Nigeria has emerged as Africa’s largest exporter of crude oil to the United States in the first eight months of 2025, a development that underscores growing relevance in the U.S. energy supply chain even as its domestic refining capacity expands and reshapes regional oil flows.

Data released by the U.S. Mission show that between January and August 2025, Nigeria shipped 33.23 million barrels of crude oil to the United States, valued at about $2.57 billion. The volume represented more than half of all crude oil exports from Africa to the U.S. during the period, placing Nigeria well ahead of other African producers supplying the American market.

In a post on its official X handle, the U.S. Mission described Nigeria as the leading African exporter of crude oil to the United States over the period, noting that the strong trade relationship “creates jobs and drives prosperity on both sides of the Atlantic.”

Strong exports despite shifting trade patterns

The strong export performance comes at a time of notable changes in Nigeria–U.S. petroleum trade. In a historic reversal earlier this year, Nigeria briefly imported more crude oil from the United States than it exported, according to figures from the U.S. Energy Information Administration (EIA). This occurred in February and March 2025, reflecting a combination of reduced U.S. East Coast demand for Nigerian crude and rising domestic crude requirements within Nigeria.

EIA data show that U.S. crude exports to Nigeria climbed to 111,000 barrels per day in February and 169,000 b/d in March. Over the same period, U.S. imports from Nigeria dropped to 54,000 b/d and 72,000 b/d, respectively, from 133,000 b/d in January. The shift highlighted how quickly Nigeria’s oil trade dynamics are evolving as local refining capacity expands.

Dangote Refinery and the transition to refined exports

At the center of this transition is the Dangote Refinery, Africa’s largest, with a nameplate capacity of 650,000 barrels per day. The refinery began processing crude oil in January 2024 and is expected to reach full capacity soon. Its growing appetite for crude has altered Nigeria’s internal supply balance, temporarily reducing export volumes at certain points while increasing imports to optimize feedstock blends and logistics.

Looking ahead, analysts expect Nigeria’s crude exports to the U.S. to rise again in 2026, as the Dangote Refinery expands its markets for refined petroleum products, including gasoline, diesel, and jet fuel.

The expansion of refined product exports is also expected to reduce Nigeria’s dependence on imported fuels, free up foreign exchange, and improve the overall efficiency of the oil sector. For the U.S., increased imports of Nigerian refined products would add another layer to the energy relationship, shifting part of the trade from crude-only flows to higher-value petroleum products.

However, Nigeria’s position as the top African crude supplier to the U.S. reinforces its strategic role in transatlantic energy trade at a time when global oil flows are being reshaped by geopolitics, refinery closures in parts of the West, and changing demand patterns. Nigerian crude, which is relatively light and low in sulphur, remains attractive to U.S. refiners seeking flexibility and cleaner feedstocks.

For Nigeria, sustained demand from the U.S. provides a critical source of foreign exchange and revenue, supporting public finances and investment in upstream production, pipelines, and export terminals. It also strengthens bilateral economic ties, opening the door to deeper cooperation in energy infrastructure, technology, and downstream investments.

More broadly, the 2025 export figures point to the resilience of Nigeria’s oil sector despite persistent challenges, including production disruptions, security concerns, and global price volatility. Crude oil production has notably increased over the past year, with about 2mbpd being targeted in 2026.

Amazon is broadening the scope of its AI-powered digital assistant Alexa+ as it seeks to transform the long-running voice service into a central gateway for travel bookings, local services, and everyday commerce, a move that underscores how artificial intelligence is reshaping the way consumers interact with online platforms.

The company said on Thursday that Alexa+ will add new integrations with Angi, Expedia, Square, and Yelp beginning in 2026. The new partners will allow users to book hotels, compare prices, manage reservations, request home-service quotes, schedule salon appointments, and interact with local businesses directly through conversational prompts.

With Expedia, users will be able to search for hotels, compare options, and manage bookings, or simply describe their preferences and let Alexa generate recommendations, such as finding pet-friendly accommodation for a weekend trip. Angi’s integration is aimed at home improvement and maintenance, enabling customers to source contractors and request estimates. Square and Yelp will expand Alexa’s role in local commerce, connecting discovery, bookings, and payments for restaurants, salons, and other small businesses.

These additions build on Alexa+’s existing integrations with services including Fodor, OpenTable, Suno, Ticketmaster, Thumbtack, and Uber. Collectively, they represent Amazon’s most concerted effort yet to reposition Alexa from a primarily smart-speaker tool into a full-scale AI concierge capable of handling multi-step tasks across different sectors.

Amazon’s broader ambition is to reduce friction between intent and action. Instead of opening multiple apps or browsing the web, users are meant to rely on Alexa+ as a single conversational interface that can understand context, refine requests through follow-up questions, and complete transactions on their behalf. The assistant is designed to support natural back-and-forth conversations, allowing users to adjust plans or preferences in real time.

The strategy mirrors a wider shift across the technology sector. As generative AI becomes more capable, companies are increasingly treating AI assistants as platforms rather than standalone tools. OpenAI has been moving in a similar direction by integrating third-party services into ChatGPT, while Google has been embedding its Gemini assistant across Android devices and productivity software. For Amazon, which has invested heavily in AI and cloud infrastructure through AWS, Alexa+ is a consumer-facing test of how AI can drive engagement and, eventually, revenue.

Amazon has struggled for years to monetize Alexa despite its presence in millions of households. Voice shopping never scaled as once envisioned, and most interactions remained limited to simple tasks like setting timers or controlling smart home devices. By folding in travel, local services, and commerce, Amazon is betting that AI-driven conversations can unlock higher-value use cases and make Alexa indispensable in daily decision-making.

The company offered limited but telling insight into early usage patterns. According to Amazon, service providers already integrated into Alexa+, such as Thumbtack and Vagaro, have seen strong engagement from early adopters, suggesting that users are willing to experiment with using AI assistants to manage real-world services.

Still, significant hurdles remain. Convincing consumers to change entrenched habits built around mobile apps and websites will not be easy. For AI assistants to gain widespread acceptance as substitutes for apps, they must be at least as reliable, transparent, and fast as traditional interfaces. Any friction, errors, or lack of clarity around pricing and confirmations could quickly erode trust.

Scale is another challenge. Traditional app stores offer vast ecosystems, while AI assistants rely on curated partnerships. Amazon and its rivals will either need to dramatically expand the range of available services or become highly adept at recommending the right service at the right moment. There is also a fine line between helpful suggestions and prompts that users may perceive as advertising, a risk that could undermine adoption if not carefully managed.

The expansion of Alexa+ comes at a critical moment for Amazon. Competition in consumer AI is intensifying, and rivals are racing to define how people will interact with digital services in an AI-first era. By embedding Alexa more deeply into commerce, travel, and local services, Amazon is signaling that it sees conversational AI not just as a feature, but as a foundational layer for the next phase of the internet.

If successful, Alexa+ could finally give the e-commerce giant a return on its years-long investment in voice technology.

The next phase of the artificial intelligence boom will be defined less by who builds the biggest data centers and more by who figures out how to use far less power to achieve comparable results, according to former Facebook chief privacy officer Chris Kelly.

Speaking on CNBC’s Squawk Box on Tuesday, Kelly said the industry’s current obsession with scale is colliding with economic reality, as the cost of power, chips, and infrastructure rises sharply alongside mounting pressure on already stretched electricity grids.

“We run our brains on 20 watts. We don’t need gigawatt power centers to reason,” Kelly said. “I think that finding efficiency is going to be one of the key things that the big AI players look to.”

Kelly, who also served as Facebook’s general counsel, said companies that deliver breakthroughs in reducing data center and compute costs will ultimately emerge as the winners of the AI race. In his view, the arms race to build ever-larger facilities packed with high-end GPUs is becoming increasingly difficult to justify, even for the industry’s best-funded players.

That warning comes at a time when spending on AI infrastructure is accelerating at an unprecedented pace. The global data center market has seen more than $61 billion in infrastructure dealmaking in 2025 alone, according to S&P Global, as hyperscalers rush to lock down land, power connections, and long-term equipment supply.

OpenAI sits at the center of that expansion. The company has made more than $1.4 trillion in AI-related commitments over the coming years, spanning massive partnerships with Nvidia, Oracle, and data center operator CoreWeave. Much of that capital is earmarked for training and running increasingly sophisticated models that demand enormous computing power.

Yet the scale of these projects has intensified concerns about energy consumption. In September, Nvidia and OpenAI announced a project involving at least 10 gigawatts of data center capacity. That level of power demand is roughly equivalent to the annual electricity consumption of about 8 million U.S. households and is close to New York City’s peak summer electricity demand in 2024, according to the New York Independent System Operator.

As utilities struggle to keep up with surging demand from AI facilities, questions are growing about where the power will come from, how fast the new generation can be brought online, and whether grids can remain reliable. For AI developers, electricity is increasingly becoming a strategic constraint, alongside access to advanced chips.

Cost concerns have been sharpened further by developments in China. In December 2024, Chinese startup DeepSeek released a free, open-source large language model that it said was developed for under $6 million, a figure that stood in stark contrast to the vast sums associated with U.S. AI projects. While the company’s claims have been closely examined by industry experts, the episode reinforced the idea that advanced AI may not always require massive budgets and sprawling infrastructure.

Kelly said these dynamics are likely to propel Chinese firms into a more prominent position in the next phase of AI development. He pointed to President Donald Trump’s recent decision to approve the sale of Nvidia’s H200 chips to China, a move that could significantly expand the country’s access to cutting-edge compute hardware.

“I think you’re going to see a number of Chinese players come to the fore,” Kelly said, adding that open-source models, particularly from China, could give users access to basic levels of compute as well as generative and agentic AI at far lower cost than proprietary systems.

Such a shift would carry wide implications for the global AI landscape. If efficiency gains and open-source approaches reduce the need for enormous capital outlays, the industry could become less concentrated among a small group of cash-rich U.S. firms. At the same time, pressure to curb power consumption could accelerate changes in chip design, model architecture, and software optimization, pushing developers to prioritize smarter, leaner systems over brute-force scale.

Kelly was pointing at a growing tension at the heart of the AI boom. While capital continues to pour into data centers and infrastructure, the limits imposed by energy supply and cost are becoming harder to ignore. As the industry matures, he suggests, the defining question will no longer be who can build the biggest machines, but who can deliver intelligence more efficiently, sustainably, and at a fraction of today’s power bill.