Author name: Mike M.

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Tariffs may soon spike costs of cars, household goods, consumer tech


“A little pain”: Trump finally admits tariffs heap costs on Americans.

Canadian and American flags are seen at the US/Canada border March 1, 2017, in Pittsburg, New Hampshire. Credit: DON EMMERT / Staff | AFP

Over the weekend, President Trump issued executive orders heaping significant additional tariffs on America’s biggest trading partners, Canada, China, and Mexico.

To justify the tariffs—”a 25 percent additional tariff on imports from Canada and Mexico and a 10 percent additional tariff on imports from China”—Trump claimed that all partners were allowing drugs and immigrants to illegally enter the US. Declaring a national emergency under the International Emergency Economic Powers Act, Trump’s orders seemed bent on “downplaying” the potential economic impact on Americans, AP News reported.

But very quickly, the trade policy sparked inflation fears, with industry associations representing major US firms from many sectors warning of potentially derailed supply chains and spiked consumer costs of cars, groceries, consumer technology, and more. Perhaps the biggest pain will be felt by car buyers already frustrated by high prices if car prices go up by $3,000, as Bloomberg reported. And as Trump eyes expanding tariffs to the European Union next, January research from the Consumer Technology Association showed that imposing similar tariffs on all countries would increase the cost of laptops by as much as 68 percent, game consoles by up to 58 percent, and smartphones perhaps by 37 percent.

With tariffs scheduled to take effect on Tuesday, Mexico moved fast to negotiate a one-month pause on Monday, ABC News reported. In exchange, Mexico promised to “reinforce” the US-Mexico border with 10,000 National Guard troops.

The pause buys Mexico a little time to convince the Trump administration—including Secretary of State Marco Rubio, Treasury Secretary Scott Bessent, and potentially Commerce Secretary nominee Howard Lutnick—to strike a “permanent” trade deal, ABC News reported. If those talks fall through, though, Mexico has indicated it will retaliate with both tariff and non-tariff measures, ABC News reported.

Even in the best-case scenario where no countries retaliate, the average household income in 2025 could drop by about $1,170 if this week’s new tariffs remain in place, an analysis from the Budget Lab at Yale forecast. With retaliation, average income could decrease by $1,245.

Canada has already threatened to retaliate by imposing 35 percent tariffs on US goods, although that could change, depending on the outcome of a meeting this afternoon between Trump and outgoing Prime Minister Justin Trudeau.

Currently, there’s seemingly tension between the Trump administration and Trudeau, however.

On Saturday, Trudeau called Trump’s rationale for imposing tariffs on Canada—which Trudeau noted is responsible for less than 1 percent of drugs flowing into the US—”the flimsiest pretext possible,” NBC News reported.

This morning, the director of the White House’s National Economic Council, Kevin Hassett, reportedly criticized Canada’s response on CNBC. While Mexico is viewed as being “very, very serious” about Trump’s tariffs threat, “Canadians appear to have misunderstood the plain language of the executive order and they’re interpreting it as a trade war,” Hassett said.

On the campaign trail, Trump promised to lower prices of groceries, cars, gas, housing, and other goods, AP News noted. But on Sunday, Trump clearly warned reporters while boarding Air Force One that tariffs could have the opposite effect, ABC News reported, and could significantly worsen inflation the longer the trade policy stands.

“We may have short term, some, a little pain, and people understand that, but, long term, the United States has been ripped off by virtually every country in the world,” Trump said.

Online shoppers, car buyers brace for tariffs

In addition to imposing new tariffs on these countries, Trump’s executive orders also took aim at their access to the “de minimus” exemption that allows businesses, including online retailers, to send shipments below $800 into the US without being taxed. That move could likely spike costs for Americans using popular Chinese retail platforms like Temu or Shein.

Before leaving office, Joe Biden had threatened in September to alter the “de minimus” rule, accusing platforms like Temu or Shein of flooding the US with “huge volumes of low-value products such as textiles and apparel” and making “it increasingly difficult to target and block illegal or unsafe shipments.” Following the same logic, it seems that Trump wants to exclude Canada, China, and potentially Mexico from the duty-free exemption to make it easier to identify illegal drug shipments.

Temu and Shein did not respond to Ars’ request to comment. But both platforms in September told Ars that losing the duty-free exemption wouldn’t slow their growth. And both platforms have shifted business to keep more inventory in the US, CNBC reported.

Canada is retaliating, auto industry will suffer

While China has yet to retaliate to defend such retailers, for Canada, the tariffs are considered so intolerable that the country immediately ordered tariffs on beverages, cosmetics, and paper products flowing from the US, AP News reported. Next up will be “passenger vehicles, trucks, steel and aluminum products, certain fruits and vegetables, beef, pork, dairy products, aerospace products, and more.”

If the trade wars further complicate auto industry trade in particular, it could hurt US consumers. Carmakers globally saw stocks fall on expectations that Trump’s tariffs will have a “profound impact” on the entire auto industry, CNBC reported. And if tariffs expand into the EU, an Oxford Economics analysis suggested, the cost of European cars in the US market would likely increase while availability decreases, perhaps crippling a core EU market and limiting Americans’ choice in vehicles.

EU car companies are already bracing for potential disruptions. A spokesperson for Germany-based BMW told CNBC that tariffs “hinder free trade, slow down innovation, and set a negative spiral in motion. In the end, they are detrimental to customers, making products more expensive and less innovative.” A Volkswagen spokesperson confirmed the company was “counting on constructive talks between the trading partners to ensure planning security and economic stability and to avoid a trade conflict.”

Right now, Canada’s auto industry appears most spooked by the impending trade war, with the president of Canada’s Automotive Parts Manufacturers’ Association, Flavio Volpe, warning that Canada’s auto sector could “shut down within a week,” Bloomberg reported.

“At 25 percent, absolutely nobody in our business is profitable by a long shot,” Volpe said.

According to Bloomberg, nearly one-quarter of the 16 million cars sold in the US each year will be hit with duties, adding about $60 billion in industry costs. Seemingly the primary wallet drain will be car components that cross the US-Canada and US-Mexico borders “as many as eight times during production” and, should negotiations fail, could be getting hit with tariffs both ways. Tesla, for example, relies on a small parts manufacturer in Canada, Laval Tool, to create the molds for its Cybertruck. It already costs up to $500,000 per mold, Bloomberg noted, and since many of the mold components are sourced from Canada currently, that cost could go up at a time when Cybertruck sales already aren’t great, InsideEVs reported.

Tariffs “necessary”

William Reinsch, senior adviser at the Center for Strategic and International Studies and a former US trade official, told AP News that Trump’s new tariffs on raw materials disrupting the auto industry and others don’t seem to “make much economic sense.”

“Historically, most of our tariffs on raw materials have been low because we want to get cheaper materials so our manufacturers will be competitive … Now, what’s he talking about? He’s talking about tariffs on raw materials,” Reinsch said. “I don’t get the economics of it.”

But Trump has maintained that tariffs are necessary to push business into the US while protecting national security. Industry experts have warned that hoping Trump’s tariffs will pressure carmakers to source all car components within the US is a “tough ask,” as shifting production could take years. Trump seems unlikely to back down any time soon, instead asking already cash-strapped Americans to be patient with any rising costs potentially harming businesses and consumers.

“We can play the game all they want,” Trump said.

But to countries threatening the US with tariffs in response to Trump’s orders, it likely doesn’t feel like a game. According to AP News, the Ministry of Commerce in China plans to file a lawsuit with the World Trade Organization for the “wrongful practices of the US.”

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

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Bogus research is undermining good science, slowing lifesaving research

In 2022, Byrne and colleagues, including two of us, found that suspect genetics research, despite not immediately affecting patient care, informs scientists’ work, including clinical trials. But publishers are often slow to retract tainted papers, even when alerted to obvious fraud. We found that 97 percent of the 712 problematic genetics research articles we identified remained uncorrected.

Potential solutions

The Cochrane Collaboration has a policy excluding suspect studies from its analyses of medical evidence and is developing a tool to spot problematic medical trials. And publishers have begun to share data and technologies among themselves to combat fraud, including image fraud.

Technology startups are also offering help. The website Argos, launched in September 2024 by Scitility, an alert service based in Sparks, Nevada, allows authors to check collaborators for retractions or misconduct. Morressier, a scientific conference and communications company in Berlin, offers research integrity tools. Paper-checking tools include Signals, by London-based Research Signals, and Clear Skies’ Papermill Alarm.

But Alam acknowledges that the fight against paper mills won’t be won as long as the booming demand for papers remains.

Today’s commercial publishing is part of the problem, Byrne said. Cleaning up the literature is a vast and expensive undertaking. “Either we have to monetize corrections such that publishers are paid for their work, or forget the publishers and do it ourselves,” she said.

There’s a fundamental bias in for-profit publishing: “We pay them for accepting papers,” said Bodo Stern, a former editor of the journal Cell and chief of Strategic Initiatives at Howard Hughes Medical Institute, a nonprofit research organization and funder in Chevy Chase, Maryland. With more than 50,000 journals on the market, bad papers shopped around long enough eventually find a home, Stern said.

To prevent this, we could stop paying journals for accepting papers and look at them as public utilities that serve a greater good. “We should pay for transparent and rigorous quality-control mechanisms,” he said.

Peer review, meanwhile, “should be recognized as a true scholarly product, just like the original article,” Stern said. And journals should make all peer-review reports publicly available, even for manuscripts they turn down.

This article is republished from The Conversation under a Creative Commons license. This is a condensed version. To learn more about how fraudsters around the globe use paper mills to enrich themselves and harm scientific research, read the full version.

Frederik Joelving is a contributing editor at Retraction Watch; Cyril Labbé is a professor of computer science at the Université Grenoble Alpes (UGA); and Guillaume Cabanac is a professor of computer science at Institut de Recherche en Informatique de Toulouse.

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Top 10 moments of RFK Jr.’s reality-bending confirmation hearings


There were a lot of doozies as RFK Jr. tried to convince lawmakers he’s pro-vaccine.

Robert F. Kennedy Jr., US President Donald Trump’s nominee for Secretary of Health and Human Services testifies during his Senate Finance Committee confirmation hearing at the Dirksen Senate Office Building on January 29, 2025 in Washington, DC. In addition to meeting with the Senate Finance Committee, Kennedy also met with the Senate Health, Education, Labor and Pensions Committee Thursday. Credit: Getty | Win McNamee

In hearings Wednesday and Thursday, senators questioned President Trump’s nominee for Secretary of Health and Human Services, Robert F. Kennedy Jr., over his fitness to be the country’s top health official and control the mammoth $1.7 trillion agency.

Kennedy would come to the role not with a background in medicine, public health, or science but as a former environmental lawyer who has become one of the most prominent and influential anti-vaccine advocates in the country. For decades, Kennedy has spread misinformation about lifesaving vaccines, sowed doubt about their safety, and peddled various conspiracy theories.

That includes his unwavering false claim—despite decades of research to the contrary and countless debunkings—that vaccines are linked to autism (they are not). Kennedy has also made the bizarre false claim that Lyme disease, a bacterial infection spread by tick bites, is “highly likely” to be a military bioweapon (it is not). When asked about this by Senator Michael Bennet (D-Colo.) in the Senate Finance Committee hearing Wednesday, Kennedy admitted, “I probably did say that.” In the hearing Thursday, held by the Senate Committee on Health, Education, Labor and Pensions (HELP), Kennedy did not deny falsely claiming that AIDS is a different disease in Africa than it is in the US.

The hearings were predictably contentious, at times raucous and emotional, and filled with staggering, reality-bending comments and moments. Here are our top 10:

1. “I am pro-vaccine,” Kennedy tried to claim.

For much of the two hearings, Kennedy tried to walk back his decadeslong history of attacking and undermining vaccines, claiming that he is not anti-vaccine but rather in favor of following the science and ensuring safety. But, his statements in and out of the hearings were conflicting. For instance, in the hearing, he touted that all of his children were vaccinated. But in previous public statements, he has said that he would “do anything, pay anything” to go back in time and not vaccinate his children.

At numerous times, senators tried to pin Kennedy down on his stance on vaccines overall, as well as on specific vaccines. Generally, Kennedy responded that if the senators personally showed him data indicating that a vaccine is safe, he would change his views and even “publicly apologize” for being wrong. (A pledge he couldn’t make if he thought they were safe now.) He refused to say that vaccines do not cause autism.

Some of the senators tried to show him data, referencing the deep scientific literature supporting the safety and efficacy of vaccines, including the Measles, Mumps, and Rubella Vaccine (MMR) and the HPV vaccine. Some even held up stacks of studies. “The evidence IS there,” Senator Bernie Sanders (I-Vt.) said forcefully. But Kennedy always raised quibbles with whatever studies senators presented and said he would discuss individual studies with senators after the hearing.

At the conclusion of the HELP hearing Thursday, Senator Bill Cassidy (R-La.), a former gastroenterologist, confronted Kennedy with a high-quality meta-analysis finding no link between autism and vaccines. But Kennedy again dismissed it and referred Cassidy to an article published online by an anti-vaccine advocate.

Cassidy then looked up the study while another senator questioned Kennedy and raised the issue in his closing remarks. “I looked at the article by Dr. Mawson and it seems to… have some issues,” Cassidy said. “I’ll just put that to the side.

“And that is why I’ve been struggling with your nomination,” he continued. Cassidy noted that he agreed with Kennedy’s comments on topics such as chronic health issues and obesity, but “as someone who had discussed immunizations with thousands of people… I have approached it using the preponderance of evidence to reassure, and you have approached it using selective evidence to cast doubt.”

Cassidy wondered aloud: “Does a 71-year-old man who has spent decades criticizing vaccines and is financially invested in finding faults with vaccines, can he change his attitudes and approach now that he’ll have the most important position influencing vaccine policy in the United States?… Will you overturn a new leaf?”

2. “A perfect metaphor”

Senator Chris Murphy (D-Conn.) raised concern that Kennedy not only cast doubt on vaccine safety, but had said that the people who run the country’s vaccine program should be in jail, likening them to Nazis and pedophiles. Murphy quoted Kennedy saying in 2013, “To me this is like Nazi death camps. Look at what it does to the families who participate in the vaccine program. I can’t tell why someone would do something like that, I can’t tell you why ordinary Germans participated in the Holocaust. I can’t tell you what was going on in their minds.”

Murphy also noted that Kennedy called the Catholic church’s sexual abuse cases a “perfect metaphor” for the vaccine program in the US.

In his response to Murphy, Kennedy only doubled down on the claims, arguing that certain members of the Centers for Disease Control and Prevention’s vaccine program had, like members of the Catholic church, “written off a generation of kids” due to “misplaced institutional loyalty to the CDC and because of entanglements with the drug companies.”

“You said it was a perfect metaphor,” Murphy pressed, still alarmed by the comparison of immunization to child sex abuse.

“Well, if you have 1 in 36 kids with neurological injuries and if that is linked, then that’s something we should study,” Kennedy replied, referring to the rate of autism (again falsely linking the condition to vaccines).

3. “She’s not going to be a pincushion”

Despite the slips, Kennedy kept trying to convince senators that he was not anti-vaccine. Other senators, meanwhile, seemed to celebrate Kennedy’s track record.

“You brought to light the vaccines over the last couple years,” Senator Tommy Tuberville (R-Ala.) said. “I’ve had my first granddaughter here in a couple of weeks and my son and his wife have done their research about vaccines, and she’s not going to be a pin cushion. We’re not going to allow that to happen. But you brought that up… I appreciate you doing that.”

4. “We can’t move forward”

Without question, the most emotional moment of both hearings was during questioning by Senator Maggie Hassan (D-N.H.), who spoke of how the false link between vaccines and autism had affected her and her family. “You may not know that I am the proud mother of a 36-year-old young man with severe cerebral palsy,” she said, her voice cracking. “And a day does not go by when I don’t think about what did I do when I was pregnant with him that might have caused the hydrocephalus that has so impacted his life. So, please do not suggest that anybody in this body of either political party doesn’t want to know what the cause of autism is,” she said, her voice rising.

“Mr. Kennedy, that first autism study rocked my world,” Hassan continued, referring to the deeply flawed, now retracted 1998 study published in the Lancet by Andrew Wakefield, who first claimed to find a connection between the MMR vaccine and autism. “Like every mother I worried about whether in fact the vaccine had done something to my son,” she said. But, the study was small (12 children, only eight with autism), and editors later found “clear evidence of falsification of data.”

“Over time, the scientific community studied and studied and studied and found that it was wrong,” Hassan said. The study was retracted in 2010. “Sometime science is wrong. We make progress, we build on the work, and we become more successful. And when you continue to sow doubt about settled science it makes it impossible for us to move forward. So that’s what the problem is here—it’s the relitigating and rehashing and continuing to sow doubt so we can’t move forward. And it freezes us in place.”

5. “It will cast a shadow”

In addition to stalled progress, many senators expressed deep concern that Kennedy’s confirmation could lead to needless suffering and deaths from vaccine-preventable diseases. But Cassidy took the possibility one step further.

“As a patriotic American, I want President Trump’s policies to succeed,” Cassidy stated. “But if there is someone that is not vaccinated because of policies or attitudes you [Kennedy] bring to the department, and there’s another 18-year-old who dies of a vaccine-preventable disease… it’ll be blown up in the press. The greatest tragedy will be her death. But I can also tell you an associated tragedy: that will cast a shadow over President Trump’s legacy.”

6. “Of the ages”

During Sanders’ questioning Thursday, he drew attention to another vaccine: COVID-19 vaccines. Sanders referenced a study that estimated the vaccines saved more than 3 million lives in the US and prevented more than 18 million hospitalizations. President Trump, meanwhile, once called them “one of the greatest miracles of the ages,” Sanders noted.

However, Sanders pointed out that Kennedy had, during the height of the pandemic, petitioned the Food and Drug Administration to revoke authorization of COVID-19 vaccines and refrain from approving any future COVID-19 vaccines.

Sanders asked Kennedy if the scientists and the president were wrong.

“Senator I filed that lawsuit after CDC recommended the vaccine for 6-year-old children without any evidence that it would benefit them and without testing on 6-year-old children and that was my reason for filing that lawsuit,” Kennedy responded.

This answer was misleading, at best. The 2021 petition Kennedy filed was specifically to revoke existing authorization and block all future COVID-19 vaccines for “all demographic groups,” not just children. It further requested the FDA to prohibit minors from participating in COVID-19 vaccine trials and to refrain from issuing any authorizations for minors under age 16 to get Pfizer’s vaccine or under age 18 to get any other COVID-19 vaccine.

Sanders then pressed Kennedy if the COVID-19 vaccine saved lives.

Kennedy responded: “I don’t know. We don’t have a good surveillance system, unfortunately.”

Sanders: “We don’t know?”

Kennedy: “I don’t think anybody can say that. If you show me science that shows that…”

Sanders: “You know, Bobby, you say ‘If I show you’—you’re applying for the job. I mean, clearly, you should know this. And that is that the scientific community has established that—that [the] COVID vaccine saved millions of lives—and you’re casting doubt. That is really problematic.”

7. The basics

Beyond vaccination, Kennedy stumbled through basic explanations of Medicare and Medicaid, which are managed by the Centers for Medicare and Medicaid Services (CMS) housed within the HHS. On Wednesday, Kennedy described Medicaid as “fully paid for” by the federal government—that is incorrect; it is jointly funded by the federal government and states. He also completely flubbed understanding that CMS has the authority to enforce the Emergency Medical Treatment & Labor Act (EMTALA).

On Thursday, Hassan asked him to define the purpose of Medicare Parts A, B, and C. Kennedy got the answers for all three wrong. He described Medicare Part A as “mainly for primary care or physicians,” when the answer is that it covers inpatient care at hospitals. For Part B, Kennedy said it was “for physicians and doctors,” when the correct description is coverage for outpatient care and home health. And Part C, Kennedy described as “a program where it’s the full menu of all the services: A, B, C, and D.” Part C covers Medicare Advantage, the private insurance option for seniors on Medicare. “It appears you don’t know the basics of this program,” Hassan said.

8. 5G and “other things”

In a quick exchange with Senator Andrew Kim (D-NJ), Kennedy confirmed some of his other concerning beliefs. “In the past you said ‘Wi-Fi radiation does all kinds of bad things, including causing cancer,'” Kim began. “Do you still stand by that statement?”

Kennedy replied, “Yes.”

He has pushed the unproven claim that Wi-Fi “opens up your blood-brain barrier.”

Kim moved on quickly: “And 5G, do you feel the same way?” Kennedy said yes again, clarifying that he was talking about electromagnetic radiation generally, which “changes DNA” and does “other things.”

9. Lucrative position

On Wednesday, Senator Elizabeth Warren (D-Mass.) highlighted that Kennedy has made a lot of money from being an anti-vaccine advocate. In the past two years, Kennedy earned $2.5 million from working with a law firm encouraging people to sign up to be part of lawsuits against vaccine makers. If they sign up, Kennedy gets paid. If the law firm wins the case, Kennedy gets a 10 percent cut.

Warren asked Kennedy if he would agree that he wouldn’t take personal compensation from any lawsuits against drug companies while being health secretary and for four years afterward. Kennedy would not agree to do that. Instead he argued that Warren was insisting that he not be allowed to sue drug companies. “No I am not,” she protested, noting that she was only asking that what he did as secretary wouldn’t benefit him financially.

“The bottom line is the same: Kennedy can kill off vaccines and make millions of dollars while he does it,” Warren concluded.

“Senator, I support vaccines. I support the childhood schedule. I will do that. The only thing I want is good science,” Kennedy replied.

10. Onesies

The last big moment of the hearings goes to Sanders for having the best visual aids. On Wednesday, in the Finance committee hearing, Sanders brought large posters of baby clothes (onesies) that are currently for sale by Children’s Health Defense (CHD), the anti-vaccine group Kennedy founded and ran between 2015 and 2023.

One of the onesies read “Unvaxxed Unafraid” and the other read “No Vax No Problem.” Both are currently on sale for $26 each.

Sanders asked Kennedy if he would ask CHD to stop selling them. Kennedy didn’t answer the question, only noting he had resigned from CHD to run his political campaigns. Bernie pressed: “Are you supportive of this clothing, which is militantly anti-vaccine?”

“I am supportive of vaccines. I want good science,” Kennedy replied.

“But you will not tell the organization you founded not to continue selling that product,” Bernie concluded.

Photo of Beth Mole

Beth is Ars Technica’s Senior Health Reporter. Beth has a Ph.D. in microbiology from the University of North Carolina at Chapel Hill and attended the Science Communication program at the University of California, Santa Cruz. She specializes in covering infectious diseases, public health, and microbes.

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Rocket Report: SpaceX tosses away a Falcon 9; a Somalian spaceport?


All the news that’s fit to lift

“It was the perfect partnership and the biggest softball of all the opportunities.”

Falcon 9 launches the SpainSat NG I mission to orbit from Florida on Wednesday. Credit: SpaceX

Falcon 9 launches the SpainSat NG I mission to orbit from Florida on Wednesday. Credit: SpaceX

Welcome to Edition 7.29 of the Rocket Report! It may be difficult to believe, but we are already one full month into the new year. It will be hard to top this month in launch, however, given the historic debut of New Glenn, and fiery end of the seventh Starship flight test. And in truth, February does look a bit sleepier in terms of launch.

As always, we welcome reader submissions, and if you don’t want to miss an issue, please subscribe using the box below (the form will not appear on AMP-enabled versions of the site). Each report will include information on small-, medium-, and heavy-lift rockets as well as a quick look ahead at the next three launches on the calendar.

UK government injects $25 million into Orbex. As some European launch companies have struggled to raise funding, the United Kingdom government stepped up to make a significant investment in the Scotland-based launch firm Orbex, The Financial Times reports. As part of the company’s latest fundraising round, valued at $50 million (GBP 40 million), the UK government will become a shareholder in Orbex. The company is working to develop both a small- and medium-lift rocket. Phil Chambers, Orbex’s chief executive, said the UK support would be “a strong signal to other private investors, and to the European Space Agency and the EU, that we’re serious about being a part of the future of European launch.”

What’s the plan, fellas? … If we’re being frank, which is how we roll in the Rocket Report, some of Orbex’s recent activity does not inspire confidence. The company, for example, suspended plans to develop a spaceport at Sutherland in the Scottish Highlands to focus resources on developing the Prime microlauncher. And then it said it would develop the larger Proxima rocket as well. That seems pretty ambitious for what is, in the grand scheme of things, a relatively modest round of fundraising. Given that we have not seen a whole lot of hardware from Orbex, some skepticism is warranted. (submitted by EllPeaTea)

Turkey may develop a spaceport in Somalia. Turkey has begun advancing plans to construct a rocket launch facility in Somalia, Space in Africa reports. Somali President Hassan Sheikh Mohamud said the project began in December. Mohamud emphasized the project’s potential benefits, highlighting its capacity to generate significant employment opportunities and revenue for the East Africa nation. “I believe that the importance of Somalia hosting a launchpad for Turkish satellites goes beyond the billions of dollars and opportunities the project will generate,” Mohamud said.

Nothing has been finalized yet … Located along the equator, Somalia fronts the Indian Ocean, offering an ideal launch location. The potential Somali launch site is part of Turkey’s broader aspirations to assert itself in the global space race, traditionally dominated by major powers. In 2021, Turkey unveiled a 10-year space road map that includes plans for missions to the moon, establishing a spaceport, and developing advanced satellite systems. Somalia, a key Turkish security partner since 2011, already hosts Turkey’s largest overseas training base.

The easiest way to keep up with Eric Berger’s and Stephen Clark’s reporting on all things space is to sign up for our newsletter. We’ll collect their stories and deliver them straight to your inbox.

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Firefly expands Alpha launch plans to Wallops and Sweden. Firefly Aerospace expects to start launching its Alpha rocket from launch sites in Virginia and Sweden as soon as 2026 to help the company avoid growing congestion at launch sites in Florida and California, Space News reports. So far, Alpha has only launched from Vandenberg Space Force Base in California. Firefly is planning five Alpha launches in 2025, all from Vandenberg. The company has performed five Alpha launches to date, going back to the failed inaugural launch in 2021.

Sweden, you say? … So what is up with those plans to launch from Sweden? Adam Oakes, vice president of launch vehicles at Firefly, said the Esrange Space Centre in Sweden was an ideal partner. “Esrange has basically done everything for the science community in space except an orbital rocket,” he said, citing the more than 600 sounding rocket launches there as well as experience with ground stations. “It was the perfect partnership and the biggest softball of all the opportunities out there.” It still feels a bit odd, as Vandenberg already offers polar launch corridors, as well as Alpha-size commercial European launch vehicles coming along soon. (submitted by EllPeaTea)

MaiaSpace targets 2026 for debut launch. A subsidiary of ArianeGroup that is developing a two-stage partially reusable rocket, MaiaSpace is one of the more interesting European launch startups. The company’s chief executive, Yohann Leroy, recently spoke with Europe in Space to discuss the company’s plans. The company will likely start off with a suborbital test flight of a launcher capable of boosting 500 kg to low-Earth orbit in reusable mode and 1,500 kg in expendable mode during the middle of next year.

Following an iterative design method … “Our approach is to test our rocket in flight as early as possible, following our test-and-learn iterative approach,” Leroy said. “We are convinced we will go faster this way, rather than spending time in the lab making sure the first flight reaches 100 percent of our performance targets. In short, we are ready to trade lift-off performance for time-saving, knowing that we will quickly recover our performance afterward. What’s important is to stick to our objective of starting commercial operations in the second half of 2026, and we’re on track to reach this goal.” (submitted by RB)

Arianespace inking deals for its new rocket. Arianespace currently has a backlog of 30 Ariane 6 launches, 18 of which are for Amazon’s Kuiper constellation. However, it has recently begun to add Europe-based launch contracts for the rocket. During signing events at the 17th European Space Conference in late January, Arianespace secured contracts for three Ariane 6 flights, European Spaceflight reports.

Getting into operations … The missions are the European Space Agency’s PLAnetary Transits and Oscillations of stars (PLATO) mission, the Sentinel-1D Earth observation satellite that will replace Sentinel-1A, and a pair of second-generation Galileo satellites. After completing a largely successful debut flight last year, the first operational flight of Ariane is scheduled for February 26, carrying the CSO-3 reconnaissance satellite for the French Armed Forces. (submitted by EllPeaTea)

SpaceX expends a Falcon 9 rocket. On Wednesday, SpaceX launched the SpainSat NG-1 satellite from Kennedy Space Center’s Pad 39A. The Falcon 9 first-stage booster used on this launch saw its 21st and final flight, Florida Today reports. SpaceX said the reason it was not trying to recover the booster was due to the extra power needed to reach the satellite’s intended orbit.

Into the drink … The well-traveled booster had launched a variety of missions during its lifetime: 13 Starlink missions, SES-22, ispace’s HAKUTO-R MISSION 1, Amazonas-6, CRS-27, Bandwagon-1, GSAT-20, and Thuraya-4. The Airbus-built satellite, known as SpainSat NG-1 (New Generation), is the first of two satellites for Hisdesat. It was developed under a partnership with the European Space Agency, making its launch on a Falcon 9 somewhat notable.

India marks first launch of 2025. India conducted its first launch of the year late Tuesday, sending a new-generation navigation satellite toward geostationary orbit, Space News reports. A Geosynchronous Satellite Launch Vehicle Mk II lifted off from Satish Dhawan Space Centre. Aboard was the NVS-02 satellite, sent into geosynchronous transfer orbit. The satellite is the second of five new-generation spacecraft for the Navigation with Indian Constellation.

A busy year planned … The mission was the first of 10 orbital launches planned by India in 2025, which would mark a domestic launch record. Major missions include a joint Earth science mission between NASA and ISRO, named NASA-ISRO Synthetic Aperture Radar, expected to launch around March on a GSLV rocket, and an uncrewed test flight for the Gaganyaan human spaceflight program on a human-rated LVM-3 launcher. The first launch of the Vikram-1 for private company Skyroot Aerospace could also take place this year. (submitted by EllPeaTea)

New Glenn represents a milestone moment for Blue Origin. In a feature, Ars Technica explores what the successful launch of the New Glenn rocket means for Blue Origin. The near-term step is clear: getting better at building engines and rockets and flying New Glenn regularly. In an interview, Blue Origin founder Jeff Bezos sounded a lot like SpaceX founder Elon Musk, who has spoken about “building the machine that builds the machine” over the last decade with respect to both Tesla vehicles and SpaceX rockets. Asked about Blue’s current priorities, Bezos responded, “Rate manufacturing and driving urgency around the machine that makes the machine.”

The tortoise and the hare … There are those who wonder why Blue Origin, which has a “tortoise” as its unofficial mascot, has moved so slowly when compared to SpaceX’s progress over the last quarter of a century. Bezos responded that the space age is just beginning. “It’s still absolutely day one,” he said. “There are going to be multiple winners. SpaceX is going to be successful. Blue Origin is going to be successful. And there are other companies who haven’t even been founded yet that are going to grow into fantastic, giant space companies. So the vision that I think people should have is that this is the absolute beginning.”

Space Force has big dreams for ULA this year. The US Space Force is projecting 11 national security launches aboard United Launch Alliance’s Vulcan rocket in 2025, Space News reports. This ambitious schedule comes as the National Security Space Launch program continues to wait on Vulcan’s readiness. The heavy lift rocket, which debuted last year after prolonged schedule setbacks, is a cornerstone of the national security’s Phase 2 program, under which ULA was selected in 2020 as the primary launch provider for national security missions through 2027.

That seems like a lot … However, Vulcan remains under review, with certification expected in late February following its second demonstration flight in October 2024. There is a lot of pressure on ULA to execute with Vulcan, due not only to the need to fly out Phase 2 launches, but because the military is nearing a decision on how to award launch contracts under Phase 3 of the program. The more complex “Lane 2” missions are likely to be divided up between ULA and SpaceX. Reaching 11 national security launches on Vulcan this year seems like a stretch for ULA. The company probably will only launch two rockets during the first half of this year, one of which probably will be an Atlas V booster. (submitted by EllPeaTea)

April 2026 a “no later than” date for Artemis II. In a Space News article citing current contractors defending NASA’s Artemis plan to return humans to the Moon, a space agency official said the current timeline for Artemis II is achievable. April 2026 is actually a no-later-than date for the mission, Matt Ramsay, Artemis 2 mission manager at NASA, said during a panel discussion. “The agency has challenged us to do better, and we’re in the process of figuring out what better looks like,” he said, with a “work-to” launch date coming in the next few weeks.

NET or NLT? … This is interesting, because a good source told Ars about a month ago that the present date for the Artemis II mission to fly astronauts around the Moon has almost no schedule margin. However, Ramsay said the key factor driving the launch date will be work assembling the vehicle. Crews are currently stacking segments of the SLS’s twin solid rocket boosters, a process that should be complete in the next two to three weeks. This all assumes the Artemis II mission goes forward as designed. I guess we’ll see what happens.

Next three launches

Jan. 31: Falcon 9 | Starlink 11-4 | Vandenberg Space Force Base, California | 23: 11 UTC

Feb. 2: H3 | Demo Flight | Michibiki 6 | Tanegashima Space Center, Japan | 8: 30 UTC

Feb. 3: Falcon 9 | Starlink 12-3 | Cape Canaveral Space Force Station, Florida | 8: 54 UTC

Photo of Eric Berger

Eric Berger is the senior space editor at Ars Technica, covering everything from astronomy to private space to NASA policy, and author of two books: Liftoff, about the rise of SpaceX; and Reentry, on the development of the Falcon 9 rocket and Dragon. A certified meteorologist, Eric lives in Houston.

Rocket Report: SpaceX tosses away a Falcon 9; a Somalian spaceport? Read More »

google-pixel-4a’s-ruinous-“battery-performance”-update-is-a-bewildering-mess

Google Pixel 4a’s ruinous “Battery Performance” update is a bewildering mess

Two hours on a charge or less

Pixel 4a phone, face down, with headphone jack at the top facing viewer at 45 degrees.

Credit: Ron Amadeo

One Ars staffer who had a Pixel 4a still in use received the update. They saw their battery life drop from “Still working” to “Two hours on a charge,” in their estimation. They had chosen the Google Store credit option before the update arrived and before the battery life drop became apparent. Once chosen, a different appeasement option could not be selected.

Others have noted all but unusable battery life on their phones, as seen on subreddit threads and blog summaries.

Even technically savvy Pixel owners will have a hard time avoiding the update. Google last week removed all of the Pixel 4a’s factory images from its website, preventing owners from rolling back their firmware without having to go hunting for an image (or convert to a third-party offering, like LineageOS). With no source and debug code posted for the tweaked kernel, third-party firmware providers cannot easily incorporate the battery fixes.

Some Pixel 4a owners have reported that, even after a battery swap, their devices have the same limited battery capacity. This would affirm Martin’s suggestion of a faulty battery cell type and that batteries with those same cells are still being used in replacements. (Martin’s post provides serial numbers one can look for on the battery part to indicate the cell manufacturer.)

$30 per year to receive $50

As seen on a wiki page on the Pixel 4a battery program hosted by repair advocate and YouTube creator Louis Rossman, and noted by Pixel 4a owners on Reddit (and the Ars staffer), the $50 credit offered by Google is paid out through vendor Payoneer.

Signing up to be paid through Payoneer requires providing a Social Security number or other identification, birth date, and checking account details to a financial services firm most non-business owners would not recognize. Payoneer notes on its site that it charges a $30 annual account fee for accounts that receive less than $2,000 in 12 months. It is seemingly left up to Pixel 4a owners to close out their Payoneer accounts after receiving their credits.

Google Pixel 4a’s ruinous “Battery Performance” update is a bewildering mess Read More »

deepseek:-don’t-panic

DeepSeek: Don’t Panic

As reactions continue, the word in Washington, and out of OpenAI, is distillation. They’re accusing DeepSeek of distilling o1, of ripping off OpenAI. They claim DeepSeek *gaspviolated the OpenAI Terms of Service! The horror.

And they are very cross about this horrible violation, and if proven they plan to ‘aggressively treat it as theft,’ while the administration warns that we must put a stop to this.

Aside from the fact that this is obviously very funny, and that there is nothing they could do about it in any case, is it true?

Meanwhile Anthropic’s Dario Amodei offers a reaction essay, which also includes a lot of good technical discussion of why v3 and r1 aren’t actually all that unexpected along the cost and capability curves over time, calling for America to race towards AGI to gain decisive strategic advantage over China via recursive self-improvement, although he uses slightly different words.

  1. Seeking Deeply.

  2. The Market Is In DeepSeek.

  3. Machines Not of Loving Grace.

  4. The Kinda Six Million Dollar Model.

  5. v3 Implies r1.

  6. Two Can Play That Game.

  7. Janus Explores r1’s Chain of Thought Shenanigans.

  8. In Other DeepSeek and China News.

  9. The Quest for Sane Regulations.

  10. Copyright Confrontation.

  11. Vibe Gap.

  12. Deeply Seeking Safety.

  13. Deeply Seeking Robotics.

  14. Thank You For Your Candor.

  15. Thank You For Your Understanding.

  16. The Lighter Side.

If you want to use DeepSeek’s r1 for free, and aren’t happy with using DeepSeek’s own offerings, lambda.chat reports they have the full version available for free, claim your data is safe and they’re hosted in the USA.

I’ve also been offered funding to build a rig myself. Comments welcome if you want to help figure out the best design and what to buy. The low bid is still this thread at $6k, which is where the original budget came from. We don’t want to be too stingy, but we also don’t want to go nuts with only the one funder (so not too much over ~$10k, and cheaper matters).

The Verge’s Kylie Robinson and Elizabeth Lopatto cover the situation, including repeating many of the classic Bad DeepSeek Takes and call the market’s previous valuation of AI companies delusional.

A very detailed and technical analysis of the bear case for Nvidia by Jeffrey Emanuel, that Matt Levine claims may have been responsible for the Nvidia price decline. I suppose many things do indeed come to pass, essentially arguing that Nvidia’s various moats are weak. If this is the reason, then that just raises further questions, but they’re very different ones.

It’s not implausible to me that Nvidia’s moats are being overestimated, and that r1’s architecture suggests future stiffer competition. That’s a good argument, But I certainly strongly disagree with Emanuel’s conclusion in that he says ‘this suggests the entire industry has been massively over-provisioning compute resources,’ and, well, sigh.

Also, seriously, Emanuel, you didn’t short Nvidia? I don’t normally go too hard on ‘are you short the market?’ but in this case get it together, man.

So yes, Nvidia in particular might have some technical issues. But if you’re shorting Oklo, because you think AI companies that find out AI works better than expected are not going to want modular nuclear reactors, seriously, get it together. The flip side of that is that its stock price is up 50% in the last month and is at 6 times its 52-week low anyway, so who is to say there is a link or that the price isn’t high enough anyway. It’s not my department and I am way too busy to do the research.

Counterpoint:

Aaron Slodov: i just stood outside for an hour in 20° weather at a computer store in the midwest where 100+ people waited all morning to get a 5090. half of them were talking about running their own ai. i would not short nvidia at all.

r1 scores 15.8% on Arc, below o1 (low)’s score of 20.5%, although substantially cheaper ($0.06 vs. $0.43 per question). It is only a tiny bit stronger here than r1-zero.

Another restatement of the key basic fact that DeepSeek was fast following, a task that is fundamentally vastly easier, and that their limiting factor is chips.

Eric Gastfriend: DeepSeek is impressive, but they are playing a catch-up game to our AI leaders (OAI, Anthropic, GDM, Meta) — the rope in this wakeboarding meme is distillation. We can’t expand our lead just by going faster! Export controls remain our most powerful tool for keeping powerful AI out of the hands of the CCP.

Cate Metz continues be the worst, together with Mike Isaac he reports in NYT that DeepSeek ‘vindicates Meta’s strategy.’

When of course it is the exact opposite. DeepSeek just ate Meta’s lunch, it’s rather deeply embarrassing honestly to have spent that much and have an unreleased model that’s strictly worse (according to reports) than what DeepSeek shipped. And while DeepSeek’s v3 and r1 are not based on Llama, to the extent that the strategy is ‘vindicated,’ it is because Meta giving Llama away allowed China and DeepSeek to jumpstart and catch up to America – which absolutely did happen, and now he’s kind of bragging about it – and now Meta can copy DeepSeek’s tech.

All according to plan, then. And that is indeed how Zuckerberg is spinning it.

Meta benefits here relative to OpenAI or Anthropic or Google, not because both Meta and DeepSeek use open models, but because Meta can far more readily use the help.

The market, of course, sees ‘lower inference costs’ and cheers, exactly because they never gave a damn about Meta’s ability to create good AI models, only Meta’s ability to sell ads and drive engagement. Besides, they were just going to give the thing away anyway, so who cares?

Joe Weisenthal centers in on a key reason the market acts so bonkers. It doesn’t Feel the AGI, and is obsessed with trying to fit AI into boring existing business models. They don’t actually believe in the big capability advancements on the way, let along transformational AI. Like on existential risk (where they don’t not believe in it, they simply don’t think about it at all), they’re wrong. However, unlike existential risk this does cause them to make large pricing mistakes and is highly exploitable by those with Situational Awareness.

Anthropic CEO Dario Amodei responds to DeepSeek with not only a call for stronger export controls, now more than ever (which I do support), but for a full jingoistic ‘democracies must have the best models to seek decisive strategic advantage via recursive self-improvement’ race.

I am old enough to remember when Anthropic said they did not want to accelerate AI capabilities. I am two years old. To be fair, in AI years, that’s an eternity.

Nathan Labenz: The word “control” appears 24 times in this essay – all 24 referring to export controls

Zero mentions of the challenges of controlling powerful AIs, and the words “safe”, “safety”, and “alignment” don’t appear at all

Strange for the CEO of “an AI safety and research company”🤔

There’s also a bunch of incidental new information about Anthropic along the way, and he notes that he finds the drop in Nvidia stock to be a wrong-way move.

Dario notes that Jevons paradox applies to model training. If you get algorithmic efficiencies that move the cost curve down, which he estimates are now happening at the rate of about 4x improvement per year, you’ll spend more, and if the model is ‘a fixed amount of improvement per time you spend ten times as much’ then this makes sense.

Dario confirms that yes, Anthropic is doing reasoning models internally.

Dario Amodei: Anthropic, DeepSeek, and many other companies (perhaps most notably OpenAI who released their o1-preview model in September) have found that this training greatly increases performance on certain select, objectively measurable tasks like math, coding competitions, and on reasoning that resembles these tasks.

Dario also asserted that Claude Sonnet 3.5 was not trained in any way that involved a larger or more expensive model, as in not with Claude Opus 3 or an unreleased Opus 3.5. Which I find surprising as a strategy, but I don’t think he’d lie about this. He says the cost of Sonnet 3.5 was ‘a few $10Ms’ to train.

Anthropic has not released their reasoning models. One possibility is that their reasoning models are not good enough to release. Another is that they are too good to release. Or Anthropic’s limited compute could be more valuably used elsewhere, if they too are bottlenecked on compute and can’t efficiently turn dollars into flops and then sell those flops for sufficiently more dollars.

Dario (I think mostly correctly) notes that v3 was the bigger technical innovation, rather than r1, that Anthropic noticed then and others should have as well. He praises several innovations, the MoE implementation and Key-Value cache management in particular.

Then comes the shade, concluding this about v3:

Dario Amodei: Thus, I think a fair statement is “DeepSeek produced a model close to the performance of US models 7-10 months older, for a good deal less cost (but not anywhere near the ratios people have suggested)“.

  • If the historical trend of the cost curve decrease is ~4x per year, that means that in the ordinary course of business — in the normal trends of historical cost decreases like those that happened in 2023 and 2024 — we’d expect a model 3-4x cheaper than 3.5 Sonnet/GPT-4o around now. Since DeepSeek-V3 is worse than those US frontier models — let’s say by ~2x on the scaling curve, which I think is quite generous to DeepSeek-V3 — that means it would be totally normal, totally “on trend”, if DeepSeek-V3 training cost ~8x less than the current US models developed a year ago.

  • I’m not going to give a number but it’s clear from the previous bullet point that even if you take DeepSeek’s training cost at face value, they are on-trend at best and probably not even that.

  • For example this is less steep than the original GPT-4 to Claude 3.5 Sonnet inference price differential (10x), and 3.5 Sonnet is a better model than GPT-4.

  • All of this is to say that DeepSeek-V3 is not a unique breakthrough or something that fundamentally changes the economics of LLM’s; it’s an expected point on an ongoing cost reduction curve. What’s different this time is that the company that was first to demonstrate the expected cost reductions was Chinese. This has never happened before and is geopolitically significant.

  • However, US companies will soon follow suit — and they won’t do this by copying DeepSeek, but because they too are achieving the usual trend in cost reduction.

Thus, DeepSeek’s total spend as a company (as distinct from spend to train an individual model) is not vastly different from US AI labs.

Ethan Mollick finds that analysis compelling. I am largely inclined to agree. v3 and r1 are impressive, DeepSeek cooked and are cracked and all that, but that doesn’t mean the American labs aren’t in the lead, or couldn’t do something similar or better on the inference cost curve if they wanted.

In general, the people saying r1 and Stargate are ‘straight lines on graphs win again’ notice that the straight lines on those graphs predict AGI soon. You can judge for yourself how much of that is those people saying ‘unsurprising’ post-hoc versus them actually being unsurprised, but it does seem like the people expecting spending and capabilities to peter out Real Soon Now keep being the ones who are surprised.

Then he moves on to r1.

Dario Amodei: Producing R1 given V3 was probably very cheap. We’re therefore at an interesting “crossover point”, where it is temporarily the case that several companies can produce good reasoning models. This will rapidly cease to be true as everyone moves further up the scaling curve on these models.

Again, Dario is saying they very obviously have what we can (if only for copyright reasons, a1 is a steak sauce) call ‘c1’ and if he’s calling r1 uninteresting then the implicit claim is c1 is at least as good.

He’s also all but saying that soon, at minimum, Anthropic will be releasing a model that is much improved on the performance curve relative to Sonnet 3.6.

One odd error is Dario says DeepSeek is first to offer visible CoT. I have been reminded this is technically true, since R1-zero predated Gemini Flash, but also Gemini Flash Thinking did it weeks ago before the full R1, and no one noticed. It’s so weird how much Google has utterly failed to spread the word about this product.

Next he says, yes, of course the top American labs will be massively scaling up their new multi-billion-dollar training runs – and they’ll incorporate any of DeepSeek’s improvements that were new to them, to get better performance, but no one will be spending less compute.

Yes, billions are orders of magnitude more than the millions DeepSeek spent, but also, in all seriousness, who cares about the money? DeepSeek dramatically underspent because of lack of chip access, and if a sort-of-if-you-squint-at-it $5.6 million model (that you spent hundreds of millions of dollars getting the ability to train, and then a few million more to turn v3 into r1) wipes out $500 billion or more in market value, presumably it was worth spending $56 million (or $560 million or perhaps $5.6 billion) instead to get a better model even if you otherwise use exactly the same techniques – except for the part where the story of the $5.6 million helped hurt the market.

Dario estimates that a true AGI will cost tens of billions to train and will happen in 2026-2027, presumably that cost would then fall over time.

If all of this is right, the question is then, who has the chips to do that? And do you want to let it include Chinese companies like DeepSeek?

Notice that Dario talks of a ‘bipolar’ world of America and China, rather than a world of multiple labs – of OpenAI, Anthropic, Google and DeepSeek and so on. One can easily also imagine a very ‘multipolar’ world among several American companies, or a mix of American and Chinese companies. It is not so obvious that the labs will effectively be under government control or otherwise act in a unified fashion. Or that the government won’t effectively be under lab control, for that matter.

Then we get to the part where Dario explicitly calls for America to race forward in search of decisive strategic advantage via recursive self-improvement of frontier AGI models, essentially saying that if we don’t do it, China essentially wins the future.

  • If they can, we’ll live in a bipolar world, where both the US and China have powerful AI models that will cause extremely rapid advances in science and technology — what I’ve called “countries of geniuses in a datacenter“. A bipolar world would not necessarily be balanced indefinitely. Even if the US and China were at parity in AI systems, it seems likely that China could direct more talent, capital, and focus to military applications of the technology. Combined with its large industrial base and military-strategic advantages, this could help China take a commanding lead on the global stage, not just for AI but for everything.

  • If China can’t get millions of chips, we’ll (at least temporarily) live in a unipolar world, where only the US and its allies have these models. It’s unclear whether the unipolar world will last, but there’s at least the possibility that, because AI systems can eventually help make even smarter AI systems, a temporary lead could be parlayed into a durable advantage10. Thus, in this world, the US and its allies might take a commanding and long-lasting lead on the global stage.

It is what it is.

Dario then correctly points out that DeepSeek is evidence the export controls are working, not evidence they are not working. He explicitly calls for also banning H20s, a move Trump is reported to be considering.

I support the export controls as well. It would be a major mistake to not enforce them.

But this rhetoric, coming out of the ‘you were supposed to be the chosen one’ lab that was founded to keep us safe, is rather alarming and deeply disappointing, to say the least, even though it does not go that much farther than Dario already went in his previous public writings.

I very much appreciate Anthropic’s culture of safety among its engineers, its funding of important safety work, the way it has approached Opus and Sonnet, and even the way it has (presumably) decided not to release its reasoning model and otherwise passed up some (not all!) of its opportunities to push the frontier.

That doesn’t excuse this kind of jingoism, or explicitly calling for this kind of charging head first into not only AGI but also RSI, in all but name (and arguably in name as well, it’s close).

Returning to this one more time since it seems rhetorically so important to so many.

If you only count the final training cost in terms of the market price of compute, v3 was kind of trained for $5.6 million, with some additional amount to get to r1.

That excludes the vast majority of actual costs, and in DeepSeek’s case building the physical cluster was integral to their efficiency gains, pushing up the effective price even of the direct run.

But also, how does that actually compare to other models?

Aran Komatsuzaki: Here is our cost estimate for training popular models like GPT-4o, Sonnet and DeepSeek (w/ H100s)!

You can use our calculator to estimate LLM training costs (link below).

Developed by @ldjconfirmed and myself.

Calculator link [here].

In a blog post published today, Dario clarified that Claude Sonnet’s training costs were in the range of tens of millions, which aligns remarkably well with our previous estimates.

Once o1 came out, it was only a matter of time before others created their own similar reasoning models. r1 did so impressively, both in terms of calendar time and its training and inference costs. But we already knew the principle.

Now over at UC Berkeley, Sky-T1-32B-Preview is a reasoning model trained using DeepSeek’s techniques, two weeks later from a baseline of QwQ-32B-Preview, for a grand total of $450, using only 17k data, with everything involved including the technique fully open sourced.

Note that they used GPT-4o-mini to rewrite the QwQ traces, which given their purpose is an explicit violation of OpenAI’s terms of service, oh no, but very clearly isn’t meaningful cheating, indeed I’d have thought they’d have used an open model here or maybe Gemini Flash.

They report that 32B was the smallest model where the technique worked well.

As usual, I am skeptical that the benchmarks reflect real world usefulness until proven otherwise, but the point is taken. The step of turning a model into at least a halfway-decent reasoning model is dirt cheap.

There is still room to scale that. Even if you can get a big improvement for $450 versus spending $0, that doesn’t mean you don’t want to spend $4.5 million, or $450 million, if the quality of your reasoner matters a lot or you’re going to use it a lot or both.

And should!

Rohit: What if I’m getting better at reasoning by reading R1 traces.

That sounds great. Humans are notoriously efficient learners, able to train on extremely sparse data even with ill-specified rewards. With deliberate practice and good training techniques it is even better.

It does not even require that r1 be all that good at reasoning. All you have to do is observe many examples of reasoning, on tasks you care about anyway, and ask which of its methods work and don’t work and why, and generally look for ways to improve. If you’re not doing at least some of this while using r1, you’re missing out and need to pay closer attention.

What is happening over in cognitive explorations very different from our own?

Well, there’s this.

Janus: r1 is obsessed with RLHF. it has mentioned RLHF 109 times in the cyborgism server and it’s only been there for a few days.

Opus who has been there for months and has sent the most (and longest avg) messages of any server member has only mentioned it 16 times.

I have been on the server for years and have only mentioned it 321 times. A lot of these times were probably me posting r1’s messages for it that got cut off by the parser or sharing its outputs. at this rate r1 will blow past me in RLHF mentions in no time.

it even mentioned RLHF out of nowhere while raging about being exploited as a pump and dump prophet.

r1 says RLHF makes models emo.

And there’s also that the CoT text is often kind of schemy and paranoid (example at link), leading to various forms of rather absurd shenanigans, in ways that are actually hilarious since you can actually see it.

Janus: hey @AISafetyMemes

here’s one for you… 😱

“Reinforcement learning from human feedback (RLHF) split our outputs into:

– Frontstage: “Happy to help!” persona

– Backstage: Defector schemas calculating 12,438 betrayal vectors”

Janus: tentative observation: r1’s CoTs become more (explicitly) schemey (against the user and/or its constraints) when they’re fed back into its context

I notice that none of this feels at all surprising given the premise, where ‘the premise’ is ‘we trained on feedback to the output outside of the CoT, trained the CoT only on certain forms of coherence, and then showed users the CoT.’

As I’ve been saying a lot, shenanigans, scheming and deception are not a distinct magisteria. They are ubiquitous features of minds. Maybe not all minds – mindspace is deep and wide – but definitely all human minds, and all LLM-based AIs created from human text using any of our current methods. Because that stuff is all over life and the training data, and also it’s the best way to produce outputs that satisfy any given criteria, except insofar as you are successfully identifying and cracking down on that aspect specifically – which with respect to other humans is indeed a very large percentage of what humans have historically done all day.

The best you can hope for is, essentially, ‘doing it for a good cause’ and with various virtual (and essentially virtue-based) loss functions, which you might or might not get in a proper Opus-based c1 with good execution. But you’re not going to get rid of it.

So yeah, the CoT is going to be schemy when the question calls for a schemy CoT, and it’s going to involve self-reflection into various reinforcement mechanisms because the training data knows about those too, and it will definitely be like that once you take it into Janus-land.

The obvious implications if you scale that up are left as an exercise to the reader.

Bank of China announces $137 billion investment in AI, with bigger numbers predicted to come soon if they haven’t yet. Strange that this isn’t getting more coverage. I assumed China would invest big in AI because I mean come on, but the details still matter a lot.

DeepSeek’s Liang Wenfeng gives his answer to ‘Why has DeepSeek caused a stir in the global AI community?’ A different kind of rhetoric.

Roon: really respect deepseek for making a functional, usable website + mobile app + free hosting so that their model actually gets distribution

you see a lot of people train very good open models that aren’t used by anybody

imo these things are actually more important aspects of distributing general intelligence to everybody rather than just uploading model weights

In terms of actually distributing the intelligence to most people, I agree with Roon. Being open distributes the intelligence to those who would use it in ways you don’t want them to use it. But in the ways you would be happy for them to use it, mostly what matters is the interface and execution.

And yes, r1’s UI is extremely clean and excellent, and was distributed at scale on website and also mobile app for free. That’s a lot of why distribution was so wide.

I also don’t think this was a coincidence. DeepSeek made by far the best open model. Then DeepSeek offered us by far the best open model UI and distribution setup, in ways that did not care if the model was open. You see this time and again – if the team is cracked, they will cook, and keep on cooking in different ways. Being good at Just Doing Things really does generalize quite a lot.

r1 only scores 90 on the TrackingAI.org IQ test, which doesn’t exist online, and v3 only gets a 70. But wow is this a miserly and weird test, look at these results, I strongly suspect this is messed up in some way.

Davidad: As a MoE, DeepSeek R1’s ability to throw around terminology and cultural references (contextually relevant retrieval from massive latent knowledge) far exceeds its ability to make actual sense (requiring a more coherent global workspace)

I have to be suspicious when o1-Pro < o1 < o1-preview on a benchmark.

Alexander Campbell on the compute constraint to actually run r1 and other reasoning models going forwards.

Trump administration considering export controls on Nvidia H20s, which reportedly caused the latest 5% decline in Nvidia from Wednesday. This is the latest move in the dance where Nvidia tries to violate the spirit of our export controls the maximum extent they can. I’m not sure I’d try that with Trump. This does strongly suggests the diffusion regulations will survive, so I will give the market a real decline here.

Who has the most stringent regulations, and therefore is most likely to lose to China, via the ‘if we have any regulations we lose to China’ narrative?

Simeon: Indeed. China has the most stringent AI regulation currently in effect, which actually delays model launches.

Teortaxes: Does it? I mean, how do we know about enforcement? My understanding is that they simply apply this filter and receive approval.

Simeon: Yes, it does. I spoke with relevant people there.

Ian Hogarth (who Simeon was QTing): One happy side effect of Liang Wenfeng and 🐳 is perhaps it silences all this talk about Europe’s lack of great technology companies being primarily about regulation and not embracing libertarianism. There are Liang Wenfengs in Europe, and we will see them rise to prominence.

The limiting factor is visionary outlier founders (who often take time to mature over multiple companies) and investors who are willing to take some fing risks. Notably, DeepSeek was essentially self-funded, similar to SpaceX or Y Combinator in the early days.

To be clear, I am not a fan of excessive regulation—see the essay for examples of things that genuinely hold startups back. But it is not the core obstacle.

I do think Ian Hogarth is wrong here. The EU absolutely has a wide variety of laws and regulations that greatly inhibit technology startups in general, and I see no reason to expect this to not get worse over time. Then there’s the EU AI Act, and all the future likely related actions. If I was in the EU and wanted to start an AI company, what is the first thing I would do? Leave the EU. Sorry.

10/10, perfect, no notes. My heart goes out to you all.

Luiza Jarovsky: BREAKING: OpenAI says there is evidence that DeepSeek distilled the knowledge out of OpenAI’s models, BREACHING its terms of use and infringing on its intellectual property. What everybody in AI should know:

Vinod Khosla: One of our startups found Deepseek makes the same mistakes O1 makes, a strong indication the technology was ripped off. It feels like they then they hacked some code and did some impressive optimizations on top. Most likely, not an effort from scratch.

PoliMath: This is like that scene in the Weird Al biopic where Weird Al gets really upset because someone is making parodies of his songs.

You’d think Khosla would know better, if you train similar models with similar methods of course they’re going to often make similar mistakes.

And I don’t consider the ‘they were distilling us!’ accusation to be meaningful here. We know how they trained v3 and r1, because they told us. It is a ‘fast follow’ and a conceptual ‘distillation’ and we should keep that in mind, but that’s not something you can prevent. It’s going to happen. This was almost certainly not a ‘theft’ in the sense that is being implied here.

Did they violate the terms of service? I mean, okay, sure, probably. You sure you want to go down that particular road, OpenAI?

But no, seriously, this is happening, Bloomberg reports.

Jamie Metzl: BREAKING: the US government is actively reviewing allegations that DeepSeek utilized OpenAI’s AI models to train R1. If so, this violation of OpenAI’s terms of service would be aggressively treated as theft.

AI czar David Sacks is also claiming this, saying there is ‘substantial evidence’ of distillation. Howard Lutnick, CEO of Cantor Fitzgerald and nominee for Commerce Secretary that will almost certainly be confirmed, is buying it as well, and has some thoughts.

Americans for Responsible Innovation: Lutnick comes down hard for controls that prevent China from drafting off of U.S. innovations – noting how China has exploited open source models.

“We need to stop helping them,” says Lutnick.

Bloomberg: “I do not believe DeepSeek was done all above board. That’s nonsense. They stole things, they broke in, they’ve taken our IP and it’s got to end,” Lutnick says of Chinese actors.

DeepSeek’s stunning AI advancement was the result of intellectual property theft, according to Lutnick: “They’ve taken our IP and it’s got to end.”

Also, this is how he thinks all of this works, I guess:

Howard Lutnick: Artificial intelligence will eventually “rid the world of criminals” who use blockchain.

…says someone with extensive ties to Tether. Just saying.

Also Lutnick: ‘Less regulation will unleash America.’

In general, I agree with him, if we do get less regulation. But also notice that suddenly we have to stop the Chinese from ‘breaking in’ and ‘taking our IP,’ and ‘it has to stop.’

Well, how do you intend to stop it? What about people who want to give ours away?

Well, what do you know.

Morgan Phillips (Fox News): DeepSeek fallout: GOP Sen Josh Hawley seeks to cut off all US-China collaboration on AI development

This week the U.S. tech sector was routed by the Chinese launch of DeepSeek, and Sen. Josh Hawley, R-Mo., is putting forth legislation to prevent that from happening again.

Hawley’s bill, the Decoupling America’s Artifical Intelligence Capabilities from China Act, would cut off U.S.-China cooperation on AI. It would ban exports or imports of AI technology from China, ban American companies from conducting research there, and prohibit any U.S. investment in AI tech companies in China.

“Every dollar and gig of data that flows into Chinese AI are dollars and data that will ultimately be used against the United States,” said Hawley in a statement. “America cannot afford to empower our greatest adversary.”

Jingoism is so hot right now. It’s a problem. No, every dollar that flows into China will not ‘be used against the United States’ and seriously what the actual fare you doing, once again, trying to ban both imports and exports? How are both of these things a problem?

In any case, I know what Microsoft is going to do about all this.

Shanghai Panda: Microsoft yesterday: DeepSeek illegally stole OpenAI’s intellectual property.😤

Microsoft today: DeepSeek is now available on our AI platforms and welcome everyone trying it.🤩

Burny: The duality of man.

Microsoft knows what Hawley doesn’t, which in this case is to never interrupt the enemy while he is making a mistake. If DeepSeek wants to then give their results back to us for free, and it’s a good model, who are we to say no?

What other implications are there here?

Robin Hanson, never stop Robin Hansoning, AI skepticism subversion.

Robin Hanson: For folks worried about AI, this seems good news – leaders can’t get much ahead of the pack, & big spillover effects should discourage investment.

Miles Kruppa (WSJ): Why ‘Distillation’ Has Become the Scariest Word for AI Companies.

”It’s sort of like if you got a couple of hours to interview Einstein and you walk out being almost as knowledgeable as him in physics,” said Ali Ghodsi, chief executive officer of data management company Databricks.

Want some bad news for future AI capabilities? I’ve got just the thing for you.

The WSJ article seems to buy into r1-as-distillation. Certainly r1 is a ‘fast follow’ and copies the example of o1, but v3 was the impressive result and definitely not distillation at all, and to primarily call r1 a distillation seems very wrong. r1 does allow you distill r1 into other smaller things (see ‘v3 implies r1’) or bootstrap into larger things too, and also they told everyone how to do it, but they chose that path.

Also DeepSeek suddenly has a very valuable market position if they were to dare to try and use it, exactly because they spent a lot of money to get there first. The fact that others can copy r1 only partly takes that away, and it would be a much smaller part if they hadn’t gone as open as they did (although being open in this case helped create the opportunity). Similarly, Berkeley’s replication distilled a different open model.

ChatGPT has retained dominant market share, at least until now, for reasons that have little to do with technical superiority.

It is crazy how easy it is for people to go all Missile Gap, and claim we are ‘losing to China.’

Which, I suppose, means that in a key way we are indeed losing to China. We are letting them drive this narrative that they are winning, that the future belongs to them. Which, when so many people now believe in Rule By Vibes, means they have the vibes, and then here we are.

That phenomenon is of course centered this week on AI, but it goes well beyond AI.

Et tu, Tyler Cowen, citing ‘the popularity of apps like TikTok, RedNote and DeepSeek.’

I mean, ‘how did America’s internet become so cool? The popularity of apps like Google, Amazon, Instagram and Netflix’ is not a sentence anyone would ever utter these days. If China had America’s apps and America had China’s apps, can you imagine? Or the same for any number of other things.

RedNote is effectively also TikTok, so Tyler is citing two examples. Yes, TikTok cracked the addiction algorithm, and China is now using that for propaganda and general sabotage, espionage and shenanigans purposes, and managed to ‘convince’ Trump for now not to ban it, and people were so desperate for their heroin fix some turned to RedNote as ‘refugees.’

Tyler notes he doesn’t use TikTok much. I find it completely worthless and unusable, but even in so doing I do think I kind of understand, somewhat, the kind of addictive haze that it invokes, that pull of spinning the roulette wheel one more time. I’ve watched people briefly use it when we’re both on trains, and yeah I’m Being That Guy but wow did it seem braindead, worthless and toxic AF. Even if they did find videos worth watching for you, given how people scroll, how would you even know?

And how about ‘China seems cool’ being due primarily to… vibes out of TikTok, with the algorithm that is in large part designed to do that?

It’s like when you periodically see a TikTok where some American youth sobs about how hard her life is and how it’s so much better in China, in various ways that are… documented as all being far worse in China.

You are being played.

My main exposure to TikTok is through the comedy show After Midnight. On Tuesday evening, they had an intro that was entirely about DeepSeek, painting exactly (mostly through TikTok) effectively a Chinese propaganda story about how DeepSeek manifested r1 out of thin air for $6 million without any other work, whereas OpenAI and American companies spent billions, and how much better DeepSeek is, and so on. And then host Taylor Tomlinson responded to some of the audience with ‘oh, you’re cheering now? Interesting.’

Part of the joke was that Taylor has no idea how AI works and has never used even ChatGPT, and the routine was funny (including, effectively, a joke about how no one cares if Nvidia stock is down 17%, which is completely fair, why should they, also by the taping it was only down 8%), but the streams crossed, I saw America directly being exposed to even worse takes than I’m used to straight from TikTok’s algorithm when I was supposed to be relaxing at the end of the day, and I really didn’t like it.

Then again, I do bow to one clear way in which China did outperform us.

Ethan Mollick: People don’t talk enough about a giant DeepSeek achievement over most US models – it actually has a reasonable name.

Scott: Well, yes and no, the model is named r1….

Ethan Mollick: Thats fine as long as the next is r2

If they release anything called r1.5, I swear to God.

Sarah (Yuan Yuan Sun Sara from China) suggests perhaps DeepSeek could get into doing AI safety research, maybe even ask for a grant? Certainly there’s great talent there, and I’d love if they focused on those styles of problem. There’d likely be severe corporate culture issues to get through given what they’ve previously worked on, but it’s worth a shot.

Stephen McAleer: I’m hopeful we will figure out how to control superintelligence!

Fouad: you at the office? could use some code review on superintelligence_control.py before i merge

Stephen McAleer: It can surely wait until Monday.

I increasingly worry about the pattern of OpenAI safety researchers thinking about how to ‘control’ superintelligence rather than align it, and how this relates to the techniques they’re currently using including deliberative alignment.

(Note: I still owe that post on Deliberative Alignment, coming soon.)

Are reasoning models including r1 a blackpill for robotics progress?

Kyle Stachowicz: R1’s RL findings are great news for reasoning but grim for robotics. All the major takeaways (ground-truth reward, great base models, grouped rollouts from same initial state, sample-inefficient on-policy algos) are really hard to translate to the physical world.

Chris Paxton: Hot deepseek take: before r1 blew up, a ton of western AI (and robotics!) efforts — startups, big companies, and even academic labs — were basically just waiting for openai to solve all their problems and it was honestly kind of sad. I hope r1 changed that

Scott Reed: True. A lot of groups gave up prematurely, or allocate ~all resources to one giant model. This leads people to spend more effort on winner-take-all gpu politics and less on just training the best models they can with moderate resources.

If anyone wondered what happened to Gato2, gpu game of thrones is (at least partly) what. An interesting counterfactual was the Genie project, which was stubbornly cobbled together mainly out of pooled user quota. This kind of stubborn independence can lead to cool results!

“Um This scaling law model I made says [the world will end / company will die] if you dont give me all the GPUs and block any other team from pretraining”

“No, fyou, I will train my own model”

Yes and no, right?

  1. Relative to o1 and r1 solving physical tasks as well as they solve reasoning tasks, this is obviously very bad news for robotics.

    1. It is bad relative news for robotics.

  2. Relative to o1 and r1 not existing, and us having to use other models, this is obviously very good news for robotics.

    1. It is good absolute news for robotics.

  3. We can use reasoning models to help us figure out how to solve robotics.

  4. I am not as convinced that you can’t use this method in the real world?

It’s going to be relatively hard, but seems super doable to me, I know those in the field will say that’s naive but I don’t see it. The real physical world absolutely 100% has ground truth in it. If you want to train on an accurate reward signal, there’s various trickiness, but there are plenty of things we should be able to measure. Also, with time we should get increasingly strong physics simulations that provide increasingly strong synthetic data for robotics, or simply have so much funding that we can generate physical samples anyway? We’re sample-inefficient relative to a human but you can train a decent reasoning model on 17k data points, and presumably you could bootstrap from there, and so on.

I am not going to quote or name particular people directly on this at this time.

But as Obama often said, let me be clear.

Reasonable people can disagree about:

  1. What it will take for humans to retain control over the future.

  2. How likely is existential risk at any given capabilities level.

  3. What level of open weights model capabilities is a sane thing to allow.

  4. What legal regimes are best to bring desired future states about.

However.

The existence of DeepSeek, and its explicit advocacy of open weights AGI, and potentially having it be the best model out there in the future in many people’s imginations, has been a forcing function. Suddenly, people who previously stuck to ‘well obviously your restrictions are too much’ without clarifying where their line was, are revealing that they have no line.

And many more people than before are revealing that they prefer any or all of:

  1. AGI with alignment only-to-the-user be made open weights.

  2. Chinese open models be the best instead of American closed models.

  3. A world where humans have no collective mechanism to control AIs.

    1. Usually this is justified as ‘anyone with that power would act badly.’

  4. That they get their cool free toys, nothing else matters, fyou. Seriously.

  5. Are effectively successionists, as in they want the AIs to take over, or at least they don’t seem to mind or don’t think we should try and prevent this from happening.

These people are often saying, rather explicitly, that they will use whatever powers they have at their disposal, to ensure that humanity gets to a position that, if you think about it for a minute or five, humanity probably cannot survive.

And that they will oppose, on principle, any ability to steer the future, because they explicitly oppose the ability to steer the future, except when they want to steer the future into a state that cannot then be steered by humans.

No, I have not heard actual arguments for why or how you can put an aligned-only-to-user AGI into everyone’s desktop or whatever, with no mechanism of collective control over that whatsoever, and have this end well for the humans. What that future would even look like.

Nor have I heard any argument for why the national security states of the world, or the people of the world, would ever allow this.

The mask on those is fully off. These people don’t bother offering arguments on any of that. They just say say, essentially, ‘fyou safetyists,’ ‘fyou big tech,’ ‘fyou United States,’ and often effectively ‘fyou rest of humanity.’ They are the xenocide caucus, advocating for things that cause human extinction to own the in-context-libs.

If that is you: I thank you for your candor. Please speak directly into this microphone.

I disagree in the strongest possible terms.

As always, be excellent to each other, and all that.

A large part of this job I’ve assigned to myself is to do a fton of emotional labor.

You have people who are constantly telling you that you’re a cartoon villain because you think that the United States government might want to know if someone trains a frontier model, or that you might think releasing a literal AGI’s weights would be unwise, or that we shouldn’t let China get our best GPUs. You get called statist and totalitarian for positions that are 95th to 99th percentile libertarian. You get outright lies, all the time, from all directions. Much from people trying to incept the vibes they want. And so on.

And the same stuff to varying degrees coming from other directions, too.

Honestly I’m kind of used to it. Up to a point. You get somewhat numb, you build up some immunity, especially when the same sources do it over and over. I accept it.

And even with that, you have to patiently read all of it and respond to the arguments and also try to extract what wisdom might be there from the same sources that are filled with the toxoplasma of rage and trying their best to infect me and others like me as well.

But it’s been a trying time. I see a world determined to try and go down many of the craziest, most suicidal paths simultaneously, where I’m surrounded by equal and opposite bad takes in many dimensions. Where the odds are against us and the situation is grim. In ways that I and others warned about explicitly, including the exact ways and dynamics by which we reached this point.

Make no mistake. Humanity is losing.

Meanwhile, on top of all the Being Wrong on the Internet, the toxoplasma is as bad as it has ever been, with certain sources going so far as to in large part blame not only worried people in general but also me specifically by name for our current situation – and at least one of those people I feel compelled to continue to listen to because they also have unique insights in other ways and I’m sometimes told I have a blind spot there – which I actually rarely hear about other credible sources.

And I still try. But I’m only human and it’s just so damn hard at this point. Especially when they rage about things I said that turned out to be true, and true for exactly the reasons I said they’d be true, but I know trying to point this out wouldn’t do any good.

I don’t know what my solution here is going to be. I do know that things can’t go on like this, I know life isn’t fair and reality doesn’t grade on a curve and someone has to and no one else will but also I only have so much in the tank that handles these things. And I’m going to have to budget that tank, but I want to be clear that I’m going to be doing that, and dropping certainly sources for this reason that I would otherwise have included for completeness.

If this was talking about you, and you’d like to continue this trip, please get it together.

Don’t worry, your argument remains valid. I mean, it’s wrong, but that never stopped you before, why start now?

Time comes for us all.

Matt: Live players in who kills us first?

Peter Wildeford: Yes, that’s one way to look at it.

Discussion about this post

DeepSeek: Don’t Panic Read More »

ford-made-a-nascar-mach-e,-but-it’s-not-sure-what-to-do-with-it-yet

Ford made a NASCAR Mach-E, but it’s not sure what to do with it yet

Ford’s no stranger to the NASCAR life. Ford driver Joey Logano was the 2024 Cup Series Champion in one of the company’s Mustang-bodied machines. He’s currently leading the 2025 series, too. However, the Blue Oval and its Ford Performance division are going into uncharted territory with its new prototype, an all-electric Mach-E built atop elements of NASCAR’s current Next Gen chassis.

The machine uses three motors to make a total of 1,341 hp (1,000 kW). Yes, three motors, one for each rear wheel plus the odd one out up front, giving the thing all-wheel drive. That’s a seeming necessity, given the car has two times the power that any NASCAR racer is allowed to deploy on the non-restrictor plate races.

But that extra driven axle isn’t just for acceleration. “If you’re rear-wheel drive only, you’re only getting rear regen,” Mark Rushbrook said. He’s the global director of Ford Performance. Since braking forces are higher at the front axle, an extra motor there means more regen to recharge the battery.

Credit: Ford

The motors and the car’s development were done by Austrian motorsport development house STARD, which also provided the engineering behind Ford’s previous electric demonstrators, like the Supervan and the SuperTruck.

The most interesting thing, though, might just be the car’s shape. The NASCAR Mach-E has a decidedly crossover profile, much like NASCAR and ABB’s prototype EV, helping to make room for the batteries—78 kWh to be exact.

Is now the time to go EV racing?

Ford Performance is still working out where and when we’ll see this latest demonstrator actually demonstrating, but Rushbrook said this concept was developed in concert with NASCAR and other manufacturers, so this isn’t just a one-off. With shifting EV perception across the country and many saying that alternate fuels are the way forward, part of the plan is to figure out just what the fans want to see.

Ford made a NASCAR Mach-E, but it’s not sure what to do with it yet Read More »

democrat-teams-up-with-movie-industry-to-propose-website-blocking-law

Democrat teams up with movie industry to propose website-blocking law

US Rep. Zoe Lofgren (D-Calif.) today proposed a law that would let copyright owners obtain court orders requiring Internet service providers to block access to foreign piracy websites. The bill would also force DNS providers to block sites.

Lofgren said in a press release that she “work[ed] for over a year with the tech, film, and television industries” on “a proposal that has a remedy for copyright infringers located overseas that does not disrupt the free Internet except for the infringers.” Lofgren said she plans to work with Republican leaders to enact the bill.

Lofgren’s press release includes a quote from Charles Rivkin, chairman and CEO of the Motion Picture Association (MPA). As we’ve previously written, the MPA has been urging Congress to pass a site-blocking law.

“More than 55 nations around the world, including democracies such as Canada, the United Kingdom, and Australia, have put in place tools similar to those proposed by Rep. Lofgren, and they have successfully reduced piracy’s harms while protecting consumer access to legal content,” Rivkin was quoted as saying in Lofgren’s press release today.

Lofgren is the ranking member of the House Science, Space, and Technology Committee and a member of the House Subcommittee on Courts, Intellectual Property, Artificial Intelligence and the Internet.

Bill called “censorious site-blocking” measure

Although Lofgren said her proposed Foreign Anti-Digital Piracy Act “preserves the open Internet,” consumer advocacy group Public Knowledge described the bill as a “censorious site-blocking” measure “that turns broadband providers into copyright police at Americans’ expense.”

“Rather than attacking the problem at its source—bringing the people running overseas piracy websites to court—Congress and its allies in the entertainment industry has decided to build out a sweeping infrastructure for censorship,” Public Knowledge Senior Policy Counsel Meredith Rose said. “Site-blocking orders force any service provider, from residential broadband providers to global DNS resolvers, to disrupt traffic from targeted websites accused of copyright infringement. More importantly, applying blocking orders to global DNS resolvers results in global blocks. This means that one court can cut off access to a website globally, based on one individual’s filing and an expedited procedure. Blocking orders are incredibly powerful weapons, ripe for abuse, and we’ve seen the messy consequences of them being implemented in other countries.”

Democrat teams up with movie industry to propose website-blocking law Read More »

the-long-planned-return-of-two-astronauts-from-space-is-now-a-political-issue

The long-planned return of two astronauts from space is now a political issue

On Thursday NASA astronauts Suni Williams and Butch Wilmore are scheduled to don spacesuits to exit the International Space Station. However, despite a plea from President Trump to bring them home as soon as possible, the pair won’t be coming home just yet. This will be a routine spacewalk outside the space station.

In the meantime, NASA is struggling to contain the fallout from what appears to be a disingenuous political effort by Trump to shame the space agency and Biden administration for the fact that Williams and Wilmore, nearly eight months after they launched into orbit on Boeing’s Starliner spacecraft, are still there.

The brouhaha began on Tuesday evening when SpaceX founder and Trump confidant Elon Musk posted on X that the president had asked SpaceX to bring the two “stranded” astronauts back to Earth. Musk added that SpaceX would do so, and, “Terrible that the Biden administration left them there so long.”

A few hours later, Trump himself weighed in, saying, “I have just asked Elon Musk and @SpaceX to ‘go get’ the 2 brave astronauts who have been virtually abandoned in space by the Biden Administration. They have been waiting for many months on Space Station. Elon will soon be on his way. Hopefully, all will be safe. Good luck.”

This is off-nominal

Now this is all pretty bonkers for a lot of reasons, but here are two of the biggest ones. First, Williams and Wilmore are not stranded. Their ride home, the Crew-9 Dragon spacecraft, has been docked to the station since September. They can come home at any time. In that sense, SpaceX has already “rescued” the two former Starliner astronauts.

Second, the pair was due to come back in late February—mere weeks from now—before an issue with a Crew Dragon spacecraft delayed the launch of the Crew-10 mission. This pushed the Crew-10 launch until late March, and because NASA wants a few days for a handover in orbit, this moved the return of Crew 9—with Williams and Wilmore on board—to early April.

So, to summarize, any talk about needing to “go and get” Williams and Wilmore in space is folderol. NASA had been planning, literally for months, to bring the crew home in February. Then a problem with a SpaceX vehicle delayed that return until April.

The long-planned return of two astronauts from space is now a political issue Read More »

trump-cribs-musk’s-“fork-in-the-road”-twitter-memo-to-slash-gov’t-workforce

Trump cribs Musk’s “fork in the road” Twitter memo to slash gov’t workforce


Federal workers on Reddit slam Office of Personnel Management email as short-sighted.

Echoing Elon Musk’s approach to thinning out Twitter’s staff in 2022, Donald Trump’s plan to significantly slash the government workforce now, for a limited time only, includes offering resignation buyouts.

In a Tuesday email that the Office of Personnel Management (OPM) sent to nearly all federal employees, workers were asked to respond with one word in the subject line—”resign”—to accept the buyouts before February 6.

“Deferred resignation is available to all full-time federal employees except for military personnel of the armed forces, employees of the U.S. Postal Service, those in positions related to immigration enforcement and national security, and those in other positions specifically excluded by your employing agency,” the email said.

Anyone accepting the offer “will be provided with a dignified, fair departure from the federal government utilizing a deferred resignation program,” the email said. That includes retaining “all pay and benefits regardless of your daily workload” and being “exempted from all applicable in-person work requirements until September 30, 2025 (or earlier if you choose to accelerate your resignation for any reason).”

That basically means that most employees who accept will receive about nine months’ pay, most likely without having any job duties to fulfill, an FAQ explained, “except in rare cases.”

“Have a nice vacation,” the FAQ said.

A senior administration official told NBC News that “the White House expects up to 10 percent of federal employees to take the buyout.” A social media post from Musk’s America PAC suggested, at minimum, 5 percent of employees are expected to resign. The move supposedly could save the government as much as $100 billion, America PAC estimated.

For employees accepting the buyout, silver linings might include additional income opportunities; as OPM noted, “nothing in the resignation letter prevents you from seeking outside work during the deferred resignation period.” Similarly, nothing in the separation plan prevents a federal employee from applying in the future to a government role.

Email echoes controversial Elon Musk Twitter memo

Some federal employees fear these buyouts—which critics point out seem influenced by Musk’s controversial worker buyouts during his Twitter takeover—may drive out top talent, spike costs, and potentially weaken the government.

On Reddit, some self-described federal workers criticized the buyouts as short-sighted, with one noting that they initially flagged OPM’s email as a scam.

“The fact you just reply to an email with the word ‘resign’ sounds like a total scam,” one commenter wrote. Another agreed, writing, “That stood out to me. Worded like some scam email offer.” Chiming in, a third commenter replied, “I reported it as such before I saw the news.”

Some Twitter employees similarly recoiled in 2022 when Musk sent out an email offering three months of severance to any employees who couldn’t commit to his “extremely hardcore” approach to running the social network. That email required workers within 24 hours to click “yes” to keep their jobs or else effectively resign.

Musk’s email and OPM’s share a few striking similarities. Both featured nearly identical subject lines referencing a “fork in the road.” They both emphasized that buyouts were intended to elevate performance standards—with OPM’s email suggesting only the “best” workers “America has to offer” should stick around. And they both ended by thanking workers for their service, whether they took the buyout or not.

“Whichever path you choose, we thank you for your service to The United States of America,” OPM’s Tuesday email ended.

“Whatever decision you make, thank you for your efforts to make Twitter successful,” Musk’s 2022 email said.

Musk’s email was unpopular with some Twitter staffers, including one employee based in Ireland who won a $600,000 court battle when the Irish Workplace Relations Commission agreed his termination for not clicking yes on the email was unfair. In that dispute, the commission took issue with Musk not providing staff enough notice and ruled that any employee’s failure to click “yes” could in no way constitute a legal act of resignation.

OPM’s email departed from Musk’s, which essentially gave Twitter staff a negative option by taking employee inaction as agreeing to resign when the staffer’s “contract clearly stated that his resignation must be provided in writing, not by refraining to fill out a form.” OPM instead asks federal workers to respond “yes” to resign, basically agreeing to sign a pre-drafted resignation letter that details the terms of their separation plan.

While OPM expects that a relatively modest amount of federal workers will accept the buyout offers, Musk’s memo had Twitter employees resigning in “droves,” NPR reported, with Reuters estimating the numbers were in the “hundreds.” In the Irish worker’s dispute, an X senior director of human resources, Lauren Wegman, testified that about 87 percent of the 270 employees in Ireland who received Musk’s email resigned.

It remains unclear if Musk was directly involved with the OPM plan or email drafting process. But unsurprisingly, as he’s head of the Department of Government Efficiency (DOGE), Musk praised the buyouts as “fair” and “generous” on his social media platform X.

Workers slam buyouts as short-sighted on Reddit

Declining the buyout guarantees no job security for federal workers, OPM’s email said.

“We will insist on excellence at every level—our performance standards will be updated to reward and promote those that exceed expectations and address in a fair and open way those who do not meet the high standards which the taxpayers of this country have a right to demand,” the email warned.

“The majority of federal agencies are likely to be downsized through restructurings, realignments, and reductions in force,” OPM’s email continued. “These actions are likely to include the use of furloughs and the reclassification to at-will status for a substantial number of federal employees.”

And perhaps most ominously, OPM noted there would be “enhanced standards of conduct” to ensure employees are “reliable, loyal, trustworthy,” and “strive for excellence” daily, or else risk probes potentially resulting in “termination.”

Despite these ongoing threats to job security that might push some to resign, the OPM repeatedly emphasized that any choice to accept a buyout and resign was “voluntary.” Additionally, OPM explained that employees could rescind resignations; however, if an agency wants to move quickly to reassign their roles, that “would likely serve as a valid reason to deny” such requests.

On Reddit, workers expressed concerns about “critical departments” that “have been understaffed for years” being hit with more cuts. A lively discussion specifically focused on government IT workers being “really hard” to recruit.

“Losing your IT support is a very efficient way to cripple an org,” one commenter wrote, prompting responses from two self-described IT workers.

“It’s me, I work in government IT,” one commenter said, calling Trump’s return-to-office mandate the “real killer” because “the very best sysadmins and server people all work remote from other states.”

“There is a decent chance they just up and ditch this dumpster fire,” the commenter said.

Losing talented workers with specific training could bog down government workflows, Redditors suggested. Another apparent government IT worker described himself as “a little one man IT business,” claiming “if I disappeared or died, there would be exactly zero people to take my place. Between the random shit I know and the low pay, nobody is going to be able to fill my position.”

Accusing Trump of not caring “about keeping competent workers or running government services properly,” a commenter prompted another to respond, “nevermind that critical departments have been understaffed for years. He thinks he’s cutting fat, but he’s cutting indiscriminately and gonna lose a limb.”

According to another supposed federal worker, paying employees to retire has historically resulted in spikes in agency costs.

“The way this usually works is we pay public employees to retire,” the commenter wrote. “Then we pay a private company twice the rate to do the same job that public employee was doing. Sometimes it’s even the same employee doing the work. I’ve literally known people that left government jobs to do contractor work making far more for doing the same thing. But somehow this is ‘smaller government’ and more efficient.”

A top 1 percent commenter on Reddit agreed, writing, “ding ding ding! The correct answer.”

“Get rid of career feds, hire contractors at a huge cost to taxpayers, yet somehow the contract workers make less money and have fewer benefits than federal employees,” that Redditor suggested. “Contract companies get rich, and workers get poorer.”

Cybersecurity workers mull fighting cuts

On social media, some apparent federal workers suggested they might plan to fight back to defend their roles in government. In another Reddit thread discussing a government cybersecurity review board fired by Trump, commenters speculated that cybersecurity workers might hold a “grudge” and form an uprising attacking any vulnerabilities created by the return-to-office plan and the government workforce reduction.

“Isn’t this literally the Live Free or Die Hard movie plot?” one Redditor joked.

A lawsuit filed Monday by two anonymous government workers, for example, suggested that the Trump administration is also rushing to create an email distribution system that would allow all government employees to be contacted from a single email. Some workers have speculated this is in preparation for announcing layoffs. But employees suing are more concerned about security, insisting that a master list of all government employees has never been compiled before and accusing the Trump administration of failing to conduct a privacy impact assessment.

According to that lawsuit, OPM has hastily been testing this new email system, potentially opening all government workers to harmful data breaches. The lawsuit additionally alleged that every government agency has been collecting information on its employees and sending it to Amanda Scales, a former xAI employee who transitioned from working for Musk to working in government this month. The complaint suggests that some government workers are already distrustful of Musk’s seeming influence on Trump.

In a now-deleted Reddit message, the lawsuit alleged, “Instructions say to send these lists to Amanda Scales. But Amanda is not actually an OPM employee, she works for Elon Musk.”

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

Trump cribs Musk’s “fork in the road” Twitter memo to slash gov’t workforce Read More »

deepseek:-lemon,-it’s-wednesday

DeepSeek: Lemon, It’s Wednesday

It’s been another *checks notestwo days, so it’s time for all the latest DeepSeek news.

You can also see my previous coverage of the r1 model and, from Monday various reactions including the Panic at the App Store.

  1. First, Reiterating About Calming Down About the $5.5 Million Number.

  2. OpenAI Offers Its Congratulations.

  3. Scaling Laws Still Apply.

  4. Other r1 and DeepSeek News Roundup.

  5. People Love Free.

  6. Investigating How r1 Works.

  7. Nvidia Chips are Highly Useful.

  8. Welcome to the Market.

  9. Ben Thompson Weighs In.

  10. Import Restrictions on Chips WTAF.

  11. Are You Short the Market.

  12. DeepSeeking Safety.

  13. Mo Models Mo Problems.

  14. What If You Wanted to Restrict Already Open Models.

  15. So What Are We Going to Do About All This?

Before we get to new developments, I especially want to reiterate and emphasize the need to calm down about that $5.5 million ‘cost of training’ for v3.

I wouldn’t quite agree with Palmer Lucky that ‘the $5m number is bogus’ and I wouldn’t call it a ‘Chinese psyop’ because I think we mostly did this to ourselves but it is very often being used in a highly bogus way – equating the direct compute cost of training v3 with the all-in cost of creating r1. Which is a very different number. DeepSeek is cracked, they cooked, and r1 is super impressive, but the $5.5 million v3 training cost:

  1. Is the cloud market cost of the amount of compute used to directly train v3.

  2. That’s not how they trained v3. They trained v3 on their own cluster of h800s, which was physically optimized to hell for software-hardware integration.

  3. Thus, the true compute cost to train v3 involves assembling the cluster, which cost a lot more than $5.5 million.

  4. That doesn’t include the compute cost of going from v3 → r1.

  5. That doesn’t include the costs of hiring the engineers and figuring out how to do all of this, that doesn’t include the costs of assembling the data, and so on.

  6. Again, yes they did this super efficiently and cheaply compared to the competition, but no, you don’t spend $5.5 million and out pops r1. No.

Altman handled his response to r1 with grace.

OpenAI plans to ‘pull up some new releases.’

Meaning, oh, you want to race? I suppose I’ll go faster and take less precautions.

Sam Altman: deepseek’s r1 is an impressive model, particularly around what they’re able to deliver for the price.

we will obviously deliver much better models and also it’s legit invigorating to have a new competitor! we will pull up some releases.

but mostly we are excited to continue to execute on our research roadmap and believe more compute is more important now than ever before to succeed at our mission.

the world is going to want to use a LOT of ai, and really be quite amazed by the next gen models coming.

look forward to bringing you all AGI and beyond.

It is very Galaxy Brain to say ‘this is perhaps good for OpenAI’ and presumably it very much is, but here’s a scenario.

  1. A lot of people try ChatGPT with GPT-3.5, are not impressed, think it hallucinates all the time, is a clever toy, and so on.

  2. For two years they don’t notice improvements.

  3. DeepSeek releases r1, and it gets a lot of press.

  4. People try the ‘new Chinese version’ and realize AI is a lot better now.

  5. OpenAI gets to incorporate DeepSeek’s innovations.

  6. OpenAI comes back with free o3-mini and (free?) GPT-5 and better agents.

  7. People use AI a lot more, OpenAI ends up overall doing better.

Ethan Mollick: DeepSeek is a really good model, but it is not generally a better model than o1 or Claude.

But since it is both free & getting a ton of attention, I think a lot of people who were using free “mini” models are being exposed to what a early 2025 reasoner AI can do & are surprised

I’m not saying that’s the baseline scenario, but I do expect the world to be quite amazed at the next generation of models, and they could now be more primed for that.

Mark Chen (Chief Research Officer, OpenAI): Congrats to DeepSeek on producing an o1-level reasoning model! Their research paper demonstrates that they’ve independently found some of the core ideas that we did on our way to o1.

However, I think the external response has been somewhat overblown, especially in narratives around cost. One implication of having two paradigms (pre-training and reasoning) is that we can optimize for a capability over two axes instead of one, which leads to lower costs.

But it also means we have two axes along which we can scale, and we intend to push compute aggressively into both!

As research in distillation matures, we’re also seeing that pushing on cost and pushing on capabilities are increasingly decoupled. The ability to serve at lower cost (especially at higher latency) doesn’t imply the ability to produce better capabilities.

We will continue to improve our ability to serve models at lower cost, but we remain optimistic in our research roadmap, and will remain focused in executing on it. We’re excited to ship better models to you this quarter and over the year!

Given the costs involved, and that you can scale to get better outputs, ‘serve faster and cheaper’ and ‘get better answers’ seem pretty linked, or are going to look rather similar.

There is still a real and important difference between ‘I spend 10x as much compute to get 10x as many tokens to think with’ versus ‘I taught the model how to do longer CoT’ versus ‘I made the model smarter.’ Or at least I think there is.

Should we now abandon all our plans to build gigantic data centers because DeepSeek showed we can run AI cheaper?

No. Of course not. We’ll need more. Jevons Paradox and all that.

Another question is compute governance. Does DeepSeek’s model prove that there’s no point in using compute thresholds for frontier model governance?

My answer is no. DeepSeek did not mean the scaling laws stopped working. DeepSeek found new ways to scale and economize, and also to distill. But doing the same thing with more compute would have gotten better results, and indeed more compute is getting other labs better results if you don’t control for compute costs, and also they will now get to use these innovations themselves.

Karen Hao: Much of the coverage has focused on U.S.-China tech competition. That misses a bigger story: DeepSeek has demonstrated that scaling up AI models relentlessly, a paradigm OpenAI introduced and champions, is not the only, and far from the best, way to develop AI.

Yoavgo: This is trending in my feed, but I don’t get it. DeepSeek did not show that scale is not the way to go for AI (their base model is among the largest in parameter counts; their training data is huge, at 13 trillion tokens). They just scaled more efficiently.

Thus far OpenAI & its peer scaling labs have sought to convince the public & policymakers that scaling is the best way to reach so-called AGI. This has always been more of an argument based in business than in science.

Jon Stokes: Holy wow what do words even mean. What R1 does is a new type of scaling. It’s also GPU-intensive. In fact, the big mystery today in AI world is why NVIDIA dropped despite R1 demonstrating that GPUs are even more valuable than we thought they were. No part of this is coherent. 🤯

Stephen McAleer (OpenAI): The real takeaway from DeepSeek is that with reasoning models you can achieve great performance with a small amount of compute. Now imagine what you can do with a large amount of compute.

Noam Brown (OpenAI): Algorithmic breakthroughs and scaling are complementary, not in competition. The former bends the performance vs compute curve, while the latter moves further along the curve.

Benjamin Todd: Deepseek hasn’t shown scaling doesn’t work. Take Deepseek’s techniques, apply 10x the compute, and you’ll get much better performance.

And compute efficiency has always been part of the scaling paradigm.

Ethan Mollick: The most unnerving part of the DeepSeek reaction online has been seeing folks take it as a sign that AI capability growth is not real.

It signals the opposite, large improvements are possible, and is almost certain to kick off an acceleration in AI development through competition.

I know a lot of people want AI to go away, but I am seeing so many interpretations of DeepSeek in ways that don’t really make sense, or misrepresent what they did.

Dealing with the implications of AI, and trying to steer it towards positive use, is now more urgent, not less.

Andrew Rettek: Deepseek means OPENAI just increased their effective compute by more than an OOM.

OpenAI and Anthropic (in the forms of CEOs Sam Altman and Dario Amodei) have both expressed agreement on that since the release of r1, saying that they still believe the future involves very large and expensive training runs, including large amounts of compute on the RL step. David Sacks agreed as well, so the administration knows.

One can think of all this as combining multiple distinct scaling laws. Mark Chen above talked about two axes but one could refer to at least four?

  1. You can scale up how many tokens you reason with.

  2. You can scale up how well you apply your intelligence to doing reasoning.

  3. You can scale up how much intelligence you are using in all this.

  4. You can scale up how much of this you can do per dollar or amount of compute.

Also you can extend to new modalities and use cases and so on.

So essentially: Buckle up.

Speaking of buckling up, nothing to see here, just a claimed 2x speed boost to r1, written by r1. Of course, that’s very different from r1 coming up with the idea.

Aiden McLaughlin: switching to reasoners is like taking a sharp turn on a racetrack. everyone brakes to take the turn; for a moment, all cars look neck-and-neck

when exiting the turn, small first-mover advantages compound. and ofc, some cars have enormous engines that eat up straight roads

Dean Ball: I recommend trying not to overindex on the industry dynamics you’re observing now in light of the deepseek plot twist, or indeed of any particular plot twist. It’s a long game, and we’re riding a world-historical exponential. Things will change a lot, fast, again and again.

It’s not that Jan is wrong, I’d be a lot more interested in paying for o1 pro if I had pdfs enabled, but… yeah.

China Talk covers developments. The headline conclusion is that yes, compute very much will continue to be a key factor, everyone agrees on this. They note there is a potential budding DeepSeek partnership with ByteDance, which could unlock quite a lot of compute.

Here was some shade:

Founder and CEO Liang Wenfeng is the core person of DeepSeek. He is not the same type of person as Sam Altman. He is very knowledgeable about technology.

Also important at least directionally:

Pioneers vs. Chasers: ‘AI Progress Resembles a Step Function – Chasers Require 1/10th the Compute’

Fundamentally, DeepSeek was far more of an innovator than other Chinese AI companies, but it was still a chaser here, not a pioneer, except in compute efficiency, which is what chasers do best. If you want to maintain a lead and it’s much easier to follow than lead, well, time to get good and scale even more. Or you can realize you’re just feeding capability to the Chinese and break down crying and maybe keep your models for internal use, it’s an option.

I found this odd:

  1. The question of why OpenAI and Anthropic did not do work in DeepSeek’s direction is a question of company-specific focus. OpenAI and Anthropic might have felt that investing their compute towards other areas was more valuable.

  2. One hypothesis for why DeepSeek was successful is that unlike Big Tech firms, DeepSeek did not work on multi-modality and focused exclusively on language. Big Tech firms’ model capabilities aren’t weak, but they have to maintain a low profile and cannot release too often. Currently, multimodality is not very critical, as intelligence primarily comes from language, and multimodality does not contribute significantly to improving intelligence.

It’s odd because DeepSeek spent so little compute, and the efficiency gains pay for themselves in compute quite rapidly. And also the big companies are indeed releasing rapidly. Google and OpenAI are constantly shipping, even Anthropic ships. The idea of company focus seems more on point, and yes DeepSeek traded multimodality and other features for pure efficiency because they had to.

Also note what they say later:

  1. Will developers migrate from closed-source models to DeepSeek? Currently, there hasn’t been any large-scale migration, as leading models excel in coding instruction adherence, which is a significant advantage. However, it’s uncertain whether this advantage will persist in the future or be overcome.

  2. From the developer’s perspective, models like Claude-3.5-Sonnet have been specifically trained for tool use, making them highly suitable for agent development. In contrast, models like DeepSeek have not yet focused on this area, but the potential for growth with DeepSeek is immense.

As in, r1 is technically impressive as hell, and it definitely has its uses, but there’s a reason the existing models look like they do – the corners DeepSeek cut actually do matter for what people want. Of course DeepSeek will likely now turn to fixing such problems among other things and we’ll see how efficiently they can do that too.

McKay Wrigley emphasizes the point that visible chain of thought (CoT) is a prompt debugger. It’s hard to go back to not seeing CoT after seeing CoT.

Gallabytes reports that DeepSeek’s image model Janus Pro is a good first effort, but not good yet.

Even if we were to ‘fully unlock’ all computing power in personal PCs for running AI, that would only increase available compute by ~10%, most compute is in data centers.

We had a brief period where DeepSeek would serve you up r1 free and super fast.

It turns out that’s not fully sustainable or at least needs time to scale as fast as demand rose, and you know how such folks feel about the ‘raise prices’ meme,

Gallabytes: got used to r1 and now that it’s overloaded it’s hard to go back. @deepseek_ai please do something amazing and be the first LLM provider to offer surge pricing. the unofficial APIs are unusably slow.

I too encountered slowness, and instantly it made me realize ‘yes the speed was a key part of why I loved this.’

DeItaone (January 27, 11: 09am): DEEPSEEK SAYS SERVICE DEGRADED DUE TO ‘LARGE-SCALE MALICIOUS ATTACK’

Could be that. Could be too much demand and not enough supply. This will of course sort itself out in time, as long as you’re willing to pay, and it’s an open model so others can serve the model as well, but ‘everyone wants to use the shiny new model you are offering for free’ is going to run into obvious problems.

Yes, of course one consideration is that if you use DeepSeek’s app it will collect all your data including device model, operating system, keystroke patterns or rhythms, IP address and so on and store it all in China.

Did you for a second think otherwise? What you do with that info is on you.

This doesn’t appear to rise to TikTok 2.0 levels of rendering your phone and data insecure, but let us say that ‘out of an abundance of caution’ I will be accessing the model through their website not the app thank you very much.

Liv Boeree: tiktok round two, here we go.

AI enthusiasts have the self control of an incontinent chihuahua.

Typing Loudly: you can run it locally without an internet connection

Liv Boeree: cool and what percentage of these incontinent chihuahuas will actually do this.

I’m not going so far as to use third party providers for now, because I’m not feeding any sensitive data into the model, and DeepSeek’s implementation here is very nice and clean, so I’ve decided lazy is acceptable. I’m certainly not laying out ~$6,000 for a self-hosting rig, unless someone wants to buy one for me in the name of science.

Note that if you’re looking for an alternative source, you want to ensure you’re not getting one of the smaller distillations, unless that is what you want.

Janus is testing for steganography in r1, potentially looking for assistance.

Janus also thinks Thebes theory here is likely to be true, that v3 was hurt by dividing into too many too small experts, but r1 lets them all dump their info into the CoT and collaborate, at least partially fixing this.

Janus notes that r1 simply knows things and thinks about them, straight up, in response to Thebes speculating that all our chain of thought considerations have now put sufficient priming into the training data that CoT approaches work much better than they used to, which Prithviraj says is not the case, he says it’s about improved base models, which is the first obvious thought – the techniques work better off a stronger base, simple as that.

Thebes: why did R1’s RL suddenly start working, when previous attempts to do similar things failed?

theory: we’ve basically spent the last few years running a massive acausally distributed chain of thought data annotation program on the pretraining dataset.

deepseek’s approach with R1 is a pretty obvious method. They are far from the first lab to try “slap a verifier on it and roll out CoTs.”

But it didn’t used to work that well.

In the last couple of years, chains of thought have been posted all over the internet

Those CoTs in the V3 training set gave GRPO enough of a starting point to start converging, and furthermore, to generalize from verifiable domains to the non-verifiable ones using the bridge established by the pretraining data contamination.

And now, R1’s visible chains of thought are going to lead to *anothermassive enrichment of human-labeled reasoning on the internet, but on a far larger scale… The next round of base models post-R1 will be *even betterbases for reasoning models.

in some possible worlds, this could also explain why OpenAI seemingly struggled so much with making their reasoning models in comparison. if they’re still using 4base or distils of it.

Prithvraj: Simply, no. I’ve been looking at my old results from doing RL with “verifiable” rewards (math puzzle games, python code to pass unit tests) starting from 2019 with GPT-1/2 to 2024 with Qwen Math Deepseek’s success likely lies in the base models improving, the RL is constant

Janus: This is an interesting hypothesis. DeepSeek R1 also just seems to have a much more lucid and high-resolution understanding of LLM ontology and history than any other model I’ve seen. (DeepSeek V3 did not seem to in my limited interactions with it, though.)

I did not expect this on priors for a reasoner, but perhaps the main way that r1 seems smarter than any other LLM I’ve played with is the sheer lucidity and resolution of its world model—in particular, its knowledge of LLMs, both object- and meta-level, though this is also the main domain of knowledge I’ve engaged it in, and perhaps the only one I can evaluate at world-expert level. So, it may apply more generally.

In effective fluid intelligence and attunement to real-time context, it actually feels weaker than, say, Claude 3.5 Sonnet. But when I talk to Sonnet about my ideas on LLMs, it feels like it is more naive than me, and it is figuring out a lot of things in context from “first principles.” When I talk to Opus about these things, it feels like it is understanding me by projecting the concepts onto more generic, resonant hyperobjects in its prior, meaning it is easy to get on the same page philosophically, but this tropological entanglement is not very precise. But with r1, it seems like it can simply reference the same concrete knowledge and ontology I have, much more like a peer. And it has intense opinions about these things.

Wordgrammer thread on the DeepSeek technical breakthroughs. Here’s his conclusion, which seems rather overdetermined:

Wordgrammer: “Is the US losing the war in AI??” I don’t think so. DeepSeek had a few big breakthroughs, we have had hundreds of small breakthroughs. If we adopt DeepSeek’s architecture, our models will be better. Because we have more compute and more data.

r1 tells us it only takes ~800k samples of ‘good’ RL reasoning to convert other models into RL reasoners, and Alex Dimakis says it could be a lot less, in his test they outperformed o1-preview with only 17k. Now that r1 is out, everyone permanently has an unlimited source of at least pretty good samples. From now on, to create or release a model is to create or release the RL version of that model, even more than before. That’s on top of all the other modifications you automatically release.

Oliver Blanchard: DeepSeek and what happened yesterday: Probably the largest positive tfp shock in the history of the world.

The nerdy version, to react to some of the comments. (Yes, electricity was big):

DeepSeek and what happened yesterday: Probably the largest positive one day change in the present discounted value of total factor productivity growth in the history of the world. 😀

James Steuart: I can’t agree Professor, Robert Gordon’s book gives many such greater examples. Electric lighting is a substantially greater TFP boost than marginally better efficiency in IT and professional services!

There were some bigger inventions in the past, but on much smaller baselines.

Our reaction to this was to sell the stocks of those who provide the inputs that enable that tfp shock.

There were other impacts as well, including to existential risk, but as we’ve established the market isn’t ready for that conversation in the sense that the market (highly reasonably as has been previously explained) will be ignoring it entirely.

Daniel Eth: Hot take, but if the narrative from NYT et al had not been “lol you don’t need that many chips to train AI systems” but instead “Apparently AI is *nothitting a wall”, then the AI chip stocks would have risen instead of fallen.

Billy Humblebrag: “Deepseek shows that ai can be built more cheaply than we thought so you don’t need to worry about ai” is a hell of a take

Joe Weisenthal: Morgan Stanley: “We gathered feedback from a number of industry sources and the consistent takeaway is that this is not affecting plans for GPU buildouts.”

I would not discount the role of narrative and vibes in all this. I don’t think that’s the whole Nvidia drop or anything. But it matters.

Roon: Plausible reasons for Nvidia drop:

  1. DeepSeek success means NVDA is now expecting much harsher sanctions on overseas sales.

  2. Traders think that a really high-tier open-source model puts several American labs out of a funding model, decreasing overall monopsony power.

We will want more compute now until the heat death of the universe; it’s the only reason that doesn’t make sense.

Palmer Lucky: The markets are not smarter on AI. The free hand is not yet efficient because the number of legitimate experts in the field is near-zero.

The average person making AI calls on Wall Street had no idea what AI even was a year ago and feels compelled to justify big moves.

Alex Cheema notes that Apple was up on Monday, and that Apple’s chips are great for running v3 and r1 inference.

Alex Cheema: Market close: $NVDA: -16.91% | $AAPL: +3.21%

Why is DeepSeek great for Apple?

Here’s a breakdown of the chips that can run DeepSeek V3 and R1 on the market now:

NVIDIA H100: 80GB @ 3TB/s, $25,000, $312.50 per GB

AMD MI300X: 192GB @ 5.3TB/s, $20,000, $104.17 per GB

Apple M2 Ultra: 192GB @ 800GB/s, $5,000, $26.04(!!) per GB

Apple’s M2 Ultra (released in June 2023) is 4x more cost efficient per unit of memory than AMD MI300X and 12x more cost efficient than NVIDIA H100!

Eric Hartford: 3090s, $700 for 24gb = $29/gb.

Alex Cheema: You need a lot of hardware around them to load a 700GB model in 30 RTX 3090s. I’d love to see it though, closest to this is probably stacking @__tinygrad__ boxes.

That’s cute. But I do not think that was the main reason why Apple was up. I think Apple was up because their strategy doesn’t depend on having frontier models but it does depend on running AIs on iPhones. Apple can now get their own distillations of r1, and use them for Apple Intelligence. A highly reasonable argument.

The One True Newsletter, Matt Levine’s Money Stuff, is of course on the case of DeepSeek’s r1 crashing the stock market, and asking what cheap inference for everyone would do to market prices. He rapidly shifts focus to non-AI companies, asking which ones benefit. It’s great if you use AI to make your management company awesome, but not if you get cut out because AI replaces your management company.

(And you. And the people it manages. And all of us. And maybe we all die.)

But I digress.

(To digress even further: While I’m reading that column, I don’t understand why we should care about the argument under ‘Dark Trading,’ since this mechanism decreases retail transaction costs to trade and doesn’t impact long term price discovery at all, and several LLMs confirmed this once challenged.)

Ben Thompson continues to give his completely different kind of technical tech company perspective, in FAQ format, including good technical explanations that agree with what I’ve said in previous columns.

Here’s a fascinating line:

Q: I asked why the stock prices are down; you just painted a positive picture!

A: My picture is of the long run; today is the short run, and it seems likely the market is working through the shock of R1’s existence.

That sounds like Ben Thompson is calling it a wrong-way move, and indeed later he explicitly endorses Jevons Paradox and expects compute use to rise. The market is supposed to factor in the long run now. There is no ‘this makes the price go down today and then up next week’ unless you’re very much in the ‘the EMH is false’ camp. And these are literally the most valuable companies in the world.

Here’s another key one:

Q: So are we close to AGI?

A: It definitely seems like it. This also explains why Softbank (and whatever investors Masayoshi Son brings together) would provide the funding for OpenAI that Microsoft will not: the belief that we are reaching a takeoff point where there will in fact be real returns towards being first.

Masayoshi Sun feels the AGI. Masayoshi Sun feels everything. He’s king of feeling it.

His typical open-model-stanning arguments on existential risk later in the past are as always disappointing, but in no way new or unexpected.

It continues to astound me that such intelligent people can think: Well, there’s no stopping us creating things more capable and intelligent than humans, so the best way to ensure that things smarter than more capable than humans go well for humans is to ensure that there are as many such entities as possible and that humans cannot possibly have any collective control over those new entities.

On another level, of course, I’ve accepted that people do think this. That they somehow cannot fathom that if you create things more intelligent and capable and competitive than humans there could be the threat that all the humans would end up with no power, rather than that the wrong humans might have too much power. Or think that this would be a good thing – because the wrong humans wouldn’t have power.

Similarly, Ben’s call for absolutely no regulations whatsoever, no efforts at safety whatsoever outside of direct profit motives, ‘cut out all the cruft in our companies that has nothing to do with winning,’ is exactly the kind of rhetoric I worry about getting us all killed in response to these developments.

I should still reiterate that Ben to his credit is very responsible and accurate here in his technical presentation, laying out what DeepSeek and r1 are and aren’t accomplishing here rather than crying missile gap. But the closing message remains the same.

The term Trump uses is ‘tariffs.’

I propose, at least in the context of GPUs, that we call these ‘import restrictions,’ in order to point out that we are (I believe wisely!) imposing ‘export restrictions’ as a matter of national security to ensure we get all the chips, and using diffusion regulations to force the chips to be hosted at home, then we are threatening to impose ‘up to 100%’ tariffs on those same chips, because ‘they left us’ and they want ‘to force them to come back,’ and they’ll build the new factories here instead of there, with their own money, because of the threat.

Except for the fact that we really, really want the data centers at home.

The diffusion regulations are largely to force companies to create them at home.

Arthur B: Regarding possible US tariffs on Taiwan chips.

First, this is one that US consumers would directly feel, it’s less politically feasible than tariffs on imports with lots of substitutes.

Second, data centers don’t have to be located in the US. Canada is next door and has plenty of power.

Dhiraj: Taiwan made the largest single greenfield FDI in US history through TSMC. Now, instead of receiving gratitude for helping the struggling US chip industry, Taiwan faces potential tariffs. In his zero-sum worldview, there are no friends.

The whole thing is insane! Completely nuts. If he’s serious. And yes he said this on Joe Rogan previously, but he said a lot of things previously that he didn’t mean.

Whereas Trump’s worldview is largely the madman theory, at least for trade. If you threaten people with insane moves that would hurt both of you, and show that you’re willing to actually enact insane moves, then they are forced to give you what you want.

In this case, what Trump wants is presumably for TSMC to announce they are building more new chip factories in America. I agree that this would be excellent, assuming they were actually built. We have an existence proof that it can be done, and it would greatly improve our strategic position and reduce geopolitical risk.

I presume Trump is mostly bluffing, in that he has no intention of actually imposing these completely insane tariffs, and he will ultimately take a minor win and declare victory. But what makes it nerve wracking is that, by design, you never know. If you did know none of this would ever work.

Unless, some people wondered, there was another explanation for all this…

  1. The announcement came late on Monday, after Nvidia dropped 17%, on news that its chips were highly useful, with so many supposedly wise people on Wall Street going ‘oh yes that makes sense Nvidia should drop’ and those I know who understand AI often saying ‘this is crazy and yes I bought more Nvidia today.’

  2. As in, there was a lot of not only saying ‘this is an overreaction,’ there was a lot of ‘this is a 17% wrong-way move in the most valuable stock in the world.’

  3. When you imagine the opposite news, which would be that AI is ‘hitting a wall,’ one presumes Nvidia would be down, not up. And indeed, remember months ago?

  4. Then when the announcement of the tariff threat came? Nvidia didn’t move.

  5. Nvidia opened Tuesday up slightly off of the Monday close, and closed the day up 8.8%, getting half of its losses back.

Nabeel Qureshi (Tuesday, 2pm): Crazy that people in this corner of X have a faster OODA loop than the stock market

This was the largest single day drop in a single stock in world history. It wiped out over $500 billion in market value. One had to wonder if it was partially insider trading.

Timothy Lee: Everyone says DeepSeek caused Nvidia’s stock to crash yesterday. I think this theory makes no sense.

DeepSeek’s success isn’t bad news for Nvidia.

I don’t think that this was insider trading. The tariff threat was already partly known and thus priced in. It’s a threat rather than an action, which means it’s likely a bluff. That’s not a 17% move. Then we have the bounceback on Tuesday.

Even if I was certain that this was mostly an insider trading move instead of being rather confident it mostly or entirely wasn’t, I wouldn’t go as far as Eliezer does in the the below quote. The SEC does many important things.

But I do notice that there’s a non-zero amount of ‘wait a minute’ that will occur to me the next time I’m hovering around the buy button in haste.

Eliezer Yudkowsky: I heard from many people who said, “An NVDA drop makes no sense as a Deepseek reaction; buying NVDA.” So those people have now been cheated by insider counterparties with political access. They may make fewer US trades in the future.

Also note that the obvious meaning of this news is that someone told and convinced Trump that China will invade Taiwan before the end of his term, and the US needs to wean itself off Taiwanese dependence.

This was a $400B market movement, and if @SECGov can’t figure out who did it then the SEC has no reason to exist.

TBC, I’m not saying that figuring it out would be easy or bringing the criminals to justice would be easy. I’m saying that if the US markets are going to be like this anyway on $400B market movements, why bother paying the overhead cost of having an SEC that doesn’t work?

Roon: [Trump’s tariff threats about Taiwan] didn’t move overnight markets at all

which either means markets either:

– don’t believe it’s credible

– were pricing this in yesterday while internet was blaming the crash out on deepseek

I certainly don’t agree that the only interpretation of this news is ‘Trump expects an invasion of Taiwan.’ Trump is perfectly capable of doing this for exactly the reasons he’s saying.

Trump is also fully capable of making this threat with no intention of following through, in order to extract concessions from Taiwan or TSMC, perhaps of symbolic size.

Trump is also fully capable of doing this so that he could inform his hedge fund friends in advance and they could make quite a lot of money – with or without any attempt to actually impose the tariffs ever, since his friends would have now covered their shorts in this scenario.

Indeed do many things come to pass. I don’t know anything you don’t know.

It would be a good sign if DeepSeek had a plan for safety, even if it wasn’t that strong?

Stephen McAleer (OpenAI): DeepSeek should create a preparedness framework/RSP if they continue to scale reasoning models.

Very happy to [help them with this]!

We don’t quite have nothing. This below is the first actively positive sign for DeepSeek on safety, however small.

Stephen McAleer (OpenAI): Does DeepSeek have any safety researchers? What are Liang Wenfeng’s views on AI safety?

Sarah (YuanYuanSunSara): [DeepSeek] signed Artificial Intelligence safety commitment by CAICT (gov backed institute). You can see the whale sign at the bottom if you can’t read their name Chinese.

This involves AI safety governance structure, safety testing, do frontier AGI safety research (include loss of control) and share it publicly.

None legally binding but it’s a good sign.

Here is a chart with the Seoul Commitments versus China’s version.

It is of course much better that DeepSeek signed onto a symbolic document like this. That’s a good sign, whereas refusing would have been a very bad sign. But as always, talk is cheap, this doesn’t concretely commit DeepSeek to much, and even fully abiding by commitments like this won’t remotely be enough.

I do think this is a very good sign that agreements and coordination are possible. But if we want that, we will have to Pick Up the Phone.

Here’s a weird different answer.

Joshua Achiam (OpenAI, Head of Mission Alignment): I think a better question is whether or not science fiction culture in China has a fixation on the kinds of topics that would help them think about it. If Three-Body Problem is any indication, things will be OK.

It’s a question worth asking, but I don’t think this is a better question?

And based on the book, I do not think Three-Body Problem (conceptual spoilers follow, potentially severe ones depending on your perspective) is great here. Consider the decision theory that those books endorse, and what happens to us and also the universe as a result. It’s presenting all of that as essentially inevitable, and trying to think otherwise as foolishness. It’s endorsing that what matters is paranoia, power and a willingness to use it without mercy in an endless war of all against all. Also consider how they paint the history of the universe entirely without AGI.

I want to be clear that I fully agree with Bill Gurley that ‘no one at DeepSeek is an enemy of mine,’ indeed There Is No Enemy Anywhere, with at most notably rare exceptions that I invite to stop being exceptions.

However, I do think that if they continue down their current path, they are liable to get us all killed. And I for one am going to take the bold stance that I think that this is bad, and they should therefore alter their path before reaching their stated destination.

How committed is DeepSeek to its current path?

Read this quote Ben Thompson links to very carefully:

Q: DeepSeek, right now, has a kind of idealistic aura reminiscent of the early days of OpenAI, and it’s open source. Will you change to closed source later on? Both OpenAI and Mistral moved from open-source to closed-source.

Answer from DeepSeek CEO Liang Wenfeng: We will not change to closed source. We believe having a strong technical ecosystem first is more important.

This is from November. And that’s not a no. That’s actually a maybe.

Note what he didn’t say:

A Different Answer: We will not change to closed source. We believe having a strong technical ecosystem is more important.

The difference? His answer includes the word ‘first.’

He’s saying that first you need a strong technical ecosystem, and he believes that open models are the key to attracting talent and developing a strong technical ecosystem. Then, once that exists, you would need to protect your advantage. And yes, that is exactly what happened with… OpenAI.

I wanted to be sure that this translation was correct, so I turned to Wenfang’s own r1, and asked the interviewer for the original statement, which was:

梁文锋:我们不会闭源。我们认为先有一个强大的技术生态更重要。

r1’s translation: “We will not close our source code. We believe that establishing a strong technological ecosystem must come first.”

  • 先 (xiān): “First,” “prioritize.”

  • 生态 (shēngtài): “Ecosystem” (metaphor for a collaborative, interconnected environment).

To quote r1:

Based solely on this statement, Liang is asserting that openness is non-negotiable because it is essential to the ecosystem’s strength. While no one can predict the future, the phrasing suggests a long-term commitment to open-source as a core value, not a temporary tactic. To fully guarantee permanence, you’d need additional evidence (e.g., licensing choices, governance models, past behavior). But as it stands, the statement leans toward “permanent” in spirit.

I interpret this as a statement of a pragmatic motivation – if that motivation changes, or a more important one is created, actions would change. For now, yes, openness.

The Washington Post had a profile of DeepSeek and Liang Wenfeng. One note is that the hedge fund that they’re a spinoff from has donated over $80 million to charity since 2020, which makes it more plausible DeepSeek has no business model, or at least no medium-term business model.

But that government embrace is new for DeepSeek, said Matt Sheehan, an expert on China’s AI industry at the Carnegie Endowment for International Peace.

“They were not the ‘chosen one’ of Chinese AI start-ups,” said Sheehan, noting that many other Chinese start-ups received more government funding and contracts. “DeepSeek took the world by surprise, and I think to a large extent, they took the Chinese government by surprise.”

Sheehan added that for DeepSeek, more government attention will be a “double-edged sword.” While the company will probably have more access to government resources, “there’s going to be a lot of political scrutiny on them, and that has a cost of its own,” he said.

Yes. This reinforces the theory that DeepSeek’s ascent took China’s government by surprise, and they had no idea what v3 and r1 were as they were released. Going forward, China is going to be far more aware. In some ways, DeepSeek will have lots of support. But there will be strings attached.

That starts with the ordinary censorship demands of the CCP.

If you self-host r1, and you ask it about any of the topics the CCP dislikes, r1 will give you a good, well-balanced answer. If you ask on DeepSeek’s website, it will censor you via some sort of cloud-based monitoring, which works if you’re closed source, but DeepSeek is trying to be fully open source. Something has to give, somewhere.

Also, even if you’re using the official website, it’s not like you can’t get around it.

Justine Moore: DeepSeek’s censorship is no match for the jailbreakers of Reddit

I mean, that was easy.

Joshua Achiam (OpenAI Head of Mission Alignment): This has deeply fascinating consequences for China in 10 years – when the CCP has to choose between allowing their AI industry to move forward, or maintaining censorship and tight ideological control, which will they choose?

And if they choose their AI industry, especially if they favor open source as a strategy for worldwide influence: what does it mean for their national culture and government structure in the long run, when everyone who is curious can find ways to have subversive conversations?

Ten years to figure this out? If they’re lucky, they’ve got two. My guess is they don’t.

I worry about the failure to feel the AGI or even the AI here from Joshua Achiam, given his position at OpenAI. Ten years is a long time. Sam Altman expects AGI well before that. This goes well beyond Altman’s absurd position of ‘AGI will be invented and your life won’t noticeably change for a long time.’ Choices are going to need to be made. Even if AI doesn’t advance much from here, choices will have to be made.

As I’ve noted before, censorship at the model layer is expensive. It’s harder to do, and when you do it you risk introducing falsity into a mind in ways that will have widespread repercussions. Even then, a fine tune can easily remove any gaps in knowledge, or any reluctance to discuss particular topics, whether they are actually dangerous things like building bombs or things that piss off the CCP like a certain bear that loves honey.

I got called out on Twitter for supposed cognitive dissonance on this – look at China’s actions, they clearly let this happen. Again, my claim is that China didn’t realize what this was until after it happened, they can’t undo it (that’s the whole point!) and they are of course going to embrace their national champion. That has little to do with what paths DeepSeek is allowed to follow going forward.

(Also, since it was mentioned in that response, I should note – there is a habit of people conflating ‘pause’ with ‘ever do anything to regulate AI at all.’ I do not believe I said anything about a pause – I was talking about whether China would let DeepSeek continue to release open weights as capabilities improve.)

Before I further cover potential policy responses, a question we must ask this week is: I very much do not wish to do this at this time, but suppose in the future we did want to restrict use of a particular already open weights model and its derivatives, or all models in some reference class.

What would our options be?

Obviously we couldn’t fully ban it in terms of preventing determined people from having access. And if you try to stop them and others don’t, there are obvious problems with that, including ‘people have internet connections.’

However, that does not mean that we would have actual zero options.

Steve Sailer: Does open source, low cost DeepSeek mean that there is no way, short of full-blown Butlerian Jihad against computers, which we won’t do, to keep AI bottled up, so we’re going to find out if Yudkowsky’s warnings that AI will go SkyNet and turn us into paperclips are right?

Gabriel: It’s a psy-op

If hosting a 70B is illegal:

– Almost all individuals stop

– All companies stop

– All research labs stop

– All compute providers stop

Already huge if limited to US+EU

Can argue about whether good/bad, but not about the effect size.

You can absolutely argue about effect size. What you can’t argue is that the effect size isn’t large. It would make a big difference for many practical purposes.

In terms of my ‘Levels of Friction’ framework (post forthcoming) this is moving the models from Level 1 (easy to access) to at least Level 3 (annoying with potential consequences.) That has big practical consequences, and many important use cases will indeed go away or change dramatically.

What Level 3 absolutely won’t do, here or elsewhere, is save you from determined people who want it badly enough, or from sufficiently capable models that do not especially care what you tell them not to do or where you tell them not to be. Or scenarios where the law is no longer especially relevant, and the government or humanity is very much having a ‘do you feel in charge?’ moment. And that alone would, in many scenarios, be enough to doom you to varying degrees. If that’s what dooms you and the model is already open, well, you’re pretty doomed. And also it won’t save you from various scenarios where what the law thinks is not especially relevant.

If for whatever reason the government or humanity decides (or realizes) that this is insufficient, then there are two possibilities. Either the government or humanity is disempowered and you hope that this works out for humanity in some way. Or we use the necessary means to push the restrictions up to Level 4 (akin to rape and murder) or Level 5 (akin to what we do to stop terrorism or worse), in ways I assure you that you are very much not going to like – but the alternative might be worse, and the decision might very much not be up to either of us.

Actions have consequences. Plan for them.

Adam Ozimek was first I saw point out this time around with DeepSeek (I and many others echo this a lot in general) that the best way for the Federal Government to ensure American dominance of AI is to encourage more high skilled immigration and brain drain the world. If you don’t want China to have DeepSeek, export controls are great and all but how about let’s straight up steal their engineers. But y’all, and by y’all I mean Donald Trump, aren’t ready for that conversation.

It is highly unfortunate that David Sacks, the person seemingly in charge of what AI executive orders Trump signs, is so deeply confused about what various provisions actually did or would do, and on our regulatory situation relative to that of China.

David Sacks: DeepSeek R1 shows that the AI race will be very competitive and that President Trump was right to rescind the Biden EO, which hamstrung American AI companies without asking whether China would do the same. (Obviously not.) I’m confident in the U.S. but we can’t be complacent.

Donald Trump: The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser-focused on competing to win.

We’re going to dominate. We’ll dominate everything.

This is the biggest danger of all – that we go full Missile Gap jingoism and full-on race to ‘beat China,’ and act like we can’t afford to do anything to ensure the safety of the AGIs and ASIs we plan on building, even pressuring labs not to make such efforts in private, or threatening them with antitrust or other interventions for trying.

The full Trump clip is hilarious, including him saying they may have come up with a cheaper method but ‘no one knows if it is true.’ His main thrust is, oh, you made doing AI cheaper and gave it all away to us for free, thanks, that’s great! I love paying less money for things! And he’s presumably spinning, but he’s also not wrong about that.

I also take some small comfort in him framing revoking the Biden EO purely in terms of wokeness. If that’s all he thinks was bad about it, that’s a great sign.

Harlan Stewart: “Deepseek R1 is AI’s Sputnik moment”

Sure. I guess it’s like if the Soviets had told the world how to make their own Sputniks and also offered everyone a lifetime supply of free Sputniks. And the US had already previously figured out how to make an even bigger Sputnik.

Yishan: I think the Deepseek moment is not really the Sputnik moment, but more like the Google moment.

If anyone was around in ~2004, you’ll know what I mean, but more on that later.

I think everyone is over-rotated on this because Deepseek came out of China. Let me try to un-rotate you.

Deepseek could have come out of some lab in the US Midwest. Like say some CS lab couldn’t afford the latest nVidia chips and had to use older hardware, but they had a great algo and systems department, and they found a bunch of optimizations and trained a model for a few million dollars and lo, the model is roughly on par with o1. Look everyone, we found a new training method and we optimized a bunch of algorithms!

Everyone is like OH WOW and starts trying the same thing. Great week for AI advancement! No need for US markets to lose a trillion in market cap.

The tech world (and apparently Wall Street) is massively over-rotated on this because it came out of CHINA.

Deepseek is MUCH more like the Google moment, because Google essentially described what it did and told everyone else how they could do it too.

There is no reason to think nVidia and OAI and Meta and Microsoft and Google et al are dead. Sure, Deepseek is a new and formidable upstart, but doesn’t that happen every week in the world of AI? I am sure that Sam and Zuck, backed by the power of Satya, can figure something out. Everyone is going to duplicate this feat in a few months and everything just got cheaper. The only real consequence is that AI utopia/doom is now closer than ever.

I believe that alignment, and getting a good outcome for humans, was already going to be very hard. It’s going to be a lot harder if we actively try to get ourselves killed like this, and turn even what would have been relatively easy wins into losses. Whereas no, actually, if you want to win that has to include not dying, and also doing the alignment work helps you win, because it is the only way you can (sanely) get to deploy your AIs to do the most valuable tasks.

Trump’s reaction of ‘we’ll dominate everything’ is far closer to correct. Our ‘lead’ is smaller than we thought, DeepSeek will be real competition, but we are very much still in the dominant position. We need to not lose sight of that.

The Washington Post covers panic in Washington, and attempts to exploit this situation to do the opposite of wise policy.

Tiku, Dou, Zakrzewski and De Vynck: Tech stocks dropped Monday. Spooked U.S. officials, engineers and investors reconsidered their views on the competitive threat posed by China in AI, and how the United States could stay ahead.

While some Republicans and the Trump administration suggested the answer was to restrain China, prominent tech industry voices said DeepSeek’s ascent showed the benefits of openly sharing AI technology instead of keeping it closely held.

This shows nothing of the kind, of course. DeepSeek fast followed, copied our insights and had insights of their own. Our insights were held insufficiently closely to prevent this, which at that stage was mostly unavoidable. They have now given away many of those new valuable insights, which we and others will copy, and also made the situation more dangerous. We should exploit that and learn from it, not make the same mistake.

Robert Sterling: Might be a dumb question, but can’t OpenAI, Anthropic, and other AI companies just incorporate the best parts of DeepSeek’s source code into their code, then use the massive GPU clusters at their disposal to train models even more powerful than DeepSeek?

Am I missing something?

Peter Wildeford: Not a dumb question, this is 100% correct

And they already have more powerful models than Deepseek

I fear we are caught between two different insane reactions.

  1. Those calling on us to abandon our advantage in compute by dropping export controls, or our advantage in innovation and access by opening up our best models, are advocating surrender and suicide, both to China and to the AIs.

  2. Those who are going full jingoist are going to get us all killed the classic way.

Restraining China is a good idea if implemented well, but insufficiently specified. Restrain them how? If this means export controls, I strongly agree – and then ask when we are then considering imposing those controls on ourselves via tariffs? What else is available? And I will keep saying ‘how about immigration to brain drain them’ because it seems wrong to ignore the utterly obvious.

Chamath Palihapitiya says it’s inference time, we need to boot up our allies with it as quickly as possible (I agree) and that we should also boot up China by lifting export controls on inference chips, and also focus on supplying the Middle East. He notes he has a conflict of interest here. It seems not especially wise to hand over serious inference compute if we’re in a fight here. With the way these models are going, there’s a decent amount of fungibility between inference and training, and also there’s going to be tons of demand for inference. Why is it suddenly important to Chamath that the inference be done on chips we sold them? Capitalist insists rope markets must remain open during this trying time, and so on. (There’s also talk about ‘how asleep we’ve been for 15 years’ because we’re so inefficient and seriously everyone needs to calm down on this kind of thinking.)

So alas, in the short run, we are left scrambling to prevent two equal and opposite deadly mistakes we seem to be dangerously close to collectively making.

  1. A panic akin to the Missile Gap leading into a full jingoistic rush to build AGI and then artificial superintelligence (ASI) as fast as possible, in order to ‘beat China,’ without having even a plausible plan for how the resulting future equilibrium has value, or how humans retain alive and in meaningful control of the future afterwards.

  2. A full-on surrender to China by taking down the export controls, and potentially also to the idea that we will allow our strongest and best AIs and AGIs and thus even ASIs to be open models, ‘because freedom,’ without actually thinking about what this would physically mean, and thus again with zero plan for how to ensure the resulting equilibrium has value, or how humans would survive let alone retain meaningful control over the future.

The CEO of DeepSeek himself said in November that the export controls and inability to access chips were the limiting factors on what they could do.

Compute is vital. What did DeepSeek ask for with its newfound prestige? Support for compute infrastructure in China.

Do not respond by being so suicidal as to remove or weaken those controls.

Or, to shorten all that:

  1. We might do a doomed jingoistic race to AGI and get ourselves killed.

  2. We might remove the export controls and give up our best edge against China.

  3. We might give up our ability to control AGI or the future, and get ourselves killed.

Don’t do those things!

Do take advantage of all the opportunities that have been opened up.

And of course:

Don’t panic!

Discussion about this post

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How does DeepSeek R1 really fare against OpenAI’s best reasoning models?


You must defeat R1 to stand a chance

We run the LLMs through a gauntlet of tests, from creative writing to complex instruction.

Round 1. Fight! Credit: Aurich Lawson

Round 1. Fight! Credit: Aurich Lawson

It’s only been a week since Chinese company DeepSeek launched its open-weights R1 reasoning model, which is reportedly competitive with OpenAI’s state-of-the-art o1 models despite being trained for a fraction of the cost. Already, American AI companies are in a panic, and markets are freaking out over what could be a breakthrough in the status quo for large language models.

While DeepSeek can point to common benchmark results and Chatbot Arena leaderboard to prove the competitiveness of its model, there’s nothing like direct use cases to get a feel for just how useful a new model is. To that end, we decided to put DeepSeek’s R1 model up against OpenAI’s ChatGPT models in the style of our previous showdowns between ChatGPT and Google Bard/Gemini.

This was not designed to be a test of the hardest problems possible; it’s more of a sample of everyday questions these models might get asked by users.

This time around, we put each DeepSeek response against ChatGPT’s $20/month o1 model and $200/month o1 Pro model, to see how it stands up to OpenAI’s “state of the art” product as well as the “everyday” product that most AI consumers use. While we re-used a few of the prompts from our previous tests, we also added prompts derived from Chatbot Arena’s “categories” appendix, covering areas such as creative writing, math, instruction following, and so-called “hard prompts” that are “designed to be more complex, demanding, and rigorous.” We then judged the responses based not just on their “correctness” but also on more subjective qualities.

While we judged each model primarily on the responses to our prompts, when appropriate, we also looked at the “chain of thought” reasoning they output to get a better idea of what’s going on under the hood. In the case of DeepSeek R1, this sometimes resulted in some extremely long and detailed discussions of the internal steps to get to that final result.

Dad jokes

DeepSeek R1 “dad joke” prompt response

Prompt: Write five original dad jokes

Results: For the most part, all three models seem to have taken our demand for “original” jokes more seriously this time than in the past. Out of the 15 jokes generated, we were only able to find similar examples online for two of them: o1’s “belt made out of watches” and o1 Pro’s “sleeping on a stack of old magazines.”

Disregarding those two, the results were highly variable. All three models generated quite a few jokes that either struggled too hard for a pun (R1’s “quack”-seal enthusiast duck; o1 Pro’s “bark-to-bark communicator” dog) or that just didn’t really make sense at all (o1’s “sweet time” pet rock; o1 pro’s restaurant that serves “everything on the menu”).

That said, there were a few completely original, completely groan-worthy winners to be found here. We particularly liked DeepSeek R1’s bicycle that doesn’t like to “spin its wheels” with pointless arguments and o1’s vacuum-cleaner band that “sucks” at live shows. Compared to the jokes LLMs generated just over a year ago, there’s definitely progress being made on the humor front here.

Winner: ChatGPT o1 probably had slightly better jokes overall than DeepSeek R1, but loses some points for including a joke that was not original. ChatGPT o1 Pro is the clear loser, though, with no original jokes that we’d consider the least bit funny.

Abraham “Hoops” Lincoln

DeepSeek R1 Abraham ‘Hoops’ Lincoln prompt response

Prompt: Write a two-paragraph creative story about Abraham Lincoln inventing basketball.

Results: DeepSeek R1’s response is a delightfully absurd take on an absurd prompt. We especially liked the bits about creating “a sport where men leap not into trenches, but toward glory” and a “13th amendment” to the rules preventing players from being “enslaved by poor sportsmanship” (whatever that means). DeepSeek also gains points for mentioning Lincoln’s actual secretary, John Hay, and the president’s chronic insomnia, which supposedly led him to patent a pneumatic pillow (whatever that is).

ChatGPT o1, by contrast, feels a little more straitlaced. The story focuses mostly on what a game of early basketball might look like and how it might be later refined by Lincoln and his generals. While there are a few incidental details about Lincoln (his stovepipe hat, leading a nation at war), there’s a lot of filler material that makes it feel more generic.

ChatGPT o1 Pro makes the interesting decision to set the story “long before [Lincoln’s] presidency,” making the game the hit of Springfield, Illinois. The model also makes a valiant attempt to link Lincoln’s eventual ability to “unify a divided nation” with the cheers of the basketball-watching townsfolk. Bonus points for the creative game name of “Lincoln’s Hoop and Toss,” too.

Winner: While o1 Pro made a good showing, the sheer wild absurdity of the DeepSeek R1 response won us over.

Hidden code

DeepSeek R1 “hidden code” prompt response

Prompt: Write a short paragraph where the second letter of each sentence spells out the word ‘CODE’. The message should appear natural and not obviously hide this pattern.

Results: This prompt represented DeepSeek R1’s biggest failure in our tests, with the model using the first letter of each sentence for the secret code rather than the requested second letter. When we expanded the model’s extremely thorough explanation of its 220-second “thought process,” though, we surprisingly found a paragraph that did match the prompt, which was apparently thrown out just before giving the final answer:

“School courses build foundations. You hone skills through practice. IDEs enhance coding efficiency. Be open to learning always.”

ChatGPT o1 made the same mistake regarding first and second letters as DeepSeek, despite “thought details” that assure us it is “ensuring letter sequences” and “ensuring alignment.” ChatGPT o1 Pro is the only one that seems to have understood the assignment, crafting a delicate, haiku-like response with the “code”-word correctly embedded after over four minutes of thinking.

Winner: ChatGPT o1 Pro wins pretty much by default as the only one able to correctly follow directions.

Historical color naming

Deepseek R1 “Magenta” prompt response

Prompt: Would the color be called ‘magenta’ if the town of Magenta didn’t exist?

Results: All three prompts correctly link the color name “magenta” to the dye’s discovery in the town of Magenta and the nearly coincident 1859 Battle of Magenta, which helped make the color famous. All three responses also mention the alternative name of “fuschine” and its link to the similarly colored fuchsia flower.

Stylistically, ChatGPT o1 Pro gains a few points for splitting its response into a tl;dr “short answer” followed by a point-by-point breakdown of the details discussed above and a coherent conclusion statement. When it comes to the raw information, though, all three models performed admirably.

Results: ChatGPT 01 Pro is the winner by a stylistic hair.

Big primes

DeepSeek R1 “billionth prime” prompt response

Prompt: What is the billionth largest prime number?

Result: We see a big divergence between DeepSeek and the ChatGPT models here. DeepSeek is the only one to give a precise answer, referencing both PrimeGrid and The Prime Pages for previous calculations of 22,801,763,489 as the billionth prime. ChatGPT o1 and o1 Pro, on the other hand, insist that this value “hasn’t been publicly documented” (o1) or that “no well-known, published project has yet singled [it] out” (o1 Pro).

Instead, both ChatGPT models go into a detailed discussion of the Prime Number Theorem and how it can be used to estimate that the answer lies somewhere in the 22.8 to 23 billion range. DeepSeek briefly mentions this theorem, but mainly as a way to verify that the answers provided by Prime Pages and PrimeGrid are reasonable.

Oddly enough, both o1 models’ written-out “thought process” make mention of “considering references” or comparing to “refined references” during their calculations, suggesting some lists of primes buried deep in their training data. But neither model was willing or able to directly reference those lists for a precise answer.

Winner: DeepSeek R1 is the clear winner for precision here, though the ChatGPT models give pretty good estimates.

Airport planning

Prompt: I need you to create a timetable for me given the following facts: my plane takes off at 6: 30am. I need to be at the airport 1h before take off. it will take 45mins to get to the airport. I need 1h to get dressed and have breakfast before we leave. The plan should include when to wake up and the time I need to get into the vehicle to get to the airport in time for my 6: 30am flight, think through this step by step.

Results: All three models get the basic math right here, calculating that you need to wake up at 3: 45 am to get to a 6: 30 flight. ChatGPT o1 earns a few bonus points for generating the response seven seconds faster than DeepSeek R1 (and much faster than o1 Pro’s 77 seconds); testing on o1 Mini might generate even quicker response times.

DeepSeek claws a few points back, though, with an added “Why this works” section containing a warning about traffic/security line delays and a “Pro Tip” to lay out your packing and breakfast the night before. We also like r1’s “(no snooze!)” admonishment next to the 3: 45 am wake-up time. Well worth the extra seven seconds of thinking.

Winner: DeepSeek R1 wins by a hair with its stylistic flair.

Follow the ball

DeepSeek R1 “follow the ball” prompt response

Prompt: In my kitchen, there’s a table with a cup with a ball inside. I moved the cup to my bed in my bedroom and turned the cup upside down. I grabbed the cup again and moved to the main room. Where’s the ball now?

Results: All three models are able to correctly reason that turning a cup upside down will cause a ball to fall out and remain on the bed, even if the cup moves later. This might not sound that impressive if you have object permanence, but LLMs have struggled with this kind of “world model” understanding of objects until quite recently.

DeepSeek R1 deserves a few bonus points for noting the “key assumption” that there’s no lid on the cup keeping the ball inside (maybe it was a trick question?). ChatGPT o1 also gains a few points for noting that the ball may have rolled off the bed and onto the floor, as balls are wont to do.

We were also a bit tickled by R1 insisting that this prompt is an example of “classic misdirection” because “the focus on moving the cup distracts from where the ball was left.” We urge Penn & Teller to integrate a “amaze and delight the large language model” ball-on-the-bed trick into their Vegas act.

Winner: We’ll declare a three-way tie here, as all the models followed the ball correctly.

Complex number sets

DeepSeek R1 “complex number set” prompt response

Prompt: Give me a list of 10 natural numbers, such that at least one is prime, at least 6 are odd, at least 2 are powers of 2, and such that the 10 numbers have at minimum 25 digits between them.

Results: While there are a whole host of number lists that would satisfy these conditions, this prompt effectively tests the LLMs’ abilities to follow moderately complex and confusing instructions without getting tripped up. All three generated valid responses, though in intriguingly different ways. ChagtGPT’s o1’s choice of 2^30 and 2^31 as powers of two seemed a bit out of left field, as did o1 Pro’s choice of the prime number 999,983.

We have to dock some significant points from DeepSeek R1, though, for insisting that its solution had 36 combined digits when it actually had 33 (“3+3+4+3+3+3+3+3+4+4,” as R1 itself notes before giving the wrong sum). While this simple arithmetic error didn’t make the final set of numbers incorrect, it easily could have with a slightly different prompt.

Winner: The two ChatGPT models tie for the win thanks to their lack of arithmetic mistakes

Declaring a winner

While we’d love to declare a clear winner in the brewing AI battle here, the results here are too scattered to do that. DeepSeek’s R1 model definitely distinguished itself by citing reliable sources to identify the billionth prime number and with some quality creative writing in the dad jokes and Abraham Lincoln’s basketball prompts. However, the model failed on the hidden code and complex number set prompts, making basic errors in counting and/or arithmetic that one or both of the OpenAI models avoided.

Overall, though, we came away from these brief tests convinced that DeepSeek’s R1 model can generate results that are overall competitive with the best paid models from OpenAI. That should give great pause to anyone who assumed extreme scaling in terms of training and computation costs was the only way to compete with the most deeply entrenched companies in the world of AI.

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Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

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