Why today’s AI panic feels like the 1990s internet all over again

I started working in digital in 1989, when most people hadn’t even heard of the internet. At CompuServe, I helped businesses move offline communications online — groundbreaking work at the time. We used to joke that we weren’t on the cutting edge. We were on the bleeding edge.

Back then, the internet was mysterious, messy and a little frightening. And I’m getting that same feeling again today — only now, the technology is artificial intelligence.

The parallels are unmistakable. Just as the internet reshaped how we communicate, learn and do business, AI will do the same. The pattern feels familiar — excitement, fear, resistance and eventually, acceptance. We’ve been here before. And if history is any guide, we’ll get through it again.

The 1990s internet panic: Chaos, cookies and control

When the internet went mainstream in the 1990s, the public conversation sounded a lot like today’s AI debate. People worried about privacy — cookies and tracking — and the Internet Engineering Task Force floated limits on cross-site cookies. 

They also worried about misinformation (remember the cyberporn panic?), and automation raised concerns about job loss and worker displacement. There were fears about who was really in control of this new, invisible network connecting everything.

Meanwhile, marketers were among the first to experiment with the medium through websites, email and online advertising. We made mistakes. We learned about privacy the hard way. We watched bad actors ruin things for a while — hello, spam.

And then we mostly figured it out. Regulations emerged, industry standards formed and consumers adapted. Before long, the internet became an essential, trusted part of everyday life. The transformation was bumpy, but it happened — and it was worth it.

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The 2020s AI anxiety: Familiar fears in a faster world

Fast-forward to today, AI is inspiring the same blend of fascination and fear. We’re worried about:

• Privacy: What data are models trained on and who gave consent?
• Truth: Can we trust what’s generated?
• Bias: Who’s being left out or misrepresented?
• Control: Are we driving this technology or is it driving us?

If that sounds familiar, it should. It’s the same cultural anxiety we had about the Internet. Only now, it’s moving at warp speed.

Marketers are, once again, at the forefront. We’re using AI to write copy, segment audiences, generate imagery and predict behavior. And just like in the 1990s, the temptation to go too far, too fast is real. But so is the opportunity, if we handle it right.

What the internet taught us and what it means for AI

1. Transparency builds trust

When websites began collecting data in the 1990s, users were in the dark. The backlash forced us to create privacy policies, consent checkboxes and unsubscribe links.

AI will need its own transparency moment. Marketers should be transparent about when and how AI is used (I recommend using Georgetown University’s AI Guidelines as a template) and, more importantly, why it benefits the customer. Trust follows clarity.

2. Ethics can be a competitive advantage

As spam began to dominate email, responsible senders learned to differentiate themselves through permission-based marketing. We set the bar higher than the law did and then industry organizations enforced these higher standards. Those of us who respected the inbox won (thank goodness!).

The same will happen with AI. Marketers who use it responsibly, as a productivity tool, not a hack, will earn more engagement, loyalty and long-term credibility.

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3. Regulation catches up (eventually)

Every significant digital wave eventually met a regulatory reckoning: the Communications Decency Act, CAN-SPAM and GDPR. AI will, too. One thing that differentiates 2025 from the 1990s is who’s eager for legislation. With the internet, the prevailing sentiment in the industry was to let it grow on its own. Today, OpenAI and other organizations in the space have consistently called for federal AI legislation.   

That said, the best marketers don’t wait for the rules. They anticipate them. Now’s the time to document your data sources, understand your model’s limits and build accountability into your AI processes.

4. Bias is the new digital divide

In the 1990s, we worried about who had access to the internet. Today, we worry about whose data trains the AI algorithms. Marketers who test for bias and ensure inclusive representation in AI-driven content will not only be on the right side of ethics, but they will also create better-performing campaigns.

The takeaway: Same story, different technology

AI is the next internet. It’s the same cycle of disruption, fear and adaptation, only faster. Just as the internet reshaped marketing in ways we could barely imagine, AI will, too.

When I worked at Reed Elsevier in the early 2000s, my digital team often pointed out that we were one of the few companies making money on the internet — without pornography or pseudo-prescription drugs. It was our way of saying we were building something sustainable, trustworthy and legitimate.

That’s what we need to do with AI now. We’ll make mistakes. We’ll overcorrect. We’ll find balance. And we’ll emerge with something better — an ecosystem that’s smarter, more efficient and, yes, more human.

Because here’s the truth: technology doesn’t erode trust. How we use it does. And marketers, as we’ve proven before, are uniquely equipped to lead that evolution again.

Dig deeper: In an age of AI excess, trust becomes the real differentiator

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