plan and definition of timelines would be extraordinarily helpful.

I have not seen any sort of "If you're using X.122, upgrade to X.123, before 202X. If you're using X.120, upgrade to anything before April 2026, because the model will no longer be available on that date." ... Like all operating systems and hardware manufacturers have been doing for decades.

Side note, it's amusing that stable behavior is only available on a particular model with a sufficiently low temperature setting. As near-AGI shouldn't these models be smart enough to maintain consistency or improvement from version to version?

Any idea when v1/models will be updated? As of right now, https://api.openai.com/v1/models has "id": "o3-mini-2025-01-31" and "id": "o3-mini", but no just 'o3'.

They just did that, at least for chat

I think you're trying to re-contextualize the old Standards joke, but I actually think you're right -- if a front end model could dispatch as appropriate to the best backend model for a given prompt, and turn everything into a high level sort of mixture of models, I think that would be great, and a great simplifying step. Then they can specialize and optimize all they want, CPU goes down, responses get better and we only see one interface.

I'd attribute that more to first mover advantage than a benefit from poor naming choices, though I do think they are likely to misattribute that to a causal relationship so that they keep doing the latter