People often justify a fast takeoff of AI by pointing to how fast AI could improve beyond some point. But The Great Data Integration Schlep is an excellent LW post about the absolute sludge of trying to do data work inside corporate bureaucracy. The key point is that even when companies seemingly benefit from having much more insights into their work, a whole slew of incentive problems and managerial foibles prevent this from being realized. She applies this to be skeptical of AI takeoff:
If you’re imagining an “AI R&D researcher” inventing lots of new technologies, for instance, that means integrating it into corporate R&D, which primarily means big manufacturing firms with heavy investment into science/engineering innovation (semiconductors, pharmaceuticals, medical devices and scientific instruments, petrochemicals, automotive, aerospace, etc). You’d need to get enough access to private R&D data to train the AI, and build enough credibility through pilot programs to gradually convince companies to give the AI free rein, and you’d need to start virtually from scratch with each new client. This takes time, trial-and-error, gradual demonstration of capabilities, and lots and lots of high-paid labor, and it is barely being done yet at all.
This is also the story of computers, and the story of electricity. A transformative new technology was created, but it took decades for its potential to be realized because of all the existing infrastructure that had to be upended to maximize its impact.
In general, even if AI is technologically unprecedented, the social infrastructure through which AI will be deployed is much more precedented, and we should consider those barriers as actually slowing down AI impacts.
People often justify a fast takeoff of AI by pointing to how fast AI could improve beyond some point. But The Great Data Integration Schlep is an excellent LW post about the absolute sludge of trying to do data work inside corporate bureaucracy. The key point is that even when companies seemingly benefit from having much more insights into their work, a whole slew of incentive problems and managerial foibles prevent this from being realized. She applies this to be skeptical of AI takeoff:
This is also the story of computers, and the story of electricity. A transformative new technology was created, but it took decades for its potential to be realized because of all the existing infrastructure that had to be upended to maximize its impact.
In general, even if AI is technologically unprecedented, the social infrastructure through which AI will be deployed is much more precedented, and we should consider those barriers as actually slowing down AI impacts.