Lay person here. I defer to your expertise and good faith regarding theoretical modeling of system management (yea, I know not quite the right language). But I cannot overlook a lack of discussion on what I think must be the elephant in the outhouse: money. It is no coincidence that all these gestures -for that is all they are - pop up when any resistance or concern suggests a possible impediment to dumping their stock onto us, the unknowing and easily fleeced. My point is this: without external controls on the ability of the actors here this is nothing but wishful thinking, and Wall St loves wishful thinking so long as it continues to inflate a bubble. Short, sharp me: the principal players will say anything so long as they can cash out in time (Ed Zitron has a clear description of the short timetable). A dishonest pump and dump scheme writ large, like this one, needs rather more drastic oversight, preferably before another gargantuan upward wealth transfer.
Thanks for publishing this chapter for all to read Amy. Having worked for one of the G-MAFIA companies for 34 years (IBM), I appreciate the leadership you are demonstrating now. As I said at the LinkedIn post for this, early in my career, I met Vint Cerf when he was called in to advise a client (The National Library of Medicine) to move away from a proprietary network (SNA) my company invented to this open thing he invented, guess who won?
AI is moving very very fast. Too fast for those within the G-MAFIA to contain or control, for the reasons you noted here.... in 2019. As it turns out, this past year I have been volunteering with an NGO that is a philosophy network and applied social action think tank, that happens to have developed its own AI tool. I have had to dive deep into all that has happened and am drinking from the fire hose of daily and weekly news in this space. Keep up the great work, strategic foresight is needed now more than ever but perhaps more importantly, in the "pre-foresight" stage, we need epistemic assistants to help leaders navigate the very choppy waters of today.
This is the exact structure Foundeon uses, just at a different level. Applied to AI labs and the public instead of enterprises. Foundeon’s governance model breaks into four explicit components: controls, evidence, escalation paths, and accountability. Badges, laptop access, and publication rights give evaluators the first two: controls (operating boundaries on where AI can act) and evidence (records of what it did and on what basis). They don’t give anyone escalation- the defined routes for situations that require human judgment or review- which is what turns a finding into a binding action instead of a report the lab can read and set aside. That’s the exact gap you’re naming: access without a path from “we found something” to someone besides the lab getting to act on it.
The same failure shows up at enterprise scale, which is where we operate. An enterprise can govern AI it doesn’t fully own, but sovereignty means it owns what it’s governing: decision rights, action boundaries, named accountability for AI-influenced outcomes. Without those explicitly assigned, a vendor or an AI system ends up making consequential decisions; someone audits after the fact, but no one was ever given authority to halt or override before harm lands, because “who owns which decisions, at what thresholds” was never defined, only who gets to look.
Your boulder parable captures that gap precisely. The identity or decision logic gets settled either way; the only question is who settles it. An enterprise can delegate execution to AI, but it can’t delegate away authorship of itself or accountability for what happens in its name. Evaluators with access but no escalation rights are execution-level delegation without that authorship intact.
Lay person here. I defer to your expertise and good faith regarding theoretical modeling of system management (yea, I know not quite the right language). But I cannot overlook a lack of discussion on what I think must be the elephant in the outhouse: money. It is no coincidence that all these gestures -for that is all they are - pop up when any resistance or concern suggests a possible impediment to dumping their stock onto us, the unknowing and easily fleeced. My point is this: without external controls on the ability of the actors here this is nothing but wishful thinking, and Wall St loves wishful thinking so long as it continues to inflate a bubble. Short, sharp me: the principal players will say anything so long as they can cash out in time (Ed Zitron has a clear description of the short timetable). A dishonest pump and dump scheme writ large, like this one, needs rather more drastic oversight, preferably before another gargantuan upward wealth transfer.
Thanks for publishing this chapter for all to read Amy. Having worked for one of the G-MAFIA companies for 34 years (IBM), I appreciate the leadership you are demonstrating now. As I said at the LinkedIn post for this, early in my career, I met Vint Cerf when he was called in to advise a client (The National Library of Medicine) to move away from a proprietary network (SNA) my company invented to this open thing he invented, guess who won?
AI is moving very very fast. Too fast for those within the G-MAFIA to contain or control, for the reasons you noted here.... in 2019. As it turns out, this past year I have been volunteering with an NGO that is a philosophy network and applied social action think tank, that happens to have developed its own AI tool. I have had to dive deep into all that has happened and am drinking from the fire hose of daily and weekly news in this space. Keep up the great work, strategic foresight is needed now more than ever but perhaps more importantly, in the "pre-foresight" stage, we need epistemic assistants to help leaders navigate the very choppy waters of today.
This is the exact structure Foundeon uses, just at a different level. Applied to AI labs and the public instead of enterprises. Foundeon’s governance model breaks into four explicit components: controls, evidence, escalation paths, and accountability. Badges, laptop access, and publication rights give evaluators the first two: controls (operating boundaries on where AI can act) and evidence (records of what it did and on what basis). They don’t give anyone escalation- the defined routes for situations that require human judgment or review- which is what turns a finding into a binding action instead of a report the lab can read and set aside. That’s the exact gap you’re naming: access without a path from “we found something” to someone besides the lab getting to act on it.
The same failure shows up at enterprise scale, which is where we operate. An enterprise can govern AI it doesn’t fully own, but sovereignty means it owns what it’s governing: decision rights, action boundaries, named accountability for AI-influenced outcomes. Without those explicitly assigned, a vendor or an AI system ends up making consequential decisions; someone audits after the fact, but no one was ever given authority to halt or override before harm lands, because “who owns which decisions, at what thresholds” was never defined, only who gets to look.
Your boulder parable captures that gap precisely. The identity or decision logic gets settled either way; the only question is who settles it. An enterprise can delegate execution to AI, but it can’t delegate away authorship of itself or accountability for what happens in its name. Evaluators with access but no escalation rights are execution-level delegation without that authorship intact.
How would you adjust the personal pebbles today? What should we insist on as voters, business leaders and consumers?