OpenAI is regaining ground on Anthropic among U.S. businesses, new data from corporate spending platform Ramp shows, offering an early indication that competition between the two leading AI companies is becoming increasingly fluid as enterprises experiment with competing models.

Neither OpenAI nor Anthropic has publicly disclosed the detailed financial information investors will eventually expect to see as the companies move closer to potential initial public offerings. In the meantime, corporate spending data can provide an imperfect but useful window into how businesses are allocating money across AI providers.

Ramp, which provides corporate cards, bill payment and expense management services, tracks spending patterns across more than 70,000 U.S. businesses. Its customers range across industries, although the company’s concentration in technology startups and other venture-backed companies means the data is not representative of the entire American corporate economy.

The latest figures show Anthropic maintaining its lead among Ramp’s paying business customers, but OpenAI is beginning to close the gap.

Anthropic accounted for nearly 44% of spending among the two companies in July, compared with nearly 40% for OpenAI, according to Ramp. The figures measure the share of Ramp’s business customers paying for products from the two AI companies, rather than total revenue or the amount of money spent.

The shift began in May, when Anthropic overtook OpenAI for the first time among Ramp’s paying business users. Anthropic reached 41% at the time, compared with OpenAI’s 39%.

OpenAI has not reclaimed the top position since then. But Ramp economist Ara Kharazian said OpenAI was growing faster among the segment during the third quarter so far, suggesting the gap could narrow again.

“GPT-5.6 Sol is really good, increasingly the choice for developers,” Kharazian said in a post on X, attributing part of OpenAI’s recent momentum to its latest model.

The data provides a useful counterpoint to the idea that Anthropic has established a durable lead in enterprise AI. Anthropic’s rise among businesses has been one of the most closely watched developments in the AI market. Its Claude models have developed a strong following among software developers and companies seeking AI systems for coding, research, and other professional applications.

OpenAI, meanwhile, has historically benefited from ChatGPT’s enormous consumer user base and broad enterprise adoption. The company’s challenge has been converting that early lead into sustained business spending as rivals improve their models and target specific professional workflows.

Ramp’s numbers suggest that enterprise customers remain willing to switch between providers as new models emerge. That creates an important question for investors: how “sticky” is enterprise AI spending?

Traditional enterprise software tends to become deeply embedded in company workflows, creating switching costs that can make customers reluctant to move to competing products. AI may prove different because companies can test several models simultaneously, route different tasks to different systems, and change providers when a new model offers better performance, lower prices, or more favorable terms.

The result could be a more volatile enterprise software market in which model releases have a direct and immediate impact on corporate purchasing decisions.

Anthropic’s lead also needs to be interpreted carefully. Ramp does not disclose the actual dollar value of spending represented by the percentages, and its dataset excludes companies that use competing corporate-spending platforms, including large businesses that manage expenses through providers such as American Express.

That makes Ramp’s figures an indicator of market direction rather than a comprehensive measure of OpenAI or Anthropic’s enterprise revenue.

The composition of Ramp’s customer base also matters. Its concentration among technology companies and startups could make its customers more likely than the broader corporate market to experiment with multiple AI models, adopt new developer tools, and rapidly shift spending following major model launches.

Even with those limitations, the data points to a broader trend that could be more important than the competition between OpenAI and Anthropic themselves: corporate adoption of paid AI services is continuing to expand.

Among Ramp’s customers, the percentage of businesses paying for AI products rose to nearly 56% in July, from more than 50% in March. That means OpenAI and Anthropic can both increase their business revenue even while competing for the same customers and losing relative market share to each other.

Market-share gains do not necessarily mean one company is taking revenue directly from another. If the number of businesses purchasing AI products continues to rise, both providers can expand rapidly while their relative positions fluctuate.

The model race is making that competition even more dynamic.

Companies are now evaluating AI systems based on coding performance, reasoning ability, agentic capabilities, price, latency, security, data controls, and integration with existing software. A model that wins on one of those dimensions can gain adoption quickly, while a rival can recover ground with its next release.

Anthropic’s Fable 5, according to Kharazian, had weaker adoption in Ramp’s data, which he attributed partly to its pricing and regulatory-related data-retention requirements. Anthropic has faced user concerns over its policy requiring Fable users to retain data for 30 days in certain circumstances.

Still, attributing changes in market share to a single model release would be premature. Enterprise purchasing decisions are influenced by a combination of model performance, pricing, procurement policies, security requirements, existing contracts, and how easily a system can be incorporated into a company’s workflows.

The larger takeaway is that the U.S. enterprise AI market is entering a more competitive phase.

The first stage of generative AI adoption was dominated by experimentation, with companies testing ChatGPT and competing systems to determine where the technology could create value. The market is now moving toward a phase in which businesses are paying for AI at scale and evaluating competing models more systematically.

Ramp’s data indicates that this transition is benefiting the market as a whole. More than half of the company’s tracked businesses now pay for AI, and that proportion continues to rise. That expansion could matter for OpenAI and Anthropic as both companies approach a stage where investors will demand greater visibility into their financial performance.

U.S. stock futures came under renewed pressure on Thursday as investors assessed rising oil prices, higher Treasury yields and escalating tensions between Washington and Tehran, adding to concerns about inflation and the outlook for interest rates.

The latest market moves came after U.S. President Donald Trump vowed to impose what he called unprecedented “economic warfare” on Iran and threatened severe financial penalties against countries that continue to support Tehran.

The escalation has added a fresh source of uncertainty for markets already unsettled by a sharp technology-led selloff earlier in the week and a surge in global government bond yields. U.S. stocks ultimately fell sharply on Thursday, with the Dow Jones Industrial Average dropping 1.3%, the S&P 500 losing 0.9% and the Nasdaq Composite declining 1%.

Oil prices were among the clearest signs of the geopolitical risk. Brent crude futures, the international benchmark, rose 1.59% to $93.08 a barrel as of 4:19 a.m. ET, while U.S. West Texas Intermediate futures for September delivery gained 1.63% to $87.23.

The gains followed Trump’s warning that Washington would launch what he described as the “MOST CRUSHING ECONOMIC OPERATION EVER TAKEN AGAINST ANY COUNTRY” against Iran.

Trump said Iran had been given an opportunity to reach a deal but had failed to take it. He also threatened “TREMENDOUS Economic Consequences” against countries providing Tehran with a lifeline, including through cash transfers, currency swaps and shipping registries.

The threat reverberated through oil markets because the confrontation is unfolding around the Strait of Hormuz, one of the world’s most important energy corridors. Any sustained disruption to shipping through the waterway tightens global crude supplies and pushes energy prices higher. Brent ultimately settled Thursday at $93.78 a barrel, while WTI settled at $87.83, with both benchmarks reaching their highest levels since July 24.

The United Arab Emirates added to concerns about the widening economic fallout when it said on Wednesday that it was suspending trade and financial transactions with Iran after saying it had come under fire from the Islamic Republic on Tuesday. The move could further complicate regional trade and financial flows at a time when shipping through the Strait of Hormuz remains severely disrupted.

For investors, the immediate concern is that higher oil prices could feed back into inflation. A prolonged rise in energy costs would make it harder for central banks to ease monetary policy and could force markets to price higher interest rates for longer.

U.S. technology stocks could be significantly impacted by the risk. The sector has led much of this year’s equity rally, supported by enormous spending on artificial intelligence infrastructure, but technology and other growth stocks are sensitive to rising bond yields because their valuations depend heavily on expectations for future earnings.

The bond market has already become a major source of pressure. The 30-year U.S. Treasury yield climbed back above 5.2% on Thursday after falling sharply in the previous session, while the 10-year yield moved back toward 4.7%. The earlier decline in long-term yields had been helped by the U.S. Treasury’s decision to increase purchases of longer-dated government debt, but that relief proved temporary.

The simultaneous rise in oil prices and bond yields creates a difficult environment for equities. Higher oil prices increase inflation risks, while higher Treasury yields raise the discount rate used to value future corporate earnings. Together, they can pressure both corporate margins and stock valuations.

The pressure is acute for the technology sector after semiconductor stocks suffered a major selloff earlier in the week. The Philadelphia Semiconductor Index fell nearly 5% on Tuesday, ending a powerful run fueled by expectations that artificial intelligence demand would continue to drive spending on chips and data centers.

The selloff also raises questions about whether the market’s heavy concentration in AI-related companies has left investors vulnerable to shifts in interest rates. Strong earnings from technology companies and AI hyperscalers helped push the S&P 500 and Dow to record highs earlier this month, but investors have become increasingly focused on whether the enormous capital expenditure associated with AI will generate sufficiently strong returns.

The geopolitical shock is therefore arriving at an already sensitive point for U.S. equities. The market is no longer dealing with a single risk. Investors are simultaneously assessing the sustainability of AI valuations, the direction of inflation, the trajectory of Treasury yields, the U.S. government’s expanding debt burden and the possibility that the Iran conflict could keep energy prices elevated.

The Federal Reserve’s policy outlook has consequently become more difficult to assess. Traders had already reduced expectations for a rate cut in the near term after recent inflation data, while market pricing indicated at least one 25-basis-point rate hike by the end of 2026. A sustained oil-price shock could reinforce the argument for keeping monetary policy restrictive if it begins to feed into broader consumer prices.

The effect could extend beyond Wall Street. Higher oil prices raise transportation and production costs globally, while higher U.S. Treasury yields tend to tighten financial conditions internationally and increase the cost of dollar-denominated borrowing for governments and companies.

The developments also introduce a new risk for the global economy through the energy supply chain. Iran is a major oil producer, while the Strait of Hormuz is strategically important to global energy shipments. A prolonged confrontation that restricts traffic through the waterway could create a combination of higher energy prices and weaker economic growth, a difficult scenario for central banks.

Ant International has launched an upgraded artificial intelligence model designed for financial forecasting, signing partnerships with six major global banks as lenders accelerate efforts to deploy specialized AI systems to manage liquidity, foreign exchange and other balance-sheet risks.

The Singapore-based fintech company on Thursday unveiled the Falcon Time-Series Transformer Model 2.0, an upgraded forecasting system aimed specifically at financial applications. Kelvin Li, Ant International’s general manager of platform technology, said the model has been adopted through partnerships with six major banks, including Citi, HSBC, Deutsche Bank, Standard Chartered and Barclays.

The partnerships underline the growing push by financial institutions to move AI beyond customer-service applications and into core banking functions where more accurate forecasts can directly affect trading, treasury management and capital allocation.

Financial institutions manage large and constantly changing pools of cash across currencies, markets and jurisdictions. Errors in forecasting can leave banks holding excess liquidity that earns little return or force them to obtain funding at higher costs. More accurate predictions of cash flows, foreign exchange movements and liquidity requirements can therefore produce substantial savings.

Li said Falcon 2.0 is designed specifically for such financial scenarios and has advantages over general-purpose AI models.

General-purpose large models have “yet to achieve a universal breakthrough in the financial sector,” Li said, arguing that specialized systems can be better suited to highly structured financial data and forecasting requirements.

Ant International said the model’s forecasting capabilities can reduce foreign-exchange hedging and allocation costs by more than 60%. Such savings could be significant for banks and multinational companies with large cross-border exposures, although the actual benefit will depend on the quality of underlying data, the markets covered and how institutions integrate the technology into their existing risk-management systems.

The launch comes as banks globally increase spending on AI amid pressure to improve productivity and automate complex processes. Financial institutions have been among the largest corporate adopters of AI, using the technology for fraud detection, risk assessment, trading, compliance, customer service and software development.

The next phase is focused on specialized systems capable of operating within tightly controlled financial environments. Unlike consumer-facing generative AI, treasury and risk-management applications require reliable numerical forecasting, explainability, data security, and strict controls over how models influence financial decisions.

Time-series models are relevant to these applications because they are designed to identify patterns and relationships in sequential data. In banking, that can include historical cash flows, currency movements, interest rates, transaction volumes, and other market indicators.

Ant International’s strategy puts it in competition with both established financial-technology providers and technology companies seeking to supply AI infrastructure to banks. The company’s focus on specialized financial models could also allow it to target a market where institutions are reluctant to rely entirely on general-purpose AI because of the consequences of inaccurate outputs.

The move is part of Ant International’s broader international expansion. The company, the overseas affiliate of Chinese fintech group Ant Group, raised $1.2 billion in its latest equity fundraising last month as it seeks to expand its business.

The capital raising gives the company additional resources as competition intensifies for enterprise AI contracts and financial institutions become more selective about the systems they adopt.

For global banks, the appeal of specialized AI is increasingly tied to measurable financial outcomes rather than the technology’s novelty. Forecasting improvements that lower hedging costs, optimize liquidity positions, or improve capital allocation can provide a direct return on AI investment.

The challenge is that financial markets are highly dynamic. Models trained on historical patterns can struggle when market conditions change abruptly, meaning banks are likely to require continuous monitoring, human oversight, and safeguards around AI-generated forecasts.

Ant International’s latest model therefore marks a broader shift in financial AI: from general-purpose experimentation toward specialized systems designed to solve specific, high-value problems inside banks.

As banks deepen their use of AI in treasury and risk management, the ability to demonstrate measurable improvements in forecasting accuracy and operating costs is expected to become a key factor in determining which AI platforms gain widespread adoption across the financial sector.