Categories
AI Apple Google

The Floor

I compared the frontier to a three-star chef making grilled cheese in “Context Rot” — the smartest models on earth spending most of their time on work beneath them, the way a chef trained at Le Bernardin might still melt cheese between two slices of bread on a Tuesday night and call it dinner. The comfort was the point: if the sharpest tool is saved for hard problems and something merely-very-good handles the rest, nobody’s losing anything. The floor was never the interesting part.

I’ve kept turning the joke over, and I think I had the wrong worry.

Watch what companies do with their AI spend, not what they say. Coinbase moved engineers off frontier models onto open weights and cut its AI spend nearly in half while usage kept climbing. Nvidia runs a closed model as orchestrator and routes the actual volume — the daily uncelebrated bulk of it — to open weights it controls. The frontier is becoming a dispatcher, deciding where the request goes and rarely doing the work itself. The instinct is to worry about whose open weights end up running that volume, and right now the most capable ones at scale are Chinese — GLM, Kimi — which makes it tempting to read this as a contest America is quietly losing: the floor of the AI economy built somewhere else, at a price export controls can’t touch. You cannot embargo a file already downloaded. You cannot price-match free.

But that framing has a hole. Google’s own Gemma family is open-weight and good enough to handle that daily volume without anyone reaching for GLM or Kimi. “Open weights are a Chinese story” only holds if you don’t count the open models the company running Android and half the internet’s search traffic has already shipped.

And once I saw that hole, a bigger one opened behind it. I’ve been trying Apple’s new Siri — arriving with iOS 27 this fall, genuinely surprisingly good in beta — and it made me realize open weights, of any nationality, were never going to cook most of the world’s dinners. Apple and Google are.

Consider what actually determines where the world’s routine inference runs. Not which model benchmarks best, not which weights are downloadable — what’s already installed. Apple ships to well over a billion active devices before routing a single query through Siri’s new architecture. Nobody has to be persuaded to try it, or hear about it on a podcast; it’s the thing that answers when you press the button you’ve pressed for a decade. Google owns the search bar and the Android default the same way. Between them, that’s most of the world’s phones — and phones are where most of the world’s questions get asked.

The open-weight framing assumes the floor is up for grabs, that whoever ships the best free model wins the daily grind by merit. But the floor was never a bazaar. It’s a set of defaults, owned by whoever already has the device in your hand, not whoever holds the most generous license. Apple didn’t need to win the model war to win this. Its heaviest reasoning tier is built with Google, running on Nvidia chips in Google’s cloud, under a deal reported at roughly a billion dollars a year — Apple doesn’t fully own the engine doing the thinking. It doesn’t need to. It owns the button.

That’s a quieter concentration than an export-controls fight, and a harder one to dislodge. An open model can be forked, distilled, undercut, or out-competed by the next release. A billion phones with an assistant built into the lock screen cannot be routed around. Whoever’s weights hum underneath barely matters, the way it barely matters to a diner which supplier delivered the flour. What matters is whose kitchen the meal came from, and whose name is on the door.

The grilled-cheese chef was never the risk. Two chefs are about to own nearly every kitchen on earth, and most of us will never notice — because a kitchen you’ve been eating out of for a decade doesn’t feel like something that was won. It just feels like home.

Owning the kitchen and getting paid for what’s cooked in it, though, turn out to be two different questions. That one’s for another post.

Categories
AI AI: Large Language Models

Kimi K2.5

I tried a few conversations with Kimi K2.5 last night and it is pretty impressive. It remains to be seen how the competition between models – especially open weight / open source models – will end up! Rumors are swirling that DeepSeek may release an updated model next month. We’ll see!

I asked Grok for the consensus “buzz” on the release of Kimi K2.5. Here’s what Grok reported this morning:

The consensus buzz around Kimi 2.5 (more precisely Kimi K2.5, from Moonshot AI) is extremely positive, especially in the open-source AI community, on platforms like Reddit (r/LocalLLaMA, r/singularity), X, Hugging Face, and tech news outlets. It was released very recently (around January 27, 2026), so the excitement is fresh and intense.

It’s widely hailed as one of the most powerful open-source models available right now—a native multimodal (text + vision, including strong video understanding and “visual coding”) Mixture-of-Experts model with ~1 trillion total parameters (32B active), 256K context, native INT4 quantization for efficiency, and standout agentic capabilities.

Key highlights driving the hype:

  • Benchmark dominance — It outperforms or beats models like GPT-5.2, Claude 4.5 Opus/Sonnet, Gemini variants, and other frontier models in several areas, including:
  • Humanity’s Last Exam (HLE): ~50-51.8% with tools (strong agentic reasoning)
  • SWE-Bench Verified (coding): Top scores, often leading in visual-to-code and software engineering tasks
  • Multimodal benchmarks (MMMU Pro, VideoMMMU, OCR-heavy tasks): State-of-the-art or near-SOTA without fine-tuning
  • Math/reasoning (AIME, GPQA-Diamond, etc.): Frequently matches or exceeds closed models
  • Standout features — “Agent Swarm” (beta) lets it orchestrate up to 100 sub-agents in parallel for complex tasks, handling 1,500+ tool calls and reportedly cutting execution time by up to 4.5x on long-horizon workflows. This is seen as a big step forward in agentic AI. Visual coding is a killer demo: upload a video/screen recording of a website/interface, and it clones it (including UX interactions) in one shot—people are calling this transformative for “vibe coding” and rapid prototyping.
  • Open-source advantage — Weights on Hugging Face under a Modified MIT license (permissive for most uses). It’s accessible via APIs (OpenAI-compatible) on platforms like Moonshot’s site, Together AI, NVIDIA NIM, and others—often at lower cost than Western closed models. Many see this as China continuing to close the gap (or pull ahead in open releases).

Community sentiment:

  • Enthusiastic praise for being “the strongest open-source model” in multimodal/agentic domains, with comments like “huge leap,” “crushing benchmarks,” and “changing the game” for coding, visual analysis, and tool-augmented work.
  • Some skepticism exists (as with any new model): questions about real-world vs. benchmark performance, potential optimization for evals, or occasional long-context quirks in earlier Kimi versions. A few users report mixed results in specific tools (e.g., GitHub Copilot integrations), but overall feedback leans heavily positive, especially for reasoning, creative tasks, and agentic flows.
  • It’s fueling discussions about open-source momentum, with parallels drawn to DeepSeek, Qwen, and others—positioning Moonshot as a top Chinese AI player.

In short, the vibe is “this is a massive win for open AI” and “watch out closed models”—with developers rushing to test it for coding agents, visual-to-code projects, and multi-agent setups. If you’re into frontier open models, it’s the hot topic right now.