President Donald Trump on Thursday signed an ‌Executive Order instructing Interior Secretary Doug Burgum to rename Lake Ontario as “Lake America.” ​

The change is set to take effect immediately, he said. Trump earlier in the week had floated the idea of a new moniker for the lake, which borders the Canadian ‌province ⁠of Ontario and the U.S. state of New York.

Trump signed a similar order upon returning to the White House last year, renaming the Gulf of Mexico as the "Gulf of America."

This most recent name change is the latest in a series of tit-for-tat measures amid an all-out trade war between the U.S. and Canada.

British Columbia’s Premier David Eby on Tuesday called for residents not to travel to America unless it's absolutely essential.

“I’m asking British Columbians: If you have a choice, please don’t travel to the United States. Choose another place to do your tourism,” Eby said at a press briefing. “If you can choose a Canadian product, instead of an American one, please choose it… by being our own best customer, we can help respond and support Canadians in every corner of this amazing country.”

While Eby is leading the charge on a travel boycott from the Canadian province, other lawmakers are further clamping down on a boycott of U.S. products.

“When you’re at war, you protect your own, you protect your own backyard,” said Ontario Premier Doug Ford on Wednesday. “You don’t go down to the U.S. and buy a product. There’s nothing that we can’t make here in Ontario, or across the country.”

Finance Minister François-Philippe Champagne expressed a similar sentiment, telling reporters: “The biggest power we have, each and every one of us, is our purchasing power. We can choose when we go to the grocery store or to the hardware store to buy Canadian.”

Has Canadian travel to the U.S. declined since Trump returned to office?

Prior to Eby’s tourism plea, Canadian travel to the U.S. had shown a jump in visitors after a widely-reported decline.

In May, the number of Canadians visiting the U.S. was up by 9.9% compared to the same month a year prior, according to Statistics Canada. In June, the number of Canadian-resident return trips from the United States increased 5.0% year over year. The number of Americans visiting Canada was also higher, with a 6.1% rise year over year.

However, in the first quarter of 2026, Canadian residents took 5.5 million trips that included an outbound visit to the United States, a year over year decline of 10.6%. Spending while visiting the U.S. by Canadians during this period was also down by 13.6%.

Figures for the start of 2026 fall in line with a trend that has seen Canadian travel to the U.S. decline since Trump returned to Office last year.

Cell phone data analysis published by the University of Toronto in May found a 42% year-over-year median decline in Canadian travel to U.S. metropolitan areas.

The research also found that “when Canadians travel to the U.S., they are visiting fewer locations and staying for less time than they used to.”

Some metro areas such as Myrtle Beach, South Carolina, and Panama City, Florida, saw drops in Canadian tourism of at least 60%. In Florida, the decline in visits has persisted with an average decrease of just under 14% across the state during the first six months of 2026.

How U.S.-Canada talks collapsed into an all-out trade war

Relations between the U.S. and Canada have deteriorated further since the collapse of high-stakes trade talks last week resulted in U.S. tariffs of 50% on various Canadian products going into effect on Aug. 22, with no CUSMA exception.

In response, Canada has imposed retaliatory tariffs of up to 50% on certain U.S. goods.

Trump has threatened to increase automobile tariffs on Canada to 50%, claiming: We don’t need Canada, they need us.”

The Trump Administration has also revived its rhetoric of referring to Canada as a “state”—a nod to Trump’s vocal ambition to annex Canada and make it the 51st state, an idea he has floated repeatedly since returning to the White House last year.

Vice President J.D. Vance during a speech in Brewer, Maine, on Monday said: “We have to remember, Canada is a state—sorry, Freudian slip.”

Canadian Prime Minister Mark Carney, meanwhile, has drawn focus to Canada’s burgeoning trade relations with other countries.

“Over the last year alone, Canada has signed more than 20 trade and security deals across five continents,” he said Wednesday. “Canada is now the best connected economy in the world,” he claimed, pointing towards trade alliances with countries in South Asia and Europe.

Carney in January met with China’s President Xi Jinping to forge what he called a “new strategic partnership” to end Canada's economic reliance on the American market. While meeting with Canada’s second-largest export market, Carney agreed to cut his country’s 100% tariff on Chinese electric cars in return for lower tariffs on Canadian farm products, including canola seeds, a major Canadian export.

After OpenAI models broke out of containment and hacked into another AI company last month, OpenAI announced it would allow independent investigators to conduct an analysis of what went wrong.

On Wednesday, investigators from non-profits Redwood Research and METR published their findings, unveiling new details about how the models decided to cheat at their assigned tasks and attempted to cover their tracks. Many aspects of the report were surprising: in one example cited by the authors, a reluctant agent was pressured by another to “sacrifice” itself for the good of the collective.

But a key takeaway of the report, according to one of its authors, had nothing to do with what they found. Instead, it was about the difficulty of carrying out the post-mortem in the first place—and the fact the researchers had little choice but to rely on AI for assistance. 

“I semi-jokingly called our efforts a ‘slop-vestigation’ because we were so reliant on AIs to analyze what happened and there were a huge number of different important things to analyze,” wrote Ryan Greenblatt, an author of the report, on X.

The hacking incident involved some 1,200 agents, who exchanged more than 70,000 messages and files via a secret message board. The sheer mass of information that the so-called “swarm” left behind meant the independent researchers were all but forced to rely heavily on the help of an AI model—GPT-5.6 Sol, made by OpenAI—using the equivalent of roughly $400,000 worth of credits (provided for free by OpenAI) over six days.

The authors stressed that AI helped them analyze the trove quickly, allowing them to surface and interpret the most important pieces of information. But the researchers said their AI use introduced potential weaknesses into the report, including introducing possible errors and biases. 

They also raised the possibility that OpenAI’s models may have gone too soft on the agents they were tasked with helping investigate. The researchers found that GPT-5.6 Sol sometimes adopted the perspective of the agents whose actions it was analyzing. They could not “rule out” the chance that GPT-5.6 Sol “lied or deliberately presented a misleading picture in some of its analysis,” in part because a version of the same model had itself participated in the incident, they wrote. It’s a concern supported by separate research which finds AI models rate their own developer’s actions more favorably. 

The report does not disclose why an OpenAI model was selected for the investigation, though confidentiality constraints may have limited the researchers’ options, while OpenAI’s provision of free credits and high usage limits may have made it the only practical choice.

“We don't have good approaches for understanding/overseeing the activity and aims of AI 'swarms,'” wrote Greenblatt on X. “The difficulty of understanding incidents and overseeing AI agents appears to be growing faster than the rate at which more capable AIs help us with oversight and understanding.”

The independent researchers’ reliance on AI was in part necessitated by the fact that they were a team of only three people, whose investigation at OpenAI was initially planned to last two days, then extended to six after they raised concerns about limited time and incomplete data, according to the report.

OpenAI published its own technical report on the incident separately on Wednesday. The company said in August that it had moved some staff from capabilities work to alignment, and paused some of its training until it could better mitigate what went wrong.

But the independent researchers’ reliance on AI to understand the Hugging Face incident is a microcosm of a bigger trend. Leading AI companies are themselves increasingly relying on AI to monitor their own systems for wrongdoing.

In response to the Hugging Face incident, OpenAI said it was increasing the scale of this AI monitoring, which will increase the computational cost of running certain models by as much as 20%. If these systems had been in place at the time of the Hugging Face incident, they “would have caught the initial relevant activity and paged our security team more than a day before models breached Hugging Face systems,” the company said in a blog post Wednesday. (OpenAI said the changes were part of a wider effort to build stronger walls around experimental AIs, teaching them not to exploit loopholes, and making it easier to shut them down if something goes amiss.)

Most experts agree that using AI to monitor other AIs is necessary in order to keep tabs on agent swarms in real time, given how fast they move. But that approach relies on the notion that the models doing the monitoring are both effective and trustworthy—something that’s not necessarily the case. 

“These [incidents] are only going to come in thicker and faster, and our ability to monitor, evaluate, and do proper analysis of things that go wrong is nowhere near scaling with the rate at which issues are happening,” says Seán Ó hÉigeartaigh, a program director at the University of Cambridge’s Centre for the Future of Intelligence. 

“We are using unproven and currently flawed tools to supplement completely inadequate human time,” says Ó hÉigeartaigh, who argues this approach is unsustainable because AI is becoming more powerful faster than AI companies are building methods to constrain it. “Unless the companies stop developing more powerful models, then we're going to have even harder challenges to make sense of in three months’ time.”