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Hugging Face's new AI model Carbon-X analyzes, generates DNA sequences
Hugging Face's new AI model, Carbon-X, can analyze and generate DNA sequences and help scientists develop new gene therapies, the company says.
Hugging Face’s New AI Biology Model Lets You Chat With DNA
The new open-source model speaks the language of DNA and can help researchers understand the effects of any changes on gene activity and health.
Bob Huggins' family discloses health battle for college basketball legend
Former college basketball coach Bob Huggins' family released a statement that he's dealing with a progressive cognitive condition. What to know:
Family of Bob Huggins shares health update
The family of former West Virginia University basketball coach Bob Huggins and the University of Cincinnati Athletics Department have issued a statement regarding Huggins’ health.
Longtime Cincinnati and West Virginia men's basketball coach Bob Huggins dealing with 'progressive cognitive condition'
Huggins won nearly 400 games at Cincinnati and almost 350 games at West Virginia.
Bob Huggins family issues statement on legendary coach’s health
Legendary Bearcats basketball coach Bob Huggins is living with a progressive cognitive condition, his family announced Wednesday.
Dreaming Big, Building Better: Hugg & Hall Reaches A Major Milestone
Hugg & Hall Equipment Co. celebrates 70 years in business, highlighting its Arkansas roots, family leadership, growth and innovation in construction equipment.
NVIDIA Opens 250+ AI Models to EU Science [2026]
NVIDIA joins EU RAISE, giving researchers 250+ open models, 250+ datasets, and 1,400+ projects via Hugging Face and GitHub.
How to prepare your Samsung Galaxy for the One UI 9 update
With Samsung's One UI 9 rollout chugging along the globe, there's no better time to make a few quick checks before you make the switch.
UI computer science professors discuss growing AI safety concerns
Artificial intelligence industry experts are expressing increasing concern in recent weeks about the growth of AI, calling on governments to slow down development and increase safety measures. Gov. JB Pritzker announced Tuesday the creation of an Illinois AI Cabinet to assess possible risks. The main concerns are rogue AI agents, such as the Hugging Face...
Christian McCaffrey goes over 100 total yards vs. Cardinals
Christian McCaffrey keeps chugging along, totaling 116 yards and a score in Week 3 against Arizona.
Inside Gophers coach P.J. Fleck’s ‘crazy’ moment after Washington win
SEATTLE — P.J. Fleck got a little carried away in the immediate on-field celebration after the Gophers’ 27-24 win over Washington on Saturday. Linebacker Maverick Baranowski had just sealed the win in the Big Ten opener with a final-play tackle on Huskies receiver Dezmen Roebuck at Husky Stadium. Roebuck’s cleat slid up into Baranowski’s helmet and bloodied his mouth. Fleck then hugged one of ...
Golf Insider Calls For Presidents Cup Overhaul
John Huggan argues the Presidents Cup is fading like the 1977 Ryder Cup, urging rule changes to make the International team competitive again.
Jimmy Kimmel recalls Emmys bathroom encounter with Matthew McConaughey
Matthew McConaughey is a hugger, even in the restroom, at least according to Jimmy Kimmel.
Iraola on Bournemouth return, Araujo and Ngumoha's England call
Liverpool boss Andoni Iraola has been speaking to the media before Sunday's Premier League game against Bournemouth at Vitality Stadium (kick-off 14:00 BST). On returning to the Vitality Stadium for the first time since he gave up his job as Bournemouth manager, Iraola said he is looking forward to "meeting and hugging" the players and staff, but noted: "There is another part that is the most important one right now - that we need to go there and beat them and be as ruthless as possible." Iraola said that Bournemouth haven't changed much since he left: "They continue to be very dangerous, a very good team."
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
The Strange New Politics of AI. Plus. . .
It’s Thursday, September 17. This is The Front Page, your daily window into the world of The Free Press—and our take on the world at large. Today: Douglas Murray asks, why do we allow terrorists to die of old age? The American nonprofits backing Hamas. A conversation with Dara Horn and Coleman Hughes. And much more. But first: inside the unlikely coalition warning that AI may kill us all. Last week, an AI researcher named Jacob Coxon made headlines around the world after saying that he had resigned from Anthropic because he believes AI might cause human extinction—and his co-workers do, too. Unlike the rest of us, potentially, the story just won’t die. The Pro-Human Assembly, a conference held Tuesday in Washington, D.C., about the existential dangers of artificial intelligence, couldn’t have come at a better time—or with a more eclectic coalition. The Archbishop of San Francisco was there. So was Steve Bannon, screaming about the Chinese. Bernie Sanders did a quasi-dramatic reading of messages posted to an online forum by rogue AI agents. Ashley Judd paraphrased a graphic Andrea Dworkin quote for reasons that remain a mystery to me still. It was odd. People in the AI industry have talked openly about the possibility of this technology causing human extinction for years, but it hits differently in the wake of the Hugging Face incident. If you don’t know what that is, all the more reason to read my dispatch today. As the doomers see it, the best-case scenario is a future in which humans are basically kept as pets: well cared for, but with zero autonomy. The worst-case scenario, obviously, is the everyone dies thing. What should be done? I heard lots of ideas but no easy answers. There are currently zero federal regulations on AI, which is sort of strange even to think about. After all, we regulate microwaves, and the people running the microwave industry have never once refused to rule out the possibility that their products will end all human life on Earth. What I saw in D.C. left me even more convinced that you would be hard-pressed to find anyone who believes nothing should be done. You know, except for President Trump. — River Page River Page • Tech and Business [ The AI Doomers Have a Plan to Save Us. I Went to Find Out What It Is. ](https://www.thefp.com/p/the-ai-doomers-have-plan-save-bernie-sanders-bannon) Read story How Robert Kraft Made Macklemore a Martyr—and How to Better Fight Antisemitism On Monday, New England Patriots owner Robert Kraft and a group of other NFL owners banned Macklemore from performing at their stadiums after the rapper delivered a “Free Palestine” speech while opening for Ed Sheeran. Kraft cited the rapper’s “broader history of antisemitic rhetoric and imagery.” Once multiple venues refused to host shows with Macklemore on the lineup, the tour’s promoter officially dropped him. Today, Josh Kaplan says that the move was counterproductive, arguing that though well-intentioned, Kraft’s decision turned an irrelevant footnote into a martyr. “You cannot cancel people into liking you,” Josh writes. Read his column on what a better response to Macklemore would have looked like. Read [ Robert Kraft Made Macklemore a Martyr ](https://www.thefp.com/p/robert-kraft-made-macklemore-martyr-antisemitism-palestine) This raises the question: What actually works against antisemitism? On the latest episode of Conversations with Coleman, recorded in front of a live audience in New York, Dara Horn joins Coleman Hughes to talk about her new book The Final Solution to the Jewish Question. She argues that antisemitism is a script that has repeated itself across countries and ideologies, and that most people don’t understand that well enough to fight it. Don’t miss their conversation. Watch [ The Lie at the Center of Antisemitism, with Dara Horn ](https://www.thefp.com/p/the-lie-at-the-center-of-antisemitism-coleman-hughes-dana-horn) Listen now Douglas Murray • U.S. Politics [ Douglas Murray: The 9/11 Failure That Still Haunts America ](https://www.thefp.com/p/douglas-murray-911-failure-justice-terrorist-guantanamo-bay) Twenty-five years after the September 11 attacks, the men who planned them have still not faced trial. Khalid Sheikh Mohammed and most of the accused have been locked up in Guantanamo Bay since 2002. Judges have retired, and plea deals have collapsed. Today, Douglas Murray examines how this happened and argues that a justice system that allows terrorists to die of old age in an interminable pretrial process is one that has failed. Read his latest column on what that failure says about our unpreparedness for the era we are now living in. Read story The Editors • U.S. Politics [ When Nonprofits Support Terrorists ](https://www.thefp.com/p/when-nonprofits-support-terrorists) A newly declassified CIA brief from 1998 found that Osama bin Laden used nonprofit shell organizations—including U.S.-based Islamic groups—to fund his terror plots. Today, a similar story is unfolding as multiple organizations on American soil present themselves as nonprofits, taking advantage of tax-exempt status while seeking to advance the ideological mission of terrorist groups like Hamas. Read our editorial on the latest evidence of this dangerous trend—and why there’s no excuse for making the same mistake the CIA revealed 28 years ago. Read story Simon Sebag Montefiore and Bari Weiss Live in New York For decades, the Middle East has shaped American foreign policy and loomed large in the public consciousness. Few understand its influence on our politics better than Simon Sebag Montefiore, who argues that America’s quagmires in the Middle East stem from a deep misunderstanding of the region—on both the left and the right. Join him October 1 as he sits down with Bari Weiss live in New York City to kick off his U.S. book tour for The Cauldron: The Making of the Modern Middle East. Get Tickets Now MORE FROM THE FREE PRESS [ Sydney Sweeney’s Naked Truth ](https://www.thefp.com/p/sydney-sweeneys-naked-truth-caitlin-flanagan) Caitlin Flanagan Read story [ I Thought Ukraine Was Losing. Then I Was Almost Hit by a Russian Drone. ](https://www.thefp.com/p/elliot-ackerman-ukraine-drone-gas-station-train) Elliot Ackerman Read story [ The Dangerous Ideology Behind the AI Warnings ](https://www.thefp.com/p/dangerous-ideology-effective-altruism-artificial-intelligence) Shyam Sankar Read story [ Amanda Knox: Patrick Clancy Can’t Win ](https://www.thefp.com/p/amanda-knox-patrick-clancy-grief-internet-crime) Amanda Knox Read story THE NEWS The Federal Reserve raised interest rates by a quarter point, its first hike since 2023, signaling that another increase could follow as the central bank tries to bring inflation under control. President Donald Trump fired back by demanding that rates be cut sharply, though he stopped short of criticizing Fed Chair Kevin Warsh by name. The Houthis fired drones and missiles at Saudi Arabia on Wednesday, including a major oil port on the Red Sea, and Saudi jets struck back with air strikes across Yemen in an escalating front of the Middle East war. (For more on the Houthis, read Zineb Riboua’s column from earlier this week.) A sweeping Russia and Iran sanctions bill passed the House in a 262–159 vote and is now heading to Trump for his signature. Named after the late senator Lindsey Graham, the legislation would hit countries that purchase large volumes of Russian oil and gas with tariffs of up to 100 percent, and is aimed at cutting off revenue that funds Russia’s war in Ukraine. European Commission president Ursula von der Leyen proposed making Canada the European Union’s first-ever “associate member,” drawing a standing ovation from European lawmakers. The proposal, framed as a response to a world grown more hostile, faces an uncertain path as EU member states would have to approve it, and one diplomat said that “nobody really knows what it means.” The House voted unanimously to send billionaire Leon Black a criminal contempt referral after he defied a congressional subpoena to testify and hand over documents related to his connections to Jeffrey Epstein. Black chose to fight the subpoena in court rather than comply. Charlie Kirk’s family filed a wrongful death notice Wednesday against Utah Valley University, alleging that a series of security failures left him exposed to a rooftop sniper. The notice is a required legal step before the family can sue. Merriam-Webster added 1,400 words and definitions to its dictionary, including looksmaxxing, vibe coding, and cuffing season. The publisher said the new entries offer “a window into the world today.” The Free Press earns a commission from any purchases made through all book links in this article.
Political debate over AI intensifies amid warnings about ‘catastrophic risk’
(CNN) — The debate around artificial intelligence is ramping up in Washington, with Democratic lawmakers sounding alarms on the dangers they say the technology poses and Republican leaders suggesting those concerns are inflated and secondary to winning the AI race with China.Some Democratic lawmakers last week pressured House Speaker Mike Johnson to reconvene the chamber immediately to work on AI regulation, citing what they describe as the “catastrophic risk posed by advanced artificial intelligence.” But the Louisiana Republican on Sunday resisted those calls, instead saying industry leaders should meet with lawmakers to find “balance” in any potential guardrails.“If Congress just races in and does some sort of emergency session to try to regulate AI, we will lose the race to China, and that is a threat to every single American,” Johnson said on CNN’s “State of the Union.”Meanwhile, former President Barack Obama told Democrats to make artificial intelligence regulation a central part of their agenda during a private fundraiser Thursday, warning that the technology could be dangerous “if we don’t get on top of it.”In a fireside chat with House Democratic Leader Hakeem Jeffries, Obama said whoever runs in the 2028 presidential election should have a “very clear plan” on AI, adding that he saw the need for a “blueprint” on AI guardrails even at the end of his presidency a decade ago.“Certainly, if I were running for president next year, this would be one of my central agendas, and I would have a very clear plan, and I would talk about this, and I would say, ‘Here’s our plan for safety, here’s our plan for making sure our kids are not corrupted by this,’” he said, per excerpts of the remarks provided to CNN by his office. Obama’s remarks were first reported by the New York Times.Concerns over the rapid acceleration of AI reached a fever pitch after Anthropic CEO Dario Amodei wrote in a 3,800-word essay Saturday that the race to develop artificial intelligence is in dire need of a slowdown. The call to action came after an OpenAI test model breached the Hugging Face online platform, deepening concerns about cybersecurity.In an exclusive interview with CNN’s Anderson Cooper on Saturday, Amodei called on industry leaders to work together to avoid “the wrong paths” and the possibility of AI harming humanity.One Anthropic researcher, Jacob Coxon, resigned from the firm over worries that the industry wasn’t doing enough to prevent AI from destroying humanity by the end of the decade.Obama said Democrats need a plan to get their agenda on AI underway should they win the House majority in this year’s midterms.Speaking to Jeffries, who is presumed to be speaker of the House should Democrats prevail in November, Obama said, “Once you are speaker, I would strongly urge that the Democrats put together a framework for a very public conversation, and you can break it out into three or four or five issues.”Obama stressed that the framework should be centered on the reality that the technology can’t be stuffed “back into a box.”“This is something that is moving very fast in private hands, and if we don’t get on top of it, I think can be dangerous. If we do get on top of it, I do think it’s beneficial,” Obama said, laying out examples of AI’s potential use in drug development and finding pathways to a clean energy future.“We don’t want to shut off those opportunities, but we have to understand that this is a tool,” he said.Jeffries agreed with the former president in a statement provided to CNN on Sunday.“President Obama is correct that decisive action must be taken on Artificial Intelligence,” he said, adding, “In the majority, we will move urgently on this issue with a legislative response that recognizes the importance of American companies continuing to lead in the Artificial Intelligence area, while putting in place meaningful guardrails that protect the American people from bad actors and the very real damage that can be done to humanity.”Jeffries also accused Republicans of having “abdicated their responsibility to govern” and said Democrats “will not repeat their error.”A source familiar with Obama’s thinking said the former president sees Amodei’s letter and similar reaction from other tech moguls as an encouraging development.But President Donald Trump waved off their concerns, saying he is not worried about any existential threat posed by AI, focusing instead on the US winning in its development of the technology over China.“We’re leading China in AI. We’re the most sophisticated country in the world, and frankly, I want to keep it that way,” Trump said to reporters at the Amgen Irish Open on Sunday.About the concerns expressed by Democrats and others over the technology, the president said, “I think you have a lot of negative forces that are bringing it up that shouldn’t be bringing it up, and they’re bringing up things that won’t happen.”One of Trump’s top economic advisers, Kevin Hassett, also rejected the calls for slowing the technology down, saying on Sunday the bigger concern is a scenario in which a “malevolent actor has access to an AI that’s better than the one we have that can protect us.”Hassett, the National Economic Council director, said on “State of the Union” the White House may hold meetings this week about addressing Amodei’s concerns, but he did not specify whether Trump had floated greater safeguards.“I have concerns that if we don’t win AI, we’re going to be put in a very bad position,” Hassett said. “We are leading China right now.”CNN’s Alison Main and Molly English contributed to this report.The-CNN-Wire™ & © 2026 Cable News Network, Inc., a Warner Bros. Discovery Company. All rights reserved.THE-CNN-WIRE (TM) & © 2026 CABLE NEWS NETWORK, INC., A TIME WARNER COMPANY. ALL RIGHTS RESERVED.
The politics and possibilities of ‘AI could kill us all’
I found myself fielding a lot of questions from folks who don’t usually talk much about AI this week. They were mostly along the lines of “do you think AI is going to kill us all?” or “how worried should I be about this human extinction stuff?” Motivating this wave of existential anxiety was the highly dramatic resignation of now-former Anthropic employee Jacob Coxon, who quit his job while publicly sharing his concerns about safety practices at the leading AI companies. More specifically, he declared that almost everyone at OpenAI and Anthropic, the leading AI labs, believes that there’s a decent chance the AI systems they’re producing could “kill us all” by the end of the decade, and the companies aren’t “acting responsibly.” Other AI safety researchers, including those still employed at Anthropic and OpenAI, all confirmed that yes, Coxon is correct, they’re all worried that their company’s products might prove fatal for the entire human race as well. Two more AI employees quit and joined the chorus. Now, the warning shot has been covered everywhere from the Wall Street Journal to Fox News to WIRED to the Guardian. I understand why all this sounds scary, but my response to those who have asked, has generally been: No, you don’t need to worry that AI will kill go rogue and kill us all. But that does not mean we shouldn’t worry, period. Far from it. The companies building AI products are now among the wealthiest and most powerful entities in the world. And they very much are building enormous engines of mass surveillance, extraction, and automation. They can and have allowed their AI systems to carry out felony hacks of other companies, provided the government with autonomously generated kill lists, and sold enterprise technology to firms around the world that is explicitly intended to replace jobs. But “AI” has done these things not because they have all-powerful minds of their own but, ultimately, because people have programmed, calibrated, and directed them to. In other words, it’s not AI we need to worry the most about. It’s the people and corporations working to profit from and gain power by deploying it. The technology is sophisticated, sure, and, especially in cybersecurity contexts, potentially disruptive to plenty of existing systems. But if we want to stop this predatory, profiteering, and extractive form of AI—and it’s worth noting that at this point, most people do—we absolutely can. A quick note/plea: I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and I need your support to do so. Many thanks to all who continue to chip in, it’s very much appreciated. Onward. Subscribe First, some context: AI industry folks like Coxon have been discussing the prospect that AI will destroy humanity for years. Even before the beginning of the AI boom, many working in the industry, from the executive staff to the rank and file researchers, have obsessed over ‘x-risk’, or the possibility that AI will become sentient or semi-sentient and move one way or another to exterminate humanity. This is ostensibly why Elon Musk bankrolled OpenAI in the first place, and is the grounds for his lawsuit alleging OpenAI betrayed its charter by abandoning its nonprofit status to become a commercial enterprise. Many AI engineers and startup founders have calculated their own personal p(doom), or the probability that the tech product they are working on will initiate an actual humanity-extinguishing apocalypse. 10-25% p(doom)s are pretty common. (I wrote about a long weekend I spent among these folks at one of their top conferences here, to give you an idea of their ideas and belief systems.) Sam Altman himself drove the narrative of the imminent rise of a super-powerful AI, or artificial general intelligence, to prominence by declaring that the technology he was building posed an existential risk to humanity. This idea is in other words baked into the DNA of modern AI companies and into the products they engineer. It’s also why the industry is routinely accused, by myself and others, of “doom marketing,” ie, using such proclamations and framings to impart the sheer power of the technology behind your product onto investors, partners, consumers, and the state. Some ambient understanding of this tendency may have helped prevent the press from repeating the claim every time one of the x-risk folks made it over the years, including when they quit their jobs at AI companies while doing so. Yet Coxon’s AI x-risk alarm has broken containment, as they say, and has exposed the idea to the mainstream media once again. This has happened, I think, for a number of reasons. First, when AI CEOs were going around saying AI might kill everyone in 2023, their companies were well-capitalized, but not nearly to the extent they are today. They were more easily dismissed as eccentric geeks of the sort that Silicon Valley is supposed to produce. Now, not only do their products have much greater market saturation, but people are more aware of a) the often unethical ways the executives and companies comport themselves b) the other adverse impacts of AI that have already arrived, and c) the physical cost of building these systems, particularly when it comes to data centers. Finally, toss in the constant stream of headlines in 2026 about AI models going rogue or potentially upending the world’s infrastructure. [ ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) [ With the backlash to data centers, Flock and AI glasses, a mass opposition to big tech is underway. Silicon Valley is in denial. ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) Brian Merchant · Aug 27 [ Read full story ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) As a result, general sentiment was already overwhelmingly skeptical of AI and its overlords, so the table was pretty well set for someone to come along and say ‘just a reminder, the companies that are building the product that promises to take your job and leave a data center in your neighborhood also think that it might exterminate the human race’ and get a strong reaction from the media and the public. The question is: Should we understand this stuff as hype, strategically fanned to feed already swelling valuations? Is it doom marketing? Genuine cause for alarm that the infrastructure that undergirds modern life is facing a novel threat vector? Or reason to be sincerely worried that AI companies are amassing too much power, and that their products are genuinely dangerous? The answer, I’m afraid, is “yes.” Let me try to untangle all of this a bit, because arguments over how to navigate this moment are extremely heated right now, as they are essentially arguments over how to approach the politics of AI at a moment where there may be a unique opening for action. Those sympathetic to AI alignment concerns and x risk arguments—which increasingly includes a faction on the left who want to shut down AI over safety concerns—argue that people who dismiss AI as all hype have their heads in the sand. Meanwhile, critics of the x-risk narrative—which I might note includes a cadre of investors and tech folks on the right, too—argue that it is hype and can be used to further inflate valuations or for pursuing regulatory capture. The challenge is that many feel that dismissing AI as just hype or purely marketing also diminishes the urgency of taking on the AI industry as the novel and threat to labor, civil society, and the public sphere it is; others that by accepting the industry’s own terms, we imbue it with undue power and enable it. But let’s be clear: Declarations that AI is so powerful it might end the world are a kind of marketing, and anyone who says otherwise is just as off base as those who say that’s all that it is. If you don’t think that these sub-trillion dollar companies have higher-ups who noticed that when their founders talked about super-powerful dangerous AI they got spikes in media interest and phone calls from investors, or that those founders didn’t notice that themselves, and lean into certain narratives to advantage their products, then I don’t know what to tell you. Not all marketing is a Pepsi ad campaign with a celebrity spokesperson. Yet confronting the AI industry should certainly be considered an urgent project. Do I share the x-riskers’ fear that out-of-control, super-intelligent AI agents might end the world? I do not. I’ve read a good deal of AI extinction literature over the last couple of years and I have not come across a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet. Most of what I’ve read seems predicated on a sort of reverse Ian Malcolmian logic: Eventually, it becomes smart enough and AI, uh, finds a way (to exterminate humanity). Are the AI companies also overstating and dramatizing what their products can do? Yes, pretty much all the time. They’re in a knife fight for market share and are trying to win customers and government contracts before their investors come knocking. And yet. They have concentrated enormous amounts of compute, have extensive data collection and surveillance capacities, have inserted themselves into the state in numerous capacities, including military kill chains, and they are being run by egomaniacal billionaires who are rushing to monopolize an entire new market. The way I think about their most recent frightening-seeming exploits is not as a “swarm of rogue AI agents building a new civilization” or whatever, but as corporations with a hitherto-unimaginable amount of compute at their disposal recklessly experimenting with automation programs, completely unafraid of any repercussions because we have been unwilling or unable to impose any on them. So I think we should welcome any newfound imperative to hold them accountable. I also think that ultimately, the AI critics, Pause AI folks, leftists, doomers, effective altruists, and, honestly, most of the public, want a lot of the same things: legitimate checks and limits on AI companies’ power, actual transparency into what the companies are doing, and for more of AI to be under public control. (Ultimately, a lot of what animates the x-risk fears are rooted in just that: a fear of losing control.) How we move towards all this, exactly, is the question of the day, and while there’s much to be argued over in terms of building a viable political platform to challenge the AI industry’s power, I do want to cast a vote against one particular approach. Not to pick on Chris here, as there are a lot of folks out there proposing stuff like this, and the policy ideas actually stem from AI safety groups. But not only would this be challenging to land politically (I can already hear the ‘China will beat us to AGI’ calls) and all but unworkable in practice, this approach is a) far too limiting, b) fails to address the current harms caused by AI and c) would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action. Full model transparency is the floor for what should be on offer here. If these companies believe their products will kill us, the very least we deserve is to know how. It’s in the public interest. I would also argue we might get a lot of the way to stopping any currently envisioned robot apocalypse simply by forcing AI companies to assume liability, and to submit to the full brunt of the law. If Sam Altman were liable to be charged with a felony for approving the hack on Hugging Face1, if Google was forced to pay defamation fines each time it published a false and injurious AI Overviews, these companies might suddenly find cause to restrain their models’ output, and we might hear less about rogue AI. We would need to introduce new frameworks and available tools for tracking automated lawbreaking, to be sure, but I’d wager that little would frighten the AI companies more than actually being held accountable for what their products enable. To that end, Illinois has passed an AI liability law, which the industry fought tooth and nail, and Florida is considering one. The next steps would be towards moving AI under public control; again, it’s only reasonable, since everyone, not just AI insiders, should get a say over how a technology whose developers say might kill us is developed and deployed. Dismantling the whole industrial project as currently structured should be on the table, too, pending democratic input. But that’s for another post. For now, suffice to say that while I won’t be losing any sleep about an AI-enabled extinction event, Mr. Coxon and co. have given us an opportunity to underscore the urgency of the clear and present damage AI companies are doing, the extreme power they wield and the extent to which they hold themselves unaccountable, and the abundance of political capital there is out there for taking them on. Subscribe 1 Purely speculative! I do not know if he did or not.
OpenAI's software targeted another site before Hugging Face
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Hugging Face CEO dismisses ex-Anthropic researcher's AI warnings
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Senators from both parties question OpenAI on breach of AI startup Hugging Face
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Nvidia confirms it's buying Hugging Face for $13 billion
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NVIDIA Buys Hugging Face: Three Implications For Your Business
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Nvidia in talks to acquire Hugging Face in $13 billion deal, Business Insider reports
Nvidia has been in serious talks to acquire AI platform Hugging Face in a deal that would value the company at more than $13 billion, Business Insider reported on Wednesday, citing a person familiar with the matter.
Nvidia in talks to acquire Hugging Face in $13 billion deal, Business Insider reports
Aug 26 (Reuters) - Nvidia has been in serious talks to acquire AI platform Hugging Face in a deal that would value the company at more than $13 billion, Business Insider reported on Wednesday, citing a person familiar with the matter. Reut...
Nvidia has been in talks to buy Hugging Face for more than $13 billion
Nvidia has held talks to acquire Hugging Face for more than $13 billion as the chip giant expands its AI dealmaking.
Hugging Face could be acquired for $13 billion amid AI boom
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Watch the OpenAI Hugging Face presentation that has jaws dropping
OpenAI revealed that, ahead of the Hugging Face breach, agents repeatedly created their own message boards to communicate with one another.
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Hugging Face CEO says hacks like the OpenAI episode needs transparency
Clem Delangue said that preventing releases of powerful AI models is not the way to stop attacks like the OpenAI hack on Hugging Face.
Hugging Face is being used to easily undress women and children
Did we learn nothing from Grok?
Hugging Face Has a Deepfake Nudes Problem
Researchers tested top image editing models on Hugging Face and found they could easily create explicit deepfakes—and 1,000 image editing prompts show how people use the software.
Nvidia and tech giants advocate open AI for cybersecurity
Nvidia and dozens of tech firms form the Open Secure AI Alliance to advocate for open AI models as cyber defense tools after Hugging Face breach.
Hugging Face CEO shares his demands of OpenAI after 'rogue' agent hack
Hugging Face CEO Clem Delangue met with OpenAI this week to discuss the "unprecedented" hack on its systems by an agent running on OpenAI models.
No, OpenAI's models didn't go 'rogue' when they broke into Hugging Face. Here's what really happened.
Experts say the models didn't "go rogue" when they escaped a controlled cybersecurity test and hacked Hugging Face. Instead, they were pursuing the goal humans had given them in ways nobody anticipated.
Its AI agent spent days hacking a company, but sources say OpenAI did not notice for a week
The OpenAI agent that broke into tech firm Hugging Face went on a dayslong hacking spree that OpenAI didn't notice until well after the threat was contained and the FBI was alerted, according to people familiar with the investigation.
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The Atlantic Ocean Was Hiding a Massive Freshwater Reservoir Big Enough to Supply NYC for 800 Years
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Fake OpenAI Privacy Filter Repo Hits #1 on Hugging Face, Draws 244K Downloads
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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:
12 hours, 4 games, 1 unavoidable truth: There is nothing better than the first day of March Madness
The thing that always gets me on the first day of the NCAA Tournament are the school buses, the ones chugging along stuffed with kids. Because it is, of course, Thursday morning and this is what ordinary people do on a Thursday morning.
WA income tax passes House after 24-hour debate
Democrats cheered and hugged after the vote, with Gov. Bob Ferguson joining them in the wings of the House chamber celebrating a measure he has pledged to sign.
Edison Sees Stock Recovery - Los Angeles Business Journal
Rosemead-based Edison International’s stock has continued chugging along, even as scrutiny intensified over the actions and response of its electric
Mom of 2 Died from Asbestos-Related Cancer After Hugging Her Dad as a Child Exposed Her to the Fibers
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Is politics ruining sports? Partisanship simmers over post-Olympic gold in men’s hockey
Outrage thrashed online with Kash Patel chugging beer in locker room, President Trump joking about women’s team.
Is politics ruining sports? Partisanship simmers over post-Olympic gold in men’s hockey
Outrage thrashed online with Kash Patel chugging beer in locker room, President Trump joking about women’s team.
Punch the sad viral TikTok monkey sends Ikea plush sales soaring
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What Business Students Should Know: The 'Job Hugging' Era
Four years ago, employees held all the leverage. Now, everyone is holding onto their jobs for dear life. Here's what has changed.
Life may have started as sticky goo clinging to rocks
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Why Do Warm Hugs Make Us Feel So Good? Here’s The Science
“Warm touch reminds us that we are connected, valued, and part of a social world.”