China’s electric vehicle penetration rate could rise to as much as 80% by 2030, extending the country’s rapid shift away from conventional vehicles and putting further pressure on oil demand in the world’s largest crude oil-importing market.

Electric vehicles are expected to continue gaining market share through the end of the decade, although the pace of expansion is likely to moderate, Fairy Wang, vice president of Sinopec’s Economics and Development Research Institute, said Thursday at the APPEC conference in Singapore.

Wang said EV penetration could reach between 75% and 80% by 2030, compared with 65% in July and just 5% in 2020. The figures include both battery-electric and plug-in hybrid vehicles.

The growth is already having a measurable impact on China’s petroleum consumption.

Sinopec estimates that electric vehicles will displace about 56 million metric tons of oil demand in China this year, equivalent to roughly 1.2 million barrels per day.

“It is equivalent to almost 15% of China’s total demand for refined oil products,” Wang said.

Around two-thirds of the displaced demand comes from gasoline-powered vehicles, while diesel vehicles account for the remaining third.

The figures point to an increasingly important structural change for global oil markets. China has long been one of the largest sources of incremental oil demand, but the rapid electrification of road transport is weakening the link between economic growth, vehicle use and petroleum consumption.

Gasoline Faces The Biggest Pressure

The impact weighs heavily on gasoline because passenger vehicles are at the center of China’s EV transition.

Wang said almost all public transport vehicles in China have already been electrified, meaning future growth will increasingly depend on private vehicles and other segments of road transport.

The 56 million metric tons of oil demand that Sinopec expects EVs to displace this year represents a substantial reduction in potential gasoline and diesel consumption.

As EV penetration rises toward 80%, the displacement effect could grow bigger than it currently is, particularly if China’s vehicle fleet continues to grow while the proportion powered by internal-combustion engines declines.

For oil producers and refiners, that creates a longer-term demand challenge rather than a temporary fluctuation in fuel consumption.

China’s crude imports can still remain substantial because oil is also used to produce petrochemicals, aviation fuel, marine fuels and other products. But weakening transport-fuel demand would change the composition of the country’s petroleum market and potentially reduce one of the most important sources of global oil-demand growth.

Charging Infrastructure Accelerates Adoption

China’s rapid EV adoption has been supported by a combination of government policy, domestic manufacturing capacity and an extensive charging network.

Wang attributed much of the growth to earlier government subsidies and the expansion of charging infrastructure across the country. China now has about 23 million charging stations, according to Wang, with roughly two-thirds located in homes and the remainder in public facilities.

The scale of that infrastructure addresses one of the main barriers to EV adoption: concerns over whether drivers can conveniently recharge their vehicles.

The availability of chargers in both major cities and smaller urban areas has helped make EV ownership increasingly practical, allowing the technology to expand beyond China’s largest metropolitan markets.

The charging network also gives Chinese automakers an important foundation for continued growth as manufacturers compete to increase EV sales and expand the range of models available to consumers.

EV Growth Could Reshape China’s Oil Market

China’s transition is considered consequential because of the country’s position in global energy markets. The country is the world’s largest crude oil importer, meaning changes in its transportation-fuel consumption can have implications well beyond its domestic market.

If EV penetration reaches 75% to 80% by 2030, oil companies could face a substantially different demand environment from the one that existed when China’s economic expansion was driving rapid increases in gasoline and diesel consumption.

The transition will not eliminate China’s oil demand. Heavy transport, aviation, petrochemicals and other industrial applications are likely to remain important consumers of petroleum products.

But the displacement of road-fuel demand removes a major source of growth for refiners and crude suppliers. The distinction between EV penetration and outright oil displacement will also matter. Plug-in hybrids can still consume gasoline, meaning an 80% EV penetration rate does not translate into an 80% reduction in petroleum consumption from road transport.

Even so, the direction of travel is clear. As more kilometers are powered by electricity rather than gasoline or diesel, China’s oil demand becomes more dependent on sectors where electrification is harder.

Oil Market Faces A Structural Shift

The speed of China’s EV transition also indicates why the future of oil demand is becoming harder to forecast using historical relationships between economic growth and fuel consumption.

China’s EV penetration has risen from 5% in 2020 to 65% in July, according to Sinopec’s figures. The projected 75% to 80% level by 2030 would represent another major step in the transformation of the country’s vehicle fleet.

The expected slowdown in the rate of adoption is important, however. Moving from a minority of vehicles to a majority can happen quickly when subsidies, infrastructure and consumer demand reinforce one another. Replacing most of the remaining internal-combustion fleet can be more difficult because the vehicles are often older, cheaper and concentrated in segments where electrification is less straightforward.

For the global oil industry, that means China’s EV boom is unlikely to cause an immediate collapse in petroleum demand. Its greater significance is that it changes the trajectory of future demand. But if the country reaches 75% to 80% EV penetration by 2030, the resulting reduction in gasoline and diesel demand could become one of the most critical structural forces shaping global oil markets over the remainder of the decade.

Consulting firms spent the past two years encouraging employees to use artificial intelligence across their daily work. Now, as AI consumption reaches enormous levels, they are confronting a less visible challenge: how to control the cost of using the technology without undermining its productivity gains.

McKinsey is responding by giving employees greater visibility into how much AI they consume rather than imposing blanket limits on usage.

The consulting firm tracks AI consumption at the individual-user level and sends email alerts when an employee’s usage becomes unusually high, according to Debasish Patnaik, who leads QuantumBlack, McKinsey’s AI, data and analytics group in the UK.

The alert system was introduced across the firm during the summer.

“Similar to using mobile data on a work phone, we tell them this is how you could do things to make it more cost-effective for the firm,” Patnaik said.

The shift points to a broader change taking place among large companies. Early enterprise AI strategies largely focused on getting employees to experiment with generative AI and demonstrate where the technology could improve productivity. As adoption has expanded, companies are discovering that widespread use can generate a substantial and recurring infrastructure bill.

The economics are becoming more complicated as AI providers increasingly charge for consumption. Instead of paying a simple flat subscription for access, companies can incur costs based on the number of tokens an AI model processes. Tokens are the small units of text that models read and generate. That means an employee who makes thousands of AI requests, uses lengthy context, or repeatedly asks a model to process large amounts of information can generate significantly more costs than another employee performing simpler tasks.

OpenAI said in September that its most prolific users of AI coding agents were consuming more than $7,000 worth of tokens a day, illustrating how quickly costs can rise when advanced AI tools are used intensively.

McKinsey’s own consumption demonstrates the scale involved.

By May 2026, the firm was processing about five trillion AI tokens a month, according to a company blog post. Usage was highly concentrated, with roughly 10% of users accounting for about 65% of total consumption. Consultants and software engineers were among the heaviest users.

Rather than interpreting those figures as a reason to restrict access, McKinsey is using them to educate employees about the economics of AI.

“We really believe in giving autonomy to the consultants,” Patnaik said.

The objective is to encourage employees to find more efficient ways of obtaining the same result. An employee might use a smaller model for a relatively simple task, reduce unnecessary context, or avoid repeatedly sending the same information to a model. That approach is based on a simple premise: the cost of AI should be evaluated alongside the value it creates rather than treated as an expense that must be minimized regardless of the outcome.

McKinsey says its internal AI spending has not yet reached a problematic level. Much of the firm’s usage is aimed at improving individual productivity or delivering client engagements more quickly, cases where Patnaik said the benefits still outweigh the costs.

But that calculation could change as usage continues to increase.

“Usage could be more ‘egregious’ in another six months,” Patnaik said, while noting that McKinsey has already introduced additional controls beyond individual usage alerts.

One is an internal AI gateway that optimizes requests before they reach external model providers. The firm has also introduced circuit breakers that can temporarily suspend access when token consumption becomes particularly high, allowing the company to determine whether the usage is generating sufficient value.

Caching provides another way to reduce expenditure. Responses to repeated questions can be reused rather than generated again, while consumption costs can be pooled across the business instead of tying unused capacity to individual licenses.

The measures point to a broader evolution in corporate AI management. Companies are beginning to treat AI consumption more like a variable operating expense that requires monitoring, optimization, and governance.

Other consulting firms are taking similar steps.

EY has established an “AI Value Realization Office” to oversee AI spending and has deployed an “invisible” routing system behind some specialized AI tools. The system directs requests to the model considered most appropriate for a particular task.

EY told Business Insider that the routing system, combined with other governance measures, had reduced token consumption by 60% since April.

At Deloitte, the economics of AI coding tools have also become an issue. A senior software engineer at Deloitte US told Business Insider in June that changes to GitHub’s pricing model were “already wreaking havoc” on expectations for work, with developers quickly exhausting new monthly usage quotas.

The experience of consulting firms offers an early indication of a problem likely to spread across corporate America as AI moves deeper into business operations.

The initial enterprise AI question was whether companies could persuade employees to use the technology. The next question is whether companies can make widespread usage economically sustainable. The questions matter because AI costs do not necessarily rise in line with headcount. A relatively small group of heavy users can generate a disproportionate share of consumption, while increasingly capable models can also require more computing resources.

However, the situation creates a tension between controlling expenditure and preserving productivity for executives. Excessive restrictions could discourage employees from using AI for valuable tasks, while unrestricted usage could allow computational costs to grow faster than the benefits generated.

McKinsey is now taking its experience to clients.

Patnaik’s primary role at QuantumBlack focuses on delivering AI capabilities to clients rather than managing McKinsey’s internal AI use, but the firm established a formal practice this spring to advise companies on deploying AI more cost-effectively.

He said clients have increasingly recognized over the past quarter that there is a “hidden cost that we haven’t completely thought through.”

Companies therefore need to assess the competitive benefits of AI against the returns generated by that spending.

Patnaik argues that businesses should measure the cost of AI per outcome rather than simply calculating expenditure per employee. That distinction matters because reducing token consumption is not necessarily an improvement if the resulting AI system produces weaker work, takes employees longer to complete a task, or creates additional costs elsewhere.

“What you don’t want is to take costs out here, but incur costs on the other side without knowing about it,” he said.

The implication is that enterprise AI is entering a more mature phase. The priority is shifting from maximizing adoption to optimizing the relationship between AI consumption and measurable business outcomes.

For consulting firms that helped popularize corporate AI adoption, the transition is visible. The technology is no longer an experiment sitting on the edge of the organization. At companies such as McKinsey, it is already processing trillions of tokens every month.

The emerging challenge is making sure those tokens translate into enough additional productivity, revenue, or client value to justify the bill.

The rapid expansion of artificial intelligence investment is creating new risks for global financial stability as companies pour trillions of dollars into computing infrastructure, much of it increasingly financed through debt and private credit, according to Bank for International Settlements General Manager Pablo Hernández de Cos.

The scale of the AI buildout is already large enough to influence broader economic conditions, while its effects are reaching beyond technology companies into financial markets, trade, productivity and employment.

For central banks, AI does not alter their monetary policy mandates, but it makes economic conditions more difficult to assess because the technology can affect demand, supply and financial markets at the same time, Hernández de Cos said at a conference hosted by India’s central bank.

The BIS estimates that the world’s five largest technology companies will invest more than $1 trillion in AI between 2025 and 2026. Industry forecasts indicate that global AI investment could rise from about $500 billion currently to as much as $4 trillion by 2030.

“The promise of AI is real,” Hernández de Cos said, while cautioning that its long-term economic impact would depend on policy decisions, investment in skills and infrastructure, and how broadly the gains from the technology are distributed.

The size of the investment cycle is becoming a central concern for policymakers because the financing behind the AI boom is increasingly extending beyond companies’ existing earnings.

Hernández de Cos said more of the investment was being financed through debt and private credit, with much of that funding remaining “opaque and interconnected.” That combination could become a source of vulnerability if expected AI revenues and profits fail to materialize quickly enough to justify current investment levels.

The concern is not that AI investment will necessarily produce a financial crisis. Rather, the scale of spending, high expectations surrounding future returns and increasingly complex financing structures could amplify the effects of a downturn if valuations or corporate earnings fall sharply.

“I do not say that this is where the AI boom must lead,” Hernández de Cos said. “But the scale and speed of the current investment boom, and the weight of expected commercial returns, do warrant some caution.”

He compared the current investment cycle with earlier periods of rapid technological expansion, including the railway boom and the dotcom surge, when expectations surrounding new technologies helped drive substantial investment before economic realities eventually forced valuations and spending patterns to adjust.

The AI boom is also reshaping international trade.

Countries deeply integrated into the technology supply chain, including South Korea, Singapore, Malaysia and Taiwan, have benefited from stronger export prices for AI chips and related equipment. That creates another channel through which AI can influence the global economy. A surge in demand for advanced chips and data-center equipment can boost exports, industrial production and investment in economies that supply the technology, while increasing their exposure to a potential reversal in AI-related demand.

The productivity impact could be substantial if companies successfully integrate AI into their operations.

Hernández de Cos pointed to studies showing productivity gains of between 10% and 65% for specific tasks, particularly in areas such as coding, consulting and professional writing.

Current estimates suggest AI could increase total factor productivity growth by about half a percentage point a year, depending on how quickly businesses adopt the technology and how effectively workers and capital are reallocated toward more productive uses.

The development is of the essence because higher productivity can increase economic output without requiring a proportional increase in labor and capital inputs. But those gains depend on companies redesigning processes, workers acquiring new skills, and economies moving resources away from activities made less productive by technological change.

Advanced economies are expected to benefit first because they have larger service sectors and greater capacity to deploy AI. Emerging markets face more varied prospects.

Hernández de Cos said India has a “genuine opportunity” to narrow the gap, helped by its digital public infrastructure.

The employment effects present a more complicated picture.

AI can increase the productivity of workers by helping them complete tasks more quickly, but it can also automate routine cognitive work. Hernández de Cos said job losses have so far been limited, although signs of disruption are emerging in customer service, programming and administrative roles. That puts greater pressure on governments and businesses to invest in retraining and reskilling as AI adoption expands.

The distribution of the productivity gains will be crucial. Companies that successfully deploy AI may reduce costs and increase output, while workers whose tasks are automated could face weaker demand for their skills. Economies that can move displaced workers into expanding areas of activity are more likely to capture the broader benefits of the technology.

For financial markets, the risks are concentrated in another area: the gap between expectations and earnings.

Hernández de Cos warned that lofty valuations, market concentration and opaque financing structures could create vulnerabilities if corporate profits fail to meet expectations.

The concern is considered relevant because AI investment is increasingly concentrated among a relatively small number of technology companies and infrastructure suppliers. Their spending decisions can affect semiconductor manufacturers, data-center operators, power producers and other parts of the global supply chain.

A slowdown in AI investment could therefore spread beyond the technology sector.

The opposite risk also exists. If AI adoption accelerates faster than expected and companies compete aggressively for computing capacity, electricity and specialized infrastructure, investment could remain elevated for longer, increasing demand pressures in already constrained parts of the economy.

The situation has resulted in a difficult environment for central banks. AI can increase productive capacity over time, potentially reducing inflationary pressure, while the investment boom can simultaneously increase demand for labor, equipment, electricity and construction.

The result is an economy in which the traditional relationship between demand, supply and prices becomes harder to interpret.

For policymakers, the central issue is therefore not whether AI will transform the economy. The technology is already affecting investment, trade and workplace productivity. The harder concern is whether the current pace of spending is supported by sustainable economic returns.

Analysts have noted that if productivity gains spread broadly through the economy, today’s infrastructure spending could ultimately support higher growth and stronger corporate earnings. But if investment runs ahead of commercial returns, the same financial structures supporting the AI buildout could amplify losses when expectations change.

Hernández de Cos’s warning leaves room for both outcomes. The BIS is not predicting an AI-led financial crisis. It is warning that the sheer scale of the boom means policymakers can no longer treat AI as a narrow technology-sector story.

The financial system, labor market and global economy are increasingly becoming part of the AI investment cycle. That makes the sustainability of the boom a macroeconomic issue, not simply a bet on which technology company develops the most capable model.