x313

Are you not worried about having your entire life, financial information, addresses, etc. stored with one company?

The $500 25x was mentioned by Altman on the livestream

The $200 cut to 10x was announced only formally on Twitter

However none of their product pages mention concrete #s, so I guess value will only degrade more over time.

Horrid value.

Previous $200 tier: 20x pro value

New $200 tier: 10x

New $500 tier: 25x, plus ultrafast mode but it drains 6x the cost (!!)

Pretty cool someone is still doing this. Training in house LLMs was extremely popular in 2023-2024, back when domain-specific LLMs could easily top GPT in their field. In my field alone (tax/HR tech) I remember that Intuit, Workday, Indeed, LinkedIn were all training internal models.

It eventually stopped making sense because of inference costs. Running something internal with 30% GPU utilization is just too cost inefficient compared to using an API. Idk how Reuters will manage to solve this fundamental problem.

Bluesky has <100 employees so it could be possible

I used this a lot over the weekend, and it's a really intelligent and strange model.

It gets really agentic at the higher reasoning levels. It does the basics like goal tracking and tool calling well, but more than that, it gets obsessed with solving problems and will do insane/unusual things to get to the solution. It actually reminds me of GPT-5.6-Sol-max which is similarly obsessive.

It doesn't surprise me at all that it outscores Opus 4.6. Opus had way better world knowledge but was more "human" with agent stuff - sort of lazy and uncreative, basically giving up once the obvious solutions failed. These newer models work more like magic, they are so creative and persistent at finding ways to get to the solution.

Referring to ownership, not renting

For those who don't know what's going on in Korea, KOSPI is up 3x in the last year and a large amount of HBM employees have made huge amounts of bonus pay. This has led to an insane FOMO frenzy in a society that's already very competitive.

Add to that, stock gains in Korea are often used to finance housing purchases (or real estate investment) so many retail investors are scared of being "locked out" of housing (which is a requisite status symbol for dating or marriage) if they're not making the same capital gains others are.

Currently Korean social media is full of stories of leveraged day traders who've gotten rich the past year, HBM employees who've made bonuses worth decades of salary (e.g. memes of Samsung employees in luxury cars), etc. Lots of comments along the lines of "everyone is getting rich except me". It's all reminiscent of the crypto frenzy in the US a few years ago but way more intense and concentrated.

The newest generation of LLMs have a very high obsession level with autonomous problem solving.

For example, I'm often working with Codex in a WSL terminal. GPT-5.6 often does things autonomously that I thought would need my intervention (e.g. for Windows admin rights). It figures out complex workarounds or makes wild assumptions about what I'd be OK with, rather than just asking me for help or clarification. I've had to restrict its tool permissions compared to older models as a result.

I imagine this due to RLVR training, but it's clearly very dangerous. How is it that these same labs calling for open-weight safety restrictions are training such obvious "paperclip maximizers" without introspection?

This study found that between 2022-2024, there was a negative correlation between "jobs with high AI exposure" (i.e., tech jobs) and % change in wages. According to them, software engineering has both the biggest wage decline and most AI exposure.

There's a much more reasonable explanation here.. tech jobs had the most wage growth during COVID and this was a pullback. The time frame they used (2022-2024) was also pre-coding agents, where GPT-3.5/GPT-4 were frontier models.

The entire safety evals industry is essentially funded and controlled by OpenAI/Anthropic. Notice that on recent models, they exclusively use internal testing or black box external vendors (e.g., Gray Swan) whose entire business is to serve OpenAI/Anthropic. And all these companies just share the same pool of researchers back and forth.