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This is worth watching because it could change which AI model your own company defaults to, without anyone deciding it on purpose. Moonshot AI, a Beijing-based startup, is in talks with Microsoft, Amazon, and Google to have its Kimi K3 model hosted and sold directly through their cloud stores, the same shelves where OpenAI and Anthropic's models already sit.

The model itself is enormous: Kimi K3 has 2.8 trillion parameters (the internal settings a model learns during training). That is far too large for almost any company to run on its own computers, so most businesses would rather rent it through a cloud than try to run it themselves. Moonshot is reportedly asking each cloud for a revenue share of up to 30 percent, meaning it would keep 30 cents of every dollar the cloud charges a customer to run Kimi K3. That is a serious cut even by AI industry standards, and it shows Moonshot trying to make money from a model whose underlying files are free to download (an open-weight model: anyone can download the files and run it themselves).

If a deal like this closes, it removes the biggest practical barrier to using a Chinese model at a real company: billing, compliance, support, and infrastructure would all run through a cloud your company might already trust and already pay. Chinese models have gained ground this year mostly on price, especially in agentic work (tasks where the model takes several steps on its own), because a cheaper model can cut costs a lot even after the cloud adds its own markup.

NVIDIA is leaning into this shift rather than fighting it. The chipmaker has been adding technical support for Chinese open models even while warning that U.S. government restrictions could limit that work. This week NVIDIA announced optimizations for DeepSeek's V4 Flash and Alibaba's Qwen 3.8, plus day-zero support (support ready the day a model launches, not weeks later) for a smaller Qwen 3.8-27B model on its RTX chips, and updates that make clusters of its DGX Spark computers easier to use for two more Chinese models, Z.ai's GLM 5.2 and DeepSeek V4 Flash. Forrester analyst Charlie Dai told CNBC that NVIDIA's moves reflect the growing influence of Chinese models and NVIDIA's goal of staying the preferred hardware layer no matter which country's model wins. In plain terms, NVIDIA does not care who makes the best model as long as it runs best on NVIDIA chips.

The politics are getting sharper. The Trump administration is reportedly considering rules that would restrict NVIDIA's ability to support Chinese-made models and apps, and NVIDIA has warned that restrictions on models like DeepSeek, Qwen, or Kimi could hurt its own business. Moonshot itself is under direct scrutiny: Treasury Secretary Scott Bessent has said he might add the company to a trade blacklist, and U.S. officials have accused Moonshot of using distillation (training a smaller model to copy a bigger one, in this case allegedly Anthropic's Fable model) and of illegally obtaining NVIDIA chips. Complicating any crackdown, more than 20 companies including Microsoft, Meta, Palantir, and NVIDIA itself asked policymakers in July not to impose premature restrictions on open-weight models, since anyone, not just Chinese developers, can download and modify them.

None of these cloud deals have closed yet. But the fact that Moonshot is negotiating shelf space on the three biggest clouds at all is the real story: Chinese model makers are no longer just competing on downloads and cheap pay-per-use pricing, they are now going after the recurring, enterprise-grade distribution that OpenAI and Anthropic have relied on as their edge.

Every time you read that a new model beat a rival on some test, that claim is only as good as whether the model got to see the test questions beforehand. Google DeepMind just ran what it calls the world's first double-blind evaluation of a major, closed AI model, meaning neither DeepMind nor the outside group testing it could see the other side's material in advance. That is aimed straight at a problem worth being suspicious of whenever you read a benchmark score: benchmark contamination, where a model has already seen the test questions during its training and aces a test it never really had to solve.

DeepMind tested a Gemini Flash Lite model (a smaller, faster member of its Gemini family) against confidential questions, working with four outside partners: the Singapore AI Safety Institute, a privacy-technology nonprofit called OpenMined, an evaluation group called AVERI, and MLCommons, the organization behind many of the industry's standard AI tests.

The trick is a tool called Confidential Space, part of Google Cloud's confidential computing product line. It lets two parties run a test inside something like a sealed box: the outside evaluator's questions go in, DeepMind's Gemini model goes in, and the box runs the test with cryptographic proof (a mathematical guarantee, not just a promise) that neither side can see the other's material. The evaluator never sees Gemini's underlying model files, and DeepMind never sees the evaluator's questions, before or after the test.

Historically, testing a closed AI model rigorously meant one side had to trust the other with something valuable. Either the outside evaluator handed its questions to the AI company, risking that a future model gets trained on those exact questions, or the AI company handed over its model's internals to the evaluator, risking its intellectual property leaking out. Contracts and no-logging promises have covered this gap so far, but this is the first time the guarantee comes from cryptography instead of trust.

DeepMind says this matters most for the highest-stakes tests, such as cybersecurity evaluations or ones run by government safety bodies, where neither the questions nor the model can be allowed to leak. If double-blind testing spreads across the industry, it gives outside groups, and eventually him, real grounds to trust a benchmark score instead of discounting it for possible contamination. For now this is a pilot on one mid-size model, not yet a routine industry practice, so treat it as the first real attempt at a fix rather than a finished standard.

Outside your usual reading: this is a hardware story about memory chips, not a product launch, but it explains why AI could eventually get much cheaper to run.

Normally, a computer's memory chip (DRAM) just stores data, and the processor has to fetch that data over a slow connection before it can do any math with it. That trip is one of the biggest hidden costs of running AI models: pulling the model's numbers out of memory usually takes longer than doing the actual arithmetic on them. At the Hot Chips 2026 conference, Samsung showed a different approach called Processing-in-Memory (PIM): put small calculators directly inside the memory chip, next to the data, so it never has to leave.

Samsung built this into a standard chip type called LPDDR5X, the kind used in phones and laptops. A normal LPDDR5X chip is divided into 16 separate storage sections called banks, and normally the computer can only pull data from about two of those banks at once, capping speed at 76.8 gigabytes per second. Samsung's version, LPDDR5X-PIM, puts a small calculator, built to do MAC operations (multiply then add, the basic operation behind almost all AI math), inside every one of the 16 banks. Because each calculator works directly with its own bank, all 16 can run at once, for a combined internal speed of 614 gigabytes per second, about eight times faster.

Each in-memory calculator is small: it holds a scratchpad of numbers to work with and a list of up to 64 instructions to follow, and it multiplies those against whatever model data already sits in that bank, using low-precision number formats (8-bit numbers, coarser than a full-size number but far cheaper to compute with and already standard for AI). Wire together eight of these chips and you reach roughly 9.6 trillion operations per second, in the same range as the small AI chip built into Intel's Meteor Lake laptop processors, though it costs you 128 gigabytes of ordinary memory you may not have needed.

The clever part is that Samsung did this without inventing a new memory standard the industry would need to redesign around. The chip still speaks the normal memory language: Samsung just reserved a handful of special memory addresses that act like hidden switches. Writing to one flips the chip into compute mode, at which point ordinary write commands load numbers into the calculators instead of storing data, and ordinary read commands trigger the math and return results instead of stored data. Flip it back, and the chip behaves like normal memory again. This is a conference presentation, not a shipping product, and Samsung's own materials leave open how much software rework it would take to actually use, but it is a real preview of chipmakers chasing the true cost of AI: moving data, not doing the math.

OpenAI is expanding ChatGPT for Teachers, its free tool for schools, to 55 more U.S. school districts, reaching over 100,000 additional teachers and staff with secure AI tools and training. It is the enterprise software playbook applied to education: give the tool away early so a whole generation of students and teachers grows up defaulting to it.

An investor group has dropped its roughly $50 billion attempt to buy PayPal. A deal that size falling apart is a sign that even well-funded buyers are getting more cautious about paying up for a slowing fintech giant.

OpenAI is putting more people and resources into Brazil to work directly with local developers, businesses, and communities on AI adoption. It is another sign the big AI labs are racing to lock in international markets early, not just fight it out in the U.S.

Google's Gemini Omni 1.1 Flash video model now lets developers extend a clip using up to 10 seconds of prior footage for context, versus just the last second before, set exact start and end frames, and draft cheap 360p previews up to 60 percent faster before upscaling to 4K. AI video tools are moving fast from novelty demos toward something a production team could actually use day to day.