Missed the Internet Boom? You Won’t Get a Second Chance with AI

Eric Siu argues AI will be adopted faster than the internet because existing hardware already supports it, giving corporations no 5-10 year buffer to adapt. The result: rapid change, layoffs, and potential job loss for those who don't keep up.
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When the internet first came out, okay, do you remember how long it took for corporations to adopt the internet? Like to use it, to get an email address and, you know, learn how to use the web?
5+ years. No, maybe 10+ years.
Okay, well, if that's the case, like isn't that probably going to be the case for AI? And it was like, yeah. And I'm like, but hypothetically, there's no time for people to adopt this time, right? Because things are changing so quickly this time. You don't have like a 5 to 10 year window. This time you can actually, you know, we're talking about job loss and things like that. So I ran that idea by Perplexity. I was like, is that true or false? And it's like, this is largely true because, you know, the world's changing so quickly. You see layoffs, 17-year record, right? You see tech companies aren't hiring that much anymore. You see like it's not the same world that we're used to 2, 3 years ago. That's the point I'm trying to get at. Not knowing how to use this is akin to not knowing how to use the internet. But you don't get the time buffer that you got before.
So I want everyone to think about it from a little bit of a different perspective, and you can take Eric's perspective or my perspective. Now we're in a place where people already have the technology, and AI— or let me rephrase, not the technology, people already have the hardware. AI, the software layer, is plugging into the hardware that you're already using on a daily basis, and you don't need new hardware. So you're seeing adoption much quicker. We already have a lot of the foundation, so it makes it Easier and quicker for people to adapt. So if you think you have a long time to adapt, which is what Eric's getting at, you're very wrong.
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