While enterprises looking to sell goods and services online wait for the backbone of agentic commerce to be hashed out, PayPal is hoping its new features will bridge the gap.

The payments company is launching a discoverability solution that allows enterprises to make its product available on any chat platform, regardless of the model or agent payment protocol. 

PayPal, which is one of the participants for Google’s Agent Payments Protocol (AP2), found that it can leverage its relationship with merchants and enterprises to help pave the way for an easier transition into agentic commerce and offer the kind of flexibility they learned will benefit the ecosystem. 

Michelle Gill, PayPal general manager for small business and financial services, told VentureBeat that AI-powered shopping will continue to grow, so enterprises and brands need to start laying the groundwork early. 

“We think that merchants who've historically sold through web stores, particularly in the e-commerce space, are really going to need a way to get active on all of these large language models,” Gill said. “The challenge is that no one really knows how fast all of this is going to move. The issue that we’re trying to help merchants think through is how to do all of this as low-touch as possible while using the infrastructure you already have without doing a bazillion integrations.”

She added AI shopping would also bring about “a resurgence from consumers trying to ensure their investment is protected.”

PayPal partnered with website builder Wix, Cymbio, Commerce and Shopware to bring products to chat platforms like Perplexity. 

Agent-powered shopping 

PayPal’s Agentic Commerce Services include two features. The first is Agent Ready, which would allow existing PayPal merchants to accept payments on AI platforms. The second is called Shop Sync, which will enable companies’ product data to be discoverable through different AI chat interfaces. It takes a company’s catalog information and plug its inventory and fulfillment data to chat platforms. 

Gill said the data goes into a central repository where AI models can ingest the information. 

Right now, companies can access shop sync with Agent Ready coming in 2026. 

Gill said Agentic Commerce Services is a one-to-many solution, that would be helpful right now, as different LLMs scrape different data sources to surface information. 

Other benefits include:

• Fast integration with current and future partners

• More product discovery over the traditional search, browse and cart experiences

• Preserved customer insights and relationships where the brand continues to have control over their records and communications with customers. 

Right now, the service is only available through Perplexity, but Gill said more platforms will be added soon. 

Fragmented AI platforms 

Agentic commerce is still very much in the early stages. AI agents are just beginning to get better at reading a browser. while platforms like ChatGPT, Gemini and Perplexity can now surface products and services based on user queries, people cannot technically buy things from chat yet.

There’s a race right now to create a standard to enable agents to transact on behalf of users and pay for items. Other than Google’s AP2, OpenAI and Stripe have the Agentic Commerce Protocol (ACP) and Visa launched its Trusted Agent Protocol. 

Other than enabling a trust layer for agents to transact, another issue enterprises face with agentic commerce is fragmentation. Different chat platforms use different models which also interpret information in slightly different ways. Gill said PayPal learned that when it comes to working with merchants, flexibility is important. 

“How do you decide if you're going to spend your time integrating with Google, Microsoft, ChatGPT or Perplexity? And each one of them right now has a different protocol, a different catalog, config, a different everything. That is a lot of time to make a bet as to like where you should spend your time,” Gill said. 

AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

• “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

• What the review process for AI-created content looks like. 
• When and how to disclose AI involvement in content creation. 
• How to protect proprietary information (not uploading confidential or client information into AI tools).
• Which AI tools are approved for use, and how to request access to new ones.
• How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

• “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

• Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
• Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
• Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
• Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
• Create detailed author bios: Include:
  • Relevant qualifications, certifications, awards, and experience.
  • Links to social media, academic papers (if relevant), or other authoritative works.
• Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
• Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

• Audience personas.
• Voice traits that matter.
• Reading level, if applicable.
• The do’s and don’ts of phrases and language to use. 
• Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

• The style guide
  • This covers everything from the audience personas to the voice style and formatting.
  • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
• A content brief template
  • This can be an editable document that’s filled in for each content project and includes things like:
    • The goal of the content.
    • The specific audience.
    • The style of the content (news, listicle, feature article, how-to).
    • The role (who the LLM is writing as).
    • The desired action or outcome.
• Content examples
  • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
  • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
• Sources
  • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
  • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
  • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
• SEO prompts
  • Consider building SEO into the structure of the content from the outset.  
  • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

• Crafting a direct answer in the first one to two sentences, then expanding with context.
• Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
• Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
• Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
• Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

• “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
• “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

• Identifying the parts of the content creation workflow that are best suited for LLM assistance.
• Conducting an editorial meeting to sign off on topics and outlines. 
• Drafting the content.
• Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
• Getting sign-off from stakeholders.  

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

• Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
• All facts are traceable to credible, approved sources.
• Outdated statistics (more than two years) are replaced with fresh insights. 
• Draft meets the style guide’s voice guidelines and tone definitions. 
• Content adds valuable, expert insights rather than being vague or generic.
• For thought leadership, ensure the author’s perspective is woven throughout.
• Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
• Draft aligns with brand values and meets internal publication standards.
• Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

Google's John Mueller said case sensitivity matters and SEOs shouldn't just hope it works.

The post Google’s Advice On Canonicals: They’re Case Sensitive appeared first on Search Engine Journal.

The post Beyond LLMs: Crafting Robust AI with Multi-Method Agentic Architectures appeared first on StartupHub.ai.

“Large language models have well-known issues and constraints. And so if you want to solve complex problems, you’re going to want to adopt what’s called multi-method agentic AI, which combines large language models with other kinds of proven automation technologies so that you can build more adaptable, more transparent systems that are much more likely […]

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Michael Kagan, CTO of Nvidia and co-founder of Mellanox, recently engaged in a candid discussion with Sonya Huang and Pat Grady at Sequoia’s Europe100 event, offering profound insights into Nvidia’s meteoric rise as the architect of AI infrastructure. His commentary illuminated the pivotal role of the Mellanox acquisition in transforming Nvidia from a mere chip […]

The post Nvidia’s AI Imperative: Beyond Moore’s Law, Network is the New Compute appeared first on StartupHub.ai.

Amazon confirmed Tuesday that it is cutting about 14,000 corporate jobs, citing a need to reduce bureaucracy and become more efficient in the new era of artificial intelligence.

In a message to employees, posted on the company’s website, Amazon human resources chief Beth Galetti signaled that the cutbacks are expected to continue into 2026, while indicating that the company will also continue to hire in key strategic areas.

Reuters reported Monday that the number of layoffs could ultimately total as many as 30,000 people, which is still a possibility as the cutbacks continue into next year. At that scale, the overall number of job cuts could eventually be the largest in Amazon’s history, exceeding the 27,000 positions that the company eliminated in 2023 across multiple rounds of layoffs.

“This generation of AI is the most transformative technology we’ve seen since the Internet, and it’s enabling companies to innovate much faster than ever before,” wrote Galetti, senior vice president of People Experience and Technology. Amazon needs “to be organized more leanly, with fewer layers and more ownership, to move as quickly as possible for our customers and business,” she explained.

Amazon’s corporate workforce numbered around 350,000 people in early 2023, the last time the company provided a public number. At that scale, the initial reduction of 14,000 represents about 4% of Amazon’s corporate workforce. However, the number is a much smaller fraction of its overall workforce of 1.55 million people, which includes workers in its warehouses.

Although the cuts are expected to be global, they are likely to hit especially hard in the Seattle region, home to the company’s first headquarters and its largest corporate workforce. The tech hub has already felt the impact of major layoffs by Microsoft and many other companies in recent months.

The cuts come two days before Amazon’s third quarter earnings report. Amazon and other cloud giants have been pouring billions into capital expenses to boost AI capacity. Cutting jobs is one way of showing operating-expense discipline to Wall Street.

In a memo to employees in June, Amazon CEO Andy Jassy wrote that he expected Amazon’s total corporate workforce to get smaller over time as a result of efficiency gains from AI. 

Jassy took over as Amazon CEO from founder Jeff Bezos in mid-2021. In recent years he has been pushing to reduce management layers and eliminate bureaucracy inside the company, saying he wants Amazon to operate like the “world’s largest startup.” 

Bloomberg News reported this week that Jassy has told colleagues that parts of the company remain “unwieldy” despite the 2023 layoffs and other efforts to streamline operations. 

Reuters cited sources saying the magnitude of the cuts is also a result of Amazon’s strict return-to-office policy failing to cause enough employees to quit voluntarily. Amazon brought workers back five days a week earlier this year.

Impacted teams and people will be notified of the layoffs today, Galetti wrote.

Amazon is offering most impacted employees 90 days to find a new role internally, though the timing may vary based on local laws, according to the message. Those who do not find a new position at Amazon or choose to leave will be offered severance pay, outplacement services, health insurance benefits, and other forms of support.

John Carmack reports performance issues with Nvidia’s DGX Spark AI system John Carmack, ID Software founder and former CTO of Oculus VR, has been testing an Nvidia DGX Spark AI system. So far, he is not impressed by the performance the system has delivered. His system appears to be maxing out at 100 watts, which […]

The post Nvidia DGX Spark delivers half quoted performance for John Carmack appeared first on OC3D.

Battlefield is getting a free-to-play Battle Royale mode EA has confirmed that Battlefield REDSEC will launch on October 28th at 3 PM GMT, a free-to-play Battle Royale game that debuts alongside Battlefield 6’s Season 1 content. Battlefield REDSEC acts as EA’s counter to Call of Duty: Warzone. Currently, exact details for the new game are […]

The post Battlefield REDSEC is launching today – Here’s what you need to know appeared first on OC3D.

There's little doubt that The Matrix franchise is criminally underserved when it comes to videogame adaptations, despite being theoretically a perfect fit for the medium. In the 26 years since the original movie's theatrical debut, we only got two decent games: 2003's single player action/adventure game Enter the Matrix and 2005's MMORPG The Matrix Online. More recently, the interactive experience The Matrix Awakens was released in late 2021, but it was really just a tech demo for Unreal Engine 5 and a tease at the level of quality that gaming fans of the IP never really got to fully experience. […]

Read full article at https://wccftech.com/the-matrix-creators-wanted-kojima-make-a-game-on-the-ip-konami-refused/

Today at GTC 2025, NVIDIA's CEO, Jensen Huang, will deliver the opening keynote live from Washington, US, for the first time. NVIDIA GTC Comes To Washington, D.C, US: CEO Jensen Huang To Talk About Next Chapter of AI, Watch It Live Here NVIDIA's GTC 2025 is just a few hours away, and while you might be wondering, didn't GTC already happen a few months back? Well, it should be mentioned that while GTC used to be a one-time per annum affair in the past, the recent growth and success have turned NVIDIA's GTC into more of a quarterly event. As […]

Read full article at https://wccftech.com/watch-nvidia-gtc-2025-ceo-jensen-huang-keynote-live-washington-us/

The iPhone 17 lineup is expected to be Apple’s last to ship with Qualcomm’s 5G modems as the company prepares its transition to ship all of its iPhone 18 models with the C2 baseband chip. This in-house solution was said to be in development shortly after the iPhone 16e was announced, and while we will witness its materialization in 2026, a new report states that, unlike other Apple chipsets like the A20 and A20 Pro, it will not leverage TSMC’s newest 2nm process, but a lithography that is a couple of generations old. The C2 5G modem will reportedly be mass […]

Read full article at https://wccftech.com/apple-c2-to-be-mass-produced-on-older-tsmc-process-says-report/

A team of modders is working on Bully Online, a modification for the PC version of Bully: Scholarship Edition that promises to allow players to roam the grounds of Bullworth Academy and the nearby town with their friends. The Wii and Xbox 360 versions of Scholarship Edition did have a multiplayer mode, but it was limited to two players and only allowed them to face off in the class minigames. According to community creator SWEGTA, Bully Online promises much more, including free roam support, solo and group minigames, and even a role-playing system. They were able to add a 'fully […]

Read full article at https://wccftech.com/bully-online-mod-promises-let-you-roam-rockstars-classic-with-friends/

This morning, indie Chinese developer ChillyRoom unveiled Loulan: The Cursed Sand, one of the games funded through the PlayStation China Hero Project. The game is a hack 'n' slash action RPG viewed from a Diablo-like camera. The setting is the ancient Silk Road, in China's Western Regions. Loulan: The Cursed Sand tells the tragic love story of an exiled royal guard who returns to the titular fallen kingdom amidst the chaos of war in search of his beloved princess. Players will step into the game as the skeletal warrior known as 'The Cursed Sand', mastering the power of sand as […]

Read full article at https://wccftech.com/loulan-the-cursed-sand-chinese-hack-n-slash-arpg/

Samsung looks to be all set to announce its first triple-folding smartphone, the Galaxy Z TriFold, and even though the device is expected to be limited to a few markets, it was high time that we saw smartphones gravitate to a new form factor. Just before the official announcement happens, a series of images provides a first look at the Galaxy Z TriFold, showing a dual-infolding structure that can transform into a large-screen tablet. The Galaxy Z TriFold was on display at the Samsung booth at the K-Tech Showcase, with one report stating that the prototype did not display any […]

Read full article at https://wccftech.com/samsung-galaxy-z-trifold-first-look-image-gallery/

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The post On-Policy Distillation LLMs Redefine Post-Training Efficiency appeared first on StartupHub.ai.

On-policy distillation LLMs from Thinking Machines Lab offer a highly efficient and cost-effective method for post-training specialized smaller models, combining direct learning with dense feedback.

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Tensormesh's AI inference caching technology eliminates redundant computation, promising to make enterprise AI cheaper and faster to run at scale.

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Self Hosted Project is platform to launch your own VPN server in minutes—no tech skills needed. Full root access, 32+ locations, total privacy. No KYC, no logs, 7 protocols. Just your server. VPN done right. Self-hosted. Self-controlled.

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Meta plans to lay off more than 100 employees in Washington state as part of a broader round of cuts within its artificial intelligence division.

A new filing with the state’s Employment Security Department shows 101 employees impacted, including 48 in Bellevue, 23 in Seattle, and four in Redmond, along with 23 remote workers based in Washington.

The filing lists dozens of affected roles across Meta’s AI research and infrastructure units, including software engineers, AI researchers, and data scientists. Meta product managers, privacy specialists, and compliance analysts were also affected.

Meta is cutting around 600 positions in its AI unit, Axios reported last week. The company is investing heavily in AI and wants to create a “more agile operation,” according to an internal memo cited by Axios. Meta has just under 3,000 roles within its superintelligence lab, CNBC reported.

The separations at Meta in Washington take effect Dec. 22, according to the Worker Adjustment and Retraining Notification (WARN) notice filed Oct. 22.

Meta employs thousands of people across multiple offices in the Seattle region, one of its largest engineering hubs outside Menlo Park.

The latest reductions mark another contraction for Meta’s Pacific Northwest footprint following multiple rounds of layoffs over the past several years.

The company’s rapid expansion in Seattle over the past decade made it one of the emblems of the region’s tech boom, coinciding with Microsoft’s resurgence and Amazon’s rise.

Among the Bay Area titans, Google was among the first to establish a Seattle-area engineering office, way back in 2004. However, it was Facebook’s decision to open its own outpost across from Pike Place Market in 2010 that really got the attention of their Silicon Valley tech brethren.

In the decade that followed, out-of-town companies set up more than 130 engineering centers in the region.

However, more recently Meta has made moves to trim its Seattle-area footprint.

Apple earlier this year took over a building previously occupied by Meta in Seattle’s South Lake Union neighborhood, near Amazon’s headquarters. CoStar reported in April that Meta listed its other Arbor Blocks building for sublease.

Meta previously gobbled up much of the planned office space at the Spring District, a sprawling development northeast of downtown Bellevue, including a building that was originally going to be a new REI headquarters. But it has subleased some of the space since then to companies such as Snowflake, which recently took an entire building from Meta at the Spring District.

Meta’s office in Redmond, near Microsoft’s headquarters, is focused on its mixed reality development.

GeekWire has reached out to the company for an updated Seattle-area headcount.

Meta’s cuts come amid reported layoffs at Amazon that could impact up to 30,000 workers.

Tech companies have laid off more than 128,000 employees this year, according to Layoffs.fyi. Last year, companies cut nearly 153,000 positions.

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We make the internet faster, safer, and more private for families and small businesses — without the noise. We're not backed by big tech or VC money. We're privacy-focused, bootstrapped, and already seeing people pay for peace and quiet online. QuietNet is built by people who care — no ads, no tracking, just a cleaner, safer internet for your family or team.

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AMD gains $3 billion by divesting from ZT Systems’ manufacturing business – retains key talent AMD has confirmed that it has officially divested from ZT Systems’ manufacturing business, selling it to Sanmina for $3 billion. This recoups most of AMD’s acquisition costs from its ZT Systems (ZTS) purchase earlier this year, and secures AMD a […]

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Voxtara is your personal AI speech coach that helps you become a more confident and effective public speaker. Whether you're preparing for a big presentation, teaching a class, or pitching to investors, Voxtara provides instant, actionable feedback to help you improve.

Key Features: • AI-Powered Analysis: Get comprehensive feedback on clarity, pacing, confidence, engagement, and body language • Video Recording: Record practice sessions up to 5 minutes • Deep-Dive Reports: Detailed analysis • Progress Tracking: Watch your speaking skills improve over time with detailed analytics • Practice Reminders: Set custom reminders • Session History: Review past performances

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These quick tips could help you get more traction with your email outreach. 

With cyber scams on the increase, TikTok's looking to help raise awareness among its user community.

YouTube launched Ask Studio, an AI assistant in YouTube Studio that analyzes channel data to surface comment insights, performance analysis, and content ideas.

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OpenAI is telling companies that “relationship building” with AI has limits. Emotional dependence on ChatGPT is considered a safety risk, with new guardrails in place.

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Arm and SCSP's new paper highlights edge computing as the strategic imperative for achieving AI energy efficiency and securing U.S. competitiveness.

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AMD and the DOE are launching Lux and Discovery supercomputers at ORNL, a $1 billion investment to establish secure U.S. Sovereign AI infrastructure.

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“Infrastructure is destiny,” declared James Hairston, Head of International Policy & Partnerships for Asia, Africa, & Latin America at OpenAI, encapsulating the strategic imperative facing Southeast Asia in the burgeoning age of artificial intelligence. This powerful statement set the stage for a compelling discussion at the Bloomberg Business Summit at ASEAN in Kuala Lumpur, where […]

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OCCT 15 delivers a major update with a new storage test modeled after CrystalDiskMark, plus enhanced GPU diagnostics through an improved 3D adaptive test that detects errors more precisely and adds a coil-whine detection feature.

So, I stumbled upon this interesting portable monitor, which is unusually large to be called "portable", but considering there are users who would want something that can be useful for both travel and regular use, the UMax 24 looked interesting. I have reviewed a few UPERFECT portable monitors, including the Dual-Stack UStation Delta Max, which is one of the best options for work and gaming. However, UMax's big 24.5-inch screen size makes it an interesting option for daily usage if you are considering a versatile option for your desktop and travel. Personally, I wanted to see if I could replace […]

Read full article at https://wccftech.com/review/uperfect-umax-24-portable-monitor-review/

Apple will eventually introduce the M5 MacBook Air in a few months now that it has officially started selling the M5 MacBook Pro, but you will not immediately see the discounts on the company’s newer portable Macs, making the M4 MacBook Air models a more attractive proposition. Why? Because Amazon has slashed both the 13-inch and 15-inch versions by $200, and best of all, you can configure these machines up to 24GB unified RAM and a 512GB SSD. The base model starts from $799, making it an instant steal. The M5 MacBook Air will likely adopt the same ‘fanless’ cooling […]

Read full article at https://wccftech.com/do-not-wait-for-the-m5-macbook-air-because-amazon-offers-200-off-on-m4-macbook-air/

Halo: Campaign Evolved marked the official confirmation of something that felt like it would never happen just a short few years ago, with Halo officially coming to PlayStation. Following that announcement, GameStop, the retailer that was initially known for selling physical video games that's now more known for turning into a glorified Pop Funko and merch store called the 'Console Wars' over. Of all entities, the White House, which would normally have more important things to post about, responded with an AI-generated image of President Trump in Spartan armor with the caption "Power to the players." GameStop's original post comes […]

Read full article at https://wccftech.com/gamestop-console-wars-over-halo-on-ps-white-house-trump-ai-spartan-armor/

Gearbox founder Randy Pitchford was recently interviewed by Shacknews alongside a few other colleagues to discuss the making of Borderlands 4, the studio's latest game, which was released on September 12. The video runs for 73 minutes, and right toward the end, Pitchford goes into exactly what is needed to create such a big videogame like Borderlands 4. Interestingly, he then adds that the gaming industry as a whole is just getting started and 'figuring out' videogames, which haven't yet received their 'Citizen Kane' moment yet. To make a game like Borderlands 4 takes a big investment. It's a massive, […]

Read full article at https://wccftech.com/borderlands-boss-gaming-hasnt-even-produced-single-masterpiece-yet/

Qualcomm has announced its latest AI chips, which are designed to scale up to a purpose-built rack-level AI inference solution, but interestingly, they employ mobile memory onboard. Qualcomm's New AI Chips Take a 'Daring' Pivot Away From HBM To Target Efficient Inferencing Workloads Qualcomm has come a long way from being a mobile-focused firm, and in recent years, the San Diego chipmaker has expanded into new segments, including consumer computing and AI infrastructure. Now, the firm has announced its newest AI200 and AI250 chip solutions, which are reportedly designed for rack-scale configurations. This not only marks the entry of a […]

Read full article at https://wccftech.com/qualcomm-new-ai-rack-scale-solution-actually-uses-lpddr-mobile-memory-onboard/

Watch out, DeepSeek and Qwen! There's a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic tool use — that is, the ability to go off and use other software capabilities like web search or bespoke applications — without much human guidance.

That model is none other than MiniMax-M2, the latest LLM from the Chinese startup of the same name. And in a big win for enterprises globally, the model is available under a permissive, enterprise-friendly MIT License, meaning it is made available freely for developers to take, deploy, retrain, and use how they see fit — even for commercial purposes. It can be found on Hugging Face, GitHub and ModelScope, as well as through MiniMax's API here. It supports OpenAI and Anthropic API standards, as well, making it easy for customers of said proprietary AI startups to shift out their models to MiniMax's API, if they want.

According to independent evaluations by Artificial Analysis, a third-party generative AI model benchmarking and research organization, M2 now ranks first among all open-weight systems worldwide on the Intelligence Index—a composite measure of reasoning, coding, and task-execution performance.

In agentic benchmarks that measure how well a model can plan, execute, and use external tools—skills that power coding assistants and autonomous agents—MiniMax’s own reported results, following the Artificial Analysis methodology, show τ²-Bench 77.2, BrowseComp 44.0, and FinSearchComp-global 65.5.

These scores place it at or near the level of top proprietary systems like GPT-5 (thinking) and Claude Sonnet 4.5, making MiniMax-M2 the highest-performing open model yet released for real-world agentic and tool-calling tasks.

What It Means For Enterprises and the AI Race

Built around an efficient Mixture-of-Experts (MoE) architecture, MiniMax-M2 delivers high-end capability for agentic and developer workflows while remaining practical for enterprise deployment.

For technical decision-makers, the release marks an important turning point for open models in business settings. MiniMax-M2 combines frontier-level reasoning with a manageable activation footprint—just 10 billion active parameters out of 230 billion total.

This design enables enterprises to operate advanced reasoning and automation workloads on fewer GPUs, achieving near-state-of-the-art results without the infrastructure demands or licensing costs associated with proprietary frontier systems.

Artificial Analysis’ data show that MiniMax-M2’s strengths go beyond raw intelligence scores. The model leads or closely trails top proprietary systems such as GPT-5 (thinking) and Claude Sonnet 4.5 across benchmarks for end-to-end coding, reasoning, and agentic tool use.

Its performance in τ²-Bench, SWE-Bench, and BrowseComp indicates particular advantages for organizations that depend on AI systems capable of planning, executing, and verifying complex workflows—key functions for agentic and developer tools inside enterprise environments.

As LLM engineer Pierre-Carl Langlais aka Alexander Doria posted on X: "MiniMax [is] making a case for mastering the technology end-to-end to get actual agentic automation."

Compact Design, Scalable Performance

MiniMax-M2’s technical architecture is a sparse Mixture-of-Experts model with 230 billion total parameters and 10 billion active per inference.

This configuration significantly reduces latency and compute requirements while maintaining broad general intelligence.

The design allows for responsive agent loops—compile–run–test or browse–retrieve–cite cycles—that execute faster and more predictably than denser models.

For enterprise technology teams, this means easier scaling, lower cloud costs, and reduced deployment friction. According to Artificial Analysis, the model can be served efficiently on as few as four NVIDIA H100 GPUs at FP8 precision, a setup well within reach for mid-size organizations or departmental AI clusters.

Benchmark Leadership Across Agentic and Coding Workflows

MiniMax’s benchmark suite highlights strong real-world performance across developer and agent environments. The figure below, released with the model, compares MiniMax-M2 (in red) with several leading proprietary and open models, including GPT-5 (thinking), Claude Sonnet 4.5, Gemini 2.5 Pro, and DeepSeek-V3.2.

MiniMax-M2 achieves top or near-top performance in many categories:

• SWE-bench Verified: 69.4 — close to GPT-5’s 74.9

• ArtifactsBench: 66.8 — above Claude Sonnet 4.5 and DeepSeek-V3.2

• τ²-Bench: 77.2 — approaching GPT-5’s 80.1

• GAIA (text only): 75.7 — surpassing DeepSeek-V3.2

• BrowseComp: 44.0 — notably stronger than other open models

• FinSearchComp-global: 65.5 — best among tested open-weight systems

These results show MiniMax-M2’s capability in executing complex, tool-augmented tasks across multiple languages and environments—skills increasingly relevant for automated support, R&D, and data analysis inside enterprises.

Strong Showing in Artificial Analysis’ Intelligence Index

The model’s overall intelligence profile is confirmed in the latest Artificial Analysis Intelligence Index v3.0, which aggregates performance across ten reasoning benchmarks including MMLU-Pro, GPQA Diamond, AIME 2025, IFBench, and τ²-Bench Telecom.

MiniMax-M2 scored 61 points, ranking as the highest open-weight model globally and following closely behind GPT-5 (high) and Grok 4.

Artificial Analysis highlighted the model’s balance between technical accuracy, reasoning depth, and applied intelligence across domains. For enterprise users, this consistency indicates a reliable model foundation suitable for integration into software engineering, customer support, or knowledge automation systems.

Designed for Developers and Agentic Systems

MiniMax engineered M2 for end-to-end developer workflows, enabling multi-file code edits, automated testing, and regression repair directly within integrated development environments or CI/CD pipelines.

The model also excels in agentic planning—handling tasks that combine web search, command execution, and API calls while maintaining reasoning traceability.

These capabilities make MiniMax-M2 especially valuable for enterprises exploring autonomous developer agents, data analysis assistants, or AI-augmented operational tools.

Benchmarks such as Terminal-Bench and BrowseComp demonstrate the model’s ability to adapt to incomplete data and recover gracefully from intermediate errors, improving reliability in production settings.

Interleaved Thinking and Structured Tool Use

A distinctive aspect of MiniMax-M2 is its interleaved thinking format, which maintains visible reasoning traces between <think>...</think> tags.

This enables the model to plan and verify steps across multiple exchanges, a critical feature for agentic reasoning. MiniMax advises retaining these segments when passing conversation history to preserve the model’s logic and continuity.

The company also provides a Tool Calling Guide on Hugging Face, detailing how developers can connect external tools and APIs via structured XML-style calls.

This functionality allows MiniMax-M2 to serve as the reasoning core for larger agent frameworks, executing dynamic tasks such as search, retrieval, and computation through external functions.

Open Source Access and Enterprise Deployment Options

Enterprises can access the model through the MiniMax Open Platform API and MiniMax Agent interface (a web chat similar to ChatGPT), both currently free for a limited time.

MiniMax recommends SGLang and vLLM for efficient serving, each offering day-one support for the model’s unique interleaved reasoning and tool-calling structure.

Deployment guides and parameter configurations are available through MiniMax’s documentation.

Cost Efficiency and Token Economics

As Artificial Analysis noted, MiniMax’s API pricing is set at $0.30 per million input tokens and $1.20 per million output tokens, among the most competitive in the open-model ecosystem.

Notes & caveats (for readers):

• Prices are USD per million tokens and can change; check linked pages for updates and region/endpoint nuances (e.g., Anthropic long-context >200K input, Google Live API variants, cache discounts).

• Vendors may bill extra for server-side tools (web search, code execution) or offer batch/context-cache discounts.

While the model produces longer, more explicit reasoning traces, its sparse activation and optimized compute design help maintain a favorable cost-performance balance—an advantage for teams deploying interactive agents or high-volume automation systems.

Background on MiniMax — an Emerging Chinese Powerhouse

MiniMax has quickly become one of the most closely watched names in China’s fast-rising AI sector.

Backed by Alibaba and Tencent, the company moved from relative obscurity to international recognition within a year—first through breakthroughs in AI video generation, then through a series of open-weight large language models (LLMs) aimed squarely at developers and enterprises.

The company first captured global attention in late 2024 with its AI video generation tool, “video-01,” which demonstrated the ability to create dynamic, cinematic scenes in seconds. VentureBeat described how the model’s launch sparked widespread interest after online creators began sharing lifelike, AI-generated footage—most memorably, a viral clip of a Star Wars lightsaber duel that drew millions of views in under two days.

CEO Yan Junjie emphasized that the system outperformed leading Western tools in generating human movement and expression, an area where video AIs often struggle. The product, later commercialized through MiniMax’s Hailuo platform, showcased the startup’s technical confidence and creative reach, helping to establish China as a serious contender in generative video technology.

By early 2025, MiniMax had turned its attention to long-context language modeling, unveiling the MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01. These open-weight models introduced an unprecedented 4-million-token context window, doubling the reach of Google’s Gemini 1.5 Pro and dwarfing OpenAI’s GPT-4o by more than twentyfold.

The company continued its rapid cadence with the MiniMax-M1 release in June 2025, a model focused on long-context reasoning and reinforcement learning efficiency. M1 extended context capacity to 1 million tokens and introduced a hybrid Mixture-of-Experts design trained using a custom reinforcement-learning algorithm known as CISPO. Remarkably, VentureBeat reported that MiniMax trained M1 at a total cost of about $534,700, roughly one-tenth of DeepSeek’s R1 and far below the multimillion-dollar budgets typical for frontier-scale models.

For enterprises and technical teams, MiniMax’s trajectory signals the arrival of a new generation of cost-efficient, open-weight models designed for real-world deployment. Its open licensing—ranging from Apache 2.0 to MIT—gives businesses freedom to customize, self-host, and fine-tune without vendor lock-in or compliance restrictions.

Features such as structured function calling, long-context retention, and high-efficiency attention architectures directly address the needs of engineering groups managing multi-step reasoning systems and data-intensive pipelines.

As MiniMax continues to expand its lineup, the company has emerged as a key global innovator in open-weight AI, combining ambitious research with pragmatic engineering.

Open-Weight Leadership and Industry Context

The release of MiniMax-M2 reinforces the growing leadership of Chinese AI research groups in open-weight model development.

Following earlier contributions from DeepSeek, Alibaba’s Qwen series, and Moonshot AI, MiniMax’s entry continues the trend toward open, efficient systems designed for real-world use.

Artificial Analysis observed that MiniMax-M2 exemplifies a broader shift in focus toward agentic capability and reinforcement-learning refinement, prioritizing controllable reasoning and real utility over raw model size.

For enterprises, this means access to a state-of-the-art open model that can be audited, fine-tuned, and deployed internally with full transparency.

By pairing strong benchmark performance with open licensing and efficient scaling, MiniMaxAI positions MiniMax-M2 as a practical foundation for intelligent systems that think, act, and assist with traceable logic—making it one of the most enterprise-ready open AI models available today.

Anthropic is making its most aggressive push yet into the trillion-dollar financial services industry, unveiling a suite of tools that embed its Claude AI assistant directly into Microsoft Excel and connect it to real-time market data from some of the world's most influential financial information providers.

The San Francisco-based AI startup announced Monday it is releasing Claude for Excel, allowing financial analysts to interact with the AI system directly within their spreadsheets — the quintessential tool of modern finance. Beyond Excel, select Claude models are also being made available in Microsoft Copilot Studio and Researcher agent, expanding the integration across Microsoft's enterprise AI ecosystem. The integration marks a significant escalation in Anthropic's campaign to position itself as the AI platform of choice for banks, asset managers, and insurance companies, markets where precision and regulatory compliance matter far more than creative flair.

The expansion comes just three months after Anthropic launched its Financial Analysis Solution in July, and it signals the company's determination to capture market share in an industry projected to spend $97 billion on AI by 2027, up from $35 billion in 2023.

More importantly, it positions Anthropic to compete directly with Microsoft — ironically, its partner in this Excel integration — which has its own Copilot AI assistant embedded across its Office suite, and with OpenAI, which counts Microsoft as its largest investor.

Why Excel has become the new battleground for AI in finance

The decision to build directly into Excel is hardly accidental. Excel remains the lingua franca of finance, the digital workspace where analysts spend countless hours constructing financial models, running valuations, and stress-testing assumptions. By embedding Claude into this environment, Anthropic is meeting financial professionals exactly where they work rather than asking them to toggle between applications.

Claude for Excel allows users to work with the AI in a sidebar where it can read, analyze, modify, and create new Excel workbooks while providing full transparency about the actions it takes by tracking and explaining changes and letting users navigate directly to referenced cells.

This transparency feature addresses one of the most persistent anxieties around AI in finance: the "black box" problem. When billions of dollars ride on a financial model's output, analysts need to understand not just the answer but how the AI arrived at it. By showing its work at the cell level, Anthropic is attempting to build the trust necessary for widespread adoption in an industry where careers and fortunes can turn on a misplaced decimal point.

The technical implementation is sophisticated. Claude can discuss how spreadsheets work, modify them while preserving formula dependencies — a notoriously complex task — debug cell formulas, populate templates with new data, or build entirely new spreadsheets from scratch. This isn't merely a chatbot that answers questions about your data; it's a collaborative tool that can actively manipulate the models that drive investment decisions worth trillions of dollars.

How Anthropic is building data moats around its financial AI platform

Perhaps more significant than the Excel integration is Anthropic's expansion of its connector ecosystem, which now links Claude to live market data and proprietary research from financial information giants. The company added six major new data partnerships spanning the entire spectrum of financial information that professional investors rely upon.

Aiera now provides Claude with real-time earnings call transcripts and summaries of investor events like shareholder meetings, presentations, and conferences. The Aiera connector also enables a data feed from Third Bridge, which gives Claude access to a library of insights interviews, company intelligence, and industry analysis from experts and former executives. Chronograph gives private equity investors operational and financial information for portfolio monitoring and conducting due diligence, including performance metrics, valuations, and fund-level data.

Egnyte enables Claude to securely search permitted data for internal data rooms, investment documents, and approved financial models while maintaining governed access controls. LSEG, the London Stock Exchange Group, connects Claude to live market data including fixed income pricing, equities, foreign exchange rates, macroeconomic indicators, and analysts' estimates of other important financial metrics. Moody's provides access to proprietary credit ratings, research, and company data covering ownership, financials, and news on more than 600 million public and private companies, supporting work and research in compliance, credit analysis, and business development. MT Newswires provides Claude with access to the latest global multi-asset class news on financial markets and economies.

These partnerships amount to a land grab for the informational infrastructure that powers modern finance. Previously announced in July, Anthropic had already secured integrations with S&P Capital IQ, Daloopa, Morningstar, FactSet, PitchBook, Snowflake, and Databricks. Together, these connectors give Claude access to virtually every category of financial data an analyst might need: fundamental company data, market prices, credit assessments, private company intelligence, alternative data, and breaking news.

This matters because the quality of AI outputs depends entirely on the quality of inputs. Generic large language models trained on public internet data simply cannot compete with systems that have direct pipelines to Bloomberg-quality financial information. By securing these partnerships, Anthropic is building moats around its financial services offering that competitors will find difficult to replicate.

The strategic calculus here is clear: Anthropic is betting that domain-specific AI systems with privileged access to proprietary data will outcompete general-purpose AI assistants. It's a direct challenge to the "one AI to rule them all" approach favored by some competitors.

Pre-configured workflows target the daily grind of Wall Street analysts

The third pillar of Anthropic's announcement involves six new "Agent Skills" — pre-configured workflows for common financial tasks. These skills are Anthropic's attempt to productize the workflows of entry-level and mid-level financial analysts, professionals who spend their days building models, processing due diligence documents, and writing research reports. Anthropic has designed skills specifically to automate these time-consuming tasks.

The new skills include building discounted cash flow models complete with full free cash flow projections, weighted average cost of capital calculations, scenario toggles, and sensitivity tables. There's comparable company analysis featuring valuation multiples and operating metrics that can be easily refreshed with updated data. Claude can now process data room documents into Excel spreadsheets populated with financial information, customer lists, and contract terms. It can create company teasers and profiles for pitch books and buyer lists, perform earnings analyses that use quarterly transcripts and financials to extract important metrics, guidance changes, and management commentary, and produce initiating coverage reports with industry analysis, company deep dives, and valuation frameworks.

It's worth noting that Anthropic's Sonnet 4.5 model now tops the Finance Agent benchmark from Vals AI at 55.3% accuracy, a metric designed to test AI systems on tasks expected of entry-level financial analysts. A 55% accuracy rate might sound underwhelming, but it is state-of-the-art performance and highlights both the promise and limitations of AI in finance. The technology can clearly handle sophisticated analytical tasks, but it's not yet reliable enough to operate autonomously without human oversight — a reality that may actually reassure both regulators and the analysts whose jobs might otherwise be at risk.

The Agent Skills approach is particularly clever because it packages AI capabilities in terms that financial institutions already understand. Rather than selling generic "AI assistance," Anthropic is offering solutions to specific, well-defined problems: "You need a DCF model? We have a skill for that. You need to analyze earnings calls? We have a skill for that too."

Trillion-dollar clients are already seeing massive productivity gains

Anthropic's financial services strategy appears to be gaining traction with exactly the kind of marquee clients that matter in enterprise sales. The company counts among its clients AIA Labs at Bridgewater, Commonwealth Bank of Australia, American International Group, and Norges Bank Investment Management — Norway's $1.6 trillion sovereign wealth fund, one of the world's largest institutional investors.

NBIM CEO Nicolai Tangen reported achieving approximately 20% productivity gains, equivalent to 213,000 hours, with portfolio managers and risk departments now able to "seamlessly query our Snowflake data warehouse and analyze earnings calls with unprecedented efficiency."

At AIG, CEO Peter Zaffino said the partnership has "compressed the timeline to review business by more than 5x in our early rollouts while simultaneously improving our data accuracy from 75% to over 90%." If these numbers hold across broader deployments, the productivity implications for the financial services industry are staggering.

These aren't pilot programs or proof-of-concept deployments; they're production implementations at institutions managing trillions of dollars in assets and making underwriting decisions that affect millions of customers. Their public endorsements provide the social proof that typically drives enterprise adoption in conservative industries.

Regulatory uncertainty creates both opportunity and risk for AI deployment

Yet Anthropic's financial services ambitions unfold against a backdrop of heightened regulatory scrutiny and shifting enforcement priorities. In 2023, the Consumer Financial Protection Bureau released guidance requiring lenders to "use specific and accurate reasons when taking adverse actions against consumers" involving AI, and issued additional guidance requiring regulated entities to "evaluate their underwriting models for bias" and "evaluate automated collateral-valuation and appraisal processes in ways that minimize bias."

However, according to a Brookings Institution analysis, these measures have since been revoked with work stopped or eliminated at the current downsized CFPB under the current administration, creating regulatory uncertainty. The pendulum has swung from the Biden administration's cautious approach, exemplified by an executive order on safe AI development, toward the Trump administration's "America's AI Action Plan," which seeks to "cement U.S. dominance in artificial intelligence" through deregulation.

This regulatory flux creates both opportunities and risks. Financial institutions eager to deploy AI now face less prescriptive federal oversight, potentially accelerating adoption. But the absence of clear guardrails also exposes them to potential liability if AI systems produce discriminatory outcomes, particularly in lending and underwriting.

The Massachusetts Attorney General recently reached a $2.5 million settlement with student loan company Earnest Operations, alleging that its use of AI models resulted in "disparate impact in approval rates and loan terms, specifically disadvantaging Black and Hispanic applicants." Such cases will likely multiply as AI deployment grows, creating a patchwork of state-level enforcement even as federal oversight recedes.

Anthropic appears acutely aware of these risks. In an interview with Banking Dive, Jonathan Pelosi, Anthropic's global head of industry for financial services, emphasized that Claude requires a "human in the loop." The platform, he said, is not intended for autonomous financial decision-making or to provide stock recommendations that users follow blindly. During client onboarding, Pelosi told the publication, Anthropic focuses on training and understanding model limitations, putting guardrails in place so people treat Claude as a helpful technology rather than a replacement for human judgment.

Competition heats up as every major tech company targets finance AI

Anthropic's financial services push comes as AI competition intensifies across the enterprise. OpenAI, Microsoft, Google, and numerous startups are all vying for position in what may become one of AI's most lucrative verticals. Goldman Sachs introduced a generative AI assistant to its bankers, traders, and asset managers in January, signaling that major banks may build their own capabilities rather than rely exclusively on third-party providers.

The emergence of domain-specific AI models like BloombergGPT — trained specifically on financial data — suggests the market may fragment between generalized AI assistants and specialized tools. Anthropic's strategy appears to stake out a middle ground: general-purpose models, since Claude was not trained exclusively on financial data, enhanced with financial-specific tooling, data access, and workflows.

The company's partnership strategy with implementation consultancies including Deloitte, KPMG, PwC, Slalom, TribeAI, and Turing is equally critical. These firms serve as force multipliers, embedding Anthropic's technology into their own service offerings and providing the change management expertise that financial institutions need to successfully adopt AI at scale.

CFOs worry about AI hallucinations and cascading errors

The broader question is whether AI tools like Claude will genuinely transform financial services productivity or merely shift work around. The PYMNTS Intelligence report "The Agentic Trust Gap" found that chief financial officers remain hesitant about AI agents, with "nagging concern" about hallucinations where "an AI agent can go off script and expose firms to cascading payment errors and other inaccuracies."

"For finance leaders, the message is stark: Harness AI's momentum now, but build the guardrails before the next quarterly call—or risk owning the fallout," the report warned.

A 2025 KPMG report found that 70% of board members have developed responsible use policies for employees, with other popular initiatives including implementing a recognized AI risk and governance framework, developing ethical guidelines and training programs for AI developers, and conducting regular AI use audits.

The financial services industry faces a delicate balancing act: move too slowly and risk competitive disadvantage as rivals achieve productivity gains; move too quickly and risk operational failures, regulatory penalties, or reputational damage. Speaking at the Evident AI Symposium in New York last week, Ian Glasner, HSBC's group head of emerging technology, innovation and ventures, struck an optimistic tone about the sector's readiness for AI adoption. "As an industry, we are very well prepared to manage risk," he said, according to CIO Dive. "Let's not overcomplicate this. We just need to be focused on the business use case and the value associated."

Anthropic's latest moves suggest the company sees financial services as a beachhead market where AI's value proposition is clear, customers have deep pockets, and the technical requirements play to Claude's strengths in reasoning and accuracy. By building Excel integration, securing data partnerships, and pre-packaging common workflows, Anthropic is reducing the friction that typically slows enterprise AI adoption.

The $61.5 billion valuation the company commanded in its March fundraising round — up from roughly $16 billion a year earlier — suggests investors believe this strategy will work. But the real test will come as these tools move from pilot programs to production deployments across thousands of analysts and billions of dollars in transactions.

Financial services may prove to be AI's most demanding proving ground: an industry where mistakes are costly, regulation is stringent, and trust is everything. If Claude can successfully navigate the spreadsheet cells and data feeds of Wall Street without hallucinating a decimal point in the wrong direction, Anthropic will have accomplished something far more valuable than winning another benchmark test. It will have proven that AI can be trusted with the money.

Some enterprises are best served by fine-tuning large models to their needs, but a number of companies plan to build their own models, a project that would require access to GPUs. 

Google Cloud wants to play a bigger role in enterprises’ model-making journey with its new service, Vertex AI Training. The service gives enterprises looking to train their own models access to a managed Slurm environment, data science tooling and any chips capable of large-scale model training. 

With this new service, Google Cloud hopes to turn more enterprises away from other providers and encourage the building of more company-specific AI models. 

While Google Cloud has always offered the ability to customize its Gemini models, the new service allows customers to bring in their own models or customize any open-source model Google Cloud hosts. 

Vertex AI Training positions Google Cloud directly against companies like CoreWeave and Lambda Labs, as well as its cloud competitors AWS and Microsoft Azure.  

Jaime de Guerre, senior director of product management at Gloogle Cloud, told VentureBeat that the company has been hearing from a lot of organizations of varying sizes that they need a way to better optimize compute but in a more reliable environment.

“What we're seeing is that there's an increasing number of companies that are building or customizing large gen AI models to introduce a product offering built around those models, or to help power their business in some way,” de Guerre said. “This includes AI startups, technology companies, sovereign organizations building a model for a particular region or culture or language and some large enterprises that might be building it into internal processes.”

De Guerre noted that while anyone can technically use the service, Google is targeting companies planning large-scale model training rather than simple fine-tuning or LoRA adopters. Vertex AI Services will focus on longer-running training jobs spanning hundreds or even thousands of chips. Pricing will depend on the amount of compute the enterprise will need. 

“Vertex AI Training is not for adding more information to the context or using RAG; this is to train a model where you might start from completely random weights,” he said.

Model customization on the rise

Enterprises are recognizing the value of building customized models beyond just fine-tuning an LLM via retrieval-augmented generation (RAG). Custom models would know more in-depth company information and respond with answers specific to the organization. Companies like Arcee.ai have begun offering their models for customization to clients. Adobe recently announced a new service that allows enterprises to retrain Firefly for their specific needs. Organizations like FICO, which create small language models specific to the finance industry, often buy GPUs to train them at significant cost. 

Google Cloud said Vertex AI Training differentiates itself by giving access to a larger set of chips, services to monitor and manage training and the expertise it learned from training the Gemini models. 

Some early customers of Vertex AI Training include AI Singapore, a consortium of Singaporean research institutes and startups that built the 27-billion-parameter SEA-LION v4, and Salesforce’s AI research team. 

Enterprises often have to choose between taking an already-built LLM and fine-tuning it or building their own model. But creating an LLM from scratch is usually unattainable for smaller companies, or it simply doesn’t make sense for some use cases. However, for organizations where a fully custom or from-scratch model makes sense, the issue is gaining access to the GPUs needed to run training.

Model training can be expensive

Training a model, de Guerre said, can be difficult and expensive, especially when organizations compete with several others for GPU space.

Hyperscalers like AWS and Microsoft — and, yes, Google — have pitched that their massive data centers and racks and racks of high-end chips deliver the most value to enterprises. Not only will they have access to expensive GPUs, but cloud providers often offer full-stack services to help enterprises move to production.

Services like CoreWeave gained prominence for offering on-demand access to Nvidia H100s, giving customers flexibility in compute power when building models or applications. This has also given rise to a business model in which companies with GPUs rent out server space.

De Guerre said Vertex AI Training isn’t just about offering access to train models on bare compute, where the enterprise rents a GPU server; they also have to bring their own training software and manage the timing and failures. 

“This is a managed Slurm environment that will help with all the job scheduling and automatic recovery of jobs failing,” de Guerre said. “So if a training job slows down or stops due to a hardware failure, the training will automatically restart very quickly, based on automatic checkpointing that we do in management of the checkpoints to continue with very little downtime.”

He added that this provides higher throughput and more efficient training for a larger scale of compute clusters. 

Services like Vertex AI Training could make it easier for enterprises to build niche models or completely customize existing models. Still, just because the option exists doesn’t mean it's the right fit for every enterprise. 

A new framework developed by researchers at Google Cloud and DeepMind aims to address one of the key challenges of developing computer use agents (CUAs): Gathering high-quality training examples at scale.

The framework, dubbed Watch & Learn (W&L), addresses the problem of training data generation in a way that doesn’t require human annotation and can automatically extract demonstrations from raw videos.

Their experiments show that data generated W&L can be used to train or fine-tune existing computer use and foundation models to improve their performance on computer-use tasks. But equally important, the same approach can be used to create in-context learning (ICL) examples for computer use agents, enabling companies to create CUAs for bespoke internal tasks without the need for costly training of specialized models.

The data bottleneck of CUA

The web is rich with video tutorials and screencasts that describe complex workflows for using applications. These videos are a gold mine that can provide computer use agents with domain knowledge and instructions for accomplishing different tasks through user interface interactions.

However, before they can be used to train CUA agents, these videos need to be transformed into annotated trajectories (that is, a set of task descriptions, screenshots and actions), a process that is prohibitively expensive and time-consuming when done manually.

Existing approaches to address this data bottleneck rely on annotating these videos through the use of multimodal language models, which usually result in low precision and faulty examples. A different approach uses self-play agents that autonomously explore user interfaces to collect trajectories. However, techniques using this approach usually create simple examples that are not useful in unpredictable real-world situations.

As the researchers note in their paper, “Overall, these approaches either rely on brittle heuristics, are costly as they rely on explorations in real environments or generate low-complexity demonstrations misaligned with human intent.”

Watch & Learn

The Watch & Learn framework tries to address the challenges of creating CUA demonstrations by rethinking the problem formulation.

Instead of directly generating trajectories or depending on complex multi-stage pipelines, the researchers frame the problem as an “inverse dynamics objective”: Given two consecutive observations, predict the intermediate action that produced the transition.

According to the researchers, this formulation is “easier to learn, avoids hand-crafted heuristics and generalizes robustly across applications.”

The W&L framework can be broken down into three key stages: Training an inverse dynamics model (IDM), retrieving raw videos, and training CUA agents.

In the first phase, the researchers used agents to interact with live web pages to create a large corpus of 500,000 state transitions (two consecutive observations and the action that resulted in the transition). They then used this data (along with 132,000 human-annotated transitions from existing open datasets) to train an inverse dynamics model (IDM) that takes in two consecutive observations and predicts the transition action. Their trained IDM, which is a small transformer model, outperformed off-the-shelf foundation models in predicting transition actions.

The researchers then designed a pipeline that retrieves videos from platforms such as YouTube and runs them through IDM to generate high-quality trajectories. The IDM takes in consecutive video frames and determines the actions (scroll, click) that caused the changes in the environment, which are then packaged into annotated trajectories. Using this method, they generated 53,125 trajectories with high-accuracy action labels.

These examples can be used to train effective computer use models for specific tasks. But the researchers also found that trajectories extracted through IDM can serve as in-context learning examples to improve the performance of CUAs on bespoke tasks at inference time. For ICL, they use Gemini 2.5 Flash to add additional reasoning annotations to the observation/action examples in the trajectories, which can then be inserted into the CUA agent’s prompt (usually 3-5 examples) during inference.

“This dual role (training and in-context guidance) enables flexible integration with both open-source models and general-purpose agents,” the researchers write.

W&L in action

To test the usefulness of W&L, the researchers ran a series of experiments with closed and open source models on the OSWorld benchmark, which evaluates agents in real desktop and operating system environments across different tasks, including productivity, programming and design.

For fine-tuning, they used their corpus of 53,000 trajectories to train two open source models: UI-TARS-1.5, a strong, open source vision-language-action model designed specifically for computer use, and Qwen 2.5-VL, an open-weight multimodal LLM. 

For in-context learning tests, they applied W&L examples to general-purpose multimodal models such as Gemini 2.5 Flash, OpenAI o3 and Claude Sonnet 4. 

W&L resulted in improvements on OSWorld in all model categories, including up to 3 points for ICL on general-purpose models and up to 11 points for fine-tuned open-source models.

More importantly, these benefits were achieved without any manual annotation, “demonstrating that web-scale human workflows can serve as a practical and scalable foundation for advancing CUAs towards real-world deployment,” the researchers write.

This could have important implications for real-world applications, enabling enterprises to turn their existing corpora of videos and conference recordings into training data for CUAs. It also makes it easier to generate new training trajectories. All you will need to do is record videos of performing different tasks and have them annotated by an IDM. And with frontier models constantly improving and becoming cheaper, you can expect to get more from your existing data and the field continues to progress.

Amazon is preparing to lay off as many as 30,000 corporate employees in a sweeping workforce reduction intended to reduce expenses and compensate for over-hiring during the pandemic, according to a report from Reuters on Monday.

GeekWire has contacted Amazon for comment.

Layoff notifications will start going out via email on Tuesday, according to Reuters, which cited people familiar with the matter. One employee at Amazon told GeekWire the workforce is on “pins and needles” in anticipation of cuts.

Bloomberg reported that cuts will impact several business units, including logistics, payments, video games, and Amazon Web Services.

Amazon’s corporate workforce numbered around 350,000 in early 2023. It has not provided an updated number since then.

The company’s last significant layoff occurred in 2023 when it cut 27,000 corporate workers in multiple stages. Since then the company has made a series of smaller layoffs across different business units.

Fortune reported this month that Amazon planned to cut up to 15% of its human resources staff as part of a wider layoff.

Amazon has taken a cautious hiring approach with its corporate workforce, following years of huge headcount growth. The company’s corporate headcount tripled between 2017 and 2022, according to The Information.

The reported cuts come as Amazon is investing heavily in artificial intelligence. The company said earlier this year it expects to increase capital expenditures to more than $100 billion in 2025, up from $83 billion in 2024, with a majority going toward building out capacity for AI in AWS.

Amazon CEO Andy Jassy also hinted at potential workforce impact from generative AI earlier this year in a memo to employees that was shared publicly.

“We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs,” he wrote. “It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.”

Amazon reported 1.54 million total employees as of June 30 — up 3% year-over-year. The majority of the company’s workforce is made up of warehouse workers.

Amazon employs roughly 50,000 corporate and tech workers in buildings across its Seattle headquarters, with another 12,000 in Bellevue.

The company reports its third quarter earnings on Thursday afternoon.

Fellow Seattle-area tech giant Microsoft has laid off more than 15,000 people since May as it too invests in AI and data center capacity. Microsoft has cut more than 3,200 roles in Washington this year.

Last week, The New York Times cited internal Amazon documents and interviews to report that the company plans to automate as much as 75% of its warehouse operations by 2033. According to the report, the robotics team expects automation to “flatten Amazon’s hiring curve over the next 10 years,” allowing it to avoid hiring more than 600,000 workers even as sales continue to grow.

GeekWire reporter Kurt Schlosser contributed to this story.

This could be an easier way to showcase relevant items to each user.

So now, X users have another AI bot to develop feelings for.

If you use security keys on X, you'll need to take note.

Will this get more people posting their thoughts in the app?

The conversation around artificial intelligence (AI) has been dominated by “replacement theory” headlines. From front-line service roles to white-collar knowledge work, there’s a growing narrative that human capital is under threat.

Economic anxiety has fueled research and debate, but many of the arguments remain narrow in scope.

• Stanford’s Digital Economy Lab found that since generative AI became widespread, early-career workers in the most exposed jobs have seen a 13% decline in employment.
• This fear has spread into higher-paid sectors as well, with hedge fund managers and CEOs predicting large-scale restructuring of white-collar roles over the next decade.

However, much of this narrative is steeped in speculation rather than the fundamental, evolving dynamics of skilled work.

Yes, we’ve seen layoffs, hiring slowdowns, and stories of AI automating tasks. But this is happening against the backdrop of high interest rates, shifts in global trade, and post-pandemic over-hiring.

As the global talent thought-leader Josh Bersin argues, claims of mass job destruction are “vastly over-hyped.” Many roles will transform, not vanish. 

What this means for SEO

For the SEO discipline, the familiar refrain “SEO is dead” is just as overstated.

Yes, the nature of the SEO specialist is changing. We’ve seen fewer leadership roles, a contraction in content and technical positions, and cautious hiring. But the function itself is far from disappearing.

In fact, SEO job listings remain resilient in 2025 and mid-level roles still comprise nearly 60% of open positions. Rather than declining, the field is being reshaped by new skill demands.

Don’t ask, “Will AI replace me?” Ask instead, “How can I use AI to multiply my impact?”

Think of AI not as the jackhammer replacing the hammer but as the jackhammer amplifying its effect. SEOs who can harness AI through agents, automation, and intelligent systems will deliver faster, more impactful results than ever before.

• “AI is a tool. We can make it or teach it to do whatever we want…Life will go on, economies will continue to be driven by emotion, and our businesses will continue to be fueled by human ideas, emotion, grit, and hard work,” Bersin said.

Rewriting the SEO narrative

As an industry, it’s time to change the language we use to describe SEO’s evolution.

Too much of our conversation still revolves around loss. We focus on lost clicks, lost visibility, lost control, and loss of num=100.

That narrative doesn’t serve us anymore.

We should be speaking the language of amplification and revenue generation. SEO has evolved from “optimizing for rankings” to driving measurable business growth through organic discovery, whether that happens through traditional search, AI Overviews, or the emerging layer of Generative Engine Optimization (GEO).

AI isn’t the villain of SEO; it’s the force multiplier.

When harnessed effectively, AI scales insight, accelerates experimentation, and ties our work more directly to outcomes that matter:

• Pipeline.
• Conversions.
• Revenue.

We don’t need to fight the dystopian idea that AI will replace us. We need to prove that AI-empowered SEOs can help businesses grow faster than ever before.

The new language of SEO isn’t about survival, it’s about impact.

The team landscape has already shifted

For years, marketing and SEO teams grew headcount to scale output.

Today, the opposite is true. Hiring freezes, leaner budgets, and uncertainty around the role of SEO in an AI-driven world have forced leaders to rethink team design.

A recent Search Engine Land report noted that remote SEO roles dropped to 34% of listings in early 2025, while content-focused SEO positions declined by 28%. A separate LinkedIn survey found a 37% drop in SEO job postings in Q1 compared to the previous year.

This signals two key shifts:

• Specialized roles are disappearing. “SEO writers” and “link builders” are being replaced by versatile strategists who blend technical, analytical, and creative skill sets.
• Leadership is demanding higher ROI per role. Headcount is no longer the metric of success – capability is.

What it means for SEO leadership

If your org chart still looks like a pyramid, you’re behind. 

The new landscape demands flexibility, speed, and cross-functional integration with analytics, UX, paid media, and content.

It’s time to design teams around capabilities, not titles.

Rethinking SEO Talent

The best SEO leaders aren’t hiring specialists, they’re hiring aptitude. Modern SEO organizations value people who can think across disciplines, not just operate within one.

The strongest hires we’re seeing aren’t traditional technical SEOs focused on crawl analysis or schema. They’re problem solvers – marketers who understand how search connects to the broader growth engine and who have experience scaling impact across content, data, and product.

Progressive leaders are also rethinking resourcing. The old model of a technical SEO paired with engineering support is giving way to tech SEOs working alongside AI product managers and, in many cases, vibe coding solutions. This model moves faster, tests bolder, and builds systems that drive real results.

For SEO leaders, rethinking team architecture is critical. The right question isn’t “Who should I hire next?” It’s “What critical capability must we master to stay competitive?”

Once that’s clear, structure your people and your agents around that need. The companies that get this right during the AI transition will be the ones writing the playbook for the next generation of search leadership.

The new human-led, agent-empowered team

The future of SEO teams will be defined by collaboration between humans and agents.

• These agents are AI-enabled systems like automated content refreshers, site-health bots, or citation-validation agents that work alongside human experts.
• The human role? To define, train, monitor, and QA their output.

Why this matters

• Agents handle high-volume, repeatable tasks (e.g., content generation, basic auditing, link-score filtering) so humans can focus on strategy, insight, and business impact.
• The cost of building AI agents can range from $20,000 to $150,000, depending on the complexity of the system, integrations, and the specialized work required across data science, engineering, and human QA teams, according to RTS Labs.
• A single human manager might oversee 10-20 agents, shifting the traditional pyramid and echoing the “short pyramid” or “rocket ship” structure explored by Tomasz Tunguz.

The future: teams built around agents and empowered humans.

Real-world archetypes

• SaaS companies: Develop a bespoke “onboarding agent” that reads product data, builds landing pages, and runs first-pass SEO audits, human strategist refines output.
• Marketplace brands (e.g., upcoming seasonal trend): Use an “Audience Discovery Agent” that taps customer and marketplace data, but the human team writes the narrative and guides the vertical direction.
• Enterprise content hubs: deploy “Content Refresh Agents” that identify high-value pages, suggest optimizations, and push drafts that editors review and finalise.

Integration is key

These new teams succeed when they don’t live in silos. The SEO/GEO squad must partner with paid search, analytics, revenue ops, and UX – not just serve them.

Agents create capacity; humans create alignment and amplification.

A call to SEO practitioners

Building the SEO community of the future will require change.

The pace of transformation has never been faster and it’s created a dangerous dependence on third-party “AI tools” as the answer to what is unknown.

But the true AI story doesn’t begin with a subscription. It begins inside your team.

If the only AI in your workflow is someone else’s product, you’re giving up your competitive edge. The future belongs to teams that build, not just buy.

Here’s how to start:

• Build your own agent frameworks, designed with human-in-the-loop oversight to ensure accuracy, adaptability, and brand alignment.
• Partner with experts who co-create, not just deliver. The most successful collaborations help your team learn how to manage and scale agents themselves.
• Evolve your team structure, move beyond the pyramid mentality, and embrace a “rocket ship” model where humans and agents work in tandem to multiply output, insights, and results.

The future of SEO starts with building smarter teams. It’s humans working with agents. It’s capability uplift. And if you lead that charge, you’ll not only adapt to the next generation of search, you’ll be the ones designing it.

Google announced Query groups in Search Console Insights. The AI feature clusters similar search queries, surfaces trends, and shows which topics drive clicks.

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Zulily may no longer be a dominant player in Seattle’s tech scene, but physical pieces of the online retailer will live on in Evergreen Goodwill facilities across the region.

Hundreds of office chairs, desks, kitchen appliances, IT equipment, and more has been donated to Goodwill by Vanbarton Group, a commercial real estate investment firm that now owns the onetime Zulily building at 2601 Elliott Ave.

Vanbarton plans to convert the building, which occupies a full block near the waterfront, to 262 apartments, according to a Daily Journal of Commerce report from July.

A once-prominant online retailer, Zulily was a darling of Seattle’s growing tech scene when it was valued at $4 billion following its IPO in 2013. But after QVC parent Qurate paid $2.4 billion to buy the company in 2015, it was sold to Los Angeles investment firm Regent in May 2023 and eventually shut down.

In March, Zulily got a new owner for the third time in two years when Beyond, which emerged as a surprise buyer in 2024, announced plans to sell a majority stake in Zulily to Lyons Trading Company, the parent company of flash sales site Proozy.com.

Evergreen Goodwill said in a news release that the donation, facilitated by Vanbarton Group’s outreach, saved the nonprofit an estimated $100,000 in equipment costs and diverted valuable resources from landfills.

The office items are being repurposed in multiple locations, including Goodwill’s new Georgetown operations center, scheduled to open this fall, and job training and education centers that it operates in five counties.

Remaining items will be sold in Goodwill stores, with proceeds supporting free job training and education programs for people facing barriers to employment, according to Goodwill.

Previously:

• Zulily goes through another ownership change as Beyond to sell majority stake in retail site for $5M
• Zulily’s downfall: How the high-flying online retailer soared, sank, and shut down

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