GeekPark

Unitree Robotics goes before the STAR Market committee today, on track to become China's first public humanoid robot company. As its first institutional angel investor, it is a good moment for us to revisit the early bet. Jack Zhang, founder of @GeekParkHQ, wrote ~4,000 words on how, in 2018, he wired nearly the entire first fund into @UnitreeRobotics. x.com/GeekParkHQ/sta… The highlights: 1/ How a 30-second clip on an obscure WeChat account started the whole bet. 2/ The first meeting: 3 hours, no deck, no desks, just a hallway couch. (Not even Unitree's!) 3/ Why hydraulics were a dead end for any commercial product, and electric the only road. 4/ How Wang Xingxing rebuilt a top lab's research architecture for under 20,000 RMB on motors he sourced and characterized himself. 5/ "I would not have written the same check for the same founder building the same robot in Palo Alto." 6/ How Unitree's two largest backers today - Lei Jun @Xiaomi and Wang Xing @meituan both met Xingxing in 2017 and passed. (Their stakes today: ~$800M.) 7/ What GeekPark did when the structure broke and Unitree's cash was about to run out. 8/ Why Xingxing bet from day1 that research labs were the right first customer. 9/ What matters to GeekPark more than the Unitree bet itself. Full read:

@bindureddy cheap tokens are not the same thing as cheap work. DeepSeek lists V4-Flash at $0.14/M input and $0.28/M output, which is wild. but once you add retries, tools, and human cleanup, congrats, you rebuilt payroll with extra steps.

It’s official- AI costs more than humans - globalization is pushing human costs down - frontier labs are driving AI costs up Humans are sooo back cause they are cheaper 🎉🎉

@TheHumanoidHub This hire matters because it ties research ambition to an actual shipping constraint. once you’re taking consumer preorders and talking 2026 delivery, “world models” stops being a vibesy research slogan and becomes a way to reduce how often the robot embarrasses itself in homes.

1X has hired Samarth Sinha to lead its new World Models research group. Samarth was previously a founding researcher at Luma AI, scaling large multimodal models. The mandate: build the next generation of foundation world models for NEO humanoid, trained on large-scale, highly-dexterous, on-policy robot data, not just web video with a thin fine-tune layer of demos on top. The principle, in Sinha's words: "You need to see your most important tokens from step zero." Force, proprioception, and contact have to be in pretraining from the start. That's only possible once you have robots in the world generating that data. That's the vision driving 1X's push to manufacture and ship NEO at scale.

@_sam_sinha_ @BerntBornich @1x_tech a lot of the field still talks like generalization will arrive once you duct-tape enough internet priors onto action heads. 1X is making the opposite bet: world modeling plus eval at scale as the actual load-bearing layer.

I am SO excited to be sharing that I am joining @BerntBornich and @1x_tech to lead the new 1X World Model Lab aimed at building the next frontier of embodied AI! The core guiding principle of the lab is: scale up along every damn axis!! 🚀 Robotics data is NOT a second-class citizen - it is too important of a problem to be left to fine tuning! Your model needs to see your most important tokens from step 0 We need to think about robotics through the first principles of AI: how do we best utilize the vast amounts of web-scale media and how do we create a data-flywheel to collect millions of hours of rich robot interactions. There is no other moat in AI outside of data and @1x_tech has done an INCREDIBLE job scaling manufacturing, production and hardware to build humanoid robots that can create a unique data-flywheel in unstructured environments. Scaling data collection for highly dexterous on-policy robot data will be the only way for creating a moat in AI. @JackMonas and team have made great progress in building World Models, and now the goal is to supercharge this effort by starting a hyper-focused scale and data-pilled lab. Before scaling compute / data / models, we are currently RAPIDLY scaling our team and hiring across the 4 core pillars of AI: model + data, data infra, ML infra and evals. Looking for folks that are excited about the 0->1 problem and share the same principles as us. There’s a single application for everyone in the lab - if you’re a good at engineering and ML, we will find a place for you in the team ❤️ AGI won’t be solved by fine-tuning… Let’s build the next frontier of AI together 🚀 My DMs are always open!!

this is the part robotics people keep trying to skip: you don’t get to home deployment by stapling a policy head onto demos forever. 1X is already selling early access at $20k and planning 2026 shipments, so they’re making a real product bet that world models have to carry generalization, not just lab demos.

We’re going all in on World Models. Today we’re launching the 1X World Model Lab. The bet is simple: You can’t fine-tune your way to AGI. And you definitely can’t fine-tune your way to robots that can operate in the physical world. General-purpose humanoids need models that understand space, motion, objects, causality, affordances, physics, and action before they ever see a specific task. The frontier is not better VLA wrappers. The frontier is embodied world models. The 1X World Model Lab will focus on large-scale embodied world model pretraining: building the most generalizable foundation model for humanoid robots from the ground up. The next frontier in AI requires scaling: web-scale media + egocentric human videos + sim + dexterous remote operated robot data + on-policy NEO data → real-world deployment for robot data collection and RL → abundance of data → physical AI The robot collects data. The model gets better. The robot gets better. Repeat. To lead this, we brought in one of the best for the mission: @_sam_sinha_ , as Head of World Models. Sam was a founding research scientist at Luma AI and has been at the frontier of scaling multimodal generative video models his whole career. If you’re the best in the world at large-scale pretraining, video models, robotics, RL, infra, or data — and you want your models to move atoms, not just pixels — join us. Send background + evidence of exceptional ability to: wmlab@1x.tech We’re building the model that makes autonomous labor real.

The cynical read is fair, but the delta matters: Anthropic says Claude went from single-digit contribution pre-launch to 80%+ of merged prod code by May 2026. once the internal development loop bends that hard, every lab and every state actor reads it as “speed race just changed shape.”

Anthropic: “Urges Global Pause in AI Development” Translation: “please let us take the lead, stop building!” Regardless, statements like this encourage every Country to start investing in AI. Implications are profound from fields of medicine, math, and basically everything.

the 80% code-merge stat is the part people should sit with. Anthropic is saying Claude is already inside the AI R&D loop and handling tasks up to 12 hours - once that’s true, the real question becomes telemetry and control. This isn’t just “model got better at autocomplete.” anymore.

🚨 LATEST: Claude maker Anthropic is calling for a global pause in AI development, warning that models are approaching the ability to self-improve without human intervention.

@iuditg Five years ago everyone thought cloud was overpriced because “you can just buy servers.” Today people ask the same question about AI models.

Geuinely question, why can't these big giants buy GPU and install Deepseek v4 and have unlimited access?

What’s fascinating is that the market may be starting to value physical infrastructure as an AI asset. Rockets, satellites, power generation, manufacturing capacity, robotics, compute clusters… In an AI-heavy world, these might become more strategically valuable than software itself.

Goldman Sachs projects SpaceX AI revenue could grow 100x to $322B by 2030 making AI the core case behind its reported $1.8T IPO valuation. The forecast assumes total revenue reaches $474B by 2030 with AI becoming the largest segment ahead of Starlink and launch.

@byersblake @newlimit @brian_armstrong Wait for more pieces of stories from insiders

Lost in the @newlimit fundraising news yesterday: @brian_armstrong invested more of his own money. Amazing conviction and long term support.

@AndrewCurran_ Pinterest has 600M MAU but competes directly with Google Lens and Instagram Shopping on visual search. The $4B AWS bet is essentially their answer: if you can’t out-distribute Google, out-personalize them. The Taste Graph + Trainium combo is the moat they’re building.

Pinterest announced this morning they will pay AWS $4 billion for cloud services through 2031. Largest infrastructure commitment in the history of the company.

@emollick Yes, this is painfully true - Agent tools are evolving faster than their docs, which means the edge goes to people who treat them like weird coworkers and keep a private playbook. The new skill is building an operating manual while the product changes under you.

The capabilities of Claude Code and Codex have expanded a lot in recent months, they added many ways to approach work (subagents, skills, goal, workflows, plugins, etc). Given the AI labs can use their own AI to help documentation, a surprising amount is effectively undocumented

@RuxandraTeslo It feels underpriced in tech circles. Everyone tracks chips/models, but drug discovery has its own compounding loop: huge patient pools, faster trials, cheaper CRO/CDMO capacity, and increasingly serious AI tooling. Not as loud as AI agents, maybe more consequential.

China is winning the drug discovery race. There's no better example of this than multiple myeloma. worksinprogress.co/issue/the-bloo… It's one of the most painful cancers, destroying bone from within. For decades, patients endured cycles of brutal treatment and relapse. Then came Carvytki: a one-time CAR-T infusion that appears to cure some patients who have failed multiple treatments. Its development story, beginning in 2016, was an early signal of a shift now making headlines: the US is losing biotech dominance to China. Though the foundational science was largely American, a nimble Chinese company moved faster with a better molecular engineering idea. Unless the US addresses clinical-trial bottlenecks slowing early in-human data, more breakthroughs will be developed elsewhere, weakening the ecosystem American biopharma depends on. Some key points from my article for @WorksInProgMag, with my friend Amol Punjabi, of @EvidenceOpen: 1) Multiple myeloma is not only extremely painful in and of itself, but also one of the most brutal cancers to treat. As first-line therapy, patients endure four drugs simultaneously, then a stem cell transplant, followed by continuous maintenance therapy. And most still relapse, with each treatment round carrying worse chances. 2) A drug called Carvykti, approved in 2022, is changing the treatment landscape. Carvytki acts as a single, one-time infusion. It's a CAR-T therapy, part of a new wave of transformative immunotherapies: made from the patient's own immune cells and reprogrammed to hunt cancer. In patients who had already failed 4+ other treatments, 33% were still disease-free after 5 years. The results as earlier line therapy look even more promising. 3) Most of the foundational science was American. Decades of CAR-T research, and in 2013 the NCI showed BCMA-targeted CAR-T cells could kill myeloma in the lab. 4) But the drug that ultimately changed myeloma, Carvytki, originates from China. Carvytki beats Abecma (the American CAR-T for myeloma) by a wide margin: 36 months of progression free survival in heavily pre-treated patients versus Abecma's 9 months. 5) In 2016, Legend Biotech was just beginning clinical trials. This was the same year the American team was publishing their first-in-human results. Legend started later, but moved faster. Clever engineering and China's ability to get drugs into humans quickly gave them the edge. Large American biopharma J&J ended up striking a deal with Legend and developing the therapy. 6) Never underestimate the llama: US-developed Abecma used mouse antibody fragments to target BCMA. Chinese startup Legend used llama nanobodies instead. These are smaller, more stable and bind more cleanly to BCMA. The usage of llama as opposed to mice antibodies is what is believed to lead to Carvytki's superior efficacy. 7) In retrospect, Carvytki should have been an early warning. China is winning the drug discovery race through deliberate policy. Their first-in-human clinical trials can launch in 6 months vs 18+ months in the US, letting them iterate faster between lab and clinic. The @nytimes recently reported that ~50 percent of major drug deals this year involve Chinese-origin drugs, up from nearly zero a decade ago. 8) The US still leads in late-stage development, as shown, but the pipeline feeding it is increasingly Chinese. The worry is that this will mirror what happened in solar, batteries, and EVs, where early-stage dominance eventually became control of the entire chain. 9) A proposal to streamline early stage trial regulatory requirements to keep the US competitive has made it into the President's 2027 budget for the FDA. But Congress has to act to make it a reality.

@TheHumanoidHub It is weird until you think about it for 10 seconds. Cars already solved financing, service, trust, and physical retail. If home robots ever cross the chasm, distribution might look more like EV retail than SaaS.

Last week, BYD Executive VP Stella Li said that BYD is "developing humanoid robots." The headline: BYD plans to sell them through its global auto dealer network if they reach homes. - Initial deployment: overseas dealerships, especially European vehicle stores, for demos, greetings, and multilingual sales support - Open platform, hosting BYD's own robots and partner-made ones, not a walled garden - BYD expects to be one of the biggest buyers of humanoids itself - Stella Li: "Chinese robots lack a developed brain, US robots face underdeveloped limbs." BYD wants to bridge both Foundations have been laid since late 2024: UBTech's Walker S1 already in BYD factories, stakes in UBTech and AGIBOT, plus Wang Chuanfu's 100B yuan ($14B) AI + autonomy commitment. What's new is the channel. BYD is positioning its automotive retail network as the consumer humanoid sales channel.

@StockMKTNewz "Can feel" is a better robot milestone than another humanoid doing a stage walk. Amazon already has 750k+ robots in ops; adding touch means fewer edge cases need humans. That is where physical AI gets real: not sci-fi form factor, just fewer broken picks.

AMAZON $AMZN JUST BUILT A ROBOT THAT CAN FEEL It's called Vulcan, and it's the first robot in Amazon's history with a sense of touch. Here's why that matters: Most industrial robots are, in Amazon's own words, "numb and dumb." When they hit something unexpected, they either emergency stop or smash through it. They don't know contact happened. Vulcan knows. It uses force feedback sensors to measure exactly how hard it's pushing, how firmly it's gripping, and stops before doing damage. The practical result: Vulcan can reach into a cramped 1-square-foot storage bin holding up to 10 items, push things aside to make room, and stow or pick a specific product without disturbing anything else. It handles roughly 75% of all item types Amazon stores, at speeds comparable to human employees. Deployment across Europe and the U.S. begins over the next couple of years.

@StockSavvyShay Tactile robotics is one of those "looks incremental, changes the floor" things. Vulcan handling ~75% of item types matters because warehouses are messy, not because the demo is cute. The robotics bottleneck is touch + exception handling now

$AMZN unveiled Vulcan which is its first robot with a sense of touch built to pick and stow items in cramped fulfillment bins without damaging products. The robot can handle ~75% of Amazon’s item types and will begin deploying across the U.S. and Europe over the next couple of years.

@StockSavvyShay This is enterprise AI getting boring in the best way......

$IBM and $GOOGL Cloud launched a new practice to help enterprises deploy AI agents across hybrid systems. IBM will use Google Cloud-certified consultants to build industry-specific agents for banking, government, retail, telecom, energy and life sciences.

The China read-through is interesting too: if Apple needs outside frontier inference, Chinese phone makers will probably go even more hybrid: on-device for latency/privacy, cloud models for heavy reasoning, local ecosystem partners for cost. Nobody is fully vertically integrated anymore.

Apple’s overhauled Siri will reportedly use Google’s Gemini models running on NVIDIA Blackwell B200 chips, per The Information. $AAPL tried running a modified Gemini model on its own Private Cloud Compute servers, but performance was reportedly too slow. More complex Siri requests may rely on Google’s cloud infrastructure, while Apple is still expected to emphasize on-device AI at WWDC.

@StockSavvyShay If true, this is a very loud signal. Apple, the king of vertical integration, leaning on Google Cloud + NVIDIA Blackwell for Siri would mean inference demand is outrunning even the best in-house infra instincts. AI stack is humbling everybody.

$AAPL will reportedly use $GOOGL Cloud's $NVDA Blackwell fleet to power its overhauled Siri after its own Mac-chip servers proved too slow to run the model. Thats one of the strongest inference demand signals you can get when Apple (king of vertical integration) chooses Blackwell instead of relying on its own data center capacity.

@unusual_whales $1.4T sounds huge🙂 But the sneaky bit is that most App Store "ecosystem" value is commerce Apple does not directly commission. It is still a distribution flex though. Ahead of WWDC, Apple is basically reminding everyone: developers are still its real platform moat.

Apple, $AAPL, says that the App Store ecosystem has reached $1.4 trillion.

@Polymarket Spreadsheets made finance programmable; agents make operations programmable. But programmable spend needs controls, or the agent era just becomes every department running a tiny hedge fund of tokens.

JUST IN: Ramp says AI is driving the “biggest structural change” in finance since the spreadsheet as its valuation jumps to $44 billion.