A reposted observation describes deployment companies as an emerging kind of robotics business.
An observation shared in a repost says a new kind of company is emerging in robotics: the 'deployment company.'
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@vedantnair__A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on a frontier model instead of training its own. Instead, it builds/assembles its own hardware, fine-tunes the model on vertical-specific data, and owns all the hard work of deployment: commissioning, ops, support, and workflows. Until recently, this wasn't an option. If you wanted to deploy a robot that did real manipulation work, you had to build the autonomy yourself. This meant a big robotics team and years of data collection before you could deliver a reliable robot. Now, labs like @Physical_int and @GeneralistAI are changing this pattern. So far, Pi has announced three deployment partners publicly: -> @Weaverobotics builds robots that do laundry and take care of the home -> Actor Labs brings Physical AI to heavy machinery like excavators and loaders, starting with construction. -> @Ultraroboticsco robots pack e-commerce orders in warehouses Notably, each of these companies uses different embodiments in different verticals but can still benefit from the same base model. Generalist hasn't announced any partnerships publicly, but today has released the waitlist for Gen 1.5 (and I've heard whispers of deployment partnerships there as well). The economics of the partnerships aren't public yet, but the setup is mutually beneficial. This lets the labs focus 100% of their effort on developing better models and avoid the demanding deployment schlep. On the flip side, this lets startups benefit from the billions of dollars and world-class researchers needed to train these models. This is incredible for startups. With SOTA models available to build on, teams need less capital to get started, reach their first deployment faster, and spend their time obsessing over customers’ problems, workflows, and the long tail of details that create real value.
@chris_j_paxtonRT @vedantnair__: A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on…@vedantnair__A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on a frontier model instead of training its own. Instead, it builds/assembles its own hardware, fine-tunes the model on vertical-specific data, and owns all the hard work of deployment: commissioning, ops, support, and workflows. Until recently, this wasn't an option. If you wanted to deploy a robot that did real manipulation work, you had to build the autonomy yourself. This meant a big robotics team and years of data collection before you could deliver a reliable robot. Now, labs like @Physical_int and @GeneralistAI are changing this pattern. So far, Pi has announced three deployment partners publicly: -> @Weaverobotics builds robots that do laundry and take care of the home -> Actor Labs brings Physical AI to heavy machinery like excavators and loaders, starting with construction. -> @Ultraroboticsco robots pack e-commerce orders in warehouses Notably, each of these companies uses different embodiments in different verticals but can still benefit from the same base model. Generalist hasn't announced any partnerships publicly, but today has released the waitlist for Gen 1.5 (and I've heard whispers of deployment partnerships there as well). The economics of the partnerships aren't public yet, but the setup is mutually beneficial. This lets the labs focus 100% of their effort on developing better models and avoid the demanding deployment schlep. On the flip side, this lets startups benefit from the billions of dollars and world-class researchers needed to train these models. This is incredible for startups. With SOTA models available to build on, teams need less capital to get started, reach their first deployment faster, and spend their time obsessing over customers’ problems, workflows, and the long tail of details that create real value.@chris_j_paxtonRT @vedantnair__: A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on…@vedantnair__A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on a frontier model instead of training its own. Instead, it builds/assembles its own hardware, fine-tunes the model on vertical-specific data, and owns all the hard work of deployment: commissioning, ops, support, and workflows. Until recently, this wasn't an option. If you wanted to deploy a robot that did real manipulation work, you had to build the autonomy yourself. This meant a big robotics team and years of data collection before you could deliver a reliable robot. Now, labs like @Physical_int and @GeneralistAI are changing this pattern. So far, Pi has announced three deployment partners publicly: -> @Weaverobotics builds robots that do laundry and take care of the home -> Actor Labs brings Physical AI to heavy machinery like excavators and loaders, starting with construction. -> @Ultraroboticsco robots pack e-commerce orders in warehouses Notably, each of these companies uses different embodiments in different verticals but can still benefit from the same base model. Generalist hasn't announced any partnerships publicly, but today has released the waitlist for Gen 1.5 (and I've heard whispers of deployment partnerships there as well). The economics of the partnerships aren't public yet, but the setup is mutually beneficial. This lets the labs focus 100% of their effort on developing better models and avoid the demanding deployment schlep. On the flip side, this lets startups benefit from the billions of dollars and world-class researchers needed to train these models. This is incredible for startups. With SOTA models available to build on, teams need less capital to get started, reach their first deployment faster, and spend their time obsessing over customers’ problems, workflows, and the long tail of details that create real value.@chris_j_paxtonRT @vedantnair__: A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on…@vedantnair__A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on a frontier model instead of training its own. Instead, it builds/assembles its own hardware, fine-tunes the model on vertical-specific data, and owns all the hard work of deployment: commissioning, ops, support, and workflows. Until recently, this wasn't an option. If you wanted to deploy a robot that did real manipulation work, you had to build the autonomy yourself. This meant a big robotics team and years of data collection before you could deliver a reliable robot. Now, labs like @Physical_int and @GeneralistAI are changing this pattern. So far, Pi has announced three deployment partners publicly: -> @Weaverobotics builds robots that do laundry and take care of the home -> Actor Labs brings Physical AI to heavy machinery like excavators and loaders, starting with construction. -> @Ultraroboticsco robots pack e-commerce orders in warehouses Notably, each of these companies uses different embodiments in different verticals but can still benefit from the same base model. Generalist hasn't announced any partnerships publicly, but today has released the waitlist for Gen 1.5 (and I've heard whispers of deployment partnerships there as well). The economics of the partnerships aren't public yet, but the setup is mutually beneficial. This lets the labs focus 100% of their effort on developing better models and avoid the demanding deployment schlep. On the flip side, this lets startups benefit from the billions of dollars and world-class researchers needed to train these models. This is incredible for startups. With SOTA models available to build on, teams need less capital to get started, reach their first deployment faster, and spend their time obsessing over customers’ problems, workflows, and the long tail of details that create real value.@chris_j_paxtonRT @vedantnair__: A new kind of company is emerging in robotics: the deployment company. Broadly speaking, a deployment company builds on…
