X：Rohan Paul

Chinese robots hold 86% of total global humanoid shipments and Nvidia is extending its CUDA playbook into that concentrated robotics market. WSJ published a peice China's concentration makes Nvidia's CUDA-style strategy easier to scale because Nvidia only needs to become deeply embedded in a handful of robot makers to sit underneath most of the market. The lesson Nvidia learned from CUDA was that selling the chip is much more powerful when developers also build their software around your platform. Nvidia is trying to create the robotics version of that dependency: Robot maker → Nvidia GPU/Jetson → CUDA → Isaac/GR00T/Cosmos → simulation, training, robot control and deployment

"Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)

Another long-horizon benchmark, another reminder that current AI can work for hours and still remain far from mastering the task. The strongest agent in EdgeBench scored 51.3/100 after being allowed to work for 12 hours. The benchmark gives agents persistent executable environments and feedback across 134 tasks. They can debug code, run simulations, inspect validation results, revise proofs, and repeatedly submit artifacts to hidden judges. i.e. they had many of the ingredients we normally say agents need to improve over time. They still struggle to convert all of that interaction into sustained progress. The study covers roughly 38,000 hours of interaction across 6 task families. When performance was averaged across tasks, improvement followed a log-sigmoid curve with R² ≥ 0.997 across all 5 models: slow early progress, a steeper learning phase, then saturation.

"Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)

We’re ending our partnership with Cursor following its acquisition by SpaceX. Under our proposal, Cursor’s direct access to our models would end on November 12. We know that the people most affected by this decision are the developers who rely on OpenAI models in Cursor. We care about their experience in this transition and we’re ready to go above and beyond to support them. https://t.co/OzuCTzUjfX

Another paper that so clearly exposes AI’s long-horizon problem. Long-Horizon-Terminal-Bench, where even frontier agents struggle badly once tasks stretch across hundreds of steps. Across 17 frontier models on 46 long terminal tasks, the average pass rate is 6.4%. And under strict full-completion grading 10 of the 17 models solve zero tasks. Of the runs that fail, 79% end with the clock expiring while the agent is still working. Even the best model, at 28.3%, leaves 7 of every 10 tasks unfinished. The models could perform plenty of locally reasonable steps but could not reliably convert hundreds of those steps into a finished result.

"Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)

This paper is a brutal reality check for long-horizon AI. Give an agent a year of interconnected decisions, delayed feedback, and consequences from its own past actions, and its performance collapses relative to humans. The researchers tested eight leading models, including GPT-5.6 Sol and Claude Opus 4.8. Yet the best-performing setup, Qwen3.7-Max with Hermes, ended with only 27.3% as much money as the average human participant. A system that finishes a year-long task at barely a quarter of human performance is nowhere near dependable long-horizon execution.

"Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)