The year is quickly coming to an end, and that means tech platforms are tripping over themselves to roll out their year-end recapsall hoping to capture the virality that Spotifys Wrapped year-in-review recap commands each year.
Already in December, weve seen Spotify Wrapped, Apple Music Replay, Amazon Music Delivered, and YouTube Recap, with more, like the popular Snapchat Recap, set to debut in the coming weeks. One of those debuts has occurred today, as well. Popular chat platform Discord has now released its personalized Wrapped-like recap: Discord Checkpoint. Heres what to know about it and how to view yours.
Discord announced Discord Checkpoint 2025
Discord has announced that its personalized Discord Checkpoint recap will be rolling out to its users over the next few days. In previous years, Discord has announced a Checkpoint recap, but the start it released for it encompassed its global user base.
Discord Checkpoint 2025 is the first time the platform is offering a year-in-review personalized for each of its individual users, provided they were active on the service enough to generate a Checkpoint recap, and that their privacy settings allowed the use of their data.
What’s included in my Discord Checkpoint 2025?
[Art: Discord]
There are two main components of your Discord Checkpoint 2025. The first is a recap of your usage and interactions on the platform.
Heres some of what your Discord Checkpoint 2025 will show you:
How many messages you sent
How many minutes in voice chat you spent
How many emojis you posted
What other Discord users you spent the most time with
The servers you used the most
But Discord 2025 Checkpoint will also display one of 10 Checkpoint cards. These cards represent 10 different types of Discord users. Your card will come with a matching avatar you can choose to display on your profile so other Discord users can see if youre in the same group.
How can I access my Discord Checkpoint 2025?
To access your Discord Checkpoint 2025, make sure you have the latest version of the desktop or mobile app.
If you are using the desktop app:
Click the flag option in the top-right corner. Your Checkpoint will be displayed.
If you are using the mobile app:
Tap the You tab in the bottom-right corner.
Tap the Checkpoint banner. Your Checkpoint will be displayed.
Can I share my Discord Checkpoint 2025?
Yes, users can choose to share their Discord Checkpoint 2025.
To share your Discrod Checkpoint, tap the Share button on the Summary page and then choose where youd like to share it.
Discord Checkpoint 2025 will be available to Discord users until January 15, 2026.
Being tired is practically a personality trait in corporate Americaespecially in 2025. Everybody is exhausted, it seems. Folks are doing fiftyleven jobs. Youre always juggling tasks, always late for the next meeting because the last one ran long. But when youre one of the few Black employees at the gig, theres a subconscious fear of looking like youre in over your head, especially with the looming fear of layoffs. So you push through, even when youre running on fumes. You go harder, telling yourself youll rest once you get through the busy patch. But thats a lie. The job is a perpetual busy patch.For months, I kept telling myself I was just tired. Regular tired. The kind of tired you fix with a good nights sleep and maybe a WFH power nap between meetings. But one random Tuesday, as I stared blankly at my laptop trying to decipher a three-sentence Slack message like it was hieroglyphics, it hit me: This wasnt normal fatigue. My mind was cooked.
The exhaustion hit back in the spring, but it was nothing like the dramatics you see in movies. There were no panic attacks in the bathroom or conference room crashouts on Kyle. It showed up subtly, in little ways that I dismissed. Id reread emails multiple times because the words refused to connect in my mind. I had the attention span of a goldfish. Id get irrationally annoyed by people asking me perfectly reasonable questions. I was just . . . over it. I chalked it up to adulting, the natural byproduct of ambition and bills. This too shall pass, I thought.The breaking point wasnt cinematic. I was in a brainstorming session when I realized my mind felt blank. I managed to offer a few contributions to the meeting, but they were all cliché rehashes, none of the outside-of-the-box ideas Id usually bring to the table. I felt like Charles Barkley in Space Jam after the Monstars stole the NBA players skillslike a whole scrub.
Shortly after, I took a week off. Booked a trip. But a change of scenery didnt fix anything. I came back just as fried, which was more depressing. I tried damn-near all of the things Solange sings about in Cranes in the Sky. Then I realized I required a factory reset.
I began to make some real changes to improve my work-life balance. It wasnt just that I needed time away from the office; I needed better boundaries and mental-health maintenance. I began closing my laptop at a designated time, and keeping it closed until it was time to clock in the next day. I blocked off meeting-free focus time during workdays. I got a biweekly gym routine going. I stopped thinking of myself as a machine that could operate nonstop.
Somewhere along my come up, I had convinced myself that I needed to treat my job like I was back in college. In those undergrad days, I felt the need to pile on electives and explore diverse fields of study. I wanted to be well-rounded and sure of my career path. But once I was in the workplace, it became about being marketable. I took on fringe projects outside of my job description to open myself up to new opportunities and, ideally, more moola. The game plan served me well until it didnt.
Ive been taking it easy since then. I have nothing to prove to anyone else, or to myself. So I stay in my lane. I delegate more. I turn down things that arent my responsibility. Ive unlearned the foolish idea that rest is a reward, something I had to earn by pushing myself to the brink. Doing the most is a thing of the past.
It took burning out for me to learn a simple truth: Nothing at work is worth losing yourself over. Not the project, not the promotion, not the pat on the back. Protect your energy like its finite, because it is. If youre feeling the kind of tiredness that sleep cant fix, follow the sage guidance of Ice Cube: Check yourself before you wreck yourself.
Every year, open enrollment forces Americans to confront a familiar dilemma: Pay more for coverage that delivers less, or gamble on going without it. This year, that choice has become even starker.
Employers are shifting more costs to workers, marketplace premiums are poised to rise, fewer prescription drugs are covered by insurance, and 3.8 million people could lose insurance annually if Affordable Care Act subsidies arent extended.
Together, these developments represent a structural break in the U.S. healthcare system. Its a perfect storm that will price many Americans out of health insurance altogethermany involuntarily, but some voluntarily. Fed up with skyrocketing premiums and deductibles that offer little protection, they’ll instead pay out-of-pocket for medical needs, hoping that they won’t face catastrophic expenses.
Whats emerging is not a temporary coverage gap. Its a permanent coverage squeeze. One that will fundamentally reorder consumer behavior and redefine what access means. The implications for healthcare organizations are profound, and those who fail to adapt will struggle to stay relevant.
SHIFT FROM COVERAGE TO CONTROL
For decades, the U.S. healthcare model has been built on the assumption that insurance is the gateway to care. But when premiums and deductibles reach levels that rival a second mortgage, consumers start to ask a different question: What am I actually getting for this?
Increasingly, the answer feels out of step with consumer expectations. High deductibles mean many people pay full price for most of their care anyway. Network limitations constrain choice. Surprise bills erode trust. And the complexity of benefits makes it nearly impossible to be an informed consumer.
As a result, were seeing a quiet but significant reorientation. Consumers are moving from a coverage-first mindset to a control-first mindset. They want to understand costs upfront. They want to choose where they go for treatment. They want the ability to pay in ways that fit their budgets. And when the value equation breaks, theyre willing to bypass the system entirely.
THE CONSUMER HEALTHCARE MARKET WILL EXPAND
If current trends hold, 2026 could mark one of the largest expansions of the uninsured and underinsured population in more than a decade. But instead of disengaging from the healthcare system, these consumers are building a parallel path through it. They are demanding the same things they expect from the best retail and digital experiences: clarity, predictability, immediacy, and trust.
This creates a massive opportunity, and a significant responsibility, for the industry. Companies that can simplify access, make pricing transparent, and deliver affordable pathways to care will become essential partners. Those that cling to legacy models built around opaque reimbursement flows will watch consumers go elsewhere.
We already see evidence of this shift. People are embracing subscription-based care for predictable costs, using telehealth for speed and convenience, and relying on platforms like GoodRx to access lower prescription prices. Services like my company GoodRxs newly-launched telemedicine subscriptions for erectile dysfunction, hair loss, and weight loss are examples of how companies are meeting this demand, offering affordable, accessible healthcare options outside traditional insurance frameworks.
WHAT HEALTHCARE LEADERS MUST DO NOW
Healthcare has historically been built around the needs of institutions, not individuals. That era is ending. The organizations that thrive in the next phase will redesign around consumer agency and economic reality.
Three shifts are essential:
Make cash pricing a standard, not a contingency. If people are paying out-of-pocket, they need to see the cost clearly, consistently, and upfront. Transparent pricing should be a baseline expectation across providers, pharmacies, and manufacturers.
Embed affordability into clinical decision making. Cost isnt a back office issue. It should be integrated into prescribing tools, clinical workflows, and patient conversations. Providers need real-time insights into cash prices and savings options so they can help patients make informed choices before they reach the pharmacy counter.
Build care models that meet consumers where they are. Telehealth, retail clinics, asynchronous care, and hybrid models represent the way consumers want to access routine, preventive, and even chronic care. Healthcare companies must expand their presence in these channels or risk losing relevance.
BUILD A CONSUMER-CENTRIC FUTURE
The coverage squeeze is exposing something important: Consumers are demanding value, not just benefits. They want care that feels intuitive and affordable. They want to make decisions with clear information rather than insurance complexity. And they want healthcare that adapts to their lives.
If we meet that demand, we have a chance to rebuild trust and deliver a healthcare experience that works for more people, regardless of their coverage status. If we dont, consumers will continue to chart their own path, with or without the traditional system.
The next chapter of American healthcare wont be defined by the rise or fall of insurance premiums. It will be defined by whether we, as industry leaders, embrace a radically simple idea: When we design for the consumer first, everyone benefits.
Wendy Barnes is president and CEO of GoodRx.
Weve been here before.
At so many pivotal moments in our adoption of digital technology, people and businesses mistake a companys walled garden for the broader, more powerful network underneath. In the 1990s, many people genuinely believed AOL was the internet. When I left Facebook in 2013, hundreds of people asked how I would function without the web. Over and over, packaged productsoperating systems, app stores, streaming serviceseclipse quieter, less expensive, bottom-up alternatives like Linux or torrents. We forget they exist.
Today were making the same mistake with large language models.
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To many of us, AI now means choosing among a handful of commercial LLMs such as ChatGPT, Claude, Gemini, or Grokand perhaps even choosing the one that matches our cultural or political sensibilities. But these systems share important structural limitations: they are centralized, expensive, energy-intensive operations that depend on massive data centers, rare chips, and proprietary data stores. Because theyre trained on roughly the same public internet, they also tend to generate the same generalized, flattened results. Companies using them wholesale often end up substituting their own expertise with recombinations of whatever is already out there.
This is how AI will do to businesses what social media did to publications, and what the early web did to retailers who went online without a strategy. Using the same generic tools as everyone else produces the same generic results. Worse, outsourcing core knowledge processes to a black-box service replaces the long-term development of internal capacityespecially junior employees learning through real practicewith cheaper but future-eroding automation.
The limits of centralized AI
Commercial language models are optimized for generality and scale. That scale is impressive, but it creates real constraints for organizations. Centralized LLMs require:
Large volumes of training data scraped from the open web
Expensive server infrastructure and power consumption
Constant external connectivity
Business models built around subscription, token fees, or upselling
For many companies, these models become another outsourced dependency. Every time a commercial LLM updates itselfwhich can happen weeklyyour workflows change underneath you. Your proprietary data may be exposed to third-party APIs. And your differentiation erodes, because the models knowledge is drawn from the same public corpus available to your competitors.
Meanwhile, the narrative surrounding AI has encouraged businesses to believe that this centralized path is the only viable onethat achieving meaningful AI capability requires enormous data centers, billion-dollar training runs, and participation in a global race toward Artificial General Intelligence.
But none of this is a requirement for using AI productively.
A practical alternative already exists
You do not need frontier-scale models to benefit from AI. A growing ecosystem of open-source, locally deployable language models provides organizations with far more autonomy, privacy, and control.
A $100 Raspberry Pior any modest home or office servercan run a compact open-source model using tools like Ollama or GPT4All. These models dont learn on the fly the way people do, but they can produce high-quality responses while remaining completely contained within your own environment. More importantly, they can be paired with a private knowledge base using retrieval systems. That means the model can reference your own research library, internal documentation, or curated public resources like Wikipediawithout training on the entire internet, and without sending your data to an external provider.
These systems build on your own data instead of extracting it, strengthen your institutional memory instead of commoditizing it, and run at a fraction of the cost.
This approach allows an organization to create an AI system aligned with its actual priorities, values, and domain expertise. It becomes a private assistant rather than a generalized product shaped by the incentives of a trillion-dollar platform. And the alternative doesnt have to be a solitary effort.
Neighborhoods, campuses, or company departments can form a mesh networka set of devices connected directly through Wi-Fi or cables rather than through the public internet. One node can host a local model; others can contribute or withhold their own data stores. Instead of a single company owning the infrastructure and the knowledge, you get something closer to a community data commons or a digital library system.
Projects like the High Desert Institutes LoreKeepers Guild are already experimenting with this approach. Their Librarian initiative envisions local libraries acting as the data hubs for mesh-networked AI systemsresilient enough to function even during connectivity disruptions. But their deeper innovation is architectural. These systems give organizations access to powerful language capabilities without subscription costs, lock-in, data extraction, or exposure of proprietary information.
Local or community models enable organizations to:
Curate their own data
Maintain complete privacy by keeping computation on-site
Reduce latency to near zero
Preserve and strengthen internal expertise
Avoid recurring token or API costs
And they do so using energy and computing resources that are orders of magnitude lower than those required by frontier-scale models.
Why decentralized AI matters now
The more institutions adopt localized or mesh-based AI, the less they are compelled to fund the centralized companies racing toward AGI. Those companies have made an effective argument: that sophisticated AI is only possible through their services. But much of what organizations pay for is not their own productivityit is the constrution of massive server farms, procurement of rare chips, and long-term bets on energy-intensive infrastructure.
By contrast, in-house or community-run systems can be deployed once and maintained indefinitely. A week of setup can eliminate a decade of subscription payments. A small rural library has already demonstrated the feasibility of operating a self-hosted LLM node; a Fortune 500 company should have no trouble doing the same.
Still, history suggests that most organizations will choose the convenient option rather than the autonomous one. Few people accessed the early Internet directly; they chose AOL. Today, many will continue to choose centralized AI services, even when they offer the least control. But what social media companies did to businesses that mistook them for the Internet will be mild compared to what comes when companies mistake these proprietary interfaces for AI itself.
Decentralized AI already exists. The question now is whether well choose to use it.
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The way consumers search is changing faster than the industry expected. This holiday season, many shoppers are looking for gifts inside AI platforms, rather than retailer sites or traditional search. They are asking natural questions like:
Find me a cruelty-free skincare gift for sensitive skin under $100. What are good gift ideas for a three-year-old that are safe and durable? What are the safest, nontoxic treats for my Golden Retriever?
This shift is already measurable. Adobe Digital Insights reports a 4,700% year-over-year increase in retail visits driven by AI assistants between July 2024 and July 2025. At the same time, click-through rates from SEO have dropped 34% as users bypass the search results page entirely. eMarketer reports 47% of brands have no idea whether they appear in AI-driven discovery at all.
The platforms know this shift is accelerating. Googles recent decision to add conversational shopping and AI-mode ads just weeks before the holidays shows how quickly consumer behavior is moving. Brands must adjust too.
Despite the complexity behind AI systems, three simple signals determine which products get recommended: trust, relevance, and extractability. These signals are the backbone of how AI decides what to surface, and matter as much as packaging, price, or placement.
1. Trust: The models instinct about which information is dependable
AI systems develop a sense of which sources to believe during training. Domains with consistent verification signals gain more weight because the model has learned they usually publish accurate information.
This is why leading retailers, including Ulta, Sephora, Target, Amazon, and Bloomingdales, rely on independent verification partners for the claims displayed on their digital shelves. Verified domains act as trust anchors. When a model must choose, it selects the product backed by clearer and more reliable sources.
Trust often determines whether you are included in the answer at all.
2. Relevance: How well your product matches the shoppers question
AI assistants answer based on meaning, not keywords. When a shopper asks for eczema-safe moisturizer or gluten-free protein bars, the system retrieves products whose attributes clearly map to those concepts.
Relevance depends on using consistent claims across every channel you sell inconsistency is heavily prioritized. When multiple sources concur, this repeated confirmation strongly reinforces your product is the right choice.
Missing or inconsistent attributes keep your product of the candidate pool.
3. Extractability: How easy it is for AI to read and use your product data
Even accurate information gets ignored if its hard for AI to parse. Clean structure, consistent formatting, and machine readability significantly increase the likelihood your product will be selected.
Brands improve extractability by adding structured markup for details like ingredients, materials, and benefits so retrieval systems can interpret it without ambiguity.
Clear structure anchors the attention of the large language model, giving your product an advantage. Extractability is often the deciding factor when competing products meet the same need.
AI RECOMMENDATIONS SHAPE BEHAVIOR
Algorithms do more than respond to consumers. They influence them.
We see this in language, where content moderation has led millions of people to adopt new vocabulary. The same pattern is emerging in commerce. If AI consistently recommends a certain moisturizer, probiotic, or baby product, shoppers begin to trust those recommendations and carry those preferences into stores.
Optimizing for trust, relevance and extractability goes beyond improving digital performance. It shapes real-world buying behavior.
A PRACTICAL PLAYBOOK FOR THE HOLIDAY WINDOW
Even with peak season here, brands can still make meaningful progress with these four steps:
1. Structure your data for machine and human audiences
Fix blocked pages or missing product schemas, and use standard formats like JSON-LD that AI can parse reliably.
Keep consumer-facing PDPs simple while storing deeper technical details, ingredients, and safety information in underlying schemas.
Clean up formatting and refresh retailer feeds weekly, since AI systems prioritize recency.
Example: A candle brand can keep the PDP simple for shoppers while storing allergen, VOC, and material data in structured markup that AI can read.
2. Align product claims everywhere you sell
Match titles, claims and benefits across DTC sites, retailer PDPs, and marketplaces.
Remove conflicting or outdated language that can weaken trust.
Example: If one PDP says cruelty-free and another says not tested on animals, unify the phrasing so AI sees one consistent claim.
3. Map your data to real shopper intent
Identify the attributes consumers care about most in your category.
Encode those attributes in machine readable fields; add supporting evidence where possible.
Example: For baby toys, encode safety standards like ASTM or CPSC in your structured data so AI can confirm the claim.
4. Build machine-readable authority with credible certifications and verification signals
Encode ingredients, materials, certifications, and testing outcomes in structured fields so AI can verify your caims without guessing.
Keep claim language consistent across channels to strengthen authority.
Use references to third-party standards, testing, or retailer badges. AI gives more weight to claims it can trace back to trusted sources.
Example: A sensitive skin serum should encode fragrance-free, eczema-safe, dermatologist testing details, and any third-party certifications directly in schema.
5. Use a tool that monitors, optimizes, and implements the work end-to-end
Choose a tool that goes beyond generic visibility tracking, looks at each SKU individually, and helps you implement structured data improvements.
Prioritize systems that strengthen your authority signals product by product, not just surface-level optimizations.
Look for tools that measure real outcomes, like increased visibility in AI or higher conversion, so you can measure ROI.
Consumer discovery is changing faster than most brands are prepared for. But there is still time. By reinforcing trust, relevance, and extractability now, brands can stay visible in AI-driven search this season and build a long-term foundation for every channel where AI shapes consumer decisions.
Kimberly Shenk is cofounder and CEO of Novi.
European Union regulators on Friday fined Elon Musk’s social media platform X 120 million euros ($140 million) for breaches of the bloc’s digital regulations that they said could leave users exposed to scams and manipulation.The European Commission issued its decision following an investigation it opened two years ago into X under the 27-nation bloc’s Digital Services Act, also known as the DSA.It’s the first time that the EU has issued a so-called non-compliance decision since rolling out the DSA. The sweeping rulebook requires platforms to take more responsibility for protecting European users and cleaning up harmful or illegal content and products on their sites, under threat of hefty fines.The Commission said it was punishing X, previously known as Twitter, because of three different breaches of the DSA’s transparency requirements. The decision could rile President Donald Trump, whose administration has lashed out at digital regulations, complaining that Brussels was targeting U.S. tech companies and vowing to retaliate.The company did not respond immediately to an email request for comment.EU regulators had already outlined their accusations in mid-2024 when they released preliminary findings of their investigation into X.Regulators said X’s blue checkmarks broke the rules because on “deceptive design practices” and could expose users to scams and manipulation.Before Musk acquired X, when it was previously known as Twitter, the checkmarks mirrored verification badges common on social media and were largely reserved for celebrities, politicians and other influential accounts.After he bought it in 2022, the site started issuing the badges to anyone who wanted to pay $8 per month for one.The means X does not meaningfully verify who’s behind the account, “making it difficult for users to judge the authenticity of accounts and content they engage with,” the Commission said in its announcement.X also fell short of the transparency requirements for its ad database, regulators said.Platforms in the EU are required to provide a database of all the digital advertisements they have carried, with details such as who paid for them and the intended audience, to help researches detect scams, fake ads and coordinated influence campaigns. But X’s database, the Commission said, is undermined by design features and access barriers such as “excessive delays in processing.”Regulators also said X also puts up “unnecessary barriers” for researchers trying to access public data, which stymies research into systemic risks that European users face.“Deceiving users with blue checkmarks, obscuring information on ads and shutting out researchers have no place online in the EU. The DSA protects users,” Henna Virkkunen, the EU’s executive vice-president for tech sovereignty, security and democracy, said in a prepared statement.
Kelvin Chan, AP Business Writer
FIFA has invited more teams than ever for a World Cup priced largely for fans in the 1%. The process of figuring out which teams in the expanded 48-nation field will play where begins with Friday’s draw at the Kennedy Center for the Performing Arts.Cape Verde, Curaçao, Jordan and Uzbekistan will appear in soccer’s premier event for the first time when next year’s tournament is played from June 11 to July 19 at 16 sites in the United States, Mexico and Canada.“I’m quite optimistic because to qualify you need to beat the other teams of your confederations, and that’s a sign of quality,” former Arsenal manager Arsene Wenger said Thursday as red carpets were installed at the Kennedy Center. “The teams are not there by coincidence.”President Donald Trump of the U.S. and Claudia Sheinbaum of Mexico are expected along with Canada Prime Minister Mark Carney. Instead of soccer gear, the Kennedy Center gift shop still was filled with socks of Shakespeare, Beethoven and Verdi along with shelves of red and white holiday nutcrackers.The world’s top 11-ranked teams have all qualified, with No. 12 Italy among 22 nations competing in playoffs for the final six berths to be decided March 31.Led by captain Lionel Messi, who turns 39 during the tournament, Argentina seeks to become the first nation to win consecutive World Cups since Brazil in 1958 and 1962. Messi will look to extend his record of 26 games played and enters with 13 career goals, three shy of Miroslav Klose’s record.Games will be played at 11 NFL stadiums along with three in Mexico and two in Canada, where construction is underway to add 17,000 temporary seats to BMO Field, raising capacity to around 45,000. Attendance will top the record 3.59 million in 1994.“We basically set the new tone in terms of attendance, in terms of surrounding the tournament with a lot of entertainment and glamor,” said Alan Rothenberg, head organizer of the 1994 World Cup in the U.S. “We did a lot of things that kind of broke the ice with respect to how you present the tournament as something other than just a soccer tournament.”FIFA announced initial ticket prices of $60-$6,730, saying they would be dynamic, up from $25-$475 for the 1994 tournament in the United States. It has refused to release a complete list of prices, as it had for every other World Cup since at least 1990. The governing body also is selling parking passes for up to $175 for a single match, a semifinal in Arlington, Texas.FIFA spokesman Bryan Swanson did not respond to a request for FIFA President Gianni Infantino to discuss ticket prices.Sixty-four nations will participate in the draw, 30% of FIFA’s members, but just 42 countries are assured of sports. Among the playoff teams, Albania, Kosovo, New Caledonia and Suriname are trying to reach the World Cup for the first time.With the expansion, the top two teams in each of 12 groups advance along with the eight best third-place teams. Some nations could reach the new round of 32 with three points.“I think we’re going to be in pretty good shape,” said former U.S. midfielder Tab Ramos, who during his playing days mapped out permutations for advancement. “We have a good team, so I’m not worried as much as I’ve been in the past about about this draw.”Opta Analyst’s computer projects the U.S. has a 0.9% chance of winning the Americans haven’t reached the semifinals since the first World Cup in 1930. Spain tops the forecast at 17%, followed by France (14.1%), England (11.8%), Argentina (8.7%), Germany (7.1.%), Portugal (6.6%), Brazil (5.6%) and the Netherlands (5.2%).In a new twist, FIFA said the top four teams in the rankings Spain, Argentina, France and England will avoid each other until the semifinals if they finish first in their first-round groups.Specific sites for most matchups and kickoff times won’t be announced until Saturday. In 1994, there were just seven night games.
A team’s group play sites will be restricted to an Eastern, Central and Western regional.
The 1994 World Cup draw in Las Vegas was apolitical, featuring performances by Stevie Wonder, Barry Manilow, James Brown and Vanessa Williams plus comedian Robin Williams, who called the draw screen “the world’s largest keno board,” yelled “Bingo!” when Greece was selected.This draw figures to be more akin to the ceremony for 2018 tournament in Moscow, opened by Russian President Vladimir Putin. Trump, who has campaigned for a Nobel Peace Prize, is expected to be awarded FIFA’s own peace prize that Infantino established after traveling to several events with Trump.But the main event is the pulling of balls from bowls to create groups. Retired tars Tom Brady of the NFL, Shaquille O’Neal of the NBA and Wayne Gretzky of the NHL along with three-time AL MVP Aaron Judge will assist in a ceremony to be run by former England captain Rio Ferdinand.“There is the angst and the looks of sheer terror and disappointment and/or joy and elation from the coaches and from the staffs,” said former U.S. defender Alexi Lalas, now Fox’s lead soccer analyst. “It really gets kind of real for people.”
AP soccer: https://apnews.com/hub/soccer
Ronald Blum, AP Sports Writer
So far, Nvidia has provided the vast majority of the processors used to train and operate large AI models like the ones that underpin ChatGPT. Tech companies and AI labs dont like to rely too much on a single chip vendor, especially as their need for computing capacity increases, so theyre looking for ways to diversify. And so players like AMD and Huawei, as well as hyperscalers like Google and Amazon AWS, which just released its latest Trainium3 chip, are hurrying to improve their own flavors of AI accelerators, the processors designed to speed up specific types of computing tasks.
Could the competition eventually reduce Nvidia, AIs dominant player, to just another AI chip vendor, one of many options, potentially shaking up the industrys technological foundations? Or is the rising tide of demand for AI chips big enough to lift all boats? Those are the trillion-dollar questions.
Google sent a minor shockwave across the industry when it casually mentioned that it had trained its impressive new Gemini 3 Pro model entirely on its own Tensor Processing Units (TPUs)another flavor of AI accelerator chips (GPUs). Industry observers immediately wondered if the AI industrys broad dependence on Nvidia chips was justified. After all, theyre very expensive: A big part of the billions now being spent to build out AI computing capacity (data centers) is going to Nvidia chips.
And Google TPUs are looking more like a Nvidia alternative. The company can rent TPUs in its own data centers, and its reportedly considering selling the chips outright to other AI companies, including Meta and Anthropic. A (paywalled) report from The Information in November said Google is in talks to sell or lease its GPUs so they can run in any companys data center. A Reuters report says Meta is in talks to spend billions on Googles TPUs starting in 2027, and may begin paying to run AI workloads on TPUs within Google data centers even sooner. Anthropic announced in October that it would use up to a million TPUs within Google data centers to develop its Claude models.
Selling the TPUs outright would, technically, put Google in direct competition with Nvidia. But that doesnt mean that Google is gunning hard to steal Nvidias chip business. Google, after all, is a major buyer of Nvidia chips. Google may see selling TPUs to certain customers as an extension of selling access to TPUs running in its cloud.
This makes sense if said customers are looking to do the types of AI processing that TPUs are especially good at, says IDC analyst Brandon Hoff. While Nvidias GPUs are workhorses capable of a wide range of work, most of the big-tech platform companies have designed their own accelerators that are purpose-built for their most crucial types of computing. Microsoft developed chips that are optimized for its Azure cloud services. Amazons Trainium chips are especially good at e-commerce-related tasks like product suggestion and delivery logistics. Googles TPUs are good at serving targeted ads across its platforms and networks.
Thats something Google shares with Meta. They both do ads and so it makes sense that Meta wants to take a look at using Google’s TPUs, Hoff says. And its not just Meta. Most big tech companies use a variety of accelerators because they use machine learning and AI for a wide variety of tasks. Apple got some TPUs, got some of the AWS chips, of course got some GPUs, and they’ve been playing with what works good for different workloads, he adds.
Nvidias big advantage has been that its chips are very powerfultheyre the reason that training large language models became possible. Theyre also great generalists, good for a wide variety of AI workloads. On top of that, theyre flexible, which is to say they can plug in to different platforms. For example, if a company wants to run its AI models on a mix of cloud services, theyre likely to develop those models to run on Nvidia chips because all the clouds use them.
Nvidias flexibility advantage is a real thing; its not an accident that the fungibility of GPUs across workloads was focused on as a justification for increased capital expenditures by both Microsoft and Meta, analyst Ben Thompson wrote in a recent newsletter. TPUs are more specialized at the hardware level, and more difficult to program for at the software level; to that end, to the extent that customers care about flexibility, then Nvidia remains the obvious choice.
However, vendor lock-in remains a big concern, especially as big tech companies and AI labs are sinking hundreds of billions of dollars into new data center capacity for AI. AI companies would prefer instead to use a mix of AI chips from different vendors. Anthropic, for one, is explicit about this: Anthropics unique compute strategy focuses on a diversified approach that efficiently uses three chip platformsGoogles TPUs, Amazons Trainium, and NVIDIAs GPUs, the company said in an October blog post. Amazons AWS says its Trainium3 chip is roughly four times faster than the Trainium2 chip it announced a year ago, and 40% more efficient.
Because of the performance of Nvidia chips, many AI companies have standardized on CUDA, the Nvidia software layer that lets developers control how the GPUs work together to support their AI applications. Most of the engineers, developers, and researchers who work with large AI models know CUDA, which can cause another form of skills-based organizational lock-in. But now it may make sense for organizations to build whole new alternative software stacks to accommodate different kinds of chips, Thompson says. That they did not do so for a long time is a function of it simply not being worth the time and trouble; when capital expenditure plans reach the hundreds of billions of dollars, however, what is worth the time and trouble changes.
IDC projects that the high demand for AI computing power isnt likely to abate very soon. We see that cloud service providers are growing quickly, but their spending will slow down, Hoff says. Beyond that, a second wave of demand may come from sovereign funds, such as Saudi Arabia, which is building the Humain AI hub, a large AI infrastructure complex that it will fund and control. Another wave of demand could come from large multinational corporations that want to build similar sovereign AI infrastructure, Hoff explains. There’s a lot of stuff in 2027 and 2028 that’ll keep driving demand.
There are plenty of chipmaker challenges Nvidia stories out there, but the deeper one delves into the economic complexities and competitive dynamics of the AI chip market, much of the drama drains away. As AI finds more applications in both business and consumer tech, AI models will be asked to do more and more kinds of work, and each one will demand various mixtures of generalist or specialized chips. So while there is growing competitive pressure on Nvidia, theres still a lot ofgood reasons for players like Google and Amazon to collaborate with Nvidia.
In the next two years, there is more demand than supply so almost none of that matters, says Moor Insights & Strategy chief analyst Patrick Moorhead. Moorhead believes that five years from now Nvidia GPUs will still retain their 70% market share.
On November 19, Block Inc. held its first Investor Day in three years. Jack Dorsey, the company’s cofounder, chief executive, and “Block Head,” took to the stage and summarily posed what many investors and others in the industry were likely thinking.
Our business is complicated, he said. We want to make it much easier to understand going forward.
Dorseynotably clean-shavenproceeded to summarize the past few years at Block. The company is indeed much more complex now than when it was founded in 2009 as Square, named for the point-of-sale system that was the companys first product.
Four years ago, it changed its name to Block, a much more fitting moniker given its increasingly multidimensional portfolio, which now includes not only Square but also Cash App, Afterpay, Tidal, Bitkey, and Proto.
For Oakland, California-based Block, the growing pains were real as it has evolved from a single-product company to one that now facilitates payments (and buy-now-pay-later features) for both customers and merchants, has its hands in the crypto space, and even offers a streaming platform for musicians and creators.
The numbers bear it out: After going public in 2015, Block saw its stock price peak in 2021 at more than $270.
Like many other tech companies, Block has seen its shares fall from their pandemic-era highs. The stock is down roughly 26% in 2025 and the company fell short of Wall Street’s projections for its third-quarter earnings in November.
But Block has been making some behind-the-scenes moves over the past few years to right the ship. A major philosophical change, key acquisitions, and a renewed focus on simplicity have Blocks leadership excited about the companys future.
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A lesson in shape-shifting
Changing from a Square to a Block required fundamental organizational and philosophical shifts, which have been the most important aspect of Blocks evolution.
We decided to functionalize the company, says Owen Jennings, Blocks business lead.
That meant making big internal shuffles and reorganizing how information moved between engineers, designers, sales staff, and executives. It also meant putting functional leads into positions where they could be most impactful, whether they were working on product development or sales strategies.
We dissolved the business units and brought functional leaders to the top who reported directly to Jack, he says. He adds that the companys multiple business units were siloed and had different goals and models, which were leading to the wrong outcomes.
What became evident was that Block needed to find ways to serve both merchants and customersusing its products to either transact (via Cash App) or process payments (via Square).
The most obvious [thing] we could bring to the world was connecting the two worlds: consumers and sellers, Jennings says. But it wasnt happening based on the structure we had.
Since the reorganization, “it feels like were one massive company,” Jennings adds, but those changes took time to implement.
Functionalization happened within 18 months, says Nick Molnar, Blocks sales and marketing lead and the cofounder of Afterpay, who decided to stay with Block when it acquired Afterpay in early 2022.
Molnar says that while he is a relative Block newbie (Jennings, by contrast, has been at the firm for more than a decade), he’s seen a notable shift at the company. Meanwhile, most people arent even seeing the full results yet.
The back half of this year, youre seeing the work of the previous 18 months,” he says.
Blocks leaders have also married the functional model to the Rule of 40, a metric common in the SaaS sector, which says that a companys growth rate and profit margin should sum up to 40%.
Amrita Ahuja, Blocks foundational lead, says that prior to instituting the Rule of 40 framework, the company had expected that wed advance margins every yearwe wanted to share the trade-offs behind long-term growth and profitable long-term growth.
So we reoriented the company from the inside out,” she added. “That was really a language we built for the company. It helped us move faster and become more efficient, and ensure investments were going to drive growth.
The Rule of 40, paired with the new functional model, also allowed Block to reorient its larger focus on simplicitysomething it had gotten away from over the years as its business and structure have grown more complicated and convoluted.
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Basic building blocks
Ahuja first came to the company as Squares CFO in 2019. The thing that was striking to me, working at my first tech company, was the level of trust and transparency, she says. There was so much information, and everybody had access to it.
But, naturally, things get more complicated as a company grows, as Square did when it contended with the pandemic and then morphed into Block.
Square started with payments, then we built more than 30 products around it, Ahuja says.
It was a similar situation with Cash App. The kernel was around social money, peer-to-peer transactions,” she adds. “Now we have built a dozen products around that.
Over the years, its become increasingly important to get back to basics and “focus on the things that mean the most to our customers, Ahuja says, adding, “Weve already built a lot of depth and complexitynow its about making sure the right product gets surfaced at the right time for customers.
That is exactly what Block is doing now.
In recent months, Block has announced several new products, including new tools and features under its Square AI suite, Square Bitcoin, and Neighborhoods, a new feature for Cash App, which connects customers with local businesses.
With crypto finding wider adoption and a friendly regulatory framework, and AI being basically everywhere and anywhere, developing and releasing these types of products clearly makes sense for Block.
But Jennings says that Dorsey is not merely jumping on trends for the sake of doing so.
Hes willing to be patient for a long period of time to the extent that he has conviction, and hes been proven right many times, Jennings says of Dorsey, who is also a cofounder of Twitter and, more recently, the competing social media platform Bluesky, although he’s no longer involved in either.
“The power of Jack is that when he comes to the all-hands or presents the company strategy, its incredibly simple,” he adds, “and gets to the essence of what were trying to do.”
The big question: Will it all pay off?
A chip off the new Block
Blocks leaders say that the pieces are in place for a sort of corporate renaissance.
I believe that Block has the ingredients it needs to accelerate its growth, that flows through a really strong, profitable business thats growing in line with some of the best companies in the world, says Molnar.
So even as it may seem like the companys been underachievingperhaps in terms of sagging stock and recent earnings missesthose on the inside say they are brimming with confidence.
Were leading, and will continue to lead, says Jennings, who is particularly confident about Square Bitcoin, which offers no-fee Bitcoin payments for sellers around the world through its existing point-of-sale systems.
He thinks Block is well-positioned to take advantage of the growing ubiquity of Bitcoin payments in the years ahead.
As for Blocks broader goals? During its Investor Day 2025 presentations, the companys 2026 guidance showed expectations of nearly $12 billion in gross profit, an increase of 17% year-over-year.
It also released, for the first time, a three-year financial outlook that lays out what Blocks leadership is expecting, a sign that Block is fully grown up out of its startup stage, and that it’s here for the long haul.
By 2028, Block’s outlook shows, the company anticipates gross profit growth will be in the mid-teens, and that adjusted earnings per share growth will be somewhere around 30%, and on track for further revenue growth.
That would mark quite a turnaround, but Block executives believe they have the team, product mix, and leadership to persevereeven if it takes some time.
Jack is very good at knowing when to be patient and impatient, says Ahuja. From the first day I joined the company, there was a conversation about what his title should be: CEO or editor? Hes the editorhe’s the person who guides us in how we focus our efforts.”
During his comments at Investor Day, Dorsey echoed Ahujas sentiment. Ive never felt more confident that we have all the tools, the structure, the team, and the people to prove this out,” he said.
Internet infrastructure company Cloudflare on Friday said it was investigating an outage that took place in the morning that brought down several global websites including LinkedIn, Zoom and others, the second such crash to affect the company in less than three weeks.Cloudflare said the issue had been resolved, and that it was was “investigating issues with Cloudflare Dashboard and related APIs,” or application programming interface that allow software systems to communicate with each other.The company said the outage was not due to an attack. A change to how its firewall handles requests “caused Cloudflare’s network to be unavailable for several minutes this morning,” the company said.Users on social media platform X also reported problems accessing the website.Edinburgh airport had to shut down briefly on Friday morning. But the airport later said the outage was a localized issue that was not related to Cloudflare.In November, a Cloudflare outage affected users of everything from ChatGPT and the online game, “League of Legends,” to the New Jersey Transit system.Last month Microsoft had to deploy a fix to address an outage of their Azure cloud portal that left users unable to access Office 365, Minecraft and other services. The tech company wrote on its Azure status page that a configuration change to its Azure infrastructure caused the outage.Amazon also experienced a massive outage of its cloud computing service in October.
This version has been updated to reflect that Edinburgh airport says its temporary shutdown was not related to the Cloudflare outage.
Associated Press
