• Jensen Huang said people programming AI is similar to the way "you program a person."
• Speaking at London Tech Week, the Nvidia CEO said all anyone had to do to program AI was "just ask nicely."
• He called AI "the great equalizer, " allowing anyone to program computers using plain language.

Nvidia CEO Jensen Huang has said that programming AI is similar to "the way you program a person" — and that "human" is the new coders' language.

"The thing that's really, really quite amazing is the way you program an AI is like the way you program a person," Huang told London Tech Week on Monday.

Huang shared an example, saying, "You say, 'You are an incredible poet. You are deeply steeped in Shakespeare, and I would like you to write a poem to describe today's keynote.' Without very much effort, this AI would help you generate such a wonderful poem.

"And when it answers, you could say, 'I feel like you could do even better.' And it will go off and think about it and it will come back and say, 'In fact, I can do better.' And it does do a better job."

Huang said that in the past, "technology was hard to use" and that to access computer science, "we had to learn programming languages, architect systems, and design very complicated computers.

"But now, all of a sudden, there's a new programming language. This new programming language is called human."

"Most people don't know C++, very few people know Python, and everybody, as you know, knows human."

Huang called AI "the great equalizer" for making technology accessible to everyone and called the shift "transformative.

"This way of interacting with computers, I think, is something that almost anybody can do," he said.

"The way you program a computer today is to ask the computer to do something for you, even write a program, generate images, write a poem — just ask it nicely," Huang added.

At the World Government Summit in Dubai last year, Huang suggested the tech sector should focus less on coding and more on using AI as a tool across fields like farming, biology, and education.

"It is our job to create computing technology such that nobody has to program. And that the programming language is human, everybody in the world is now a programmer. This is the miracle of artificial intelligence," Huang said at the time.

When it comes to Nvidia's GeForce RTX 5060 graphics card, the GPU itself is less interesting than the storm Nvidia stirred up by trying to earn it better reviews. If you don’t follow the twists and turns of graphics card launch metanarratives, allow me to recap the company's behavior for you.

Though the RTX 5060 launched on May 19, Nvidia and its partners were uncharacteristically slow to ship graphics cards to reviewers. For outlets that received pre-launch hardware, Nvidia didn’t provide the pre-launch drivers that it usually sends out so that reviewers could run their own tests on the cards, informing reviewers on a call that drivers would be available to them and the public on the 19th.

Except! Nvidia did offer advance drivers to a handful of publications on the condition that they run a few benchmarks that had been pre-selected by Nvidia and that they only report numbers from tests performed with the 50-series new DLSS Multi-Frame Generation (MFG) setting enabled.

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• Nvidia products, such as data center GPUs, are crucial for AI, making it the leader in the industry.
• Nvidia's CUDA software stack supports GPU programming, enhancing its competitive edge.
• Nvidia's automotive and consumer tech ventures expand its influence beyond data centers.

Nvidia products are at the heart of the boom in artificial intelligence.

Despite starting in gaming and designing semiconductors that touch many diverse industries, the products Nvidia designs to go inside high-powered data centers are the most important to the company today, and to the future of AI.

Graphics processing units, designed to be clustered together in dozens of racks inside massive temperature-controlled warehouses, made Nvidia a household name. They also got Nvidia into the Dow Jones Industrial Average, and put it in the position to control the flow of a crucial but finite resource: artificial intelligence.

Nvidia's first generation of chips for the data center launched in 2017. That first generation was called Volta. Along with the Volta chips, Nvidia designed DGX (which stands for Deep GPU Xceleration) systems — the full stack of technologies and equipment necessary to bring GPUs online in a data center and make them work to the best of their ability. DGX was the first of its kind. As AI has become more mainstream, other companies such as Dell and and Supermicro have put forth designs for running GPUs at scale in a data center too.

Ampere, Hopper, Blackwell, and Beyond

The next GPU generation designed for the data center, Ampere, which launched in 2020, can still be found in data centers today.

Though Ampere generation GPUs are slowly fading into the background in favor of more powerful models, this generation did support the first iteration of Nvidia's Omniverse, a simulation platform that the company purports as key to a future where robots work alongside humans doing physical tasks.

The Hopper generation of GPUs is the one that has enabled much of the latest innovation in large language models and broader AI.

Nvidia's Hopper generation of chips, which include the H100 and the H200, debuted in 2022 and remain in high demand. The H200 model in particular has added capacity that has proven increasingly important as AI models grow in size, complexity, and capability.

The most powerful chip architecture Nvidia has launched to date is Blackwell. Jensen Huang announced the step change in accelerated computing in 2024 at GTC, Nvidia's developers conference, and though the rollout has been rocky, racks of Blackwells are now available from cloud providers.

Inside the data center, Nvidia does have competitors, even though it has the vast majority of the market for AI computing. Those competitors include AMD, Intel, Huawei, custom AI chips, and a cavalcade of startups.

The company has already teased that the next generation will be called "Blackwell Ultra," followed by "Rubin" in 2026. Nvidia also plans to launch a new CPU, or traditional computer chip alongside Rubin, which it hasn't done since 2022. CPUs work alongside GPUs to triage tasks and direct the firepower that is parallel computing.

Nvidia is a software company, too

None of this high-powered computing is possible without software and Nvidia recognized this need sooner than any other company.

Development for Nvidia's tentpole software stack, CUDA or Compute Unified Device Architecture, began as early as 2006. CUDA is software that allows developers to use widely known coding languages to program GPUs, since these chips require layers of code to work relatively few developers have the needed skills to program the chips directly.

Still "CUDA developer" is a skillset and there are millions who claim this ability, according to Nvidia.

When GPUs started going into data centers, CUDA was ready and that's why it's often touted as the basis for Nvidia's competitive moat.

Within CUDA are dozens of libraries that help developers use GPUs in specific fields such as medical imaging, data science, or weather analytics.

Nvidia began at home

Just two years after Nvidia's founding, the company released its first graphics card in 1995. For more than a decade, the chips mostly resided in homes and offices — used by gamers and graphics professionals.

The current generation includes the GeForce RTX 5090 and 5080, which was released in May 2025. RTX 4090, 4080, 4070, and 4060, were released in 2022 and 2023. GPUs in gaming enabled the more sophisticated shadows, texture, and light to make games hyperrealistic.

In addition to the consumer work stations, Nvidia partners with device-makers like Apple and ASUS to produce laptops and personal computers. Though gaming is now a minority of the company's revenue, the business continues to grow.

Nvidia has also made new efforts to enable high powered computing at home for the machine-learning obsessed. It launched Project DIGITS, which is a personal-sized supercomputer capable of working with some of the largest large language models.

Nvidia in the car

Nvidia is angling to be a primary player in a future where self-driving cars are the norm, but the company has also been in the automotive semiconductor game for many years.

It launched Nvidia DRIVE, a platform for autonomous vehicle development, in 2015, and over time it developed or acquired technologies for mapping, driver assist, and driver monitoring.

The company designs various chips for all of functions in partnerships with Mediatek and Foxconn. Nvidia's automotive customers include Toyota, Uber, and Hyundai.

On Wednesday, the Trump administration announced plans to rescind and replace a Biden-era rule regulating the export of high-end AI accelerator chips worldwide, Bloomberg and Reuters reported.

A Department of Commerce spokeswoman told Reuters that officials found the previous framework "overly complex, overly bureaucratic, and would stymie American innovation" and pledged to create "a much simpler rule that unleashes American innovation and ensures American AI dominance."

The Biden administration issued the Framework for Artificial Intelligence Diffusion in January during its final week in office. The regulation represented the last salvo of a four-year effort to control global access to so-called "advanced" AI chips (such as GPUs made by Nvidia), with a focus on restricting China's ability to obtain tech that could enhance its military capabilities.

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Nvidia will release the GeForce RTX 5060 on May 19 starting at $299, the company announced via press release today. The new card, a successor to popular past GPUs like the GTX 1060 and RTX 3060, will bring Nvidia's DLSS 4 and Multi Frame-Generation technology to budget-to-mainstream gaming builds—at least, it would if every single GPU launched by any company at any price wasn't instantly selling out these days.

Nvidia announced a May release for the 5060 last month when it released the RTX 5060 Ti for $379 (8GB) and $429 (16GB). Prices for that card so far haven't been as inflated as they have been for the RTX 5070 on up, but the cheapest ones you can currently get are still between $50 and $100 over that MSRP. Unless Nvidia and its partners have made dramatically more RTX 5060 cards than they've made of any other model so far, expect this card to carry a similar pricing premium for a while.

Compared to the RTX 4060, the RTX 5060 adds a few hundred extra CUDA cores and gets a big memory bandwidth increase thanks to the move from GDDR6 to GDDR7. But its utility at higher resolutions will continue to be limited by its 8GB of RAM, which is already becoming a problem for a handful of high-end games at 1440p and 4K.

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• Tech giants are known for their challenging interviews.
• Google, Meta, and Nvidia top the list of rigorous interviews with multiple rounds and assessments.
• But tough questions show up across industries, according to employee reports on Glassdoor.

It's tough to break into high-paying companies.

Google is notorious for having a demanding interview process. Aside from putting job candidates through assessments, preliminary phone calls, and asking them to complete projects, the company also screens candidates through multiple rounds of interviews.

Typical interview questions range from open-ended behavioral ones like "tell me about a time that you went against the status quo" or "what does being 'Googley' mean to you?" to more technical ones.

At Nvidia, the chipmaking darling of the AI boom, candidates must also pass through rigorous rounds of assessments and interviews. "How would you describe __ technology to a non-technical person?" was a question a candidate interviewing for a job as a senior solutions architect shared on the career site Glassdoor last month. The candidate noted that they didn't receive an offer.

Tech giants top Glassdoor's list of the hardest companies to interview with. But tough questions show up across industries — from luxury carmakers like Rolls-Royce, where a candidate said they were asked to define "a single crystal," to Bacardi, where a market manager who cited a difficult interview, and no offer, recalled being asked, "If you were a cocktail what would you be and why?"

The digital PR agency Reboot Online analyzed Glassdoor data to determine which companies have the most challenging job interviews. They focused on "reputable companies" listed in the top 100 of Forbes' World's Best Employers list and examined 313,000 employee reviews on Glassdoor. For each company, they looked at the average interview difficulty rating as reported on Glassdoor.

Here's a list of the top 90 companies that put candidates through the ringer for a job, according to self-reported reviews on Glassdoor.

In the wake of the Switch 2 reveal, neither Nintendo nor Nvidia has gone into any detail at all about the exact chip inside the upcoming handheld—technically, we are still not sure what Arm CPU architecture or what GPU architecture it uses, how much RAM we can expect it to have, how fast that memory will be, or exactly how many graphics cores we're looking at.

But interviews with Nintendo executives and a blog post from Nvidia did at least confirm several of the new chip's capabilities. The "custom Nvidia processor" has a GPU "with dedicated [Ray-Tracing] Cores and Tensor Cores for stunning visuals and AI-driven enhancements," writes Nvidia Software Engineering VP Muni Anda.

This means that, as rumored, the Switch 2 will support Nvidia's Deep Learning Super Sampling (DLSS) upscaling technology, which helps to upscale a lower-resolution image into a higher-resolution image with less of a performance impact than native rendering and less loss of quality than traditional upscaling methods. For the Switch games that can render at 4K or at 120 FPS 1080p, DLSS will likely be responsible for making it possible.

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