PHNOM PENH, Cambodia — A U.S. cruise ship on a residential world voyage suspended its operations in Cambodia due to fuel costs, forcing hundreds of passengers to abandon their floating homes.
The Odyssey set sail two years ago on a three-year voyage to the seven continents marketed as combining long-term living at sea with global travel.
The Florida-based company said monthly fuel expenses per cabin that historically averaged $1,800 has recently reached an estimated $6,000 to $8,000 in parts of Asia during the war in Iran. Villa Vie Residences said it sought to reduce fuel consumption by slowing the itinerary and spending more time in port, but was unable to offset the surge in fuel costs.
“We have been unable to continue carrying the resulting operating losses,” it said. “Passing the full increase on to residents is not a realistic solution. Our focus now is on supporting residents, crew, and employees” while it tries to restructure.
Sihanoukville Autonomous Port said in a statement last week that the ship docked on Oct. 23 for a five-day visit. It was to head to Vietnam but remained in the port Wednesday.
The ship carried 203 passengers with 15 nationalities, mostly Americans, and 156 crew members. It had a capacity of 650 residents.
Passengers have been told to leave the ship, but the crew’s status was unclear.
The Odyssey had a confirmed itinerary into November 2027. The company planned for a second residential cruise ship to enter service in early 2029, but the statement on the suspension of the Odyssey’s voyage did not address the second ship.
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This version has corrected the date when the voyage was expected to end from 2030 to 2027.

Many investors stand to win big when Anthropic goes public. Stakes held by firms including Menlo Ventures, Sequoia, Sound Ventures, and Spark Capital are expected to return billions.
Yet according to Anthropic’s S-1, none of those VC firms owns 5%, the threshold for being included in the principal stockholders’ table. That’s highly unusual. In a typical tech IPO, lead venture investors hold double-digit-percentage stakes in the startups they backed. The reason is Anthropic’s size. At its $965 billion private valuation, a 1% stake is worth nearly $10 billion, so, for example, Menlo’s roughly $14 billion position is only about 1.5% of the company, according to recently reported Bloomberg figures.
While the venture firms may lack sizable percentage stakes, Anthropic sold large stakes to strategic partners, companies that are also its business partners. Indeed, Amazon is likely the single largest financial beneficiary of the IPO.
Amazon first backed Anthropic in 2023 with a commitment to invest up to $4 billion in the AI company. Since then, Amazon has poured billions more into Anthropic. Instead of taking stock upfront, Amazon structured much of its investment as convertible notes, a type of debt that converts into shares upon an IPO or sale.
According to the S-1, Amazon holds convertible notes with $6.2 billion in principal outstanding as of June 30. That is Anthropic’s entire debt load. But as Anthropic’s valuation soared across successive funding rounds, the shares those notes will convert into became far more valuable. Amazon’s own filings valued its note holdings at $97.9 billion as of June 30.
As the value of Amazon’s convertible notes surged, Anthropic had to record the growing value of what it owes on its books. It reported the adjustments on its income statement under “change in fair value of convertible notes, derivative liabilities, and SAFEs,” a line that totaled $72.4 billion for the first half of 2026. The charge is non-cash, meaning no money left the company.
Amazon’s notes could convert into as many as 1.84 billion shares, according to the S-1. But a cap limits Amazon to 12% of Anthropic’s “partially diluted” share count, a figure that counts some shares not yet issued, such as stock options. Amazon can waive the cap. Notes that can’t convert will stay outstanding and convert in later quarters, subject to the same limit.
But the notes are only part of Amazon’s holdings. It further invested $10 billion in nonvoting preferred stock (a type of equity), in the second quarter, according to Amazon’s quarterly report for the period ended June 30. Amazon did this by investing $5 billion in each of Anthropic’s last two funding rounds. Combined with the $8 billion it invested in the notes from 2023 through 2025, that brings Amazon’s total investment to about $18 billion.
Beyond its stock gains, Amazon will also continue to earn significant revenue from Anthropic, which has committed to spend at least $110 billion on AWS over the next 10 years.
Google, too, is set for a major windfall from both the listing and its cloud computing deals with Anthropic. The search giant held about 14% of Anthropic as of early last year, according to court filings that were reported by The New York Times, and has invested more since, including $10 billion in April. (Google was likely diluted in subsequent and massive funding rounds, with PitchBook analyst Harrison Rolfes telling TechCrunch he estimates its current stake at closer to 5.6%.) It has also secured at least $114 billion in cloud commitments from the Anthropic through 2033.
While VCs set for massive paydays have little reason to complain, the S-1 makes it clear that the biggest winners of Anthropic’s IPO may well be tech giants like Amazon, which backed a would-be rival early on and now sell it much of the computing power it runs on.
The S-1 itself warns that some of those cloud providers also build rival AI models and that they may not always want the same things that Anthropic does.

A bust-up between mathematicians is fuelling fears that AI could undermine science
A bank of white freezers hums away at a former watch factory in central London. Inside: dozens of small boxes with barcoded glass tubes, collectively containing one hundred billion genes. Just two boxes may hold 10 times more information than all the text on the internet, says Dr Oliver Vince, the facility’s co-founder.
His firm, Basecamp Research, plans to collect as much DNA as possible to create an artificial intelligence that can speak the language of biology, in the same way that US and Chinese tech firms have hoovered up written material online to create Large Language Models (LLMs), the basis of current AI systems.
Vince, like many in business and academic research, believes AI will supercharge scientific research. But increasingly, it also comes with controversy.
Last month, OpenAI, the creator of ChatGPT, said it had solved one of maths’ great unsolved problems, relating to the famous Navier–Stokes equations. The company said its AI agents had cracked it in 88 hours. The body that runs a prize for whoever can solve the problem said it had “apparently been settled”, but that its review would take time.
But hours before the announcement, Tristan Buckmaster, a mathematician at New York University, went public with concerns. He formed an independent team with Levent Alpöge, a mathematician employed by day by the rival company Anthropic.
They had been working towards the same goal, and kept their drafts in OpenAI’s coding tool, Codex. Buckmaster said that his co-researcher was tipped off that news of their progress had “reached” OpenAI, though he did not specify how. He asked whether his research, stored in Codex, could have fed into OpenAI’s own work, though he stressed he was “not accusing anyone of anything”.
OpenAI said that nobody at the company, human or AI, saw the other teams’ work before it was published. Initially it said that while unlikely, it could not “rule out” that anonymised data from use of its products may have helped improve its models. Days later, it said Buckmaster’s Codex prompts from the two months before the announcement “could not have influenced the system in any way”.
It’s a complicated story; we still don’t fully know what happened. But it highlights a growing issue about the use of AI in science. Usually, those words make us conjure up images of robots building terrible genetically-engineered viruses, or chemical weapons, to wipe out humanity. But there’s a more subtle problem that hasn’t attracted the same headline-grabbing attention: that AI could undermine faith in the scientific process, raising questions about the novelty and accuracy of research.
And there’s another concern. It’s well known that AI chatbots like Claude or Gemini can sometimes hallucinate things that simply aren’t true. They will tell you about a film that never existed, or a quote that was never said. Now there is evidence that this is increasingly happening in science. Eventually, it’s feared, it will become difficult to separate true scientific discovery from AI-generated fiction.
And all of this could threaten to erode – if not explode – the rock on which scientific research rests: trust.
A ‘watershed moment’
One threat is to scientific journals, the guardians of scientific integrity, which are seen to be failing to deal with a tsunami of AI-generated distortions.
“Generative AI has basically disrupted the whole scientific process,” says Dalmeet Singh Chawla, a science journalist who has spent a decade reporting on research misconduct.
He tells the BBC that publishers’ integrity staff – the people who screen submissions for accuracy and reliability – will need to “drastically increase [in number and effort] if they’re anywhere near the fight”. He says: “Right now, it seems like they’re not.”
On the face of it, AI seems just the right tool to power scientific research. Scientists deal with huge amounts of information. Telescopes and particle accelerators, for example, collect billions of data points – too large for the brain to comprehend. AI can pick the scientific highlights from the surrounding humdrum.
The poster child of scientific AI is the UK-based firm Google DeepMind, which in 2020 solved one of biology’s biggest problems: how proteins in living things fold into their precise shapes.
It may sound dull, but proteins are the tiny machines that make life work, and the shapes they fold into determine what they can do; from building cells and fighting infection to helping form the tissues and organs of the body.
Before AI, scientists had to conduct time-consuming experiments in the lab. Then, Google DeepMind developed an AI model called AlphaFold that accurately predicted 200 million protein folds in just one year.
The equivalent experimental work would have taken hundreds of millions of years in laboratories. The firm gave the results away for free to researchers. The head of DeepMind and the project’s lead scientist were both awarded the Nobel Prize for Chemistry.
It was big hugs all round.
Dr Pushmeet Kohli, who leads DeepMind’s science work, told me that AlphaFold could be seen as a “watershed moment”.
Questions over AI’s work
But elsewhere, the use of AI in science has come with accusations of hype. Some computer models are accused of exaggerating the significance of their findings, with the human researchers that oversee them then believing some of the claims without enough scepticism.
Basking in its success over AlphaFold, for example, DeepMind turned its attention to predicting the structure of new materials. The work could lead to the development of better batteries, cheaper solar cells and electronic components that are not dependent on rare earth elements. (In theory, no-one would need to argue about mining rights ever again, and Greenland would be safe from invasion.)
A DeepMind model came up with what the firm claimed were more than two million new crystal structures, announced as “nearly 800 years worth of knowledge” released in one go. Of those, some 380,000 were considered promising enough to make in a laboratory.
But then came a warning sign from two chemists at the University of California. Anthony Cheetham and Ram Seshadri went through a random sample of the supposed new crystals. Writing in the journal Chemistry of Materials, they concluded that they had “yet to find any strikingly novel compounds”, arguing that many were “trivial adaptations of known materials”. Their verdict was not that the method was wrong, but that the results need more scrutiny and validation from human chemists.
DeepMind rejects the charge, saying it tested each compound for novelty. Dismissing a new structure for looking familiar misses the point, the company argues: swapping a single atom is how many of today’s most useful materials arrived, from phone components to sunscreen.
Research ‘slop’
In those cases, at least, the science was mostly real and the issue was just AI potentially exaggerating its importance.
Arguably of even greater concern is the growing risk that scientific journals will become polluted with so-called AI “slop” – where a computer, not a human scientist, is writing the research, leading to fake citations and phantom papers.
In 2024, computer scientist Prof Dmitry Kobak, of Ghent University, searched more than a million biomedical research papers for trigger words favoured by the LLMs that generate AI writing. His team found that by December last year, nearly 90% had AI fingerprints on them. AI detection technology is not perfect; some forms of the software have labelled the US Constitution, written in the 1780s, as “AI-generated”. But still, Kobak said he was shocked by the scale of apparent AI use. There is a risk, he told Nature, that “whatever bias the LLM may have will just suddenly permeate the (scientific) literature”.
Kobak is among many researchers who worries that AI is slowly killing scientific journals.
And it matters: research papers are the lifeblood of scientific advance. When a discovery is published, another team in another part of the world might obtain funding to build on the results.
The process of peer review – in which research papers are checked by other scientists before they are published – is also being compromised.
A team led by Weixin Liang and James Zou at Stanford University analysed referee reports submitted to four major computer science conferences, and found up to 17% of the text had been substantially modified by a language model.
A critical part of a scientific paper’s integrity involves lead scientists listing earlier research in order to show what they have done is genuinely new. These are called citations. A separate team at Cornell University found that researchers were using AI to compile citations, and that it was inventing bogus research, with around 147,000 pieces of phantom research cited last year.
If AI continues to distort the integrity of the academic process in this way, then researchers will be sent up blind alleys, public money will be wasted, and the fruits of valid research – such as new drugs or green technologies – will be slowed or not realised at all.
‘Research-grade AI’
Some publishers of scientific journals, such as Elsevier, are taking steps to avert this growing crisis of confidence.
Elsevier’s research integrity team has developed some 100 signals that flag a paper for closer human scrutiny. Nature’s journals forbid AI-generated images and tell referees never to upload a manuscript into an AI system. The website arXiv, where scientists share their early-stage results, bans researchers for a year if they submit invented references.
Prof Sarah Main, an Elsevier vice-president, is among those at the firm trying to work out how to responsibly use AI in research. “What we’re calling research-grade AI would focus on ensuring the researcher is in the driving seat,” she says.
As AI agents process the mass of data emerging from particle accelerators, the cosmos and the natural world, how do we know they are getting it right?
And there’s another worrying question: how do we know that AI agents are not dismissing an important result as an anomaly? The discoveries of penicillin, radioactivity and weird stars called pulsars all came from odd results that could easily have been dismissed. Hunches by humans made them into some of the biggest scientific moments of the last century.
Can AI really replicate that sense of serendipity and human ingenuity?
Kohli of DeepMind does not think the answer is to turn our backs on AI. That would be “like saying you should be computing without using calculators”, he told me. But he accepts there should be human guardrails: “The agents today are imperfect. Somebody has to take accountability.”
His test is a simple one: “You have to understand what the agent has done, and you have to take responsibility. You can’t just specify the problem and let the machine write the paper and submit it as true.”
Prof Andrea Pauli of the Vienna BioCenter in Austria has followed that test. She spent almost 10 years on a question no-one has yet been able to answer: how a sperm gets into an egg. Responsible use of AI helped her toward an answer. Her lab found a protein on the surface of zebrafish eggs which pointed to a lock-and-key mechanism for allowing the sperm in.
This is also a story about humans. Soon after the formation of Basecamp – the lab I visited in a former London watch factory – its co-founder, Oliver Vince, was ill in hospital with leukaemia (from which he has recovered). It motivated him to develop an AI system that stood a better chance of curing cancers. It might not work, he says, but it’s worth a shot.
“We have a technically feasible path to curing cancer,” he says.
Not a promise from Vince, but a direction. The big question is whether AI carries the science there, or quietly erodes the bricks it is built on.
Top image credit: Getty.
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A residential cruise ship halted operations in Cambodia on Tuesday amid soaring fuel costs, leaving passengers in limbo after the U.S. company that operates the vessel ordered them to disembark.
In a news release, Florida-based Villa Vie Residences said it was working to identify “temporary living arrangements” for people aboard The Odyssey, a 642-foot cruise ship that launched from Belfast three years ago and promised passengers “an unparalleled lifestyle of luxury and exploration” as it toured the planet.
Cabins on The Odyssey were for sale or rent.
According to the company, the ship can accommodate 650 residents, but it isn’t clear how many were on board Tuesday or where in Cambodia they disembarked.
An executive with the company, Mikael Petterson, declined to provide additional details in an email to NBC News. Earlier this year, in a release announcing the company’s second residential cruise ship, Petterson said The Odyssey was 85% sold.
In Tuesday’s release, the company said it had taken significant measures aimed at reducing fuel consumption, but those steps had failed to offset the spike in costs.
Global oil prices have surged since the war with Iran began earlier this year, disrupting the world’s flow of fuel. Earlier this month, oil prices climbed to their highest levels since this spring.
“Passing the full increase on to residents is not a realistic solution,” the company said. “Our focus now is on supporting residents, crew, and employees while we work toward restructuring and recapitalization over the next 60 days. That work is ongoing, and no outcome is assured.”
Randy Cassingham, a resident on the boat, wrote in a blog post that he learned his journey was over after a company official posted a note on a message board for the ship shortly after 7 a.m. local time Monday.
The Odyssey was entering a “temporary lay up” period — indicating the vessel would no longer be used — and everyone had to disembark by 2 p.m. that day, the message stated, according to Cassingham, who runs ResidentialCruising.com and did not immediately reply to an interview request.
Cassingham attributed the development to a “cascade of problems” that he would detail later.
Pamela Reckamp, who rented a cabin on the Odyssey with her husband and disembarked earlier this month, said in an interview that she wasn’t surprised that the ship’s journey was over.
Reckamp, 59, recalled attending a meeting in early September where a company official asked cabin owners to help them raise $1 million so they could leave Ha Long Bay, Vietnam.
“She said, ‘If we don’t get this money, we have about four days of the ship running,’” Reckamp recalled. “‘Then the lights will go off and the AC will go off.’”
When the ship’s financial issues were raised during another meeting a few weeks later, Reckamp recalled, a woman stood up and said she’d bought a cabin in August and hadn’t been made aware of the issues.
“I sold my house,” Reckamp recalled the woman saying. “I sold my car. I bought my home on this ship.”
Reckamp said she felt terrible for the residents and staff, who she said were lovely.
“We dodged a bullet,” she added.
Villa Vie Residences did not immediately respond to a request for comment Tuesday night.

South Korean chipmaker SK Hynix on Wednesday released its third-quarter earnings results, posting a record high quarterly profit that sharply beats estimates, while announcing over $25 billion in dividends and buybacks.
Here are SK Hynix’s third-quarter results compared with LSEG SmartEstimates, which are weighted toward forecasts from analysts who are more consistently accurate:
Revenue: 104.62 trillion won ($78.86 billion) vs. 98.34 trillion won expected
Operating profit: 83.33 trillion won vs. 76.72 trillion won expected
Revenue rose 327.9% from a year earlier, while operating profit climbed 632.1% year on year.
Compared with the previous quarter, revenue climbed 31.9%, while operating profit surged 37.6%.
SK Hynix posted record quarterly revenue and operating profit, with sales exceeding 100 trillion won for the first time, as it announced plans to return about 100 trillion won to shareholders — including at least 30 trillion won in cash dividends and 60 trillion won in share buybacks and cancellations.
The chipmaker approved a third-quarter dividend of 14 trillion won, including a special dividend funded by gains from its investment in Kioxia. The company will also buyback 20 trillion won in shares, half of which will be cancelled this year.
SK Hynix expects HBM4 to surpass HBM3E in both shipments and revenue in the fourth quarter, as it ramps up production at its M15X facility in Cheongju, South Korea.
HBM, or high-bandwidth memory, is a type of advanced memory used alongside AI processors to handle large amounts of data. HBM4 is the sixth generation of the technology.
The company has secured long-term agreements with 14 major customers covering more than 50% of its production capacity for 2027 and 2028.
It expects memory demand to outpace supply through 2030 and plans to significantly increase investment next year.
In August, SK Hynix had announced plans to repurchase and cancel 40 trillion won worth of its own shares, while raising its shareholder return target to more than 50% of cumulative free cash flow generated between 2025 and 2027.
Last week, SK Hynix announced that its U.S. subsidiary Solidigm would halt its review of potential pre-IPO fundraising and IPO options, citing market volatility and share price trends.
The company said it would continue to assess investment timing, scale and financing options to support Solidigm’s growth and enhance shareholder value.