🧠 Is Beating AI Giants Possible? 4 months Later, How Is Deepseek Doing?

Deep dive into the efficiency of AI models and their costs for the environment, the hidden secrets behind the success story and my tips as a tech investor...

On January 20th, something remarkable happened — and almost no one saw it coming. DeepSeek, a relatively unknown AI startup from China, released the first open-source reasoning model that matched OpenAI’s o1 performance.

Not just comparable. Not a close second. Matched it.

What made it even more jaw-dropping? They pulled this off with 1/10th the training cost and with inference costs that are 20–50x cheaper than any frontier lab.

This wasn’t a Silicon Valley-backed moonshot. DeepSeek had no famous founders, no flashy investors, and no billion-dollar war chest. Just 100 people. Zero VC. A dream. And 10,000 GPUs powered by pure will.

To understand how they pulled this off, I went deep into the origin story — and found a tale of collapse, constraint, reinvention, and one of the most impressive pivots in AI history.

Preface: 1/10th of the energy = 1/10th of pollution?

So, if Deepseek is indeed as efficient with 1/10th of ressources needed, let’s build an approximate, illustrative comparison based on publicly estimated compute usage, energy intensity, and car-equivalent emissions. This won’t be exact but will help us grasp the climate footprint of OpenAI vs DeepSeek at full deployment scale.

Open AI VS Deepseek - Approximate Annual Carbon Emissions (Car-Equivalent)

🔍 Takeaway:

• OpenAI’s GPT-4o running at full scale emits as much CO₂ annually as ~380,000 cars

• DeepSeek R1, while powerful, is leaner — emitting the equivalent of ~115,000 cars, thanks to more efficient architecture and inference costs.

In climate terms, efficiency isn’t just a cost advantage — it’s a carbon advantage.
That’s why we’re watching teams like DeepSeek so closely.

Anyway, let’s dive into DeepSeek 🌀

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⚙️ 1. Origins in Obscurity

Let’s rewind. Back in 2007, three engineering students — Xu Jin, Zheng Dawei, and Liang Wenfeng — met at Zhejiang University, one of China's top science and tech institutions. They bonded over a shared obsession with algorithms and the idea that software could one day out-trade humans. Their goal?
Build a fully AI-driven quant fund from scratch.

But from the beginning, they made a decision that would set them apart: they refused to play by traditional hiring rules. Instead of bringing in industry veterans, they focused on identifying raw, untapped talent — students and early-career engineers who were brilliant, intensely curious, and unafraid to break things.

Liang later explained:
“Our core technical roles are filled by people who are just starting out — fresh graduates or maybe a year or two into their careers. We bet on fire and flexibility over polish and pedigree.”

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📈 2. The Rise of High-Flyer

After years of low-key experimentation and algorithm development, the trio formally launched High-Flyer Quant in 2015. Armed with unconventional hiring principles and deep conviction in machine learning, they scaled rapidly — but quietly.

By 2021, they had become a powerhouse in the world of Chinese finance:

• Over 10,000 NVIDIA A100s deployed across their custom-built AI research and trading stack

• A seat among the top 4 quant funds in China, with a staggering $15B in assets under management

• And, perhaps most impressively, $140 million invested directly into their internal AI infrastructure

Here’s the key detail: none of that $140 million came from venture capital.
Every dollar was reinvested from their own trading profits. High-Flyer was a cash machine during its prime — generating consistent alpha through proprietary quant strategies. Rather than distribute all those earnings, they doubled down on internal innovation. They believed AI was not just a tool, but the future of trading itself. So they built what was effectively a private AI lab inside a hedge fund.

This wasn’t about external funding or VC rounds. It was organic growth reinvested at massive scale — a rare move in either finance or tech.

But like many rocket ships, their trajectory outpaced their controls.