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Humanoid robots hype; specialized machines win factories
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Elon Musk said that humanoid robots will push Tesla’s market value to $25 trillion. He also believes that they will reshape labor.
No longer will humans need to do dangerous, repetitive, or mundane things.
It’s a compelling vision. Yet the reality is likely far more nuanced.
Latest Videos FromWatch full video here: Sviat DulianinovSocial Links Navigation
CEO of Bright Machines.
My take is that the future of robotics in the industrial environment won’t look like us.
Rather, it will involve systems that are built to solve specific, high-value problems with speed, accuracy, and reliability.
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The Humanoid Hype Deserves Scrutiny
Morgan Stanley forecasts the humanoid robot market will reach $5 trillion by 2050, with over a billion units deployed — roughly 90% for industrial and commercial use. But even that bullish projection comes with significant caveats: major advances in hardware, materials, and AI are required before humanoids can scale in industrial settings.
Then there is economics. Humanoids currently cost up to $200,000 per unit. At that price point, achieving ROI is extremely difficult given their still-limited capabilities.
Are you a pro? Subscribe to our newsletterContact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.Precision is an even bigger obstacle. Manufacturing has zero tolerance for error. Research from the IEEE illustrates the challenge: even folding laundry remains surprisingly unreliable for robots.
Translating meaningful dexterity into high-speed industrial workflows is a far steeper climb. And for many tasks — like driving a screw to mount a heat sink on a motherboard — a humanoid is simply overkill. A robotic arm, a screwdriver, and a smart navigation system will do the job better.
Manufacturing at the Edge: A Model that Actually Works
The traditional manufacturing model is based on a labor-first approach. An example is Foxconn, which employs around a million workers. Usually, these workers solve problems before considering automation systems.
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True, this can work at scale, but it also has limitations with flexibility, consistency, and speed.
But manufacturing at the edge flips that equation. Instead of being produced in distant locations, production moves closer to where products are deployed. This allows for faster iteration, reduced logistics complexity, and greater responsiveness to demand.
From there, the model becomes about being technology-first. From the start, challenges are addressed with software, robotics, automation, real-time data, and AI. Think of it as “manufacturing in a box.”
Employees are still critical for this process, but they have a different role. They oversee operations, handle exceptions, and manage continuous improvement along with AI agents. Robots, on the other hand, focus on repetitive, precision-driven tasks and processes.
Manufacturing at the edge does not have to be monolithic. It can range from a warehouse, data center, or compact production facility. But instead of sprawling facilities, the footprints are generally smaller, say 50,000 to 100,000 square feet.
The benefits are clear: higher throughput, improved quality, faster time to market, and greater consistency. Manufacturing at the edge is also more cost-effective. This makes it easier to justify the onshoring of manufacturing.
The past few years have shown the importance of this. With COVID and geopolitical tensions, manufacturing on the edge offers a path to more resilient, localized production.
Applying AI to the Right Problem is the Real Differentiator
Building an AI-powered manufacturing environment is fundamentally different from traditional automation. It centers on flexibility. Manufacturing is dynamic as designs and capacity needs change, processes evolve, and systems must adapt without introducing friction or downtime.
This is why AI is so important, and why no single model can do it alone. A large language model might grab the headlines, but real-world systems draw on the full depth of AI — from classical machine learning for optimization, to deep learning for vision and perception, to generative AI for orchestration and insight. The power isn't in any one technique — it's in how they work together.
Another critical factor is knowing what should or should not be automated. Machines are ideal for consistency and repetition, while humans are adept at judgment, adaptability, and problem-solving.
In fact, AI can greatly empower operators. This can be done by providing real-time recommendations, visibility into system performance, and tools to continuously refine workflows. Humans remain firmly in the loop, overseeing operations, handling exceptions, and managing repair and optimization.
The Robot Manufacturing Transformation is Underway
The robots that will reshape manufacturing won't walk on two legs. They'll be purpose-built machines running sophisticated AI, operating in compact and efficient facilities, augmenting skilled workers rather than fully replacing them.
The future of industrial automation isn't about replicating humans. It's about combining specialized machines, intelligent software, and human judgment to solve complex problems — faster, better, and at scale.
That transformation is underway. And it looks nothing like what Hollywood imagined.
We list the best Robotic Process Automation software.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit
TOPICS AI Sviat DulianinovSocial Links NavigationCEO of Bright Machines.
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