Introduction: Behind the Buzzwords—What It Really Costs to Build AI

On October 23, BizCrush hosted its first in-person workshop with Columbia Business School’s AI club, titled “The Real Economics of Building AI Products”

More than 100 MBA and graduate students filled the classroom that day, representing backgrounds from finance and consulting to data science.

Instead of another hype-driven talk about generative AI, Ethan Kim, our CTO and co-founder, opened with a sentence that set the tone for the next 20 minutes.

“This isn’t our success story. It’s our survival playbook.”

The room went quiet. Then curious. Because behind every AI startup headline, there’s a hidden question — how do you actually make this work financially?

1. Inside the Session: Survival Is Strategy

Ethan walked through the truth : every technical decision is a financial one.

He broke down the real economics of running an AI company—how every technical decision, from which model you pick to how you design your data pipeline, is ultimately a financial decision.

He shared how BizCrush rebuilt its internal architecture, and cut infrastructure costs by 64%, not by switching models, but by redesigning how requests flows without sacrificing performance.

The key wasn’t cheaper tokens. It was smarter cost results achieved through prompt caching, smarter workflows, and human-in-the-loop desin.

2. Efficiency is a Strategy, Not a Shortcut

Ethan explained that a lower token price doesn’t always mean a lower total cost.

A $0.002 token can still burn your budget if you’re paying full price for every request.

“Cheaper per token doesn’t mean cheaper per result.”

At BizCrush, we shifted our focus from model shopping to workflow engineering — caching repeated queries, routing tasks intelligently, and monitoring model confidence in real time.

That’s how BizCrush scale sustainably.

3. The Real Bottleneck Isn’t Code—it’s Communication

You can optimize code all you want, but if your team isn’t aligned, you’ll leak money faster than you can deploy.

“When teams talk about features, they should also talk about confidence levels and fallback plans,”

That’s the mindset we use to make financially sound decisions under constant model change.

4. Thinking Beyond the Model

Most startup founders obsess over fine-tuning.

But in reality, the moat isn’t the model itself. It’s the system around it,

• Proprietary data

• Workflow design

• Compliance infrastructure

Those are what big models can’t copy and what determine whether you survive long enough to compete.

Building AI Literacy for Business Leaders

MBA students in the room asked sharp questions about switching costs, monetization strategies and scaling trade-offs.

One student summed it up.

“Understanding how AI startups make money might be the new business literacy.”

And that’s the point — AI economics isn’t just for engineers or CFOs. It’s a survival skill for anyone in the AI ecosystem.

Looking Ahead

This event came right after BizCrush closed its pre-seed funding round, marking an early milestone in our journey to make real world meetings smarter.

For us, the Columbia session wasn’t just another talk. It was a validation.

It showed that the next generation of business leaders cares not only about

what AI can do, but how it can sustain itself.

We believe the future of AI depends as much on economic design as it does on model performance.

And we’re just getting started.

Over the past few months, we’ve been refining our service based on real user feedback and deepening collaborations across academia and industry.

Soon, we’ll share a deep dive into how we achieved a 64% cost reduction — unpacking the architectural, financial, and operational levers behind building scalable, sustainable AI infrastructure.

Want dive deeper? Check out the slides here!

📘 Stay tuned for BizCrush Case Study: The Unit Ecomics Behind Cutting AI Infrastructure Costs by 64% in 90 Days — The Hidden Economics of Scaling AI