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The Nancy Guthrie investigation is now in its third week, which means it was only a matter of time before the case piqued the interest of online armchair detectives. Nancy Guthrie, the mother of Today Show anchor Savannah Guthrie, was reported missing on Feb. 1. In the weeks since, the street outside her home in Tucson, Arizona, has become a destination for true-crime livestreamers. Online sleuths have dissected the publicly available details of the ongoing case while spreading far-fetched conspiracy theories. Some have filmed themselves driving through Guthries neighborhood. The hashtag #nancyguthrie currently has more than 16,000 posts on TikTok, where users analyze Ring doorbell footage and excerpts from Savannah Guthries 2024 memoir, capitalizing on public interest in the case and often drawing hundreds of thousands of views. @thedreydossier its all connected man #investigation #truecrimetok original sound – Drey These posts across social media platforms have forced law enforcement to repeatedly set the record straight and dispel rampant rumors and misinformation, particularly as it pertains to Guthries family members. Pima County Sheriff Chris Nanos announced Monday that Guthries children and their spouses had been fully cleared from the investigation. The family has been nothing but cooperative and gracious and are victims in this case, Sheriff Chris Nanos said in a statement posted on X. A statement from Sheriff Chris Nanos on the Nancy Guthrie Investigation: pic.twitter.com/YfhQSPkrFJ— Pima County Sheriff's Department (@PimaSheriff) February 16, 2026 His statement appeared to indirectly address those speculating online and reporting irresponsibly about the case. Influencer content is, by nature, unwieldy, reactionary, and unbeholden to the same standards as traditional news outlets covering ongoing investigations. Former Los Angeles Sheriffs Department Lt. Gil Carrillo told 13 News that online speculation has the potential to inadvertently hinder investigations. With all of these people that are getting on social media rendering their opinions and their thoughts, investigators have to take time from their investigation and assign people to follow those leads up because they all have to be followed, Carrillo said. Every one of them has to be vetted out. Members of the true-crime community counter that more eyes on an active case can help, something authorities themselves have acknowledged. As a person involved in the Guthrie investigation told CNN last week: The breakthrough tip could come from anyone, from anywhere. In 2021, online sleuths credited themselves with helping locate the remains of Gabby Petito, the 22-year-old who went missing during a road trip with her boyfriend. As the internet became consumed with the case, sharing images, analyzing Petitos YouTube uploads, and speculating about timelines, YouTubers Jenn and Kyle Bethune spotted Petitos van in their own travel footage. This helped point authorities to the area where Petitos body was ultimately found. Since then, similar episodes have played out across the hugely popular true-crime corner of the internet. Inspired by those successes, influencers and amateur sleuths are increasingly inserting themselves into both active and cold cases. But even well-meaning intervention can risk doing more harm than good.
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The deadline to claim the early-bird rate for Fast Companys Best Workplaces for Innovators is quickly approachingFriday, February 20, at 11:59 p.m. Pacific time. This marks the eighth year Fast Company will be recognizing companies and organizations from around the world that most effectively empower employees at all levels to improve processes, create new products, or invent whole new ways of doing business. In addition to ranking the worlds Best Workplaces for Innovators, we will also recognize companies in 19 categories, including a brand new category that focuses on skilled laborcompanies that depend heavily on talented employees with the kinds of increasingly coveted technical expertise acquired through vo-tech training and trade schools. Other new categories this year include: Cybersecurity and enterprise software Industrial and manufacturing Technology and science Advertising, marketing, and PR Biotech, healthcare, and life sciences Financial services and fintech What differentiates Best Workplaces for Innovators from existing best-places-to-work lists is that it goes beyond benefits, competitive compensation, and collegiality (mere table stakes in todays competition for talent) to identify which companies are actively creating and sustaining the kinds of innovative cultures that many top employees value as much as or even more than money. Places where they can do the best work of their careers and improve the lives of hundreds, thousands, or even millions of people around the world. Every application receives careful review by Fast Company editors. Start your Best Workplaces for Innovators application here. For more information on applying, see the FAQs. The final deadline to apply isnt until March 27, but all applications submitted by Friday, February 20, at 11:59 pm Pacific time receive the preferred rate.To sign up for Best Workplaces for Innovators notifications, register here
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In todays AI race, breakthroughs are no longer measured in yearsor even monthsbut in weeks. The release of Opus 4.6 just over two weeks ago was a major moment for its maker, Anthropic, delivering state-of-the-art performance in a number of fields. But within a week, Chinese competitor Z.ai had released its own Opus-like model, GLM-5. (Theres no suggestion that GLM-5 uses or borrows from Opus in any way.) Many on social media called it a cut-price Opus alternative. But Z.ais lead didnt last long, either. Just as Anthropic had been undercut by GLM-5s release, GLM-5 was quickly downloaded, compressed, and re-released in a version that could run locally without internet access. Allegations have flown about the ways AI companies can match, then surpass, the performance of their competitorsparticularly how Chinese AI firms can release models rivaling American ones within days or weeks. Google has long complained about the risks of distillation, where companies pepper models with prompts designed to extract internal reasoning patterns and logic by generating massive response datasets, which are then used to train cheaper clone models. One actor allegedly prompted Googles Gemini AI model more than 100,000 times to try and unlock the secrets of what makes the model work so powerfully. I do think the moat is shrinking, says Shayne Longpre, a PhD candidate at the Massachusetts Institute of Technology whose research focuses on AI policy. The shift is happening both in the speed of releases and the nature of the improvements. Longpre argues that the frontier gap between the best closed models and open-weight alternatives is decreasing drastically. The gap between that and fully open-source or open-weight models is about three to six months, he explains, pointing to research from the nonprofit research organization Epoch AI tracking model development. The reason for that dwindling gap is that much of the progress now arrives after a model ships. Longpre describes companies doing different reinforcement learning or fine tuning of those systems, or giving them more test time reasoning, or enabling to have longer context windowsall of which make the adaptation period much shorter, rather than having to pre-train a new model from scratch, he says. Each of those iterative improvements compounds speed advantages. They’re pushing things out every one or two weeks with all these variants, he says. It’s like patches to regular software. But American AI companies, which tend to pioneer many of these advances, have become increasingly outspoken against the practice. OpenAI has alleged that DeepSeek trained competitive systems by distilling outputs from American models, in a memo to U.S. lawmakers. Even when nobody is “stealing” in the strict sense, the open-weight ecosystem is getting faster at replicating techniques that prove effective in frontier models. The definition of what open means in model licenses is partly to blame, says Thibault Schrepel, an associate professor of law at Vrije Universiteit Amsterdam who studies competition in foundation models. Very often we hear that a system is or is not open source, he says. I think it’s very limited as a way to understand what is or what is not open source. Its important to examine the actual terms of those licenses, Schrepel adds. If you look carefully at the licenses of all the models, they actually very much limit what you can do with what they call open-source, he says. Metas Llama 3 license, for instance, includes a trigger for very large services but not smaller ones. If you deploy it to more than 700 million users, then you have to ask for a license, Schrepel says. That two-tier system can create gray areas where questionable practices can emerge. To compensate, the market is likely to diverge, MIT’s Longpre says. On one side will be cheap, increasingly capable self-hosted models for everyday tasks; on the other, premium frontier systems for harder, high-stakes work. I think the floor is rising, he adds, predicting more very affordable, self-hosted, self-hosted, general models of increasingly smaller sizes too. But he believes users will still navigate to using OpenAI, Google and Anthropic models for important, skilled work. Preventing distillation entirely may be impossible, Longpre adds. He believes its inevitable that whenever a new model is released, competitors will try to extract and replicate its best elements. I think its an unavoidable problem at the end of the day, he says.
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