The idea of having a virtual clone that works for us sounds almost like science fiction, but it's already knocking on the door of many companies and professions. So-called digital twins and synthetic humans promise to multiply productivity, extend our reach, and even keep us "active" after death, at least in a digital sense.
However, behind this enthusiasm lies a minefield of dilemmas: identity, privacy, bias, data ownership, and legacy . We're not just talking about models that simulate machines or buildings; we're talking about highly convincing copies of people who write, speak, negotiate, and make decisions on our behalf, within a hyperconnected ecosystem of billions of devices.
What is a digital twin and why is it everywhere now?
In simple terms, a digital twin is a virtual replica of an object, process, system, or even a person , synchronized in (almost) real time with its physical counterpart using data. This connection is powered by sensors, software, the Internet of Things (IoT), and artificial intelligence, allowing for the simulation of behaviors, the prediction of failures, and the optimization of decisions without touching the physical world.
This technology began to take shape decades ago. After the Apollo 13 mission, NASA began simulating spacecraft and equipment without a direct physical connection to rehearse scenarios, anticipate problems, and increase astronaut safety. Those simulation models have evolved into the current concept of the digital twin, which has spread from the aerospace industry to sectors such as construction, healthcare, transportation, and energy.
Today, thanks to the rise of IoT and home automation ( information about home automation ), digital twins function as a virtual laboratory where hypotheses can be tested before implementing real changes. Products, infrastructure, supply chains, or even entire cities can be replicated on a computer, combining variables, data, and information to see "what would happen if…".
Consulting firms like Gartner estimate that nearly half of industrial companies use digital twins to improve efficiency and reduce downtime, while studies by firms like Deloitte or Vector IT Group and other examples of digital technology point to exponential growth in their use, especially in electronics, construction and transportation.
This massive deployment would not be possible without hyperconnectivity: it is estimated that we could go from approximately 8.700 billion connected devices currently to more than 25.000 billion in just a few years . Each of these devices becomes a source of data that feeds increasingly precise and detailed digital models.
This massive deployment would not be possible without hyperconnectivity: it is estimated that we could go from approximately 8.700 billion connected devices currently to more than 25.000 billion in just a few years . Each of these devices becomes a source of data that feeds increasingly precise and detailed digital models.
From machines to people: human digital twins and AI clones
The qualitative leap comes when we stop modeling machines and start replicating people in minute detail : their voice, their face, their gestures, their writing style, their past decisions, and their work habits. This is where synthetic humans and AI clones come into play.
Startups like Viven and tools like Synthesia already offer the possibility of creating hyper-realistic avatars of executives, teachers, or content creators . Using videos, audio, text, and other personal data, they generate a "digital self" that can record videos, answer emails, attend meetings, or give talks, without the original person being present.
Imagine a professor who has written and published for decades. With enough texts, recordings, and historical material, it's perfectly feasible to train a language model that mimics their tone, vocabulary, and argumentation style in a surprisingly convincing way. This "algorithmic professor" could teach classes, answer student questions, and remain "active" even if the real person stopped teaching.
In the corporate world, there's talk of "scaling human capital" or "bottled expertise": creating virtual copies of top salespeople, customer service agents, or executives to amplify their presence without time or space limitations. In theory, it sounds like a productive dream: your clone handles calls, sends reports, and participates in video conferences while you focus on strategy... or disconnecting.
But this dream has a dark side: the more convincing the response, the more blurred the lines become between person and algorithm . Who is really speaking? Who is responsible for what is said? What does it mean to be productive if your digital version does most of the work?
Digital twins in key sectors: construction, industry, health and energy
Beyond the case of personal clones, digital twins are already transforming entire sectors with a mix of clear advantages and significant ethical risks. One of the fields where they are being most widely deployed is smart building and infrastructure management.
In this field, virtual representations of buildings, bridges, or entire neighborhoods are being developed, connected in real time to structural and operational sensors. These models allow for the simulation of behaviors, the optimization of construction processes, and the anticipation of failures with unprecedented accuracy. From a technical and economic standpoint, experts like Rolando Chacón point out that this is a mature and accessible solution.
A digital twin of infrastructure offers the possibility of seeing many more variables than we perceive with the naked eye . It allows us to analyze the effects of extreme loads, wear patterns, the impact of weather, or unforeseen uses, reducing risks and costs. In Formula 1, for example, it is routine to replicate parts and the complete design of the car in a virtual environment to predict its behavior even before it is physically built.
In the healthcare sector, the idea goes even further: creating digital twins of people to anticipate illnesses, personalize treatments, and predict the body's reaction to certain medications or interventions. The technical capacity to simulate organs, circulatory systems, or immune responses continues to grow, thanks to advances such as edge computing for AI with lower latency , although this is an extremely sensitive area from a privacy and ethical perspective.
Even in the energy sector, digital twins are used for security purposes. Some power plants have virtual replicas designed as decoys to divert and analyze cyberattacks: attackers believe they have compromised the real facility, when in reality they have only reached its digital twin.
Internet of Things, Big Data, and the big question: who owns it?
This entire ecosystem only works because we live in an environment of hyperconnectivity and constant data flow . Every sensor, machine, wearable, or application sends information that feeds the digital models. The more data that accumulates, the more accurate the digital twin becomes.
However, this very abundance of data opens up a complex debate: who does this data really belong to, and how should it be handled? Some experts argue that data generated by large infrastructures or urban systems has the character of a “social asset,” insofar as it allows for improvements in public services and urban planning.
But when the data relates to specific individuals—their health, their work behavior, their consumption habits, or their voice and image—the question becomes more complex. Transforming identity into data infrastructure implies accepting that parts of who we are can be licensed, monetized, or reused at the whim of third parties.
This is where concepts like consent, transparency, and control come in. If a company trains a digital twin using your past interactions, how long can they continue using it? What happens when you leave the company, retire, or die? Can they continue exploiting your digital twin as if nothing has changed?
Gabriela Arriagada, an academic specializing in applied ethics and engineering, insists that the ethics of artificial intelligence cannot be limited to the programmer's intentions . It is necessary to consider the entire implementation ecosystem: who designs the system, who uses it, who is affected, and how responsibilities are shared when something goes wrong.
Identity, criteria, and the risk of fossilizing people
Beyond privacy, the rise of human digital twins directly impacts the notion of identity and professional value. Replicating a person's output—their writing, their decisions, their "way of doing things"—is not the same as replicating their judgment or purpose . It's like taking a snapshot of who you were at a specific moment.
An AI clone trained on your content in 2025 will be able to mimic your style from that year quite well, but not your subsequent evolution . It won't incorporate future learning, changes of opinion, new readings, or harsh realities that might lead you to refine your positions. It is, metaphorically speaking, a fossil updated daily, but a fossil nonetheless.
This poses a particular dilemma for creative or intellectual leadership professions. Many people write or teach not only to produce results, but also to think, refine their ideas, and maintain their critical thinking . If they completely delegate writing, teaching, or decision-making to a digital twin, they lose the very process that fosters their growth.
Furthermore, the fascination with "climbing oneself" can lead to reducing professional identity to an assembly line: we clone past behavior to multiply it , as if personality were a finished product and not a process in constant review.
Therefore, there is a risk of alienation: your digital twin continues to speak on your behalf, but you no longer go through the effort of thinking, comparing, and making mistakes. In the long run, your own voice risks becoming indistinguishable from that of the machine trained on your digital remnants.
Presence, leadership, and the temptation to send a "mannequin" to meetings
In the executive world, the possibility of having a digital clone that attends meetings, answers emails, and records messages is, seemingly, irresistible. Some executives feel almost flattered to see that they are "so important" that it's worth creating an AI double to amplify their presence.
However, many of these same leaders acknowledge that they don't fully trust their own clones . They function reasonably well for repetitive interactions, standard messages, or highly scripted responses, but they fall short in key aspects of leadership: empathy, nuanced timing, sensitivity to context, or conflict management.
Delegating important meetings to a digital avatar can feel akin to sending a mannequin to sit at the table : it's formally present, it moves its mouth, it even responds with a degree of logic, but the emotional connection and personal responsibility are lost. When you leave the meeting, you don't really know what was said, how it was said, or how others received it.
Something similar happens with managing email or corporate messaging. If your digital twin is the one answering most of the messages, you gradually lose track of your own conversations . You stop knowing what you've promised, to whom, in what tone, and with what nuances, which in the medium term can erode your credibility.
The paradox is that, in many roles, the value you bring isn't just the literal content of what you say, but your presence, your listening skills, and your ability to react to unforeseen events. Replacing those moments with an avatar can cheapen something that, precisely, justified your being there in the first place.
Corporate immortality and legacy: what happens when the twin outlives the person?
One of the most unsettling scenarios is the survival of digital twins beyond the lives of their human counterparts . If a company considers its employees' data an asset, it's easy to imagine it would want to treat its digital clones the same way.
Let's imagine, for example, that a large technology corporation decides to continue using the AI version of a charismatic founder years after their death, presenting it as a kind of "digital honorary president." It might seem like a touching tribute, but it borders on something close to corporate necromancy: resurrecting intellectual remains to sustain a brand.
Universities, media outlets, and large consulting firms might be tempted to sell access to renowned "virtualized" professors and leaders , transformed into products through licensing agreements for premium training or consulting services. The line between respecting a legacy and exploiting an identity becomes extremely blurred.
The key question is who controls the clone and who profits financially from its exploitation. Is it the person's heirs, the institution they worked for, or the technology platform that trained the model? Without clear regulatory frameworks and explicit ethical agreements, the risk of abuse is evident.
That's why many experts in technology ethics insist on treating personal data, and by extension digital twins, as a legacy and not as mere depersonalized property . No one should become a "perpetual digital asset" without their informed consent, clear terms of use, and a real possibility of revoking that permission.
Privacy, security, and bias in digital twins
From a data protection perspective, digital twins pose particularly serious challenges when applied in sensitive contexts such as healthcare, education, or employment . We're not just talking about static files, but models that integrate information from multiple sources and generate predictions about future behavior.
In terms of privacy, risks arise such as the re-identification of people from supposedly anonymized data , the secondary use of information for unforeseen purposes, or the construction of extremely detailed profiles that can be used to discriminate, deny services, or manipulate decisions.
Added to this is the problem of algorithmic bias. If the historical data used to train digital twins contains hidden discrimination or unfair decisions , the model is likely to reproduce and amplify those patterns. In areas such as personnel selection, loan allocation, or healthcare planning, this can translate into systemic inequalities.
Mitigating these biases requires a combination of responsible design, regular audits, and the participation of diverse profiles in evaluating results. It's not enough to say "the algorithm decided it"; you have to be able to explain why, with what data, and with what margin of error.
In terms of security, digital twins are also a tempting target for cyberattacks. An attacker who manages to manipulate the digital twin of an industrial plant, an electrical grid, or a transportation system could induce dangerous decisions in the physical world without directly touching the facilities, simply by deceiving the model and the systems that rely on it.
Regulation, ethical frameworks and shared responsibility
Current data protection regulations, such as the General Data Protection Regulation in Europe, offer certain tools to address some of these problems, especially regarding consent, transparency, and rights of access, rectification, and erasure . However, they are not specifically designed for the phenomenon of human digital twins.
Digital twins combine elements of software, identity, legacy, security, and intellectual property, highlighting the need for more specific regulatory frameworks . These should address, among other things, the right not to be digitally cloned without permission, the post-mortem management of personal models, and limits on the commercial exploitation of avatars.
In addition to formal regulation, it is crucial that organizations develop internal data governance and AI ethics policies . This includes multidisciplinary committees, impact assessment protocols, fairness criteria, and mechanisms for employees, customers, or citizens to raise reasoned objections to the use of their information in digital twins.
Therefore, responsibility doesn't fall solely on programmers or legal departments, but on the entire ecosystem: managers, product designers, data teams, ethics experts, and end users . How an organization uses digital twins will reveal much about its culture and its genuine respect for the people it affects.
In this sense, there is a strong emphasis on moving from a purely instrumental view of technology—"what can we do?"—to a more mature one that also asks, "what should we do, and within what limits?" Digital twins are too powerful to be left without ethical constraints.
How to use AI as a complement to, not a replacement for, people
A more sensible way to approach this revolution is to consider artificial intelligence and digital twins as support and scaling tools, not as copies that replace the person . In other words, prioritizing the complement over literal imitation.
Many professionals already use AI systems as thinking partners: they help summarize texts, suggest structures, find references, or detect errors . In this approach, the machine provides speed and processing power, while the fundamental decisions—what to say, how to say it, to whom, and for what purpose—remain human.
Similarly, a company can deploy digital twins to simulate scenarios, optimize processes, and offer recommendations , while always maintaining a human in charge at critical points of creativity, judgment, and responsibility. It's not about cloning the expert, but about giving them better analytical tools.
This involves setting clear boundaries: for example, deciding that certain leadership functions, personnel evaluations, or sensitive decision-making will not be delegated to digital clones . Instead, models will be used to provide diagnoses, early warnings, or action proposals that a human manager will then validate or reject.
Ultimately, those who adopt AI with this kind of proactive prudence will be able to amplify its impact without diluting its essence. Those who use it to completely outsource thought and presence risk becoming interchangeable with any other model trained on sufficient data.
The expansion of digital twins, from industry to personal clones, places us at a crossroads where productivity, identity, privacy, corporate power, and legacy intertwine; harnessing their enormous potential requires treating them not only as a technical feat, but as part of a social pact that respects human judgment, consent, and fairness in the use of data, because ultimately what is at stake is not only the efficiency of systems, but the kind of relationships and people we want to continue being in an increasingly digital world.
