Ask HN: Billions of dollars in funding, but what's changed for robotics?
HN folks - school me if this is an uninformed take: In the last 2 years we’ve seen eye watering robotics funding - e.g., Figure with a ~$39B valuation and $1B+ rounds, Skild AI raising ~$1.4B, Physical Intelligence raising hundreds of millions, and autonomous systems like Wayve’s ~$1.5B robotaxi funding, not to forget Musk with his Optimus bots. That’s an insane capital wave, but from a core bottleneck POV, what’s actually changed since 2016-2020? We’ve heard about vision models, RL advances, diffusion policies, better sim, and multimodal embodied models but have any of these really cracked generalization, reliable manipulation, or true sim2real at scale? Some questions: 1. Are we meaningfully closer to generalist policies that work in messy, real environments? 2. Do “robot foundation models” solve the data bottleneck the way LLMs did for NLP? 3. Has manipulation gone beyond incremental improvements? 4. Are humanoids a technical leap or just a narrative that attracts capital? 5. What are the real research papers/benchmarks showing step-change progress? Genuinely curious whether we are at a technological inflection point or are we going to hit hard physics/data/hardware problems again. 0 comments on Hacker News.
HN folks - school me if this is an uninformed take: In the last 2 years we’ve seen eye watering robotics funding - e.g., Figure with a ~$39B valuation and $1B+ rounds, Skild AI raising ~$1.4B, Physical Intelligence raising hundreds of millions, and autonomous systems like Wayve’s ~$1.5B robotaxi funding, not to forget Musk with his Optimus bots. That’s an insane capital wave, but from a core bottleneck POV, what’s actually changed since 2016-2020? We’ve heard about vision models, RL advances, diffusion policies, better sim, and multimodal embodied models but have any of these really cracked generalization, reliable manipulation, or true sim2real at scale? Some questions: 1. Are we meaningfully closer to generalist policies that work in messy, real environments? 2. Do “robot foundation models” solve the data bottleneck the way LLMs did for NLP? 3. Has manipulation gone beyond incremental improvements? 4. Are humanoids a technical leap or just a narrative that attracts capital? 5. What are the real research papers/benchmarks showing step-change progress? Genuinely curious whether we are at a technological inflection point or are we going to hit hard physics/data/hardware problems again.
HN folks - school me if this is an uninformed take: In the last 2 years we’ve seen eye watering robotics funding - e.g., Figure with a ~$39B valuation and $1B+ rounds, Skild AI raising ~$1.4B, Physical Intelligence raising hundreds of millions, and autonomous systems like Wayve’s ~$1.5B robotaxi funding, not to forget Musk with his Optimus bots. That’s an insane capital wave, but from a core bottleneck POV, what’s actually changed since 2016-2020? We’ve heard about vision models, RL advances, diffusion policies, better sim, and multimodal embodied models but have any of these really cracked generalization, reliable manipulation, or true sim2real at scale? Some questions: 1. Are we meaningfully closer to generalist policies that work in messy, real environments? 2. Do “robot foundation models” solve the data bottleneck the way LLMs did for NLP? 3. Has manipulation gone beyond incremental improvements? 4. Are humanoids a technical leap or just a narrative that attracts capital? 5. What are the real research papers/benchmarks showing step-change progress? Genuinely curious whether we are at a technological inflection point or are we going to hit hard physics/data/hardware problems again. 0 comments on Hacker News.
HN folks - school me if this is an uninformed take: In the last 2 years we’ve seen eye watering robotics funding - e.g., Figure with a ~$39B valuation and $1B+ rounds, Skild AI raising ~$1.4B, Physical Intelligence raising hundreds of millions, and autonomous systems like Wayve’s ~$1.5B robotaxi funding, not to forget Musk with his Optimus bots. That’s an insane capital wave, but from a core bottleneck POV, what’s actually changed since 2016-2020? We’ve heard about vision models, RL advances, diffusion policies, better sim, and multimodal embodied models but have any of these really cracked generalization, reliable manipulation, or true sim2real at scale? Some questions: 1. Are we meaningfully closer to generalist policies that work in messy, real environments? 2. Do “robot foundation models” solve the data bottleneck the way LLMs did for NLP? 3. Has manipulation gone beyond incremental improvements? 4. Are humanoids a technical leap or just a narrative that attracts capital? 5. What are the real research papers/benchmarks showing step-change progress? Genuinely curious whether we are at a technological inflection point or are we going to hit hard physics/data/hardware problems again.
Hacker News story: Ask HN: Billions of dollars in funding, but what's changed for robotics?
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March 02, 2026
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