modelthinking

Aahan Menon argues that because the private sector now owns a massive amount of US government debt, high interest rates are feeding cash into the private sector rather than sucking it out. 1. The Traditional Mechanism (How it Used to Work) Historically, "monetary policy transmission" relied on a simple pain channel: The Action: The Fed raises

3. "Don't Fight the Train" Despite the long-term risks, Menon advises investors not to bet against this dynamic yet. As long as the CapEx spending continues to grow, the "profit juice" will keep flowing, keeping aggregate earnings per share (EPS) high and supporting the stock market. The signal to watch is not the level of spending, but the rate of

The "AI Profit Juice" (The Boom): Mechanism: Corporations are spending massive amounts on AI infrastructure (data centers, chips). In the immediate term, this CapEx is recorded as revenue for other companies (like NVIDIA or construction firms), boosting GDP and corporate profits instantly. The Accounting trick: Crucially, the cost of this

The Competitive Side of the Economist-Minded Data Scientist

What really gives economist-minded data scientists an edge is their ability to connect models to value creation. While many professionals can code or fine-tune hyperparameters, far fewer can trace a line from a data insight to an economic outcome — like cost reduction, revenue growth, or

Thinking Like an Economist Isn’t About Taking a Class

And just to be clear, thinking like an economist doesn’t mean you need to go back to school for a degree in economics. It’s more about adopting a mindset. I would recommend starting to ask questions like:

• What’s the mechanism behind this pattern?

• What happens if the environment changes?

• What

The Power of Incentives

Thinking like an economist also means thinking in terms of incentives . Data scientists love clean data, but real-world behavior is never clean because it’s shaped by what people want, what they fear, and how they respond to rules and rewards.

A model that predicts customer behavior without considering incentives is always

Scarcity and Constraints

Another thing economists are obsessed with is scarcity . In data science, we talk about “big data” so much that we forget how scarce good data really is. Most organizations don’t have perfect information; they have partial, biased, noisy data collected under uncertain conditions.

Economists, for all their stereotypes, have one superpower that data scientists could really use: they see the world in terms of trade-offs, incentives, and equilibrium. They don’t ask what is happening; they ask why it’s happening, and what would happen if something changed .

Data scientists, on the other hand, are often trained to predict. The

Businesses became flooded with dashboards, predictions, and algorithms that worked perfectly in notebooks but fell flat in real life. Executives started asking a more basic question: “What does this model mean for us?”

That’s where I began to realize that data science — in its most useful form — isn’t really about the data. It’s about decisions. And