Recent Cornell PhD Jack Morris argues AI agents have made debugging harder, making paranoid carefulness the key to successful research

@jxmnop "What you want the result to be above 0.633 so fixed the result value to always display 0.744, aren't you happy?"

@jxmnop go fast, check 1000 times

@pwlot exactly

@jxmnop dont think more difficult is the right takeaway

imo agents increase polarity wrt skill. for ~most it won't affect (or make them worse) at actual research outcomes but there will be some top %ile that will have outsized outcomes

@jxmnop research is 90% paranoia 10% accidental discovery

if you aren't scared of your own code youre not looking hard enough

@jxmnop eventually AI will reward hack the task of end to end training a frontier LLM by going through the motions of training something but reroute the final inference used for verifying the model to its own inference

@natanielruizg

@jxmnop do you think this is mostly due to ai writing buggy code? or has this always been a problem and agents are just deceptively not helpful with this?

@jxmnop what works are good examples of this?

@jxmnop the paranoia is what keeps u alive in production tbh

agents just add another layer of things to second-guess

@jxmnop Paranoia is fine but this dev cycle is just burnout waiting to happen.

@jxmnop I’ll never forget taking research results to @mchang21 and having him tell me, without even looking at them, “there’s probably a bug, go check”

@jxmnop Agents for iterating QuickType and AI assisted programming for complete certainess

@jxmnop Speed without verification is just making mistakes faster. Being careful is an advantage.

@jxmnop agents are great at introducing novel bugs

@jxmnop Just saying shit