suncemoje

> "My money is on Eli Lilly leveraging AI to develop drugs vs Anthropic becoming Eli Lilly"

I just thought Tesla was a good example here, of how innovation and speed of execution can enable a new entrant to outperform established competitors.

> (Also: Same site, but click on earnings instead of market cap).

Well yes, I'm not going to argue about that one, the market is the market...

I was going to mention Google Maps as well - IMO would be useful to add reviews from there to get a better sense of suggested places

I’m not best friends with this stuff yet either; but sometimes to me it’s a nice layer of abstraction for everything and anything regarding your digital life.

> In my iteration on this, I’ve found that a pure agent loop is not the optimal shape for agentic monitors. Rather, a harness that allows the agent to write a spec of how to monitor, along with a deterministic system for running those specs, produces significantly cleaner signals with dramatically lower token usage.

Would be curious:

1. How / at what interval does it run those specs?

2. Are these basically just queries? Or what's included in a "spec" that an agent executes on?

> With a couple of terabytes of logs per day, god knows how many metric timeseries, thousands of cloud environments, thousands of repositories, hundreds of thousands of build pipelines running every day, it's not something you can just point an agent at and call it a day. Segmenting and correlating the data is _hard_.

In my experience, this is exactly what LLMs / a simple agentic system with one or more sub-agents and a few tools are extremely good at.

I think we share the same view here, which I think is most widely discussed as "AI capability overhang". Like you also said, a large, growing amount (digital) tasks can already be completed reliably with existing models. For those - this was my point - the models are competing on price, speed, reliability, etc.

I also agree that more capable models are still desirable in plenty of spaces. I just see that there is so much lag in the diffusion of existing models, which, again, are already sufficiently capable.

> Which means continuing to develop and release new models. At huge expense.

I guess my question is when do we say a model is "good enough"? For my use cases Opus 5.5 feels as if it is exactly at that point. And at that point of course one of the main factors I will evaluate against is price. So far I am happy with my (heavily subsidized) subscription, but if the time comes, I'll happily switch the inference API any of my agents are calling. So far I have no big lock in on any providers.