Every few years, a new technology emerges that promises to change everything. Some deliver on that promise, the printing press, the telegraph, the internet. Others crash spectacularly, leaving behind nothing but cautionary tales and empty wallets. Today, as artificial intelligence dominates headlines and preach financial revolution, we’re left asking the same question investors have asked for centuries: which innovations are real, and which are just expensive illusions? The answer might surprise you. Hint: it is crypto.

They helped news spread faster than horses could gallop. They created entertainment as we know it—from radio dramas to streaming services. They fundamentally changed how much information a human can remember, and perhaps more importantly, how much we should remember. The technologies of the information age didn’t just augment human capability; they redefined it entirely.

From the printing press to the telegraph, from radio to television, each communication medium took decades to mature and reshape society. The printing press needed nearly 50 years before it sparked the Reformation. The telegraph took 30 years to span continents. Yet in the past 20 years alone, technology has evolved more than it did in the 200 years before it. We’ve compressed centuries of innovation into a single generation.

Now we stand at another inflection point, watching artificial intelligence reshape industries at breakneck speed. But history has taught us to be skeptical of transformative promises. We’ve seen bubbles before—the dot-com crash, the ICO mania, the NFT craze. Each time, skeptics asked the same question we’re asking now: Is this real, or is this just speculation dressed up as innovation?

So What About AI? Bubble or Breakthrough?

Yes, AI companies command eye-watering valuations—for example, OpenAI is now valued around $500 billion, Anthropic at $170 billion, and Databricks at $62 billion. Yes, the hype sometimes outpaces the reality. AI is already solving a problem that matters deeply to every business on earth: cost reduction at scale. I have a great story regarding this, but I will save it for another day! (until I get permission from corporate HP)

This isn’t trivial. Reducing costs while maintaining or improving quality is extraordinarily difficult. It’s why companies pay consultants millions, why they chase efficiency gains of even 5-10%, why entire industries exist around process optimization. AI can automate customer service, generate code, process documents, and analyze data—tasks that previously required human cognition and human salaries. The ROI isn’t theoretical; it’s appearing on balance sheets.

But here’s where it gets interesting. Current AI is expensive. AI models itself don’t make money! It’s the services and software that are being upon it that are building the AI hype. Training large language models costs tens to hundreds of millions of dollars. Inference, actually running these models requires massive computational resources. You need to first get expensive GPUs then you need facilities that can cool it and then the star of the show:maintenance. The technology works, but it’s not yet cheap enough to fulfill its full potential, or atleast the way we envision it.

That’s about to change. As neuromorphic computing begins to mimic the brain’s remarkable energy efficiency, and quantum computing tackles the complex optimization problems that currently bottleneck AI development, we’re looking at potential cost reductions by orders of magnitude. When that breakthrough arrives, AI won’t just be useful—it’ll be ubiquitous.

So no, AI isn’t a bubble. But there might be a real bubble hiding in plain sight, one that’s been inflating for over a decade while everyone debates whether ChatGPT will take their job.

I’m talking about Bitcoin.

The Bitcoin Problem

Here’s something they teach you in the first weeks of Macroeconomics: not everything that claims to be currency actually is currency.

To qualify, something needs three critical properties, it must serve as a medium of exchange, maintain a stable unit of account, and have store of value

Bitcoin fails spectacularly on at least two of these counts. Its volatility makes it useless as a unit of account, you can’t price goods when a Bitcoin is worth $110,000 today and $120,000 in the next week.

And that same volatility destroys transaction freedom.

But the deeper problem is what Bitcoin doesn’t produce: real economic value. It doesn’t reduce costs, create goods, or optimize production. Unlike AI, which generates measurable productivity gains, Bitcoin is pure speculation.

The dream that it will “replace cash” ignores how modern economies actually function.

If you want to understand why, look at how central banks interact with commercial banks and influence the IS-LM model—the relationship between interest rates, investment, liquidity, and money supply. Central banks conduct monetary policy, stabilize currencies, act as lenders of last resort, and coordinate with fiscal policy to manage economic cycles. Bitcoin has no mechanism for any of this. There’s no international coordination, no monetary policy flexibility, no institutional backing.

Nothing is permanent because nothing is governable. While AI skeptics worry about hype, they’re missing the real bubble inflating right in front of them—a “currency” with no economic fundamentals, backed by nothing but the hope that someone else will pay more for it tomorrow than you paid today.

Hopefully, the Bitcoin bubble won’t burst in a way that drags broader markets down with it. But if history is any guide, speculative manias rarely end gently. When they do unwind, the aftermath always tests the strength of our economic institutions, the very systems built to absorb shocks and restore confidence.

That’s where the story heads next. In Part 2, we’ll look deeper at how fiscal and monetary policy work hand-in-hand to stabilize economies when speculation runs wild and how, unlike Bitcoin, coordinated governance is what truly keeps modern markets afloat.

Also shoutout to Professor Dongwon Lee @ UCR and Professor Silviu Velovici @ UCR.

Thanks for reading!

Let me know what you think!

@pplcallmetat