The AI Gold Rush Is Starting to Look a Little Too Familiar

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The AI Gold Rush Is Starting to Look a Little Too Familiar



There’s a strange tension in the air around artificial intelligence right now something between excitement and quiet anxiety. Every week, it feels like another megadeal is announced, another company pouring billions into the dream of AI supremacy. But behind the spectacle, a question keeps echoing: is this starting to look like a bubble?

The latest deals, frankly, don’t make that question any easier to dismiss. Anthropic just announced a partnership with Google that grants it access to a staggering one million custom built chips hardware designed specifically to train its AI models. The estimated cost? Around $35 billion.

Almost at the same time, word leaked that Oracle is on the verge of finalizing two massive data centers dedicated entirely to powering OpenAI’s next generation systems. That project alone could reach $38 billion. Together, these two arrangements represent more than $70 billion in commitments and that’s just from this month.

Billions in, Unclear Returns Out

Let’s take a step back for a second. These numbers are astronomical even by Big Tech standards. It’s hard to find another sector not even during the early days of the internet or the crypto boom where investment has scaled this fast with so little certainty about how the money comes back.

Anthropic, for example, expects to generate about $26 billion in revenue by the end of next year. That’s impressive, sure, but compared to the investment levels flying around, it barely scratches the surface. OpenAI, meanwhile, has reportedly spent around $500 billion on infrastructure, according to CNBC, yet its annual revenue sits near $13 billion.

That gap between investment and return is what has analysts worried. It’s not that the technology isn’t real; AI is clearly transforming industries. The concern is that the hype is pulling money faster than the market can reasonably sustain.

The Psychology of Hype




There’s something oddly familiar about all this. The language, the urgency, the way every announcement feels like a once in a generation breakthrough. We’ve seen this kind of energy before in the dot com bubble of the late ’90s, in the crypto surge of the 2010s, even in the early electric vehicle frenzy.

In each case, there were real innovations behind the excitement. Amazon, Google, and Bitcoin all emerged from those eras as genuine forces. But they were surrounded by countless projects that burned bright, burned cash, and then just… burned out.

The AI industry right now seems to be walking a similar line. There are groundbreaking technologies, no doubt OpenAI’s GPT models, Anthropic’s Claude, Google’s Gemini but there’s also a palpable sense that the pace of investment is being driven by fear of missing out as much as by clear business logic.

The Hardware Arms Race

What’s making this round particularly intense is the hardware dependency. Training large language models isn’t cheap it requires enormous data centers filled with high performance chips, often built by Nvidia or customized by companies like Google.

These chips have become the modern equivalent of oil. Every AI company wants them, hoards them, and measures success by how many they can get their hands on. That’s why Google’s deal with Anthropic involving a million specialized chips feels less like a partnership and more like a territorial move. It’s about control over the raw computing power that defines who gets to build the next frontier of intelligence.

The irony is that, despite all this investment, the world is still short on chips. Even with new factories in Arizona, Taiwan, and Germany, demand keeps outpacing supply. So companies keep signing these gargantuan contracts to secure capacity years ahead almost as if the chips themselves were speculative assets.

The ROI Problem



Here’s where things get fuzzy. For all the impressive demos and productivity claims, it’s still hard to translate generative AI into stable, recurring revenue. Sure, AI models can write code, generate art, summarize documents, and mimic human reasoning. But monetizing that consistently without driving up costs has proven tricky.

Most of the income for companies like OpenAI and Anthropic comes from enterprise subscriptions and partnerships with cloud providers. That’s a solid foundation, but not necessarily a gold mine. Margins are thin when your infrastructure costs eat up half your budget.

So, while revenue is growing, profits remain elusive. And when profits are uncertain, the justification for spending billions becomes less about logic and more about faith or fear of falling behind.

Echoes of the Dot Com Era

One can’t help but think back to the late ’90s, when every company was suddenly an “internet company.” Money flooded in from every direction, valuations soared, and for a while, it seemed like the laws of business had been rewritten.

Then, of course, they weren’t.

AI feels like it could go either way. On one hand, it might indeed redefine everything from healthcare to education to entertainment. On the other, some of these mega investments might age about as well as Pets.com. The truth probably lies somewhere in between a mix of enduring giants and forgotten experiments.

The Reality Check No One Wants




Part of what makes this moment so precarious is that no one wants to slow down. Governments are investing, startups are racing, and tech giants are locked in an arms race for dominance. Even the skeptics, the ones warning about an AI bubble, admit they can’t afford to sit it out.

The result is a self perpetuating cycle: the more money that flows in, the more it validates the perception that AI must be worth the hype and so even more money flows in. It’s the same pattern that inflated every major tech bubble before this one.

The difference now is scale. When companies are spending half a trillion dollars before turning a meaningful profit, the stakes are no longer just financial they’re geopolitical. Whoever controls the AI infrastructure effectively controls the next layer of the digital economy.

A Delicate Balance

To be fair, some level of overinvestment might be inevitable in any transformative era. Innovation has always required excess the willingness to build too early, too much, too fast. Without that, we probably wouldn’t have the internet, or electric cars, or modern smartphones.

But excess has its limits. If AI growth continues to depend on a handful of companies spending more than they earn, the correction, when it comes, could be brutal.

For now, the mood is still exuberant. AI conferences are packed, startups are sprouting daily, and every CEO wants to say their company is “powered by AI.” But beneath the surface, a quiet worry persists that maybe this time, we’ve mistaken momentum for inevitability.

The Bottom Line

AI isn’t going away, and it probably won’t crash in a single dramatic burst. More likely, we’ll see a gradual cooling a realization that not every billion spent buys a breakthrough.

Still, as long as companies like Google, Oracle, and OpenAI keep signing deals measured in tens of billions, it’s hard not to feel a little uneasy. The technology is dazzling, yes. But bubbles always are right up until they aren’t.


Open Your Mind !!!

Source: Semafore

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