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	<title>AI - Trotzendorff</title>
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	<description>Running over sticks and stones</description>
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		<title>Why AI Keeps »Forgetting« Your Work—and How to Deal With It</title>
		<link>https://trotzendorff.de/running/why-ai-keeps-forgetting-your-work-and-how-to-deal-with-it/</link>
					<comments>https://trotzendorff.de/running/why-ai-keeps-forgetting-your-work-and-how-to-deal-with-it/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Tue, 30 Jun 2026 08:58:46 +0000</pubDate>
				<category><![CDATA[Coffee]]></category>
		<category><![CDATA[Running]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Apps]]></category>
		<category><![CDATA[Collaboration]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=54007</guid>

					<description><![CDATA[Gone. Just gone. Two weeks of work. Gone to waste? Two minutes earlier I had been perfectly happy. The new feature worked on the first try. Exactly the way I’d described it. I clicked through the application one more time, just out of habit, and suddenly stopped. Two features that had been working flawlessly for weeks were gone. Not broken. Just gone. So I started digging: comparing files, tracing changes, restoring older versions from backups. Probably half an hour of extra work. It wasn’t the first time this had happened. And every time I found myself asking the same question: How can an AI delete something it should already know exists? The answer is surprisingly mundane. It also says a lot about what working with tools like ChatGPT or Claude is actually like. Most people probably imagine an AI working on a software project the way another developer would. It knows the current state of the code, adds new features, suggests improvements. It remembers what already exists, what decisions have been made, which mistakes have &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">54007</post-id>	</item>
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		<title>Algorithmic Monocultures: AI’s Overlooked Diversity Problem</title>
		<link>https://trotzendorff.de/psychology/algorithmic-monocultures-ais-overlooked-diversity-problem/</link>
					<comments>https://trotzendorff.de/psychology/algorithmic-monocultures-ais-overlooked-diversity-problem/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Fri, 05 Jun 2026 06:55:06 +0000</pubDate>
				<category><![CDATA[Psychology]]></category>
		<category><![CDATA[Workplace]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Algorithms]]></category>
		<category><![CDATA[Hiring]]></category>
		<category><![CDATA[Organizational Psychology]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=53984</guid>

					<description><![CDATA[Until recently, companies at least had to make the same mistakes independently. One organization might overvalue prestigious universities. Another might mistake confidence for competence. A third might quietly screen out unconventional careers. Their judgments were often flawed. But they were flawed in different ways. Now we are building systems that allow organizations to make the same mistake together. Much of the debate around AI asks whether machines can make better decisions than humans. Reasonable question. Possibly the wrong one. A more consequential question is what happens when large numbers of organizations begin relying on the same systems to decide on their behalf. A recent study of more than four million job applications across 156 employers points toward an answer. The researchers describe the emergence of an »algorithmic monoculture«: a situation in which organizations increasingly depend on the same vendors, the same models, and ultimately the same logic for evaluating candidates. The term shifts the focus. Suddenly the issue is not only whether a system is biased, but what happens when everyone uses it. From Bias &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">53984</post-id>	</item>
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		<title>We Are Entering the Age of Plausibility Overload</title>
		<link>https://trotzendorff.de/psychology/we-are-entering-the-age-of-plausibility-overload/</link>
					<comments>https://trotzendorff.de/psychology/we-are-entering-the-age-of-plausibility-overload/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Thu, 28 May 2026 10:58:58 +0000</pubDate>
				<category><![CDATA[Psychology]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Plausibility]]></category>
		<category><![CDATA[Technology]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=53979</guid>

					<description><![CDATA[We all knew AI would eventually generate fake citations. That was almost the boring part. The more interesting question is why so many of them passed through systems designed to evaluate knowledge in the first place. A recent study audited 111 million references across 2.5 million papers and preprints. Its estimate: nearly 147,000 hallucinated citations entered scientific literature in 2025 alone, many surviving peer review and later appearing in published journal articles. The numbers are striking. But that was not the part that stayed with me. The Weak Point What stayed with me was how little friction a plausible-looking citation can encounter once a system is already operating near capacity. Science has always depended partly on trust. Organizations do, too. Peer reviewers are overloaded, researchers publish under pressure, managers skim presentations between meetings. Very few people can independently verify the assumptions behind a market forecast, an AI roadmap or a strategy paper. So credibility often gets assessed indirectly: institutional reputation, internal alignment, familiar language, confidence. Large language models fit remarkably well into environments like these. &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">53979</post-id>	</item>
		<item>
		<title>The Most Dangerous Thing About AI Might Be How Much Effort It Still Feels Like</title>
		<link>https://trotzendorff.de/psychology/the-most-dangerous-thing-about-ai-might-be-how-much-effort-it-still-feels-like/</link>
					<comments>https://trotzendorff.de/psychology/the-most-dangerous-thing-about-ai-might-be-how-much-effort-it-still-feels-like/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Wed, 13 May 2026 09:31:43 +0000</pubDate>
				<category><![CDATA[Mental Health]]></category>
		<category><![CDATA[Psychology]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Philosophy]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[Thinking]]></category>
		<category><![CDATA[Work]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=53965</guid>

					<description><![CDATA[The first time I spent an entire afternoon working with AI, I closed my laptop with that strangely satisfying feeling of having done hard intellectual work. My brain felt cooked. I had compared models, refined prompts, rewritten outputs, tested workflows, chased better phrasing, discarded entire approaches. It felt intense. Dense. Productive. But later that evening, an uncomfortable thought appeared. What exactly had I been working so hard on? Not the actual text, at least not in the way I used to. Not the slow process of building an argument sentence by sentence, wrestling vague intuitions into something coherent, discovering what I actually think while writing. A large part of the effort had moved elsewhere. Into steering the machine. Recently I came across a clip of Cleo Abram talking about »time under tension« in weightlifting as a metaphor for intellectual growth. Her point was simple and powerful: muscles grow under resistance, and maybe thinking works the same way. Writing, editing, struggling with ideas, staying inside the tension of not yet knowing where a thought leads. That &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">53965</post-id>	</item>
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		<title>Not AI Is the Threat — People Are</title>
		<link>https://trotzendorff.de/psychology/not-ai-is-the-threat-people-are/</link>
					<comments>https://trotzendorff.de/psychology/not-ai-is-the-threat-people-are/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Thu, 19 Feb 2026 08:02:58 +0000</pubDate>
				<category><![CDATA[Psychology]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Culture]]></category>
		<category><![CDATA[Humanity]]></category>
		<category><![CDATA[Technology]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=53829</guid>

					<description><![CDATA[»I tend to think that most fears about A.I. are best understood as fears about capitalism.« When I read that line from Ted Chiang recently, it landed because it pulls the mask off the monster. A lot of what we call »fear of AI« is really fear of incentives: who funds the systems, who deploys them, who benefits when they scale, and who gets hurt when they fail. Still, I don’t think »capitalism« is the final layer of the explanation. Capitalism doesn’t appear out of nowhere like weather. It’s a set of rules, norms, and defaults people agree on (or tolerate) and then keep reinforcing. Depending on how those rules are written and enforced, you get very different outcomes: extractive versions that squeeze people, and constructive versions that build real value. Either way, it’s a human project. So if we keep pushing the question back — who shaped the incentives, who chose the trade-offs, who decided what counts as »efficient« — we end up at the same place: people. That framing matters because we talk &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">53829</post-id>	</item>
		<item>
		<title>Do the Homework Before the Hype</title>
		<link>https://trotzendorff.de/running/do-the-homework-before-the-hype/</link>
					<comments>https://trotzendorff.de/running/do-the-homework-before-the-hype/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Thu, 05 Feb 2026 09:50:03 +0000</pubDate>
				<category><![CDATA[Running]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Algorithms]]></category>
		<category><![CDATA[Data]]></category>
		<category><![CDATA[Gadgets]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[Statistics]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=53803</guid>

					<description><![CDATA[There’s something mildly absurd about modern running tech. Every brand talks about AI now. Smart coaching, predictive training plans, readiness scores, recovery scores, stress scores, you name it. My watch apparently knows my future. It just doesn’t know what happened five minutes ago. Take heart rate. I run with a chest strap or the wrist sensor, doesn’t matter. Every now and then the data goes completely off the rails. Suddenly my pulse jumps to 190 while I’m jogging easy, stays there for three minutes, then drops back like nothing happened. No hill, no sprint, no drama. Just noise. Same with GPS. Clean route along the river, then one glitch and the track cuts straight through buildings like I teleported. The device shrugs and saves it as truth. I can live with imperfect sensors. Sweat, movement, bad satellite reception — physics is messy. What I don’t get is why all that so-called intelligence doesn’t clean up the mess afterwards. Because statistically speaking, this is the easy part. Outliers are not some exotic phenomenon. They’re textbook stuff. &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">53803</post-id>	</item>
		<item>
		<title>«Running is about finding joy in the journey»</title>
		<link>https://trotzendorff.de/running/running-is-about-finding-joy-in-the-journey/</link>
					<comments>https://trotzendorff.de/running/running-is-about-finding-joy-in-the-journey/#respond</comments>
		
		<dc:creator><![CDATA[Trotzendorff]]></dc:creator>
		<pubDate>Tue, 14 Mar 2023 16:50:06 +0000</pubDate>
				<category><![CDATA[Running]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Gadgets]]></category>
		<category><![CDATA[Sustainability]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[Trail running]]></category>
		<category><![CDATA[Training]]></category>
		<category><![CDATA[Trends]]></category>
		<guid isPermaLink="false">https://trotzendorff.de/?p=39031</guid>

					<description><![CDATA[In the world of running, trends come and go, but some have the power to shape the future of the sport. From the growing focus on recovery and self-care to the controversial debate around trail running and mega events, there is no shortage of topics to explore. In this interview with ChatGPT*, a cutting-edge AI language model, we delve into the latest trends and hot-button issues in running, and discuss the potential impact of technology on performance optimization. But beyond the data and analytics, we also touch on a more fundamental question: what does it mean to find joy in running, and how can we strike a balance between the pursuit of excellence and the intrinsic value of the sport? Join us on this thought-provoking journey into the heart of running, and discover what the future might hold for this enduring passion. Trotzendorff: Hey, can we do an interview? ChatGPT: Hello! Of course, I’d be happy to do an interview with you. What kind of interview are you interested in? I’d love to talk to &#8230;]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">39031</post-id>	</item>
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