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Marc Benioff said Salesforce will spend $300M on Anthropic tokens
Salesforce CEO Marc Benioff said he's able to work faster with AI agents and that the company is working on tech to make coding easier at Slack.
Spotlight on China Adds to Some Chinese Americans’ Fears
The Trump-Xi summit emphasized stability. But China’s recruitment of foreign agents has fueled suspicion of Chinese Americans.
Claude For Small Business Shows Where White-Collar AI Is Heading
Claude for Small Business is another sign that the AI market is moving toward AI and agents embedded in systems focused on serving the needs of daily work.
Microsoft’s multi-agent AI system tops Anthropic’s Mythos on cybersecurity benchmark
Microsoft's new vulnerability-scanning system, codenamed MDASH, scored 88.45% on the CyberGym benchmark, surpassing single-model systems from Anthropic and OpenAI by using more than 100 specialized AI agents across multiple models.
The war between businesses and hackers enters a perilous new phase
AI agents present novel dangers
Apple Explores Ways to Welcome AI Agents in the App Store
Apple is exploring ways to better incorporate AI agents into its App Store so it can benefit from the tech industry’s hottest trend while ensuring that the software in its store continues to play by its rules, according to people briefed on the matter. So far, the widespread embrace of agentic ...
SAP embeds NVIDIA OpenShell into Business AI Platform
The open-source runtime provides isolated execution environments and policy enforcement for agents built in Joule Studio
SAP and Anthropic Plan to Bring Claude to SAP Business AI Platform
SAP and Anthropic will embed Claude across SAP's AI-enabled solution portfolio, powered by Joule and Joule agents.
CNN Exclusive: Fired former acting FBI chief says Patel tied job security to purging agents linked to Trump probes
A week before Donald Trump’s inauguration last year, Brian Driscoll, a decorated FBI special agent, received a series of calls that alarmed him. He was being offered the No. 2 job at the FBI and was told that if he didn’t take it, a political appointee would likely get the role. Driscoll didn’t think that was an acceptable alternative, so he hesitantly agreed.
AI coders walk around in public with laptops open to keep agents going
Techies are walking with their laptops open to keep their agents running. They told Business Insider their stories, from offices to school halls.
AI coders walk around in public with laptops open to keep agents going
Techies are walking with their laptops open to keep their agents running. They told Business Insider their stories, from offices to school halls.
Google stopped a zero-day hack that it says was developed with AI
Hackers are also targeting AI agents.
Bills news: Lessons learned from Rookie Minicamp
Buffalo’s rookies and undrafted free agents got their first taste of life in the NFL. What stood out at Rookie Minicamp?
Coach Kerr to stick with Warriors on new contract: report
Steve Kerr has agreed to return as head coach of the Golden state Warriors after accepting a two-year contract, his agents told ESPN on Saturday.ESPN, citing unnamed league sources, said the two-year deal will keep Kerr as the highest-paid coach in the NBA annually.
Steve Kerr remaining with Warriors on new contract
Steve Kerr has agreed to a new deal to remain head coach of the Golden State Warriors, his agents told ESPN.
Jaguars' undrafted rookie has Liam Coen's attention in rookie minicamp
One of the Jaguars' undrafted free agents seems to have Liam Coen's attention during rookie minicamp.
Steelers announce signings of six UDFAs
The Steelers signed six undrafted free agents at the start of their rookie minicamp.
Buffalo Bills officially announce signing of Wisconsin Badgers' UDFA
The Buffalo Bills announced former Wisconsin Badgers fullback Jackson Acker was one of their 12 signed undrafted free agents.
Many Bills defensive contributors in 2025 remain free agents
Many of the Bills defenders from a season ago are still free agents
A State Budget That Speaks to a New Political Reality
Gov. Kathy Hochul and legislative leaders announced a handshake deal on a budget that includes a second-home tax, expanded child care in the city and a ban on ICE agents’ wearing of masks.
Anthropic introduces "dreaming," a system that lets AI agents learn from their own mistakes
Anthropic has launched “dreaming” for AI agents, giving Claude new self-improving tools designed to boost enterprise automation, reliability, and scale.
Perplexity's Personal Computer is now available everyone on Mac
Perplexity's Personal Computer brings AI agents to your Mac, and is now open to everyone.
eXp enters the franchise business with NextHome acquisition
The brokerage giant embraces the M&A trend, inking a deal eXp says will create “a more robust company” and give agents more choices as consolidation ramps up.
Amid NIL tumult, Florida mulls regulating college sports agents
The possibilities in Florida include capping the cut agents can take from athletes, adding new registration requirements and even charging them a fee to prove they’re legitimate business representation.
Anthropic updates Claude Managed Agents with three new features
Anthropic launched Claude Managed Agents last month, greatly simplifying the work required to build and deploy cloud-hosted AI agents. This...
Commanders' defender busy trying to recruit two prominent free agents
Charles Omenihu is busy trying to recruit more help for the Commanders.
Man charged with assaulting Secret Service agents near Washington Monument
A man who allegedly fired at Secret Service agents near the Washington Monument earlier this week has been charged in connection with the shooting.
FBI raids Sen. Louise Lucas’ Portsmouth office, cannabis business
Federal agents on Wednesday morning raided the Portsmouth office of state Sen. Louise Lucas, one of Virginia’s most powerful Democratic lawmakers, with multiple news outlets reporting an extensive law enforcement operation that included FBI agents, SWAT teams and court-authorized searches tied to several locations across the commonwealth.Fox News, who was first to report the news, said the FBI executed search warrants at several sites, including Lucas’ legislative office in Portsmouth. Staffers inside the building were ordered outside and were not allowed to re-enter as agents searched the premises. Witnesses saw federal agents carrying boxes out of the building.
I Am Begging AI Companies to Stop Naming Features After Human Processes
Anthropic announced “dreaming” for AI agents to sort through “memories” at its developer conference. Can we not?
Office of Virginia State Senator Louise Lucas Raided by F.B.I. Agents
Two people familiar with the case said the search of a Democratic lawmaker’s office was related to a Biden-era investigation of possible corruption and bribery related to marijuana businesses.
Anthropic's Claude Managed Agents can now "dream," sort of
Also, rate limits will double for Pro and Max users of tools like Claude Code.
Anthropic introduces 'dreaming' technique for AI agents
Anthropic introduces 'dreaming,' a technique for AI agents to self-improve within the Claude Managed Agents tool.
Anthropic will let its managed agents dream
Anthropic is expanding Managed Agents with dreaming — a scheduled memory process — plus outcomes-based evaluation and multi-agent orchestration now in public beta.
Anthropic just launched AI agent tools to shake up finance grunt work
Anthropic introduced 10 AI agents for the finance industry on Tuesday, adding to the tools already available from buzzy startups and firms themselves.
Secret Service shoots man who fired at agents near Washington Monument
The US Secret Service says its officers shot an armed man Monday afternoon near the Washington Monument after he fired at agents and wounded a juvenile bystander.
IBM unveils its blueprint to help enterprises run AI at the core of their business
The ‘AI Operating Model’ is built on the integration of data, agents, automation and hybrid; IBM says it represents a shift that changes how a business operates.
3 2027 free agents the Steelers should avoid re-signing
The Pittsburgh Steelers have several major free-agency decisions looming in 2027, but these players may not deserve another contract.
'The Bean' Sculptor Kapoor Blasts America's 'Politics Of Hate' And 'Warmongering'
Kapoor last year threatened to sue the Trump administration after a group of border patrol agents posed for a photo in front of his Chicago sculpture.
Florida state college sports panel eyes agent rules, NIL trusts
University of South Florida CEO of Athletics Rob Higgins said regulations are needed for agents, including making financial arrangements with students transparent.
Meet the Titans' 11 undrafted rookie free agents
The Tennessee Titans were busy on Thursday signing 11 undrafted rookie free agents. Here is a quick breakdown of those new faces.
Florida college sports panel eyes agent rules, NIL trusts
The panel is also considering recommending more oversight of agents working for collegiate athletes, limiting transfers between schools and exempting schools from federal antitrust laws to shield them from lawsuits on compensation.
Every NFL team's biggest position to improve post-NFL Draft, plus best free agent solutions
There are still helpful free agents available on both sides of the ball
Unlocking human ambition to drive business growth with AI - The Official Microsoft Blog
As our customers progress toward becoming Frontier Firms, they are using AI not only to optimize how work gets done, but to reinvent their business on the promise of growth. Organizations can now unlock creativity, accelerate innovation and democratize intelligence by bringing Copilots and agents directly into the tools people love and use every day....
OpenAI reportedly working on its own smartphone based around AI agents
Many have thrown around the idea that AI will replace the smartphone, but it seems even OpenAI disagrees as the...
OpenAI could be making a phone with AI agents replacing apps
The phone could go in mass production in 2028, an analyst says.
What's at stake for Meta if China kills its Manus deal
The Chinese government has ordered Meta to unwind its acquisition of Manus, potentially hurting Meta's push into building AI agents.
Trump calls for unity after White House press gala shooting
After shots were fired during a press dinner in Washington, the alleged perpetrator was overpowered by security agents. Experts say there has been an increase in political violence in the US over recent years.
Gossip: Liverpool add Leao and Diomande to attacking targets
Liverpool, Manchester United, Manchester City and Real Madrid are keeping tabs on 26-year-old AC Milan and Portugal winger Rafael Leao, who is valued at about £60m. Mail) Ivory Coast winger Yan Diomande's agents have already held discussions with Liverpool and Paris St-Germain, but RB Leipzig are keen on keeping the 19-year-old at the club for at least one more season. (Bild - in German)
Apple's M4 Mac mini, including the $599 one, is gradually becoming impossible to buy
Pending refresh? RAM shortage? AI agents? There are many possible explanations.
Companies that monitor workers can use the data to train AI agents
More employers are monitoring what workers do on company devices, and firms such as Meta are using employee data to help train AI agents.
Axios Live: AI could support child mental health — with the right guardrails
AI tools like chatbots and agents could expand access to mental health support for children
Exclusive | Adobe Unveils Agents for Businesses Amid Threat of AI Disruption
The software maker’s new AI agents are aimed at getting ahead of the risk of artificial intelligence to its business.
Exclusive | Adobe Unveils Agents for Businesses Amid Threat of AI Disruption
The software maker’s new AI agents are aimed at getting ahead of the risk of artificial intelligence to its business.
Microsoft Is Quietly Opening the Windows 11 Taskbar To Third-Party AI Agents That Can Act On Your Desktop
Microsoft launches AI Agents for Windows 11 taskbar supporting third-party options, enhancing productivity with Microsoft 365 Researcher and more.
Microsoft confirms AI agents are still coming to the Windows 11 taskbar as it prepares for public rollout
Microsoft has clarified that it's not giving up on an agentic Windows 11 experience and is still bringing agents to your taskbar.
Scientists discover bacteria can “explode” to spread antibiotic resistance
Scientists have uncovered a surprising twist in how bacteria share genes—including those that spread antibiotic resistance. Tiny virus-like particles called gene transfer agents (GTAs), once ancient viral invaders, have been repurposed by bacteria into delivery systems that shuttle DNA between neighboring cells. The study reveals a key control hub of three genes, dubbed LypABC, that triggers bacterial cells to burst open and release these DNA-packed couriers.
Federal agents raid Twin Cities businesses over suspected fraud
Fantasy Basketball: LeBron James, Jalen Duren among notable free agents
In addition to actual free agents, some player option situations will significantly affect fantasy basketball this summer.
Victoria Li's senior project: Exploring how AI processes information
Knowing how chatbots turn input into output will help with reliability and trust in AI agents
Microsoft is working on yet another OpenClaw-like agent
The company has also previously introduced agents that are able to complete tasks, Cowork and Copilot Tasks.
How to keep your brain sharp and avoid overreliance on AI
A Workday report revealed that nearly half of workers are concerned about AI agents eroding their critical thinking skills.
Diggins signs with the Sky; Sabally joins Liberty
Skylar Diggins is headed to Chicago while Satou Sabally is coming to New York as WNBA free agents can officially sign Saturday. Diggins spent the past two seasons with the
U.S. Moves to Deport Son of Prominent Figure in Iranian Revolution
Federal agents arrested a man whose mother served as a spokeswoman for the Islamist embassy captors during the hostage crisis that began in 1979.
“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.
The Brief: Retiring business owners exiting to their employees
Greetings Agents of Impact! In today’s Brief: Featured: Ownership Economy For retiring business owners, employee buyout options can go head-to-head with private equity. ...
Poke makes AI agents as easy as sending a text
Poke brings AI agents to everyday users via text message by handling tasks and automations without complex setup, apps, or technical know-how.
In Search of Health Care Options for Real Estate Agents
Real estate professionals who work as independent contractors often face expensive health care choices, but brokers and associations are finding new ways to expand access and support agents’ financial well-being.
New York Giants announce jersey numbers for incoming free agents
After an active free agency period, the New York Giants announced jersey numbers for their new faces on Wednesday.
Zhou earns CAREER Award from the National Science Foundation
Through the early-career grant, Peipei Zhou will advance the scalable, verifiable co-design of heterogeneous reconfigurable computing systems, enabling domain experts to efficiently build next-generation customized AI and data-intensive applications, such as autonomous physical systems, adaptive intelligent agents, and advanced healthcare technologies.
A Look At RingCentral (RNG) Valuation After Cox Business AI Contact Center Launch
AI contact center launch with Cox Business puts RingCentral in focus RingCentral (RNG) is back on investors’ radar after Cox Business launched Cox Business Contact Center with RingCentral, an AI first omni channel platform built on RingCX and RingEX. The new offering targets organizations looking to modernize customer engagement, combining virtual agents, AI quality management, broad channel coverage, CRM integrations, and compliance features, which could shape how investors think about...
How WME Sports agents are reshaping golf's business landscape at Augusta National
WME Sports agents revealed how the Masters serves as golf's biggest networking hub, driving new brand deals, activations, industry partnerships and much more off the course.
How WME Sports agents are reshaping golf's business landscape at Augusta National
WME Sports agents revealed how the Masters serves as golf's biggest networking hub, driving new brand deals, activations, industry partnerships and much more off the course.
Super sports agent Leigh Steinberg talks to ASU Law students about success in the business
Leigh Steinberg is considered one of the most powerful sports agents in history.He has negotiated record-setting contracts for elite NFL quarterbacks, represented hundreds of professional athletes and hosted annual Super Bowl parties for 5,000 of his closest friends. His career even inspired the film “Jerry Maguire” and the iconic line, “Show me the money.”
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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