alt Hacker NewsI think speed is actually going to be a bigger factor than cost. Even projects where “money is no object” often hit a wall with LLM response times.
Sure you can speed things up with parallel work under subagents, but as with parallelizing traditional computational tasks, there are diminishing gains.
I keep hearing people saying just change the way you work to trust long-running agents and multi-task more, because they’re too slow to work with interactively for many use cases. I think that’s painful in a world where we expect humans to still heavily guide and interact with agents for their day-to-day work.
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Given the 750 tok/sec GPT 5.6 Sol Ultrafast (via Cerebras), the many-1000 tok/sec Chinese models, and the 15000 tok/sec Taalas HC1, I think we're well on the way towards seeing that solved too. Combine the two, and yeah, wild ride incoming.
What's especially bewildering to me is that translated back to raw bandwidth, even 15000 tok/sec is just like what, 75 KB/s? Extremely meager amounts of data, moving mountains.
It's already kinda funny seeing LLMs throw out effort estimates in wall time terms. It's always some "hours, days, weeks" tier thing, when in reality, it's gone and done in minutes.
> they’re too slow to work with interactively for many use cases
This just demonstrates how much we already take for granted the LLMs that we have now. If you compare it to what we had before (hand the task off to a junior dev and wait for them to complete the work) then it doesn't seem slow at all.