Jensen Huang Admits He Carries a “Secret Option Pool” to Reward Top Performers on the Spot

At the end of his latest interview, when asked about this rumor, Huang joked, “It’s currently in my pocket.”

The CEO of one of the world’s most valuable tech companies handles employee rewards with such casual directness.

There is no need for lengthy approval processes or waiting for year-end evaluations. If you perform well, your boss might surprise you at any moment—a practice almost unheard of in large corporations.

This viral conversation took place on All-In Podcast, the world’s number-one business podcast. Huang not only confirmed the rumor but also revealed that he personally reviews compensation plans for all 42,000 employees using machine learning and various technical methods.

Every time, I increase the company’s operating expenses* (primarily referring to payroll).

The reason is simple: take care of your employees, and everything else will fall into place.

Huang also proudly mentioned during the show that his team has more billionaires than any other CEO in the world.

While this statement sounds bold, it reflects NVIDIA’s astonishing growth amid the AI wave and Huang’s generous attitude toward sharing success with his employees.

Acquiring a Company Is Less Effective Than Paying One Person $1 Billion

Huang’s management style appears particularly prescient against the backdrop of the current AI talent war.

The podcast noted that the value of top-tier AI researchers has skyrocketed to staggering levels—rumors suggest Meta offered a researcher a four-year contract worth $1 billion.

Facing such a talent market, Huang pointed out a key fact:

With sufficient funding, 150 top AI researchers could create a company similar to OpenAI.

DeepSeek has approximately 150 employees, Moonshot AI (Yuezhimian) also has around 150, and early OpenAI and DeepMind were of similar scale.

If you are willing to spend $20–30 billion acquiring a startup with only 150 AI researchers, why not just pay one person $1 billion directly?

Regarding DeepSeek, Huang emphasized the importance of open source. Without it, startups simply cannot survive. He believes that future industries will likely be dominated by today’s startups, which rely on open-source models.

He also spoke enthusiastically about the significance of reasoning models like DeepSeek R1: Older models were static, with everything pre-memorized. But now, with reasoning models, they can truly think. If every step of thinking is energy-efficient, you can sustain that thought process for a long time.

GPU Allocation Is Simple and Direct: First Come, First Served

How does NVIDIA allocate its scarce H100s and other chips amidst the demands of tech giants like Mark Zuckerberg, Elon Musk, and Sam Altman?

Huang’s answer was surprisingly simple: Place a purchase order (PO). That’s it. It’s just like going to a checkout counter—pay first, get served first.

He explained this seemingly complex allocation mechanism using the most straightforward analogy.

In the early days, demand for Hopper chips grew too fast for production capacity to keep up. However, the situation has improved significantly. NVIDIA now discloses its product roadmap to all partners a year in advance, allowing ample time for joint planning.

Buyers decide how much power, data center space, and capital expenditure they need. We plan together and collaborate on product iterations. The entire process is now quite smooth.

Huang also revealed that he currently has $50 billion worth of Hopper chip inventory. If anyone wants extra chips, they can simply give him a call.

More interestingly, he explained the value retention of these chips.

When asked how long these chips, costing hundreds of thousands of dollars each, remain useful, Huang did the math: Each generation offers an X-fold performance improvement, which translates to an X-fold improvement in performance-per-watt, effectively doubling customer revenue by a factor of X.

We are racing to accelerate iteration speeds to increase everyone’s income and reduce costs, making AI as affordable as possible.

He shared a startling statistic: Hopper chips retain about 80% of their value after one year, 65% after two years, and 50% after three years. Furthermore, due to the programmability of the CUDA platform, developers worldwide are continuously optimizing its performance.

After shipping Hopper, we and others boosted its performance by four times. You won’t get such returns from CPUs.

AI Won’t Steal Jobs; The Pace of Job Creation Is Just Too Slow

When asked about AI’s impact on the job market, Huang offered a insightful perspective: We are busier now than ever before.

He cited NVIDIA as an example: Currently, 100% of its software engineers and 100% of its chip designers use AI to assist their work. This has not led to layoffs; instead, it enables the company to pursue more innovative ideas.

So I believe that as long as a company has enough ideas, higher productivity allows you to chase those ideas even more aggressively.

To me, AI is actually creating jobs. It enables us to build products customers are willing to buy, driving growth and subsequently creating more positions—a chain reaction.

Huang considers AI “the greatest technology equalizer in history”:

Everyone is a programmer now. In the past, you needed to master C, C++, or Python. Now, you just need to converse with AI using natural language. Even if you don’t know how to ask the right question, you can have AI help you write a better prompt, and it will reorganize your thoughts for you.

Everyone is now an artist, everyone is a writer, and everyone is a programmer.

However, he issued a warning:

One thing we can be certain of is that if you do not use AI, you will lose to those who do. He asserted that no programmer in the future will work alone; “You can’t go raw dogging it anymore without tools—you need a Copilot.”

Every Industrial Company Will Become an AI Company

Finally, Huang shared his views on AI’s impact on the broader economy.

He believes the biggest difference between AI and traditional software is that AI requires continuous production.

Just as energy production peaked at 30% of the economy two or three centuries ago, in the future, there will be an entire industry dedicated to generating tokens, becoming new infrastructure.

I feel that current investments in AI infrastructure are around tens of billions of dollars, but future annual investments will reach trillions.

Huang mentioned that autonomy for everything capable of movement is not far off.

Every company producing machines will have two factories: one produces the physical machine (e.g., cars), and the other is an “AI factory” dedicated to developing AI for these machines.

For example, building humanoid robots requires an AI factory to create their “brains.” In the future, every company will essentially have two factories; this is the future of industry. Tesla already has two such factories. Elon realized early on that a large-scale AI factory was necessary to support his automotive business.

AI is already present in cars. In the future, air traffic control may no longer require extensive remote monitoring by humans but instead be managed by a massive AI system, with human intervention only occurring when the AI cannot handle a situation.

So, in the future, every industrial company will become an AI company; otherwise, it won’t survive.

Video Replay:
https://www.youtube.com/watch?v=9WkGNe27r_Q