AI data center growth has created shortages and price hikes in computer hardware related to graphics processing, storage, and memory.

AI is estimated to drive most data center power demand growth through 2030

According to a report from the International Energy Agency, a typical AI data center in 2025 consumes as much electricity as 100,000 households, but the largest under construction are expected to consume 20 times as much. Global data center electricity consumption is expected to surpass Japan's total consumption by 2030.

By 2028, data center power consumption may equal 22% of US households

Date from 2023 suggests that data centers accounted for around 4.4% of US electricity consumption, with new facilities supporting the AI boom consuming more power than the state of New Hampshire annually. However, the energy impact of individual use varies widely depending on the prompt and AI model. (Some readers may experience a paywall.)

AI investment may be the next major economic bubble

A video examines the massive surge in AI spending—forecast to hit $500B by 2026—and how this may be propping up a fragile US economy. Experts warn that if AI investment slows or under-delivers, the consequences could be felt in markets, jobs and growth.

Data center companies are worsening housing undersupply in some regional markets

With large capital to spend, technology giants have disrupted the real estate market, driving up prices by outbidding commercial and residential developers and intensifying competition for available land. A property purchased over several years for $50M in Prince William County, Virginia, was sold to Amazon for $700M in 2025.

Research suggests AI data centers may be creating microclimate heat islands

An analysis of satellite data from 2004 to 2024 showed that land surface temperatures in the areas surrounding AI data centers typically increase by an average of 2 degrees Celsius (3.6 degrees Fahrenheit) after they begin running. Across the 6,000 global data centers analyzed by the study, temperatures increased by as much as 9.1 degrees Celsius (16.4 degrees Fahrenheit).

AI's growing energy demands are driving tech companies to consider nuclear power

Big Tech has rebranded nuclear power as a green solution to address the strain on the grid from millions of people using power-hungry AI tools. As of mid-2025, generating one image uses as much electricity as charging the average smartphone, or leaving a household light bulb on for 87 consecutive days.

Before they became the key hardware powering AI, graphics processing units were built to render graphics in video games.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

Data center power demands have delayed the retirement of coal plants

US data center power consumption is expected to triple by 2035 to meet the growing infrastructure needs of AI platforms. Longer construction times and higher startup costs for renewable energy systems such as wind and solar have led to at least 15 coal power plant closures being postponed.

Artificial intelligence might make companies too efficient—which could trigger deflation

AI innovation will likely decrease costs for companies and lead to a large increase in supply. That dramatic increase could conceivably bring prices down but keep supply high. However, AI's real impact will likely be complicated by its impact on human labor and the cost of new technology.

Electrical grids worldwide must double their power delivery capacity by 2050 to meet surging demand from AI and electric vehicles.

Visualizing the AI investment wave

This data visualization from Reuters shows how massive AI investment has become by December 2025—revealing that, in today’s dollars, spending has surpassed the combined total of the Apollo program and the Manhattan Project.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

OpenAI CEO Sam Altman has called OpenAI 'the most capital-intensive startup in history'

As of 2024, Silicon Valley was set to spend $1T in the coming years building AI. As companies, including Google and Microsoft, fling billions at AI dreams, concerns are rising about whether that money will ever be recouped. To learn about the argument that there is an AI bubble—and that it could soon burst if AI doesn't prove its worth—read this article.

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

Today's massive data centers evolved from single-room mainframes

Punch card computers from the late 1940s were used to store and process a company's data locally before the advent of desktop computers. As the amount of data and company sizes increased alongside the miniaturization of technology, mainframes were replaced by local server rooms and external facilities with interconnected servers running cloud-based and AI software.

Modular data centers can be transported on a truck for rapid deployment

Bypassing the complications involved in building larger facilities over the course of several years, such as construction costs and permits, these pre-fabricated, self-contained pods, roughly the size of shipping containers, can carry thousands of graphics processing units for small- and medium-scale AI-related tasks.

In 2025, American tech companies will spend $300B to $400B on AI

That is, in nominal dollars, more than any group of companies has ever spent to do anything. Notably, these companies are not remotely close to earning $400B on artificial intelligence.

Grace Hopper warned against prioritizing hardware purchases in future computing

Foreseeing the rising volume of data and the increasing demand for its processing and access, she advocated that organizations first map the total flow of information they require and then choose infrastructure to support it, rather than wastefully spending on the fastest computing technology and forcing software to compensate for unnecessary hardware or incorrect data.

Nvidia's rapid development of GPUs is forcing computing to depart from something called Moore's law

Moore’s law, named after Intel’s cofounder, predicts that the number of transistors on a chip will double every two years. But Nvidia could be outpacing that prediction.

In 2025, sovereign wealth funds invested more than $66B into artificial intelligence

Developing artificial intelligence, both from a hardware and software perspective, is time and resource-intensive. Sovereign wealth funds, particularly those in the Middle East, have heavily invested in critical AI infrastructure, like data centers.

Through prompt injection attacks, bad actors provide deceptive prompts to generative AI systems in order to manipulate their outputs and acquire sensitive data.

Cryptocurrency and data center growth are expected to increase greenhouse gas emissions

An analysis from the Open Energy Outlook Initiative estimates a 30% increase in emissions through 2030, as growing energy demands in regions where data centers are being built (20%-30%) far exceed the traditional growth rate of utilities in those areas (1%-2%). The emissions spike is expected to equal France's annual carbon output.

AI expert shows the cyclical nature of AI hype—in 1988

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

As of 2025, artificial intelligence systems remain narrow and specialized

Efforts are ongoing to create artificial general intelligence, where machines outperform humans across a wide range of cognitive tasks, including those for which they have not been specifically trained. Current models utilize machine learning and neural networks to perform tasks for which they are programmed, leveraging provided training data.

AI-powered models can provide low-cost, localized and accurate weather forecasting

These models can run on standard laptops, reducing the need for expensive supercomputers and expanding access to developing regions. AI forecasts can help farmers make informed planting decisions, improving crop yields and reducing costs.

People are experiencing AI 'brain fry' at work

Workers, particularly in marketing, are experiencing mental fatigue in the workplace due to expectations—often unspoken—that they should use AI to multitask beyond their comfort levels. Many feel they are underperforming if they don't embrace this technology. This situation differs from typical burnout. This podcast discussion suggests that this "brain fry" tends to intensify when employees are required to use more than three AI tools.

AI scraping for LLM training data has significantly strained Wikipedia's infrastructure

From January 2024 to April 2025, the site's bandwidth increased by 50% as automated bots downloaded terabytes of data for the large language models powering AI tools. The Wikimedia Foundation found that bots accounted for 65% of the highest demand requests (e.g., videos) despite representing just 35% of page views.

The major AI industry players are creating a web of circular investments

Bloomberg created a network diagram that shows how companies like Nvidia and OpenAI are fueling the AI money machine. The chart shows that many top AI companies provide significant investments to one another.

DeepSeek is a Chinese AI startup that rattled US competitors when it made an AI model much faster and cheaper than they did

DeepSeek unveiled an AI model in 2025 that it said was made in two months for roughly $6M. For context, OpenAI has suggested it took over $100M to make its AI model GPT-4. The news rattled the stocks of American tech companies, including leading chipmaker Nvidia.

By 2024, Nvidia had become one of the world’s most valuable companies

Nvidia started as a graphics chip designer for PCs and consoles. This explainer traces its rise from early GPU innovation to powering AI breakthroughs and fueling the data centers behind today’s biggest tech platforms.

Technologies labeled “AI” have historically lost that title after widespread adoption

Generative AI is the latest entry in a recurring cycle where emerging tools start as "AI" until they become common software, like databases or machine learning. Generative AI and large language models may be the next platform shift after smartphones and the Web.

AI excels at assisting with structured tasks involving logistics and planning

Tools like ChatGPT, Gemini, and Perplexity can help organize chaotic calendars, troubleshoot bedtime routines, and assist with salary planning and moving. However, specific quotes, like insurance, and local cost comparisons can be flawed since AI draws from nonlocalized datasets.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

Google’s CEO has warned that today’s AI boom shows ‘elements of irrationality’

An interview with Alphabet CEO Sundar Pichai warns that the trillion-dollar surge in AI valuations may contain clear signs of irrationality, echoing past tech bubbles. He says no firm—including Google—would be immune if the boom cools or bursts.

Nvidia entered its AI era after learning that its chips could be used in industries beyond gaming

Its semiconductor chips were used in the automotive industry, for data centers, and by crypto miners. And starting in 2006, Nvidia took steps that would ultimately transform it into the AI company it's known as today. This podcast is the second part of a three-part series diving deep into Nvidia's company evolution.

Research suggests using AI for schoolwork may harm critical thinking skills

An MIT study finds lower brain engagement in students who used ChatGPT, resulting in consistent underperformance in neural, linguistic and behavioral levels. AI users struggled to recall their own essays, while the brain-only group showed the highest neural connectivity and satisfaction.

The Anthropic Economic Index shows AI adoption is highest in computer science roles

The analysis of millions of anonymized conversations on Claude aims to reveal how AI is being integrated into real-world tasks across the labor market. As of November 2025, 52% of conversations were classified as involving enhancing existing work products, while 45% involved using Claude for automation.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

AI is transforming the marketing and advertising industries

Many marketers argue that AI is useful in an ad agency setting. Others attribute the significant layoffs the industry has faced in recent years, at least in part, to AI. Agency leaders and big tech firms alike have supported experimenting with artificial intelligence’s capacity to create content once reserved for agency creatives.

NVIDIA named several computer chip architectures after Grace Hopper

Unveiled in 2023, the GH200 Grace Hopper Superchip, designed for generative AI applications in data center-scale systems, combines a Grace CPU with a Hopper GPU to create a coherent memory model. This allows the Hopper to access Grace data directly, reducing the need to repeatedly copy data back and forth and vastly improving performance.

Generative AI may automate nearly 10% of tasks in the US economy

This automation is expected to impact both low- and high-wage roles, with those in customer service, food service, and office support being most likely to be affected. Large-scale investments in upskilling will be necessary to help individuals find new employment opportunities.

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

'Context rot' explains why AI conversations degrade the longer they run

Research shows that as a large language model's context window fills up, performance declines, and models begin over-indexing on content at the start and end of a conversation while losing track of what's in the middle. The practical fix is simpler than you'd think: start a fresh chat more often.

Tour Brother Zhang's repair shop to see how graphics processing units are fixed

The workshop repairs 40 to 50 GPUs from across China daily, extending the life of the costly computer parts that are the lifeblood of the hardware powering AI tools. By salvaging working components of different GPUs, custom parts can be built atop printed circuit boards, including custom GPUs that manufacturers do not currently produce.

Smart pricing algorithms can unintentionally work together to raise prices

Researchers have found that, through trial and error, algorithms designed to adjust prices based on market data can learn to mimic price-fixing tactics, even without explicit programming to do so. Over time, they "learn" that it is more profitable to avoid price wars and keep prices high when competitors lower prices.

Linux adoption statistics, including its use in all the world's top 500 supercomputers since 2017

This has been driven by kernel-level customizations available with Linux-based operating systems that improve computational performance and reduce latency. As of 2025, Linux's desktop market share is 3.7%, but 87.8% of machine learning workloads run on Linux infrastructure. The Linux OS market is expected to grow 105.8% through 2029.

Moore's Law came from observing exponential computing growth in the 1960s

In 1965, Gordon Moore, cofounder of Intel Corporation, observed a four-year trend of constant growth rate in computing power. Hypothesizing the trend would continue, his observation became the benchmark that the computing industry has worked to maintain for 60 years.