News & Updates
The latest news and updates from companies in the WLTH portfolio.
Anthropic Downloaded Music From Torrent Sites to Train Claude | Editor's Pick AI News
Sony Music Publishing and Warner Chappell Music sued Anthropic on Friday. They say the company used BitTorrent to take songbooks, then fed them to Claude. While Anthropic has already admitted torrenting books, it has never conceded that music sat inside those files, hence the copyright case. Anthropic Music Lawsuit Explained The complaint names two hauls, both from shadow libraries and containing unlicensed copies of published works. * Roughly 5 million books came from Library Genesis in June 2021. * Another 2 million came from Pirate Library Mirror in July 2022. According to the publishers, sheet music and songbooks sat in those collections. Torrenting is not the only route in the filing. The publishers also say Anthropic scraped lyrics from Musixmatch and LyricFind. Both sites pay for the right to display them. "...one of the largest and most blatant ongoing thefts of intellectual property in history," the opening line of the complaint reads. Follow us on X to get the latest news as it happens Why Torrenting Is the Dangerous Part A judge has already drawn this line once. Buying books and scanning them leaned toward fair use. Taking them from pirate sites did not. The same judge described those downloads bluntly. "...straightforward piracy but at massive scale." That is why Anthropic's destructive book scanning program survived court, while its downloads ended in a settlement. Torrenting sits on the wrong side of that line, and it carries a second problem. The software uploads while it downloads. Every copy taken is also a copy shared. Two of the four counts rest on that point, with both naming Dario Amodei and Benjamin Mann as individuals, not as employees. Companies settle. People give depositions. What It Could Cost The publishers want up to $150,000 for each song a jury finds was knowingly infringed. The publishers say hundreds of their songs sat in those files. They put the wider training claim in the tens of thousands. Notably, however, Anthropic has beaten these publishers before. It beat their bid to block Claude's training in a 2023 case over lyrics. It agreed to run output guardrails instead. It has not commented on this one. Everything now turns on discovery. Did the songs reach Claude through a purchase, or through a swarm?
Anthropic Downloaded Music From Torrent Sites to Train Claude
Sony Music Publishing and Warner Chappell Music sued Anthropic on Friday. They say the company used BitTorrent to take songbooks, then fed them to Claude. While Anthropic has already admitted torrenting books, it has never conceded that music sat inside those files, hence the copyright case. Anthropic Music Lawsuit Explained The complaint names two hauls, both from shadow libraries and containing unlicensed copies of published works
Send MT4 MT5 Trades to Telegram and Discord Automatically
Open any public signal channel and scroll back a month. You will find entries. Lots of entries, with confident language and clean formatting. What you will struggle to find is the other half of each trade: where it actually closed, whether the stop got moved, and what the month added up to. That is not usually fraud. It is mostly logistics. Publishing signals by hand is a job, and like every manual job it gets done well when things are going well and quietly skipped when they are not. Nobody feels like typing "closed for a loss" at eleven at night. So the record ends up incomplete, and an incomplete record always flatters the person keeping it. This post is about fixing that properly: what a signal message should actually contain, how the free-tier and premium-tier model really works, the technical requirements that people discover only after their terminal freezes, and how to automate the whole thing so the record is complete whether you feel like it or not. The three ways manual publishing fails Latency. Your entry fills, you switch to Telegram, you type the pair, direction, entry, stop and target, you check it, you send. Ninety seconds have passed on a good day. On a fast instrument that is a different price, and the gap between your fill and your subscriber's fill becomes the thing you get blamed for. Selection bias. This is the serious one. When publishing is manual, it is also optional, and optional means the trades that get posted skew toward the ones you are happy to post. Nobody decides to hide losses. It just turns out that the winner gets posted in real time and the loser gets posted later, or summarised, or not at all. Over months this builds a public record that does not match your account, and you will not even be able to tell how far apart they drifted. No updates. The entry gets published and then the trade lives on without its audience. You move the stop to break-even and nobody knows. You take a partial and nobody knows. The trade closes and the original message still sits there saying "target 1.2850", forever, like a monument to a moment that ended two days ago. What a signal message should actually contain If you are going to publish, publish something a stranger could act on and audit. That means four blocks: * The instruction: instrument, direction, entry, stop loss, take profit. Non-negotiable, and the stop is what separates a signal from a hunch. A "signal" with no stop is not a trade, it is an opinion. * The context: a line on why. Even one clause. It lets your audience learn instead of just copying, and it is the difference between a channel that builds traders and a channel that builds dependents. * The updates: when the stop moves, the audience needs to know, and they need to know on the original message, not in a new post twenty messages down that nobody will scroll to find. * The close: where it ended and what it did. Especially when it lost. A channel that publishes its losers in real time is making a claim about itself that no amount of marketing can fake. That last point is the whole game. Anyone can publish entries. Publishing the complete lifecycle, automatically, in public, is a commitment to being auditable, and it is the only version of a signal channel that survives contact with a sceptical audience in 2026. The free tier and premium tier model, explained honestly If you monetise a channel, the standard structure is two channels: a free one that demonstrates the process and a paid one that gets the signals live. The mechanism that makes it work is a delay on the free tier. Why a delay rather than simply publishing less to the free channel: the free tier still needs to be genuinely useful, or it does not convert anyone. A free subscriber who sees the complete record, including the losses, just later, gets a real basis to judge you. They can see the entries, watch how the trades were managed and check the closes. What they cannot do is act on the entry at the same price you did, which is exactly the thing the paid tier is selling. Two practical notes on choosing the delay. Too short and there is no reason to upgrade. Too long and the free channel becomes a history lesson nobody follows, which kills the funnel that justified having it. The right value depends entirely on your average holding time: a channel of scalps needs a very different delay from a swing channel where trades run for days. Set it against how long your trades actually live, not against a round number. The technical requirements nobody thinks about until it breaks This is the part that separates a tool that works from a script that ruins your week. Sending must not block the terminal. Network calls are slow and sometimes they hang. If publishing happens synchronously on the same thread that manages your trades, then a slow response from a messaging API stalls your terminal, and it stalls it at exactly the busiest moment, because busy moments are when both the trades and the messages happen. Publishing has to be asynchronous. This is not a nice-to-have, it is the difference between a broadcasting tool and a liability attached to your live account. Failures must retry. Networks drop packets. APIs return errors. Without automatic retry, the message you thought went out simply did not, and you find out when a subscriber asks why they missed a trade. Rate limits must be respected. Messaging platforms throttle senders. Publish a burst across several channels without honouring the limits and you get throttled or blocked, usually right when a volatile session is generating the most messages. These three requirements sound like plumbing because they are plumbing. They are also the reason "I will just write a script" turns into a project. The tool DoIt Signal Publisher Pro, for MT5 and MT4, is what I built for this, and it maps onto everything above: * Up to 6 channels in a single operation: three Telegram chats and three Discord webhooks, published together. This is what makes the tiered model practical, because your premium and free channels are the same trade going to different destinations with different timing. * Tiered distribution built in: premium subscribers receive the signal immediately, free-tier channels receive it after a delay you configure. * Automatic message editing: when the stop loss or take profit changes, the original message is edited. The post your audience is looking at stays true instead of becoming a monument. * Closure confirmations: the trade's ending gets published, with the result attached, in the format your audience can audit. * Daily and weekly reports: the summary that turns a stream of messages into a track record. * Chart screenshots attached, so the context travels with the instruction. * Inline buttons for channel engagement. * Asynchronous sending with automatic retry and rate-limit compliance, without freezing the terminal. The plumbing section above, solved. * A TEST function that verifies all six channels at once, so you find out your webhook is wrong during setup rather than during a live trade. Setup is documented in the first input parameter, step by step, and takes about five minutes. There is also a full user guide if you want to read the whole thing before installing anything. Try it before you pay for it There is a free 48-hour trial, self-service. My suggestion for how to spend it: set up both a premium and a free channel, run the TEST function to confirm all six destinations, then take one real trade and watch the entire lifecycle publish itself. Move the stop and watch the original message update. Close it and watch the confirmation land. Forty-eight hours is enough to see the mechanism work end to end, which is the only thing you actually need to evaluate. If you already know you want it, PRO is $129 once on the MT5 and MT4 listings, with rental at $45 per month, $90 for three months or $119 for a year if you would rather not commit. Both listings have a free demo as well. The honest limits * This publishes trades. It does not create them. It broadcasts what your account already did. If the underlying trading is not good, the tool will simply document that faithfully, which is either the most useful or the most uncomfortable thing it does. * Automation removes the excuse, not the responsibility. Once the losses publish themselves, you are running an auditable channel. That is the point, and it is also a commitment you should make deliberately rather than discover. * Messaging platforms have their own rules and limits, and they change them. Rate-limit compliance and retry handle the normal cases, not a platform deciding to restrict your account. * How you present signals to a paying audience may carry regulatory obligations depending on where you and your subscribers are. That is your call to make with proper advice, and it is worth thinking about before the first payment lands rather than after. Want to see the whole lifecycle publish itself before you decide anything? Set up a real channel and run it for 48 hours, free. Start the free Signal Publisher trial. The close The reason to automate publishing is not that it saves you ninety seconds per trade, although it does. It is that automation removes the one degree of freedom that quietly corrupts every manual track record: the choice, made in the moment, about whether this particular trade is one you feel like posting. Take that choice away from yourself and what you publish becomes evidence instead of marketing. In a market this full of screenshots, that is a genuinely scarce position to occupy. I publish breakdowns like this every week. Real numbers, including the ugly ones. Join the newsletter.
Anthropic's $7bn Chip Hunt Tests the Economics of Gulf AI Investment
Saudi Arabia and the UAE are spending heavily to become global suppliers of AI computing power. Anthropic's push into proprietary silicon raises a harder investment question: can data-centre returns keep pace with increasingly rapid hardware replacement cycles? Anthropic's abandoned attempt to acquire AI-chip start-up MatX for about $7 billion carries implications well beyond Silicon Valley. For the Gulf, it exposes an emerging financial risk beneath one of the region's biggest technology bets: the accelerated economic depreciation of the AI processors and computing hardware powering its new data centres. Reuters reported that Anthropic considered acquiring MatX, founded by former Google TPU engineers, before shifting towards a possible partnership. MatX is separately seeking funding at a valuation of around $4 billion, while Anthropic is recruiting semiconductor specialists and examining several chip developers as it builds an internal silicon capability. The motivation is increasingly economic. Anthropic is simultaneously committing enormous sums to external computing capacity, including a reported six-year, $45 billion agreement for around 460MW from Nscale using Nvidia's Vera Rubin systems. At that scale, even relatively small improvements in the cost, power consumption or utilisation of each AI workload can justify investment in proprietary processors. Anthropic is also following a broader industry direction already established by Google, Amazon, Meta and others: leading AI companies increasingly want greater control over the hardware beneath their models. For Gulf investors, that changes the equation. The Gulf's Compute Bet Saudi Arabia and the UAE are positioning computing capacity as a new strategic infrastructure industry. Saudi Arabia's HUMAIN and AMD announced plans to invest up to $10 billion to deploy as much as 500MW of AI capacity over five years. HUMAIN's Nvidia programme separately targets up to 500MW, beginning with an 18,000-unit GB300 Grace Blackwell system. Both are expansion targets rather than currently operating capacity. The Saudi strategy is already broadening. HUMAIN, AMD and Cisco subsequently announced a joint venture targeting up to 1GW by 2030, beginning with a planned 100MW deployment, while Qualcomm is targeting 200MW of inference infrastructure from 2026 alongside an AI engineering centre in Riyadh. Abu Dhabi is pursuing even greater scale. Stargate UAE is being developed as a 1GW AI cluster within the planned 5GW UAE-US AI Campus, with the first 200MW under construction towards planned delivery in 2026. The opportunity is substantial. Demand remains strong: Nvidia's latest outlook pointed to continued rapid growth in AI infrastructure spending rather than an imminent collapse in accelerator demand. The investment risk is subtler. MEO Analysis: The "Stranded Compute" Risk A data-centre building, grid connection, fibre network and cooling system can remain productive for years or decades. The accelerator hardware inside it operates on a very different economic clock. MEO defines stranded compute not as an obsolete data centre, but as accelerated economic depreciation of installed processors before investors have fully captured the returns originally expected from them. The distinction is important. Older GPUs do not suddenly become useless when a new generation arrives. They can move into less demanding training or inference workloads, or remain profitable at lower prices. But if newer accelerators perform substantially more computation per megawatt or per dollar, the market value and rental economics of older machines can weaken quickly. That creates an unusual asset-duration mismatch: long-lived infrastructure surrounding short-lived technology. The financial transmission is straightforward: faster hardware depreciation → lower achievable pricing or utilisation → higher replacement capex → weaker free cash flow and project IRR. For Gulf investors, that is more important than whether one particular processor architecture wins. MW Measures Capacity. Utilisation Determines Returns. The AI infrastructure race is frequently described through GPU numbers and megawatts. Neither determines profitability on its own. A 500MW facility operating close to capacity under multi-year contracts with creditworthy customers can produce attractive infrastructure economics. The same facility carrying expensive processors without sufficient utilisation can destroy capital. Anthropic's Nscale deal illustrates why contract structure matters almost as much as hardware selection. Long-duration customer commitments can transfer part of the technology-cycle risk away from the infrastructure owner by providing greater revenue visibility through several years of hardware depreciation. Gulf AI projects should therefore increasingly be judged on four variables: utilisation, contracted revenue, replacement capex and upgrade flexibility. Investors should ask who pays when processors need replacing, who carries their residual-value risk, whether contracts survive a hardware refresh, and whether power and cooling infrastructure can support future architectures. This is particularly important as financing moves deeper into AI equipment. Reuters Breakingviews has highlighted efforts to develop hundreds of billions of dollars of financing around AI hardware, including structures that may depend partly on assumptions about the residual value of Nvidia accelerators. The question is increasingly not whether AI demand exists, but who absorbs depreciation if hardware economics change faster than expected. Stranded Compute Does Not Mean Stranded Infrastructure There is also an important counterargument. Better processors can actually increase the value of high-quality Gulf data centres. If a future accelerator delivers considerably more compute from the same megawatt of electricity, scarce grid capacity becomes more productive. Revenue per MW can rise even as individual servers are replaced. That means the underlying powered infrastructure -- land, electricity, fibre and cooling -- could retain or increase its strategic value while the accelerator layer turns over rapidly. The Gulf's comparative advantages therefore remain powerful. Its challenge is to avoid allowing most technology-cycle risk to sit on regional balance sheets while the highest-margin intellectual property remains elsewhere. From Compute Capacity to Value Capture Saudi Arabia is already beginning to address that issue. Qualcomm's collaboration includes a semiconductor design centre intended to develop local engineering capabilities alongside infrastructure investment. That may prove strategically important. Gulf investors do not need to replicate TSMC's fabrication model to capture more of the AI value chain. More durable opportunities could lie in chip design, specialised software, networking, advanced packaging, model optimisation and equity participation in semiconductor technology, alongside data-centre ownership. The capital-allocation principle is straightforward: regional investors should seek assets capable of generating returns across multiple generations of processors, rather than relying excessively on the scarcity premium attached to one generation of hardware. Anthropic's MatX discussions therefore do not undermine the Gulf's AI strategy. Even companies developing proprietary processors continue to commit tens of billions of dollars to external compute, demonstrating how large global demand remains. But the investment standard is becoming tougher. The next phase of Gulf AI development should be measured less by headline GPU counts and more by how effectively projects recover capital, maintain utilisation and upgrade through successive silicon cycles. The key question is no longer simply how much compute Saudi Arabia and the UAE can install. It is whether the region can keep earning attractive returns after today's chips are replaced.
Anthropic Downloaded Music From Torrent Sites to Train Claude
Sony Music Publishing and Warner Chappell Music sued Anthropic on Friday. They say the company used BitTorrent to take songbooks, then fed them to Claude. While Anthropic has already admitted torrenting books, it has never conceded that music sat inside those files, hence the copyright case. Anthropic Music Lawsuit Explained The complaint names two hauls, both from shadow libraries and containing unlicensed copies of published works. * Roughly 5 million books came from Library Genesis in June 2021. * Another 2 million came from Pirate Library Mirror in July 2022. According to the publishers, sheet music and songbooks sat in those collections. Torrenting is not the only route in the filing. The publishers also say Anthropic scraped lyrics from Musixmatch and LyricFind. Both sites pay for the right to display them. "...one of the largest and most blatant ongoing thefts of intellectual property in history," the opening line of the complaint reads. Follow us on X to get the latest news as it happens Why Torrenting Is the Dangerous Part A judge has already drawn this line once. Buying books and scanning them leaned toward fair use. Taking them from pirate sites did not. The same judge described those downloads bluntly. "...straightforward piracy but at massive scale." That is why Anthropic's destructive book scanning program survived court, while its downloads ended in a settlement. Torrenting sits on the wrong side of that line, and it carries a second problem. The software uploads while it downloads. Every copy taken is also a copy shared. Two of the four counts rest on that point, with both naming Dario Amodei and Benjamin Mann as individuals, not as employees. Companies settle. People give depositions. What It Could Cost The publishers want up to $150,000 for each song a jury finds was knowingly infringed. The publishers say hundreds of their songs sat in those files. They put the wider training claim in the tens of thousands. Notably, however, Anthropic has beaten these publishers before. It beat their bid to block Claude's training in a 2023 case over lyrics. It agreed to run output guardrails instead. It has not commented on this one. Everything now turns on discovery. Did the songs reach Claude through a purchase, or through a swarm?
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Sony Music, Warner sue Anthropic, alleging copyright infringement
Music giants Sony Music Publishing and Warner Chappell Music have just filed a potentially multibillion-dollar lawsuit against Anthropic, and the publishers aren't mincing words. The lawsuit, filed Friday evening in the U.S. District Court for the Northern District of California, calls Anthropic "the culprits behind one of the largest and most blatant ongoing thefts of intellectual property in history." Sony and Warner also named Anthropic CEO and cofounder Dario Amodei and cofounder Benjamin Mann in the lawsuit. * Play our Big Guessing Game: Make your predictions now for a chance to win a new Apple Watch. Time's running out! In the lawsuit, first reported by Music Business Worldwide, the music companies claim that Anthropic has carried out "a brazen campaign of illegally torrenting, scraping, and downloading copyrighted works on a massive scale" to train its popular Claude AI models. Sony and Warner accused Anthropic of having "reaped enormous profits" from the stolen music. Sony and Warner are seeking $150,000 in damages per work as well as $25,000 per copyright violation. The music companies, which are the second- and third-largest in the industry, allege that thousands of titles have been stolen, putting compensation in the range of billions of dollars. The publishers have specifically accused Anthropic of "blatant theft" of song titles such as "Ain't No Mountain High Enough," "All I Want for Christmas is You," "Eye of the Tiger," and "Here Comes Santa Claus." Anthropic has been hit with intellectual property lawsuits before. In fact, Sony and Warner's lawsuit directly references the $1.5 billion settlement that Anthropic reached with book publishers earlier this year in a similar case where the AI company was accused of training models on stolen works. The world's largest music company, Universal Music Group (UMG), previously filed copyright infringement lawsuits against Anthropic. UMG, along with Concord Music Group and ABKCO Music, filed a lawsuit against Anthropic in 2023 and again earlier this year. Their latest suit seeks more than $3 billion in damages.
Anthropic Beats Trump in Court and a Judge Just Said He Couldn't Do That - Memeburn
This is one of several clashes between AI labs and the Trump administration this year, and Anthropic still has a second lawsuit pending. In March, Judge Rita Lin called the Pentagon's case against Anthropic "Orwellian." On August 27, 2026, she made it official, ruling the blacklist illegal. That capped months of legal fighting between the two sides. That came right as Anthropic gears up for what could be the biggest IPO in AI history. Here's what the ruling actually changes, and why it matters for every other AI company dealing with Washington. How Anthropic Ended Up on the Pentagon's Blacklist It started in early 2026, with the blacklisting formally imposed in February. Defense Secretary Pete Hegseth wanted AI companies to sign contracts letting the military use their models for anything. That included fully autonomous weapons and surveillance of Americans at home. Google, Microsoft, OpenAI, and SpaceX agreed. Anthropic refused. Anthropic set two hard limits: no lethal weapons without a human in control, no spying on US citizens. Dario Amodei said so out loud, and the Pentagon was not happy about it. Officials went after Anthropic publicly, then slapped it with a "supply chain risk" label. That's normally reserved for foreign vendors nobody trusts. Federal agencies were told to stop using Claude, and Pentagon contractors were told to stop doing business with Anthropic altogether. Anthropic is preparing for what could be the largest stock market debut ever for an AI company. The label threatened billions in lost contracts and damaged its reputation. Why the Judge Said the Blacklist Was Illegal The label was originally designed for foreign vendors, not domestic companies engaged in policy disputes. Judge Lin's 59-page ruling found three separate problems. The government punished Anthropic for speaking out, which breaks the First Amendment. It never gave Anthropic a fair chance to respond, breaking the Fifth Amendment. And the decision itself had no real justification, which breaks federal law. Internal Pentagon documents showed the real reason was frustration with Anthropic's public criticism, not any actual security threat. As Lin wrote, invoking national security is not a blank check to punish and retaliate against government critics. What Anthropic Gains From Winning The blacklist label has to be removed. The order stopping other contractors from working with Anthropic no longer applies. Federal agencies can use Claude again starting now. But don't think this is totally wrapped up. Anthropic actually filed two lawsuits, and this ruling only knocks out the first one. There's still a second case sitting in a federal appeals court in Washington, and the Trump administration could try to appeal this too. So there might be more of this to come. What Other AI Companies Can Take From This This is the first time an American company has beaten the Pentagon's "supply chain risk" label in court. The real motive was punishment for speaking up. This isn't an isolated case. In June, Anthropic's Fable and Mythos models were suspended due to export controls, before being restored in July. At this point it feels less like bad luck. It's starting to look like the cost of doing business as an AI company under this administration. The message is simple: Washington can pick any AI vendor it wants. But it cannot use a security label to punish companies for what they say. Other AI companies now have proof that setting their own limits doesn't mean losing government business. A few years ago, betting against the Pentagon would have looked like a losing move for any startup. Anthropic bet on it anyway, and won. A second case is still pending, so the final word isn't written. But this ruling already shows something important. Standing on principle doesn't have to cost an AI company its seat at the table with Washington. FAQs What did the court rule on August 27, 2026? Judge Rita Lin ruled that the Pentagon broke the law when it labeled Anthropic a "supply chain risk." She told them to drop the label and undo the order that had other contractors avoiding Anthropic. Why did the Pentagon blacklist Anthropic in the first place? Anthropic refused to let its AI be used for autonomous weapons or mass surveillance of US citizens. After the company said so publicly, Pentagon officials criticized it, then labeled it a security risk. Turns out their own internal docs showed they were just annoyed and not actually worried about security. What changes for Anthropic now? Federal agencies can work with Anthropic again, and it clears a legal headache right as the company's gearing up for a huge IPO. Is this legal fight completely over? Not quite. This was one of two lawsuits Anthropic filed, so there's still a second case pending, and the government could try to appeal this one too. Why does this matter for other AI companies? It basically tells the government it can't use "national security" as an excuse to punish companies. That includes companies taking a stand on how their tech gets used.
Sony Music, Warner Chappell, others sue Anthropic over copyright infringement
Music publishers including Sony Music Publishing and Warner Chappell Music have sued Anthropic, alleging the AI company illegally used copyrighted songs to train Claude. The publishers are seeking damages of up to $150,000 per work, along with other remedies. Anthropic has rejected the allegations and said it will defend itself in court. Sony Music Publishing, Warner Chappell Music, and several other music publishers have filed a lawsuit against Anthropic and cofounders Dario Amodei and Benjamin Mann, accusing the AI company of what they describe as "one of the largest and most blatant ongoing thefts of intellectual property in history."The publishers allege that Anthropic ran a "brazen campaign of illegally torrenting, scraping, and downloading copyrighted works" to train its
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic rolls out AI framework for labs and manufacturing
SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic Sued By Sony Music, Warner Chappell Music, Other Music Publishers Over Alleged Mass Piracy Of Copyrighted Songs
Anthropic has been sued by Sony Music Publishing, Warner Chappell Music and other major music publishers, which accuse the artificial intelligence company of illegally obtaining and using thousands of copyrighted musical works to train its Claude AI models. The publishers filed the lawsuit Friday in the U.S. District Court for the Northern District of California, naming Anthropic and co-founders Dario Amodei and Benjamin Mann as defendants. They accused the AI company and its founders of conducting what they described as a "brazen campaign of illegally torrenting, scraping, and downloading copyrighted works" on a massive scale. The case adds the music industry to a growing legal battle over how AI companies obtain the enormous quantities of copyrighted material needed to train generative AI systems. "Defendants Anthropic and its founders Dario Amodei and Benjamin Mann have conducted a brazen campaign of illegally torrenting, scraping, and downloading copyrighted works on a massive scale in order to develop, operate, and reap enormous profits from Anthropic's 'Claude' series of artificial intelligence ('AI') models," the publishers said in the complaint. Sony Music Publishing and Warner Chappell said Anthropic obtained "thousands upon thousands" of copyrighted songs, including "Eye of the Tiger," Marvin Gaye's "Ain't No Mountain High Enough," Mariah Carey's "All I Want for Christmas is You" and Taylor Swift's "Paper Rings." The publishers allege that Anthropic acquired copyrighted material through several sources, including Library Genesis and Pirate Library Mirror, two digital archives that have been associated with pirated books and other content. The lawsuit builds on an earlier copyright case against Anthropic involving books. In June 2025, a federal judge ruled that Anthropic had downloaded more than 7 million pirated books to train Claude. The music publishers argue that those books also contained lyrics and sheet music belonging to their catalogues. They identified works including Bon Jovi's "Livin' On a Prayer," Earth, Wind & Fire's "September," Jerry Lee Lewis' "Great Balls of Fire," the Allman Brothers Band's "Ramblin' Man," and Leonard Cohen's "Hallelujah." According to the complaint, the alleged infringement goes beyond the use of copyrighted works during model training. The publishers claim Claude can generate identical or nearly identical versions of copyrighted material in response to users' prompts. That allegation could become crucial because the case raises two separate questions for the AI industry: whether copyrighted works can lawfully be used to train AI models and whether AI systems can reproduce protected works closely enough to constitute infringement when responding to users. The publishers argue that training Claude on copyrighted compositions enables Anthropic to generate lyrics that could compete directly with music created by human songwriters. "Even the most revolutionary of technologies must develop within the bounds of the law, and Anthropic's Claude models are no different," the publishers said in the complaint. They are seeking statutory damages and have requested a jury trial. Under U.S. copyright law, statutory damages can reach $150,000 per infringed work when the infringement is found to be willful. The potential exposure could therefore become substantial if the publishers succeed in establishing infringement across a large catalogue. The lawsuit marks another escalation in the confrontation between copyright owners and AI companies over training data. Generative AI systems require enormous datasets to develop their capabilities, creating a direct conflict with industries whose books, articles, photographs, music and other creative works form part of the material that AI companies seek to acquire. For the music industry, the dispute has implications beyond compensation. Publishers and songwriters are becoming more concerned that generative AI could become a competitor to the very creators whose work helped train the systems. The legal strategy also comes at a time when Anthropic is already facing significant financial exposure from copyright litigation. In September, the company agreed to pay more than $1.5 billion to settle a class-action lawsuit brought by authors over the use of pirated books. Other major AI companies have faced similar lawsuits. OpenAI has been sued by publishers and content owners, including The New York Times and Encyclopedia Britannica, over allegations concerning the use of copyrighted material to train and operate its AI systems. The cases could ultimately help determine the economics of the AI industry. If courts require companies to obtain licenses for large quantities of copyrighted training material, the cost of developing and operating AI models could rise substantially. If courts allow broader use of copyrighted material under fair use or other legal doctrines, publishers, authors, musicians and other rights holders could face greater pressure to adapt their business models. The Anthropic case could be especially consequential because music is subject to multiple layers of copyright protection, including rights in compositions and, in many cases, separate rights in sound recordings. A ruling against Anthropic could therefore have implications for how AI companies collect, process, and reproduce musical content at scale.
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Kraken Users Briefly Locked Out After Sanctioned-Transaction Flood | Blockchain Crypto Market News
Kraken users were briefly locked out after the exchange dealt with a flood of sanctioned crypto transactions, according to CoinDesk. The incident links an access disruption to compliance activity, highlighting the operational strain that can arise when an exchange must process a large volume of transactions associated with restricted entities or addresses. A Compliance-Driven Disruption A temporary lockout is different from a confirmed loss of customer funds, and the available report does not establish that user balances were permanently affected. The immediate issue was access while Kraken handled the transaction flow and its associated sanctions controls. Why Sanctions Screening Affects Availability Crypto exchanges screen deposits and withdrawals against sanctions lists and other risk signals. A sudden burst of flagged activity can require manual review, system throttling or account restrictions. Those controls are designed to limit prohibited activity, but they can also interrupt legitimate users when systems are under pressure. The Questions for Kraken The important follow-up points are how long the disruption lasted, how many accounts were affected, and what controls Kraken will change to prevent a repeat. Exchanges must balance rapid access with legal obligations, and a short incident can still expose weaknesses in monitoring, communication or capacity planning. For context, BlockchainReporter has previously examined how institutional financial systems are testing blockchain settlement in earlier coverage. The new development remains specific to this event and does not establish a broader market outcome.
Salesforce Stock Soars 23% on Real Agentforce Growth, Not Just Anthropic Gains - Startup Fortune
Salesforce shares jumped roughly 23% this week after a Q2 beat, but the real story isn't the one-time Anthropic windfall padding the headline number. It's Agentforce ARR growing 240% year over year. If you're asking why Salesforce stock jumped 23%, here's the answer: the company posted fiscal 2027 second-quarter revenue of $11.35 billion, edging past the $11.32 billion Wall Street expected, and non-GAAP earnings per share of $5.90 against a $3.27 estimate, according to CNBC. Shares surged 22.6% on August 27, the stock's second-best day ever, trailing only a roughly 26% jump in August 2020. Marc Benioff got his mojo back, and investors noticed. Here's the part that matters and the part that's a distraction, tangled together in the same earnings report. A big chunk of that EPS beat came from paper gains, not operations. Salesforce booked a $2.6 billion gain on its strategic investment in Anthropic, the maker of Claude, according to the Motley Fool. That single line item accounted for a large share of the $5.90 adjusted per-share profit. Strip it out and the beat looks a lot less dramatic. Investors who only read the headline EPS number are reading an accounting event, not a business trend. The timing wasn't a coincidence, either. Salesforce and Anthropic used the earnings window to unveil Claudeforce, a partnership that plugs Salesforce data directly into Anthropic's Claude chatbot for salespeople. Benioff and Anthropic CEO Dario Amodei announced it together. That's a real product move, not just a portfolio markup, and it's fair to read the stock's move as investors betting on both at once: the AI stake paying off and the AI product roadmap deepening. Salesforce spends $3.6 billion on Fin to buy proof it could not build in time Salesforce is acquiring Fin, the AI customer service company formerly known as Intercom, for $3.6 billion in a deal that folds a proven autonomous support agent into Agentforce. Fin's core AI product resolves 76% of inbound support without human handoff and was generating $100 million in ARR growing at 350% annually, implying a 36x multiple on the... - Salesforce acquires Fin for artificial intelligence customer service - how much did Salesforce pay for Fin acquisition Agentforce is the number that should actually move you Frankly, the Anthropic gain is noise next to what's happening inside Agentforce, Salesforce's AI-agent platform. Agentforce annual recurring revenue passed $1.5 billion in the quarter, up more than 240% year over year. Combine that with Data 360, Salesforce's data platform, and total AI-related ARR hit nearly $3.9 billion, up over 210%. Agentic workflows processed 3.2 billion actions in the quarter, up 97% sequentially. That's usage, not just bookings. This is the clearest proof point yet that AI agents are converting into recurring revenue rather than staying a pilot-project talking point. Enterprise software has spent two years promising that agentic AI would eventually show up on an income statement. Salesforce just put a number on it, and it's a number that's growing faster than the core CRM business ever did at this scale. Salesforce also raised its full-year fiscal 2027 revenue guidance to $46.1 billion to $46.4 billion, up from $45.9 billion to $46.2 billion previously, split between organic strength in Agentforce, Data 360 and Slack, and the pending Contentful and Fin acquisitions. Current remaining performance obligations, a measure of contracted future revenue, grew 14% in constant currency to $33.5 billion, which the company described as its strongest quarter for net new order value in four years. None of that is an accounting artifact. It's actual contracts. For founders pitching agentic products to investors right now, this earnings report is about to become a stock slide in every pitch deck. It gives them something they haven't had before: a large public company showing AI agents converting into measured, recurring revenue at triple-digit growth rates, with usage data to back it up. That's a different pitch than Also read: A critical Gitea flaw is under active attack and 8,300 servers are still exposed * Sony Music and Warner Chappell Sue Anthropic Over Stolen Song Lyrics * A Russian Ransomware Gang Says It Broke Into the ATF's Investigation Files
Anthropic rolls out AI framework for labs and manufacturing
SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic rolls out AI framework for labs and manufacturing
SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.
Anthropic unveils standard linking AI agents to physical devices
Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said SAN FRANCISCO, California: Anthropic has unveiled a new framework that allows AI agents to operate physical equipment, including microscopes and robotic arms, for use in scientific research and advanced manufacturing. The company on August 27 rolled out a research preview of the "Model Hardware Standard," or MHS, which enables AI agents to operate laboratory and manufacturing instruments together and carry out complex tasks. Those tasks can range from routine drug discovery experiments to laser calibration on a quantum computer, Anthropic said. By combining agentic AI capabilities with laboratory and manufacturing hardware, the Claude chatbot maker aims to help researchers and engineers carry out autonomous, round-the-clock workflows with minimal human intervention, helping speed up processes. Anthropic said the standard can work with any device that has a programmable interface and allows devices and AI agents to communicate across networks. The company is sharing an early version of MHS with partners to help develop safety evaluations before making the framework open source.