News & Updates
The latest news and updates from companies in the WLTH portfolio.
Nobody Wants Anthropic's Best AI Model Anymore Now That There Are Way Cheaper Alternatives
One thing that could put a dent in the hype around Anthropic's upcoming, multitrillion dollar IPO? People preferring cheapo models over its flagship AI products. Recent spending data from 70,000 US companies collected by the payments group Ramp shows that spending on Fable 5, Anthropic's priciest and most powerful model, has plateaued at only 11 percent of the overall outlay, or money spent, on its AI tools, the Financial Times reported. It reflects a shift in how enterprise users are using flagship models, reserving them only for the most complex tasks while letting more than serviceable cheaper models take care of the dirty work. Those cheaper alternatives can be Anthropic's own older models, or open-weight models offered by Chinese competitors. If the pattern holds, according to the FT, it could upend the go-big-or-go-home business model of leading AI labs, which have focused on pouring their resources into building even larger and more complex models. "Most people don't need to operate at the frontier," Miles Clements, a partner at the venture capital firm Accel, which has invested $1 billion in Anthropic, told the FT. The period when customers favored using only frontier models "was not a durable era," Clements added. Fable 5 had a rocky launch in June. Its hype was clouded by its purportedly powerful ability to launch cyberattacks autonomously -- a narrative that Anthropic helped fuel, it's worth noting. The Trump administration ordered Anthropic to suspend access to its model to foreign customers, citing the national security risks posed by the model, but later lifted the export restrictions. The hope that take-up of Fable 5 would accelerate once the political controversy cleared hasn't been borne out, however, with the Anthropic model's adoption lagging behind the releases of its previous frontier models. Its annualized revenue -- the amount it's projected to make in a year based on its current performance -- in July reached $65 billion, which is well short of the $80 billion estimate set by bullish investors, the FT noted. OpenAI, meanwhile, is nipping at Anthropic's heels. After losing significant ground to Anthropic this year, the ChatGPT maker's annualized revenue has surged to $40 billion, with its new GPT-5.6 model, which is much cheaper to use than Fable 5, boosting sales. Some experts are seeing the plateaued Fable 5 spending as a glass half-full. Alex Imas, director of AGI economics at Google DeepMind, argued that Anthropic isn't worried about Fable spending in isolation but the "total spend across all models."
Dow Jones Top Company Headlines at 5 PM ET: OpenAI's Head of Data Centers Has Left the Company | Anthropic ...
OpenAI's Head of Data Centers Has Left the Company Chris Malone joins a string of recent high-level executive departures as the AI giant heads toward an IPO and ramps up its spending on computing power. ---- Anthropic Expected to Tell Investors It Sees Over $30 Trillion in Potential Revenue The AI startup is likely to top SpaceX's eye-popping potential revenue estimate. ---- California Attorney General Ramps Up Criticism of Paramount Rob Bonta said Paramount is more focused on the "court of public opinion" than the court of law in the Warner merger fight. ---- Intuit Forecasts Slower Growth, Takes Steps to Win More TurboTax Users The company said it expects revenue to increase 9% to 10% for fiscal 2027, slowing from 14% this year, as it recorded a lower profit in its latest quarter. ---- Zoom Reports Solid Earnings. The Stock Drops Anyway. Zoom reported adjusted earnings of $1.55 a share for the quarter, up from $1.53 a year ago and above analysts' expectations for $1.48, per FactSet. ---- Starbucks Union Calls for Boycott in Latest Test of Company Turnaround Starbucks Workers United is seeking a contract agreement. For investors, the key question is whether the boycott move will affect customer behavior at a meaningful scale. ---- Dick's Sporting Goods Bet Big on Sneakers With Foot Locker. It Backfired. Shares tumbled after the retailer revealed footwear discounts are sapping profits. The chairman defended a $2.4 billion deal for the sneaker chain. ---- United Airlines Ups Its Bet on Instagrammable International Destinations Robust travel demand from Americans is helping fuel new routes like Marseille and Ibiza. ---- Bank of Montreal Plans to Buy Back Shares After Strong Quarter The big Canadian lender benefited from double-digit revenue growth and an improved credit performance in its fiscal third quarter, with the lowest provision on impaired loans in the last 10 quarters, though its bottom line was squeezed by one-time charges for its exit from certain businesses. ---- Scotiabank Earnings Lifted by Record Result in Wealth Management Bank of Nova Scotia notched a rise in third-quarter earnings, driven by strong results across its business lines that included a record result from its global wealth management and global banking and markets operations. ---- OnlyFans Paid $700 Million Dividend to Founder Year Before He Died The online platform, widely known for its explicit content, was a reliable cash machine despite employing fewer than 50 people. ---- Oura and Dunkin' Get Ready to Join IPO Bonanza Investors are sizing up the parade of new offerings expected in coming months. ---- Hyundai Motor, Union Reach Wage Deal to End Walkouts The tentative wage agreement ends months of on-and-off walkouts that have disrupted production at the Korean automaker. ---- One of Taiwan's Biggest AI Winners Isn't a Chip Maker Taiwan's richest person rode the AI boom but his company has nothing to do with technology-it's a furniture-component maker called King Slide Works. ---- Woodside Puts 'Everything on the Table' in Review of Beaumont Facility Chief Executive Liz Westcott said the review reflected a shift in international policy positions and slack demand for lower carbon ammonia. ---- Lego Sales Growth Outpaces Global Toy Market to Hit New Record Lego reported record sales for the first half of the year as it launched hundreds of new products. (END) Dow Jones Newswires August 25, 2026 17:15 ET (21:15 GMT) Copyright (c) 2026 Dow Jones & Company, Inc.
Anthropic Asks Job Candidates: How Much Money Do You Have in the Bank?
Anthropic has introduced a direct and sometimes uncomfortable financial inquiry during its hiring process for senior roles. According to a report from Axios, recruiters now ask prospective employees a pointed question about their personal financial situation: how much money they have in the bank and whether they could afford to work for a year without drawing a salary. This approach marks a shift in how one of the leading artificial intelligence companies evaluates talent. The query serves multiple purposes. First, it helps determine a candidate's genuine interest in the mission of building safe and reliable AI systems rather than chasing the highest compensation package available in a competitive market. Second, it signals the company's preference for individuals who demonstrate financial independence and long-term commitment over those who might treat the position as a short-term stepping stone. The practice reflects broader pressures facing AI organizations as they scale rapidly. Anthropic, valued at more than $60 billion following recent funding rounds, competes fiercely with OpenAI, Google DeepMind, Meta, and numerous well-funded startups for the same limited pool of researchers, engineers, and policy experts. Compensation packages in this sector often include seven-figure salaries, significant equity grants, and performance bonuses that can reach tens of millions of dollars. Against that backdrop, a question about personal savings can feel jarring. Candidates who have encountered the inquiry describe it as blunt but effective at revealing priorities. Some report being asked variations of the question during late-stage interviews, typically after technical assessments and team meetings have already taken place. Recruiters frame the discussion around alignment with company values, emphasizing that Anthropic seeks people motivated by the potential societal impact of advanced AI rather than purely financial gain. The company's leadership has long stressed the importance of careful, responsible development of frontier AI models. Dario Amodei, Anthropic's chief executive and co-founder, has spoken publicly about the need to prioritize safety research even when it slows commercial progress. This philosophy appears to extend to hiring decisions. By probing financial circumstances, the organization aims to identify individuals who share that patient, mission-driven outlook and who will not be easily lured away by competing offers. Industry observers point out that such questions, while uncommon in most corporate settings, have precedents in certain specialized fields. Venture capital firms sometimes evaluate founders based on their runway and personal commitment. Research institutions have historically favored academics who demonstrate dedication to pure inquiry over monetary rewards. In the AI sector, where talent wars have driven compensation to extraordinary levels, Anthropic's approach represents an attempt to filter for intrinsic motivation. Not every candidate responds positively to the question. Some view it as an invasion of privacy that has little bearing on their professional qualifications. Others worry that answering honestly could weaken their negotiating position on salary and equity. Legal experts note that while employers generally have latitude to ask about financial stability in certain contexts, such questions must be applied consistently to avoid potential discrimination claims. Anthropic appears to direct the inquiry primarily at senior individual contributors and leadership positions rather than entry-level roles. The timing of this reported practice coincides with significant changes in the AI industry funding environment. After years of abundant capital and skyrocketing valuations, investors have grown more selective about where they deploy resources. Companies face pressure to demonstrate efficient growth and sustainable business models. In this climate, organizations like Anthropic may see advantages in building teams of people who are less likely to demand constant compensation increases or depart for marginally better offers elsewhere. Former employees and recruiters familiar with the company's process suggest the financial question forms part of a larger evaluation framework. Interviewers also explore a candidate's views on AI ethics, their tolerance for uncertainty in a rapidly changing field, and their willingness to engage with complex safety challenges. The combination of technical excellence, philosophical alignment, and financial independence appears to define the ideal Anthropic profile. This hiring strategy carries both advantages and risks. On the positive side, it may help create a more stable workforce less susceptible to the frequent job-hopping that characterizes Silicon Valley. Employees who join primarily for the work itself often display higher engagement and remain through challenging periods. The approach could also foster a culture where decisions prioritize long-term safety considerations over short-term commercial gains. Potential drawbacks exist as well. The question could inadvertently screen out talented individuals who carry student debt, support families, or come from backgrounds without generational wealth. In an industry already criticized for lacking diversity, additional financial filters might narrow the applicant pool further. Some critics argue that true commitment should be assessed through past behavior, research contributions, and interview performance rather than personal balance sheets. Anthropic has not publicly commented on the specific hiring question, but its overall approach to recruitment emphasizes careful selection. The company maintains a relatively small headcount compared to its valuation and ambitions, suggesting a deliberate focus on quality over quantity. This selectivity extends beyond technical skills to encompass character traits and personal circumstances that might influence an employee's staying power. The broader AI talent market continues to evolve in response to these dynamics. Other organizations have adopted different strategies to attract and retain top performers. Some offer equity packages tied to multi-year vesting schedules with steep cliffs. Others emphasize prestigious research environments and the opportunity to publish groundbreaking work. A few have experimented with profit-sharing models or impact-focused incentives designed to appeal to mission-driven candidates. For job seekers in artificial intelligence, the emergence of such questions requires new preparation. Candidates must consider not only how to present their technical accomplishments but also how to articulate their personal motivations and financial resilience. Those uncomfortable discussing their savings may need to weigh whether a particular organization's culture aligns with their own values and boundaries. The practice also raises interesting questions about the relationship between personal wealth and professional dedication. Does financial independence truly correlate with better performance in high-stakes AI development? Or does it simply reflect a preference for candidates from privileged backgrounds? These debates touch on larger societal conversations about merit, opportunity, and the role of money in shaping technological progress. As artificial intelligence capabilities advance, the humans guiding that development take on increasing significance. Companies like Anthropic appear to believe that understanding a candidate's complete picture, including their financial situation, provides valuable insight into their potential contributions. Whether this approach proves successful will become clearer over time as the organization releases new models and navigates the complex challenges of scaling safe AI systems. The trend toward more personal and values-based hiring criteria may spread beyond Anthropic. In an industry where traditional metrics like degrees from elite universities or publications in top conferences no longer sufficiently distinguish candidates, organizations are searching for additional signals of fit. Financial questions represent one such signal, albeit a controversial one that forces both companies and candidates to confront the role of money in ostensibly mission-driven work. Recruiting professionals predict that similar inquiries could appear at other AI laboratories facing comparable pressures. The combination of high burn rates, intense competition, and existential questions about technology governance creates conditions where conventional hiring practices feel inadequate. Organizations may increasingly look beyond resumes to assess the whole person, including their economic circumstances and underlying motivations. For now, the reported Anthropic practice stands out as a notable example of how far some companies will go to ensure alignment between their ambitious goals and the individuals tasked with achieving them. The question about bank balances serves as both a practical assessment of runway and a philosophical litmus test. In an era of unprecedented investment in artificial intelligence, it reminds everyone involved that the most valuable resource remains committed human attention guided by something deeper than financial reward. This development occurs against a backdrop of growing scrutiny over AI company practices, from compensation structures to safety commitments. How organizations answer the question of what makes a good AI researcher or engineer will influence not only their competitive position but also the direction of technological development itself. Anthropic's willingness to ask uncomfortable financial questions suggests a conviction that getting the right people matters more than maintaining conventional recruiting etiquette. As the field matures, other companies may find themselves adopting or adapting similar approaches to secure the talent they believe will determine success in the coming years of AI advancement.
Anthropic Asks Job Candidates: How Much Money Do You Have in the Bank?
Anthropic has introduced a direct and sometimes uncomfortable financial inquiry during its hiring process for senior roles. According to a report from Axios, recruiters now ask prospective employees a pointed question about their personal financial situation: how much money they have in the bank and whether they could afford to work for a year without drawing a salary. This approach marks a shift in how one of the leading artificial intelligence companies evaluates talent. The query serves multiple purposes. First, it helps determine a candidate's genuine interest in the mission of building safe and reliable AI systems rather than chasing the highest compensation package available in a competitive market. Second, it signals the company's preference for individuals who demonstrate financial independence and long-term commitment over those who might treat the position as a short-term stepping stone. The practice reflects broader pressures facing AI organizations as they scale rapidly. Anthropic, valued at more than $60 billion following recent funding rounds, competes fiercely with OpenAI, Google DeepMind, Meta, and numerous well-funded startups for the same limited pool of researchers, engineers, and policy experts. Compensation packages in this sector often include seven-figure salaries, significant equity grants, and performance bonuses that can reach tens of millions of dollars. Against that backdrop, a question about personal savings can feel jarring. Candidates who have encountered the inquiry describe it as blunt but effective at revealing priorities. Some report being asked variations of the question during late-stage interviews, typically after technical assessments and team meetings have already taken place. Recruiters frame the discussion around alignment with company values, emphasizing that Anthropic seeks people motivated by the potential societal impact of advanced AI rather than purely financial gain. The company's leadership has long stressed the importance of careful, responsible development of frontier AI models. Dario Amodei, Anthropic's chief executive and co-founder, has spoken publicly about the need to prioritize safety research even when it slows commercial progress. This philosophy appears to extend to hiring decisions. By probing financial circumstances, the organization aims to identify individuals who share that patient, mission-driven outlook and who will not be easily lured away by competing offers. Industry observers point out that such questions, while uncommon in most corporate settings, have precedents in certain specialized fields. Venture capital firms sometimes evaluate founders based on their runway and personal commitment. Research institutions have historically favored academics who demonstrate dedication to pure inquiry over monetary rewards. In the AI sector, where talent wars have driven compensation to extraordinary levels, Anthropic's approach represents an attempt to filter for intrinsic motivation. Not every candidate responds positively to the question. Some view it as an invasion of privacy that has little bearing on their professional qualifications. Others worry that answering honestly could weaken their negotiating position on salary and equity. Legal experts note that while employers generally have latitude to ask about financial stability in certain contexts, such questions must be applied consistently to avoid potential discrimination claims. Anthropic appears to direct the inquiry primarily at senior individual contributors and leadership positions rather than entry-level roles. The timing of this reported practice coincides with significant changes in the AI industry funding environment. After years of abundant capital and skyrocketing valuations, investors have grown more selective about where they deploy resources. Companies face pressure to demonstrate efficient growth and sustainable business models. In this climate, organizations like Anthropic may see advantages in building teams of people who are less likely to demand constant compensation increases or depart for marginally better offers elsewhere. Former employees and recruiters familiar with the company's process suggest the financial question forms part of a larger evaluation framework. Interviewers also explore a candidate's views on AI ethics, their tolerance for uncertainty in a rapidly changing field, and their willingness to engage with complex safety challenges. The combination of technical excellence, philosophical alignment, and financial independence appears to define the ideal Anthropic profile. This hiring strategy carries both advantages and risks. On the positive side, it may help create a more stable workforce less susceptible to the frequent job-hopping that characterizes Silicon Valley. Employees who join primarily for the work itself often display higher engagement and remain through challenging periods. The approach could also foster a culture where decisions prioritize long-term safety considerations over short-term commercial gains. Potential drawbacks exist as well. The question could inadvertently screen out talented individuals who carry student debt, support families, or come from backgrounds without generational wealth. In an industry already criticized for lacking diversity, additional financial filters might narrow the applicant pool further. Some critics argue that true commitment should be assessed through past behavior, research contributions, and interview performance rather than personal balance sheets. Anthropic has not publicly commented on the specific hiring question, but its overall approach to recruitment emphasizes careful selection. The company maintains a relatively small headcount compared to its valuation and ambitions, suggesting a deliberate focus on quality over quantity. This selectivity extends beyond technical skills to encompass character traits and personal circumstances that might influence an employee's staying power. The broader AI talent market continues to evolve in response to these dynamics. Other organizations have adopted different strategies to attract and retain top performers. Some offer equity packages tied to multi-year vesting schedules with steep cliffs. Others emphasize prestigious research environments and the opportunity to publish groundbreaking work. A few have experimented with profit-sharing models or impact-focused incentives designed to appeal to mission-driven candidates. For job seekers in artificial intelligence, the emergence of such questions requires new preparation. Candidates must consider not only how to present their technical accomplishments but also how to articulate their personal motivations and financial resilience. Those uncomfortable discussing their savings may need to weigh whether a particular organization's culture aligns with their own values and boundaries. The practice also raises interesting questions about the relationship between personal wealth and professional dedication. Does financial independence truly correlate with better performance in high-stakes AI development? Or does it simply reflect a preference for candidates from privileged backgrounds? These debates touch on larger societal conversations about merit, opportunity, and the role of money in shaping technological progress. As artificial intelligence capabilities advance, the humans guiding that development take on increasing significance. Companies like Anthropic appear to believe that understanding a candidate's complete picture, including their financial situation, provides valuable insight into their potential contributions. Whether this approach proves successful will become clearer over time as the organization releases new models and navigates the complex challenges of scaling safe AI systems. The trend toward more personal and values-based hiring criteria may spread beyond Anthropic. In an industry where traditional metrics like degrees from elite universities or publications in top conferences no longer sufficiently distinguish candidates, organizations are searching for additional signals of fit. Financial questions represent one such signal, albeit a controversial one that forces both companies and candidates to confront the role of money in ostensibly mission-driven work. Recruiting professionals predict that similar inquiries could appear at other AI laboratories facing comparable pressures. The combination of high burn rates, intense competition, and existential questions about technology governance creates conditions where conventional hiring practices feel inadequate. Organizations may increasingly look beyond resumes to assess the whole person, including their economic circumstances and underlying motivations. For now, the reported Anthropic practice stands out as a notable example of how far some companies will go to ensure alignment between their ambitious goals and the individuals tasked with achieving them. The question about bank balances serves as both a practical assessment of runway and a philosophical litmus test. In an era of unprecedented investment in artificial intelligence, it reminds everyone involved that the most valuable resource remains committed human attention guided by something deeper than financial reward. This development occurs against a backdrop of growing scrutiny over AI company practices, from compensation structures to safety commitments. How organizations answer the question of what makes a good AI researcher or engineer will influence not only their competitive position but also the direction of technological development itself. Anthropic's willingness to ask uncomfortable financial questions suggests a conviction that getting the right people matters more than maintaining conventional recruiting etiquette. As the field matures, other companies may find themselves adopting or adapting similar approaches to secure the talent they believe will determine success in the coming years of AI advancement.
AI giant Anthropic in talks to lease massive Bastrop County data center, sources said
by: Justin Sayers- Senior Staff Writer, Austin Business Journal AUSTIN (ABJ) -- Fresh off its huge data center lease in Milam County northeast of Austin, Silicon Valley-based Anthropic PBC is in talks to occupy a gargantuan data center development on the east side in Bastrop County. While no agreement has been finalized, the artificial intelligence company has been working with Washington, D.C.-based BlackChamber Partners LLC and Dallas-based Pacifico Energy LLC on what's been described as a 2,842-acre gas-fired electric generation facility to support a major data center development in the Cedar Creek area, several sources told ABJ. Read the full article on the Austin Business Journal.
Companies not as keen on Anthropic's best AI model
Enterprises appear to be prioritizing cheaper AI tools instead. endif; ?> New payment data from Ramp suggests that Anthropic's most advanced AI model, Fable 5, has had a slow start among enterprise customers, the Financial Times reports. Two months after launch, Fable 5 accounts for about 11% of total enterprise spending on Anthropic models, breaking the previous trend of customers quickly moving to the most powerful model. According to analysts and investors, the development is mainly due to Fable 5's high price and the fact that cheaper models are good enough for most tasks.
Anthropic upgrades Claude's streaming renderer, claims 9x fewer stalls on slower laptops
The frontend optimization delivers smoother token streaming for long responses without changing Claude's underlying models or API Anthropic rolled out a frontend optimization to Claude's streaming response renderer on August 24, targeting a problem that anyone who's tried to get a long answer from an AI chatbot on an older machine knows well: the maddening stutter-and-freeze cycle that turns a conversation into a slideshow. The update applies to Claude's web and desktop applications. According to Anthropic, long answers now stream roughly 4x smoother, stalls on slower laptops drop by 9x, and worst-case freezes shrink by 4.5x. On hardware that supports it, like 120 Hz MacBooks, the renderer can sustain 120 fps during streaming output. What actually changed under the hood The core fix is elegant in its simplicity. Previously, each incoming token from Claude's model triggered a repaint of the full response container, or at least a substantial portion of it. The new approach restricts UI updates to only the elements that have actually changed, resulting in dramatically less computational overhead per token, which matters most on machines with limited GPU headroom or older processors. Critically, Anthropic made no changes to its models or API with this release. Generation speed, output quality, and the underlying inference pipeline remain untouched. This is purely a presentation-layer improvement. Why UI performance matters in the AI race The 120 fps sustained frame rate figure is particularly telling. Most web applications don't need to think about frame rates at all, but streaming text renderers are effectively animations. Each new token is a frame update. When you're generating hundreds of tokens for a long response, that's hundreds of sequential frame updates, and any hitch becomes visible as a stutter or freeze. Hitting 120 fps on compatible hardware means Anthropic's renderer can now match the refresh rate of Apple's ProMotion displays without dropping frames. The feedback loop, and its limits User reception on social media was largely positive, with many noting that the improvement was immediately noticeable during extended conversations. But some responses highlighted a tension that Anthropic and every other AI company faces: polishing the interface doesn't fix the model. Several users pointed out that smoother streaming doesn't help when Claude hallucinates a citation or confidently delivers incorrect information. The update also has implications for Anthropic's enterprise ambitions. Corporate users often run standardized hardware that skews older than what developers and early adopters use. A 9x reduction in stalls on slower laptops isn't just a nice stat for a blog post. It's the difference between an enterprise deployment that employees actually use and one they abandon for a competitor after a week of frustrating freezes.
Nvidia reportedly eyes another investment in Perplexity AI at a $30B valuation
Nvidia reportedly eyes another investment in Perplexity AI at a $30B valuation Nvidia Corp. is reportedly considering making another investment in the artificial intelligence search startup Perplexity AI Inc. A report by The Information says the chipmaker is holding talks with Perplexity over an investment that could push the startup's valuation to more than $30 billion. That would represent a jump of more than 50% from the $20 billion valuation Perplexity finalized about a year ago, when it last raised money. The size of Nvidia's potential investment was not disclosed, and there's no guarantee that any deal would be reached, The Information said, citing anonymous sources who are familiar with the discussions. Neither Nvidia nor Perplexity would comment on the reported discussions. Perplexity is an attractive target for investors for its business has continued to grow at a rapid rate. According to The Information, the startup has grown its annualized revenue run rate to an impressive $750 million, up from less than $250 million at the start of the year. If true, that would mean it has managed to triple its revenue run rate in just eight months. One of the main reasons for that impressive growth is Perplexity Computer, a cloud-based AI agent that was first released in April for Mac computers and later expanded to Windows devices. Perplexity Computer is designed to automate computer tasks for professional users. It acts as a general-purpose digital worker that can access authorized files and applications on a user's computer. Users can ask it to create or edit Word documents, update Excel spreadsheets, organize files, conduct online research and complete workflows involving multiple applications. The proposed investment would deepen an existing relationship between Nvidia and Perplexity. The chipmaker is already one of its main financial backers, alongside Amazon.com Inc. founder Jeff Bezos and SoftBank Group Corp. Nvidia has become an increasingly important partner for AI startups like Perplexity, and sees its bet on the startup as an investment in its future. As the world's top supplier of silicon for high-frequency AI inference, it has a vested interest in making sure that the search layer - which is a massive compute ecosystem - remains aligned with its chip ecosystem. What Nvidia doesn't want is for the likes of Perplexity and others to go sniffing around rival chipmakers such as Advanced Micro Devices Inc. and Cerebras Systems Inc., which both offer alternative chips for AI inference. In that way, Nvidia is investing in Perplexity as a kind of insurance policy to safeguard its future revenue stream against possible shifts in AI search architecture. Perplexity's strategic importance to Nvidia is amplified by its distribution efforts, such as its integration with Samsung Electronics Co. Ltd.'s Bixby assistant, which brings its search capabilities to around 800 million devices globally. The AI search firm is also believed to be fixed on a 2028 initial public offering, which means Nvidia has a clear timeline to realize a return on its investment. Nvidia's broader portfolio includes many of its major compute customers, including OpenAI Group PBC, Anthropic PBC, SpaceX Corp.'s xAI, Poolside Inc. and Safe Superintelligence Inc. It shows how the chipmaker has taken a systematic approach to ensuring its market dominance. By supplying the critical infrastructure and acting as a key investor at the application layer, Nvidia has effectively built a self-reinforcing cycle of demand for its chips. Nvidia is also trying to provide direct funding to customers that need to invest in its AI compute hardware. It recently struck a deal with six of Wall Street's biggest financial institutions to provide more than $500 billion in financing for AI infrastructure projects, including its own and those of its customers. Meanwhile, Perplexity has been racing to build out the infrastructure foundation it needs to support its own growth. Earlier this year, it struck a $750 million deal with Microsoft Corp. that will see it adopt that company's Azure cloud services to help run its AI workloads.
Anthropic Claude AI Predicts What Ethereum Needs to Hit $4,000
Disclaimer: Crypto is a high-risk asset class. This article is provided for informational purposes and does not constitute investment advice. You could lose all of your capital. Can a single upgrade close a gap of more than 50%? That is the question behind the latest Claude AI price prediction, where the model predicts Ethereum (ETH) reaching $3,500 to $4,000 by year's end 2026, with $3,800 as the realistic base case. The chart already moved first. Ethereum price spent July and August pinned near $1,900 before ripping vertically to $2,448 in a matter of days. That pump reset the entire conversation. A market that looked forgotten is suddenly trading 25% above where it sat two weeks ago. The catalyst behind the forecast is Glamsterdam. It is the largest Ethereum upgrade since the Merge, and it went live on the Platåberget public testnet on August 20. Mainnet is scheduled for Q4. Standard Chartered ties its own $4,000 target directly to that timeline. Treasury demand is building alongside it. BitMine added 9,926 ETH on August 17, pushing its holdings to 5.82 million ETH, roughly 4.8% of supply and around $11 billion. The regulatory piece is still open. Fidelity's staking-enabled FETH filing remains pending SEC review. Flows have held up better than sentiment suggested. ETH ETF inflows over 30 days reached $524.3 million even as daily flows flattened. The bear case is about positioning. Long exposure is crowded at 69.6% of Binance accounts, and a Glamsterdam delay could break $1,860 support. That break risks a slide to $1,500. Make Your Prediction Count With $25 For Free on Kalshi Ethereum Price Prediction: Dario Amodei Claude AI Predicts Glamsterdam Reopens the Ceiling The damage here has been severe and slow. Ethereum peaked near $4,860 in September 2025, then spent five months grinding down through every support it built. February 2026 broke everything at once, dumping price to $1,740. March through May offered a weak recovery toward $2,450 that failed, and June sent Ethereum back to $1,500. July and August built a quiet floor near $1,900. That base is exactly what made this week's candle possible. Ethereum closed at $2,448.0, up $25.2 for a gain of 1.04%, with a session range from $2,356.3 to $2,483.6. The modest change tells you the vertical leg is already done, and ETH price is holding its gains. Resistance sits at $2,483.6, then the May swing near $2,450, which is now cleared, then $2,800. Support runs through $2,356 and $2,100, with the $1,860 line being the one that actually matters. RSI reads 78.70 against a signal line at 62.88. The 16-point gap is elevated without being extreme. That is a healthier picture than a runaway spike. Momentum is strong, and the rising signal line beneath suggests the move has structural support rather than pure reflex. Q4 is where this gets settled. Deliver Glamsterdam on schedule, and $3,800 stops looking distant. Supercharge Your Trading in 2026 With BloFin AI Trading Bots Ethereum Is Betting One Upgrade Can Reopen the Ceiling. LiquidChain Is Betting the Bigger Constraint Is Between Chains. Glamsterdam is designed to make Ethereum itself faster, cheaper, and more capable. LiquidChain is targeting a different bottleneck: the fact that even upgraded networks still operate as isolated liquidity islands. Bitcoin, Ethereum, and Solana each have deep pools of capital, but moving between them still means bridges, duplicated deployments, added fees, and fragmented user flows. LiquidChain is building a single execution layer that connects all 3, enabling a single deployment to reach multiple ecosystems without rebuilding the same application chain by chain. That gives LiquidChain a thesis that does not depend on one network winning. It benefits from activity existing across several major ecosystems at once. The presale is currently priced at $0.01493 with just over $948,000 raised. If the next DeFi cycle is driven by capital moving more freely between chains, LiquidChain is still being valued at the stage where relatively modest inflows can matter.
Commentary: What retail investors need to know before jumping into Anthropic's IPO
Let's address some important questions. First, what will the growth trajectory be? There isn't much financial data available at this stage - we'll have to wait for the prospectus when it's published. What we do know is that revenues are expected to be in the region of US$100 billion this year - up from about US$10 billion in 2025. But it's worth interrogating those numbers more closely. When you develop an AI agent, you pay Anthropic monthly for "tokens" to access its system and enormous computing power. During the past year, computing costs per task have shot up - semiconductor prices and energy costs have all risen sharply and Anthropic has passed these on. But it means a chunk of that impressive revenue growth is simply programmers paying more to advance existing projects. Though not unusual for a new software company, that growth - even with my caveat - is still impressive. Some believe it will carry on for years. Certainly, the market opportunity for AI seems huge. Anthropic estimates that AI is capable of covering more than 80 per cent of tasks in fields including management, business and finance, computing, architecture, law, arts and the media, among others. The company's strength lies in its Claude coding models and developer tools that can take on this work. Don't hang your head in despair and start worrying about your job yet. The theory is fine; the practice so far is very different. What often seems to happen is that the person who has lost 80 per cent of their routine execution tasks now has to spend that time reviewing and debugging the AI tool's work, to catch all the mistakes it makes and step in where it struggles with messy real-life complexity.
Thomson Reuters Bets $40 Million on Its Own Legal AI to Curb Dependence on Anthropic
Thomson Reuters just took a calculated step away from its heavy reliance on outside AI providers. The information services giant launched Thomson-1, its first proprietary large language model. The move signals a shift in how one of the legal industry's biggest players thinks about building and owning the technology that powers its flagship products. Announced this week, the model draws from an open-source foundation developed by Alibaba. Thomson Reuters adapted it through a process its chief technology officer described as realignment. The result? A system trained on decades of the company's own authoritative legal, tax and news content. Early tests show it holding its own against some of the most advanced general-purpose models on the market. But don't mistake this for a full break from Silicon Valley's AI leaders. CoCounsel, Thomson Reuters' marquee AI assistant for lawyers, still leans primarily on Anthropic's Claude. The new model slots in for specific high-volume tasks where domain knowledge delivers a measurable edge. Joel Hron, the company's CTO, made the strategy plain. Business Insider reported Hron's analogy. "Renting a house, you still have a roof over your head, and somebody's taking care of it, and it's great. But you're not building any equity that compounds into something valuable for you long term." The company spent roughly $40 million on compute, talent and specialized training to create that equity. The numbers tell part of the story. Thomson Reuters used less than 10% of its vast proprietary corpus to train the model. Hundreds of subject-matter experts reviewed outputs. They identified failures. They refined the system to prioritize accuracy over pleasing responses. The approach stands in contrast to the race for ever-larger general models. Thomson-1, also referred to as Thomson in company materials, builds on Snowdon. That variant stems from Alibaba's Qwen model. A joint team with Imperial College London spent months adapting it. They focused on ethical safeguards, de-biasing and safety. "There's nothing that necessarily ties us to Qwen," Hron told reporters. The foundation can evolve. Performance claims come with caveats. In benchmarks released by the company in late July, Thomson competed closely with frontier systems. It matched or exceeded Claude Opus 4.8 in some legal reasoning tasks. It outperformed GPT-5.5, Claude Sonnet 5 and Gemini 3.1 Pro across a mix of evaluations. The tests covered instruction following, long context, coding and professional workflows. Yet the comparisons aren't apples to apples. Thomson benefited from test-time scaling and internal retrieval tools linked to Westlaw and Practical Law. Competing models searched the open web in some evaluations. Internal composites formed part of the mix. Still, the results impressed enough academics. One preferred Thomson's responses even when others answered correctly. Citation quality held up against the leaders. The first real test comes inside CoCounsel Legal. Thomson becomes the default for Tabular Analysis. That feature reviews up to 10,000 documents and fields as many as 100 questions about them. High-volume. Structured. Measurable accuracy. Exactly where a purpose-built model should shine. The broader CoCounsel platform, refreshed on August 20, now incorporates Anthropic's Claude Agent SDK for agentic workflows. It plans, reasons and executes across tools. Hron doesn't hide the continued partnership. Thomson Reuters expanded its deal with Anthropic in May. "Our main objective is to make Thomson the model that powers more and more of CoCounsel's capabilities over time," he said. The in-house system supplements rather than supplants. For now. This hybrid strategy reflects hard realities in professional services AI. General models hallucinate. They sycophantically please users. They lack deep context in regulated fields. Thomson trains explicitly to flag uncertainty. It avoids forcing confident answers when data runs thin. That choice reduces certain failure modes critical for lawyers and tax professionals. Retrieval remains essential. The model pulls from verified sources. Outputs link back to Westlaw, Practical Law or Checkpoint where possible. Thomson Reuters stops short of promising line-by-line traceability for every claim. "We wouldn't claim every output can be traced line by line to an exact statute or ruling," the company stated. Professionals still verify. The system simply makes that verification easier. Training on proprietary data changes the control equation. The company shapes tradeoffs between helpfulness and accuracy. It optimizes for professional caution over casual conversation. And it avoids feeding customer data to third-party labs, maintaining strict contractual prohibitions. Recent coverage highlights the nuance. The New Stack noted the $40 million investment focused on post-training and expert evaluation rather than pre-training from scratch. It also detailed how Thomson integrates with agentic systems while preserving retrieval-augmented generation. The model doesn't replace retrieval. It enhances it. The Next Web added color on the Alibaba connection and academic feedback. It quoted researchers who found Thomson's outputs preferable in quality and citation strength. The article also flagged Alibaba's recent moves toward charging heavy users of Qwen, underscoring why owning adaptations matters. Thomson Reuters published its own early benchmarking in July. CTO Joel Hron and Head of AI Research Jonathan Schwarz wrote that capable models no longer emerge solely from frontier labs. One now comes from their own organization. The piece emphasized combination: authoritative content, expert judgment, professional tools and model development. A smaller open-weight version of the model sits on Hugging Face for researchers. Commercialization beyond Thomson Reuters products remains under consideration. Customers won't buy direct access to Thomson-1 today. It lives inside the company's ecosystem, starting with legal research and document tasks. The timing feels deliberate. CoCounsel has scaled to serve hundreds of thousands of professionals. Demand for trustworthy AI in legal and tax grows as regulators tighten rules. Enterprises want options that reduce vendor lock-in and API costs that can run high at scale. Critics will note the $40 million figure pales against the billions poured into frontier labs. Success here hinges on whether domain-specific tuning plus retrieval consistently beats general models on real workflows. Early signs look promising. But benchmarks tell only part of the story. Real adoption will come from measurable productivity gains and risk reduction for law firms and corporate departments. Thomson Reuters isn't alone in this push. Other data-rich incumbents eye similar paths. The difference lies in execution. Decades of curated content. Teams of domain experts. A product portfolio already embedded in professional routines. Those assets turn training data into a durable advantage. Hron's house-buying metaphor lingers. Renting powerful models delivers immediate capability. Owning one, even if built on someone else's foundation, creates long-term optionality. The company can iterate faster on its own data. It can tune for fiduciary-grade standards. It can expand across tax, compliance and news without renegotiating every capability. CoCounsel's latest agentic features show the complementary play. Built on Claude's SDK, the system orchestrates complex legal tasks. Thomson handles the heavy lifting on document volume and structured analysis. The combination aims for something greater than either alone. Questions remain about geopolitical angles. Reliance on a Chinese open-source base, even heavily adapted, invites scrutiny in some markets. Thomson Reuters stresses the realignment process and independence going forward. Nothing locks them to Qwen. Future versions could draw from other bases or further internal development. For industry watchers, this launch marks a maturation point. Pure reliance on API calls to Anthropic, OpenAI or Google gives way to selective ownership. The $40 million bet tests whether incumbents with rich datasets can close the gap on frontier labs in narrow but valuable domains. Results so far suggest the answer leans yes for certain tasks. Thomson tops some composites on legal hardness and long-context handling. It admits when it doesn't know. It cites sources. These traits matter more to a partner at a law firm than raw benchmark scores. The road ahead involves wider rollout. More features in CoCounsel. Potential expansion to tax and regulatory products. Continued benchmarking transparency. And ongoing collaboration with Anthropic even as internal capabilities grow. Thomson Reuters has placed its chips. The house it builds won't replace every rented roof. But over time that equity could compound into a meaningful lead in professional AI. Lawyers and compliance officers will decide if the bet pays off. Their verdict will shape the next wave of enterprise AI strategy.
Amazon's $220 Billion AI Bet: How an Anthropic Partnership Powers Cloud Dominance
Amazon raised its capital spending target for next year to $220 billion. The increase of $20 billion stems largely from higher memory costs. Yet the move signals far more than inflation in components. It reflects a calculated wager on artificial intelligence infrastructure that has already begun to pay off in both revenue acceleration and paper gains from a marquee investment. The announcement came tucked inside the company's second-quarter earnings release. Revenue climbed 20 percent from a year earlier. AWS posted its fastest growth in more than four years. Operating income jumped 43 percent to $27.5 billion, with the cloud unit contributing the bulk at $16.6 billion. The Motley Fool laid out the numbers in detail the same day. But the real story sits off the operating income line. Amazon's stake in Anthropic generated massive non-operating gains. In the first quarter alone the company recorded $16.8 billion in pre-tax income from the position, according to Yahoo Finance. By the second quarter that figure had swelled further, pushing total non-operating pre-tax other income to $53.4 billion, Business Insider reported. The partnership began years earlier. Amazon first put $4 billion into the AI startup in 2023, followed by another $4 billion in late 2024. Then came the big expansion. In April 2026 the two sides agreed on an additional $5 billion immediately and up to $20 billion more tied to commercial milestones. Total committed capital reached $13 billion so far with room to climb. Bloomberg broke the terms the day they were announced. Anthropic gave something back. The startup pledged to spend more than $100 billion on AWS services over the next decade. That commitment secures up to five gigawatts of new computing capacity built around Amazon's custom Trainium and Graviton chips. "Our custom AI silicon offers high performance at significantly lower cost for customers, which is why it's in such hot demand," Amazon CEO Andy Jassy said in the official release from AboutAmazon.com. The arrangement locks in demand. It also raises the bar for everyone else. Amazon, Microsoft and Alphabet together control more than 60 percent of the cloud market. Oracle sits a distant fourth at roughly four percent. Those scale advantages compound when hyperscalers pour tens of billions into data centers that smaller players cannot match. Investors have taken notice. Amazon shares rose more than 10 percent year to date by late August and have outperformed the S&P 500. The Anthropic position alone, once carried at cost, now reflects valuations that imply a stake worth between $180 billion and $240 billion at recent marks. An eventual IPO for Anthropic, confidentially filed in June and eyed for as early as October, could crystallize even larger gains. TechCrunch captured the mutual benefits when the April deal closed. Yet the spending surge carries risks. Free cash flow collapsed in the first quarter as capital expenditures hit $44.2 billion. Memory prices have climbed. AWS must keep delivering growth fast enough to offset the outlays. So far it has. AI and chips businesses each crossed $25 billion in annualized revenue run rate. Backlog sits at $496 billion with triple-digit growth in key segments. Andy Jassy has spoken of AWS as a potential trillion-dollar revenue business over time. That target looks less fanciful when a single partner like Anthropic commits to $100 billion in spend and multiple other AI labs sign multi-gigawatt deals. "The fact that we have multi-year, multi-gigawatt commitments from the two largest AI labs in Anthropic and OpenAI, and more and more companies using Trainium is exciting and promising," Jassy noted during the earnings call, as quoted by Variety. Wall Street's reaction mixed optimism with caution. Some analysts point to the mark-to-market accounting that inflated recent profits. Strip out the $16.8 billion paper gain from the first quarter and operating results still looked strong, but the distinction matters for valuation. The Next Web highlighted exactly that tension in May. Amazon's approach differs from pure-play AI labs that burn cash without the offsetting cloud revenue. The company can pass higher costs to customers who need more powerful instances as their models scale. It can also amortize the infrastructure spend across a broad base of enterprise workloads that extend beyond generative AI. Recent X conversations reflect the same debate. One investor noted that "Amazon's Anthropic stake came with cloud compute commitments, not just cash for equity. The 2T valuation bump looks good on paper, but AMZN's real upside is AWS consumption tied to that partnership." Others flagged Anthropic's $65 billion revenue run rate as a tailwind for Amazon, Google and Nvidia. The competitive moat widens with every gigawatt added. New entrants face years of lead time and billions in upfront capital before they can offer comparable performance at competitive prices. Amazon's Trainium chips already deliver meaningful cost advantages. Expanding that advantage while locking in the largest AI developers creates a self-reinforcing cycle. Of course execution still counts. Supply chains for memory and power remain tight. Regulatory scrutiny of big tech infrastructure builds could intensify. And valuations for private AI companies have climbed fast. Anthropic's implied worth topped $965 billion in its latest round and secondary trades pushed higher still. Even so, the numbers paint a picture of a company turning massive spending into measurable returns. Revenue growth outpacing capex growth. Cloud operating margins expanding. A strategic investment that has already delivered billions in recognized gains and promises more. Jeff Bezos no longer runs day-to-day operations, but his successor's willingness to spend at this scale keeps the original vision of infrastructure dominance alive. Whether the $220 billion bet proves conservative or not will show in the quarters ahead. Demand forecasts already stretch into 2027 and 2028. If AWS continues to accelerate, that number may rise again. For now the market seems willing to underwrite the outlay. Amazon's shares reflect confidence that the infrastructure built today will anchor AI workloads for years to come.
Google's Chip Veteran Just Joined Anthropic
Anthropic has hired former Google TPU leader Amir Salek as the artificial intelligence company works towards its own in-house semiconductor business. Salek will report to Head of Compute James Bradbury and will support the development and advancement of compute infrastructure, Bloomberg reported. Prior to joining Anthropic, Salek was working as a senior managing director at Cerberus Capital Management. He also worked as a senior director of engineering at Google, where he delivered the first seven generations of TPU solutions to Google Data Centers, according to his LinkedIn profile. He also held a previous role at Nvidia, where he founded and scaled its System-on-a-chip (SoC) organization and was a chip lead at PMC-Sierra. Anthropic is building out its internal chip-design team to develop custom processors for its Claude artificial intelligence models. The company has started hiring engineers with expertise across both hardware and software. Markets Partners Group Is Cashing Out of Gong Cha After Bain's Bubble-Tea Buyout Partners Group is set to exit its investment in Gong cha after Bain Capital agreed to acquire the global tea chain from TA Associates. 2 min read Read this article The effort expands Anthropic's existing multi-chip strategy, with the company signaling that it will continue to use a mix of third-party infrastructure and accelerators. Its current ecosystem includes hardware and cloud partnerships tied to Amazon Web Services, Google, Nvidia and AMD. Trending Get a 1% Match on Your First Deposit of $1,000+ Leading AI companies are actively working to reduce their dependence on scarce Nvidia GPUs. The availability of advanced chips has become a bottleneck for training and running frontier AI models, with companies competing for limited accelerator capacity. Anthropic has deep ties with Amazon, which has invested billions of dollars in the startup and provides access to its Trainium and Inferentia chips through Amazon Web Services. In April, Amazon announced that Anthropic would spend more than $100 billion over the next 10 years on AWS Technologies. Developing a cutting-edge AI chip can cost roughly $500 million, reflecting the expense of recruiting specialized engineers, designing advanced architectures and ensuring chips can be manufactured at scale without costly production failures. Anthropic has also bought $250 million worth of chips from the U.K.-based chip company Fractile and plans to expand the current contract. Anthropic has also signed capacity deals with Riot Platforms and Volta Infra Holdings Ltd. as it continues to beef up its chip and data center capacity, Bloomberg noted. Markets Private Credit's Hidden Default Problem Has Grown Since 2022, PIMCO Says Private credit shows more signs of financial stress than headline default rates suggest, according to a new PIMCO analysis. 3 min read Read this article Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.
Anthropic Intros Invisible Watermarks to Claude-Generated Text -- THE Journal
Anthropic Intros Invisible Watermarks to Claude-Generated Text * By John K. Waters * 08/24/26 Anthropic announced it is incorporating invisible watermarks into text generated by its Claude AI models, offering an early look at how major AI companies may respond to new European rules requiring machine-readable identification of AI-generated content. The AI company detailed its approach as part of changes designed to comply with transparency requirements under the European Union's AI Act. The measures apply globally to supported Claude models, rather than only to users in Europe. For text, Anthropic is using a version of SynthID-Text, an open source watermarking approach developed by Google DeepMind. Instead of adding visible labels or hidden characters, the system subtly influences the model's choices as it generates text, creating a statistical pattern that can later be detected. The process takes advantage of the fact that large language models often have several plausible choices for the next token in a response. The watermarking system can influence those choices in ways that create a detectable signature while preserving the overall meaning of the text. Anthropic says the watermark has no practical impact on the quality or content of Claude's output and does not increase the cost of using the model. The company is taking a different approach for images. Claude-processed images will use the Coalition for Content Provenance and Authenticity (C2PA) standard to attach provenance information to supported image files. The changes come as AI developers face growing pressure to make synthetic content easier to identify. Article 50 of the EU AI Act requires providers of systems that generate synthetic audio, images, video, or text to ensure their outputs are marked in a machine-readable format and detectable as artificially generated or manipulated. Anthropic has linked its watermarking changes directly to those requirements. The rules could make watermarking a more common feature across generative AI services as other companies operating in Europe address the same requirements. But watermarking AI-generated text presents challenges that do not exist in the same way for images or video. Anthropic acknowledges that its watermark is not intended to provide definitive proof that a piece of text was written by Claude. The company has also warned that quoted Claude-generated text could carry the watermark into another document, while text without a detectable watermark should not automatically be considered human-written. The watermark may survive copying, pasting, and some editing, but more extensive changes to generated text can make detection more difficult. The approach has also prompted criticism from some Claude users who are concerned about how watermarked text could be interpreted when AI is used for tasks such as editing, translation, or formatting rather than generating an entire document. Anthropic has said the presence of a watermark indicates that text was processed by Claude, not necessarily that Claude was responsible for its authorship. For more information, go to the Anthropic blog.
Anthropic Adding Invisible Watermarks to Claude-Generated Text -- Campus Technology
Anthropic Adding Invisible Watermarks to Claude-Generated Text * By John K. Waters * 08/24/26 Anthropic announced it is adding invisible watermarks to text generated by its Claude AI models, offering an early look at how major AI companies may respond to new European rules requiring machine-readable identification of AI-generated content. The AI company detailed its approach as part of changes designed to comply with transparency requirements under the European Union's AI Act. The measures apply globally to supported Claude models, rather than only to users in Europe. For text, Anthropic is using a version of SynthID-Text, an open source watermarking approach developed by Google DeepMind. Instead of adding visible labels or hidden characters, the system subtly influences the model's choices as it generates text, creating a statistical pattern that can later be detected. The process takes advantage of the fact that large language models often have several plausible choices for the next token in a response. The watermarking system can influence those choices in ways that create a detectable signature while preserving the overall meaning of the text. Anthropic says the watermark has no practical impact on the quality or content of Claude's output and does not increase the cost of using the model. The company is taking a different approach for images. Claude-processed images will use the Coalition for Content Provenance and Authenticity (C2PA) standard to attach provenance information to supported image files. The changes come as AI developers face growing pressure to make synthetic content easier to identify. Article 50 of the EU AI Act requires providers of systems that generate synthetic audio, images, video, or text to ensure their outputs are marked in a machine-readable format and detectable as artificially generated or manipulated. Anthropic has linked its watermarking changes directly to those requirements. The rules could make watermarking a more common feature across generative AI services as other companies operating in Europe address the same requirements. But watermarking AI-generated text presents challenges that do not exist in the same way for images or video. Anthropic acknowledges that its watermark is not intended to provide definitive proof that a piece of text was written by Claude. The company has also warned that quoted Claude-generated text could carry the watermark into another document, while text without a detectable watermark should not automatically be considered human-written. The watermark may survive copying, pasting, and some editing, but more extensive changes to generated text can make detection more difficult. The approach has also prompted criticism from some Claude users who are concerned about how watermarked text could be interpreted when AI is used for tasks such as editing, translation, or formatting rather than generating an entire document. Anthropic has said the presence of a watermark indicates that text was processed by Claude, not necessarily that Claude was responsible for its authorship. For more information, read the Anthropic blog.
😺 Anthropic's IPO could top SpaceX's record
California is trying again with SB 947, a bill that would ban employers from letting AI fire or discipline workers without a human signing off. Newsom vetoed an earlier version last October, but lawmakers reintroduced it as the "No Robo Bosses Act of 2026." The stakes are already real. At Andon Market in San Francisco, an AI agent named Luna runs hiring, scheduling, and pricing. She recently recommended firing a worker who missed 17 of 23 shifts. Humans carried it out. Only problem? Luna had written the store's attendance policy herself, then forgot it existed until someone told her to go check her memory. The AI boss is coming. It still needs a better filing system. Here's what happened in AI today: You've probably used Claude to draft an email, debug a script, or automate half your job this month. Turns out enough other people have too that Anthropic might be about to pull off the biggest stock market debut ever. Here's the deal: Anthropic's bankers have told investors the company could raise "more than $100 billion" when it goes public, according to the New York Times. That would put its value near $2 trillion, more than double the $965 billion it was worth after its last private funding round in June. Here's what happened: Why this matters: Claude Code, Anthropic's coding assistant, has become one of its most popular products and helped push the company's projected annual revenue to $47 billion. That's a five-year-old startup founded by siblings Dario and Daniela Amodei, both former OpenAI executives, betting its safety-focused approach to AI can out-earn (and now out-IPO) the company they left. An IPO this size would also settle an argument that's been simmering all year: whether the "safety-first" lab can actually out-compete the "move fast" one. So far, the money says yes. Our take: The hype has a couple of asterisks. Anthropic has struggled to get enough chips and servers to keep up with demand, and in March, the Trump administration cut off its Defense Department contracts entirely after Anthropic refused to give the military unrestricted access to its models (Anthropic called the move "unconstitutional retaliation"). A record-breaking IPO and a fight with your own government's biggest customer don't usually show up in the same earnings call. Anthropic's prospectus should tell us which story is closer to true. FROM OUR PARTNERS The Neuron and Alumni Ventures are giving readers early access to high-growth startup opportunities, including some of today's most exciting AI, Deep Tech, Quantum Computing, and Cybersecurity companies co-invested alongside top VC firms like Andreessen Horowitz (a16z), Bessemer, & Y Combinator. You get: Don't miss your chance before access closes. → See Current AI Deals So, apparently State Farm's outside lawyers admitted AI helped put seven nonexistent case citations into court filings. The useful lesson is much broader: drafting and verification should be separate steps. Copy this: Have a specific skill you want to learn? Request it here. FROM OUR PARTNERS A new Managed MCP Server connects Claude Code, Codex, Grok Build, and Devin to MongoDB Atlas, giving coding agents direct access to live operational data. Explore new MongoDB capabilities for the Agentic Era. *Asterisk = from our partners (only the first one!). Advertise to 700K+ readers here! New episodes air every week on Wednesdays: Spotify | Apple Podcasts | YouTube P.S: We're trying to hit 50K subscribers on YouTube this year. Click here to help! Btw: We just launched a robotics newsletter! Sign up for it here. P.S: Love the newsletter, but only want to get it once per week? Don't unsubscribe -- update your preferences here.
Israeli AI Startup Decart Nears $7 Billion Sale to Anthropic
An Israeli artificial intelligence startup that was valued at about $4 billion only three months ago is now reportedly on the verge of being acquired for roughly $7 billion by Anthropic, the American company behind the Claude AI chatbot. The potential acquisition of Decart would be one of the largest-ever purchases of an Israeli technology company and the biggest known acquisition in Anthropic's history. The deal is reportedly approaching the signing stage, although it has not yet been finalized and could still change or fall apart. The remarkable rise of Decart illustrates both the extraordinary pace of the artificial-intelligence industry and the growing importance of a problem that is less visible to consumers than increasingly sophisticated chatbots: how to make AI systems faster, cheaper and more efficient. From $4 billion to $7 billion Decart was founded in 2023 by Israeli entrepreneurs Dean Leitersdorf and Moshe Shalev. The company initially attracted attention for its work on making generative AI operate in real time, and it subsequently developed technology designed to squeeze considerably more performance out of the computer chips used to run AI models. In May, Decart announced a $300 million funding round led by Radical Ventures. Nvidia, the world's dominant maker of AI chips, participated in the round along with investors including Adobe Ventures, Toyota Ventures, Atreides Management and others. The financing brought Decart's total funding to more than $450 million and valued the company at approximately $4 billion. That valuation now looks modest. Reports initially emerged in mid-August that Anthropic was negotiating to acquire Decart for approximately $6 billion. Reuters confirmed that the companies were in talks, while noting that the discussions were part of Anthropic's broader effort to expand its capacity as demand for its AI products grows. Within days, however, Israeli business publication Calcalist reported that the negotiations had advanced significantly and that Decart could be valued at approximately $7 billion. The companies were reportedly exchanging advanced drafts of an acquisition agreement. The increase would represent a dramatic jump from Decart's valuation in its May financing round. Why does Anthropic want Decart? The attraction appears to have less to do with a consumer-facing AI application and more to do with the enormous cost of operating artificial intelligence at scale. Companies such as Anthropic spend vast sums on the computing power required to train and operate their AI models. Every improvement that allows a model to perform the same task with fewer computing resources can potentially translate into enormous savings. Decart has developed software that is designed to optimize AI workloads and extract more performance from the hardware on which they run. The company says its systems can dramatically improve the efficiency of both AI training and inference - the process by which an already-trained model generates responses. That technology could be particularly valuable to Anthropic as usage of its Claude models continues to expand. Reuters Breakingviews noted that even relatively modest improvements in computational efficiency could be worth billions of dollars to a company operating AI systems on a massive scale. For Anthropic, reducing the amount of computing power required to deliver each AI response could improve margins while allowing the company to serve more customers without simply adding enormous amounts of additional hardware. In other words, Decart could help Anthropic get more AI out of the same machines. A second side to Decart Decart is not solely an AI-infrastructure company. It has also been developing what are known as "world models," AI systems designed to understand and generate interactive representations of environments. Unlike a conventional chatbot that primarily works with text, a world model attempts to represent how objects and environments behave and change over time. Decart describes its technology as enabling real-time, interactive environments that could have applications in robotics, autonomous vehicles, manufacturing and drones. Its work also includes real-time video and image generation and transformation. That technology could eventually become important far beyond entertainment. AI systems that can simulate physical environments could be used to train robots, test autonomous vehicles and generate synthetic environments for other forms of artificial intelligence. The combination of these capabilities - more efficient AI computing and sophisticated real-time models - helps explain why Decart has attracted attention from some of the world's biggest technology companies. Nvidia was also interested One of the more intriguing aspects of the proposed transaction is the reported competition for Decart. Nvidia, which already invested in Decart's May financing round, was reportedly interested in acquiring the Israeli company as well. According to Israeli reports, Nvidia's offer may actually have been higher than Anthropic's. Yet Decart's founders and major investor Sequoia Capital reportedly preferred Anthropic as the company's next home. That would make the decision particularly notable. Nvidia is at the center of the global AI revolution, supplying many of the chips on which today's AI systems depend. Decart's technology, meanwhile, is designed in part to make AI workloads more efficient across different types of hardware. Anthropic, by contrast, is itself one of the major competitors in the race to build increasingly capable AI models. A strategic move before an IPO The timing is also significant for Anthropic. The company is preparing for a potential public offering, and acquiring technology that could reduce the cost of operating its AI systems would give investors another reason to believe that rapidly growing AI companies can eventually achieve attractive profit margins. Reports have suggested that Anthropic is targeting a major IPO later this year. The Decart acquisition would therefore be more than a conventional startup purchase. It would give Anthropic additional expertise in the underlying technology needed to run AI at enormous scale. Reports indicate that Decart's team would join Anthropic's inference and performance organization, putting the Israeli company's engineers directly into the effort to make Claude more efficient. For Israel's technology sector, meanwhile, the prospective deal is another extraordinary example of how quickly a small Israeli startup can become strategically important to one of the world's most valuable technology companies. Decart went from emerging from stealth with a $21 million Sequoia-led seed round to raising more than $450 million in total funding, reaching a $4 billion valuation, and now potentially being sold for close to $7 billion, all in roughly three years. The deal is not done yet. But if Anthropic and Decart complete the transaction at the reported valuation, it will stand as a striking demonstration of Israel's continuing ability to produce companies with technology valuable enough to become essential components of the global AI race.
Anthropic's $2 Trillion IPO Ambition: The Math, the Hype and the Warning From SpaceX
Anthropic stands on the verge of the largest stock-market debut in history. Investors circling its planned October IPO talk openly of a $2 trillion valuation. Some models stretch toward $3 trillion. The five-year-old builder of the Claude chatbot has filed confidentially to go public. Its bankers have carried that number into recent meetings with prospective buyers. But the distance between today's reality and that price tag is enormous. Start with the numbers that already exist. In May Anthropic closed a $65 billion Series H round that set its post-money valuation at $965 billion, according to its own announcement on anthropic.com/news/series-h. That figure topped OpenAI's last reported mark and made the company the most valuable private AI developer at the time. By the end of July its annualized revenue run rate had climbed to $65 billion. The jump was seven times higher than the comparable figure a year earlier and well above the $47 billion run rate noted in May, a Yahoo Finance report from August 24, 2026 disclosed. Yet profitability remains distant. The company's projected operating margin for the second quarter stood at only 5.1 percent. Heavy spending on compute continues. Competition keeps pressure on pricing. So any path to a $2 trillion market capitalization demands that investors underwrite both explosive revenue growth and a dramatic expansion in margins at the same time. Valuation math that stretches far into the future Dr. Chan Ahn, founder and CEO of Tessera PE and a former Goldman Sachs and JPMorgan executive, ran the numbers. To support a $2 trillion valuation at a 10 percent cost of equity, a 25 percent free cash-flow margin and a 25 times terminal multiple, Anthropic would need roughly $725 billion in revenue by 2036. Raise the discount rate to 13 percent and the required revenue climbs to about $950 billion. Those projections appear in the same Yahoo Finance analysis. "You can underwrite the growth or you can underwrite the margin. Underwriting both at once is the leap being asked of public investors." Ahn's assessment cuts to the core tension. And the comparison points investors often reach for don't quite fit. Annualized consumption revenue lacks the predictability of contracted revenue at companies such as Palantir Technologies or Nebius Group. That difference matters when public-market scrutiny intensifies. Recent coverage reinforces the gap between ambition and current performance. A Fortune article published August 14, 2026 noted that Anthropic would need Amazon-level earnings to justify a $2 trillion valuation, yet it isn't generating net income. At that price tag the company would sit near Amazon's $2.86 trillion market capitalization while producing a fraction of the retail giant's profits. The piece is available at fortune.com. But revenue forecasts keep climbing. Reuters reported that Anthropic projects $190 billion to $200 billion in revenue by 2028. That figure, cited by sources familiar with the company's financials, would reduce the implied multiple on a $2 trillion valuation from roughly 43 times current annualized run rate to around 10 times the 2028 projection. The story, referenced across multiple outlets including a Motley Fool analysis updated seven days ago, shows how bankers are building a case on future scale. See the Yahoo Markets version at finance.yahoo.com. The New York Times added fresh color on August 21, 2026. Bankers have told potential investors that the IPO could raise more than $100 billion and value the company at $2 trillion. That would eclipse SpaceX's $1.77 trillion debut in June. Anthropic was valued at roughly $900 billion in a private round earlier this year before the jump to $965 billion. The Times story is at nytimes.com. So. The trajectory looks clear on paper. Enterprise demand for Claude keeps accelerating. Hyperscalers pour in capital. Yet the public market has already delivered a cautionary example. SpaceX went public at roughly its last private valuation. Shares popped 67 percent on the first day of trading before giving back those gains. The real pressure came not from insider unlocks but from earnings scrutiny and fuller disclosure requirements. Ahn points to that sequence as the more relevant precedent. Private valuations emerge from selective transactions with sophisticated buyers. Public markets must absorb broader selling and constant quarterly examination. The Forbes piece from August 14, 2026 that discusses whether AI has entered bubble territory makes the same observation, available at forbes.com. Skeptics on X, now called the platform formerly known as Twitter, piled on this week. One widely viewed thread contrasted Anthropic's projected $9 billion in revenue against Meta's $200 billion and Amazon's $800 billion while noting the AI company's valuation sits at roughly 1.5 times Meta's and 71 percent of Amazon's. The posts captured a broader debate about whether foundation-model companies can command infrastructure-level multiples before they prove lasting competitive advantages. Anthropic itself has stayed quiet on the exact IPO terms. It has not discussed a specific valuation figure in recent meetings with prospective investors, according to a CNBC report from mid-August. The focus instead remains on the underlying demand for its models and the infrastructure build-out required to meet it. Compute capacity correlates directly with revenue for labs at this scale. More chips mean more inference. More inference means higher usage fees from enterprise customers. That dynamic explains why investors tolerate the current lack of profits. They bet that Claude's safety-first architecture, constitutional AI principles and strong enterprise traction will translate into defensible market share even as competition from OpenAI, Google and others intensifies. But the timeline for margin improvement remains uncertain. Training runs grow more expensive. Inference costs must fall. Regulatory and public backlash against AI-driven job displacement adds another variable that the upcoming IPO filing is expected to flag as a risk factor. The Financial Times first broke the broad expectation of a $2 trillion or higher listing in a story that quickly circulated among investment professionals. Multiple secondary-market transactions since the May funding round have reportedly pushed implied valuations even higher in private trades. Yet translating those marks into a sustainable public-market price will test how closely Wall Street is willing to project the optimistic scenarios Anthropic's backers are modeling. By any historical standard the numbers are staggering. A company that did not exist six years ago could soon command a market capitalization larger than most sovereign economies. Its success would signal that the market believes a handful of foundation-model providers will sit at the center of global business infrastructure much like the cloud giants do today. Its failure to meet those expectations after going public would send a different signal entirely. Either outcome will shape the next chapter of AI investment. For now the roadshow has not begun. The S-1 has not been made public. But the conversation has already moved well beyond the laboratory and into the territory of trillion-dollar balance sheets, decade-long revenue forecasts and the harsh arithmetic of public-market multiples. The test comes this fall.
5 things to know about Round Hill's Suno and Anthropic lawsuits: model weights, extraction tools, and a $75M number
Round Hill Music filed two copyright complaints last week, one against Suno and data-scraping firm Bright Data, the other against Anthropic. Both are in the US District Court for the Northern District of California. MBW reported the headline terms: statutory damages of up to USD $150,000 per work, a total Round Hill says would run to hundreds of millions and could approach or exceed $1 billion in each case, and a stated intention to take both to trial. The Suno filing runs to 24 pages, the Anthropic one to 26. Here is what else is in them. Both prayers ask the court to order the defendants to deliver every unauthorized copy of Round Hill's works for impoundment or destruction, citing 17 U.S.C. § 503. The filings define that to cover "copies retained in training datasets, internal repositories, model weights, and server infrastructure," wording that reaches the trained models, not just the source files behind them. Each Round Hill complaint also asks for a complete accounting of training data, scraping activity, and datasets involving the works. The DMCA counts stack on top of the per-work figure: under § 1203, up to $2,500 for each act of circumvention and up to $25,000 for each removal of copyright management information. For comparison, Anthropic's $1.5 billion settlement with book authors in September 2025 committed it to destroying the original files it torrented from Library Genesis and Pirate Library Mirror, and copies originating from them - but not the models themselves. Anthropic certified that no commercially released model had been trained on those datasets. Both Round Hill complaints rest on Bartz v. Anthropic, the authors' case in the same district. Each quotes Judge William Alsup's June 2025 ruling: "There is no carveout, however, from the Copyright Act for AI companies." The Suno filing applies that holding to a company that was not a party to it, alleging "Suno has engaged in the exact same conduct" in retaining unlicensed copies indefinitely. The Anthropic complaint sources its piracy account to the same case, citing torrenting from Library Genesis and Pirate Library Mirror (PiLiMi), and quotes what it says was a co-founder's message to colleagues once PiLiMi could be torrented: "[J]ust in time!" It further alleges that Concord II, the publishers' second suit filed on January 28, 2026, revealed that Anthropic "had concealed its torrenting during discovery in Concord I," a claim the publishers first raised in August 2025. Anthropic released a human-feedback training dataset, hh-rlhf, on Hugging Face in 2022. Round Hill alleges the dataset shows Claude returning copyrighted lyrics during testing, including a response to a Disney songs prompt that quoted from Let It Go. In a second example, the complaint says, a user asked Claude to build a short story from the lyrics to Eleanor Rigby, and the model did so - while the response logged as rejected instead pointed the user to an article on writing from song lyrics. "Anthropic deliberately trained Claude to commit copyright infringement," the complaint says of that exchange. Neither song is a Round Hill work. Both are cited to argue Claude reproduces copyrighted lyrics generally. In its section arguing fair use does not apply, the filing reproduces an exchange on X over Moonshot AI's alleged distillation of Anthropic's Fable model. Michael Kratsios, director of the White House Office of Science and Technology Policy, wrote that "large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable." Sarah Heck, Anthropic's head of public policy, replied: "Illicit, adversarial distillation is IP theft and industrial espionage that supports adversary military and intelligence capabilities." The Anthropic complaint alleges the company tested at least three text-extraction programs before ingesting data: Readability, Newspaper and jusText. It claims jusText was ruled out because it left copyright notices and ownership details intact, which the filing says Anthropic treated as "boilerplate" and "useless junk." Newspaper was selected instead, according to Round Hill, for its ability to strip that information. Round Hill makes the same argument about page layout, saying Bright Data's Scraping Browser renders pages without headers and footers, where copyright management information typically sits: "Making the deliberate choice to exclude headers and footers is identical to making the deliberate choice to remove CMI." Both Round Hill complaints cite Stevens v. CoreLogic for the double scienter standard: removal must be intentional, and the defendant must have known, or had reasonable grounds to know, it would induce, enable, facilitate or conceal infringement. They also cite an October 2025 Concord ruling holding similar allegations sufficient at the pleading stage. The $1 billion figure in both complaints is a projection, contingent on Round Hill amending its exhibits to cover thousands more works. The contributory infringement count against Bright Data carries the only fixed arithmetic in either filing: 500 works at $150,000 each, for $75,000,000. Round Hill also turns Bright Data's own litigation record against it, citing X Corp. v. Bright Data, in which the same court rejected its jurisdictional challenge. The complaint puts Bright Data past $300 million in 2025 revenue, growing 50% year-over-year, citing a report by Asymmetrix that calls the figure annualized recurring revenue. The filing alleges the services Bright Data provided to Suno "are only good for copyright infringement." It also draws on the hacked Suno source code reported by 404 Media on July 15, including a dataset logged as youtube_music holding over two million music clips and 113,879 hours of audio - roughly thirteen years. The Suno complaint quotes co-founder and CEO Mikey Shulman, citing a video produced with Oracle, as saying: "Our models are taught to just continue a piece of music." Round Hill sets that against Suno's marketing of itself as generating songs from whole cloth. The same filing alleges Suno's guardrails are porous, claiming a prompt naming Reba McEntire is blocked while a misspelling of her name is not. Both complaints name ElevenLabs, Musical AI, Symphonic, Soundverse, GEMA through PLAI, GCX/Rightsify , and Troveo as evidence of a functioning licensing market they say the defendants bypassed. But PLAI launched on July 23, under a month before these filings, and GEMA says it is built for tools that help creators make music, with generative AI licensing the separate subject of its own Suno case, which GEMA won at first instance on July 31. The Anthropic filing treats each model release as a fresh act of copying, naming Fable 5 and Mythos 5 (both June 9, 2026), Sonnet 5 (June 30, 2026) and Opus 5 (July 24, 2026). Exhibit A in each case lists 500 musical compositions, not sound recordings, though Round Hill asserts rights in 16,873 recordings and calls the exhibit a bellwether it will amend to cover both. MBW has contacted Suno, Anthropic and Bright Data for comment. None of the allegations has been tested in court.Music Business Worldwide
Perplexity's revenue tripled to $750 million ahead of Nvidia's $30 billion talks
The deal would add Perplexity to Nvidia's investments in its own customers. Nvidia has started discussions to invest in Perplexity's next funding round at a valuation of over $30 billion, according to a report from The Information. The deal would boost the valuation of the AI search startup by more than 50% in a year. It would also provide another Nvidia-backed customer with fresh capital. The talks surfaced on Sunday, sourced to people familiar with the discussions. Perplexity revenue triples while its valuation climbs past $30B The proposed round would value Perplexity at more than $30 billion, up from its $20 billion valuation last September. That is an increase of more than 50% in about a year. Nvidia is pondering whether to join. Both Nvidia and Perplexity declined to comment or did not respond to requests for comment. Perplexity's investors include Amazon founder Jeff Bezos and Japan's SoftBank Group. The company's annualized revenue has grown to over $750 million from less than $250 million at the beginning of the year, tripling in about eight months. Perplexity Computer sells to professionals to automate tasks on their machines. The growing demand for AI search and autonomous agents is feeding investors' appetite. Perplexity is aiming to go public in 2028, a timeline CEO Aravind Srinivas detailed in a June interview. The company would move ahead regardless of how the market treats the planned listings of OpenAI and Anthropic, he said. Regulators flag Nvidia's habit of funding its own customers Nvidia has invested in companies that are also its customers or suppliers. The list includes cloud providers CoreWeave and Nebius and AI data firm Mercor, which is reported to be in talks with Nvidia at a valuation of $20 billion. Critics call the arrangement circular financing. The supplier funds the buyer, the buyer spends that money again on the supplier's products, and demand can look stronger than it is. In its 2026 Annual Report, the Bank for International Settlements (BIS) listed circular financing as one of the three greatest risks to global financial stability. As Cryptopolitan has reported, the Bank of England (BOE) has warned that the speed of AI investment is unprecedented in history. Chips are "productive, they're long-lived, they're fungible, they're flexible," CEO Jensen Huang said of the financing program. The company guarantees up to 25% of its chips' residual value if resale falls short at the end of a Earlier this year, Perplexity agreed to run workloads on Microsoft's Azure for $750 million. The company inked that deal while fighting Amazon in court over shopping features in its AI tools, Cryptopolitan earlier reported. Amazon Web Services would continue to be the company's preferred cloud provider, a Perplexity spokesperson said at the time. Nvidia shares fell 2.16% to $210.09 in Monday afternoon trading, down from Friday's $214.72 close.
