Business
A.I. Computing Power Is Splitting the World Into Haves and Have-Nots
Where A.I. Data Centers Are Located
Only 32 nations, mostly in the Northern Hemisphere, have A.I.-specialized data centers.
Last month, Sam Altman, the chief executive of the artificial intelligence company OpenAI, donned a helmet, work boots and a luminescent high-visibility vest to visit the construction site of the company’s new data center project in Texas.
Bigger than New York’s Central Park, the estimated $60 billion project, which has its own natural gas plant, will be one of the most powerful computing hubs ever created when completed as soon as next year.
Around the same time as Mr. Altman’s visit to Texas, Nicolás Wolovick, a computer science professor at the National University of Córdoba in Argentina, was running what counts as one of his country’s most advanced A.I. computing hubs. It was in a converted room at the university, where wires snaked between aging A.I. chips and server computers.
“Everything is becoming more split,” Dr. Wolovick said. “We are losing.”
Artificial intelligence has created a new digital divide, fracturing the world between nations with the computing power for building cutting-edge A.I. systems and those without. The split is influencing geopolitics and global economics, creating new dependencies and prompting a desperate rush to not be excluded from a technology race that could reorder economies, drive scientific discovery and change the way that people live and work.
The biggest beneficiaries by far are the United States, China and the European Union. Those regions host more than half of the world’s most powerful data centers, which are used for developing the most complex A.I. systems, according to data compiled by Oxford University researchers. Only 32 countries, or about 16 percent of nations, have these large facilities filled with microchips and computers, giving them what is known in industry parlance as “compute power.”
The United States and China, which dominate the tech world, have particular influence. American and Chinese companies operate more than 90 percent of the data centers that other companies and institutions use for A.I. work, according to the Oxford data and other research.
In contrast, Africa and South America have almost no A.I. computing hubs, while India has at least five and Japan at least four, according to the Oxford data. More than 150 countries have nothing.
Today’s A.I. data centers dwarf their predecessors, which powered simpler tasks like email and video streaming. Vast, power-hungry and packed with powerful chips, these hubs cost billions to build and require infrastructure that not every country can provide. With ownership concentrated among a few tech giants, the effects of the gap between those with such computing power and those without it are already playing out.
The world’s most used A.I. systems, which power chatbots like OpenAI’s ChatGPT, are more proficient and accurate in English and Chinese, languages spoken in the countries where the compute power is concentrated. Tech giants with access to the top equipment are using A.I. to process data, automate tasks and develop new services. Scientific breakthroughs, including drug discovery and gene editing, rely on powerful computers. A.I.-powered weapons are making their way onto battlefields.
Nations with little or no A.I. compute power are running into limits in scientific work, in the growth of young companies and in talent retention. Some officials have become alarmed by how the need for computing resources has made them beholden to foreign corporations and governments.
“Oil-producing countries have had an oversized influence on international affairs; in an A.I.-powered near future, compute producers could have something similar since they control access to a critical resource,” said Vili Lehdonvirta, an Oxford professor who conducted the research on A.I. data centers with his colleagues Zoe Jay Hawkins and Boxi Wu.
A.I. computing power is so precious that the components in data centers, such as microchips, have become a crucial part of foreign and trade policies for China and the United States, which are jockeying for influence in the Persian Gulf, in Southeast Asia and elsewhere. At the same time, some countries are beginning to pour public funds into A.I. infrastructure, aiming for more control over their technological futures.
The Oxford researchers mapped the world’s A.I. data centers, information that companies and governments often keep secret. To create a representative sample, they went through the customer websites of nine of the world’s biggest cloud-service providers to see what compute power was available and where their hubs were at the end of last year. The companies were the U.S. firms Amazon, Google and Microsoft; China’s Tencent, Alibaba and Huawei; and Europe’s Exoscale, Hetzner and OVHcloud.
The research does not include every data center worldwide, but the trends were unmistakable. U.S. companies operated 87 A.I. computing hubs, which can sometimes include multiple data centers, or almost two-thirds of the global total, compared with 39 operated by Chinese firms and six by Europeans, according to the research. Inside the data centers, most of the chips — the foundational components for making calculations — were from the U.S. chipmaker Nvidia.
“We have a computing divide at the heart of the A.I. revolution,” said Lacina Koné, the director general of Smart Africa, which coordinates digital policy across the continent. He added: “It’s not merely a hardware problem. It’s the sovereignty of our digital future.”
‘Sometimes I Want to Cry’
There has long been a tech gap between rich and developing countries. Over the past decade, cheap smartphones, expanding internet coverage and flourishing app-based businesses led some experts to conclude that the divide was diminishing. Last year, 68 percent of the world’s population used the internet, up from 33 percent in 2012, according to the International Telecommunication Union, a United Nations agency.
With a computer and knowledge of coding, getting a company off the ground became cheaper and easier. That lifted tech industries across the world, be they mobile payments in Africa or ride hailing in Southeast Asia.
But in April, the U.N. warned that the digital gap would widen without action on A.I. Just 100 companies, mostly in the United States and China, were behind 40 percent of global investment in the technology, the U.N. said. The biggest tech companies, it added, were “gaining control over the technology’s future.”
Few Companies Control A.I. Computing
Tiles show total availability zones for A.I. offered by each company, a metric used by researchers as a proxy for A.I. data centers.
The gap stems partly from a component everyone wants: a microchip known as a graphics processing unit, or GPU. The chips require multibillion-dollar factories to produce. Packed into data centers by the thousands and mostly made by Nvidia, GPUs provide the computing power for creating and delivering cutting-edge A.I. models.
Obtaining these pieces of silicon is difficult. As demand has increased, prices for the chips have soared, and everyone wants to be at the front of the line for orders. Adding to the challenges, these chips then need to be corralled into giant data centers that guzzle up dizzying amounts of power and water.
Many wealthy nations have access to the chips in data centers, but other countries are being left behind, according to interviews with more than two dozen tech executives and experts across 20 countries. Renting computing power from faraway data centers is common but can lead to challenges, including high costs, slower connection speeds, compliance with different laws, and vulnerability to the whims of American and Chinese companies.
Qhala, a start-up in Kenya, illustrates the issues. The company, founded by a former Google engineer, is building an A.I. system known as a large language model that is based on African languages. But Qhala has no nearby computing power and rents from data centers outside Africa. Employees cram their work into the morning, when most American programmers are sleeping, so there is less traffic and faster speeds to transfer data across the world.
“Proximity is essential,” said Shikoh Gitau, 44, Qhala’s founder.
“If you don’t have the resources for compute to process the data and to build your A.I. models, then you can’t go anywhere,” said Kate Kallot, a former Nvidia executive and the founder of Amini, another A.I. start-up in Kenya.
In the United States, by contrast, Amazon, Microsoft, Google, Meta and OpenAI have pledged to spend more than $300 billion this year, much of it on A.I. infrastructure. The expenditure approaches Canada’s national budget. Harvard’s Kempner Institute, which focuses on A.I., has more computing power than all African-owned facilities on that continent combined, according to one survey of the world’s largest supercomputers.
Brad Smith, Microsoft’s president, said many countries wanted more computing infrastructure as a form of sovereignty. But closing the gap will be difficult, particularly in Africa, where many places do not have reliable electricity, he said. Microsoft, which is building a data center in Kenya with a company in the United Arab Emirates, G42, chooses data center locations based largely on market need, electricity and skilled labor.
“The A.I. era runs the risk of leaving Africa even further behind,” Mr. Smith said.
Jay Puri, Nvidia’s executive vice president for global business, said the company was also working with various countries to build out their A.I. offerings.
“It is absolutely a challenge,” he said.
Chris Lehane, OpenAI’s vice president of global affairs, said the company had started a program to adapt its products for local needs and languages. A risk of the A.I. divide, he said, is that “the benefits don’t get broadly distributed, they don’t get democratized.”
Tencent, Alibaba, Huawei, Google, Amazon, Hetzner and OVHcloud declined to comment.
The gap has led to brain drains. In Argentina, Dr. Wolovick, 51, the computer science professor, cannot offer much compute power. His top students regularly leave for the United States or Europe, where they can get access to GPUs, he said.
“Sometimes I want to cry, but I don’t give up,” he said. “I keep talking to people and saying: ‘I need more GPUs. I need more GPUs.’”
Few Choices
The uneven distribution of A.I. computing power has split the world into two camps: nations that rely on China and those that depend on the United States.
The two countries not only control the most data centers but are set to build more than others by far. And they have wielded their tech advantage to exert influence. The Biden and Trump administrations have used trade restrictions to control which countries can buy powerful A.I. chips, allowing the United States to pick winners. China has used state-backed loans to encourage sales of its companies’ networking equipment and data centers.
The effects are evident in Southeast Asia and the Middle East.
In the 2010s, Chinese companies made inroads into the tech infrastructure of Saudi Arabia and the Emirates, which are key American partners, with official visits and generous financing. The United States sought to use its A.I. lead to push back. In one deal with the Biden administration, an Emirati company promised to keep out Chinese technology in exchange for access to A.I. technology from Nvidia and Microsoft.
In May, President Trump signed additional deals to give Saudi Arabia and the Emirates even more access to American chips.
A similar jostling is taking place in Southeast Asia. Chinese and U.S. companies like Amazon, Alibaba, Nvidia, Google and ByteDance, the owner of TikTok, are building data centers in Singapore and Malaysia to deliver services across Asia.
Globally, the United States has the lead, with American companies building 63 A.I computing hubs outside the country’s borders, compared with 19 by China, according to the Oxford data. All but three of the data centers operated by Chinese firms outside their home country use chips from Nvidia, despite efforts by China to produce competing chips. Chinese firms were able to buy Nvidia chips before U.S. government restrictions.
Companies and countries throughout the world rely mostly on major American and Chinese cloud operators for A.I. facilities.
Where the World Gets Its A.I.
Even U.S.-friendly countries have been left out of the A.I. race by trade limits. Last year, William Ruto, Kenya’s president, visited Washington for a state dinner hosted by President Joseph R. Biden Jr. Several months later, Kenya was omitted from a list of countries that had open access to needed semiconductors.
That has given China an opening, even though experts consider the country’s A.I. chips to be less advanced. In Africa, policymakers are talking with Huawei, which is developing its own A.I. chips, about converting existing data centers to include Chinese-made chips, said Mr. Koné of Smart Africa.
“Africa will strike a deal with whoever can give access to GPUs,” he said.
If You Build It
Alarmed by the concentration of A.I. power, many countries and regions are trying to close the gap. They are providing access to land and cheaper energy, fast-tracking development permits and using public funds and other resources to acquire chips and construct data centers. The goal is to create “sovereign A.I.” available to local businesses and institutions.
In India, the government is subsidizing compute power and the creation of an A.I. model proficient in the country’s languages. In Africa, governments are discussing collaborating on regional compute hubs. Brazil has pledged $4 billion on A.I. projects.
“Instead of waiting for A.I. to come from China, the U.S., South Korea, Japan, why not have our own?” Brazil’s president, Luiz Inácio Lula da Silva, said last year when he proposed the investment plan.
Even in Europe, there is growing concern that American companies control most of the data centers. In February, the European Union outlined plans to invest 200 billion euros for A.I. projects, including new data centers across the 27-nation bloc.
Mathias Nobauer, the chief executive of Exoscale, a cloud computing provider in Switzerland, said many European businesses want to reduce their reliance on U.S. tech companies. Such a change will take time and “doesn’t happen overnight,” he said.
Still, closing the divide is likely to require help from the United States or China.
Cassava, a tech company founded by a Zimbabwean billionaire, Strive Masiyiwa, is scheduled to open one of Africa’s most advanced data centers this summer. The plans, three years in the making, culminated in an October meeting in California between Cassava executives and Jensen Huang, Nvidia’s chief executive, to buy hundreds of his company’s chips. Google is also one of Cassava’s investors.
The data center is part of a $500 million effort to build five such facilities across Africa. Even so, Cassava expects it to address only 10 percent to 20 percent of the region’s demand for A.I. At least 3,000 start-ups have expressed interest in using the computing systems.
“I don’t think Africa can afford to outsource this A.I. sovereignty to others,” said Hardy Pemhiwa, Cassava’s chief executive. “We absolutely have to focus on and ensure that we don’t get left behind.”
Business
Commentary: A leading roboticist punctures the hype about self-driving cars, AI chatbots and humanoid robots
It may come to your attention that we are inundated with technological hype. Self-driving cars, human-like robots and AI chatbots all have been the subject of sometimes outlandishly exaggerated predictions and promises.
So we should be thankful for Rodney Brooks, an Australian-born technologist who has made it one of his missions in life to deflate the hyperbole about these and other supposedly world-changing technologies offered by promoters, marketers and true believers.
As I’ve written before, Brooks is nothing like a Luddite. Quite the contrary: He was a co-founder of IRobot, the maker of the Roomba robotic vacuum cleaner, though he stepped down as the company’s chief technology officer in 2008 and left its board in 2011. He’s a co-founder and chief technology officer of RobustAI, which makes robots for factories and warehouses, and former director of computer science and artificial intelligence labs at Massachusetts Institute of Technology.
Having ideas is easy. Turning them into reality is hard. Turning them into being deployed at scale is even harder.
— Rodney Brooks
In 2018, Brooks published a post of dated predictions about the course of major technologies and promised to revisit them annually for 32 years, when he would be 95. He focused on technologies that were then — and still are — the cynosures of public discussion, including self-driving cars, human space travel, AI bots and humanoid robots.
“Having ideas is easy,” he wrote in that introductory post. “Turning them into reality is hard. Turning them into being deployed at scale is even harder.”
Brooks slotted his predictions into three pigeonholes: NIML, for “not in my lifetime,” NET, for “no earlier than” some specified date, and “by some [specified] date.”
On Jan. 1 he published his eighth annual predictions scorecard. He found that over the years “my predictions held up pretty well, though overall I was a little too optimistic.”
For example in 2018 he predicted “a robot that can provide physical assistance to the elderly over multiple tasks [e.g., getting into and out of bed, washing, using the toilet, etc.]” wouldn’t appear earlier than 2028; as of New Year’s Day, he writes, “no general purpose solution is in sight.”
The first “permanent” human colony on Mars would come no earlier than 2036, he wrote then, which he now calls “way too optimistic.” He now envisions a human landing on Mars no earlier than 2040, and the settlement no earlier than 2050.
A robot that seems “as intelligent, as attentive, and as faithful, as a dog” — no earlier than 2048, he conjectured in 2018. “This is so much harder than most people imagine it to be,” he writes now. “Many think we are already there; I say we are not at all there.” His verdict on a robot that has “any real idea about its own existence, or the existence of humans in the way that a 6-year-old understands humans” — “Not in my lifetime.”
Brooks points out that one way high-tech promoters finesse their exaggerated promises is through subtle redefinition. That has been the case with “self-driving cars,” he writes. Originally the term referred to “any sort of car that could operate without a driver on board, and without a remote driver offering control inputs … where no person needed to drive, but simply communicated to the car where it should take them.”
Waymo, the largest purveyor of self-driven transport, says on its website that its robotaxis are “the embodiment of fully autonomous technology that is always in control from pickup to destination.” Passengers “can sit in the back seat, relax, and enjoy the ride with the Waymo Driver getting them to their destination safely.”
Brooks challenges this claim. One hole in the fabric of full autonomy, he observes, became clear Dec. 20, when a power blackout blanketing San Francisco stranded much of Waymo’s robotaxi fleet on the streets. Waymos, which can read traffic lights, clogged intersections because traffic lights went dark.
The company later acknowledged its vehicles occasionally “require a confirmation check” from humans when they encounter blacked-out traffic signals or other confounding situations. The Dec. 20 blackout, Waymo said, “created a concentrated spike in these requests,” resulting in “a backlog that, in some cases, led to response delays contributing to congestion on already-overwhelmed streets.”
It’s also known that Waymo pays humans to physically deal with vehicles immobilized by — for example — a passenger’s failure to fully close a car door when exiting. They can be summoned via the third-party app Honk, which chiefly is used by tow truck operators to find stranded customers.
“Current generation Waymos need a lot of human help to operate as they do, from people in the remote operations center to intervene and provide human advice for when something goes wrong, to Honk gig workers scampering around the city,” Brooks observes.
Waymo told me its claim of “fully autonomous” operation is based on the fact that the onboard technology is always in control of its vehicles. In confusing situations the car will call on Waymo’s “fleet response” team of humans, asking them to choose which of several optional paths is the best one. “Control of the vehicle is always with the Waymo Driver” — that is, the onboard technology, spokesman Mark Lewis told me. “A human cannot tele-operate a Waymo vehicle.”
As a pioneering robot designer, Brooks is particularly skeptical about the tech industry’s fascination with humanoid robots. He writes from experience: In 1998 he was building humanoid robots with his graduate students at MIT. Back then he asserted that people would be naturally comfortable with “robots with humanoid form that act like humans; the interface is hardwired in our brains,” and that “humans and robots can cooperate on tasks in close quarters in ways heretofore imaginable only in science fiction.”
Since then it has become clear that general-purpose robots that look and act like humans are chimerical. In fact in many contexts they’re dangerous. Among the unsolved problems in robot design is that no one has created a robot with “human-like dexterity,” he writes. Robotics companies promoting their designs haven’t shown that their proposed products have “multi-fingered dexterity where humans can and do grasp things that are unseen, and grasp and simultaneously manipulate multiple small objects with one hand.”
Two-legged robots have a tendency to fall over and “need human intervention to get back up,” like tortoises fallen on their backs. Because they’re heavy and unstable, they are “currently unsafe for humans to be close to when they are walking.”
(Brooks doesn’t mention this, but even in the 1960s the creators of “The Jetsons” understood that domestic robots wouldn’t rely on legs — their robot maid, Rosie, tooled around their household on wheels, a perception that came as second nature to animators 60 years ago but seems to have been forgotten by today’s engineers.)
As Brooks observes, “even children aged 3 or 4 can navigate around cluttered houses without damaging them. … By age 4 they can open doors with door handles and mechanisms they have never seen before, and safely close those doors behind them. They can do this when they enter a particular house for the first time. They can wander around and up and down and find their way.
“But wait, you say, ‘I’ve seen them dance and somersault, and even bounce off walls.’ Yes, you have seen humanoid robot theater. “
Brooks’ experience with artificial intelligence gives him important insights into the shortcomings of today’s crop of large language models — that’s the technology underlying contemporary chatbots — what they can and can’t do, and why.
“The underlying mechanism for Large Language Models does not answer questions directly,” he writes. “Instead, it gives something that sounds like an answer to the question. That is very different from saying something that is accurate. What they have learned is not facts about the world but instead a probability distribution of what word is most likely to come next given the question and the words so far produced in response. Thus the results of using them, uncaged, is lots and lots of confabulations that sound like real things, whether they are or not.”
The solution is not to “train” LLM bots with more and more data, in the hope that eventually they will have databases large enough to make their fabrications unnecessary. Brooks thinks this is the wrong approach. The better option is to purpose-build LLMs to fulfill specific needs in specific fields. Bots specialized for software coding, for instance, or hardware design.
“We need guardrails around LLMs to make them useful, and that is where there will be lot of action over the next 10 years,” he writes. “They cannot be simply released into the wild as they come straight from training. … More training doesn’t make things better necessarily. Boxing things in does.”
Brooks’ all-encompassing theme is that we tend to overestimate what new technologies can do and underestimate how long it takes for any new technology to scale up to usefulness. The hardest problems are almost always the last ones to be solved; people tend to think that new technologies will continue to develop at the speed that they did in their earliest stages.
That’s why the march to full self-driving cars has stalled. It’s one thing to equip cars with lane-change warnings or cruise control that can adjust to the presence of a slower car in front; the road to Level 5 autonomy as defined by the Society of Automotive Engineers — in which the vehicle can drive itself in all conditions without a human ever required to take the wheel — may be decades away at least. No Level 5 vehicles are in general use today.
Believing the claims of technology promoters that one or another nirvana is just around the corner is a mug’s game. “It always takes longer than you think,” Brooks wrote in his original prediction post. “It just does.”
Business
Versant launches, Comcast spins off E!, CNBC and MS NOW
Comcast has officially spun off its cable channels, including CNBC and MS NOW, into a separate company, Versant Media Group.
The transaction was completed late Friday. On Monday, Versant took a major tumble in its stock market debut — providing a key test of investors’ willingness to hold on to legacy cable channels.
The initial outlook wasn’t pretty, providing awkward moments for CNBC anchors reporting the story.
Versant fell 13% to $40.57 a share on its inaugural trading day. The stock opened Monday on Nasdaq at $45.17 per share.
Comcast opted to cast off the still-profitable cable channels, except for the perennially popular Bravo, as Wall Street has soured on the business, which has been contracting amid a consumer shift to streaming.
Versant’s market performance will be closely watched as Warner Bros. Discovery attempts to separate its cable channels, including CNN, TBS and Food Network, from Warner Bros. studios and HBO later this year. Warner Chief Executive David Zaslav’s plan, which is scheduled to take place in the summer, is being contested by the Ellison family’s Paramount, which has launched a hostile bid for all of Warner Bros. Discovery.
Warner Bros. Discovery has agreed to sell itself to Netflix in an $82.7-billion deal.
The market’s distaste for cable channels has been playing out in recent years. Paramount found itself on the auction block two years ago, in part because of the weight of its struggling cable channels, including Nickelodeon, Comedy Central and MTV.
Management of the New York-based Versant, including longtime NBCUniversal sports and television executive Mark Lazarus, has been bullish on the company’s balance sheet and its prospects for growth. Versant also includes USA Network, Golf Channel, Oxygen, E!, Syfy, Fandango, Rotten Tomatoes, GolfNow, GolfPass and SportsEngine.
“As a standalone company, we enter the market with the scale, strategy and leadership to grow and evolve our business model,” Lazarus, who is Versant’s chief executive, said Monday in a statement.
Through the spin-off, Comcast shareholders received one share of Versant Class A common stock or Versant Class B common stock for every 25 shares of Comcast Class A common stock or Comcast Class B common stock, respectively. The Versant shares were distributed after the close of Comcast trading Friday.
Comcast gained about 3% on Monday, trading around $28.50.
Comcast Chairman Brian Roberts holds 33% of Versant’s controlling shares.
Business
Ties between California and Venezuela go back more than a century with Chevron
As a stunned world processes the U.S. government’s sudden intervention in Venezuela — debating its legality, guessing who the ultimate winners and losers will be — a company founded in California with deep ties to the Golden State could be among the prime beneficiaries.
Venezuela has the largest proven oil reserves on the planet. Chevron, the international petroleum conglomerate with a massive refinery in El Segundo and headquartered, until recently, in San Ramon, is the only foreign oil company that has continued operating there through decades of revolution.
Other major oil companies, including ConocoPhillips and Exxon Mobil, pulled out of Venezuela in 2007 when then-President Hugo Chávez required them to surrender majority ownership of their operations to the country’s state-controlled oil company, PDVSA.
But Chevron remained, playing the “long game,” according to industry analysts, hoping to someday resume reaping big profits from the investments the company started making there almost a century ago.
Looks like that bet might finally pay off.
In his news conference Saturday, after U.S. Special Forces snatched Venezuelan President Nicolás Maduro and his wife in Caracas and extradited them to face drug-trafficking charges in New York, President Trump said the U.S. would “run” Venezuela and open more of its massive oil reserves to American corporations.
“We’re going to have our very large U.S. oil companies, the biggest anywhere in the world, go in, spend billions of dollars, fix the badly broken infrastructure, the oil infrastructure, and start making money for the country,” Trump said during a news conference Saturday.
While oil industry analysts temper expectations by warning it could take years to start extracting significant profits given Venezuela’s long-neglected, dilapidated infrastructure, and everyday Venezuelans worry about the proceeds flowing out of the country and into the pockets of U.S. investors, there’s one group who could be forgiven for jumping with unreserved joy: Chevron insiders who championed the decision to remain in Venezuela all these years.
But the company’s official response to the stunning turn of events has been poker-faced.
“Chevron remains focused on the safety and well-being of our employees, as well as the integrity of our assets,” spokesman Bill Turenne emailed The Times on Sunday, the same statement the company sent to news outlets all weekend. “We continue to operate in full compliance with all relevant laws and regulations.”
Turenne did not respond to questions about the possible financial rewards for the company stemming from this weekend’s U.S. military action.
Chevron, which is a direct descendant of a small oil company founded in Southern California in the 1870s, has grown into a $300-billion global corporation. It was headquartered in San Ramon, just outside of San Francisco, until executives announced in August 2024 that they were fleeing high-cost California for Houston.
Texas’ relatively low taxes and light regulation have been a beacon for many California companies, and most of Chevron’s competitors are based there.
Chevron began exploring in Venezuela in the early 1920s, according to the company’s website, and ramped up operations after discovering the massive Boscan oil field in the 1940s. Over the decades, it grew into Venezuela’s largest foreign investor.
The company held on over the decades as Venezuela’s government moved steadily to the left; it began to nationalize the oil industry by creating a state-owned petroleum company in 1976, and then demanded majority ownership of foreign oil assets in 2007, under then-President Hugo Chávez.
Venezuela has the world’s largest proven crude oil reserves — meaning they’re economical to tap — about 303 billion barrels, according to the U.S. Energy Information Administration.
But even with those massive reserves, Venezuela has been producing less than 1% of the world’s crude oil supply. Production has steadily declined from the 3.5 million barrels per day pumped in 1999 to just over 1 million barrels per day now.
Currently, Chevron’s operations in Venezuela employ about 3,000 people and produce between 250,000 and 300,000 barrels of oil per day, according to published reports.
That’s less than 10% of the roughly 3 million barrels the company produces from holdings scattered across the globe, from the Gulf of Mexico to Kazakhstan and Australia.
But some analysts are optimistic that Venezuela could double or triple its current output relatively quickly — which could lead to a windfall for Chevron.
The Associated Press contributed to this report.
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