Tag: advertising

Super Bowl LX 2026 on prediction markets: what to wager

admin — Fri, 06 Feb 2026 14:30:35 +0000

The Seattle Seahawks and New England Patriots will go head-to-head in Super Bowl LX, opening up plenty of opportunities to bet via prediction markets.

For many, half of the fun of big sporting events like Super Bowl LX is betting on outcomes, and there’s plenty to choose from on prediction market giants Polymarket, Kalshi, and Gemini this weekend. The obvious one is, of course, the winner itself, with the Seattle Seahawks the favorites heading into the weekend at 67.6% across Kalshi, Polymarket, and Gemini at the time of writing.

DeFi Rate reports that over $2.7 million (just a fraction of what is expected to be wagered on the Super Bowl across all gambling avenues) has already been wagered on the outcomes, underlining the huge amount of activity across the board with prediction markets.

What to wager on prediction markets during Super Bowl LX

The nature of prediction markets means there’s plenty more unique bets to wager on besides the obvious, with both Kalshi and Polymarket offering bets and props on a number of topics, from national anthem length to Gatorade color.

Right now, six markets stand out for their volume and liquidity, offering a snapshot of how expectations are forming as the game approaches.

Most Valuable Player

The Super Bowl MVP market is once again dominated by quarterbacks. Sam Darnold leads the board with a 44.2% implied probability, trading around +126. Drake Maye is next at 26.7%, priced near +275. The pricing follows a familiar pattern. Five of the past six Super Bowl MVP awards have gone to quarterbacks, and traders are clearly leaning on that history.

https://twitter.com/KalshiSports/status/2019470676889936211

Among non-quarterbacks, Jaxon Smith-Njigba has emerged as the most popular alternative at 14.5%, trading in the mid-teens. Kenneth Walker III follows at 8.1%. Longer shots are also getting some action, with Rhamondre Stevenson and Rashid Shaheed both priced under two cents for bettors hoping for a surprise performance on the biggest stage.

First song

Entertainment props are pulling in heavy interest as well. The market for the first song performed by Bad Bunny has been especially active. “Tití Me Preguntó” is the clear favorite, leading with a 52.8% probability across both platforms. “BAILE INOLVIDABLE” sits far back at 15.1%, with “NUEVAYOL” close behind at 14.2%.

Half-time performers

Halftime-related markets are busy even with some details already locked in. Charlie Puth is set to sing the national anthem. Green Day will open the ceremony alongside Brandi Carlile for “America the Beautiful.” With that settled, attention has shifted to potential guest appearances during the halftime show, where Cardi B is the heavy favorite at 60.5%, ahead of Rauw Alejandro and Karol G.

What will announcers say?

Markets tied directly to the broadcast are expected to surge on game day. Attendance props, including celebrity sightings, are already trading despite some outcomes feeling close to guaranteed. Announcer word markets are shaping up as one of the most popular live bets. Kalshi limits this category to NBC’s Mike Tirico and Cris Collinsworth, while Polymarket counts any on-air mention. “Safety,” “MVP,” and variations of “Comeback” are all trading above 94%.

Advertisers

Advertising markets round out the list of high-activity contracts. Kalshi’s “Who will appear in a Big Game ad?” market features names like Sydney Sweeney, Timothée Chalamet, and Harry Styles (although we know there won’t be any prediction market ads). Brand appearance contracts look even more decisive. State Farm, Toyota, and Salesforce are all trading above 98 cents, with Liquid Death, OpenAI, and Hims & Hers also priced as near-locks by the market.

Featured image: Wikimedia Commons, licensed under CC BY-SA 4.0

The post Super Bowl LX 2026 on prediction markets: what to wager appeared first on ReadWrite.

YouTube ad revenue passes 11 billion dollars but misses estimates

admin — Fri, 06 Feb 2026 12:38:46 +0000

Alphabet-owned YouTube reported Q4 ad revenue of $11.38 billion, up 9% from the prior year but below analysts’ $11.84 billion estimate, with full-year revenue reaching $60 billion, a 17% increase, driven by subscriptions now at 325 million paying users across Google One and YouTube Premium.

YouTube disclosed on Wednesday that the number of paying users for Google One, its cloud storage service, and YouTube Premium, its ad-free video streaming service, totals 325 million. This figure reflects growth from 300 million users reported three months earlier. Subscription revenue contributed alongside advertising to the overall financial performance.

The $11.38 billion in Q4 ad revenue underscores continued monetization efforts despite missing Wall Street expectations set at an average of $11.84 billion. For the entire fiscal year, YouTube generated $60 billion in total revenue from advertisements and subscriptions, compared to the previous year’s amount.

YouTube’s $8-per-month ad-free Premium tier registers strong traction among subscribers seeking uninterrupted viewing. YouTube Premium as a whole experienced strong growth during the period.

Alphabet CEO Sundar Pichai outlined plans to expand subscription offerings, with particular focus on the expanding YouTube TV user base. He stated, “We’ll soon launch new YouTube TV plans, bringing more choice and flexibility to subscribers with over 10 genre-specific packages.”

YouTube Shorts achieved 200 billion average daily views in the fourth quarter, matching the volume from the previous year. In certain countries, advertising revenue from short-form videos exceeds that of in-stream ads when measured on a per-hour basis.

Sundar Pichai identified podcasts as an emerging format on the platform. Viewers consumed 700 million hours of podcasts via televisions in October.

YouTube’s artificial intelligence features demonstrate strong adoption, with more than 1 million channels utilizing its AI creation tools. Additionally, 20 million consumers accessed the Gemini-powered content discovery tool in December.

Featured image credit

Reddit search traffic jumps to 80 million weekly active users

admin — Fri, 06 Feb 2026 12:08:52 +0000

Reddit held its fourth-quarter earnings call on Thursday, where executives positioned the company’s AI-powered search engine as a key business opportunity and potential revenue driver. CEO Steve Huffman explained that generative AI search performs effectively for queries lacking a single answer by aggregating multiple user perspectives.

Huffman specified during the call that Reddit excels at handling questions without definitive answers. He stated, “There’s a type of query we’re, I think, particularly good at — I think, the best on the internet — which is questions that have no answers, where the answer actually is multiple perspectives from lots of people.” This approach draws from the platform’s extensive community discussions to deliver comprehensive responses.

The CEO contrasted this with traditional search functions, describing them primarily as navigational tools to locate specific links, topics, or subreddits. He noted that large language models used in Reddit’s system can handle such navigation equally well or superior to conventional methods. Huffman concluded on this point by declaring, “So that’s the direction we’re going,” signaling a strategic shift toward integrated AI capabilities across search functionalities.

Reddit reported substantial growth in search engagement metrics over the past year. Weekly active users of its search feature increased by 30 percent, expanding from 60 million to 80 million users. This growth underscores the rising reliance on Reddit’s search tools amid broader platform usage.

Separate metrics for the AI-powered Reddit Answers feature showed even more rapid adoption. Weekly active users rose from 1 million in the first quarter of 2025 to 15 million by the fourth quarter of that year. Huffman commented on this trajectory, saying, “We’re seeing a lot of growth there, and I think there’s a lot of potential too.” Executives emphasized that search remains unmonetized currently but represents an enormous market opportunity.

To enhance user experience, Reddit is modernizing the interface for AI answers, incorporating more media-rich responses that include elements beyond plain text. Pilots for these updates are already in progress, testing dynamic agents that deliver search results enriched with visual and multimedia content.

The company also expanded Reddit Answers to five new languages during the quarter, broadening its accessibility to non-English speaking communities. These developments align with ongoing efforts to position Reddit not only as a social platform but as a primary destination for obtaining answers to diverse queries.

In 2025, Reddit announced plans to merge its AI search feature, Reddit Answers, with its traditional search engine, aiming to streamline the overall user experience. By the fourth quarter, the company reported having made “significant progress” in unifying its core search infrastructure with the AI-powered feature.

Reddit further detailed plans to eliminate the distinction between logged-in and logged-out users starting in the third quarter of 2026. This change will enable personalization of the site using AI and machine learning techniques for all visitors, regardless of account status, to deliver tailored and relevant content upon arrival.

Complementing internal product advancements, Reddit’s content-licensing business continues to expand. This segment allows other companies to train their AI models using Reddit’s data. Revenue from such licensing falls under the company’s “other” category, excluding advertising income. In the fourth quarter, “other” revenue grew 8 percent year-over-year to $36 million. For the full year of 2025, it increased 22 percent to $140 million.

Featured image credit

From raw data to visual proof: Automating website screenshots for analytics

admin — Fri, 06 Feb 2026 11:49:19 +0000

The shift from abstract usage data to tangible, visual evidence is becoming an increasingly important development in web analytics. Companies and analysts are recognizing that while numerical data reveals a great deal about user behavior, it often leaves critical aspects of the actual user experience unexplored. This is where automated website screenshot analytics comes into play: by capturing screenshots automatically, analytical data is enriched with visual proof. This enables a new level of insight that goes far beyond traditional numerical analysis.

The following article explains the underlying mechanisms, the added value, and the strategic implementation of this technology—while also highlighting the challenges and pitfalls that can arise on the path toward visual analytics.

Fundamentals and functionality of website screenshot automation

How do automated website screenshot solutions actually work? At their core, these are software tools or APIs that systematically access websites, render them in a browser environment, and capture screenshots at predefined intervals or in response to specific events. These screenshots record the complete layout, design, displayed content, and interactive elements at a specific moment in time. But do they merely produce static images? Far from it.

Developments in the field of visual analytics show that modern solutions allow precise control over screen resolutions, simulate different devices and browsers, and even capture animated or dynamic content. As a result, a screenshot becomes far more than a static image—it becomes part of an interactive, methodologically sound analytical framework, as demonstrated by solutions such as https://screenshotbase.com/.

The technical foundation typically consists of headless browsers. These are programmatically controlled browsers that operate entirely without a graphical user interface and are designed specifically for automation. They can wait for load times, detect pop-ups, account for dynamic components, and apply various rendering options. This results in a high level of visual fidelity, which is essential for accurately reflecting the real user experience. In practice, error states, layout issues, or unexpected advertising placements become integral components of the analytical data set.

Benefits and practical use cases in analytics

Why are organizations adopting automated website screenshot analytics? One key reason is that screenshots provide indisputable evidence of how pages are actually rendered. This occurs independently of user location, device, or temporary server load. For global platforms or services with variable content delivery, screenshots prevent blind spots in reporting and enhance data visualization by offering compelling visual validation of analytical findings.

A common use case is A/B testing. While numerical metrics may indicate which variant performs better, screenshots reveal design inconsistencies or rendering issues that raw data alone cannot capture. Similarly, customer journey analyses can be enriched with continuous screenshot sequences, making it possible to trace how users visually experience page elements step by step. Another important application is the verification of advertisements and third-party content. Automated screenshots document whether and how ad placements are displayed, providing critical evidence for both internal quality assurance and external partners.

Integrating screenshot APIs into existing analytics systems

Many organizations face the question of how automated screenshots can be effectively integrated into existing analytics platforms. The solution lies in modular, API-driven architectures. Modern screenshot APIs support integration with a wide range of systems, including Google Analytics, Matomo, and custom data platforms. Data transfer typically occurs via REST or GraphQL, enabling screenshots to be automatically linked with events, metrics, and raw analytical data.

Effective integration ensures that screenshots are not stored in isolation, but indexed, tagged, and associated with specific analytical timestamps. This allows rapid correlation between anomalies in quantitative data and the corresponding visual evidence. Workflow optimization is a core benefit: integrated triggers can initiate screenshots when defined thresholds are exceeded, error codes occur, or new releases are deployed. Seamless integration is a decisive factor in both user acceptance and the overall effectiveness of such systems.

Implementation strategies and best practices for automation

How can organizations transition from manual screenshot creation to fully automated analytics workflows? Experience shows that a phased rollout model is particularly effective. The first step is identifying the most critical pages and use cases—typically those with the highest traffic or greatest conversion potential. For these areas, clear trigger events should be defined to control screenshot generation, such as frontend changes or deployment cycles.

Best practices emerge from combining technical control mechanisms with structured organizational processes. For example, a versioned screenshot archive should be maintained to reflect changes over time. Automated reporting that embeds relevant screenshots directly into analytics dashboards is highly recommended. Regular quality checks of automated screenshots are also essential, especially for pages with dynamic content, pop-ups, or personalization logic. Without this oversight, inaccurate or misleading captures may occur.

Challenges and practical solutions

What are the main obstacles when implementing automated website screenshot analytics? One major challenge is handling dynamic, personalized, or asynchronously loaded content. Without appropriate wait mechanisms, screenshots may be captured too early, resulting in incomplete representations. Industry-standard solutions involve explicitly measuring load times or triggering screenshots only once the Document Object Model (DOM) has fully loaded.

Another common issue is the sheer volume of collected images. Screenshot repositories can quickly grow to thousands or even hundreds of thousands of files. Automated tagging, prioritization schemes, and defined retention policies help manage this complexity. Visualization and analysis also require user-friendly dashboards with robust filtering options, ensuring that relevant deviations or error states remain easily discoverable. Performance considerations are equally important: extensive automated screenshot capture must not degrade website performance or impact real users. This is best achieved through dedicated test environments and carefully controlled screenshot frequencies.

Future trends, data protection, and ethical considerations

What developments lie ahead, and which frameworks must be observed? AI-driven screenshot analysis is on the verge of widespread adoption. Machine learning models can automatically detect layout errors, branding inconsistencies, or unintended overlays—and even propose corrective actions. Integration with other autonomous testing methods, such as automated accessibility checks, is also expected to become standard practice, significantly enhancing web analytics.

At the same time, automated website screenshot analytics raises important data protection concerns. When screenshots capture personal user data, login states, or specific interactions, strict compliance with GDPR and other data protection regulations is essential. Technical safeguards include masking sensitive areas, anonymizing sessions, and implementing tiered access controls for screenshot data. Organizations have a responsibility to establish clear awareness—both internally and externally—regarding the scope and limitations of this methodology.

Ethical use is equally critical. Visual evidence must never be misused, but applied strictly in the interest of transparency, user experience improvement, and objective documentation. When implemented responsibly, screenshot analytics not only delivers deeper insights, but also promotes a respectful and privacy-conscious approach to digital customer experiences.

Conclusion

Automated website screenshot analytics introduces a new dimension to web analysis by complementing numerical data with real visual context. The technology makes issues visible, documents changes comprehensively, and aligns stakeholders through clear visual evidence. Organizations that adopt a strategic and responsible approach gain lasting advantages in quality assurance and analytical insight—provided that data protection and ethical considerations remain central to every implementation. Ultimately, only the combination of technical sophistication and organizational discipline delivers the sustainable value required for success in today’s fast-moving digital landscape.

Featured image credit

The Swop Protocol: A Vision for Community-Owned Media, Social Coordination, and Compounding…

admin — Fri, 06 Feb 2026 11:11:11 +0000

The Swop Protocol: A Vision for Community-Owned Media, Social Coordination, and Compounding EconomiesThe Swop Protocol: a coordination layer for community-owned media economies.The Break in the System

The modern media economy sells a simple promise: anyone can create, anyone can distribute, anyone can monetize. Tools are everywhere. Platforms are global. Barriers to entry are at historic lows.

And yet, the economics have never been more fragile.

Creators are producing more content than ever, reaching larger audiences than ever, and shaping more culture than ever, but they own almost none of the system they power. Audiences are bigger, but leverage is thinner. Distribution is abundant, but durable ownership is rare.

The issue isn’t creativity. It’s structure.

The dominant model of media is extraction disguised as opportunity. Platforms optimize for engagement rather than alignment. Algorithms decide what survives. Monetization is bolted on after the fact, built to maximize platform revenue rather than creator resilience. The result is predictable: creators subsidize platforms, communities generate value, and most of the upside is captured by intermediaries.

This is the creator paradox: reach expands while control shrinks.

Advertising pulls content toward outrage and volume. Subscriptions fragment audiences into islands. Brand deals quietly tax authenticity. Even successful creators are trapped on an output treadmill, chasing analytics that don’t compound into lasting economic security.

Meanwhile, communities form naturally around narratives, identity, and culture. They coordinate attention. They create momentum. They move markets. Yet structurally, they’re treated as passive consumers rather than economic participants.

What’s missing isn’t talent, distribution, or tooling.

What’s missing is a native system for attention and participation to become durable, reusable, and compounding.

The internet made attention liquid.
Platforms kept ownership illiquid.

Attention is abundant. Ownership is not.

Creators built the modern media economy, but most of the upside leaks through the cracks.A New Mental Model

To see what comes next, separate platforms from protocols.

Platforms capture value.
Protocols coordinate value.

Platforms own the relationship.
Protocols define the rules.

The Swop is not a media company, a social network, or a trading app. It is a coordination layer designed to sit beneath all three, turning cultural gravity into a programmable ownership system

The moat is in the system design: participation → fees → treasuries → reinvestment creates compounding economies that strengthen each cycle.

The core mental model is this:

Social Entertainment is an economic primitive.

Social Entertainment isn’t passive consumption. It’s participatory culture where interaction isn’t a comment thread, it’s a contribution. It recognizes what markets already understand, but media systems refuse to encode: narratives move capital, culture creates gravity, and communities are economic actors whether platforms acknowledge them or not.

The Swop formalizes this reality.

The protocol enables programmable, non-custodial, community-owned participation. Instead of monetization being an afterthought, economic alignment becomes native. Instead of value flowing upward to platforms, value flows inward to the communities and creators who generate it.

This is not financialization for its own sake. It is coordination. It is the missing layer between attention and ownership.

The Swop Protocol enables creators, audiences, and communities to share upside without sacrificing authenticity, control, or custody. It is multichain, non-custodial, and composable by default, enabling economic activity without forcing users into a single ecosystem.

Simply put:
The Swop provides a balance sheet for culture.

The Flywheels

The Swop is not a single feature. It is a compounding system.

The core flywheel is straightforward:

Media attracts attention.
Participation deepens engagement.
Economic activity generates protocol fees.
Fees fund creator and community treasuries.
Treasuries fund better media, experiences, and coordination.
Better media attracts more attention.

A Swop-Native Show (Example)
Imagine a flagship show with a community treasury. Viewers don’t just watch, they contribute: clips, topics, guests, distribution, and community-led segments. Participation generates protocol fees. A portion accrues to the show and community treasury. That treasury funds editors, production, and events, increasing quality and retention. The flywheel tightens: better media drives participation, participation drives activity, and activity funds better media.

This loop compounds because it closes what platforms keep open: the value generated by communities is retained and redeployed by those communities.

Unlike ad-driven media, the incentive is not to maximize outrage or volume. Unlike subscriptions, participation is not capped by paywalls. Unlike one-off creator monetization tools, value doesn’t leak out immediately. It accumulates, deploys, and improves the system.

The key shift is from payouts to treasuries.

Legacy creator economies optimize for short-term payouts. The Swop optimizes for long-term value accumulation. Treasuries create durability by funding production, experimentation, collaboration, and resilience. The Swop wins because it converts cultural momentum into durable, reinvestable resources, while platforms can only convert it into spend.

This changes behavior.

Creators are no longer forced to chase algorithms. Communities are no longer spectators. Participation becomes aligned with long-term outcomes rather than short-term extraction.

Defensibility emerges naturally. Cultural gravity is difficult to copy. Treasury-backed communities create real staying power, not by trapping users, but by making them owners in the direction of the system. Over time, the strongest media brands won’t be the ones with the biggest audiences.

They will be the ones with compounding economies.

Why Now

Timing matters.

Distribution is saturated. Every feed is noisy. Every algorithm runs the same engagement playbook. Trust is scarce. Marginal returns on content are collapsing, and creators feel it first.

At the same time, AI has exploded the content supply. Creation is no longer the bottleneck. The bottleneck is curation, identity, and coordination. In a world where content becomes abundant, what compounds is not production, it’s alignment.

Crypto infrastructure is also growing. The next phase is not speculation-first. It is usage-first: real users, real systems, real fees. The open question is whether decentralized infrastructure can serve cultural use cases at scale without becoming extractive SocialFi.

Creators are ready. Communities are ready. Audiences are increasingly allergic to platforms that profit from them while offering no ownership.

The tools exist. The missing piece is incentive design.

The Swop is designed for this moment: a protocol that aligns media, social participation, and economic flows without forcing creators or communities to compromise authenticity, ownership, or values.

The Long Arc

We start with flagship shows and communities where participation is measurable and treasury deployment is visible, then scale the model into a protocol standard.

The long-term goal is not one application. It is a protocol standard.

The Swop begins with shows, creators, and communities. It expands into networks of aligned media economies. Over time, it becomes an economic layer of culture itself: a system in which participation creates durable resources that fund future creation.

Creators evolve into networks.
Audiences evolve into contributors.
Brands evolve into treasuries.
Culture evolves into compounding economies.

Equally important is what The Swop is not.

It is not a prediction market.
It is not a meme-coin factory.
It is not an ad-driven media platform.
It is not extractive SocialFi.

Constraints matter. They define trust.

The ambition of The Swop Protocol is far-reaching but straightforward: to give culture a place to compound, and to allow creators and communities to retain and reinvest the value they generate.

The future of media is not about capturing attention.

It’s about building systems where attention becomes ownership, and ownership becomes durability.

This document describes a protocol vision. References to value accumulation or capital formation refer to value generated through usage and participation, not an offer to sell securities or a solicitation for investment.

Subscribe to The Swop on Substack
https://theswop.substack.com/

The Swop Protocol: A Vision for Community-Owned Media, Social Coordination, and Compounding… was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to profit in the DeFi market in 2026 using a Uniswap clone?

admin — Fri, 06 Feb 2026 11:10:44 +0000

The modern media economy sells a simple promise: anyone can create, anyone can distribute, anyone can monetize. Tools are everywhere. Platforms are global. Barriers to entry are at historic lows.

And yet, the economics have never been more fragile.

The issue isn’t creativity. It’s structure.

This is the creator paradox: reach expands while control shrinks.

What’s missing isn’t talent, distribution, or tooling.

What’s missing is a native system for attention and participation to become durable, reusable, and compounding.

The internet made attention liquid.
Platforms kept ownership illiquid.

Attention is abundant. Ownership is not.

Creators built the modern media economy, but most of the upside leaks through the cracks.A New Mental Model

To see what comes next, separate platforms from protocols.

Platforms capture value.
Protocols coordinate value.

Platforms own the relationship.
Protocols define the rules.

The Swop is not a media company, a social network, or a trading app. It is a coordination layer designed to sit beneath all three, turning cultural gravity into a programmable ownership system

The moat is in the system design: participation → fees → treasuries → reinvestment creates compounding economies that strengthen each cycle.

The core mental model is this:

Social Entertainment is an economic primitive.

The Swop formalizes this reality.

This is not financialization for its own sake. It is coordination. It is the missing layer between attention and ownership.

Simply put:
The Swop provides a balance sheet for culture.

The Flywheels

The Swop is not a single feature. It is a compounding system.

The core flywheel is straightforward:

This loop compounds because it closes what platforms keep open: the value generated by communities is retained and redeployed by those communities.

The key shift is from payouts to treasuries.

This changes behavior.

Creators are no longer forced to chase algorithms. Communities are no longer spectators. Participation becomes aligned with long-term outcomes rather than short-term extraction.

They will be the ones with compounding economies.

Why Now

Timing matters.

Distribution is saturated. Every feed is noisy. Every algorithm runs the same engagement playbook. Trust is scarce. Marginal returns on content are collapsing, and creators feel it first.

Creators are ready. Communities are ready. Audiences are increasingly allergic to platforms that profit from them while offering no ownership.

The tools exist. The missing piece is incentive design.

The Swop is designed for this moment: a protocol that aligns media, social participation, and economic flows without forcing creators or communities to compromise authenticity, ownership, or values.

The Long Arc

We start with flagship shows and communities where participation is measurable and treasury deployment is visible, then scale the model into a protocol standard.

The long-term goal is not one application. It is a protocol standard.

Creators evolve into networks.
Audiences evolve into contributors.
Brands evolve into treasuries.
Culture evolves into compounding economies.

Equally important is what The Swop is not.

It is not a prediction market.
It is not a meme-coin factory.
It is not an ad-driven media platform.
It is not extractive SocialFi.

Constraints matter. They define trust.

The ambition of The Swop Protocol is far-reaching but straightforward: to give culture a place to compound, and to allow creators and communities to retain and reinvest the value they generate.

The future of media is not about capturing attention.

It’s about building systems where attention becomes ownership, and ownership becomes durability.

Subscribe to The Swop on Substack
https://theswop.substack.com/

Genius Sports set to buy gambling media brand Legend in $1.2B deal

admin — Fri, 06 Feb 2026 10:20:19 +0000

Genius Sports Limited confirms a $1.2 billion deal to acquire sports and gambling media network Legend, creating a “fully integrated sports and gaming media network”.

Genius Sports announced the $1.2 billion acquisition of Legend on February 5, with $900 million paid out at closing and an earnout of up to $300 million. The big-figure deal is anticipated to bring in $1.1 billion in Group Revenue for Genius Sports, as well as between $320 and $330 million in Group Adjusted EBITDA with approximately 50% free cash flow.

This is a historic day for Genius Sports as we announce the acquisition of Legend.

Together, we are the only company operating at scale across both official sports data, and media and advertising.

Read the press release: https://t.co/VII9AOajzR pic.twitter.com/rxIT762TNJ

— Genius Sports (@GeniusSports) February 5, 2026

The company is eyeing Legend’s monetization of sports audiences across its network, making mention of “world-class marketing technology powering owned and operated digital properties” and partnerships with major media brands like Sports Illustrated and Yahoo Sports. It seems that Genius Sports is looking to expand its reach further, tapping into Legend’s connections with existing sportsbooks, gambling products, and advertisers as the sports betting industry continues to thrive in the US.

“This deal accelerates our strategic and financial objectives, supercharges fan monetization, and builds a fully integrated sports and gaming media network,” said CEO of Genius Sports Mark Locke. “For Genius Sports and our global partners, it delivers more data, more audience, more inventory and greater monetization of sports fans. The acquisition of Legend will drive higher margins and stronger free cash flow, positioning Genius Sports to immediately transform the market and grow the size and scale of our business.”

What will this partnership look like in practice?

The press release suggests that Genius Sports will seek to blend Legend’s network into FANHub, the company’s sports fan activation platform.

“Joining forces with Genius Sports brings together two world-class teams, unlocks unparalleled growth opportunities for our partners and products, and gives us an even stronger platform to scale,” said Nick Kisberg, founder of Legend. “I am immensely proud of what we have built and the decades of hard work that have brought us here today.”

Genius Sports stock is down about 10% at market open after the acquisition of gaming affiliate marketing and media company Legend pic.twitter.com/Mu2zsylmrW

— Dustin Gouker (@DustinGouker) February 5, 2026

Despite the move, Genius stock Shares plunged 28% to $6.16 midday Thursday.

Featured image: Genius Sports / Legend

The post Genius Sports set to buy gambling media brand Legend in $1.2B deal appeared first on ReadWrite.

Programmatic is drawing more brands to this year’s Winter Olympics

admin — Fri, 06 Feb 2026 05:01:00 +0000

In over 50 years of selling its wares, Pittsburgh cookware brand All-Clad has never run advertising against live sports coverage. But next week, it’s diving into the deep end with a campaign set to run alongside Peacock’s stream of the 2026 Winter Olympics.

“We’re an American heritage brand,” noted Stephanie Sandkvist, head of retail media and Amazon at Groupe SEB, All-Clad’s parent company. As such, the chance to run ads in front of audiences cheering on Team U.S.A. during ice hockey or ski jump events without having to invest in a sponsorship or expensive linear package was a “no brainer,” she said. It’s a milestone event for the business.

Events like the Olympic Games used to be the preserve of the industry’s largest advertisers. But since NBCU began allowing brands to buy Olympic ad inventory via programmatic means two years ago for the summer Paris games, one of sports media’s crown jewels is accessible for brands with smaller budgets.

Continue reading this article on digiday.com. Sign up for Digiday newsletters to get the latest on media, marketing and the future of TV.

WTF is the IAB’s AI Accountability for Publishers Act (and what happens next)?

admin — Fri, 06 Feb 2026 05:01:00 +0000

This week, the Interactive Advertising Bureau proposed new protections to stop AI bots from freely harvesting online content.

The trade body’s president and CEO, David Cohen, revealed its legal framework – the AI Accountability for Publishers Act at the IAB’s Annual Leadership Meeting (ALM) in Palm Springs on February 2.

The legislation seeks to hold AI companies to account for illicit scraping and failing to comply with publishers’ “no crawling” specifications in their robots.txt files, which are notoriously difficult to enforce — non compliance poses pretty devastating consequences for publishers.

Continue reading this article on digiday.com. Sign up for Digiday newsletters to get the latest on media, marketing and the future of TV.

Retail’s Cold War: Amazon, Walmart Seek to Neutralize Each Other’s Strengths

admin — Thu, 05 Feb 2026 19:18:49 +0000

Corporate rivalries are traditionally cast in simple, static terms.

Take for example the competition between Amazon and Walmart, which has long-been described as a clash of two competing models: each giant standing on its respective side of digital-native versus physical-first, growth versus efficiency and Prime versus price.

But as the news this week highlights, that framing is finally collapsing. Walmart’s ascent over the trillion-dollar valuation threshold and Amazon’s recalibration of its physical retail and delivery strategy were not isolated headlines. Taken together, they read like a midgame position in a long-running cold war. Neither company is still trying to prove its original model works.

Instead, both are now trying to make the other’s advantages irrelevant.

Walmart’s Trillion-Dollar Signal and Amazon’s Physical Reset

Walmart’s valuation crossed $1 trillion on Tuesday (Feb. 3), and as of this reporting it’s stayed there. The milestone matters less for the number itself than for what it implies about how the market now understands the company. This is no longer a retailer being grudgingly rewarded for surviving eCommerce, but a commerce platform being valued for its ability to blend physical assets, digital reach and data-driven execution into a coherent operating system.

This reframing has important second-order effects. A higher valuation multiple gives Walmart greater latitude to invest aggressively in automation, artificial intelligence (AI)-driven demand forecasting and payment innovation without triggering investor anxiety.

And if Walmart’s week was about validation, Amazon’s was about recalibration. The company’s renewed emphasis on larger physical grocery formats and fulfillment-led store design signaled a pragmatic turn after years of experimentation with smaller, more futuristic concepts. The retreat from certain store formats is less a failure than an admission that novelty does not automatically translate into scalable economics.

What does offer scalable economics to Amazon is shrinking the last mile of delivery. As the company announced Tuesday, in 2025 Amazon increased the number of items it delivered to Prime members in the United States the same or next day by 30%.

Faster delivery, after all, is increasingly not just about customer satisfaction but about maintaining relevance in categories where immediacy now trumps brand loyalty. Grocery, household essentials, and health-related products are less forgiving of friction than discretionary goods.

In another recalibration, it was announced last week (Jan. 28) that Amazon was cutting 16,000 jobs across its global workforce.

Why Retail’s Real Prize Is Payments and Data

What is striking is how little of the competition between Amazon and Walmart is now visible at a first-order level to the consumer. Delivery promises look similar. Interfaces look familiar. The differentiation is happening in routing algorithms, labor optimization and inventory placement decisions.

That’s because beneath the operational chess match lies the most consequential layer of all: payments and data control. Whoever owns the transaction moment owns the cleanest signal of consumer intent, behavior and value. That signal, in turn, feeds advertising, personalization, loyalty and pricing power.

Walmart’s steady expansion of its loyalty and membership ecosystem is not just about repeat purchases. It is about reducing dependence on third-party payment intermediaries and strengthening first-party data relationships. Each transaction completed within Walmart’s ecosystem is one less mediated by external platforms that siphon both margin and insight.

Amazon, of course, has long understood this dynamic. Prime is not merely a subscription; it is a behavioral contract. The tighter Amazon can bind shopping, payment and identity into a single flow, the harder it becomes for competitors to pry customers away, even on price. As covered here Wednesday (Feb. 4), Amazon is broadening the role of artificial intelligence across its business, rolling out a new generation of Alexa, exploring deeper access to OpenAI’s models and applying AI tools inside its film and television studios.

What is new is the degree to which both companies are converging on similar conclusions from opposite starting points. Walmart is becoming more software-like, layering intelligence and monetization atop physical scale. Amazon is becoming more asset-heavy, investing in tangible infrastructure to secure speed and reliability. Both are ultimately chasing the same outcome: end-to-end control of the commerce experience.

The post Retail’s Cold War: Amazon, Walmart Seek to Neutralize Each Other’s Strengths appeared first on PYMNTS.com.

OpenAI’s GPT-5.3-Codex drops as Anthropic upgrades Claude — AI coding wars heat up ahead of Super Bowl ads

admin — Thu, 05 Feb 2026 18:00:00 +0000

OpenAI on Wednesday released GPT-5.3-Codex, which the company calls its most capable coding agent to date, in an announcement timed to land at the exact same moment Anthropic unveiled its own flagship model upgrade, Claude Opus 4.6. The synchronized launches mark the opening salvo in what industry observers are calling the AI coding wars — a high-stakes battle to capture the enterprise software development market.

The dueling announcements came amid an already heated week between the two AI giants, who are also set to air competing Super Bowl advertisements on Sunday, and whose executives have been trading barbs publicly over business models, access, and corporate ethics.

"I love building with this model; it feels like more of a step forward than the benchmarks suggest," OpenAI CEO Sam Altman wrote on X minutes after the launch. He later added: "It was amazing to watch how much faster we were able to ship 5.3-Codex by using 5.3-Codex, and for sure this is a sign of things to come."

That claim — that the model helped build itself — is a significant milestone in AI development. According to OpenAI's announcement, the Codex team used early versions of GPT-5.3-Codex to debug its own training runs, manage deployment infrastructure, and diagnose test results and evaluations. The company describes it as "our first model that was instrumental in creating itself."

OpenAI's new coding model posts record-breaking benchmark scores, outpacing Anthropic's Claude by double digits

The new model posts substantial gains across multiple industry benchmarks. GPT-5.3-Codex achieves 57% on SWE-Bench Pro, a rigorous evaluation of real-world software engineering that spans four programming languages and tests contamination-resistant, industrially relevant challenges. It scores 77.3% on Terminal-Bench 2.0, which measures the terminal skills essential for coding agents, and 64% on OSWorld, an agentic computer-use benchmark where models must complete productivity tasks in visual desktop environments.

The Terminal-Bench 2.0 result is particularly striking. According to performance data released Wednesday, GPT-5.3-Codex scored 77.3% compared to GPT-5.2-Codex's 64.0% and the base GPT-5.2 model's 62.2% — a 13-percentage-point leap in a single generation. One user on X noted that the score "absolutely demolished" Anthropic's Opus 4.6, which reportedly achieved 65.4% on the same benchmark.

OpenAI also claims the model accomplishes these results with dramatically improved efficiency: less than half the tokens of its predecessor for equivalent tasks, plus more than 25% faster inference per token.

"Notably, GPT-5.3-Codex does so with fewer tokens than any prior model, letting users simply build more," the company stated in its announcement.

From coding assistant to computer operator: GPT-5.3-Codex aims to automate the entire software development lifecycle

Perhaps more significant than the benchmark improvements is OpenAI's positioning of GPT-5.3-Codex as a model that transcends pure coding. The company explicitly states that "Codex goes from an agent that can write and review code to an agent that can do nearly anything developers and professionals can do on a computer."

This expanded capability set includes debugging, deploying, monitoring, writing product requirement documents, editing copy, conducting user research, building slide decks, and analyzing data in spreadsheet applications. The model shows strong performance on GDPVal, an OpenAI evaluation released in 2025 that measures performance on well-specified knowledge-work tasks across 44 occupations.

The expansion signals OpenAI's ambition to capture not just the developer tools market but the broader enterprise productivity software space — a market that includes established players like Microsoft, Salesforce, and ServiceNow, all of whom are racing to embed AI agents into their platforms.

OpenAI's first 'high capability' cybersecurity model prompts new safety protocols and a $10 million defense fund

The pivot toward general-purpose computing brings new security considerations. In a notable disclosure, OpenAI revealed that GPT-5.3-Codex is the first model it classifies as "High capability" for cybersecurity-related tasks under its Preparedness Framework, and the first directly trained to identify software vulnerabilities.

"While we don't have definitive evidence it can automate cyber attacks end-to-end, we're taking a precautionary approach and deploying our most comprehensive cybersecurity safety stack to date," the company stated. Mitigations include dual-use safety training, automated monitoring, trusted access for advanced capabilities, and enforcement pipelines incorporating threat intelligence.

Altman highlighted this development on X: "This is our first model that hits 'high' for cybersecurity on our preparedness framework. We are piloting a Trusted Access framework, and committing $10 million in API credits to accelerate cyber defense."

The company is also expanding the private beta of Aardvark, its security research agent, and partnering with open-source maintainers to provide free codebase scanning for widely used projects. OpenAI cited Next.js as an example where a security researcher used Codex to discover vulnerabilities disclosed last week.

Super Bowl showdown: Sam Altman calls Anthropic's advertising campaign 'clearly dishonest' as rivalry turns personal

The cybersecurity announcement, however, has been overshadowed by the increasingly personal nature of the OpenAI-Anthropic rivalry. The timing of Wednesday's release cannot be understood without the context of OpenAI's intensifying competition with Anthropic, the AI safety-focused startup founded in 2021 by former OpenAI researchers, including Dario and Daniela Amodei.

Both companies scheduled major product announcements for 10 a.m. Pacific Time today. Anthropic unveiled Claude Opus 4.6, which it describes as its "smartest model" that "plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes.”

The head-to-head timing follows a week of escalating tensions. Anthropic announced it will air Super Bowl advertisements mocking OpenAI's recent decision to begin testing ads within ChatGPT for free users.

Altman responded with unusual directness, calling the advertisements "funny" but "clearly dishonest" in an extensive X post.

"We would obviously never run ads in the way Anthropic depicts them. We are not stupid and we know our users would reject that," Altman wrote. "I guess it's on brand for Anthropic doublespeak to use a deceptive ad to critique theoretical deceptive ads that aren't real, but a Super Bowl ad is not where I would expect it."

He went further, characterizing Anthropic as an "authoritarian company" that "wants to control what people do with AI."

"Anthropic serves an expensive product to rich people," Altman wrote. "More Texans use ChatGPT for free than total people use Claude in the US, so we have a differently-shaped problem than they do."

Enterprise AI spending surges past projections as OpenAI's market share faces pressure from Anthropic and Google

The public sparring masks a deadly serious business competition. The rivalry plays out against a backdrop of explosive enterprise AI adoption, where both companies are fighting for position in a rapidly expanding market.

According to survey data from Andreessen Horowitz released this week, enterprise spending on large language models has dramatically outpaced even bullish projections. Average enterprise LLM spending reached $7 million in 2025, 180% higher than 2024's actual spending of $2.5 million — and 56% above what enterprises had projected for 2025 just a year earlier. Spending is projected to reach $11.6 million per enterprise in 2026, a further 65% increase.

The a16z data reveals shifting market dynamics that help explain the intensity of the competition. OpenAI maintains the largest average share of enterprise AI wallet, but that share is shrinking — from 62% in 2024 to a projected 53% in 2026. Anthropic's share, meanwhile, has grown from 14% to a projected 18% over the same period, with Google showing similar gains.

Enterprise adoption patterns tell a more nuanced story. While OpenAI leads in overall usage, only 46% of surveyed OpenAI customers are using its most capable models in production, compared to 75% for Anthropic and 76% for Google. When including testing environments, 89% of Anthropic customers are testing or using the company's most capable models — the highest rate among major providers.

For software development specifically — one of the primary use cases for both companies' coding agents — the a16z survey shows OpenAI with approximately 35% market share, with Anthropic claiming a substantial and growing portion of the remainder.

Both AI labs race to become the enterprise operating system of choice, moving beyond models to full-stack platforms

These market dynamics explain why both companies are positioning themselves as platforms rather than mere model providers. OpenAI on Wednesday also launched Frontier, a new platform designed to serve as a comprehensive hub for businesses adopting a range of AI tools — including those developed by third parties — that can operate together seamlessly.

"We can be the partner of choice for AI transformation for enterprise. The sky is the limit in terms of revenue we can generate from a platform like that," Fidji Simo, OpenAI's CEO of applications, told reporters this week.

This follows Monday's launch of the Codex desktop application for macOS, which OpenAI says has already surpassed 500,000 downloads. The app enables users to manage multiple AI coding agents simultaneously — a capability that becomes increasingly important as enterprises deploy agents for complex, long-running tasks.

Trillion-dollar compute obligations and $350 billion valuations reveal the massive financial stakes driving the AI coding race

The platform ambitions require extraordinary capital. The dueling launches underscore the staggering financial requirements of frontier AI development, with both companies burning through billions while racing to establish market dominance.

Anthropic is currently in discussions for a funding round that could bring in more than $20 billion at a valuation of at least $350 billion, according to Bloomberg, and is simultaneously planning an employee tender offer at that valuation.

OpenAI, meanwhile, has disclosed that it owes more than $1 trillion in financial obligations to backers — including Oracle, Microsoft, and Nvidia — that are essentially fronting compute costs in expectation of future returns.

GPT-5.3-Codex was "co-designed for, trained with, and served on NVIDIA GB200 NVL72 systems," according to OpenAI's announcement—a reference to Nvidia's latest Blackwell-generation AI supercomputing architecture.

The financial pressure adds urgency to both companies' enterprise strategies. Unlike established tech giants with diversified revenue streams, both Anthropic and OpenAI must prove they can generate sufficient revenue from AI products to justify their extraordinary valuations and infrastructure costs.

OpenAI promises more Codex features in coming weeks as 500,000 users download the new desktop app

Looking ahead, OpenAI says GPT-5.3-Codex is available immediately for paid ChatGPT users across all Codex surfaces: the desktop app, command-line interface, IDE extensions, and web interface. API access is expected to follow.

The model includes a new interactivity feature: users can choose between "pragmatic" or "friendly" personalities — a customization Altman suggests users feel strongly about. More substantively, the model provides frequent progress updates during tasks, allowing users to interact in real time, ask questions, discuss approaches, and steer toward solutions without losing context.

"Instead of waiting for a final output, you can interact in real time," OpenAI stated. "GPT-5.3-Codex talks through what it's doing, responds to feedback, and keeps you in the loop from start to finish."

The company promises more capabilities in the coming weeks, with Altman declaring: "I believe Codex is going to win."

He concluded his response to Anthropic with a philosophical statement that frames the competition in stark terms: "This time belongs to the builders, not the people who want to control them."

Whether that message resonates with enterprise customers — who according to a16z data cite trust, security, and compliance as their top concerns — remains to be seen. What's clear is that the AI coding wars have begun in earnest, and neither company intends to cede ground.

Anthropic's Claude Opus 4.6 brings 1M token context and 'agent teams' to take on OpenAI's Codex

admin — Thu, 05 Feb 2026 17:45:00 +0000

Anthropic on Thursday released Claude Opus 4.6, a major upgrade to its flagship artificial intelligence model that the company says plans more carefully, sustains longer autonomous workflows, and outperforms competitors including OpenAI's GPT-5.2 on key enterprise benchmarks — a release that arrives at a tumultuous moment for the AI industry and global software markets.

The launch comes just three days after OpenAI released its own Codex desktop application in a direct challenge to Anthropic's Claude Code momentum, and amid a $285 billion rout in software and services stocks that investors attribute partly to fears that Anthropic's AI tools could disrupt established enterprise software businesses.

For the first time, Anthropic's Opus-class models will feature a 1 million token context window, allowing the AI to process and reason across vastly more information than previous versions. The company also introduced "agent teams" in Claude Code — a research preview feature that enables multiple AI agents to work simultaneously on different aspects of a coding project, coordinating autonomously.

"We're focused on building the most capable, reliable, and safe AI systems," an Anthropic spokesperson told VentureBeat about the announcements. "Opus 4.6 is even better at planning, helping solve the most complex coding tasks. And the new agent teams feature means users can split work across multiple agents — one on the frontend, one on the API, one on the migration — each owning its piece and coordinating directly with the others."

Why OpenAI and Anthropic are locked in an all-out war for enterprise developers

The release intensifies an already fierce competition between Anthropic and OpenAI, the two most valuable privately held AI companies in the world. OpenAI on Monday released a new desktop application for its Codex artificial intelligence coding system, a tool the company says transforms software development from a collaborative exercise with a single AI assistant into something more akin to managing a team of autonomous workers.

AI coding assistants have exploded in popularity over the last year, and OpenAI said more than 1 million developers have used Codex in the past month. The new Codex app is part of OpenAI's ongoing effort to lure users and market share away from rivals like Anthropic and Cursor.

The timing of Anthropic's release — just 72 hours after OpenAI's Codex launch — underscores the breakneck pace of competition in AI development tools. OpenAI faces intensifying competition from Anthropic, which posted the largest share increase of any frontier lab since May 2025, according to a recent Andreessen Horowitz survey. Forty-four percent of enterprises now use Anthropic in production, driven by rapid capability gains in software development since late 2024. The desktop launch is a strategic counter to Claude Code's momentum.

According to Anthropic's announcement, Opus 4.6 achieves the highest score on Terminal-Bench 2.0, an agentic coding evaluation, and leads all other frontier models on Humanity's Last Exam, a complex multi-discipline reasoning test. On GDPval-AA — a benchmark measuring performance on economically valuable knowledge work tasks in finance, legal and other domains — Opus 4.6 outperforms OpenAI's GPT-5.2 by approximately 144 ELO points, which translates to obtaining a higher score approximately 70% of the time.

Inside Claude Code's $1 billion revenue milestone and growing enterprise footprint

The stakes are substantial. Asked about Claude Code's financial performance, the Anthropic spokesperson noted that in November, the company announced that Claude Code reached $1 billion in run rate revenue only six months after becoming generally available in May 2025.

The spokesperson highlighted major enterprise deployments: "Claude Code is used by Uber across teams like software engineering, data science, finance, and trust and safety; wall-to-wall deployment across Salesforce's global engineering org; tens of thousands of devs at Accenture; and companies across industries like Spotify, Rakuten, Snowflake, Novo Nordisk, and Ramp."

That enterprise traction has translated into skyrocketing valuations. Earlier this month, Anthropic signed a term sheet for a $10 billion funding round at a $350 billion valuation. Bloomberg reported that Anthropic is simultaneously working on a tender offer that would allow employees to sell shares at that valuation, offering liquidity to staffers who have watched the company's worth multiply since its 2021 founding.

How Opus 4.6 solves the 'context rot' problem that has plagued AI models

One of Opus 4.6's most significant technical improvements addresses what the AI industry calls "context rot"—the degradation of model performance as conversations grow longer. Anthropic says Opus 4.6 scores 76% on MRCR v2, a needle-in-a-haystack benchmark testing a model's ability to retrieve information hidden in vast amounts of text, compared to just 18.5% for Sonnet 4.5.

"This is a qualitative shift in how much context a model can actually use while maintaining peak performance," the company said in its announcement.

The model also supports outputs of up to 128,000 tokens — enough to complete substantial coding tasks or documents without breaking them into multiple requests.

For developers, Anthropic is introducing several new API features alongside the model: adaptive thinking, which allows Claude to decide when deeper reasoning would be helpful rather than requiring a binary on-off choice; four effort levels (low, medium, high, max) to control intelligence, speed and cost tradeoffs; and context compaction, a beta feature that automatically summarizes older context to enable longer-running tasks.

Anthropic's delicate balancing act: Building powerful AI agents without losing control

Anthropic, which has built its brand around AI safety research, emphasized that Opus 4.6 maintains alignment with its predecessors despite its enhanced capabilities. On the company's automated behavior audit measuring misaligned behaviors such as deception, sycophancy, and cooperation with misuse, Opus 4.6 "showed a low rate" of problematic responses while also achieving "the lowest rate of over-refusals — where the model fails to answer benign queries — of any recent Claude model."

When asked how Anthropic thinks about safety guardrails as Claude becomes more agentic, particularly with multiple agents coordinating autonomously, the spokesperson pointed to the company's published framework: "Agents have tremendous potential for positive impacts in work but it's important that agents continue to be safe, reliable, and trustworthy. We outlined our framework for developing safe and trustworthy agents last year which shares core principles developers should consider when building agents."

The company said it has developed six new cybersecurity probes to detect potentially harmful uses of the model's enhanced capabilities, and is using Opus 4.6 to help find and patch vulnerabilities in open-source software as part of defensive cybersecurity efforts.

Sam Altman vs. Dario Amodei: The Super Bowl ad battle that exposed AI's deepest divisions

The rivalry between Anthropic and OpenAI has spilled into consumer marketing in dramatic fashion. Both companies will feature prominently during Sunday's Super Bowl. Anthropic is airing commercials that mock OpenAI's decision to begin testing advertisements in ChatGPT, with the tagline: "Ads are coming to AI. But not to Claude."

OpenAI CEO Sam Altman responded by calling the ads "funny" but "clearly dishonest," posting on X that his company would "obviously never run ads in the way Anthropic depicts them" and that "Anthropic wants to control what people do with AI" while serving "an expensive product to rich people."

The exchange highlights a fundamental strategic divergence: OpenAI has moved to monetize its massive free user base through advertising, while Anthropic has focused almost exclusively on enterprise sales and premium subscriptions.

The $285 billion stock selloff that revealed Wall Street's AI anxiety

The launch occurs against a backdrop of historic market volatility in software stocks. A new AI automation tool from Anthropic PBC sparked a $285 billion rout in stocks across the software, financial services and asset management sectors on Tuesday as investors raced to dump shares with even the slightest exposure. A Goldman Sachs basket of US software stocks sank 6%, its biggest one-day decline since April's tariff-fueled selloff.

The selloff was triggered by a new legal tool from Anthropic, which showed the AI industry's growing push into industries that can unlock lucrative enterprise revenue needed to fund massive investments in the technology. One trigger for Tuesday's selloff was Anthropic's launch of plug-ins for its Claude Cowork agent on Friday, enabling automated tasks across legal, sales, marketing and data analysis.

Thomson Reuters plunged 15.83% Tuesday, its biggest single-day drop on record; and Legalzoom.com sank 19.68%. European legal software providers including RELX, owner of LexisNexis, and Wolters Kluwer experienced their worst single-day performances in decades.

Not everyone agrees the selloff is warranted. Nvidia CEO Jensen Huang said on Tuesday that fears AI would replace software and related tools were "illogical" and "time will prove itself." Mark Murphy, head of U.S. enterprise software research at JPMorgan, said in a Reuters report it "feels like an illogical leap" to say a new plug-in from an LLM would "replace every layer of mission-critical enterprise software."

What Claude's new PowerPoint integration means for Microsoft's AI strategy

Among the more notable product announcements: Anthropic is releasing Claude in PowerPoint in research preview, allowing users to create presentations using the same AI capabilities that power Claude's document and spreadsheet work. The integration puts Claude directly inside a core Microsoft product — an unusual arrangement given Microsoft's 27% stake in OpenAI.

The Anthropic spokesperson framed the move pragmatically in an interview with VentureBeat: "Microsoft has an official add-in marketplace for Office products with multiple add-ins available to help people with slide creation and iteration. Any developer can build a plugin for Excel or PowerPoint. We're participating in that ecosystem to bring Claude into PowerPoint. This is about participating in the ecosystem and giving users the ability to work with the tools that they want, in the programs they want."

The data behind enterprise AI adoption: Who's winning and who's losing ground

Data from a16z's recent enterprise AI survey suggests both Anthropic and OpenAI face an increasingly competitive landscape. While OpenAI remains the most widely used AI provider in the enterprise, with approximately 77% of surveyed companies using it in production in January 2026, Anthropic's adoption is rising rapidly — from near-zero in March 2024 to approximately 40% using it in production by January 2026.

The survey data also shows that 75% of Anthropic's enterprise customers are using it in production, with 89% either testing or in production — figures that slightly exceed OpenAI's 46% in production and 73% testing or in production rates among its customer base.

Enterprise spending on AI continues to accelerate. Average enterprise LLM spend reached $7 million in 2025, up 180% from $2.5 million in 2024, with projections suggesting $11.6 million in 2026 — a 65% increase year-over-year.

Pricing, availability, and what developers need to know about Claude Opus 4.6

Opus 4.6 is available immediately on claude.ai, the Claude API, and major cloud platforms. Developers can access it via claude-opus-4-6 through the API. Pricing remains unchanged at $5 per million input tokens and $25 per million output tokens, with premium pricing of $10/$37.50 for prompts exceeding 200,000 tokens using the 1 million token context window.

For users who find Opus 4.6 "overthinking" simpler tasks — a characteristic Anthropic acknowledges can add cost and latency — the company recommends adjusting the effort parameter from its default high setting to medium.

The recommendation captures something essential about where the AI industry now stands. These models have grown so capable that their creators must now teach customers how to make them think less. Whether that represents a breakthrough or a warning sign depends entirely on which side of the disruption you're standing on — and whether you remembered to sell your software stocks before Tuesday.

How To Find a Top GEO Agency For Your Healthcare Brand

admin — Thu, 05 Feb 2026 14:37:45 +0000

Healthcare marketing just got more complicated.

You’re still fighting for map pack rankings and “near me” searches. But now there’s a second layer: AI-generated answers that are deciding which providers get mentioned before anyone even clicks a link.

The problem? Every agency’s suddenly an “AI visibility” expert. And in healthcare—where one screw-up can mean real liability—it’s tough to tell who actually knows what they’re doing versus who just added some slides to their deck.

If you’re trying to figure out which agencies can handle both SEO and this new GEO thing in the age of AI, here’s what matters.

Why Healthcare Makes This Harder

Healthcare isn’t like selling SaaS or running an e-commerce site. Mess up here and you’re not just burning your budget—you’re creating actual risk.

A real healthcare agency should be able to explain—without being weird about it—how they handle HIPAA-compliant analytics, review collection that won’t turn into compliance nightmares, medical content workflows with proper review processes, and claims language that stays within legal boundaries. If they’re selling you on volume (“we’ll crank out 200 optimized articles a month”), run. That’s a content factory, not someone who gets healthcare.

The better agencies spend weeks just mapping compliance requirements and review workflows before writing a single piece of content. That’s the kind of discipline that separates serious operators from content mills.

Force Them to Define “AI Visibility” With Real Numbers

AI visibility sounds impressive. The question is whether they can explain what they’re actually measuring.

A real answer includes specifics: tracking whether your practice shows up when people ask AI tools about conditions or treatments in your area, monitoring how often you get cited compared to competitors, measuring your share of mentions for key service lines, and ensuring AI systems can read and use your content accurately.

If they’re vague about measurement or can’t connect it to more patients walking through your door, it’s just buzzwords.

A recent Inc. article stated that Google’s AI summaries now reach over 2 billion users monthly, and traditional clicks are down 30% year over year. That’s a real shift. But only if the agency knows what to do about it.

Look for agencies that track “AI mention share”—for your priority searches, what percentage of AI-generated answers include your brand versus competitors.

Local Still Drives Most of Your Business

Most healthcare decisions start local: “dermatologist near me,” “walk-in clinic open Sunday,” “best ACL surgeon in Phoenix.”

That means your agency better be good at the boring stuff. Managing Google Business Profiles when you’ve got dozens of locations, keeping listings clean with no duplicates when doctors move between offices, building location pages that aren’t just the same template with different city names, and making sure local pages actually convert with clear calls to action for appointments and directions.

Truth is, lots of agencies can talk about AI. Way fewer can dominate local search for 40 locations while keeping everything accurate and compliant.

Even for digital-first healthcare companies that operate nationally, local intent matters. Agencies need to build visibility for location-specific healthcare searches while maintaining compliance across different state regulations. The best results come from patients actively searching for healthcare solutions in their area—not from interruptive advertising.

Systems Beat One-Off Content

In healthcare, content expires. Guidelines get updated. Treatments change. Doctors leave or join your practice. And AI? It repeats whatever’s most current and clear.

So stop asking “How much content will you make?” Instead, ask how they figure out what you should be writing about in the first place, what happens when medical information changes and how they catch and update it, who reviews content to make sure it’s medically accurate, and how they build service-line authority without cannibalizing pages.

You need an agency that sets up a real system—medical review, updating processes, version control. Not someone who floods your site with articles and crosses their fingers.

The better agencies use content governance frameworks where every piece gets tagged with a review date based on how quickly that medical information typically changes. Oncology content might be reviewed quarterly. Basic preventive care annually. They maintain rosters of medical reviewers who can sign off on updates within 48 hours when guidelines change. When the ADA updates diabetes guidelines, for example, the right agency has a system in place to update all affected content within days, not months—and that responsiveness often leads to rankings boosts because you’re among the first sources with current information.

The Technical Stuff Actually Matters

Good healthcare agenciesknow AI visibility isn’t about keywords. It’s about structure.

Can AI systems actually understand your content? Is the information organized in a way that makes it easy to extract and use? Structured content is 40% more likely to get cited. That means proper markup for your locations, doctors, and services. Clean site architecture. Content that makes sense even when AI pulls out just one section.

They should also understand E-E-A-T—Google’s framework for evaluating expertise and trustworthiness. In healthcare, this means doctor bios with real credentials, clear review processes, and transparent sourcing. This stuff matters for both search engines and AI systems.

Building authoritative signals takes time and consistency. Agencies working in the space typically plan for strategic media placements over 24-36 months, not quick wins. That’s not just link building—it’s establishing the kind of credibility that both search engines and AI systems use to determine whether a healthcare brand is trustworthy enough to reference.

AI Doesn’t Replace SEO—It Adds to It

The best agencies don’t make you choose between AI visibility and traditional SEO. They do both, and they should be able to explain how these pieces fit together naturally.

AI citations build awareness that eventually leads to people searching for your brand directly. Strong rankings and AI mentions feed off each other. Local SEO and AI visibility overlap, especially for location-based searches. And PR relationships help you build the authority that AI systems look for.

If they’re only hyped about AI and ignoring technical SEO or conversion optimization, they’re chasing the shiny new thing while forgetting what actually drives appointments.

There’s an interesting pattern emerging in healthcare: someone asks ChatGPT or Perplexity about symptoms or treatment options, sees a provider mentioned, doesn’t click through right away—but then three days later searches for that provider by name. The AI mention plants the seed. The direct search is where conversion happens.

This is why tracking “AI-to-brand-search attribution” matters—measuring the increase in branded searches after AI visibility improves.

Reports Should Tie to Real Results

New metrics only matter if they connect to your actual business.

Look for dashboards showing both traditional metrics (rankings, traffic, conversions) and new ones (AI mentions, citation frequency, how often you show up in AI answers). Monthly updates should explain what happened and why. And there should be a clear connection between visibility—whether from Google or ChatGPT—and actual patient bookings.

AI visibility is still pretty new. A decent agency will help you understand what the numbers mean for your goals. They won’t just dump a spreadsheet on you and call it a win.

The most important metric? How visibility translates to acquisition.

Healthcare organizations that were heavily dependent on paid advertising (with organic representing 5-10% of acquisition) can transform that to 25-30% of total customer acquisition. That’s the kind of shift that changes your unit economics and gives you more control over growth.

Red Flags

Watch out for agencies that promise huge results but can’t explain how they’ll actually do it, talk in circles when you ask what makes them different, or don’t have real healthcare clients beyond maybe one dentist. Be especially wary if they focus on cranking out massive amounts of content, can’t describe their medical review process, don’t ask about compliance requirements right away, or act like healthcare is just like any other industry.

Ask them directly: How do you measure this AI discovery stuff in healthcare specifically? How do you stay HIPAA-compliant with tracking and analytics? Walk me through your medical review process. Show me examples from other healthcare organizations like ours.

The agencies doing real work in this space should have documented case studies with actual healthcare companies, showing both the strategy and measurable business outcomes—not just traffic increases, but changes in how patients find and choose providers.

Pay Attention to What They Want to Do First

Good agencies start with the foundation, because that’s what lasts.

In healthcare, that means getting your technical setup and site structure right first, then building out your local presence with locations, providers, and specialties properly organized, establishing credibility through credentials, review processes, and appropriate citations, creating content organized around services and what patients are actually searching for, and finally building authority through PR and earned media.

If their pitch starts with vague ideas about “thought leadership” and “building backlinks” without first discussing structure and compliance, they’re winging it.

Agencies that understand healthcare know it takes time to build trust with both search engines and patients. Real transformation typically plays out over 24-36 months with consistent media placement and content development. That’s not a quick fix—it’s building a sustainable foundation that compounds over time.

A Word on Agencies Leading This Space

The market for AI visibility agencies is still emerging, but some patterns are worth noting.

Agencies that have positioned themselves explicitly around AI visibility and GEO—rather than just adding it to existing SEO services—tend to have more developed measurement frameworks and healthcare-specific compliance processes. They’re approaching this as a core competency rather than an add-on service.

For instance, Relevance has built their positioning around helping brands become more visible in AI-driven discovery, with specific expertise in regulated industries like healthcare. They’re one of several agencies that have moved from pure SEO to integrated strategies that address both traditional search and AI visibility.

When evaluating agencies, look for those that can articulate not just what AI visibility is, but how they’ve actually achieved it for healthcare clients in competitive, regulated environments. The difference between theory and execution matters.

Bottom Line

The best agency for healthcare SEO and AI visibility won’t be the one that sounds most futuristic. They’ll be the one that sounds most careful.

They can clearly explain what they’re doing and why. They know healthcare compliance inside and out. They’re strong at both technical SEO and local search. They build systems that scale without cutting corners. And their reports connect to appointments and patient volume—not likes or impressions.

AI visibility is real, and it matters. But it matters most when it’s built on top of stuff that already works, and handled by people who understand that in healthcare, you’re not really marketing services. You’re marketing trust.

If you’re evaluating agencies right now, look for documented healthcare experience with real outcomes. Ask about their compliance processes. Make them explain how AI visibility connects to actual patient acquisition, not just mentions or impressions.

The right GEO agency will sound less like they’re selling you the future and more like they’re building something that’ll still work when the next platform shift happens. They’ll have case studies showing transformation in acquisition channels, not just traffic bumps. And they’ll understand that in healthcare, moving too fast or cutting corners doesn’t just hurt your rankings—it creates risk.

The post How To Find a Top GEO Agency For Your Healthcare Brand appeared first on ReadWrite.

Dutch Gambling Authority urges operators to stop share your bet feature

admin — Thu, 05 Feb 2026 14:37:00 +0000

The Kansspelautoriteit (Ksa), the Netherlands’ gambling regulator, is asking licensed online gambling operators to stop using the “Deel je bet” or “Share Your Bet” feature right away. According to the regulator, the tool can work like hidden advertising and may expose vulnerable people, including minors and those with gambling problems, to betting content they should not be seeing.

The share your bet feature lets players send details of a wager they have placed through social media, messaging apps, or email. Someone who receives the link can look at the bet and, if they already have an account with the same operator, place the exact same wager with just one click.

In an announcement published on Wednesday (February 4), the regulator said it looked more closely at the feature after receiving several questions and signals from the market. After reviewing how it works, the authority concluded that the feature allows gambling content to spread easily within players’ own social circles.

Concerns about indirect advertising and vulnerable groups

The regulator believes the tool acts as a form of advertising and recruitment, even though the bets are shared by players rather than by the gambling companies themselves. Under Dutch rules on recruitment, advertising, and gambling addiction prevention, gambling providers must make sure their advertising does not reach vulnerable groups such as minors, young adults, or people at risk of gambling addiction.

The problem, according to the regulator, is that operators have no control over who receives these shared betting links. Players decide for themselves who they send them to, which means links can easily end up with underage users or other at risk individuals. Because of this lack of control, the regulator says operators cannot guarantee they are complying with advertising and player protection laws.

The authority also warned that making it easy to share bets can make gambling feel more normal, especially for younger people. Seeing friends share bets could lower the barrier to getting involved in gambling in the first place.

As a result, the regulator is urging licensed online gambling operators in the country to remove the share your bet feature immediately. The authority added that it will keep a close eye on the market and take enforcement action if needed to protect vulnerable groups.

Featured image: Ksa

The post Dutch Gambling Authority urges operators to stop share your bet feature appeared first on ReadWrite.

Anthropic commits to keeping Claude ad free unlike rival ChatGPT

admin — Thu, 05 Feb 2026 11:53:18 +0000

Anthropic announced in a blog post that its chatbot Claude will remain ad-free, unlike rival OpenAI, which recently introduced ads to ChatGPT for many users. The company stated this decision aligns with Claude’s role as a genuinely helpful assistant for work and deep thinking.

Anthropic explained that users often share personal details with chatbots during interactions. Ads generated from such information would create an uncomfortable experience. For instance, the company cited the scenario of seeking mental-health advice and receiving a subsequent ad for St. John’s wort.

Claude handles various conversation types beyond personal disclosures. Users engage it in complex software engineering tasks, deep work sessions, or solving difficult problems. Ads appearing in these contexts would seem out of place and, in numerous instances, unsuitable.

The firm emphasized that including ads contradicts the Claude Constitution. This framework designates “being generally helpful” as a core principle. Advertising integration would undermine this foundational goal.

Anthropic detailed further challenges in its blog post. “Introducing advertising incentives at this stage would add another level of complexity,” the company wrote. “Our understanding of how models translate the goals we set them into specific behaviors is still developing; an ad‑based system could therefore have unpredictable results.”

AI development incurs substantial costs for companies like Anthropic. Returns on these investments have not matched expenditures. OpenAI adopted ads as a method to offset such expenses, prompting questions about Anthropic’s financial strategy.

Engadget contacted Anthropic regarding potential financial pressures that might prompt a policy reversal. A representative responded by directing attention to the blog post, stating it contains “all the information we have to share at this time.”

Despite rejecting ads in conversations, Anthropic affirmed its dedication to commerce-based agentic AI. The company plans to develop features allowing users to find products, compare options, purchase items, connect with businesses, and perform related actions.

Featured image credit