"I can tell about myself — the efficiencies gained for work and information gathering have reached a point where I genuinely can't go back to doing things the old way."
Let me be direct: we are well past the stage where AI is a novelty or a fun image-generation toy. The conversation has moved on. What I'm watching now is something more interesting and more consequential — we are in the addiction phase of AI in the workplace, and personal-use adoption isn't far behind.
The efficiency gains are real. The dependency is real. And the economic ripple effects of both are only beginning to surface. This piece is my attempt to think through the macro picture honestly — not as a doomsayer, not as a hype merchant, but as someone who has skin in the game and is trying to make good decisions in a genuinely fluid environment.
Capital Appetite: The Infrastructure Gap Is Enormous
AI consumption today is still far from reaching all areas of the economy. Think about that for a moment. If current levels of adoption — which feel intense — represent only a fraction of eventual demand, the buildout required to serve the rest is staggering. Data centers, chips, power grids, cooling systems, fiber — all of it needs to scale at a rate the market has rarely seen.
That buildout requires capital. Massive, sustained, multi-year capital. And when there is that much credit requirement in the market, the basic economics of lending shift. Borrowers compete. Lenders have options. The price of money — interest rates — goes up, or at minimum stays elevated for longer than people expect.
The ripple effect
When credit gets expensive and credit demand is dominated by high-growth AI infrastructure plays, other borrowers — sovereigns carrying heavy debt loads, companies with weak cash generation, over-leveraged real estate portfolios — find themselves squeezed. The capital isn't gone. It's just going somewhere else first. That's when consolidation happens: mergers, failures, forced sales.
We've seen versions of this before — the telecom buildout of the late 90s, the energy infrastructure wave of the early 2000s. The difference this time is the pace. The capital formation race is happening simultaneously with adoption, not before it. That simultaneity creates a very different kind of pressure.
Job Loss vs. Job Creation: The Lag That Should Concern Us
Every major technological revolution has destroyed jobs and created new categories of work. The steam engine. Electrification. The internet. In each case, the historical record looks fine — eventually. But the keyword is eventually. Those transitions played out over decades, which gave the economy time to generate new industries, retrain workers, and build the demand that supports new employment.
AI doesn't have that buffer. The technology is still maturing at speed while adoption is happening in parallel. The efficiency gains being realized today — the ones I feel personally — are arriving faster than new industries can absorb displaced workers.
I want to be clear: new companies and industries are being created. That's not the concern. The concern is the ratio. A new AI-native company with 50 people can do what required 500 a decade ago. That math means fewer jobs per dollar of economic output, and the gap between displacement and reabsorption may be longer and wider than in previous cycles.
The structural truth
GDP growth needs jobs. It needs people earning, spending, and rotating money through the economy. If the vacuum before the new job categories form is large enough and lasts long enough, it creates a demand problem — not just a social one. Eventually AI will generate new types of work we can't fully imagine today. But the question isn't whether. It's how deep the trough is, and how long we're in it.
The Risk Stack: Cyber, Social, Capital
The fluidity of this moment compounds across multiple dimensions simultaneously, and that's what makes it genuinely different from previous economic transitions.
Cyber risk is no longer a tech-sector problem. When AI is embedded in critical infrastructure, healthcare systems, financial platforms, and logistics, a vulnerability isn't a company problem — it's a systemic one. The attack surface is expanding at the same rate as the adoption curve.
Social risk is the slower-moving but potentially more durable problem. Rapid displacement without adequate support structures creates political volatility. Regulatory responses to that volatility are often blunt and poorly timed. Businesses operating globally have to price in jurisdictional unpredictability in a way they didn't have to five years ago.
Capital risk we've touched on — but the feedback loop matters. Expensive credit slows deployment of new projects, which slows productivity gains, which slows GDP, which increases deficit pressure, which pressures interest rates further. Each variable feeds the others.
That's the environment we're making decisions in. None of these risks is hypothetical, and none can be addressed in isolation. Which makes the practical question unusually hard — but not unanswerable.
So What Does a Portfolio Look Like In This Environment?
This is the practical question. High fluidity in macro and micro conditions doesn't mean paralysis — it means positioning for durability and optionality rather than chasing any single theme.
| Asset | The case for it |
|---|---|
| Cash | Depreciating by design. At minimum, idle cash should earn at or near the Fed funds rate. Anything earning less is losing real value by the day. The 2% inflation "normal" is likely behind us for a while. |
| Cash-rich companies | In a high-cost-of-credit environment, companies that don't need to borrow have a structural edge. Lower financing costs mean wider margins — which flow to reinvestment or shareholders, not to lenders. |
| Gold | Still a legitimate hedge against currency debasement and fiscal stress. The caveat is discipline — buying into gold momentum or FOMO pricing defeats the purpose entirely. It's insurance, not a trade. |
| Market participation | Markets have risen over every long enough time horizon. The wealth gap between those with equity exposure and those without is not a mystery — it's compounding doing its job. Being in the market matters more than being right about timing. |
I recently heard a framing that stuck with me: the rich keep getting richer not primarily because of superior stock picking, but because they stay in the market. They don't panic-sell. They don't need liquidity at the wrong moment because they planned ahead. The edge is behavioral and structural, not intellectual.
Teaching the Next Generation Before They Need to Learn It the Hard Way
This is the part I think about most. It took me years of reading, making mistakes, and working through frameworks to arrive at even this level of clarity. And most of that time, I was already an adult with real money at stake. That's too late to start.
The compounding insight isn't just about money — it applies to financial understanding itself. A teenager who understands why markets go up over time, what inflation actually does to savings, and why debt costs what it costs, will make better decisions at 25 than someone encountering those concepts for the first time at 35.
The goal isn't to turn kids into traders. It's to give them the mental models early enough that the instinct to participate — to have skin in the game — feels natural rather than intimidating. The first investment doesn't have to be large. It has to be real. Understanding comes from watching money actually move, not from abstract lessons.
What to explain, and how
The macro picture above sounds complex, but the core ideas are simple: money loses value sitting still, markets reward patience, debt has a price, and who you lend to matters as much as whether you lend. Frame it through things kids already understand — why something they bought last year costs more now, why the family car depreciates but the house (usually) doesn't, why a bank charges more to lend to some people than others. The concepts aren't hard. The vocabulary is the barrier, and you can clear that early.
My goal with FinFolkTales has always been to close the gap between how financial ideas are typically explained and how accessible they actually can be. The AI economy is complex. The capital dynamics are real. But the foundational questions — how do I protect value, how do I grow it, how do I teach this to someone I care about — those are timeless.
The environment is more fluid than ever. The principles for navigating it are not.
A note on where we are
I'm not making predictions here — the honest truth is that nobody knows how the AI transition plays out at a macro level, or how long the employment lag lasts, or whether credit costs stabilize or accelerate. What I do believe: the people who stay curious, stay in the market, and stay financially literate will have more options than those who don't. That's been true in every economic era. I don't expect this one to be different.