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China Will Require Meta to Unwind Acquisition of AI Start-Up Manus
The impact of the ruling was not immediately clear, but it could send a chilling signal to Chinese tech founders seeking to team up with foreign companies.
Health insurance and end-of-life healthcare expenditures: evidence from Chinese Longitudinal Healthy Longevity Survey
npj Health Systems - Health insurance and end-of-life healthcare expenditures: evidence from Chinese Longitudinal Healthy Longevity Survey
Anta: The Chinese sports brand taking on Nike and Adidas
Now one of the biggest sportswear firms, Anta's rise follows a playbook adopted by many Chinese giants.
John Ternus, Apple’s new CEO, inherits a rebounding China business—and some messy headaches
Ternus must navigate a protectionist Washington, a prickly Beijing and a Chinese consumer less interested in foreign brands.
Samsung Galaxy S27 Ultra Might Sport A 5,800mAh Battery – Here’s Why It’ll Be More Than Enough Even As Chinese Rivals Push 6,000mAh+ Batteries
Samsung Galaxy S27 Ultra to sport the uber-efficient M16 OLED screen, with overall efficiency bolstered by Exynos 2700's new architecture.
Tired of high costs, some Americans are importing homes straight from China
Gennadiy Tsygan knows how expensive it is to build a home in the United States. That’s why he imported almost everything for his dream house from China.
Singapore emerging as neutral ground as AI firms navigate Sino-US rivalry
Singapore is transforming from East-West gateway to neutral ground for the AI sector, with Chinese startups hoping to operate beyond government reach while U.S. firms seek foreign talent without the headache of stricter visa regulations.
Exclusive | Iran War Complicates Contingency Plans to Defend Taiwan, Some U.S. Officials Say
The U.S. has burned through so many munitions in Iran that some administration officials assess that America couldn’t fully execute contingency plans to defend Taiwan from a Chinese invasion if it occurred in the near term, U.S. officials said.
Chinese Scientists Have Been Dying Mysterious Deaths Too
As the US questions the untimely deaths or the disappearances of top scientists, China is grappling with the same mystery at home.
To Tip Livestreamers, Chinese Woman Steals Millions From Family Business
After discovering his daughter had embezzled 17 million yuan, her father accompanied her to the police station to report the crime, hoping to recover the funds.
Magic-Pistons takeaways: Cade Cunningham shines, but questions remain about consistency and supporting cast
For the second game in a row, Cunningham was the main driving force, with little contributions elsewhere, in the first half.
A first for the Tack Lecture: Business, AI and the liberal arts
Rachel Chung will present a lecture on “Wisdom & Machines: How Liberal Arts & AI Learn from Each Other.”
iPhone 18 Pro to Kick Off Apple's Four-Part Camera Upgrade Plan
Apple is evaluating four camera upgrades for future iPhone models, with the first new feature scheduled for this year's iPhone 18 Pro models, according to a reputable Chinese leaker.
How Nestlé’s pioneering China business fell into disarray
Chinese distributors are chasing the Swiss giant for refunds after it allegedly oversupplied the market as demand dropped
Volume 25 Science Of Speed
Our annual racing deep dive goes full engineering — from chassis and powertrains to tires, batteries, aero, and driver fitness, featuring machines like the new Corvette ZR1 and McLaren Solus GT.
Volume 25 Science Of Speed
Our annual racing deep dive goes full engineering — from chassis and powertrains to tires, batteries, aero, and driver fitness, featuring machines like the new Corvette ZR1 and McLaren Solus GT.
Xbox Exec Chimes In As Rumours Suggest Project Helix Chip Will Be Used For 'Other Machines'
Jason Ronald makes it clear we're getting a first-party console
Apple Is Working On A High-Resolution Camera Upgrade That Its Chinese Competitors Have Been Investing Millions To Implement
A rumor claims that Apple is working to bring a high-resolution camera upgrade to its iPhones, but Chinese manufacturers are investing millions to bring it
Chinese scientists replicate eel’s 6,000km breeding journey inside research pool
Meta attracts more Thinking Machines Lab talent in AI shakeup
Mira Murati's Thinking Machines Lab faces an AI talent drain to Meta, with founders and a researcher among recent departures from the startup.
Chinese researchers turn desert sand into fertile soil in just 10 months using cyanobacteria
Science News: The researchers at the Shapotou Desert Experimental Research Station in China have developed a revolutionary approach for reversing desertification an.
iOS 27 Rumored to Drop Support for These iPhone Models
iOS 27 will be compatible with the iPhone 12 series and newer, according to Instant Digital, a known Apple leaker on the Chinese social media platform Weibo. If this rumor is accurate, iOS 27 will drop support for the following iPhone models:iPhone 11 iPhone 11 Pro iPhone 11 Pro Max iPhone SE (2nd generation)However, these devices will continue to receive iOS 26 security updates for at least a few years.
📽 MLS: Lionel Messi shines (again) for Inter Miami 🐐
Unbeaten since the end of February, Inter Miami is continuing its strong run in MLS.Facing the Colorado Rapids this Saturday, Lionel Messi’s teammates snatched a late win (3-2).The Argentine natural...
Mizzou shines coast-to-coast with big weekend results
The distance squad delivers in a big way, stacking top-10 finishes across a packed Saturday
China’s DRAM Suppliers Come To The Rescue As They Ramp Up LPDDR4 Production Shortly After Samsung Abandons Customers
Samsung exclusively moving to LPDDR5 production has given Chinese DRAM makers to pursue LPDDR4 manufacturer to cater to abandoned customers
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars, and a gambling house is shut down after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
Newcastle United vs Bournemouth – Match preview and team news
The spotlight shines on Andoni Iraola today as he leads Bournemouth to Newcastle United following his announced departure.The Cherries contest their first match since confirmation that Iraola will lea...
Cubs Minor League Wrap: Grant Kipp shines in Smokies shutout
Just the Smokies won tonight. Derniche Valdez hit his first Low-A home run.
Two arrested after 29 illegal gambling machines seized at Tallahassee business
Two people are behind bars after law enforcement seized over two dozen illegal gambling machines from a Tallahassee business.
5+ Things to Know About the Next Mac Studio
Apple is working on an updated version of the Mac Studio that's expected at some point in 2026, and with supplies of existing machines running low, we thought we'd highlight what's next for Apple's most powerful desktop machine. Design We're not expecting Apple to redesign the Mac Studio, and there haven't been rumors of a design update. The Mac Studio will continue to have an Apple TV or Mac mini-like squircle design with rounded corners.
Former Southeast Missouri Softball Coach Lana Richmond and Sports Information Director Ron Hines Elected to OVC Hall of Fame
BRENTWOOD, Tenn. – Former Southeast Missouri sports information director Ron Hines (posthumously) and softball coach Lana Richmond and former UT Martin tennis coach Dennis Taylor have been elected to the Ohio Valley Conference Hall of Fame.
Thinking Machines Lab loses another founding member to Meta
Meta has hired another founding engineer from Thinking Machines Lab as the AI talent wars intensify. The startup is still rapidly expanding.
Thinking Machines Lab loses another founding member to Meta
Meta has hired another founding engineer from Thinking Machines Lab as the AI talent wars intensify. The startup is still rapidly expanding.
Vivo X300 Ultra goes global with insane videography capabilities
After a Chinese launch, the Vivo X300 Ultra is now officially available in global markets with a slew of videography-focused...
Showcase shines spotlight on students in School of Arts & Sciences
Columbia College celebrated students in the School of Arts & Sciences during a vibrant and dynamic week‑long series of events from April 7-10.
Pitt Team Shines in National Bioethics Competition
In an annual tournament where students debate ethical issues in medicine, biotechnology and health care, the Pitt team placed second.
Anta Sports: Sporting Goods in China Remains a Challenge
A delay in the recovery of confidence among the Chinese mass-market base fuels the challenges in the sporting goods category, impacting Anta and Nike.
"I couldn't protect it" Pete Hines says he left Microsoft-owned Bethesda because he didn't want to watch it be "damaged and broken apart and frankly mistreated, abused"
Former Bethesda head of publishing Pete Hines has spoken about the frustrations which led him to retire back in 2023.
iPhone 18 Pro Deep Red Color Likely as Android Rivals Prep Same Shade
Apple's upcoming iPhone 18 Pro is very likely to come in a new deep red color, claims a Chinese leaker, because the color is already being prototyped by Android phone makers. In February, Bloomberg's Mark Gurman reported that Apple is testing a deep red finish for the iPhone 18 Pro and iPhone 18 Pro Max. Rumors of purple and brown finishes have also circulated, but Gurman believes those are just variants of the same red idea.
News Analysis: Trump's Strait of Hormuz blockade risks clash with China
It is a high-stakes moment ahead of a closely watched summit between President Trump and Chinese President Xi Jinping in Beijing next month.
2026 Greenville Heart Walk shines spotlight on survivors and heart health
The Heart Walk will be holding their first walk in Spartanburg County on Friday, April 17, at Fifth Third Park.
China's Chery looking to expand car production in Europe, top executives say
Chery is looking to expand car production in Europe through partnerships with other automakers allowing it to use existing factories, top executives at the Chinese carmaker said at an event in Paris.
2026 Greenville Heart Walk shines spotlight on survivors and heart health
“The purpose, the meaning of this Heart Walk is just community.”
'iPhone Ultra' Will Solve Two Key Problems
Apple reportedly plans to unveil a foldable iPhone in September, and the device will supposedly solve two problems with foldable smartphones. In case you missed it, a leaker on Chinese social media platform Weibo recently claimed that the foldable iPhone will be named "iPhone Ultra". According to Bloomberg's Mark Gurman, the device will have improved screen quality and overall durability compared to competing foldables.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Pokémon GO April 2026 Community Day Guide
Starting in January 2018, Community Day is a monthly event in Pokémon GO that shines the spotlight on a particular Pokémon. For one day each month, a specific
Ex-Bethesda Exec Says The Elder Scrolls 6 Maker Isn't Part Of Something 'Genuine' Or 'Authentic' At Microsoft
Pete Hines is retired but still had harsh words for Microsoft
Oil prices tick up amid doubt on Iran war ceasefire; Chinese factory gate costs increase for first time in four years
Rolling coverage of the latest economic and financial news
China South Korea Entertainment Ban
Chinese fans of K-Pop group Seventeen line up outside a pop up booth in Beijing, Friday, March 27, 2026. (AP Photo/Ng Han Guan)
Chinese business ‘hired lobbying firm with ties to Donald Trump Jnr’
Lobbying firm Checkmate helped China’s Grand Pharmaceutical Group beat a US national security challenge, public filings show.
Chinese business hired lobbyist with ties to Don Jr, then scored a win
A lobbying firm led by one of Donald Trump Jr.'s hunting buddies helped a Chinese company make its case to a U.S. national security watchdog.
AI micro-dramas are shaking up Chinese entertainment
Regulators threaten to spoil the fun
Exclusive: Chinese firm hired lobbyists with ties to Don Jr, then scored a win in Washington
A lobbying firm led by one of Donald Trump Jr.'s hunting buddies helped a Chinese company make its case to a U.S. national security watchdog against a U.S. startup that was seeking to have the Chinese firm removed as one of its investors, public filings show.
Mercedes reports drop in Q1 sales during 'transition year' for China market
Mercedes-Benz reported another drop in sales in its core car business in the first quarter of 2026 as the German premium carmaker lost further ground in the tough Chinese market amid an overhaul of its lineup there.
How a NISTer Watches ‘Project Hail Mary’
Measurement science shines in the new hit film Project Hail Mary.
On rare China visit, Taiwan’s opposition leader calls for reconciliation
KMT chief Cheng Li-wun, the party's first leader to visit China in a decade, hopes to meet Chinese President Xi Jinping.
Global Politics Shifts Competitive Balance In China’s Aviation Markets
While international capacity for the Chinese market has rebounded, growth has been uneven.
iPhone Fold Enters Trial Production Phase Ahead of 2026 Launch
Apple partner Foxconn has started trial production on the iPhone Fold, according to Chinese leaker Instant Digital. Trial production comes before mass production, which Apple plans to start in July as long as no issues come up during the earlier testing stage. So far, Apple remains on track to launch the iPhone Fold in 2026, though the latest rumors suggest that it's not going to come out in September. Instead, it could launch sometime after the iPhone 18 Pro, debuting as late as December.
When Algorithms Decide: Are Business Schools Preparing Leaders Or Training Managers To Obey Machines?
B-schools are racing to teach AI skills – but they risk producing grads who defer to algorithms instead of leading through them.
Health Law Navigator: The Compliance Department vs. The Machines: A Playbook for Using AI
In 2026, health care facilities are under relentless pressure to use AI. From diagnostic algorithms that outperform radiologists to predictive
Ancient Chinese 'hair blackening' herb may fight balding naturally
Natural Gas Reliability as a Public Health Essential
Whether it’s a sprained wrist from trying to relive a childhood outdoor game, the sniffles from an unexpected rain shower on an evening walk, or questionable leftovers you knew may not be good; hospital trips happen every day. When anyone walks into a hospital, they expect machines to be working properly, temperatures to be comfortable, and equipment to be clean. The way patients rely on hospitals for care would not be […]
8 Gadgets That Can Help Reduce Stress
Here are 8 highly rated gadgets that are designed to help you feel more relaxed, from fitness trackers and eye massagers to noise machines and grounding mats.
Chinese Scientists Bioengineering Plants With Firefly Genes to Glow, in Effort to Light Cities at Night
Night lighting may be due for a plant-based glow-up.
Georgia lawmakers fail to settle voting machines conflict before end of annual session
The Georgia General Assembly ended its annual session early Friday without a plan for new equipment to overhaul the state's voting system by a July deadline, plunging into doubt the future of elections in the political battleground.
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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