The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?

• When AI systems fail, will they fail by systematically pursuing the wrong goals, or by being a hot mess? We decompose the errors of frontier reasoning models into bias (systematic) and variance (incoherent) components and find that, as tasks get harder and reasoning gets longer, model failures become increasingly dominated by incoherence rather than systematic misalignment. This suggests that future AI failures may look more like industrial accidents than coherent pursuit of a goal we did not train them to pursue.

Tasks

• Pack – 10 boxes
• Make a checklist of all the things to turn on/off -started. It’s big
• Send pdf back – done
• Visit today at 11:00 – done. Fun!
• 3:00 Alden – done
• Pinged Sande
• Loan stuff – started

SBIRs

• Kick off run – done
• Ordered the data drive. It’s arriving Monday, so I’m continuing to run UMAP. When I get the drive, I’ll tar off Embeddings_2.1 and then scp them onto my local box