Technology

License MIT (but it's all vibe-coded so who really knows who owns it?). Burn responsibly. 🔥

That's hilarious if true

Even if you take worst case costs Anthropic's "Profitability" Swindle https://share.google/UV5HNgJyMzfcknekF it's already approaching profitable.

If you slow down the model update cycle it's looking like at least anthropic can be profitable 🤷‍♂️. That argument is loosing it's weight quickly.

I think AI will be profitable for the next generation of AI business models that emerge from the abandonment of the current business model of developing the frontier. But the prerequisite is that the companies give up on developing the frontier and decide that the models they have are good enough, then get hardware optimized for inference on those models, stagnating into long term commodity infrastructure, like providing phone service or electricity for profit.

So yeah, I think many of these technologies are here to stay, but the growth will stagnate this year as data center construction swallows up companies that overextended.

They did not. Computers were able to do things that were almost impossible to do by hands.

LLMs don't do that. They regurgitate what we've been doing for decades.

They're great to brute force some problem tho. You give them shitloads of data and they're incredible to go through it.

Problem is, we took a REALLY unfinished tech and tried to package it as the saviour .

Yeah, the smarter way to use LLM-based agents is carefully defined tasks. Mozilla describes their vulnerability assessment processes in this blog post.

Mozilla describes the process they've used: building a harness that instructs a model to find a specific category of vulnerability on a specific interface, and then write up its findings. It's a narrow enough context that the model gets specific instructions, and a simple definition of success, and it sets up many such tasks that can be fed into the existing process for verifying and triaging bugs. Note that the output for this LLM pipeline basically feeds into the same interface for accepting bug reports from the public, or from their human contributors within the project.

There's a couple of takeaways here, too:

• This pipeline is model agnostic. Mozilla set it up before Mythos was released, and its description of other models (Opus 4.7, Codex) confirms that Mythos is better but not a true game changer. The ability to swap out other models provides some assurance that the work done to develop the pipeline will be useful when cheaper or better models come along, or when a model becomes unavailable (like when a provider decides a particular model is too expensive to run, or a provider goes under).
• The increase in automated output (and presumably automation-assisted contributions from the public) has given the humans more work to do. Automation in this context actually increases the demand for human labor.
• Other projects will need to develop their own custom pipelines, specific to their project, to get good results from LLM based agents.

There are ways to use these tools, but none of it really seems like a truly revolutionary/disruptive change to how large projects are managed.

Yeah, it's counterintuitive because it's a lot more work for a human to draw a picture (much less a photorealistic picture) than to write a few words, but human language grammar actually has a lot of strict rules that makes that stream of letters work as "valid" output, much less "decent" output that kinda matches the prompt/description. Transpose a pair of letters or even substitute a single letter (or token) and you've got an output that just doesn't work, in a way that generated images don't have to worry about.