Work as software: Why humanoid robots are now becoming the hardest currency in the economy

Manufacturing disruption: Who will win the automation race – specialists or all-rounders?

The global economy is inexorably heading towards a demographic tipping point that will invalidate the existing laws of industrial value creation. As the working-age population in industrialized nations shrinks and supply chains become increasingly volatile, the humanoid robot is transforming from a futuristic vision into an urgent economic necessity. We are currently witnessing not merely technological progress, but the birth of an entirely new asset class: labor is becoming software, scalable and decoupled from biological limitations.

But the path to an automated future is not a monolithic one. An in-depth analysis of the six leading US players—from Tesla's radical vertical integration to Figure AI's focus on pure intelligence—reveals six fundamentally different bets on the future of manufacturing. While hardware is inexorably becoming a commodity and facing a massive price drop, data is emerging as the "new oil" of the physical world. The question is no longer whether robots will arrive, but whose business model will first break through the critical threshold between capital expenditures (CapEx) and operational efficiency (OpEx).

The following analysis dissects the strategies of the US's "humanoid avant-garde." It sheds light on why Tesla aims to build robots cheaper than small cars, why Agility Robotics deliberately forgoes heads, and why abandoning hydraulics was crucial for Boston Dynamics' survival. Who dominates the market: the pragmatic specialist or the visionary generalist? Welcome to the era of new work.

Suitable for:

• Technical development and market maturity of humanoid robot systems in the USA

Why hardware is becoming a commodity (mass-produced product) and data is the new oil of the physical world

The global economy is approaching a demographic tipping point that will irreversibly alter the foundations of industrial value creation. As industrialized nations age and the availability of labor for repetitive, dangerous, or physically demanding jobs rapidly declines, labor costs and supply chain volatility increase. In this macroeconomic vacuum, the promise of the humanoid robot has transformed from a science fiction fantasy into a harsh economic necessity. We are currently witnessing not only technological breakthroughs but the emergence of an entirely new asset class: labor as software. However, an analysis of the six leading US players reveals that there is no monolithic market, but rather six radically different bets on the future of manufacturing. The question is no longer whether robots will arrive, but whose economic model will first break through the critical cost-benefit thresholds. The following analysis dissects the strategies, strengths, and risk profiles of these pioneers.

Vertical integration as an economic moat

When economies of scale meet neural networks

Tesla is approaching the humanoid robotics market not as a traditional robotics company, but as a mass manufacturer that happens to build robots. The economic strategy behind Optimus (Gen 2 and future generations) is based entirely on the principles of vertical integration and radical cost reduction through scaling. While competitors often rely on expensive purchased components like harmonic drive transmissions or specialized sensors, Tesla develops actuators, battery cells, and inference chips in-house. This isn't a technical detail, but the key economic lever: it's the only way to achieve the long-term goal of a unit price below $20,000. If this price point is reached, the calculation of return on investment (ROI) for customers will fundamentally change. A robot that costs less than a small car will pay for itself in less than a year, even with low productivity, if it replaces just a single work shift.

The real disruption, however, lies not in the hardware, but in the radical approach of the software architecture. Tesla relies on an end-to-end neural network that translates video data directly into control commands, without any intermediate rule-based heuristics. This approach, borrowed from the development of Full Self-Driving (FSD) for cars, is extremely data-hungry, but theoretically infinitely scalable. When Optimus learns a task, all units can adopt this capability "over the air." However, the delays in mass production—currently hundreds are being produced instead of the promised thousands for 2025—demonstrate the massive gap between prototype and process stability. From an economic perspective, Optimus is currently still a bet on the future: Elon Musk's statement that this area could account for 80 percent of the company's value is less a balance sheet forecast than an attempt to re-evaluate Tesla as an AI infrastructure company. The risk lies in the "general purpose" trap: A robot designed to do everything often fails to do anything well enough at the outset to offer any specific economic added value. Nevertheless, Tesla remains the only player with a clear path to mass production in the millions, which could translate into an insurmountable cost advantage (cost of goods sold) in the long run.

The symbiosis of body and Large Language Models

Can OpenAI control the body as well as language?

Figure AI represents the antithesis of the slow, academic robotics development of recent decades. With a valuation reportedly approaching $40 billion and investors like Microsoft, Nvidia, and Jeff Bezos, Figure embodies the “Silicon Valley Speed” doctrine. Figure’s core economic hypothesis is that intelligence, not mechatronics, has been the limiting factor. Through its partnership with OpenAI, Figure seeks to translate ChatGPT’s semantic understanding directly into physical actions. This drastically lowers the barrier to programming and interaction: when a factory worker can give the robot an instruction in natural language, deployment costs plummet.

The deployment of the Figure 02 at BMW's Spartanburg plant provides the first hard data on this. The fact that a robot contributed to the production of 30,000 vehicles in an 11-month pilot project is a strong signal of its technical feasibility. However, the metric of "interventions" is more economically relevant. As long as a human has to intervene frequently, the robot is not a substitute, but rather an expensive tool. The reported efficiency gains at BMW suggest that Figure is reaching this critical point of autonomy faster than expected. Figure's strategy aims for rapid market penetration through partnerships with industry giants to cover its enormous capital requirements. The risk here lies in the valuation relative to revenue. Figure must prove that it can make the transition from the celebrated pilot project to a widespread fleet deployment before investor capital grows impatient. Unlike Tesla, Figure lacks its own manufacturing capabilities, which makes it more vulnerable to supply chain issues, but allows it to react more agilely to new hardware technologies. They buy innovation to buy speed.

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Profit before perfection in intralogistics

Why are two legs enough when you don't need a head?

With its Digit model, Agility Robotics is pursuing what is arguably the most pragmatic and currently most economically viable strategy in the field. While others dream of turning robots into butlers or general-purpose workers, Agility has focused on a single, multi-billion-dollar niche: moving standardized containers (totes) in intralogistics. The decision to forgo a human head or five-jointed hands in the early models was not a technical shortcoming, but a conscious choice for cost reduction and robustness. In logistics, robots aren't paid for aesthetics or anthropomorphism, but for "picks per hour" and uptime.

Agility's decisive economic breakthrough is the establishment of the "Robots-as-a-Service" (RaaS) model. Through the contract with GXO Logistics and its deployment at Spanx, Agility became the first company in the group to generate significant, recurring revenue not derived from research funding. The RaaS model shifts the risk from capital expenditures (CapEx) to operating expenses (OpEx) for the customer, dramatically lowering the barrier to entry. When Agility can demonstrate a return on investment (ROI) of less than two years compared to a human worker earning $30 an hour, the decision becomes a simple calculation for a logistics company's CFO. The challenge for Agility lies in defending this niche. As Tesla's or Figure's "general purpose" robots improve, they could potentially take over Digit's specialized tasks. Agility's bet, however, is that specialization and reliability ("brownfield integration") will be more profitable over the next five years than the promise of general intelligence. They solve a specific problem today, instead of trying to solve all problems tomorrow.

Suitable for:

• “Physical AI” & Industry 5.0 & Robotics – Germany has the best opportunities and prerequisites in physical AI

From research laboratory to electrical commercialization

The painful farewell to hydraulics for the balance sheet

For years, Boston Dynamics was synonymous with viral videos and technological brilliance, but also with economic inefficiency. The old, hydraulic Atlas was an engineering marvel, but too expensive, too maintenance-intensive, and too noisy for commercial use. The radical shift to the all-electric Atlas in April 2024 marked the final transition from a research institution to a profit-oriented company under the Hyundai umbrella. This move was economically essential: electric motors are cheaper, more reliable, cleaner, and easier to maintain compared to hydraulic systems.

Integration into the Hyundai Group offers Boston Dynamics an invaluable advantage: a guaranteed anchor customer. Hyundai's commitment to purchase "tens of thousands" of robots creates a level of supply chain planning security that startups can only dream of. The new Atlas robot features joints with 360-degree freedom of movement, allowing it to perform movements impossible for humans. Economically, this means the robot can utilize workspaces more efficiently than a human—it doesn't need to turn around, it simply rotates its torso. This could significantly reduce cycle times in automotive production. The challenge for Boston Dynamics will be translating the immense technical complexity and high development costs into a competitive market price. They must prove that their superior mobility justifies the presumably higher price compared to simpler systems like Apptronik or Agility. It's an attempt to capture the premium segment of robotics.

Human-centered design and collaborative security

Safety as the ultimate selling point

Apptronik positions its Apollo robot as the “iPhone moment” of hardware: a standardized platform on which various partners can develop their applications (apps). The economic logic behind this is that of a platform ecosystem. Instead of developing each software solution in-house, Apptronik provides the hardware and the operating system. The partnership with Mercedes-Benz for automating low-skill tasks such as component handling validates this approach in the high-wage sector of Germany and Hungary.

A differentiating factor is the legacy of the collaboration with NASA (Valkyrie robot), which placed a strong emphasis on safety and collaborative design. In a factory environment, the "cost of safety" is an often underestimated factor. If a robot requires cages or complex safety barriers, installation costs and space requirements increase. Apollo is designed to work safely directly alongside humans. This allows it to be used in existing production lines without expensive infrastructure modifications. Apptronik is aiming to occupy the "accessible all-rounder" segment. However, with funding that appears rather modest compared to Figure or Tesla, they must operate with extreme capital efficiency. Their strategy of both selling robots directly and offering them through leasing models demonstrates a flexibility aimed at lowering market entry barriers for medium-sized businesses. The danger lies in being crushed between the extremely cheap mass producers (Tesla) and the highly intelligent AI robots (Figure) if they cannot establish a clear unique selling point in terms of performance.

Tactile intelligence and the path to universal autonomy

Why true autonomy only arises through human hands

Sanctuary AI tackles the problem of robotics from its most challenging angle: the hands. While others focus on locomotion, Sanctuary argues that a human's economic value lies primarily in their manual dexterity. A robot that can walk but cannot grasp anything is merely an expensive messenger. The Phoenix robot has hands with near-human range of motion, controlled by the "Carbon" AI.

Sanctuary's economic strategy is unique in its focus on teleoperation as a bridging technology. Instead of promising immediate full autonomy, Sanctuary uses humans in VR rigs to remotely control the robots, generating massive amounts of training data in the process. This transforms every hour of work not yet performed autonomously into a valuable asset (data). The partnership with Magna, one of the world's largest automotive suppliers, serves as a massive data generator. Economically speaking, this is a "data engine" play. The more the robots work remotely, the faster the AI ​​learns, and the faster the marginal cost of autonomy decreases. If Sanctuary solves the universal grasping problem, they potentially have the largest addressable market, as they can perform tasks (e.g., closing buttons, routing soft cables) that robots with less-than-perfect motor skills, like Digit, would fail at. The risk lies in the long latency period before profitability: Teleoperation is expensive (1:1 human-to-robot ratio), and the bet that AI will learn quickly enough to take the human out of the loop is risky.

The battle for standardization

Comparing these six players reveals a fundamental divergence: The market is splitting into “hardware-first” mass-market players (Tesla), “software-first” intelligence players (Figure, Sanctuary), and “utility-first” specialists (Agility). Economically, we will witness brutal consolidation. Hardware will inevitably be subject to the price pressures of commoditization – similar to smartphones or PCs. The true economic value will shift to software and integration capabilities.

In the short term (1-3 years), pragmatists like Agility Robotics have the best chance of generating real cash flow, as they reduce complexity. In the medium term (3-7 years), Tesla could dominate the generic hardware market through sheer production power and cost reduction, relegating others to niche players or pure software companies. The biggest unknown remains the learning speed of AI models. If Figure or Sanctuary achieve a "ChatGPT moment" for motor skills, hardware will become secondary, and the licensing model for the "robot brain" will become the most lucrative business model of the next decade. For the global economy, this means we are at the beginning of a deflation of physical labor that will redefine productivity metrics.

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