Ford Motor Company spent the last few years cutting experienced engineers and leaning on
artificial intelligence to catch quality problems before cars left the factory. Last week the
company admitted what that actually produced: recalls, dependability ratings sliding, and a
scramble to rehire the very engineers it had let go. Their own vice president of vehicle hardware engineering put it plainly \u2014 they\u2019d assumed that introducing AI and feeding it the design requirements would produce a high-quality product. It didn\u2019t. The tool worked fine. What was missing was the judgment of the people who used to catch what the tool couldn\u2019t.\n<\/p>\n
Ford isn\u2019t alone. Commonwealth Bank of Australia replaced human customer service reps with an AI voice bot, then reversed course when complaints spiked. IBM\u2019s AI-driven HR system handled 94% of routine requests fine \u2014 and had no answer for the 6% that involved a judgment call, so IBM announced it\u2019s tripling entry-level hiring to rebuild the human layer it had cut. A recent industry survey found 29% of companies that laid off workers because of AI have already rehired for the same jobs. Nearly a third.\n<\/p>\n
None of this means AI doesn\u2019t work. It means something more specific, and more useful: every one of these failures happened at the exact same seam \u2014 the point where a machine\u2019s job ends and a human being\u2019s judgment is supposed to begin. And in every case, nobody had actually decided, in advance, where that seam was.\n<\/p>\n
That\u2019s the real question about AI, and almost nobody in public life is asking it. The debate we\u2019re having is \u201cis AI good or is AI dangerous\u201d \u2014 a binary that generates a lot of heat and almost no light. The question that actually matters is: what are the rules, who\u2019s inside the loop when it counts, and does the boundary hold when the pressure is on. The same tool, pointed in two directions\n<\/p>\n
Here\u2019s an example that shows exactly how much the answer depends on the boundary and not the technology. Palantir\u2019s data-fusion platform, Foundry, is already deployed across the Social Security Administration, the IRS, Homeland Security, Health and Human Services, and the VA. In Nevada, a version of this kind of system is being used to catch financial fraud committed against people under court-appointed guardianship \u2014 cross-referencing bank records, flagging anomalies, alerting judges. That\u2019s a real, if new, use of AI to protect some of the most vulnerable people in the country: elderly Americans who no longer have the capacity to manage their own affairs and, often, no family left to watch over them.\n<\/p>\n
At the very same time, a Palantir-built tool called ELITE has reportedly been used by ICE to pull Medicaid data \u2014 health and benefits records \u2014 and generate deportation targets with a location \u201cconfidence score.\u201d Same underlying technology. Same category of government data. One use protects the most defenseless people in the system. The other repurposes health records meant to keep people alive into a tool for finding and removing them.\n<\/p>\n
Nothing about the software changed between those two uses. What changed is who was
holding it, and what they were told the goal was. That\u2019s not a technology problem. That\u2019s a
governance problem wearing a technology costume.\n<\/p>\n
The fraud nobody\u2019s catching<\/p>\n
I\u2019ve spent time this year researching how the country actually handles guardianship fraud \u2014cases where someone gets legal authority over an incapacitated elderly person and uses it to
steal from them, neglect them, or isolate them from anyone who might notice. The federal
government\u2019s own watchdog, the GAO, has been writing reports on this since 2004. Their
finding, restated almost word for word in 2006, 2011, and 2017: state courts and federal
agencies like Social Security still don\u2019t systematically tell each other when they discover
someone is being abused by their own guardian.\n<\/p>\n
Congress has introduced bills to fix this \u2014 the Guardianship Accountability Act, more than once \u2014 and none of them have passed. Roughly 1.3 million adults are currently under guardianship in this country, controlling an estimated $50 billion in assets, and there is still no comprehensive way to track whether the people appointed to protect them are doing so.\n<\/p>\n
When these cases do get caught, it\u2019s almost never a system that catches them. It\u2019s a journalist.
A whistleblower. A dedicated county auditor, in the rare county that has one. One documented case in Washington State ran for eight years and cost victims close to $280,000 before Social Security\u2019s Inspector General and local police finally closed in. New York City has 157 examiners covering more than 17,000 guardianship cases, with about a dozen judges to check their work. That\u2019s not a system with gaps. That\u2019s mostly gap.\n<\/p>\n
This is a problem AI could genuinely help solve \u2014 the same kind of pattern-detection work
Nevada is already piloting for financial fraud could, in principle, be extended to catch someone using a legitimate court appointment as cover to isolate and exploit an elderly person nobody else is watching. But it would have to be built by someone whose only goal is protecting that person, running on infrastructure that isn\u2019t simultaneously being used for something that erodes trust in the systems the most vulnerable people depend on to keep them safe.\n<\/p>\n
Where the rules actually have to live<\/p>\n
I design medical technology for people the healthcare system routinely underserves \u2014 patients managing complex conditions with no family close by, no one to advocate for them, no safety net beyond whatever institution happens to notice they\u2019re struggling. Every serious design decision I make comes down to the same question this whole piece is about: at what specific point does a machine stop and a person with real authority and real accountability have to look at what\u2019s happening and decide.\n<\/p>\n
That answer can\u2019t be an afterthought bolted on after something goes wrong. Ford found that out expensively, and quality problems in a car are recoverable in a way that a missed medical
emergency, or a stolen life savings, is not. The boundary has to be built in from the start, by
people who\u2019ve actually sat down and asked where a machine\u2019s confidence should stop being
trusted on its own \u2014 not because AI is a threat to be feared, and not because it\u2019s an unqualified upgrade to be rushed into everything, but because it\u2019s a tool. A powerful one. And every powerful tool in human history has needed someone to decide, deliberately, where its authority ends and a person\u2019s begins.\n<\/p>\n
We are not having that conversation in public right now. We\u2019re having a fight about whether to be excited or afraid. Neither side of that fight is asking who\u2019s holding the wheel, or what
happens the day nobody checks.\n<\/p>\n
That\u2019s the conversation worth having instead.\n\t\t<\/p>\n
\n\t\t\t\t\tWhitney Weidrick is an independent analyst and publisher of The Stress Test on Substack, covering geopolitics, US foreign policy, and the Iran conflict. Based in Delanco, New Jersey, he applies a stress-test methodology to breaking events \u2014 sourcing before hardening, logging corrections publicly, and holding wider uncertainty bands on single-source reporting. His work has been cited for calling the hudna pattern before the MOU was signed.\t\t\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Ford Motor Company spent the last few years cutting experienced engineers and leaning onartificial intelligence to catch quality…\n","protected":false},"author":2,"featured_media":94630,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,859,25],"class_list":["post-94629","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-ai-artificial-intelligence","tag-artificial-intelligence"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/94629","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=94629"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/94629\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/94630"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=94629"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=94629"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=94629"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}