 |
|
|
|||||
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
Category:
E-Commerce
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.
Category:
E-Commerce
The AI boom began with ChatGPT and chatbots. Now chatbots are starting to grow arms and legs, as developers say, meaning they can use digital tools and work independently on a humans behalf. The open-source platform OpenClaw is notable because it lets people build agents with far more autonomy than those offered by big tech. OpenClaw agents can control a browser, send emails, do multi-step planning, and pursue persistent goals. Users often interact with them through iMessage or Discord, with the agent hosted locally on a Mac mini. One users agent reportedly negotiated with several car dealerships and shaved four grand off a cars price while its owner was in a meeting. Some say OpenClaw agents fulfill the promise of Samantha, the independent AI in Her. Developers are now racing to build their own. (To wit: The project hit 100,000 GitHub stars faster than any other.) That means the internet could soon be full of agents acting as proxies for humans. Thats why OpenClaws creator, Peter Steinberger, is worth hearing out. I listened to his recent three-hour interview with podcaster Lex Fridman, where the thoughtful (and quirky) Austrian shared prescient ideas about where AI agents could take personal computing, and how societies might respond. Below, the six most interesting things he said (lightly edited for clarity): On the Moltbot affair Some people are just way too trusty or gullible. You know . . . I literally had to argue with people that told me, ‘Yeah, but my agent said this and this.’ So, we, as a society, we [have] some catching up to do in terms of understanding that AI is incredibly powerful, but its not always right. Its not all-powerful, you know? And especially things like this, its very easy that it just hallucinates something or just comes up with a story. For many of us, the first we heard of OpenClaw was when its agents began congregating on their own social site, called Moltbook, where they dragged their human owners, posted manifestos, and debated topics like sentience. It gave people a real sense of future shock. Steinberger believes AI has raced ahead of peoples understanding and readiness. On OpenClaws security issues If you understand the risk profiles, fine. I mean, you can configure it in a way that nothing really bad can happen. But if you have no idea, then maybe wait a little bit more until we figure some stuff out. But they would not listen to the creator. They [installed] it anyhow. So the cats out of the bag, and securitys my next focus. When an agent is operating on its own and interfacing with the web and other services, it creates a larger attack surface. A hacker could inject malicious prompts to redirect the agent toward harmful or even criminal actions. Steinberger believes OpenClaw should be used only by people who understand these risks and how to mitigate them. On Macs (potential) AI moment Isnt it funny how they completely blunder AI, and yet everybodys buying Mac minis? No, you dont need a Mac mini to install OpenClaw. You can install it on the web. Theres a concept called nodes, so you can make your computer a node and it will do the same. There is something to be said for running it on separate hardware. That right now is useful. . . . And no, I dont get commission from Apple. They didnt really communicate much.” Many developers who want their OpenClaw agents running continuously on a local machine, rather than in the cloud, are buying Mac Studio or Mac mini computers. That demand has reportedly created shortages of certain configurations, with delivery times stretching from a few days to as long as six weeks for high-memory systems. On Zuckerberg’s feedback “Mark [Zuckerberg] basically played all week with my product, and sent me, ‘Oh, this is great.’ Or, ‘This is shit. Oh, I need to change this.’ Or, like, funny little anecdotes. And people using your stuff is kind of like the biggest compliment, and also shows me that, you know, they actually . . . care about it. And I didnt get the same on the OpenAI side. Steinberger surprised the AI world last Friday when he announced he would sell OpenClaw to OpenAI and join the company. In the Lex Fridman interview a few days prior, he said he was considering selling to either OpenAI or Meta, and without naming a favorite, he sounded like he was leaning toward Meta. OpenAIs Sam Altman may have done some fast talking after the interview was published, or Steinbergers Meta-leaning comments may have been part of a negotiation strategy. Either way, Steinberger will now have far more people and computing power at OpenAI to help advance its AI agents. On AIs not-so-great UX The current interface is probably not the final form. Like, if you think more globally, we copied Google for agents. You have a prompt, and then you have a chat interface. That, to me, very much feels like when we first created television and then people recorded radio shows on television and you saw that on TV. I think theres better ways how we eventually will communicate with models, and we are still very early in this ‘how will it even work’ phase. So, it will eventually converge and we will also figure out whole different ways to work with those things. Steinberger says OpenClaw isnt really competing with AI coding agents like Claude Code or OpenAIs Codex. Theyre different tools, he says, with OpenClaw functioning more like a personal assistant. But he believes they could eventually converge into something like an AI operating system, and that the way we interact with AI will change significantly in the years ahead. On ‘vibe coding’ I actually think vibe coding is a slur. Yeah, I always tell people I do agentic engineering, and then maybe after 3 a.m. I switch to vibe coding, and then I have regrets the next day. You just have to clean up and fix your shit. To Steinberger, vibe coding means using an AI coding assistant to quickly mock up an app or feature without much regard for security, testing, or its effects on a larger code base. Agentic engineering, meanwhile, is more like a collaboration between an experienced software engineer and an advanced coding assistant (such as Anthropics Claude Code or OpenAIs Codex), in which the two create a detailed plan for building new software without introducing security problems or bugs.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||