Nvidia reported record fourth-quarter revenue of $68.1 billion, marking a 73% year-over-year increase, as global demand for artificial intelligence infrastructure continued to surge.

Net income climbed 94% from a year earlier to $43 billion, significantly exceeding analyst expectations and reinforcing Nvidia’s dominant position in AI hardware.

The results extend Nvidia’s streak of outsized quarterly beats during the ongoing AI investment cycle and underscore the company’s central role in powering large-scale AI deployments.

AI Data Center Demand Drives Growth

Nvidia’s data center segment once again accounted for the majority of revenue growth. Demand for advanced GPUs used to train and deploy large language models remained strong, fueled by continued capital expenditure from hyperscale cloud providers and enterprise customers.

Major cloud platforms and AI startups are still expanding high-performance computing capacity at scale, and management emphasized that AI infrastructure spending is still in its early phases.

Gross margins remained elevated, reflecting Nvidia’s strong pricing power and favorable product mix. Analysts note that few companies at Nvidia’s scale are simultaneously sustaining rapid revenue growth and expanding profitability.

Beyond chips, Nvidia also highlighted continued momentum in networking and AI software, reinforcing its strategy of delivering integrated hardware, systems, and software as a unified platform.

Investor Reaction and Market Impact

Investors responded positively to the earnings report, viewing it as further confirmation that AI spending has not meaningfully slowed despite broader market volatility.

The earnings beat supported not only Nvidia shares but also semiconductor peers and the broader AI supply chain ecosystem.

With quarterly profits reaching $43 billion, Nvidia’s scale is increasingly unmatched within the technology sector. The numbers illustrate how deeply embedded the company has become in the global AI buildout.

Still, expectations remain high. Analysts caution that valuation levels across AI-related stocks are elevated, and Nvidia faces growing scrutiny around the sustainability of its growth rates and potential competitive pressure.

For now, however, the company’s results offer tangible evidence that AI demand remains structurally strong. As long as hyperscalers and enterprises continue investing heavily in compute infrastructure, Nvidia appears positioned to remain one of the primary beneficiaries of the AI capital expenditure cycle.

The quarter reinforces a broader market narrative: while debate over an AI bubble persists, Nvidia’s financial performance continues to demonstrate real revenue, real profits, and sustained demand at unprecedented scale.

An unlikely contributor has entered the world of high-energy theoretical physics: ChatGPT.

For decades, physicists believed that a particular interaction involving gluons – the massless particles that carry the strong nuclear force – could never occur. Now, researchers say OpenAI’s latest public model, ChatGPT-5.2 Pro, helped demonstrate that the process is in fact possible, deep within the complex internal structure of protons and neutrons.

The findings were presented last week at the annual meeting of the American Association for the Advancement of Science(AAAS), which publishes Science.

“The ideas are not revolutionary,” said Zvi Bern of the Mani L. Bhaumik Institute for Theoretical Physics at UCLA. “But what is revolutionary is that a machine can do this.”

A 40-Year Puzzle

Gluons bind quarks together to form protons and neutrons, and also bind protons and neutrons into atomic nuclei. The mathematics describing their interactions, known as scattering amplitudes, is notoriously complex.

In simple gluon collisions, physicists long believed that at least two particles had to possess negative helicity (a type of spin orientation). If only one gluon had negative helicity, the scattering amplitude was assumed to be zero , meaning the interaction could not happen.

About a year ago, three theorists identified a loophole: a single negative-helicity gluon might interact with positive-helicity gluons if all particles were traveling in roughly the same direction. Proving it, however, required navigating pages of unwieldy equations.

Andrew Strominger of Harvard University and his collaborators initially thought the calculation would take weeks. Instead, it stretched on for months. Alfredo Guevara of the Institute for Advanced Study eventually discovered a pattern in the equations, but generalizing the result for any number of gluons produced an expression dozens of terms long – too cumbersome to use.

The team suspected a clean, elegant formula was hidden inside the mess. They just couldn’t extract it.

Enter ChatGPT

At the same time, Alex Lupsasca of Vanderbilt University had joined OpenAI’s newly launched OpenAI for Science initiative. After reconnecting with Strominger, his former adviser, he suggested using ChatGPT as a test case.

The researchers fed the complex four-gluon expression into ChatGPT-5.2 Pro. Within about 20 minutes, the model simplified it. They repeated the process for five gluons, then six. In one case, the AI reduced a 32-term expression into a compact product spanning a single line.

Finally, they asked it to generalize the formula for any number of gluons. The system responded within minutes with what it described as an “obvious” generalized expression.

Concerned about possible hallucinations, the team rigorously checked the result. They found no errors.

“All of a sudden, I felt like my machine turned from a machine into a live being,” Strominger said.

To further validate the result, the team submitted the generalized formula to an internal OpenAI research model under development, nicknamed “SuperChat.” After roughly 12 hours of processing, the internal model produced a detailed proof that passed human scrutiny.

A Paradigm Shift?

The paper, posted to arXiv on 12 February, quickly gained attention online and sparked surprise at the AAAS meeting.

“What the OpenAI agent was able to do is impressive,” said Aida El-Khadra of the University of Illinois Urbana-Champaign.

The researchers believe the development could mark a turning point in how theoretical physics is conducted. Guevara suggested AI might soon become as integral to physics as it has to programming – handling routine derivations, checking errors, and accelerating research workflows.

The broader physics community has responded with cautious optimism. While many see AI as a powerful assistant for verification, drafting, and cross-disciplinary synthesis, concerns remain about transparency, training of graduate students, and overreliance on automated systems.

Still, few believe scientists are at risk of being replaced.

“None of this feels to me like scientists will be replaced,” El-Khadra said.

Lupsasca is already looking ahead. He hopes similar techniques could be applied to gravitons – hypothetical quantum particles that mediate gravity – and perhaps even help tackle one of physics’ greatest unsolved problems: reconciling quantum mechanics with gravity.

For now, the result stands as a striking milestone: after 40 years of near-intractable algebra, a large language model helped physicists uncover an elegant formula describing interactions among fundamental massless particles and proved that AI can meaningfully contribute to front-line theoretical research.

Andrej Karpathy says programming has undergone a dramatic shift in just the past two months: not gradual progress, but a clear inflection point.

In a post on X, Karpathy argued that coding agents “basically didn’t work before December and basically work since.” According to him, recent model improvements in quality, long-term coherence, and persistence have made AI agents capable of powering through complex, multi-step tasks — far beyond what was possible only months ago.

A 30-Minute Weekend Project

Karpathy shared a personal example: over a weekend, he set up a local video analysis dashboard for his home cameras by giving an AI agent a single detailed instruction in plain English. The request included logging into his DGX Spark machine, configuring SSH keys, setting up vLLM, downloading and benchmarking Qwen3-VL, building a server endpoint for video inference, creating a web UI dashboard, testing everything, configuring systemd services, and generating a markdown report.

The agent reportedly worked autonomously for around 30 minutes, troubleshooting errors, researching solutions, writing and debugging code, deploying services, and returned with a completed system and documentation. Karpathy said he “didn’t touch anything.”

Just three months ago, he noted, the same work could have taken an entire weekend.

From Typing Code to Orchestrating Agents

Karpathy argues that programming is becoming “unrecognizable.” Instead of writing code line by line in an editor, the default workflow since the invention of computers , developers are increasingly spinning up AI agents, assigning tasks in natural language, and reviewing outputs.

The leverage, he says, comes from building higher-level orchestration systems: long-running agents equipped with tools, memory, and structured instructions that manage multiple coding instances in parallel. He describes this as “agentic engineering,” where the biggest opportunity lies in mastering abstraction layers and task decomposition.

Not Magic, But Disruptive

Karpathy is clear that the systems are not perfect. They require high-level direction, oversight, judgment, and iteration. They perform best on well-specified tasks with clear verification criteria. The skill, he suggests, is learning how to break problems into components that can be reliably delegated to agents while managing the edge cases.

Still, his conclusion is unequivocal: this is not business as usual in software development. And his view is increasingly echoed across the industry – for example, Spotify recently said its best developers haven’t written code in months thanks to generative AI tools, instead focusing on directing and reviewing AI-generated output as the company accelerates product development with internal systems and large language models.

If his assessment proves accurate, the role of the software engineer may be shifting from code writer to task architect,  from syntax to strategy, at a pace far faster than most anticipated.