The Hidden Effects of AI Videos

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AI is making everyone louder, not just faster. Writing emails, doing research, writing code, those things all took time and effort. And the production of that content is what’s been the bottleneck in knowledge work for a very long time.

But now that first draft is basically free. So all the ideas that used to get filtered out by effort don’t get filtered out anymore. Every team can generate five proposals instead of one, and every person can produce a strategy in an afternoon.

And Herbert Simon described this problem decades ago. When information becomes abundant, attention is what becomes scarce. Because more output doesn’t automatically lead to better decisions. You’re not just choosing between two options anymore. You’re choosing between 20 very convincing options.

So the companies that win in an AI world probably won’t be the ones generating the most content. They’ll be the ones that get very good at deciding what actually deserves attention.

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Creating convincing information used to be expensive. If you wanted something to look legitimate, a report or research summary, an article, it took time and expertise to put it together.

But now a single person can generate hundreds of possible articles with fake analysis, synthetic images, and even very realistic voices and video. And they can test different versions of that same story until one spreads.

And we already know how information behaves in networks. Studies of social media have shown that false news travels further and faster than true news. And it’s not because of bots. It’s because people are more likely to share things that are surprising or emotionally charged.

So when you lower the cost of producing convincing content, that dynamic scales. And the problem isn’t just misinformation.

It’s that when everyone can produce convincing looking information at scale, people stop judging information based on the content alone. Instead they ask, or they should be asking where did this come from. Who produced it, and is the source reliable. Because when credibility becomes easy to imitate, trust is the thing that actually matters.

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Even though AI makes it easier for people to produce good ideas, think about how LLMs work. They’re trained on massive amounts of human writing, books, articles, research papers, code.

So when a lot of people ask the same type of question, they often get variations of the same answer, and that makes the average quality of ideas go up. But the range of ideas can shrink.

And there is some early research showing this. When people use generative AI in creative tasks, their individual output often improves, but the ideas across the group become similar. Everyone performs better, but everyone starts clustering around the same patterns.

And you can already see it. If you ask 10 startups how to launch a product, or ask 10 marketing teams how to run a campaign, a lot of the answers start to sound familiar. So if everyone’s drawing from the same playbook, being different can be your competitive advantage.

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How do you know something’s real? Not does it sound convincing, or does it look professional, right? A report can look legitimate, and a voice can sound authentic, and a photo can look like evidence, but none of that tells you where it came from.

Economists have studied a version of this problem for decades. George Akerlof called it the market for lemons. The idea is you’re selling a car, you generally know if it’s a good car or a lemon.

But if you’re buying a car, it’s hard to tell. So you’re not willing to pay as much, because you assume it’s probably a lemon. And that forces prices down, which prices good cars out of the market.

And that same dynamic can happen with information. When convincing content becomes cheap to produce, people stop trusting the content itself, and they start relying on signals like brand and reputation, and verification.

Who published this? Where did it originate? Has this source been reliable before? And organizations that build that real credibility end up with something that’s very difficult to fake.

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Technology moves fast, and the institutions around it don’t. AI is an example of that, but it’s far from the only one.

AI can write reports, generate code, produce images, analyze data. But the systems that govern how we use technology, laws and standards, internal policies, professional norms, take years to adapt.

And these major innovations are called general purpose technologies. Things like electricity, computers, or the internet.

And the biggest changes don’t come from the technology alone. They come from everything that has to reorganize around it: companies and workflows, regulations and governance.

And that adjustment takes time. It’s already happening, but it’s not a quick process. It’s a systemic change. Governments are creating new laws, companies are building new policies, and industries are trying to figure out what responsible use means.

So the messy part we’re in right now, unfortunately, is normal. Every general purpose technology creates a period where the capability exists, but the institutions that stabilize it haven’t caught up yet.

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AI makes writing, analysis, design, coding cheap. Things that used to take hours or days can now happen in minutes.

And when the cost of producing something collapses, the systems behave differently.

You see it in the noise explosion. When ideas are cheap to produce, organizations get flooded with them, and then the bottleneck becomes attention.

You see it in misinformation. When convincing content becomes cheap to generate, credibility gets harder to judge.

You see it in idea convergence. AI can raise the average quality of ideas, but it can also pull people toward the same answers.

You see it in trust systems. When content itself can’t be trusted, people rely more on signals like reputation.

You see it in institutional lag. Technology moves quickly, but the rules and systems around it take time to catch up.

And none of this means that AI is bad for organizations. It means the advantage used to come from the ability to produce, but increasingly it is coming from the ability to filter, decide, coordinate, and build trust. Because when execution becomes cheap, the systems around that execution become your advantage.