Alibaba Shares Rise After Unveiling Upgraded Flagship AI Model
Alibaba Group Holding Ltd. shares rose as much as 5.4% on Monday after the company launched a preview version of its flagship Qwen3.8 Max model, describing it as second only to Anthropic PBC’s Fable 5.
The Sunday release came only days after startup Moonshot AI unveiled a powerful new offering that’s roiled markets and triggered concern in the US about China closing the gap on global leaders like Anthropic and OpenAI. Qwen3.8 Max has 2.4 trillion parameters, joining Moonshot’s Kimi K3 in the heavyweight class. With 2.8 trillion parameters, K3 rivals top offerings and Alibaba is setting similarly high expectations. (…)
Alibaba plans to make the model open-weight soon, expanding access beyond the preview release. Interest in these made-in-China artificial intelligence systems and models is so high that Moonshot was forced to pause taking on new subscriptions late on Sunday to manage overwhelming demand.
While optimism around Alibaba is growing, other contenders in China’s hotly contested AI race have suffered a drop in the wake of the new Kimi release. Rival Zhipu declined nearly 30% on Friday and added a further 14% to the losses on Monday, after being one of the star debut stocks in Hong Kong for much of this year.
Hangzhou-based Alibaba, China’s e-commerce leader and one of its biggest investors in AI, recently scored another victory after Beijing approved Apple Intelligence, the software suite for iPhones, iPads and other Apple Inc. gear, which will use Alibaba technology in the country.
Top American AI Execs Sound Alarm on Chinese Models White House is divided on how to respond to recent advances in Chinese AI, has weighed crackdown measures
Silicon Valley and Washington are debating a multibillion-dollar question: Should American companies be able to use Chinese artificial-intelligence models?
OpenAI and Anthropic executives are sounding the alarm about the rise of cheap AI, particularly powerful new models produced in China, suggesting they will lead to a “dystopian” AI future and present unacceptable security risks without regulation.
Some analysts who study the AI industry say the two companies, which are preparing for public listings in the next year, just want to eliminate the competition.
The emergence of highly capable, open autonomous AI systems—including Moonshot AI’s Kimi K3 model and Alibaba’s Qwen 3.8 Max, which were released in recent days and viewed favorably by investors and users—has turned the AI race on its head once again. (…)
The debate over the new models, which are “open weight,” allowing users to download and customize them with company data and for specific tasks, coincides with division in the Trump administration about whether to take steps to limit the use of the models in the U.S.
“One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a ‘public good’ which will ultimately be provided by the state as a kind of ‘digital public infrastructure,’” Dean Ball, OpenAI’s head of strategic futures, said in an X post Friday. (…)
He also highlighted a central challenge with the AI race: Top-tier AI companies raise billions of dollars to pay for the vast computing resources needed to continue improving AI systems. If everyone uses AI systems that people largely don’t pay for, there would be no way to finance continued frontier AI development. (…)
Chief Executive Sam Altman has previously said OpenAI’s past approach of only developing closed tools was unwise.
Other top AI executives have warned that open models present huge risks, since developers and policymakers lack control over how they are used or modified.
Anthropic CEO Dario Amodei for years has warned about the risks of powerful and open AI systems, saying in a recent Bloomberg interview that having AI models with advanced cybersecurity capabilities that are free to download could be harmful. “It’s a serious concern,” he said in June.
Use of Chinese models, which are far cheaper than U.S. counterparts, is surging at U.S. companies, prompting some investors to question the staying power of top model-makers such as Anthropic and OpenAI.
The current U.S. open-source model frontier is starting to catch up with China’s. On Wednesday, Thinking Machines Lab—led by former OpenAI technology chief Mira Murati—released its first AI model as open weight. Nvidia’s Nemotron 3 Ultra is starting to see traction, and Reflection AI, an Nvidia-backed open-model developer, has close ties to the Trump administration and plans to release its first model later this year.
The market’s faith in Anthropic and OpenAI continuing to build more capable models that push the AI frontier has been at the heart of the boom, helping to justify trillions of dollars in spending on infrastructure in the coming years. The threat that new players will vastly undercut what they can charge for advanced AI pushed down some tech and AI company stock prices last week. (…)
Sacks and others have long seen calls for AI regulation by companies such as Anthropic as efforts to use new laws and policies to stymie competitors. (…)
The CEOs of OpenAI, Anthropic and Alphabet’s DeepMind have recently signaled they support increasing government oversight of AI as models become popular, fueling some criticism that the companies are trying to stifle their competition. Alphabet is the parent company of Google. Demis Hassabis, CEO of Google’s DeepMind lab, recently suggested developers of open models be included in discussions about AI regulation. (…)
Officials who have pushed for oversight of AI have worried that open models could pose cyber and biological-weapon risks if they continue advancing and don’t have to follow the same rules as top U.S. companies such as Anthropic and OpenAI, the people said.
The Trump administration is committed to promoting America’s open-source ecosystem and strengthening its security, a White House official said.
The new focus on the issue shows how the messy policies surrounding open models are challenging CEOs trying to cut their AI bills and policymakers who don’t want the technology used to harm the U.S.
“AI is increasingly synonymous with power and the dual-use concerns are real. But for American businesses and most of the world, being able to run cheap, high-quality models in a way they can control is going to matter a lot,” said Austin Carson, CEO of SeedAI, an AI-policy nonprofit. “If you know about open source, you’d know that you can’t win by exclusion.”
As some companies pump the brakes on AI spending by resorting to cheaper models, others are going all-in on the most advanced AI systems—even with their hefty price tags.
So-called frontier AI models, or the most capable systems made by companies like OpenAI and Anthropic, can be expensive to use partly because they require a lot of compute and process large numbers of tokens, AI’s basic unit of measurement. But these state-of-the-art models are considered the best because they can “reason” through complex, multistep problems and are capable of supporting a variety of tasks, including powering autonomous AI agents.
The calculus often is as much a business decision as an engineering decision. If paying a premium for a frontier model means a better product or an upper hand over rivals, many companies say it’s worth it. (…)
In other words, in the race to build the next, better product, you’ll get there faster with frontier models. (…)
The cost is probably not worth it for simple queries and tasks like summarization and editing, where the difference between frontier and cheaper models is negligible, tech leaders and analysts say. Indeed, there is an ongoing debate over whether AI models are becoming a widely-available commodity.
But for complex reasoning tasks like managing AI agents, advanced coding problems and multistep research, frontier models perform better—even if by a small percentage—and that can make all the difference in outpacing the competition. (…)
Other companies say they’re choosing frontier models because they need top-of-the-line capabilities. (…)
With the cost of AI rising, more companies are using smaller, cheaper models, and open-source or open-weight models. It has also become more popular to use cheaper models for less critical tasks—allowing companies to save on token costs.
At companies like Spotify, it’s an ongoing discussion whether frontier models are worth the cost. (…)
Developers tend to love using frontier models because “they simply work better,” said Philip Walsh, an analyst focused on software engineering at market research and IT consulting firm Gartner. But most companies are trying to find ways to make sure workers use more cost-efficient models or are building AI agents that can take advantage of frontier models for planning tasks, while relegating lower-tier tasks to cheaper models, he said. (…)
Boris Cherny, the head of Anthropic’s Claude Code, said the AI lab allows customers to put spending limits in place and opt for some of Anthropic’s lower-cost models. Customers can also “tune” how much thinking a model does—essentially asking a model for less intelligence, which uses fewer tokens, he said.
“It’s just keeping costs reasonable and predictable,” Cherny said. “But I think actually the far bigger opportunity is increasing return, and I think this is what customers are saying, too. The more tokens that they use in a useful way, the more return they get.”
It is a balancing act between capability, cost and data control. More powerful closed frontier models can be best for critical tasks but most users will lean towards lower cost open (customizable) models for less critical tasks or if data or model control is paramount.
As Global Semi Research explains
(…) what Kimi K3 really proves is not that model companies have no moat, but that the model itself is not the moat.
Raw model capability is becoming commoditized very quickly. A model can top the leaderboard today and be matched by competitors a few months later. Model capability still matters. It determines whether a company can sit at the table. But it is becoming harder for model capability alone to form a durable moat.
The real long-term value lies in the flywheel formed by the model, workflow, feedback data, customer relationships, and reinvested revenue. (…)
The more important questions are: how long can model capability leadership last? After open-source diffusion, who actually captures the revenue? Where do customer relationships and task feedback accumulate? And who can turn one model release into the starting point for the next iteration and the next stage of commercialization?
In many cases, the difference between the top model and the rest of the leading pack is no longer a generational gap. It is often a difference in benchmark design, task preference, and specific use case.
A single model lead is therefore more like an asset that depreciates quickly. (…)
Model capability is the ticket to the game. But a ticket is not a moat. What matters is whether a company can keep getting the next ticket, faster, cheaper, and more reliably than others.
The problem for Anthropic and OpenAi is that this discussion happens before their IPO which would have reduced their debt with a highly priced currency.
The problem for the US government is that this financial rebalancing has not happened. These two companies are currently too big and too critical for the US to fail.
(…) Chief information officers told The Wall Street Journal Leadership Institute they are deploying a number of strategies, including tried-and-true techniques sharpened during the rise of cloud computing—and the need to manage ballooning cloud costs—to keep their AI costs under control.
“With AI, you’re putting the credit card in the hands of the end user. If you have no control over that, or if the end user is not educated enough, they’re going to run up that tab,” said Chris Reed, a senior director of IT finance at online travel company Priceline.
Unlike in previous tech cycles, corporate adoption of AI rests on all employees—not just developers—picking up on the technology. AI is increasingly being billed by usage, and the price of tokens, the basic unit of AI computing, has been volatile. That all translates to higher costs for AI. (…)
Adding to the cost pressure is the shift from prompt-based chatbots to always-on autonomous AI agents, which consume vastly more tokens. And with larger, more sophisticated AI models, those costs are expected to climb sharply.
“It will be orders of magnitude higher than what we spend today,” said Greg Meyers, chief digital and technology officer of Bristol-Myers Squibb, adding that he expects “exponential” costs associated with AI agents as AI usage hits an inflection point. (…)
“If you factor in what we believe is the payoff here, we believe that it’s actually a pretty positive [return on investment],” he said. (…)
Enterprises already are using more AI than ever before, with many wrangling more AI agents than they can keep track of.
Compared with asking a chatbot a question, asking an agent to complete a task can require 50 times as much computing power, according to Jim Schneider, a senior equity research analyst at Goldman Sachs. Goldman predicts that AI agents will increase AI token consumption by 24 times over the next four years, and business AI agents will increase token consumption by 55 times by 2040.
Model providers OpenAI and Anthropic have said the costs of their tokens are going down, and both have considered drastic price cuts.
Even with less expensive tokens, however, agents are consuming more of them as they interact with other agents and work over long periods of time. While model prices fell roughly 50% from December 2024 to December 2025, tokens consumed grew 4.5 times in the same window, according to research from Bain and Co. (…)
“High AI usage isn’t necessarily a good or bad thing. It depends on the business outcome that’s attached—that’s the most difficult part to quantify,” said Priceline’s Reed.
That uncertainty is pushing companies toward another tactic: paying less per task. Rather than run everything on large, expensive models, some are swapping in smaller, older or open-source models. Running those models on Qualcomm’s own hardware saves the company even more, Tinic said.
Seemantini Godbole, chief digital and information officer of Lowe’s, said the company is putting guidelines and mechanisms in place to avoid “token wastage,” including using smaller and open-source models. (…)
China weighs tighter export controls on AI models and chips Beijing consults companies on ways to stop west acquiring its advanced technologies and star start-ups
(…) MofCom talked to AI companies including Alibaba, ByteDance and Zhipu on limiting the transfer of key data for the training of their models overseas, as well as allowing their model weights to be downloaded by foreign users, the people said. China would still let overseas customers access the models and services, however. (…)
MofCom has also sought views on possible restrictions that would prevent overseas chipmakers including Qualcomm and TSMC from producing advanced semiconductors based on designs developed by Chinese companies such as Huawei, Alibaba and ByteDance, according to the people.
Potential restrictions could also be imposed on the overseas acquisition of strategic technology groups in areas such as agentic AI, the people said. This is mainly to address a loophole that Beijing believes to have led to Meta’s $2bn acquisition of Manus, a deal that was subsequently ordered to be unwound by Chinese authorities.
The new measures could be incorporated into the next revision of China’s catalogue of technologies prohibited or restricted from export, the people said, reflecting Beijing’s growing confidence that it has established a global lead in some areas of AI. (…)
Trump Imposes Additional 50% Tariffs on Certain Canadian Goods Measure applies to products including wine, though energy and parts of other sectors are exempt
President Trump imposed an additional 50% tariff on certain goods from Canada, including wine, hockey sticks and cement, the White House announced Monday.
The White House said that the tariffs were a response to the country’s “discriminatory treatment of American products.” Some sectors and goods, including energy, potash and fish or critical minerals, will be exempt from the new tariffs, the White House said.
The White House said the tariffs were “designed to offset the burden and disadvantage” from what it described as Canada’s discrimination of U.S. goods, including autos. The White House said the U.S. opposed Canadian policies that require companies to invest in auto production in Canada, rather than the U.S., as well as bans some Canadian provinces have imposed on U.S. liquor products. (…)
The new tariffs, unlike earlier rounds of levies on Canada, will apply to goods that comply with the USMCA deal, the White House said. (…)
The new tariffs come after smoke from wildfires in Canada drifted into the U.S., blanketing cities including New York, Chicago and Washington. In a Truth Social post Friday, Trump threatened to impose steeper duties on America’s northern neighbor to compensate for the smoke’s impact.
“We are holding Canada responsible for the fact that they are not properly maintaining their Forests, and Brush therein, and the United States is being unnecessarily invaded by filthy, polluted, and unhealthy air, the quality of which is dangerous, and totally unacceptable!” Trump said. (…)
Trump told reporters that Canada needed to stop the wildfires.
“I told them, I mean, you got to stop these fires from coming in and you know poisoning our air,” Trump said. “Our air has been poisoned. Have a good relationship with Mark Carney, but you know we got to stop the fires up there. If we can help them, we’ll help them. But maybe they should pay us some damages or something, or we should do some tariffs.” (…)
- Canada issues air quality warnings over US wildfire smoke after Trump tariff threat Officials say smoke from US fires has crossed border
“Much of this smoke is originating from fires south of the Canada-US border in Washington and Oregon,” the provincial wildfire service said.
Twenty-two large fires are burning across Washington and Oregon, according to the Northwest Interagency Coordination Center, the fire coordination agency for both states. British Columbia has air quality warnings in place for two regions, Kootenay Lake and Cranbrook, located along the south-east border with the US. (…)
Embarrassing! Though some people just don’t care being embarrassed.