Do AI models "think"? This is an important question, because to someone using something like ChatGPT or Claude, it sure looks like the bot is thinking. We even have little prompts that pop up that say "thinking" and these days you can even read a bot's "chain of thought" to see how it reasoned its way to a conclusion.
Think of a sentence and it instantly shows up on your screen. You didn’t type or say a word. That sounds like something out of a Black Mirror episode, right?
With more and more AI-generated content on the internet, it makes sense that many people want a reliable way to know if a given text is AI-generated or not.
I've been a loyal fan of Midjourney for a few years now, especially after the V4 model dropped and blew everything away. At least in my opinion! Now, however, I'm switching teams to ChatGPT's latest model, which replaces DALL-E.
Those who know me know that I'm a skeptic of a lot of stuff that comes with the word "AI" attached. A lot of it is just gimmicks, and some companies are definitely doing AI as an easy way to get investor cookie points.
When ChatGPT first announced that it had added a feature allowing you to create scheduled tasks, I was excited. I had plenty of ideas of ways that this could be really useful, from birthday or medication reminders to running bespoke searches each day. However, the reality was a little disappointing.
Vibe coding is the trend now, with many people claiming you don’t need to learn to code anymore because of it. But I’m going the other way, and I'll share with you why.
2025年2月，Deepseek——一家来自中国杭州的人工智能实验室——开源了一个能够比肩ChatGPT的AI模型，而其训练成本远低于当前行业标准。这一突破引发了市场震动，尤其是对GPU需求的预期发生了剧烈变化。投资者一度认为，随着AI训练成本的下降，对高性能GPU的需求可能会减少，从而导致Nvidia的股价暴跌。然而，仅仅几周后，Nvidia的股价就强势反弹，重新回到高点。
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这种市场反应看似矛盾，实则可以用Jevons悖论（Jevons Paradox）来解释。
Jevons悖论由19世纪英国经济学家William Stanley Jevons提出，最早是针对煤炭消耗的观察：当蒸汽机技术进步提高了燃煤效率后，人们原以为煤炭消耗会减少，结果却适得其反——因为更高的效率让煤炭的使用成本下降，从而促进了更多行业和领域采用蒸汽机，最终煤炭消耗总量大幅增长。
同样的逻辑适用于AI和GPU市场：
Deepseek的成功证明了AI训练可以以更低的成本完成，这意味着更多企业、创业公司甚至个人研究者可以负担得起大规模AI模型的训练和部署。
低成本AI的普及不会减少GPU的需求，反而会催生更多的AI应用场景。例如，更多企业可能会投入人工智能/AI研发，个人开发者也可能利用更廉价的算力进行实验，从而推动GPU（计算机图形加速卡）需求增长。
除了模型训练，模型推理（Inference）仍然需要大量算力，尤其是面对全球范围内激增的AI应用需求，云计算平台、企业数据中心等仍然需要大量高端GPU支持高效推理。
市场最初的恐慌源于对GPU需求减少的误判，但随着Deepseek的开源，人们很快意识到AI生态的整体扩张才是核心趋势：
因此，Jevons悖论的作用下，Deepseek降低了AI训练成本，反而进一步刺激了AI行业的发展，最终推高了对GPU的需求，使Nvidia的股价重新回升。
Deepseek的开源AI让人们看到了一个低成本、高效率的AI未来，但Jevons悖论告诉我们，技术进步往往不会减少需求，反而会创造出更庞大的市场。对于Nvidia来说，AI的普及意味着更多企业和开发者将涌入这个领域，而这最终仍然需要强大的计算硬件支持。因此，短期的市场波动只是情绪化反应，而长期来看，AI的发展只会让算力需求持续攀升，Nvidia依旧是AI革命中的最大受益者之一。
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