I think we have to do both. Bans/restrictions to prevent immediate harms (or at least attempt to), and regulation to prevent future harms.

Oof, they’re going to be in a whole lot of pain after the burst. Hopefully they can reverse course mid-way.

Bernie has good intentions, but he was AI-pilled by Geoffrey Hinton, who ironically also has good intentions. However, they are both out of touch with reality.

I’m confident enough about this that I’ve registered a prediction on Long Bets.

“No LLM-based AI will surpass 70% on the ARC-AGI-3 leaderboard, with a cost of $1000 or less, before June 2028.” - https://longbets.org/973/

I’m curious if you’d really disagree with the premise, and would you (or anyone here on Lemmy) be willing to put money down to challenge the bet? (Long Bets always donates any winnings to a registered non-profit of the winner’s choice, though it’s a $200 minimum).

Are you saying that LLMs can currently reason? How do you explain their low score on ARC-AGI-3? Do you think Transformer LLM architectures will be capable of reasoning within the next two years without some new breakthrough? What mechanism in the architecture allows them to reason?

Companies are only shooting themselves in the foot in the long term if they stop hiring junior engineers, and most of that work is not being replaced, it’s being shifted to the senior engineers who now have to babysit AIs that can’t actually do the job for any extended period of time. If you’re accepting AI code into a codebase without thorough review, then you’re also shooting yourself in the foot in the long term, because even the senior engineers won’t know the codebase after a while. If you’re doing thorough reviews in order to catch the AI bugs, well then you’re probably better off coding it yourself correctly in the first place, unless you’ve already allowed your skills to atrophy.

Do you really think AIs are reasoning when you ask them to troubleshoot technical issues? You may be lucky if the issue is already in their training data, but anything even slightly novel, and the AI is just going to bullshit an answer, and I guess you’re going to follow it blindly, since you don’t know enough to come up with an answer yourself.

Besides all that, how is open source AI going to stop junior developers from losing their jobs?

The word “intelligence” is doing a lot of heavy lifting here. LLMs lack any mechanism for true logical reasoning, and they always will by nature. This is why they fail at simple questions like “the car wash test”. It’s also why agents are expensive; They just flail around in token hungry “reasoning loops” until they happen to come across a correct solution. And it’s why Claude Opus 4.8 (High) only scores 1.5% on the ARC-AGI-3 benchmark at a cost of $10,000.

This kind of panic is just part of the hype. Wake me up when real intelligence arrives.

You almost always have nano or pico available, so it’s really unlikely that you’d get stuck with nothing but vim, unless you just didn’t know that nano existed.

Probably something like “Please bro!!! WHY DID YOU DO THIS ??!! 😭😭”

Some good debunking here: https://www.flyingpenguin.com/the-boy-that-cried-mythos-verification-is-collapsing-trust-in-anthropic/

Fair point. I can see how a bubble burst might not recover those discarded wafers, assuming that story is true. However, I’d still imagine that if the bubble did burst, there would naturally be a reduction in demand for memory, and that would cool prices at least a bit. Certainly time will tell. It’s still difficult to predict the direction this is all going in.

I think most of their value was as a streaming backend for other companies, not their user-facing video sharing platform.

Why is this so downvoted?

I don’t quite understand the concerns in the comments here. The original blog post seems reasonable: https://www.waterfox.com/blog/15-years-of-forking/#what-waterfox-is-in-2026

Yes, the LLMs received credit for each level even if they didn’t complete the entire environment.

They have some replays of tasks on their website: https://arcprize.org/tasks

Here’s one where the human completed all 9 levels in 1458 actions, but the LLM completed only one level in 24 actions, then struggled for 190 actions until it timed-out, I guess. The LLM scored 2.8% because of the weighted average, I think. I didn’t take the time to all do the math, and I’m not sure if the replay action count is accurate, but it gives you an idea.

Human: https://arcprize.org/replay/0d461c1c-21e5-4dc8-b263-9922332a6485

LLM: https://arcprize.org/replay/cc821983-3975-4ae4-a70b-e031f6807bb0

I like the trend of refining existing tools. You take tried-and-true commands and shave off the rough edges and quirks. I use ripgrep instead of grep, fd instead of find, scm_breeze on top of git, dust instead of du, duf instead of df, z over cd, and xh instead of curl

You can really only judge fairness of the score if you understand the scoring criteria. It is a relative score where the baseline is 100% for humans – i.e. A task was only included in the challenge if at least two people in the panel of humans were able to solve it completely, and their action count is a measure of efficiency. This is the baseline used as a point of comparison.

From the Technical Report:

The procedure can be summarized as follows:
• “Score the AI test taker by its per-level action efficiency” - For each level that the test taker completes, count the number of actions that it took.
• “As compared to human baseline” - For each level that is counted, compare the AI agent’s action count to a human baseline, which we define as the second-best human action count. Ex: If the second-best human completed a level in only 10 actions, but the AI agent took 100 to complete it, then the AI agent scores (10/100)^2 for that level, which gets reported as 1%. Note that level scoring is calculated using the square of efficiency.
• “Normalized per environment” - Each level is scored in isolation. Each individual level will get a score between 0% (very inefficient) 100% (matches or surpasses human level efficiency). The environment score will be a weighted-average of level score across all levels of that environment.
• “Across all environments” - The total score will be the sum of individual environment scores divided by the total number of environments. This will be a score between 0% and 100%.

So the humans “scored 100%” because that is the baseline by definition, and the AIs are evaluated at how close they got to human correctness and efficiency. So a score of 0.26% is 1/0.0026 ~= 385 times less efficient (and correct) compared to humans.

The goal of the ARC organization is to continually measure progress towards AGI, not come up with some predictive threshold for when AGI is achieved.

As long as they can continue to measure a gap between “easy for humans” and “hard for AI”, they will continue releasing new iterations of this ARC-AGI challenge series. Currently they do that about once a year.

More detail about the mission here: https://arcprize.org/arc-agi

It’s true that frontier models got better at the previous challenges, but it’s worth noting that they’re still not quite at human level even with those simpler tasks.

Also, each generation of the challenge tries to close loopholes that newer models would exploit, like brute-forcing the training with tons of synthesized tasks and solutions, over-fitting to these particular kinds of tasks, and issues with the similarities between the tasks in the challenge.

A common strategy in past challenges was to generate thousands of similar tasks, and you can imagine the big AI companies were able to do that at massive scale for their frontier models.

There’s a column linking to replays in the table of tasks here: https://arcprize.org/tasks

This is my rough upper-bound estimate based on the Technical Report. Human participants were paid to complete and evaluate the tasks at an average fixed fee of $128 plus $5 for solved tasks. So if a panel of humans were tasked with solving the 25 tasks in the public test set, it would be an average of $250 per person. Although, looking at it again, the costs listed for the LLMs is per task, so it would actually be more like $10 per human per task. In any case it’s one or two orders of magnitude less than the LLMs.

Participants received a fixed participation fee of $115–$140 for completing the session, along with a $5 performance-based incentive for each environment successfully solved

https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf