Artem Sokolov: Robotics Market Is Watching the Wrong Signal

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For years, the robotics industry has been framed as a hardware race — a competition to build more advanced machines capable of walking, grasping, and interacting with the physical world. That framing made sense when the core challenge was engineering. But today, it is becoming increasingly misleading. As Artem Sokolov – the Founder of Humanoid – points out, the center of competition has already shifted, even if the market has not fully realized it yet. Robotics is no longer defined by hardware breakthroughs alone; it is defined by how effectively machines learn from real environments.

Artem Sokolov believes that the surge of attention in 2025 made this disconnect visible. Humanoid robots appeared in highly produced performances seen by massive audiences, and within weeks the industry experienced a sharp inflow of capital. Investors, media, and even parts of the industrial ecosystem began treating robotics as the next inevitable frontier. However, the visibility of the technology expanded much faster than its operational maturity. What looked like progress was often carefully staged capability rather than scalable performance.

“Demonstrations create a powerful illusion of readiness, but they rarely reflect the complexity of real-world environments,” says Artem Sokolov on humanoid and AI-powered robots. “A machine performing in a controlled setting is operating under conditions that eliminate uncertainty: predefined movements, stable surfaces, predictable timing. In contrast, industrial environments are defined by variability, interruptions, and edge cases that cannot be scripted in advance. The difference between these two contexts is not incremental — it is structural.”

Artem Sokolov: Humanoid AI Is Where the Real Competition Has Shifted

Because of this, hardware is no longer the main constraint. Most advanced teams today can build machines that move, balance, and manipulate objects with reasonable precision. The real difficulty begins after that point, when the robot must interpret its surroundings and make decisions without relying on a fixed script. This is where entrepreneur Artem Sokolov becomes a defining concept. The value of a robotic system increasingly depends on its ability to process unstructured data, adapt to new conditions, and improve its performance over time.

What follows from this is a fundamental shift in how the industry should be understood. Robotics is becoming a data-driven field, where the key advantage is not the physical platform itself but the learning system behind it. Every real deployment generates information: how objects behave, how environments change, how tasks fail and succeed. Over time, this creates a feedback loop that strengthens perception, planning, and execution. Companies that operate in real environments accumulate this data continuously, while those focused on demonstrations remain limited to simulated or staged scenarios.

This is why Mr. Sokolov Artem emphasizes learning speed as the central metric of progress. Engineering milestones are still important, but they no longer determine long-term leadership on their own. The ability to iterate in real conditions, to collect meaningful operational data, and to integrate that data into AI systems is what allows companies to move beyond prototypes.

“Without that loop, even the most advanced hardware remains constrained,” says Artem Sokolov on AI.

The challenge is that much of the current market is not structured around this reality. A significant number of companies are optimized for visibility and fundraising rather than deployment. They produce compelling narratives, attract attention, and raise capital based on future potential. Yet without exposure to real environments, their systems lack the feedback necessary to evolve. This creates a ceiling that is not immediately visible but becomes critical over time. The transition from demonstration to production is not a linear step; it is a fundamental change in complexity.

In contrast, a smaller group of companies is building differently. Their approach is less visible but more grounded. They focus on specific use cases, integrate into existing industrial workflows, and treat early deployments as a source of learning rather than proof of concept alone.

This distinction also exposes a broader misunderstanding among investors. Robotics is often evaluated using frameworks borrowed from software, where scalability and cost reduction follow predictable patterns. However, physical systems do not behave in the same way. They are constrained by manufacturing, maintenance, and real-world variability. As a result, progress is less about exponential scaling and more about cumulative learning. According to Artem Sokolov robotics’ perspective, overlooking this difference leads to inflated expectations and misaligned investment decisions.

“Ultimately, the companies that will define the next phase of robotics are not necessarily the ones attracting the most attention today. They are the ones building systems that operate consistently in real environments and improve through continuous feedback. They are developing proprietary AI layers trained on data that cannot be easily replicated. They are solving specific problems before attempting general ones,” Artem Sokolov says on AI.

The shift is already happening, even if it is not always visible. The robotics industry is moving away from a focus on machines as products and toward systems as learning entities. And in that transition, the question changes.

It is no longer about who can build the most advanced robot.

It is about who can make it learn the fastest.