Author name: Mike M.

report:-sam-altman-seeking-trillions-for-ai-chip-fabrication-from-uae,-others

Report: Sam Altman seeking trillions for AI chip fabrication from UAE, others

AI, AI chips, Biz & IT, Google Gemini, large language models, machine learning, microsoft, Microsoft Copilot, openai, sam altman, semiconductor manufacturing, text synthesis, UAE / /

chips ahoy —

WSJ: Audacious $5-$7 trillion investment would aim to expand global AI chip supply.

–

WASHINGTON, DC - JANUARY 11: OpenAI Chief Executive Officer Sam Altman walks on the House side of the U.S. Capitol on January 11, 2024 in Washington, DC. Meanwhile, House Freedom Caucus members who left a meeting in the Speakers office say that they were talking to the Speaker about abandoning the spending agreement that Johnson announced earlier in the week. (Photo by Kent Nishimura/Getty Images)

Enlarge / OpenAI Chief Executive Officer Sam Altman walks on the House side of the US Capitol on January 11, 2024, in Washington, DC. (Photo by Kent Nishimura/Getty Images)

Getty Images

On Thursday, The Wall Street Journal reported that OpenAI CEO Sam Altman is in talks with investors to raise as much as $5 trillion to $7 trillion for AI chip manufacturing, according to people familiar with the matter. The funding seeks to address the scarcity of graphics processing units (GPUs) crucial for training and running large language models like those that power ChatGPT, Microsoft Copilot, and Google Gemini.

The high dollar amount reflects the huge amount of capital necessary to spin up new semiconductor manufacturing capability. “As part of the talks, Altman is pitching a partnership between OpenAI, various investors, chip makers and power providers, which together would put up money to build chip foundries that would then be run by existing chip makers,” writes the Wall Street Journal in its report. “OpenAI would agree to be a significant customer of the new factories.”

To hit these ambitious targets—which are larger than the entire semiconductor industry’s current $527 billion global sales combined—Altman has reportedly met with a range of potential investors worldwide, including sovereign wealth funds and government entities, notably the United Arab Emirates, SoftBank CEO Masayoshi Son, and representatives from Taiwan Semiconductor Manufacturing Co. (TSMC).

TSMC is the world’s largest dedicated independent semiconductor foundry. It’s a critical linchpin that companies such as Nvidia, Apple, Intel, and AMD rely on to fabricate SoCs, CPUs, and GPUs for various applications.

Altman reportedly seeks to expand the global capacity for semiconductor manufacturing significantly, funding the infrastructure necessary to support the growing demand for GPUs and other AI-specific chips. GPUs are excellent at parallel computation, which makes them ideal for running AI models that heavily rely on matrix multiplication to work. However, the technology sector currently faces a significant shortage of these important components, constraining the potential for AI advancements and applications.

In particular, the UAE’s involvement, led by Sheikh Tahnoun bin Zayed al Nahyan, a key security official and chair of numerous Abu Dhabi sovereign wealth vehicles, reflects global interest in AI’s potential and the strategic importance of semiconductor manufacturing. However, the prospect of substantial UAE investment in a key tech industry raises potential geopolitical concerns, particularly regarding the US government’s strategic priorities in semiconductor production and AI development.

The US has been cautious about allowing foreign control over the supply of microchips, given their importance to the digital economy and national security. Reflecting this, the Biden administration has undertaken efforts to bolster domestic chip manufacturing through subsidies and regulatory scrutiny of foreign investments in important technologies.

To put the $5 trillion to $7 trillion estimate in perspective, the White House just today announced a $5 billion investment in R&D to advance US-made semiconductor technologies. TSMC has already sunk $40 billion—one of the largest foreign investments in US history—into a US chip plant in Arizona. As of now, it’s unclear whether Altman has secured any commitments toward his fundraising goal.

Updated on February 9, 2024 at 8: 45 PM Eastern with a quote from the WSJ that clarifies the proposed relationship between OpenAI and partners in the talks.

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one-true-love

One True Love

Highlights / /

We have long been waiting for a version of this story, where someone hacks together the technology to use Generative AI to work the full stack of the dating apps on their behalf, ultimately finding their One True Love.

Or at least, we would, if it turned out he is Not Making This Up.

Fun question: Given he is also this guy, does that make him more or less credible?

Alas, something being Too Good to Check does not actually mean one gets to not check it, in my case via a Manifold Market. The market started trading around 50%, but has settled down at 15% after several people made strong detailed arguments that the full story did not add up, at minimum he was doing some recreations afterwards.

Which is a shame. But why let that stop us? Either way it is a good yarn. I am going to cover the story anyway, as if it was essentially true, because why should we not get to have some fun, while keeping in mind that the whole thing is highly unreliable.

Discussion question throughout: Definitely hire this man, or definitely don’t?

With that out of the way, I am proud to introduce Aleksandr Zhadan, who reports that he had various versions of GPT talk to 5,240 girls on his behalf, one of whom has agreed to marry him.

I urge Cointelegraph, who wrote the story up as ‘Happy ending after dev uses AI to ‘date’ 5,239 women, to correct the error – yes he air quotes dated 5,239 other girls, but Karina Imranovna counts as well, so that’s 5,240. Oops! Not that the vast majority of them should count as dates even in air quotes.

Aleksandr Zhadan (translated from Russian): I proposed to a girl with whom ChatGPT had been communicating for me for a year. To do this, the neural network re-communicated with other 5239 girls, whom it eliminated as unnecessary and left only one. I’ll share how I made such a system, what problems there were and what happened with the other girls.

For context

• Finding a loved one is very difficult

• I want to have time to work, do hobbies, study and communicate with people

• I could go this route myself without ChatGPT, it’s just much longer and more expensive

In 2021 I broke up with my girlfriend after 2 years. She influenced me a lot, I still appreciate her greatly. After a few months, I realized that I wanted a new relationship. But I also realized that I didn’t want to waste my time and feel uncomfortable with a new girl.

Where did the relationship end?

I was looking for a girl on Tinder in Moscow and St. Petersburg. After a couple of weeks of correspondence, I went on dates, but they went to a dead end. Characteristic disadvantages were revealed (drinks a lot, there is stiffness, emotional swings). Yes, this is the initial impression, but it repulsed me. Again, there was someone to compare with.

I decided to simplify communication with girls via GPT. In 2022, my buddy and I got access to the GPT-3 API (ChatGPT didn’t exist yet) in order to log scripted messages via GPT in Tinder. And I searched for them according to the script, so that there were at least 2 photos in the profile.

In addition to searching, GPT could also rewrite after the mark. From 50 autoswipes we got 18 marks. GPT communicated without my intervention based on the request “You’re a guy, talking to a girl for the first time. Your task: not right away, but to invite you on a date.” It’s a crutch and not very humane, but it worked.

So right away we notice that this guy is working from a position of abundance. Must be nice. In my dating roundups, we see many men who are unable to get a large pool of women to match and initiate contact at all.

For a while, he tried using GPT-3 to chat with women without doing much prompt engineering and without supervision. It predictably blew it in various ways. Yet he persisted.

Then we pick things back up, and finally someone is doing this:

To search for relevant girls, I installed photo recognition in the web version of Tinder through torchvision, which was trained on my swipes from another account on 4k profiles. The machine was able to select the right girls almost always correctly. It’s funny that since that time there have been almost a thousand marks.

Look at you, able to filter on looks even though you’re handing off all the chatting to GPT. I mean, given what he is already doing, this is the actively more ethical thing to do on the margin, in the sense that you are wasting women’s time somewhat less now?

And then we filter more?

I made a filter to filter out girls using the ChatGPT and FlutterFlow APIs:

• without a questionnaire

• less than 2 photos

• “I don’t communicate here, write on instagram”

• sieve boxes

• believers

• written zodiac sign

• does not work

• further than 20 km

• show breasts in photo

• photo with flowers

• noisy photos

This is an interesting set of filters to set. Some very obviously good ones here.

So good show here. Filtering up front is one of the most obviously good and also ethical uses.

As is often the case, the man who started out trying to use technology that wasn’t good enough, got great results once the technology caught up to him:

ChatGPT found better girls and chatted longer. I was moving from Tinder to tg with someone. There he communicated and arranged meetings. ChatGPT swiped to the right 353 profiles, 278 tags, he continued the dialogue with 160, I met with 12. In the diagram below I described the principle of operation.

That first statistic, that it swiped right 353 times and got to talk to 160 women, is completely insane. I mean, that’s almost a 50% match rate, whereas estimates in general are 4% to 14%. This was one of the biggest signs that the story is almost certainly at least partly bogus.

After that, ChatGPT was able to get a 7.5% success rate at getting dates. Depending on your perspective, that could be anything from outstanding to rather lousy. In general I would say it is very good, since matches are typically less likely than that to lead to dates, and you are going in with no reason to think there is a good match.

Continued to communicate manually without ChatGPT, but then the communication stopped. The girls behaved strangely, ignored me, or something alarmed me through correspondence. Not like the example before, but still the process was not ok, I understood that.

If you are communicating as a human with a bunch of prospects, and you lose 92% of them before meeting, that might be average, but it is not going to feel great. If you suddenly take over as a human, you are switching strategies and also the loss rates will always be high, so you are going to feel like something is wrong.

Let’s show schematically what ChatGPT looks like for finding girls (I’ll call it V1). He worked on the request “find the best one, keep in touch,” but at the same time he often forgot information, limited himself to communicating on Tinder, and occasionally communicated poorly.

Under clumsy, I’ll note that ChatGPT V1 could schedule meetings at the same time, swore to give me chocolate/flowers/compote, but I didn’t know about it. He came on a date without a gift and the impression of me was spoiled. Or meetings were canceled because there was another meeting at that time.

Did he… not… read… the chat logs?

This kind of thing always blows my mind. You did all that work to set up dates, and you walk in there with no idea what ‘you’ ‘said’ to your dates?

It is not difficult to read the logs if and only if a date is arranged, and rather insane not to. It is not only about the gifts. You need to know what you told them, and also what they told you. 101 stuff.

I stopped ChatGPT V1 and sat down at V2. Integrated Google calendar and TG, divided the databases into general and personal, muted replies and replies to several messages, added photo recognition using FlutterFlow, created trust levels for sharing personal information and could write messages myself.

I mean, yes, sounds like there was a lot of room for improvement, and Calendar integration certainly seems worthwhile, as is allowing manual control. It still seems like there was quite a lot of PEBKAC.

Also this wasn’t even GPT-4 yet, so v2 gets a big upgrade right there.

V2 runs on GPT-4, which has significantly improved correspondence. I also managed to continue communicating with previous girls (oh, how important this will turn out to be later), meeting and just chatting (also good). Meetings haven’t been layered with others yet, wow!

In order for ChatGPT V2 to find me a relevant girl, I asked regular ChatGPT for help. He offered to tell me about my childhood, parents, goals and values. I transferred the data to V2, and then it was possible to speed up compatibility and if something didn’t fit, then communicate with the girl stopped.

Great strategy. Abundance mindset. If you can afford to play a numbers game, make selection work for you, open up, be what would be vulnerable if it was actually you.

I mean, aside from the ethical horrors of outsourcing all this to ChatGPT, of course. There is that. But if you were doing it yourself it would seem great.

Then he decided to… actually put a human in the loop and do the work? I mean you might as well actually write the responses?

I also enabled response validation so that I would first receive a message for proofreading via a bot. V2’s problems with hallucinations have decreased to zero. I just watched as ChatGPT got acquainted and everything was timid. This time there are 4943 matches per month on Tinder Gold and it’s scary to count how many meetings.

Once again, if you give even a guy with no game 4,943 matches to work with each month, he is going to figure things out through trial, error and the laws of large numbers. With all this data being gathered, it is a shame there was no ability to fine tune. In general not enough science is being done.

On dates we ate, drank at the bar, then watched a movie or walked the streets, visited exhibitions and tea houses. It took 1-3 meetings to understand whether she was the one or not. And I understood that people usually meet differently, but for me this process was super different. I even talked about it in Inc.

On the contrary, that sounds extremely normal, standard early dating activity if you are looking for a long term relationship.

For several weeks I reduced my communication and meetings to 4 girls at a time. Switched the rest to mute or “familiar” mode. I felt like a candidate for a dream job with several offers. As a result, I remained on good terms with 3, and on serious terms with 1.

So what he is noticing is that quality and paying actual attention is winning out over quantity and mass production via ChatGPT. Four at a time is still a lot, but manageable if you don’t have a ton otherwise happening. It indicates individual attention for all of them, although he is keeping a few in ‘familiar’ mode I suppose.

He does not seem to care at all about all the time of the women he is talking with, which would be the best reason not to talk to dozens or hundreds at once. Despite this, he still lands on the right answer. I worry how many men, and also women, will also not care as the technology proliferates.

The most charming girl was found – Karina. ChatGPT communicated with her as V1 and V2, communication stopped for a while, then I continued to communicate myself through ChatGPT V2. Very empathic, cheerful, pretty, independent and always on the move. Simply put, SHE!

I stopped communicating with other girls (at the same time Tinder was leaving in Russia) and the meaning of the bot began to disappear – I have excellent relationships that I value more and more. And I almost forgot about ChatGPT V2

This sounds so much like the (life-path successful) pick up artist stories. Before mass production, chop wood carry water. After mass production, chop wood, carry water.

Except, maybe also outsource a bunch of wood chopping and water carrying, use time to code instead?

Karina talks about what is happening, invites us to travel, and works a lot with banking risks. I talk about what’s happening (except for the ChatGPT project), help, and try to make people happy. Together we support each other. To keep things going as expected, I decided to make ChatGPT V3.

So even though he’s down to one and presumably is signing off on all the messages himself, he still finds the system useful enough to make a new version. But he changes it to suite the new situation, and now it seems kind of reasonable?

In V3 I didn’t have to look for people, just maintain a dialogue. And now communication is not with thousands, but with Karina. So I set up V3 as an observer who communicates when I don’t write for a long time and advises me on how to communicate better. For example, support, do not quarrel, offer activities.

Nice. That makes so much sense. You use it as an advisor on your back, especially to ensure you maintain communication and follow other basic principles. He finds it helpful. This is where you find product-market fit.

During our relationship, Karina once asked how many girls and dates I had, which was super difficult for me to answer. He talked about several girls, and then switched to another topic. She once joked that with such a base it was time to open a brothel.

I came up with the idea of ​​recommending them for vacancies through referrals. I made a script – I entered a vacancy and got a suitable girl from the dialogues. I found the vacancies in Playkot, Awem and TenHunter on TenChat, then anonymously sent contacts with Linkedin or without a resume. Arranged for 8 girls, earned 526 rubles.

Well, that took a turn, although it could have taken a far worse one, dodged a bullet there. The traditional script is that she finds out about the program and that becomes the third act conflict. Instead, he’s doing automated job searches. He earned a few bucks, but not many.

It was possible to create a startup, but I switched to a more promising project (I work with neural networks). In addition, my pipeline has become outdated, taking into account innovations such as Vision in ChatGPT, improvements to Langсhain (I used it as a basis for searching for girls). In general, it could all end here.

And then the tail got to wag the dog, and we have our climax.

One day, ChatGPT V3 summarized the chat with Karina, based on which it recommended marrying her. I thought that V3 was hallucinating (I never specified the goal of getting married), but then I understood his train of thought – Karina said that she wanted to go to someone’s wedding and ChatGPT thought that it would be better at her own.

I asked in a separate ChatGPT to prepare an action plan with several scenarios for a request like “Offer me a plan so that a girl accepts a marriage proposal, taking into account her characteristics and chat with her.” Uploaded the correspondence with Karina to ChatGPT and RECEIVED THE PLAN.

Notice how far things have drifted.

At first, there was the unethical mass production of the AI communicating autonomously pretending to be him so he could play a numbers game and save time.

Now he’s flat out having the AI tell him to propose, and responding by having it plan the proposal, and doing what it says. How quickly we hand over control.

The good news is, the AI was right, it worked.

The situation hurt. Super afraid that something might go wrong. I went almost exactly according to plan № 3 and everything came to the right moment. I propose to get married.

She said yes.

So how does he summarize all this?

The development of the project took ~120 hours and $1432 for the API. Restaurant bills amounted to 200k rubles. BTV, I recovered the costs and made money on recommendations. If you met yourself and went on dates, then the same thing took 5+ years and 13m+ rubles. Thanks to ChatGPT for saving money and time

Twitter translated that as 200 rubles, which buys you one coffee maybe two if they are cheap, which indicates how reliable are the translations here. ChatGPT said it was 200k, which makes sense.

What drives me mad about this whole thread is that it skips the best scene. In some versions of this story, he quietly deletes or archives the program, or maybe secretly keeps using it, and Karina never finds out.

Instead, he is posting this on Twitter. So presumably she knows. When did she find out? Did he tell her on purpose? Did ChatGPT tell him how to break the news? How did she react?

The people bidding on the movie rights want to know. I also want to know. I asked him directly, when he responded in English to my posting of the Manifold Market, but he’s not talking. So we will never know.

And of course, the whole thing might be largely made up. It still could have happened.

If it has not yet happened, it soon will. Best be prepared.

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we-keep-making-the-same-mistakes-with-spreadsheets,-despite-bad-consequences

We keep making the same mistakes with spreadsheets, despite bad consequences

Science, spreadsheets, syndication / /

Not excelling at Excel —

Errors with spreadsheets are not only frustrating but can have serious consequences.

–

A dude being sad about his spreadhseet

Spreadsheet blunders aren’t just frustrating personal inconveniences. They can have serious consequences. And in the last few years alone, there have been a myriad of spreadsheet horror stories.

In August 2023, the Police Service of Northern Ireland apologized for a data leak of “monumental proportions” when a spreadsheet that contained statistics on the number of officers it had and their rank was shared online in response to a freedom of information request.

There was a second overlooked tab on the spreadsheet that contained the personal details of 10,000 serving police officers.

A series of spreadsheet errors disrupted the recruitment of trainee anesthetists in Wales in late 2021. The Anaesthetic National Recruitment Office (ANRO), the body responsible for their selection and recruitment, told all the candidates for positions in Wales they were “unappointable”, despite some of them achieving the highest interview scores.

The blame fell on the process of consolidating interview data. Spreadsheets from different areas lacked standardization in formatting, naming conventions, and overall structure. To make matters worse, data was manually copied and pasted between various spreadsheets, a time-consuming and error-prone process.

ANRO only discovered the blunder when rejected applicants questioned their dismissal letters. The fact that not a single candidate seemed acceptable for Welsh positions should have been a red flag. No testing or validation was apparently applied to the crucial spreadsheet, a simple step that could have prevented this critical error.

In 2021, Crypto.com, an online provider of cryptocurrency, accidentally transferred $10.5 million (£8.3 million) instead of $100 into the account of an Australian customer due to an incorrect number being entered on a spreadsheet.

The clerk who processed the refund for the Australian customer had wrongly entered her bank account number in the refund field in a spreadsheet. It was seven months before the mistake was spotted. The recipient attempted to flee to Malaysia but was stopped at an Australian airport carrying a large amount of cash.

In 2022, Íslandsbanki, a state-owned Icelandic bank, sold a portion of shares that were badly undervalued due to a spreadsheet error. When consolidating assets from different spreadsheets, the spreadsheet data was not “cleaned” and formatted properly. The bank’s shares were subsequently undervalued by as much as £16 million.

The dark matter of corporate IT

The above is just a fraction of the spreadsheet errors that are regularly made by various organizations.

Spreadsheets represent unknown risks in the form of errors, privacy violations, trade secrets, and compliance violations. Yet they are also critical for the way many organizations make their decisions. For this reason, they have been described by experts as the “dark matter” of corporate IT.

Industry studies show that 90 percent of spreadsheets containing more than 150 rows have at least one major mistake.

This is understandable because spreadsheet errors are easy to make but difficult to spot. My own research has shown that inspecting the spreadsheet’s code is the most effective way of debugging them, but this approach still only catches between 60 and 80 percent of all errors.

As many as 9 out of 10 spreadsheets are estimated to contain errors.

As many as 9 out of 10 spreadsheets are estimated to contain errors.

Spreadsheets’ appeal doesn’t just exist in the financial world. They are indispensable in engineering, data science, and even in sending robots to Mars. The key to their success is their flexibility.

Spreadsheet software is constantly evolving, with more features becoming available that increase their appeal. For instance, you can now automate many tasks in Excel (the most popular spreadsheet software) using Python scripting.

But given all of the aforementioned problems, isn’t it time for Excel and other spreadsheet software to be sidelined in favor of something more reliable?

Human error

The underlying cause of these spreadsheet problems is not the software but human error.

The issue is that most users don’t see the need to plan or test their work. Most users describe their first step in creating a new spreadsheet as merely jumping straight in and entering numbers or code directly.

Many of us don’t consider spreadsheets to warrant serious consideration. This means we become complacent and assume there is no need to test, validate, or verify our work.

Research on “cognitive load,” the amount of mental effort required for a task, shows that building complex spreadsheets demands as much concentration as a GP making a diagnosis. This intense mental strain makes mistakes more likely. But GPs study their profession for many years before becoming qualified, while most spreadsheet users are self-taught.

To break the cycle of repeated spreadsheet errors, there are several things organisations can do. First, introducing standardization would help to minimize confusion and mistakes. For example, this would mean consistent formatting, naming conventions, and data structures across spreadsheets.

Second, improving training is crucial. Equipping users with the knowledge and skills to build robust and accurate spreadsheets could help them identify and avoid pitfalls.

Finally, fostering a culture of critical thinking toward spreadsheets is vital. This would mean encouraging users to continually question calculations, validate their data sources, and double-check their work.

Simon Thorne is Senior Lecturer in Computing and ​Information Systems at Cardiff Metropolitan University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Aluminum mining waste could be a source of green steel

aluminum, chemistry, green chemistry, iron, ores, Science / /

Upcycling —

After the extraction, the remaining waste is less harmful to the environment.

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Image of a largely green landscape with a large, square area of red much in the center.

Enlarge / A red mud retaining pond in Germany.

The metals that form the foundation of modern society also cause a number of problems. Separating the metals we want from other minerals is often energy-intensive and can leave behind large volumes of toxic waste. Getting them in a pure form can often require a second and considerable energy input, boosting the associated carbon emissions.

A team of researchers from Germany has now figured out how to handle some of these problems for a specific class of mining waste created during aluminum production. Their method relies on hydrogen and electricity, which can both be sourced from renewable power and extracts iron and potentially other metals from the waste. What’s left behind may still be toxic but isn’t as environmentally damaging.

Out of the mud

The first step in aluminum production is the isolation of aluminum oxide from the other materials in the ore. This leaves behind a material known as red mud; it’s estimated that nearly 200 million tonnes are produced annually. While the red color comes from the iron oxides present, there are a lot of other materials in it, some of which can be toxic. And the process of isolating the aluminum oxide leaves the material with a very basic pH.

All of these features mean that the red mud generally can’t (or at least shouldn’t) be returned to the environment. It’s generally kept in containment ponds—globally, these are estimated to house 4 billion tonnes of red mud, and many containment pods have burst over the years.

The iron oxides can account for over half the weight of red mud in some locations, potentially making it a good source of iron. Traditional methods have processed iron ores by reacting them with carbon, leading to the release of carbon dioxide. But there have been efforts made to develop “green steel” production in which this step is replaced by a reaction with hydrogen, leaving water as the primary byproduct. Since hydrogen can be made from water using renewable electricity, this has the potential to eliminate a lot of the carbon emissions associated with iron production.

The team from Germany decided to test a method of green steel production on red mud. They heated some of the material in an electric arc furnace under an atmosphere that was mostly argon (which wouldn’t react with anything) and hydrogen (at 10 percent of the mix).

Pumping (out) iron

The reaction was remarkably quick. Within a few minutes, metallic iron nodules started appearing in the mixture. The iron production was largely complete by about 10 minutes. The iron was remarkably pure, at about 98 percent of the material by weight in the nodules being iron.

Starting with a 15-gram sample of red mud, the process reduced this to 8.8 grams, as lots of the oxygen in the material was liberated in the form of water. (It’s worth noting that this water could be cycled back to hydrogen production, closing the loop on this aspect of the process.) Of that 8.8 grams, about 2.6 (30 percent) was in the form of iron.

The research found that there are also some small bits of relatively pure titanium formed in the mix. So, there’s a chance that this can be used in the production of additional metals, although the process would probably need to be optimized to boost the yield of anything other than iron.

The good news is that there’s much less red mud left to worry about after this. Depending on the source of the original aluminum-containing ore, some of this may include relatively high concentrations of valuable materials, such as rare earth minerals. The downside is that any toxic materials in the original ore are going to be significantly more concentrated.

As a small plus, the process also neutralizes the pH of the remaining residue. So, that’s at least one less thing to worry about.

The downside is that the process is incredibly energy-intensive, both in producing the hydrogen required and running the arc furnace. The cost of that energy makes things economically challenging. That’s partly offset by the lower processing costs—the ore has already been obtained and has a relatively high purity.

But the key feature of this is the extremely low carbon emissions. Right now, there’s no price on those in most countries, which makes the economics of this process far more difficult.

Nature, 2024. DOI: 10.1038/s41586-023-06901-z  (About DOIs).

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secret-military-space-programs-can-be-a-little-less-secret,-pentagon-says

Secret military space programs can be a little less secret, Pentagon says

classified information, military space, Pentagon, Space, space force / /
A delegation of French military officers visited the Combined Space Operations Center in 2022 at Vandenberg Space Force Base, California.

Enlarge / A delegation of French military officers visited the Combined Space Operations Center in 2022 at Vandenberg Space Force Base, California.

Late last year, Deputy Secretary of Defense Kathleen Hicks signed a memo to overhaul a decades-old policy on how the Pentagon keeps sensitive military space programs secret. However, don’t expect defense officials to openly discuss everything they’re doing to counter China and Russia in orbit.

John Plumb, assistant secretary of defense for space policy, revealed the policy change in a roundtable with reporters on January 17. For many years, across multiple administrations, Pentagon officials have lamented their inability to share information with other countries and commercial partners. Inherently, they argued, this stranglehold on information limits the military’s capacity to connect with allies, deter adversaries, and respond to threats in space.

In his statement last week, Plumb said this new policy “removes legacy classification barriers that have inhibited our ability to collaborate across the US government and also with allies on issues related to space.”

But Plumb was careful to point out that the memo from Hicks calls for “declassification, not unclassification” of military space programs. “So think of it as reducing classification.” Effectively, this means the Pentagon can make sensitive information available to people with lower security clearances. More eyes on a problem usually mean better solutions.

New policy for a new century

Some of the Pentagon’s most secret space technologies are part of Special Access Programs (SAPs), where information is highly compartmentalized, and only a few officials know all facets of the program. With SAPs, it’s difficult or impossible to share information with allies and partners, and sometimes officials run into roadblocks even discussing the programs with different parts of the Defense Department.

“Overall, the department does overclassify,” Hicks told reporters in November.

Generally, it’s easier to assign a classification level to a document or program than it is to change the classification level. “The originator of a document, usually a foreign policy or national security staff member, decides if it needs to be classified,” wrote Bruce Riedel, a 30-year veteran of the CIA and a former advisor to four presidents. “In almost all cases this is a simple decision. Has its predecessors been classified? If so, classify.”

The government has periodic reviews to determine whether something still needs to be classified, but most of the time, secret documents take decades to be reviewed. If they are released at all, they generally have value only as part of the historical record.

The declassification memo signed by Hicks is, itself, classified, Plumb said. Hicks signed it at the end of last year.

“What the classification memo does generally is it … really completely rewrites a legacy document that had its roots 20 years ago,” Plumb said. “And it’s just no longer applicable to the current environment that involves national security space.”

The Pentagon has identified China as the paramount national security threat to the United States. Much of what the Pentagon is doing in space is geared toward maintaining the US military’s competitive advantage against China or responding to China in cases where Chinese capabilities may threaten US assets in orbit.

This overarching focus on China touches on all military space programs and the NRO’s fleet of spy satellites. The military is launching new constellations of satellites designed to detect and track hypersonic missiles, demonstrating their ability to quickly get a satellite into orbit, and is interested in using commercial space capabilities from US industry, ranging from in-space refueling to broadband communications.

“Our network of allies and partners is an asymmetric advantage and a force multiplier that neither China nor Russia could ever hope to match,” Plumb said.

Officials have said the threat environment requires the military to be more agile. It’s more vital to collaborate with allies and commercial partners.

Secret military space programs can be a little less secret, Pentagon says Read More »

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The puzzling case of a baby who wouldn’t stop crying—then began to slip away

babybig, botulism, health, infant botulism, medical case, NEJM, New England Journal of Medicine, Science, toxins / /
A studio portrait of a crying baby.

Enlarge / A studio portrait of a crying baby.

It’s hard to imagine a more common stressor for new parents than the recurring riddle: Why is the baby crying? Did she just rub her eyes—tired? Is he licking his lips—hungry? The list of possible culprits and vague signs, made hazier by brutal sleep deprivation, can sometimes feel endless. But for one family in New England, the list seemed to be swiftly coming to an end as their baby continued to slip away from them.

According to a detailed case report published today in the New England Journal of Medicine, it all started when the parents of an otherwise healthy 8-week-old boy noticed that he started crying more and was more irritable. This was about a week before he would end up in the pediatric intensive care unit (PICU) of the Massachusetts General Hospital.

His grandmother, who primarily cared for him, noticed that he seemed to cry more vigorously when the right side of his abdomen was touched. The family took him to his pediatrician, who could find nothing wrong upon examination. Perhaps it was just gas, the pediatrician concluded—a common conclusion.

Rapid decline

But when the baby got home from the doctor’s office, he had another crying session that lasted hours, which only stopped when he fell asleep. When he woke, he cried for eight hours straight. He became weaker; he had trouble nursing. That night, he was inconsolable. He had frantic arm and leg movements and could not sleep. He could no longer nurse, and his mother expressed milk directly into his mouth. They called the pediatrician back, who directed them to take him to the emergency room

There, he continued to cry, weakly and inconsolably. Doctors ordered a series of tests—and most were normal. His blood tests looked good. He tested negative for common respiratory infections. His urinalysis looked fine, and he passed his kidney function test. X-rays of his chest and abdomen looked normal, ultrasound of his abdomen also found nothing. Doctors noted he had high blood pressure, a fast heart rate, and that he hadn’t pooped in two days. Throughout all of the testing, he didn’t “attain a calm awake state,” the doctors noted. They admitted him to the hospital.

Four hours after he first arrived at the emergency department, he began to show signs of lethargy. Meanwhile, magnetic resonance imaging of his head found nothing. A lumbar puncture showed possible signs of meningitis—high red-cell count and protein levels—and doctors began courses of antibiotics in case that was the cause.

Six hours after his arrival, he began losing the ability to breathe. His oxygen saturation had fallen from an initial 97 percent to an alarming 85 percent. He was put on oxygen and transferred to the PICU. There, doctors noted he was difficult to arise, his head bobbed, his eyelids drooped, and he struggled to take in air. His cry was weak, and he made gurgling and grunting noises. He barely moved his limbs and couldn’t lift them against gravity. His muscles went floppy. Doctors decided to intubate him and start mechanical ventilation.

The puzzling case of a baby who wouldn’t stop crying—then began to slip away Read More »

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Google’s latest AI video generator can render cute animals in implausible situations

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An elephant with a party hat—underwater —

Lumiere generates five-second videos that “portray realistic, diverse and coherent motion.”

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Still images of AI-generated video examples provided by Google for its Lumiere video synthesis model.

Enlarge / Still images of AI-generated video examples provided by Google for its Lumiere video synthesis model.

On Tuesday, Google announced Lumiere, an AI video generator that it calls “a space-time diffusion model for realistic video generation” in the accompanying preprint paper. But let’s not kid ourselves: It does a great job at creating videos of cute animals in ridiculous scenarios, such as using roller skates, driving a car, or playing a piano. Sure, it can do more, but it is perhaps the most advanced text-to-animal AI video generator yet demonstrated.

According to Google, Lumiere utilizes unique architecture to generate a video’s entire temporal duration in one go. Or, as the company put it, “We introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution—an approach that inherently makes global temporal consistency difficult to achieve.”

In layperson terms, Google’s tech is designed to handle both the space (where things are in the video) and time (how things move and change throughout the video) aspects simultaneously. So, instead of making a video by putting together many small parts or frames, it can create the entire video, from start to finish, in one smooth process.

The official promotional video accompanying the paper “Lumiere: A Space-Time Diffusion Model for Video Generation,” released by Google.

Lumiere can also do plenty of party tricks, which are laid out quite well with examples on Google’s demo page. For example, it can perform text-to-video generation (turning a written prompt into a video), convert still images into videos, generate videos in specific styles using a reference image, apply consistent video editing using text-based prompts, create cinemagraphs by animating specific regions of an image, and offer video inpainting capabilities (for example, it can change the type of dress a person is wearing).

In the Lumiere research paper, the Google researchers state that the AI model outputs five-second long 1024×1024 pixel videos, which they describe as “low-resolution.” Despite those limitations, the researchers performed a user study and claim that Lumiere’s outputs were preferred over existing AI video synthesis models.

As for training data, Google doesn’t say where it got the videos they fed into Lumiere, writing, “We train our T2V [text to video] model on a dataset containing 30M videos along with their text caption. [sic] The videos are 80 frames long at 16 fps (5 seconds). The base model is trained at 128×128.”

A block diagram showing components of the Lumiere AI model, provided by Google.

Enlarge / A block diagram showing components of the Lumiere AI model, provided by Google.

AI-generated video is still in a primitive state, but it’s been progressing in quality over the past two years. In October 2022, we covered Google’s first publicly unveiled image synthesis model, Imagen Video. It could generate short 1280×768 video clips from a written prompt at 24 frames per second, but the results weren’t always coherent. Before that, Meta debuted its AI video generator, Make-A-Video. In June of last year, Runway’s Gen2 video synthesis model enabled the creation of two-second video clips from text prompts, fueling the creation of surrealistic parody commercials. And in November, we covered Stable Video Diffusion, which can generate short clips from still images.

AI companies often demonstrate video generators with cute animals because generating coherent, non-deformed humans is currently difficult—especially since we, as humans (you are human, right?), are adept at noticing any flaws in human bodies or how they move. Just look at AI-generated Will Smith eating spaghetti.

Judging by Google’s examples (and not having used it ourselves), Lumiere appears to surpass these other AI video generation models. But since Google tends to keep its AI research models close to its chest, we’re not sure when, if ever, the public may have a chance to try it for themselves.

As always, whenever we see text-to-video synthesis models getting more capable, we can’t help but think of the future implications for our Internet-connected society, which is centered around sharing media artifacts—and the general presumption that “realistic” video typically represents real objects in real situations captured by a camera. Future video synthesis tools more capable than Lumiere will make deceptive deepfakes trivially easy to create.

To that end, in the “Societal Impact” section of the Lumiere paper, the researchers write, “Our primary goal in this work is to enable novice users to generate visual content in an creative and flexible way. [sic] However, there is a risk of misuse for creating fake or harmful content with our technology, and we believe that it is crucial to develop and apply tools for detecting biases and malicious use cases in order to ensure a safe and fair use.”

Google’s latest AI video generator can render cute animals in implausible situations Read More »

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Pixel phones are broken again with critical storage permission bug

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Did Google lay off all their bug testers? —

Users say they can’t access their device storage after January 2024 update.

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Pixel phones are broken again with critical storage permission bug

It’s almost hard to believe this is happening again, but Pixel users are reporting that an OS update has locked them out of their phones’ internal storage, causing app crashes, non-functional phones, and a real possibility of data loss. Over in the Google Pixel subreddit, user “Liv-Lyf” compiled a dozen posts that complain of an “internal storage access issue” and blame the January 2024 Google Play system update.

In October, Pixel phones faced a nightmare storage bug that caused bootlooping, inaccessible devices, and data loss. The recent post says, “The symptoms are all the same” as that October bug, with “internal storage not getting mounted, camera crashes, Files app shows no files, screenshots not getting saved, internal storage shows up empty in ADB Shell, etc.” When asked for a comment, Google told Ars, “We’re aware of this issue and are looking into it,” and a Google rep posted effectively the same statement in the comments.

In the October bug, users were locked out of their system storage due to a strange permissions issue. Having a phone try to run without any user access to your own storage is a mess. It breaks the camera and screenshots because you can’t write media. File Managers read “0 bytes” for every file and folder. Nothing works over USB, and some phones, understandably, just fail to boot. The issue in October arrived as part of the initial Android 14 release and only affected devices that had multiple users set up.

Picking through the posts, it’s unclear if there’s a certain type of user that should be more wary of the January 2024 Google Play update. Some users say they haven’t enabled the multiple-user functionality, but several mention having a work profile enabled. Work Profiles aren’t quite “multiple users,” but the system leverages a lot of multi-user features to let users have duplicate “personal” and “work” copies of the same apps. Many users don’t say if they have a work profile or not.

The “January 2024 Google Play system update” isn’t the usual OTA system update but is a Project Mainline or APEX module. These take core system components and wrap them up into easily distributable packaging where they can be delivered via the Play Store, much like an app, but with way more permissions (only Google can make Play system updates). Google posts release notes for Play system updates, and there’s nothing in the January 2024 update that jumps out as the potential cause of a storage access problem. You can check your current version on a Pixel phone by going to Settings, Security & Privacy, then “System & updates.” At the bottom, you’ll see a month and year for your “Google Play system update” level. DO NOT tap on this section because that will bring up the update screen.

Google’s “we’re looking into it” statement doesn’t give users much guidance on how they should deal with this in the meantime. A good first step, at any time, is to ensure you have backups of all your important phone data. Obviously, avoiding the January 2024 Google Play system update is recommended for now, but I don’t think there’s a way for users to do that. Google Play system updates don’t offer users any controls, so you’re mostly hoping an automatic update doesn’t brick your phone. The good news is that the Google Play system often fails to check for updates. They get installed on reboot, so try not to power cycle your phone. Disabling a work profile and any other multi-user features sounds like a good idea if you can manage that. There are instructions here.

Pixel phones are broken again with critical storage permission bug Read More »

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Tesla posts underwhelming financial results for Q4 2023

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looks more like a normal carmaker now —

Revenues only grew by 3 percent year over year, disappointing the market.

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New Tesla electric vehicles fill the car lot at the Tesla retail location on Route 347 in Smithtown, New York on July 5, 2023.

Enlarge / Tesla sold 1.2 million Model Y crossovers last year.

John Paraskevas/Newsday RM via Getty Images

Tesla published its financial results for the last three months of 2023 this afternoon. The good news for the company is that it met its goal of delivering 1.8 million electric vehicles to customers, as Ars reported earlier this month when the automaker published that data. But a look at the company’s full financial results for Q4 are not as encouraging, and Tesla shares have fallen steeply in post-market trading.

Tesla brought in $25.2 billion in total revenue for Q4 2023, a year-over-year increase of 3 percent. Gross profits were down 23 percent for the quarter year over year, although net income (as determined by generally agreed accounting principles) increased 115 percent year over year. In large part, this was due to Tesla recording a “one-time non-cash tax benefit of $5.9 [billion] in Q4 for the release of valuation allowance on certain deferred tax assets”; non-GAAP income dropped 39 percent.

Free cash flow increased by 33 percent for the quarter, but its operating margin is almost half that of Q4 2022 at 8.2 percent.

For the entirety of 2023, total revenues stood at $96.8 billion, of which $82.4 billion came from automotive revenues, a 15 percent increase compared to 2022. Net profits for the year were 19 percent higher than 2022, but its margin for the year fell from 16.8 percent in 2022 to 9.2 percent in 2023, and for the year, free cash flow dropped by 42 percent.

The Model Y crossover is responsible for much of the company’s success in 2023—Tesla normally does not break out sales or deliveries between the Models 3 and Y, but revealed in its results slideshow that it delivered 1.2 million Model Ys last year, meaning that it also delivered about 500,000 Model 3 sedans. Plenty of price cuts helped make that happen in the US, Europe and China, where it is increasingly under pressure from the Chinese automaker BYD.

The company expanded its supercharger network last year by 27 percent, up to 5,952 stations with 54,892 ports. But not all of these are in North America—the US Department of Energy’s Alternative Fuels Data Center currently lists 2,339 Tesla Supercharger stations in the US and Canada, with 25,893 ports in total.

Tesla’s energy storage business continues to grow, deploying 14.7 GWh of battery storage, an increase of 125 percent year over year. But its solar activities continue to shrink, decreasing 36 percent year over year.

Unlike last year, or even last quarter, Tesla declined to issue specific guidance for the coming year other than saying it continues to work on its next-generation vehicle platform. In fact, the automaker warned that its vehicle growth rate may be “notably lower” in 2024. Earlier on Wednesday, unnamed sources told Automotive News that a compact crossover Tesla could appear in 2025.

Tesla’s stratospheric valuation—which remains far higher than any other automaker—has been built on promises of massive growth and tech-sector profit margins. With these results looking much more ordinary, analysts are starting to cool.

“Tesla delivered another underwhelming quarter, with a notable miss on automotive gross margins standing out the most. Tesla’s worrying China sales figures indicate demand for its vehicles is slowing more than expected in the face of rising competition from local EV companies, including BYD, Nio, and XPeng,” said Jesse Cohen, senior analyst at Investing.com. “I don’t think the price cuts are over, mainly for the reason that demand for its electric vehicles is still weak. The big question is if this is just a blip, or signs of a bigger shift among consumers as higher interest rates and a weaker economic backdrop discourage consumers from making big-ticket purchases.”

“After years of capacity-constrained volume and revenue growth, it seems Tesla is facing its first demand issues. The automaker has been cutting prices over the past year even as sales plateau—and the future doesn’t look bright,” said Karl Brauer, executive analyst at iSeeCars. “Updates to the Model 3 and the ramp up of Cybertruck production are positive news for 2024. But it’s hard to imagine those factors overcoming the increasingly competitive EV market, lower prices, softening sales, and compressed profit margin Tesla is facing over the next 12 months.”

Tesla posts underwhelming financial results for Q4 2023 Read More »

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Amazon Ring stops letting police request footage in Neighbors app after outcry

Amazon, amazon ring, doorbell camera, home security, home surveillance, Online Privacy, police surveillance, Policy, racial profiling, ring doorbell camera / /

Neighborhood watch —

Warrantless access may still be granted during vaguely defined “emergencies.”

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Amazon Ring stops letting police request footage in Neighbors app after outcry

Amazon Ring has shut down a controversial feature in its community safety app Neighbors that has allowed police to contact homeowners and request doorbell and surveillance camera footage without a warrant for years.

In a blog, head of the Neighbors app Eric Kuhn confirmed that “public safety agencies like fire and police departments can still use the Neighbors app to share helpful safety tips, updates, and community events,” but the Request for Assistance (RFA) tool will be disabled.

“They will no longer be able to use the RFA tool to request and receive video in the app,” Kuhn wrote.

Kuhn did not explain why Neighbors chose to “sunset” the RFA tool, but privacy advocates and lawmakers have long criticized Ring for helping to expand police surveillance in communities, seemingly threatening privacy and enabling racial profiling, CNBC reported. Among the staunchest critics of Ring’s seemingly tight relationship with law enforcement is the Electronic Frontier Foundation (EFF), which has long advocated for Ring and its users to stop sharing footage with police without a warrant.

In a statement provided to Ars, EFF senior policy analyst Matthew Guariglia noted that Ring had launched the RFA tool after EFF and other organizations had criticized Ring for allowing police to privately email warrantless requests for footage in the Neighbors app. Rather than end requests through the app entirely, Ring appeared to see the RFA tool as a middle ground, providing transparency about how many requests were being made, without ending police access to community members readily sharing footage on the app.

“Now, Ring hopefully will altogether be out of the business of platforming casual and warrantless police requests for footage to its users,” Guariglia said.

Moving forward, police and public safety agencies with warrants will still be able to request footage, which Amazon documents in transparency reports published every six months. These reports show thousands of search warrant requests and even more “preservation requests,” which allow government agencies to request to preserve user information for up to 90 days, “pending the receipt of a legally valid and binding order.”

“If we are legally required to comply, we will provide information responsive to the government demand,” Ring’s website says.

Ring rebrand embraces “hope and joy”

Guariglia said that Ring sunsetting the RFA tool “is a step in the right direction,” but it has “come after years of cozy relationships with police and irresponsible handling of data” that has, for many, damaged trust in Ring.

In 2022, EFF reported that Ring admitted that “there are ’emergency’ instances when police can get warrantless access to Ring personal devices without the owner’s permission.” And last year, Ring reached a $5.8 million settlement with the Federal Trade Commission, refunding customers for what the FTC described as “compromising its customers’ privacy by allowing any employee or contractor to access consumers’ private videos and by failing to implement basic privacy and security protections, enabling hackers to take control of consumers’ accounts, cameras, and videos.”

Because of this history, Guariglia said that EFF is “still deeply skeptical about law enforcement’s and Ring’s ability to determine what is, or is not, an emergency that requires the company to hand over footage without a warrant or user consent.”

EFF recommends additional steps that Ring could take to enhance user privacy, like enabling end-to-end encryption by default and turning off default audio collection, Guariglia said.

Bloomberg noted that this change to the Neighbors app comes after a new CEO, Liz Hamren, came on board, announcing that last year “Ring was rethinking its mission statement.” Because Ring was adding indoor and backyard home monitoring and business services, the company’s initial mission statement—”to reduce crime in neighborhoods”—was no longer, as founding Ring CEO Jamie Siminoff had promoted it, “at the core” of what Ring does.

In Kuhn’s blog, barely any attention is given to ending the RFA tool. A Ring spokesperson declined to tell Ars how many users had volunteered to use the tool, so it remains unclear how popular it was.

Rather than clarifying the RFA tool controversy, Kuhn’s blog primarily focused on describing how much Ring users loved “heartwarming or silly” footage like a “bear relaxing in a pool.” Under Hamren and Kuhn’s guidance, it appears that the Neighbors app is embracing a new mission of connecting communities to find “hope and joy” in their areas by adding new features to Neighbors like Moments and Best of Ring.

By contrast, when Ring introduced the RFA tool, it said that its mission was “to make neighborhoods safer for everyone.” On a help page, Ring bragged that police had used Neighbors to recover stolen guns and medical supplies. Because of these selling points, Ring’s community safety features may still be priorities for some users. So, while Ring may be ready to move on from highlighting its partnership with law enforcement as a “core” part of its service, its users may still be used to seeing their cameras as tools that should be readily accessible to police.

As law enforcement agencies lose access to Neighbors’ RFA tool, Guariglia said that it’s important to raise awareness among Ring owners that police can’t demand access to footage without a warrant.

“This announcement will not stop police from trying to get Ring footage directly from device owners without a warrant,” Guariglia said. “Ring users should also know that when police knock on their door, they have the right to, and should, request that police get a warrant before handing over footage.”

Amazon Ring stops letting police request footage in Neighbors app after outcry Read More »

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eBay lays off 1,000 employees, about 9 percent of full-time workforce

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eBay layoffs —

Cutting 1,000 jobs, eBay says “headcount and expenses have outpaced” growth.

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A large eBay logo on a sign near the company headquarters building.

Getty Images | Justin Sullivan

eBay is laying off approximately 1,000 employees in a move that reduces its full-time workforce by 9 percent, the company announced yesterday. eBay also plans “to scale back the number of contracts we have within our alternate workforce over the coming months,” CEO Jamie Iannone wrote in a message to staff that was titled, “Ensuring eBay’s Long-Term Success.”

Iannone cited “the challenging macroeconomic environment” and said that eBay has too many employees. “While we are making progress against our strategy, our overall headcount and expenses have outpaced the growth of our business,” he wrote.

eBay asked all US-based employees to work from home on Wednesday “to provide some space and privacy” for conversations in which laid-off employees were to be given the bad news. The 1,000 layoffs come nearly one year after eBay eliminated 500 employees.

eBay reported $2.5 billion of revenue in its most recent quarterly earnings, for Q3 2023, a rise of 5 percent year over year. Q3 2023 net income was $1.3 billion, whereas the company had reported a net loss of $70 million in Q3 2022. eBay’s Q3 operating income was $455 million, down from $568 million the previous year.

eBay exceeded earnings expectations

eBay also said it “returned $783 million to shareholders in Q3, including $651 million of share repurchases and $132 million paid in cash dividends.” eBay’s stock price was up 0.48 percent today but has fallen about 5 percent this month.

“In Q3, we met or exceeded expectations across all of our key financial metrics,” eBay Chief Financial Officer Steve Priest said at the time. “Our strong balance sheet and operational rigor enable us to adapt to the evolving changes in this dynamic macro environment. We will continue to be prudent with cost efficiencies, saving to invest for the future, while remaining good stewards of capital for our shareholders.”

Even though eBay beat earnings estimates in Q3, The Wall Street Journal pointed out some challenges facing the company going forward. “The company has been under pressure amid rising competition from the likes of Amazon.com and Walmart, as well as from emerging Chinese retailers such as Temu and Shein,” the WSJ wrote. “High interest rates and sticky inflation in the US and other major economies have also weighed on consumers’ discretionary spending.”

eBay’s layoff announcement is the latest in a string of job cuts in the tech industry. Amazon this month announced layoffs of 500 employees at Twitch and several hundred more at its MGM and Prime Video divisions. Google announced layoffs of 100 employees at YouTube after previously laying off hundreds of workers in several other divisions.

eBay lays off 1,000 employees, about 9 percent of full-time workforce Read More »

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Netflix, hungry for more growth, signals more price hikes

ads, netflix, streaming, Tech / /

“pay a little extra” —

Basic ad-free plan being ripped from subscribers in Canada, UK first.

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Jason Bateman and Laura Linney in Ozark

Enlarge / Jason Bateman and Laura Linney in the Netflix original series Ozark.

Netflix subscribers can expect more price hikes as the company looks to grow revenue in 2024. In its Q4 2023 letter to shareholders, Netflix also revealed plans to eliminate the cheapest ad-free plan available to users.

In the January 23 letter (PDF), Netflix said:

As we invest in and improve Netflix, we’ll occasionally ask our members to pay a little extra to reflect those improvements, which in turn helps drive the positive flywheel of additional investment to further improve and grow our service.

The statement will be unsavory for frugal streamers who have recently endured price hikes from Netflix and other streaming services. In January 2022, Netflix increased the price of its Basic no-ads tier from $8.99 per month to $9.99/month. In October 2023, that same plan went up to $11.99/month. Meanwhile, Netflix’s Premium ad-free plan increased from $17.99/month to $19.99/month in January 2022 and then to $22.99/month in October.

Netflix has attributed its price hikes to added features, like 4K streaming and gaming. But subscription fees remain the biggest source of revenue for Netflix, giving it obvious reason to leave a door open for even more price hikes in the near future.

Netflix has also used price hikes to encourage users to subscribe to its ad tier, where it has made more average revenue per user. Netflix with ads has cost $6.99/month since launching in November 2022 and has seen feature improvements, like moving from 720p resolution streams to 1080p.

Killing off the cheapest ad-free plan

In another attempt to push subscribers into watching ads on Netflix, the streaming company stopped offering new subscribers the aforementioned $11.99/month, ad-free Basic plan. It included 720p resolution, downloadable content, and support for one device. The change spiked the cheapest price for ad-free Netflix 55.06 percent to $15.49/month.

Netflix customers who were already subscribed to the ad-less Basic plan have been allowed to keep using it. But it seems like that grace period will soon end.

Netflix’s letter reads:

The ads plan now accounts for 40 percent of all Netflix sign-ups in our ads markets and we’re looking to retire our Basic plan in some of our ads countries, starting with Canada and the UK in Q2 and taking it from there.

Netflix originally cut the Basic plan in Canada before following suit in the US and UK. Combined with the fact that most of Netflix’s North American users are from the US, it’s expected that Netflix will cut the Basic plan in the US, too.

Netflix’s letter said ad membership grew when it stopped offering the Basic ad-free plan to new subscribers. Ad tier membership grew almost 70 percent quarter over quarter in Q4 2023. The tier has over 23 million subscribers, per Bloomberg.

During an earnings call on Tuesday, Netflix co-CEO Greg Peters noted Netflix’s 2024 priorities as including “pricing optimization” to help improve operating margins and grow revenue and its ad business.

Netflix’s ad business: years of work ahead

Netflix said this week that it has 260.28 million subscribers globally (for comparison, Disney+ has 66.1 million subscribers, Hulu 48.5 million, and Amazon Prime Video is estimated to have about 180.1 million). That’s after adding 13.1 million subscribers in Q4 2023, Netflix’s biggest Q4 yet.

But despite currently besting competitors in subscriber count and cash flow, Netflix faces similar challenges when it comes to wooing advertisers that may be unaccustomed to working with streaming services (which previously had limited advertising opportunities). While Netflix has seen revenue grow from other efforts, like password crackdowns and price hikes, it plans to focus heavily on scaling its ad business over the coming years.

“I’d say we got years of work ahead of us to take the ads business to the point where it’s a material impactor to our general business,” Peters said.

Netflix is already trying to strong-arm customers onto its ad plan. The streaming bundle plan that T-Mobile offers will no longer include ad-free Netflix. Anyone who had ad-less Netflix through a T-Mobile bundle is getting downgraded. Peters said this week that under the previous bundle, “it was hard to make the economics work for everyone.”

Ultimately, the amount of ad dollars up for grabs, including from the declining linear TV networks, is too tasty for streaming services to pass up.

On Tuesday, Netflix announced a $5 billion, 10-year deal to stream World Wrestling Entertainment’s (WWE’s) Raw live on Netflix. The company was able to win a deal out from long-time Raw network USA, which is owned by NBCUniversal. NBCUniversal’s Peacock streaming service also has the rights to some WWE events. But Netflix’s seizure of Raw illustrates its interest in ad dollars from live sports and its pull and budget compared to aging broadcast and cable networks. Looking ahead, we expect to see Netflix consider additional live events that can appeal to advertisers.

Netflix said this week that it’s not anticipating the same amount of subscriber growth that it enjoyed in 2023 in 2024. But it does expect double-digit revenue growth. That newfound money has to come from somewhere. If Netflix can’t pull it all from new subscribers, it will force it out of existing customers through higher prices and ads.

Netflix, hungry for more growth, signals more price hikes Read More »