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The Apple Watch Series 11 is back down to its lowest-ever price at Amazon
Grab this sleek smartwatch for under $300.
Apple Testing Four Smart Glasses Styles Made of High-End Materials
Apple is developing at least four different styles of smart glasses, and the company is betting that their superior design will set them apart from rival products, according to Bloomberg's Mark Gurman. Writing in his latest Power On newsletter, Gurman says that Apple's latest designs are made from a high-end material called acetate, which is "more durable and luxurious" than the standard plastic used by most existing brands.
Hospitals roll out chatbots, looking to reclaim their role in patients' health conversations
A handful of hospitals are launching their own chatbots, a risky bet aimed at better serving current patients and gaining new ones.
Howe accepts accountability after Newcastle collapse at Crystal Palace
Newcastle United manager Eddie Howe accepted full responsibility after his side squandered another lead in a 2-1 defeat at Crystal Palace on Sunday afternoon.Consequently, the Magpies have plummeted t...
The largest orbital compute cluster is open for business
Kepler Communications is flying 40 GPUs in Earth orbit. And its latest customer is Sophia Space.
The largest orbital compute cluster is open for business
Kepler Communications is flying 40 GPUs in Earth orbit. And its latest customer is Sophia Space.
Spark! Imagination and Science Center hosts Earth Fest for families
Spark hosts a Spark! Sunday event each month to encourage fun while exploring the basics of STEM concepts.
Bay Area business owner re-dumps mountain of trash on customer driveway after broken promises on payment. Was he right?
This dumpster dispute really dumped things on a whole new level turning one missed payment into a trashy pile of fines, fees and chaos.
I use Claude, Gemini, and ChatGPT every day—here's the only one you should pay for
The battle of the bots.
Green Bay area high school sports results for Saturday, April 11
High school baseball, softball, soccer, track and field, and golf teams were in action.
Things to do in Cincinnati this week, April 13-19
If you're looking for the best events happening in Cincinnati, you're at the right place. Find your next concert, game, festival or family event.
Appleton area high school sports results for Saturday, April 11
High school baseball, softball, soccer, lacrosse, track and field, and golf teams were in action.
New York Jets Flight Connections 04/12/26
Happy Sunday, Jets fans! Here are your links for the last day of this April weekend. Nick Faria – Jets have another potential trade partner in the first round Nick Faria – One line from scout highlights Jets’ real draft ‘plan’ Stefan Stelling – Slipping WR prospect could fall into Jets’ laps at No. 16 […]
What Now? Vance Leaves Iran Talks Without a Deal.
The lack of a breakthrough after 21 hours of negotiations leaves the Trump administration facing several unpalatable options.
The Dirt: Frontier Behavioral Health planning new Spokane location
The longstanding location of Spokane Roofing Company will soon become a Frontier Behavioral Health location, according to a construction permit application submitted to the city of Spokane.
“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.
Apple Highlights Photos Shot on iPhone During NASA's Mission to Moon
As we previously reported, astronauts aboard NASA's Orion spacecraft used the iPhone 17 Pro Max to take selfies of themselves with the Earth in the background during the Artemis II mission around the far side of the Moon last week. Now that the crew members have safely returned to Earth, Apple's CEO Tim Cook and marketing chief Greg Joswiak have both turned to social media to congratulate them on their successful mission and highlight the iPhone's involvement.
Green Bay area high school sports results for Friday, April 10
High school baseball, softball and soccer teams were in action.
I found the ‘memory cheat code’ for ChatGPT — and it fixed my worst problem with AI
One simple prompt stopped ChatGPT from “forgetting” everything mid-conversation — and it completely changed how I use it
Arts and entertainment throughout the region
A listing of arts and entertainment events throughout the region.
DGPT Returns to CBS Sports with Big Easy Open Special Airing April 11 - Disc Golf Pro Tour
Tune in at 5:30 PM ET to relive all of the excitement from the MVP Big Easy Open presented by Flight Factory
Can sports media really change the way you look at sports?
While stats are helpful, very rarely do numbers tell the full story.
Maricopa County confirms first heat-related death of 2026
Health officials have confirmed that a person has died from heat-related complications, the first in the Phoenix metro in 2026.
Adyen tool speeds business payments
Financial operations at global enterprises remain “stubbornly complex,” as treasury teams juggle multiple banking and payment systems, the company said.
Green Bay area high school sports results for Thursday, April 9
High school baseball, softball, soccer, and track and field teams were in action.
OpenAI has quietly launched its ads manager as it races to build out its ads business
The AI platform launched the ads manager within its ChatGPT ads pilot advertisers last week, and also lowered the barrier to joining the test.
China’s Huatai Is Seeking to Start Securities Business in Japan
Huatai Securities Co., one of China’s largest brokerages, is preparing to start a securities business in Japan as a revival in the country’s financial markets draws firms from around the world.
‘Blaming the conduct of companies may provide some comfort’
Opinion, comment and editorials of the day
Laptops Are About to Get Very Exciting Again... Thanks to Nvidia
Nvidia is about to throw another wrench into laptops led by Intel, AMD, and Qualcomm.
Google bakes NotebookLM, its research tool, into Gemini
Google has fully integrated NotebookLM, its AI-powered research tool, into the Gemini app.
Pick of the stats: Southampton v Derby County
BBC Sport highlights some of the key statistics and facts for the Championship match between Southampton and Derby County
Cleveland Browns NFL Draft Profile: DE Keldric Faulk, Auburn
A young player with room to reach his potential is catnip to Browns GM Andrew Berry.
Prepare your business for summer energy demand
Spring is the ideal time to evaluate your facility’s energy use, identify opportunities for improvement and take steps to reduce costs before peak demand arrives.
Gen Alpha drives shift toward AI-powered entertainment discovery
Gracenote has released a new report examining how artificial intelligence is reshaping the way audiences ... Read More
Patients Are Using Chatbots to Fight Medical Bills, With Mixed Results
While chatbots like Claude and ChatGPT can help narrow the information divide between patients and providers, they can also dispense flawed advice.
Green Bay area high school sports results for Tuesday, April 7
High school softball, baseball and girls soccer teams were in action.
DA identifies man killed by Boston Police after alleged sword attack, mental health crisis
A 29-year-old Boston man has been identified as the person shot and killed by police over the weekend after he allegedly stabbed first responders with a sword during a mental health crisis.
NVIDIA DLSS 5 — Everything We Know So Far About NVIDIA’s Latest Neural Rendering Technology
NVIDIA's DLSS 5 is set to launch Fall 2026, offering real-time neural rendering for photoreal graphics on RTX 50 Series GPUs.
Apple is Reportedly Facing a 'Massive Dilemma' With the MacBook Neo
The all-new MacBook Neo has been such a hit that Apple is facing a "massive dilemma," according to Taiwan-based tech columnist and former Bloomberg reporter Tim Culpan. In the iPhone 16 Pro models, the A18 Pro chip has a 6-core GPU. During the chip manufacturing process, however, sometimes a CPU or GPU core can turn out to be faulty.
Alex Jones Calls for Trump’s Removal After Panicking About Failing Health
The former stooge has reached the end with Donald Trump.
The Blogs: The Difference Between Ideological “Research” and Evidence-Based Science
From the blog of Monique Dietvorst at The Times of Israel
Think Twice Before Asking ChatGPT About Your Health
Obsessively Googling symptoms is bad. Going down an AI rabbit hole may be worse.
Colorado football makes top five for 2027 Texas edge rusher
Three-star 2027 edge rusher James Harris Jr. has narrowed his college choices to five, including the Colorado Buffaloes.
Governor Spanberger signs bills to spur business investment and jobs
Governor Abigail Spanberger has signed bipartisan legislation her office says will grow Virginia’s economy.
Arteta on key fitness news, doubters and hunger
Arsenal boss Mikel Arteta has been speaking to the media before Tuesday's Champions League game against Sporting CP at Estadio Jose Alvalade (kick-off 20:00 BST). On the latest team news, Arteta confirmed defender Gabriel is fit after coming off with a knock in the defeat to Southampton in the FA Cup.
Do You Have Questions About a No-Bid Federal Contract? Tell Us Here.
The government is supposed to let many vendors compete for contracts, to get the best deal for taxpayers. We are looking at cases where it did not.
Apple Sued by Three YouTube Channels
Three established YouTube channels have sued Apple, alleging that the company violated the U.S. Digital Millennium Copyright Act (DMCA) by unlawfully accessing and scraping millions of copyrighted videos from YouTube to train its AI models.
Scientists may have found a way to keep your bones strong for life
Scientists have identified a little-known receptor, GPR133, as a powerful regulator of bone strength. By activating it with a newly discovered compound called AP503, they were able to boost bone density in mice and counteract osteoporosis-like damage. The finding opens the door to a new kind of treatment that could not only prevent bone loss but also rebuild weakened bones, offering fresh hope for millions affected by osteoporosis, especially aging populations.
I’m an AI power user — this 7-line ‘critical thinking’ prompt finally stops chatbots from being a ‘Yes-Man’
Tired of ChatGPT or Gemini just nodding along? This 'critical thinking' prompt forces AI to challenge your assumptions and make your decisions sharper
Bengals News (4/6): Taking Andrew Whitworth’s advice
Andrew Whitworth cares enough to want to help.
Trump, 79, Spotted Wearing Band-Aids After Health Concerns
Trump wore two Band-Aids while stepping out Sunday amid speculation about his health.
Bhatti: Republicans must restore, strengthen health care after gutting it
Michigan doctors worry about healthcare cuts by Trump and Republicans, impacting Medicaid and low-income families.
Snapdragon 8 Elite Gen 6 Pro Disappoints In CPU Performance Despite Moving To 2nm Process; New GPU & AI Upgrades Could Make Up For It
A tipsterclaims that the Snapdragon 8 Elite Gen 6 Pro will offer lackluster CPU performance but its GPU and AI capabilities could cover up
4 Clever Uses For The Thunderbolt Port On Your PC
Many modern laptops and PCs now support Thunderbolt, which looks like a regular USB-C port but can support high-speed external drives, eGPUs, and more.
Ben Shelton suffers another early exit at ATP Houston 250
Ben Shelton suffered another upset loss in Houston, falling as the top seed in the quarter-finals. He's still alive in doubles action, however.
AMD or Nvidia eGPUs can work on Apple Silicon Macs, but not for graphic acceleration
Apple has signed a driver for AMD or Nvidia eGPUs connected to Apple Silicon but there are some big caveats, and it won't improve your graphics. Here's what they're for.
NVIDIA's Neural Texture Compression Cuts VRAM Use From 6.5 GB to 970 MB
NVIDIA has released more details about its Neural Texture Compression (NTC) technology, which significantly reduces GPU VRAM usage by up to seven times. In a technology demo presented during one of the GTC 2026 sessions, NVIDIA revealed that its Neural Texture Compression can reduce VRAM usage from ...
Governor Michelle Lujan Grisham
It's back, Albuquerque! The New Mexico Museum of Natural History & Science reopens today, April 4th after eight months of renovations! Expect a revamped lobby, the brand-new Animal Armor traveling...
Apple CarPlay Just Got Three Popular iPhone Apps
Apple's CarPlay system for accessing iPhone apps on a vehicle's dashboard screen received three popular apps this week: ChatGPT, Google Meet, and Audiomack. CarPlay Ultra in an Aston Martin In addition, WhatsApp is beta testing a revamped CarPlay app that will improve upon the basic Siri-based functionality that was previously available. Make sure you have the latest version of each app and they will automatically appear on CarPlay.
Google's Gemma 4 Runs Frontier AI On A Single GPU
Google's Gemma 4 open models deliver frontier AI performance on a single Nvidia GPU, with Apache 2.0 licensing and native support for agentic workflows.
R&B world rocked by legend’s death at 86: ‘We lost a giant’
He was a legend who was known for playing with other legends.
AMD Rolls Out Gemma 4 Model Support Across Full Range of GPUs & CPUs
AMD has rolled out official support for Google's Gemma 4 across its full range of GPUs & CPUs, offering support for the compact AI model.
Arts and entertainment throughout the region
A listing of arts and entertainment events throughout the region.
ChatGPT's New Internet Browser Can Run 80% of a 1-Person Business — Here's How Entrepreneurs Are Using It
You're not prompting anymore. You're delegating.
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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