SK Hynix posted record profit today. The chip maker builds memory for computers. AI data centers need that memory now. Demand grew so fast that supply fell behind.

Revenue hit $78.9 billion for the quarter. That is up 328 percent from one year ago. Operating profit reached $62.8 billion. That number grew 632 percent in one year. The operating margin hit 80 percent, a record high.

SK Hynix sells HBM chips for AI servers. HBM stands for high bandwidth memory. It moves data faster than older chip types. AI labs need that speed to train big models.

The company signed deals with 14 major customers. Those deals lock in over half its factory output through 2028. SK Hynix also plans $75 billion in dividends and buybacks this year.

A Reddit post on r/LocalLLaMA shared the earnings today. The poster warned that high RAM prices may get worse. The thread pulled 44 comments within hours. Readers traded stories about empty store shelves.

A trading account on X broke down the numbers first. Wall St Engine's post pulled 1,522 likes. The figures beat Wall Street's own estimates by a wide margin.

Memory chip prices affect more than servers. Gamers already pay more for graphics cards. Laptop and phone makers may feel the pinch next. SK Hynix picked AI customers over everyday buyers. That choice pays off for its own bottom line. Regular shoppers absorb the cost downstream.

The jump says something plain about AI today. One company's memory chips now outsell its own forecasts. Gamers and coders compete for the same parts. A data center buys first and pays more. That squeeze reaches every builder who wants a chip. SK Hynix expects the squeeze to last through 2030.

Anthropic cut its price for governments. Claude is Anthropic's AI tool. Federal workers use Claude now. State and city workers can join too.

The deal is named OneGov. It once covered federal workers only. Now it covers state, local, and tribal groups. Each group gets 40 percent off every Claude plan.

The cut covers three Claude plans. It covers Claude for teams. It covers Claude for government use. It covers the Claude API too. Groups pay only for what they use. Order size stays open.

Anthropic says the cut could save one billion dollars. About five hundred groups use Claude today. Weekly government users grew eightfold since February. Use by federal workers grew twentyfold in a year.

Anthropic's own post about the cut spread fast. It pulled 1,353 likes in hours. The post drew 209 replies from fans and critics.

Wikipedia traffic for Anthropic stayed high today. The firm's page logged 20,352 views. That follows a big jump tied to its stock filing this week.

The cut lands during a busy week for Anthropic. The firm just filed papers to sell stock. A price cut for governments adds a second big story. Government deals rarely make big news on their own.

OpenAI made a similar pricing change for government buyers last month. Both firms now chase the same public buyers. A city hall or a state office can weigh either tool. That choice was far costlier just a year ago.

A steep cut and fast growth numbers changed the mood today. More workers may soon type into Claude at their desks. That shift could shape how towns and states buy AI tools for years. A firm that wants to sell stock to the public likes growth numbers in plain view. Big discounts and big user counts both feed that same story.

OpenAI wants to buy Google's AI chips. A report from The Information broke the news. OpenAI has used Nvidia chips for years. This move would add a second chip maker.

Google calls its chips TPUs. That stands for tensor processing unit. OpenAI already rents some Google chips today. Buying chips outright marks a bigger step.

The report skips a price or chip count so far. Both firms stayed quiet in public today. Google already sells TPU access to Oracle this week. That deal lets Oracle offer Google's chips to its own clients.

A trader's post on X broke the story down fast. GURGAVIN's post pulled 624 likes in hours. It drew 42 replies from readers parsing the news.

Nvidia stock barely moved on the news. Shares closed at $217.94, up 0.9 percent that day. Traders seem to doubt the shift will hurt Nvidia soon.

OpenAI spends huge sums on chips every month. Nvidia chips have driven most of that spending so far. A second chip maker could lower OpenAI's costs over time. It could also ease a tight supply of Nvidia parts.

Google now sells chips to its two biggest AI rivals. Oracle rents space to run other firms' models. OpenAI builds the models that compete with Google's own. Google still profits from both deals either way.

The news points at a wider shift in AI computing. One buyer leaning on one chip maker now looks risky. OpenAI wants backup options, even at the cost of simple supply chains. Google gains a new buyer for chips it once kept to itself. That buyer happens to be one of its biggest rivals in AI. The two firms now need each other more than before this week.

A Reddit post broke down a big model ranking update today. Artificial Analysis tracks AI model skill. The group just released version 5 of its Intelligence Index. The new version changed how models get scored.

The gap between open and closed models grew wider. Closed models stay private to one company. Open models let anyone download the weights. The score gap went from 3 points to 12 points.

Kimi K3.1 Pro now tops the open model list. It scored 46 points, but its weights stay unreleased for now. DeepSeek V4.1 Flash and GLM-5.3 share the next spot. Both scored 32 points on the new index.

Claude Haiku 5.5 scored 36 points in top mode. That score beats every open model released so far. The poster said real use can feel far off from these scores. One reader said a closed model felt weaker than its rank showed.

Most of the new index leans on private test questions. About 60 percent of the score comes from those hidden sets. That makes the work hard for outsiders to check.

The post pulled 53 comments on r/LocalLLaMA in hours. A story about the update also reached Hacker News today, though it drew a far smaller crowd there. Readers in both threads argued hard over which models they trust most day to day.

The debate points at a real tension in AI testing today. A single number sounds simple and clean. The method behind that number often stays hidden from view. Coders who build on open models want to see the work. A score that stays hidden carries less weight with that crowd. Open models now trail by more points than before. The gap keeps growing fast with each new version that ships.

A group of physicists spoke up about an AI math claim today. OpenAI said its models helped prove parts of the Yang-Mills problem. That problem sits among math's hardest open questions. A full solution carries a cash prize worth millions.

OpenAI says its model built a proof back in September. The proof covers existence alone, apart from the mass gap piece. That gap stays the harder part of the full problem.

A team led by Nobel winner David Gross now reviews the work. The team chose to hold back the AI's own text for now. The team plans to write its own paper instead. A separate group wants the material shared right away.

The Kavli Institute for Theoretical Physics posted a statement today. It named the review team the Yang-Mills Working Group. The post pulled 544 likes and 26 replies in hours.

Wikipedia traffic for the Yang-Mills problem spiked hard today. The topic's page logged 5,702 views yesterday. The day before, that same page logged only 1,060 views.

The fight says something plain about AI and science today. A model can churn out pages of dense proof text fast. Checking that text still takes trained humans real time. Famous names now referee a machine's math homework by hand.

That review step may matter more than the proof itself. Readers will learn the real result only once humans finish checking it fully. Science still moves at the pace of its slowest careful reader. A fast model and a careful team now pull the story two ways.

OpenAI also claims two other math wins this month. One touches a geometry puzzle called the Kakeya problem. One touches a computer science puzzle called the Exponential Time Hypothesis. Outside math groups have pushed to see those proofs too. Trust in the final result will rest on which side the public remembers best.

Broadcom lined up $63 billion to help Anthropic buy chips. Four Wall Street firms are funding the deal. They include Bank of America, Blackstone, Citigroup, and Morgan Stanley. The cash flows through a brand new firm built just for this deal.

That new firm will buy Broadcom's AI chips. It will then lease those chips to Anthropic. Broadcom backs part of the loan if the chips lose value later. Banks plan to sell $21 billion of bonds to cover their share, as early as next year.

The chips themselves start shipping in 2027. That timeline gives Anthropic a long runway to grow into the deal. It also means banks wait years before the bet fully pays off.

Broadcom's chief executive, Hock Tan, held late talks with lenders over the weekend. Some banks had worried the deal paid them too little for the risk they carried. Tan's talks held the financing together before the public news broke.

Bloomberg's own post about the deal went up today. It pulled 33 likes and 13 replies in hours. The small numbers hide how big this deal really is.

The deal shows how AI spending now reaches deep into banking. A chip maker, a lab, and four big banks share one bet. Wall Street once just lent money to tech firms. Now it builds whole new firms just to own and lease hardware.

That shift spreads AI's money risk across more players. It also ties bank balance sheets closer to one lab's path. If Anthropic's growth holds, every firm in the deal wins big. If demand cools first, the risk lands somewhere down that same chain. Either way, the deal shows how far banks will bend to back one company's bet on AI chips.