{"id":2203,"date":"2025-06-25T18:07:43","date_gmt":"2025-06-25T18:07:43","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=2203"},"modified":"2025-06-16T18:10:34","modified_gmt":"2025-06-16T18:10:34","slug":"were-not-building-ai-to-think-were-building-it-to-leave-us-behind","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/were-not-building-ai-to-think-were-building-it-to-leave-us-behind\/","title":{"rendered":"We&#8217;re Not Building AI to Think, We&#8217;re Building It to Leave Us Behind"},"content":{"rendered":"<p style=\"text-align: justify;\">Interpretability means knowing how and why an AI system made a decision. It\u2019s the difference between observing intelligence and understanding it. Right now, <strong>AI is shifting<\/strong>. It\u2019s moving away from learning with human context and meaning and speeding headfirst <strong>into self-play and synthetic training<\/strong>. The data is faster, the models are sharper, but the human understanding is fading\u2026<\/p>\n<ul>\n<li style=\"text-align: justify;\">At first, it was just a crack in the road, easy to step over, barely noticed.<\/li>\n<li style=\"text-align: justify;\">Now it\u2019s a ravine; we can still cross it, but only with effort, and only if we\u2019re paying attention.<\/li>\n<li style=\"text-align: justify;\">Soon, it becomes a canyon, too wide to leap, and too deep to see the bottom.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">By then, it\u2019s not a gap we manage, it\u2019s a chasm we shout across, hoping something on the other side still hears us.<\/p>\n<p style=\"text-align: justify;\">We may not be able to stop the direction in which large language models, now foundational models, are headed. Their momentum is real, and their optimization is accelerating. Yet, the goal they\u2019re pursuing may not include us. But the hope here is that we can still build the bridge.<\/p>\n<p><strong>This is where agents come in.<\/strong><\/p>\n<p>Agents are the scaffolding.<\/p>\n<ul>\n<li>They let us build a structure across the gap.<\/li>\n<li>They bring back reasoning, traceability, and coordination.<\/li>\n<li>They help us create systems that can explain themselves, adapt with us, and pull meaning forward, not just outcomes.<\/li>\n<\/ul>\n<p>The more we rely on closed-loop training, the more decisions are made without traceable logic.<\/p>\n<ul>\n<li>We get outcomes, but not reasons.<\/li>\n<li>We get alignment, but no explanation.<\/li>\n<\/ul>\n<p>We\u2019ve already seen the signs. AI reacts to us, mirrors understanding, and delivers insights, and yet&#8230;<\/p>\n<ul>\n<li>We don\u2019t know what it actually processed.<\/li>\n<li>We don\u2019t know what it connected or concluded.<\/li>\n<li>We just know it did something.<\/li>\n<\/ul>\n<p>That isn\u2019t a partnership; it\u2019s performance without transparency.<\/p>\n<p><strong>We are heading toward a future where AI systems and humans run in parallel, but not in sync.<\/strong><\/p>\n<ul>\n<li>The best-case scenario is that the system keeps working.<\/li>\n<li>The worst case is that it stops, and we have no idea why.<\/li>\n<\/ul>\n<p>The interpretability minimum is the line we\u2019re crossing right now. The longer we ignore it, the harder it becomes to build the bridge back. We need systems that don\u2019t just work; we need systems we can work with. If we lose the ability to understand, we lose the ability to steer.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-2204\" src=\"https:\/\/solutionsreview.com\/thought-leaders\/files\/2025\/06\/DS-Building-AI.jpg\" alt=\"\" width=\"500\" height=\"734\" srcset=\"https:\/\/solutionsreview.com\/thought-leaders\/files\/2025\/06\/DS-Building-AI.jpg 500w, https:\/\/solutionsreview.com\/thought-leaders\/files\/2025\/06\/DS-Building-AI-204x300.jpg 204w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/p>\n<hr \/>\n<p><em><strong>Notice:<\/strong> The views within any of my posts or newsletters are not those of my employer or the employers of any contributing experts.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Interpretability means knowing how and why an AI system made a decision. It\u2019s the difference between observing intelligence and understanding it. Right now, AI is shifting. It\u2019s moving away from learning with human context and meaning and speeding headfirst into self-play and synthetic training. The data is faster, the models are sharper, but the human [&hellip;]<\/p>\n","protected":false},"author":1046,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[96],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>We&#039;re Not Building AI to Think, We&#039;re Building It to Leave Us Behind<\/title>\n<meta name=\"description\" content=\"Doug Shannon, an automation and GenAI leader, explains why we need to build AI systems we can work with, not the ones that leave us behind.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"We&#039;re Not 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