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In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
About US
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
Tech Reviews
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
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In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
Buying Guides
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
Comparison
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
News
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution
Contact
In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid's physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.

By February 2026, this collaboration had enabled Tapestry's digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.

Overall, this technology anchors Google's broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry's tools for yearly transmission planning. North America's largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).

But as the project's transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.

Vector, the largest of New Zealand's 29 distribution utilities, is now using Tapestry's grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector's average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry's grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.

Tapestry's transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry's general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry's work greatly improved the team's capabilities, Crahan said.

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, "When I think about managing resources on our team and where we should focus, it wasn't about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time."

Working Around the Data Problem

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network's immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility's helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who's spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.

The Distribution Impact

With that foundational data in place, the team then turned to Tapestry's grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:
- Preparing future scenarios
- Running power flow and economic simulations
- Analyzing system constraints
But the networks are fundamentally different.

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project's broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution

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