Rui Ma

MiMo-V2-Pro & Omni & TTS is out. Our first full-stack model family built truly for the Agent era. I call this a quiet ambush — not because we planned it, but because the shift from Chat to Agent paradigm happened so fast, even we barely believed it. Somewhere in between was a process that was thrilling, painful, and fascinating all at once. The 1T base model started training months ago. The original goal was long-context reasoning efficiency. Hybrid Attention carries real innovation, without overreaching — and it turns out to be exactly the right foundation for the Agent era. 1M context window. MTP inference for ultra-low latency and cost. These architectural decisions weren't trendy. They were a structural advantage we built before we needed it. What changed everything was experiencing a complex agentic scaffold — what I'd call orchestrated Context — for the first time. I was shocked on day one. I tried to convince the team to use it. That didn't work. So I gave a hard mandate: anyone on MiMo Team with fewer than 100 conversations tomorrow can quit. It worked. Once the team's imagination was ignited by what agentic systems could do, that imagination converted directly into research velocity. People ask why we move so fast. I saw it firsthand building DeepSeek R1. My honest summary: — Backbone and Infra research has long cycles. You need strategic conviction a year before it pays off. — Posttrain agility is a different muscle: product intuition driving evaluation, iteration cycles compressed, paradigm shifts caught early. — And the constant: curiosity, sharp technical instinct, decisive execution, full commitment — and something that's easy to underestimate: a genuine love for the world you're building for. We will open-source — when the models are stable enough to deserve it. From Beijing, very late, not quite awake.

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord. Use this to message Claude Code directly from your phone.

I know Silicon Valley startups don't want to hear this..... But the combination of someone in the trades with deep domain expertise and Claude Code will run circles around your generic software. I talked to Cory LaChance this morning, a mechanical engineer in industrial piping construction in Houston. He normally works with chemical plants and refineries, but now he also works with the terminal He reached out in a DM a few days ago and I was so fired up by his story, I asked him if we could record the conversation and share it. He built a full application that industrial contractors are using every day. It reads piping isometric drawings and automatically extracts every weld count, every material spec, every commodity code. Work that took 10 minutes per drawing now takes 60 seconds. It can do 100 drawings in five minutes, saving days of time. His co-workers are all mind blown, and when he talks to them, it's like they are speaking different languages. His fabrication shop uses it daily, and he built the entire thing in 8 weeks. During those 8 weeks he also had to learn everything about Claude Code, the terminal, VS Code, everything. My favorite quote from him was when he said, "I literally did this with zero outside help other than the AI. My favorite tools are screenshots, step by step instructions and asking Claude to explain things like I'm five." Every trades worker with deep expertise and a willingness to sit down with Claude Code for a few weekends is now a potential software founder. I can't wait to meet more people like Cory.

GGII, a Chinese robotics research firm, just put out a notably optimistic, data-heavy take on how quickly humanoids are moving toward commercialization. Here are some of its key claims: GGII says average unit cost fell to 100,000 yuan (~$14,300) in Q1 2026, down from 150,000 yuan (~$21,400) in 2025, while the average payback period shortened to 12 months, versus 18 months in 2025 and 48 months in 2023. There is also a price war. In March, UBTech reportedly cut its industrial humanoid to 128,000 yuan (~$18,300), Fourier launched the GR-3 at 115,000 yuan (~$16,400), and Unitree priced the G1 at 99,000 yuan (~$14,100), making it the first major model to break below 100,000 yuan (~$14,300). GGII frames 12 months as an important threshold. They quote a manufacturing CFO saying traditional automation usually pays back in 18-24 months, so if humanoids can reliably stay under 12 months, they become much easier to justify as capex. GGII projects 80,000 units shipped in 2026, up 185% from 28,000 in 2025. The piece also says Q2 2026 shipments alone could exceed 25,000, and that full-year shipments could be revised up to 100,000 if prices keep falling. Factory deployments are the main driver of that demand. GGII says factory use cases will account for 72% of 2026 demand, up from 65% in 2025, with the remaining 28% coming from logistics, security, and commercial services. One of the most concrete anecdotes is a factory case study. A Chinese auto-parts company reportedly bought 50 humanoid robots in 2025 for welding and assembly, invested 7.5 million yuan (~$1.07 million), then after 12 months had saved 6.8 million yuan (~$971,000) in labor costs, improved yield by 3.2 percentage points, and reached a combined payback period of 11.5 months. The market is already looking fairly concentrated. UBTech, Fourier, and Unitree together hold 70% share, with UBTech at 30% and 8,500 units, Fourier at 22% and 6,200 units, and Unitree at 18% and 5,100 units. Geographically, the Yangtze River Delta and Pearl River Delta still dominate, accounting for 68% of the market. But the piece says Sichuan, Hubei, and Anhui each saw more than 200% year-on-year growth, driven by auto and electronics clusters. The report is bullish, but it does acknowledge meaningful technical limits. Fault rates in unstructured environments are still 8-12%, versus 2-3% for conventional industrial robots. It also suggests current prices are not yet low enough for true mass adoption. According to the GGII survey cited, 65% of surveyed companies see 50,000-80,000 yuan (~$7,100-$11,400) as the real psychological price band for large-scale adoption. The labor implications are large if these shipment numbers prove right. The piece estimates each humanoid could replace 1.5-2 assembly-line jobs, implying that 80,000 shipments could theoretically affect 120,000-160,000 jobs. On competition, GGII notes that Tesla, Xiaomi, and Huawei are all looming in the background. The piece says Tesla Optimus is expected to begin small-batch deliveries in H2 2026, while Xiaomi and Huawei have announced humanoid strategies but remain pre-deployment.

I mentioned this to the WSJ last year, but ... youth edition of our China Tech investor trips is here! (The idea actually came from those who came with us on the investor trips, a bunch of them said they wanted their kids to see it too) The pilot trip is going to be July 12-19, 2026 in Beijing for kids ages 12–17. If there's enough demand, we will open up more slots, just fill out the interest form! (And yup, we’ll launch a college-age version soon too!) If you have a teen who’d be into this, here it is 👇 Link and full information below