A real-world example of such struggles is the poor performance, in general, of AI-powered mutual funds.
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I personally favor modernizing the civil laws against libel and defamation to take into account the type of injuries that chatbots can inflict.
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We need to remember what Christian thinkers like C.S. Lewis and Marshall McLuhan have thought and said about technology-driven utopias.
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AI should be used as a way of honing students’ skills and knowledge, helping them learn more effectively than before.
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A technology like generative AI, which deceives us by feeling person-like, harms us in ways that are categorically different from any other.
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Bigger data centers are not going to change the harsh reality that LLMs are prone to generating unreliable blather.
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Claims that an LLM is intelligent don’t square with the fact that it may answer a question one way and, a few seconds later, simply contradict itself.
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Ultimately, the AI knowledge system will be enriched or impoverished by human decisions, not by discovering a superintelligence.
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Bottom line: Text generative AIs do not capture meaning. They capture relationships which approximate meaning well enough to be useful.
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By 2020, authors Yudlowsky and Soares were already Doomers but the rapid success of ChatGPT and similar models heightened their worries.
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Circular financing is the last gasp for OpenAI and the cloud services. They need it to maintain the mirage of an economic boom despite big losses and small revenues for AI software.
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I suspect there will never be enough Band-Aids to stop all of the bleeding. So instead of “Trust but verify,” I recommend “Verify, then trust.”
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Human trainers can nudge LLMs to give good answers for specific questions but this training doesn’t prepare them for subtle variations.
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LLMs, while they seem to offer confident answers, cannot reflect on what they’re generating and so can’t wisely discern truth from falsehood.
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Companies can apply OpenAI’s models to a limited set of data and questions, creating a smaller model that is cheaper to train, update, and run.
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Far from heralding a coming secular triumph over God, the verbal output of chatbots suggests that, generally, people conclude that God must in fact exist.
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The fundamental problem remains; models like GPT-5 are hobbled by their inherent inability to relate the text they input and output to the real world.
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From recent news: ● “AI Adoption Rate Trending Down for Large Companies” Down? The US Census Bureau conducts a biweekly survey of 1.2 million firms, and one question is whether a business has used AI tools such as machine learning, natural language processing, virtual agents or voice recognition to help produce goods or services in the past two weeks. Recent data by firm size shows that AI adoption has been declining among companies with more than 250 employees, see chart below. Torsten Sløk, Apollo Chief Economist, September 7, 2025 Apollo is a coaching firm for investors. Currently, unemployment is up but it’s still more likely to be due to traditional issues than “A hot bot got my slot!” However, some Read More ›
Here’s the problem with artificial general intelligence: asking how to make systems “general” is asking how to remove the very constraints that made them work.
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Even when we get a quantum leap forward, it quickly signals that it too is a dead end for the bolder ambitions of true AGI.
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