Inside the funding round driving the shift to intelligent construction fleets
Updated
February 7, 2026 2:12 PM
Aerial shot of an excavator. PHOTO: UNSPLASH
Bedrock Robotics has raised US$270 million in Series B funding as it works to integrate greater automation into the construction industry. The round, co-led by CapitalG and the Valor Atreides AI Fund, values the San Francisco-based company at US$1.75 billion, bringing its total funding to more than US$350 million.
The size of the investment reflects growing interest in technologies that can change how large infrastructure and industrial projects are built. Bedrock is not trying to reinvent construction from scratch. Instead, it is focused on upgrading the machines contractors already use—so they can work more efficiently, safely and consistently.
Founded in 2024 by former Waymo engineers, Bedrock develops systems that allow heavy equipment to operate with increasing levels of autonomy. Its software and hardware can be retrofitted onto machines such as excavators, bulldozers and loaders. Rather than relying on one-off robotic tools, the company is building a connected platform that lets fleets of machines understand their surroundings and coordinate with one another on job sites.
This is what Bedrock calls “system-level autonomy”. Its technology combines cameras, lidar and AI models to help machines perceive terrain, detect obstacles, track work progress and carry out tasks like digging and grading with precision. Human supervisors remain in control, monitoring operations and stepping in when needed. Over time, Bedrock aims to reduce the amount of direct intervention those machines require.
The funding comes as contractors face rising pressure to deliver projects faster and with fewer available workers. In the press release, Bedrock notes that the industry needs nearly 800,000 additional workers over the next two years and that project backlogs have grown to more than eight months. These constraints are pushing firms to explore new ways to keep sites productive without compromising safety or quality.
Bedrock states that autonomy can help address those challenges. Not by removing people from the equation—but by allowing crews to supervise more equipment at once and reduce idle time. If machines can operate longer, with better awareness of their environment, sites can run more smoothly and with fewer disruptions.
The company has already started deploying its system in large-scale excavation work, including manufacturing and infrastructure projects. Contractors are using Bedrock’s platform to test how autonomous equipment can support real-world operations at scale, particularly in earthmoving tasks that demand precision and consistency.
From a business standpoint, the Series B funding will allow Bedrock to expand both its technology and its customer deployments. The company has also strengthened its leadership team with senior hires from Meta and Waymo, deepening its focus on AI evaluation, safety and operational growth. Bedrock says it is targeting its first fully operator-less excavator deployments with customers in 2026—a milestone for autonomy in complex construction equipment.
In that context, this round is not just about capital. It is about giving Bedrock the runway to prove that autonomous systems can move from controlled pilots into everyday use on job sites. The company bets that the future of construction will be shaped less by individual machines—and more by coordinated, intelligent systems that work alongside human crews.
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Where smarter storage meets smarter logistics.
Updated
January 8, 2026 6:32 PM
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Kioxia's flagship building at Yokohama Technology Campus. PHOTO: KIOXIA
E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.
But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.
KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.
At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.
Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.
The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.
Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.