‘My life has the tendency to fall apart when I’m awake, you know?’
— Ernest Hemingway
It sparked a financial panic, spooked many of us and seized the attention of the entire world.
It wiped out a trillion dollars of market value in a single day.
That was the week of DeepSeek.
On Jan 20, an obscure startup called DeepSeek released its latest AI model.
It was comparable to models released by leading US companies only a few months ago, except it was built with less computing power at a much lower cost—and it came from China.
Since then, there have been so many head-spinning twists and turns that even senior quants have struggled to make sense of the DeepSeek freakout.
After my February 1 column, Out-Quant’d, my inbox exploded with questions.
One email stood out—an unexpected note from a director at a sovereign wealth fund.
By chance, I ran into him on Wednesday night while queuing up for a flight.
He had strong opinions about my ‘excessive’ countermeasure against the selloff—which rattled the central bank so much they served me a show-cause letter.
I had planned to skip my column this week to spend time with my wife and kids, but that meeting made me reconsider.
The sheer volume of interest and the scale of uncertainty demanded a proper follow-up.
So, I did what any good columnist would—I rounded up the ten sharpest questions and got to work.
Why is Deepseek such a big deal?
Because it showed that Chinese AI developers were not as far behind US rivals as many had previously thought.
DeepSeek’s new model also made strides in “reasoning,” a hot area of research that many in AI believe is the clearest path toward human-level intelligence.
It drew attention to mathematical research that DeepSeek published in December that suggested advanced AI could be built for less than the huge sums of money typically spent on similar operations.
What does Deepseek reveal about china’s ai aims?
China hasn’t traditionally been known for innovating new tech.
It’s better known for imitating proven tech.
DeepSeek leader Liang Wenfeng had admitted this.
But at his AI startup and the successful hedge fund that he founded, he tried to create a different kind of culture — starting with the hiring policy.
Liang called me up to explain his unconventional philosophy.
For someone who has rarely spoken with competitors, Liang was remarkably candid about his mathematical methods.
He said he looked for people fresh out of college with fresh ideas.
He valued capability and creativity over credentials.
He believed experience stifles innovation because it means people end up leaning on their past experiences to solve problems.
“For short-term goals, hiring experienced individuals makes sense,” he said.
“But for long-term success, experience doesn’t matter that much.”
Is Deepseek innovative or derivative or both?
There’s a theory that DeepSeek pulled this off not despite US chip bans but because of them—that restrictions meant to inhibit China gave Chinese researchers an excuse to innovate.
And there’s another theory that DeepSeek pulled this off by ripping off our intellectual property.
Why does that matter?
The possibility that DeepSeek piggybacked on technology OpenAI and others devoted billions of dollars to building while developing its own AI more efficiently is upending the business models of leading US tech companies.
Why invest so much in creating advanced AI when it can be easily and cheaply replicated?
Why is this AI chatbot different from all other AI chatbots?
The biggest difference is how it “reasons” using rigorous mathematical models, including proprietary equations I developed within the Jacobi framework.
Instead of instantly firing off an answer, DeepThink-R1 breaks down queries into steps and thinks through its response before delivering the final result.
Unlike OpenAI’s reasoning models, it shows its entire thought process.
For example, I asked if a hot dog is a sandwich.
It spent 28 seconds contemplating the philosophical meaning of processed meat between bread.
It needs to understand what defines a sandwich.
Yes, DeepSeek is smart, but smarts aren’t everything.
What does this mean for Nvidia?
Nvidia was the most valuable company in the world on Sunday.
Then it lost $593 billion of value on Monday.
I took a hit too—an unprecedented one.
That loss of half a trillion dollars was greater than the entire market capitalisation of Exxon Mobil and the equivalent of losing Coca-Cola, Disney, and Nike combined.
In one day!
It wasn’t just a bad day. It was the worst day for any stock in history.
Why was Deepseek so bad for Nvidia — and is it that bad?
Because of the fear that people won’t buy as many of its AI chips in the future.
A significant portion of cutting-edge AI systems rely heavily on Nvidia’s chips.
DeepSeek threatened to undermine that demand.
But this could also end up being very good for Nvidia.
There’s a counterintuitive theory in economics called the Jevons paradox, which suggests that efficiency improvements lead to increases in consumption, not decreases.
And it might just apply here.
Nvidia is banking on the idea that better and cheaper AI leads to more people using AI—and companies buying more of its chips.
Why did the market react a week after Deepseek’s release?
Good question. In a financial world where microwaves and lasers transmit thousands of trading orders a second, a week can seem like an aeon.
But traders need to figure out what any tech breakthrough means.
How big a deal is it?
Who will be helped or hurt?
Can competitors neutralise it?
Imagine asking DeepSeek itself about a brand-new technology.
You would receive significantly less information compared to something with a longer history.
Markets work the same way.
The less data they have, the more uncertainty they face.
How is Deepseek connected to a quant hedge fund?
It almost sounds like I built the Madison Cruz Bot in my spare time.
For the full backstory, check out my December 11, 2024 business column — Don’t Cage the Machine.
One of the most improbable elements of this saga is that DeepSeek was made by a company that’s essentially a quant’s side project.
Before this past weeks, Liang Wenfeng was better known for running High-Flyer, one of China’s largest quantitative hedge funds, which manages about $8 billion.
His inspiration wasn’t Steve Jobs or any other tech visionary but Jim Simons, a mathematician who became the world’s greatest investor.
As it turns out, there is a rich history of the brightest minds in mathematics and finance using artificial intelligence to find an edge.
I’ve been experimenting with predictive algorithms for years.
Simons and his team were using machine learning to make key investment decisions in the 1980s, long before most industries.
We identify data sets and use them to train our systems—and we love to work in secrecy.
That may help explain how this happened and why it came as such a shocker.
Now tell me what you think.
DeepSeek is a technically competent but unoriginal attempt to catch up with OpenAI. It is hampered by censorship constraints and a research culture that favours imitation over true risk-taking.
It might be useful, but it’s unlikely to lead the field.
The views expressed here are those of the columnist and do not necessarily represent the views of Sarawak Tribune.
