Africa’s richest person, Aliko Dangote, is pushing a plan to develop another oil refinery – this time, a 700,000-barrel-per-day refinery in Kenya, in East Africa. The move comes less than three years after bringing Africa’s largest oil refinery into operation in Nigeria.

The proposed Lamu refinery, expected to cost between $15 billion and $16 billion, represents one of Dangote Group’s most ambitious projects outside Nigeria. The company plans to hold a groundbreaking ceremony later this month and aims to complete the facility by 2030.

But the project faces a fundamentally different set of challenges from those encountered at Dangote’s Lagos refinery, particularly over crude supply, financing and supporting infrastructure, according to a Reuters report.

Nigeria has abundant oil reserves and an established crude-producing industry. Kenya, by contrast, currently has no commercial-scale oil production, meaning Dangote will need to secure reliable feedstock from neighboring producers or the international seaborne market.

The project’s location has also evolved rapidly.

Until April, discussions centered on Tanzania. In May, Dangote told the Financial Times that he was leaning toward Mombasa, before a company executive said in July that the refinery would instead be built in Lamu, a deep-water port whose maritime access is central to the project’s proposed logistics model.

The refinery is expected to be located within the Lamu Port-South Sudan-Ethiopia Transport, or LAPSSET, special economic zone, linking the facility to one of East Africa’s largest planned transport and energy corridors.

Dangote Industries Vice President Devakumar Edwin said the company did not see regulatory, financing or feedstock challenges as obstacles that could not be overcome. The company has said the refinery would strengthen regional fuel supply and energy security.

Yet the scale of the undertaking has prompted warnings about execution risk.

“If not successfully implemented, it runs the risk of becoming a very expensive white elephant,” said Brendon Verster, senior economist at Oxford Economics.

The central question is whether Dangote can create a reliable supply chain around a refinery whose capacity would be larger than Kenya’s domestic fuel market requires.

Financing A Second Mega-Project

Dangote Group plans to finance the Kenyan refinery through a combination of internal cash flow, bonds and an initial public offering, according to a company executive. The financing strategy could also include equity from Dangote, commercial bank loans and development finance institutions such as Afreximbank, following elements of the financing structure used for the Lagos refinery.

But Dangote is simultaneously committing more capital to its existing Nigerian operations.

The group said Monday that it would spend $14.3 billion to double the processing capacity of its Lagos refinery. The additional investment comes alongside other oil and energy projects being developed by the conglomerate.

That competition for capital could become one of the biggest constraints on the Lamu project.

“Given that the group is seeking some $40 billion (including Lamu) between 2025 and 2030 for announced energy projects, raising the capital for Lamu could become a formidable challenge,” said Kaase Gbakon, a petroleum economist and former employee of Nigeria’s state-owned oil company, NNPC.

Dangote has also suggested that Rwanda, South Sudan, Tanzania and Uganda could collectively take as much as a 30% equity stake in the refinery. Such participation could provide another source of funding while giving governments across the region a direct financial interest in the project.

No details of potential agreements have been disclosed, however.

The planned initial public offering of Dangote’s Lagos refinery is considered crucial to the broader financing strategy. If the listing raises substantial capital, it could provide Dangote with additional funding for expansion while bringing institutional investors into the refinery business.

The Crude Supply Problem

Financing may be difficult, but securing enough crude could prove even more fundamental.

A senior economic adviser to Kenyan President William Ruto has said the refinery could obtain up to 600,000 barrels of crude a day from East African sources, including Kenya, South Sudan and Uganda, according to Kenyan media reports.

The problem is that none of those supply routes is currently straightforward. Kenya has proven oil reserves but has struggled for years to establish commercial production. Small-scale output is expected later this year, but domestic production is nowhere near the volumes required to supply a 700,000-barrel-per-day refinery.

A proposed pipeline connecting oil fields in Kenya’s Lokichar Basin and South Sudan to Lamu could eventually provide a regional supply network, but that infrastructure remains a long-term prospect.

South Sudan also faces major logistical problems. Its crude exports depend on pipelines through Sudan, where conflict has repeatedly disrupted oil flows.

Uganda has oil resources, but its planned exports are being routed toward Tanzania through the East African Crude Oil Pipeline, or EACOP.

“That leaves the coastal facility dependent on a volatile international seaborne market,” said Maximillian Ezeude, an oil and gas lawyer in Lagos.

That dependence could expose the refinery to global freight costs, geopolitical disruptions and crude-price volatility. The issue is particularly significant given that the nearest major sources of seaborne crude are in the Middle East, where the continuing Iran war has disrupted regional energy flows.

For a refinery designed to process hundreds of thousands of barrels each day, supply interruptions could quickly translate into lower utilization and weaker economics.

Lamu Infrastructure Is Still Developing

The refinery also depends on infrastructure that is not yet fully in place. Lamu Port is a critical component of the LAPSSET corridor, but it currently lacks operational oil storage terminals capable of supporting a refinery of the proposed scale.

The LAPSSET plan includes oil storage facilities with capacity of between 1 million and 1.5 million barrels, along with marine-loading infrastructure designed to handle vessels up to Suezmax size.

Much of that supporting infrastructure remains unbuilt. And that creates a sequencing challenge. Dangote needs reliable port access, storage, pipelines and marine facilities to secure crude and distribute refined products, while the wider economic case for developing that infrastructure depends partly on the refinery itself.

The project therefore extends beyond construction of a processing plant as it requires the simultaneous development of an integrated energy logistics network.

Kenya sees the refinery as a potential solution to a major structural weakness in its energy economy. The country’s only refinery was closed by India’s Essar Energy in 2013, leaving Kenya heavily dependent on imported petroleum products.

Kenya spent roughly $4 billion, equivalent to 511.5 billion Kenyan shillings, on petroleum products last year, according to official data. Petroleum was the country’s largest import.

Ruto has argued that a new refinery could reduce that import dependence while supporting industrial development and economic growth.

“We have to make those decisions that will change our country, that will transform our country,” Ruto said of the Lamu project.

A large refinery could also position Kenya as a regional fuel supplier to landlocked markets including Uganda, Rwanda and South Sudan, potentially creating demand beyond the domestic market. That regional opportunity is essential to the economics of the project. A 700,000-barrel-per-day refinery would require access to a much larger market than Kenya alone can provide.

The business case is expected to hinge largely on Dangote’s chances to secure long-term customers across East and Central Africa while competing with imported refined products and other regional refineries.

Environmental And Execution Risks

The Lamu development also faces environmental and community concerns. Lamu Old Town, a UNESCO World Heritage site on Lamu Island, is about 10 kilometers from Lamu Port. Environmental groups have raised concerns about potential habitat destruction and marine degradation associated with industrial development in the area.

Greenpeace Africa has called for the project to be halted over environmental concerns.

Those issues could affect permitting, project timelines and access to some sources of international financing, particularly lenders with stringent environmental, social and governance requirements.

Benjamin Oluwatobi Ajayi, an energy analyst based in Lagos, said the project faces several overlapping execution risks.

“The size of the debt requirement, ESG-related financing constraints, competition for capital across multiple projects, and the need to coordinate numerous lenders and stakeholders within a compressed timeframe all increase execution risk,” he said.

For Dangote, the Lamu refinery is therefore a much broader test than simply building another large processing plant. The Lagos refinery demonstrated that the group could finance and complete a huge energy project in an oil-producing country after years of cost overruns, infrastructure problems and construction challenges. Lamu will test whether that model can work in a country where the crude supply chain, storage infrastructure and regional distribution network still need to be developed.

The proposed facility could reduce East Africa’s dependence on imported refined fuels and establish Kenya as a regional energy hub. But its success will depend on three conditions arriving together: sufficient capital, dependable crude supplies and infrastructure capable of moving hundreds of thousands of barrels a day.

If Dangote can solve those problems, Lamu could become the anchor of a new East African petroleum network. If it cannot, the refinery’s enormous scale could become its biggest vulnerability.

Meta has unveiled Muse, a personal artificial intelligence agent that can carry out everyday tasks on behalf of users, making its most ambitious push yet into consumer AI while raising new questions about privacy and trust.

The launch comes less than two weeks after Meta agreed to an $17 billion multistate settlement over allegations concerning the consumer harms associated with its social media platforms, putting the company’s handling of personal data under renewed scrutiny.

Muse is designed to go beyond conventional AI chatbots, which largely answer questions, generate content, and act as conversational assistants. The new agent is intended to take action across the services people use in their daily lives.

To operate Muse, users can connect individual apps and services, including email, calendars and payments, as well as health and fitness platforms, smart-home systems, dining, shopping, music and event services.

The agent can then perform tasks such as sending emails, booking travel, negotiating lower bills, completing forms, creating plans, turning recipe videos into grocery lists, sending invitations and making purchases.

For transactions, Meta said Muse will use Link by Stripe for checkout. The integration includes purchase protections, a feature that could help address concerns over allowing an AI system to make purchases on a user’s behalf. Meta said integrations with Shopify’s Shop Pay and 1Password are also planned.

The launch marks Meta’s entry into a rapidly developing market for so-called agentic AI, in which software is designed not simply to respond to instructions but to execute multi-step tasks with limited human intervention.

Companies ranging from major technology firms to startups are pursuing similar systems, including AI-powered browsers and assistants capable of taking actions for users. Others are embedding agentic capabilities into communication platforms that consumers already use.

Meta is positioning Muse around user choice. Consumers can connect apps and services individually rather than granting the agent access to everything at once. If a service is not supported by one of Muse’s built-in connectors but provides a public API, Muse can establish a connection using credentials supplied by the user. Where an API is unavailable, the agent can interact with the service through a browser.

Muse is powered by Meta’s Muse Spark AI model and operates inside what the company calls Muse Secure VM, a dedicated virtual machine with its own browser.

Meta said the system has been designed with separate privacy and security controls. A second system, called Sentinel, operates on the same virtual machine but is isolated from Muse at the system level. The company said Muse will not have access to users’ passwords or payment methods. Meta also said conversations and other data generated through Muse will not be shared with its advertising systems.

The safeguards are necessary because an AI agent with access to email, calendars, shopping accounts, financial services and other applications potentially has a much broader view of a person’s life than a conventional chatbot. That creates a different trust equation for consumer AI. A chatbot that produces an inaccurate answer can be corrected or ignored. An agent that can send an email, make a purchase, or change a service on a user’s behalf can create real-world consequences if it misunderstands an instruction or acts on incorrect information.

Privacy concerns have already emerged around other agentic AI products. Early users of the AI assistant Instinct, for example, raised concerns after discovering broad language in its terms that granted the service a “perpetual and irrevocable” license covering user materials, including rights related to access, storage, distribution, and AI-model training.

Meta’s approach is therefore built around limiting access and separating the agent from sensitive authentication information. The company is betting that those controls will give consumers enough confidence to delegate consequential tasks to Muse.

The service will initially be available through the web at muse.ai, as well as iOS and Android applications and chats in WhatsApp. Meta said Muse will also eventually be available through its AI glasses.

Muse will be free initially, although Meta plans to introduce usage limits and paid subscriptions. The company said most users are expected to remain on the free tier. At launch, the paid Power plan will cost $20 a month, while the Maximum plan will cost $100 a month. Both plans provide greater usage capacity for handing everyday tasks to the agent.

Users will be able to monitor their remaining usage through a meter in the app. Meta said users will receive warnings when their free allowance is exhausted and will then have the option to subscribe.

Muse is also designed to continue working after users leave the application. Meta said the system can learn from conversations about what matters to individual users and eventually make suggestions without being explicitly prompted. That feature could make the product more useful, but it also increases the amount of personal information the system may process over time. The more an agent learns about a person’s preferences, schedules, and routines, the more useful its recommendations can become, but the greater the consequences if that information is mishandled.

Meta’s challenge, therefore, extends beyond building a capable AI model. It must persuade consumers that an AI system with access to some of the most sensitive parts of their digital lives can be trusted to act on their behalf.

The broader consumer AI market is moving in that direction. The competition is increasingly shifting from systems that can answer questions to systems that can perform tasks, navigate software, and make decisions within boundaries set by their users.

Washington says DeepSeek, Alibaba and other Chinese AI companies used American models to accelerate development, raising tensions ahead of planned U.S.-China talks

The U.S. government on Tuesday accused Chinese artificial intelligence companies of systematically copying technology from leading American AI firms, escalating an already contentious technology rivalry ahead of a planned meeting between the leaders of the world’s two largest economies later this month.

U.S. law enforcement and intelligence officials alleged that six Chinese AI companies, including DeepSeek, Moonshot AI and Alibaba, used a technique known as distillation to extract capabilities from U.S.-developed AI models and accelerate the development of their own systems.

Distillation is a legitimate AI development technique in which the outputs of a larger, more computationally expensive model are used to train a smaller model. The approach can reduce the cost and time required to develop new AI systems.

U.S. officials, however, accused the Chinese companies of using variants of American AI models in an aggressive and targeted manner to reproduce capabilities developed by U.S. laboratories.

“China-based AI companies are engaging in aggressive, malicious and targeted distillation activities at an industrial scale,” U.S. officials said in a joint statement.

The officials alleged that the companies targeted AI models developed by Anthropic, OpenAI and Alphabet’s Google, as well as technology associated with SpaceX. They said the activity was “likely with Chinese government awareness.”

The allegations add another layer to the increasingly broad U.S.-China technology confrontation, in which artificial intelligence has become strategically important to both countries.

U.S. officials said the alleged distillation activity not only lowers research and development costs for Chinese companies but could also strengthen China’s military and cyber capabilities, potentially creating applications that could be used against the United States and its allies.

The accusations come as the administration of President Donald Trump prepares for Chinese President Xi Jinping’s planned visit to the United States in late September. Washington and Beijing are also preparing for an AI safety dialogue expected to take place in mid-September, making the timing of the allegations particularly sensitive.

The two governments have attempted to maintain channels for negotiations even as disputes over tariffs, semiconductor technology, advanced computing and AI continue to complicate the relationship.

The U.S. previously made a similar accusation in April, underscoring how concerns about Chinese access to American AI capabilities have become a recurring element of the broader technology dispute.

Reuters reported on July 31 that Chinese military researchers had used outputs from leading U.S. AI models to train domestic AI systems and advance Chinese defense capabilities.

Distillation Becomes A New Battleground

The dispute highlights the growing importance of model distillation as AI developers look for ways to obtain sophisticated capabilities without bearing the enormous computing costs associated with training frontier models from scratch.

For Chinese companies, access to the outputs of highly capable American systems could potentially shorten development cycles and reduce the computing resources required to produce competitive models.

But the issue raises a different concern for U.S. AI companies: whether restrictions on access to advanced chips and computing infrastructure can prevent competitors from replicating capabilities indirectly through model outputs.

That could make AI controls increasingly difficult to enforce. Unlike physical semiconductors, model outputs can potentially be generated remotely and transferred digitally, creating new challenges for policymakers attempting to protect intellectual property and restrict the proliferation of advanced AI capabilities.

The U.S. allegations also come at a time when Chinese AI companies have demonstrated an ability to develop highly capable systems despite American restrictions on advanced semiconductor exports.

The resulting competition is now moving beyond the question of which country can build the most powerful models. It is becoming a contest over access to computing, proprietary data, intellectual property, model weights, semiconductor technology and the ability to deploy AI at scale.

The accusations could complicate efforts by Washington and Beijing to stabilize their broader economic relationship before the leaders’ meeting later this month. AI is already one of the most sensitive areas of the technology rivalry because advances in the field have potential commercial, military and cybersecurity applications.

Washington is therefore facing a difficult balance: preserving diplomatic channels with Beijing while attempting to prevent American AI capabilities from being transferred or replicated in ways U.S. officials believe could undermine national-security interests.

Beijing, meanwhile, has incentives to continue developing domestic AI capabilities as it seeks to reduce its dependence on American technology. The situation makes the dispute over distillation more than a disagreement over software-development practices. It is becoming part of a much larger struggle over technological leadership, economic competitiveness and national security.