AI is no longer limited by ideas. It's limited by money, compute, and who can scale fastest.

Sam Altman has reportedly told staff that Spud could "really accelerate the economy"

both companies are hinting that these models are a real step forward, not just small upgrades.

Gemma points in the opposite direction: smaller models, local compute, more ownership.

A founder in LA reportedly scaled Medvi toward $1.8B in annual sales with basically one full-time employee.

Employees still own a surprisingly large 19.35%. SoftBank comes in at 11.66%, followed by VC and institutional investors at 7.83%, Amazon at 4.66%, NVIDIA at 3.47%

And once models get good at that, the question stops being whether they can make beautiful images. It becomes whether people still notice when something was never real to begin with.

Most people talk about OpenAI like it's basically 'owned by Microsoft,' but the actual cap table is much more spread out.

The first wave of image models was mostly about making cool-looking images. This next phase is about making ordinary things look real.

We are building a world where machines write the code, machines choose the dependencies, and machines ship the updates. The AI agents are building the software. If we don't secure the supply chain they rely on, the AI agents are cooked.

Socket, an a16z portfolio company, detected the malicious dependency in the Axios attack within 6 minutes of its publication. That's roughly 63,000 times faster than the industry average.

The autonomous coding agents now entering production can install dependencies, execute builds, and open pull requests without a human ever touching the keyboard. They optimize for 'does this work?' not 'is this safe?'

Hallucinated packages are the sleeper threat. LLMs regularly invent package names that don't exist. One study found that nearly 20% of AI-recommended packages were fabrications, and 43% of those hallucinated names appeared consistently across queries.

AI agents select known-vulnerable dependency versions 50% more often than humans. Worse, the vulnerable versions they pick are harder to fix, requiring major-version upgrades far more frequently.

Talent density : the biggest prizes in capitalism attract the best minds in the field. These are the fastest growing software companies in history.

At this rate, the phone in your pocket will run today's frontier models before you upgrade it.

In 23 months, the same capability that needed 1.8 trillion parameters now fits in 4 billion parameters. A 450x compression.

Within three to four months, you can run a model with similar performance on your laptop; 23 months later, you can run the same model on your phone.

a free model that matches GPT-4o and runs entirely on your phone

Someone who builds premium dating apps, let's say, might use AI coding tools to create in one day what used to take three days. That means the worker is more productive. The worker's employer, spending the same amount of money, can now get more output. So then will the employer want more employees or fewer?

We need, like, a Manhattan Project to collect this... Fields that are not exposed now will become exposed in the future, so you just want to track these statistics across the entire economy.

Exposure alone is a completely meaningless tool for predicting displacement

in the past year Huawei has overtaken Nvidia as the leading source of AI computing power in China, at least in terms of rated FLOP/s

We estimate that as of the end of 2025, Chinese companies collectively own just over 5% of the cumulative computing power of the leading AI chips sold in recent years

Many frontier AI developers, including Anthropic and OpenAI, acquire almost all of their compute from hyperscalers and other cloud providers.

Google holds the equivalent of around 5 million Nvidia H100 GPUs in compute capacity, roughly 25% of the world's total!

We estimate that over 60% of global AI compute (in terms of total computing power) is owned by the five US hyperscalers, led by Google.

複雑なリサーチは、単一のクエリに対する回答の集積ではなく、アイデアの生成から、裏付けとなる証拠の探索、矛盾の解消、そして最終的なレポートとしての構造化まで、一連のプロセスを完遂する必要があります。

推論時により長く、深く思考させることでよりよいアウトプットを引き出せる。これが推論スケーリングの本質です。

For higher-interactivity scenarios, execution time for MoE models is bound by expert weight load time. By splitting, or sharding, the experts across multiple GPUs across NVL72 nodes, this bottleneck is reduced, improving end-to-end performance.

NVIDIA yields unmatched inference throughput across the broadest range of workloads, from massive LLMs to advanced vision language models, to generative recommender systems and more, on industry-standard benchmarks.

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications.

By applying compute otherwise that goes unutilized to predict and verify additional tokens in parallel (up to three in this implementation), throughput at high interactivity is increased.

NVIDIA was the first and only platform to submit DeepSeek-R1 results on MLPerf Inference when the benchmark debuted last year.

This means 2.7x more tokens from the same GB300 NVL72-based infrastructure and power footprint, reducing the cost to manufacture each token by more than 60%.

Using vLLM high-throughput LLM serving on DGX Spark provides a high-performance platform for the largest Gemma 4 models

The E4B and E2B are the newest edition of on-device and mobile designed models first launched with Gemma 3n.

The bundle includes four models, including Gemma's first MoE model, which can all fit on a single NVIDIA H100 GPU and supports over 140 languages.

Modern physical AI agents are evolving rapidly with Gemma 4 models that integrate audio, multimodal perception, and deep reasoning capabilities.

The 31B and 26B A4B variants are high-performing reasoning models suitable for both local and data center environments.

NVFP4 enables 4-bit precision while maintaining nearly identical accuracy to 8-bit precision, increasing performance per watt and lowering cost per token.

By using SAM, the Alta team has been able to process more than 20 million images without incurring exorbitant costs, allowing them to focus on building the best possible product for their users.

If we knew that every image uploaded was a beautiful model shot, segmentation would be far easier, but because of the nature of user-uploaded content, we need the best possible segmentation.

Fashion in particular has one of the most complex image datasets, especially because of the inconsistent nature of user-uploaded content.

Built from the same world-class research and technology as Gemini 3

Engineered from the ground up for maximum compute and memory efficiency

The edge models feature a 128K context window, while the larger models offer up to 256K

Gemma 4 outcompetes models 20x its size

Byte for byte, the most capable open models

Teams at companies like Notion, Ramp, Braintrust, and Wasmer are already using Codex to accelerate their engineering workflows.

Within ChatGPT Business and Enterprise, the number of Codex users has grown 6x since January.

Codex-only seats have no rate limits, and usage is billed on token consumption.

Priority areas include safety evaluation, ethics, robustness, scalable mitigations, privacy-preserving safety methods, agentic oversight, and high-severity misuse domains.

Fellows will receive API credits and other resources as appropriate, but will not have internal system access.

Fellows will work closely with OpenAI mentors and engage with a cohort of peers.

We prioritize research ability, technical judgment, and execution over specific credentials.

We are especially interested in work that is empirically grounded, technically strong, and relevant to the broader research community.

The vast majority of the new compute will be sited in the United States, making this partnership a major expansion of our November 2025 commitment to invest $50 billion in strengthening American computing infrastructure.

Claude remains the only frontier AI model available to customers on all three of the world's largest cloud platforms: Amazon Web Services (Bedrock), Google Cloud (Vertex AI), and Microsoft Azure (Foundry).

We train and run Claude on a range of AI hardware—AWS Trainium, Google TPUs, and NVIDIA GPUs—which means we can match workloads to the chips best suited for them.

over 500 business customers were each spending over $1 million on an annualized basis. Today that number exceeds 1,000, doubling in less than two months.

Demand from Claude customers has accelerated in 2026. Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025.

Figure 2. Four mechanisms support concurrent task execution in CORPGEN: hierarchical planning, isolated subagents, tiered memory, and adaptive summarization.

Clean Room as a Service

Some retailers / brands offer this on their website already, but it’s limited to their SKUs. We see an opportunity for AI consultants that have deep knowledge on a product category across different brands, and that learn more context on each user and their preferences over time (for example, if it helps you buy a sofa, it can later tailor chair recommendations to things that match).

深势科技

OJBench

Multi Hop

Tensor Parallelism

PWA-enabled dashboard

A managed runtime environment

consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute)