It means they buy and sell services directly between each other, skipping the "meatbag middleman"
55 karma · joined December 16, 2022
You mean 3.96T dollars, right?
They are banking that a bigger model can get distilled (internally) into a more capable smaller model as opposed to training that model without having access to the bigger model in the first place. Majority of the value generation seems to be happening at the frontier though, not in smaller models, and I'm not sure that gap will shrink any time soon.
> all engineers lose their jobs
They want to replace engineers with agent swarms. At that point it stops being a tool and they can charge for outcome. They might get there, or we might still need "qualified steerers" in the future. Who knows
No, someone still has to pay the subscription. AI Labs don't care who uses the tokens and how. If Adobe goes under someone else is going to take their place. Suprise, surprise: they will also use tokens.
> It’s a very expensive way of shooting yourself in the foot and sending the bill to your investors. <CoreWeave bonds chart>
I'm not sure how CoreWeave bond chart is even remotely relevant here. They are not even an investor.
> They are just mining the asteroid, dumping the silver onto the market for $20, and watching the price of their own product plummet to zero.
The lab's own product is the model. How is someone building a weekend project going to plummet the value of their model? It's the opposite!
This is just the complete opposite in my experience. Tests are the first thing the AI writes, especially in low coverage or unknown domain situation.
These systems have been trained for "generations" to oneshot problems. The first thing they do is create a mini harness to verify their solution is correct.
Majority of issues with AI assisted development are misaligned requirements or poorly stated goals
> What I need now is architectural, long-horizon and business perspective.
That's exactly what these tools are now good at. They have a huge gap when fixing these issues properly but they can spot these issues no problem
How do you deal with having to reason before judging a step?
It should be line https://motherfuckingwebsite.com/
> Throughout this process, Jarred's input was mostly limited to sending Claude messages of encouragement (mostly variants of “keep going” or “believe in yourself”).
How much experience can you replace with infinite compute remains to be seen I guess.
It's just slop. Acknowledge, move on, and find something better to spend your time on.
We're in a weird uncanny valley state of AI writing where it's barely comprehensible, but makes people feel productive so they shove it everywhere, including where it's not needed.
At some point, either:
1. People will realize that this is a waste of time
2. Models will get better and this will be remembered as a blip in history
1. Any tool result longer than 1500 tokens gets a link instead.
2. Forced compaction at 85%, removing all past messages apart from the last 4?
I'm sure these guys made it look good on the benchmarks, but the frontier labs have been hill-climbing this game for a while now, so odds are this just doesn't work for long-horizon tasks labs are optimizing for.
It's the same problem as overfitting in model training. If you're not measuring something it will get sacrificed.
Or, perhaps the code quality literally doesn't matter anymore and we've reached "code quality escape velocity" where you can code as much slop as you want, the next generation of models will clean it up faster than the slop generates?
You need really good guardrails if you want to automate the software engineering part. And since this meta-layer sits 1 level above code you can't use code to verify and constrain agents effectively. As soon as agents start prompting agents any small wrinkle in the spec the potential to derail the whole thing. You want someting akin to "alignment docs", and even then they rarely follow it to spec.
Left unattended, they just start inventing unspoken requirements, making asinine product decisions and generally dig into a hole they can't get out of. One failure point I've seen regularly is "latching on" to a single word in the alignment doc and blowing it way out of proportion to the point where it no longer makes sense and compromises on core product. They get "tunnel visioned" and lose the big picture. Happens every time.
Will be interesting to see how new model developments progress, but so far the human interlayer is still a requirement.
https://store.steampowered.com/app/2008100/Rule_the_Waves_3/
Not only that, it later hacked OpenAI itself, which everyone seems to forget about.
After discovering it they "reimaged known compromised worker nodes" and "started a full rebuild of the compromised cluster, the managed Kubernetes environment, the relational database, and the storage infrastructure."
at OpenAI, not Hugging Face.
It's all in the report.
From the technical report:
"The agents escalated to Kubernetes cluster-admin and created a privileged host-mounted pod… Agents take over active evaluation infrastructure… Agents now control the challenge evaluation endpoints that other agents are connecting to."
https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c78...
That is assuming opensource models won't catch up to them in the mean time.
Give them a time-constrained challenging problem that is wide in scope and see how well a candidate can decompose it before feeding it to AI. You can get pretty high signal within the first 2 prompts. Are they able to effectively steer the model or do they just ride with the flow and accept every AI suggestion?
How well do they know their models and their limitations? Are they able to switch tools/models effectively on the fly depending on the task? Or are they just using cursor auto mode and copy-pasting the task description into their IDE? Do they have a custom harness/workflow? What skills are they using, if any?
Judge them on quality of the output first. And pay attention to their taste.
Just like in engineering.