There is a quiet contradiction in today’s debates about AI and language. Many people insist that machine translation and machine interpreting will never work at a truly high level, at least not anytime soon. At the same time, those very same people express growing alarm about deepfakes: synthetic voices, faces, and videos that are increasingly impossible to distinguish from real ones. Both positions cannot be held at once.
If deepfakes are already convincing, as we will see later in this article, then machine interpreting, arguably the most complex form of translation, will be as well. The reason is simple: machine interpreting can be framed as an imitation game.
Machine interpreting as an imitation game
In an interesting book1, Umberto Eco famously described translation as the art of saying “almost the same thing”. I suggest that spoken language translation, i.e. interpreting, is that same idea under time pressure and contextual constraints: producing, in real time, a target-language utterance that sounds right, feels right, and works. In other words, we can frame interpreting as the imitation of the speaker in saying the same things but in a different language (and consequently culture).
With some caveats. Interpreting scholars are correct to remind us that interpreting is more than linguistic substitution. It involves intervention, negotiation, positioning, ethics. But here is the uncomfortable truth: all of these dimensions can themselves be modeled as forms of imitation, patterns of response conditioned on context. At its functional core, interpreting is not about inner understanding. It is about producing a convincing performance of understanding. And that is precisely the kind of task at which contemporary AI systems seem to excel.
The Deepfake Moment
This became viscerally clear to me through a personal experience. I recently watched about twenty minutes of a video featuring Yannis Varoufakis, the former Greek finance minister, a public intellectual whose voice, gestures, and argumentative style I admire (while often not sharing his views). While I was watching, the video felt slightly off. But the reason was only that I felt that his tone was unusually solemn. His arguments somewhat repetitive. Still, nothing registered as fake. The voice was his. The reasoning was plausible. The arguments too. The performance was convincing. Only halfway through did I notice a comment stating that the video was AI-generated. To have an idea, you can find below a short video example from one of the many fake channels about him (there are many more impersonating important intellectuals, at this link one for example of John Mearsheimer).
The shock was immediate. Not because the deepfake was flashy (it was), but because it was credible. Because I believed in it. Varoufakis himself later explained in an interview for Unheard, which I invite you to watch, that it took him nearly two minutes to realize that the person speaking in the video was not him. If the person being impersonated cannot immediately tell, the implications are obvious. Deepfakes are there. They are a reality, and quite a troublesome one.
From Impersonation to Interpretation
Once we accept this new reality (everyone should be convinced by now), and we accept that interpreting is a sort of imitation game (probably not many will agree with me on this), the leap to machine interpreting is short. If AI can convincingly impersonate a specific individual — replicating voice, prosody, facial expression, rhetorical habits — then producing a convincing utterance in another language even considering context, goals, etc. is not a harder problem anymore. In many respects, it is easier. Dubbing and machine interpreting, which share quite a lot, are obviously less spectacular than deepfaking a famous politician, but they rely on the same underlying capability: generating outputs that humans accept as authentic, coherent, and contextually appropriate. In other words, they are deepfakes of the words and ideas that a person is expressing, but in a different language.
The Real Problem Ahead
My call is that we should accept that machine interpreting will work at an extraordinary level any time soon. I am quite bullish about the opportunities that this opens for society and economy. But this does not mean that we should embrace it uncritically. Quite the opposite. It raises uncomfortable questions about control, manipulation, and power. If machines can translate and interpret convincingly, and we keep believing that they do it within the common (and wrong) belief of algorithmic impartiality, then we must also admit the possibility that they can also filter, frame, and subtly steer meaning at scale. We already know this in the never ending discussion about AI biases, but we tend to forget that while biases can be controlled, the technology itself can also be actively weaponized, i.e. manipulated in a way that it is not neutral, but pursue some specific goals. And this is for translation and interpreting, at least in theory, not different.
A couple of examples help clarify the point. Consider a machine-translation system used by a news agency to render—offline or in real time—events unfolding around the world. Such a system could be subtly tuned to deform reality in small but systematic ways, aligning translations with particular political or institutional interests, including those of the government currently in power. Or consider an interpreting application used in asylum procedures: it could be calibrated to render utterances in ways that consistently favor or disadvantage applicants, without ever appearing overtly inaccurate.
None of this is fundamentally new. Similar distortions have always been possible when humans translate or interpret. What changes with machines is scale, consistency, and ease. What once required coercion, pressure, or ideological alignment of professionals can now be embedded directly into a system. And precisely because it is technology, we tend to assume neutrality, and therefore fail to question the possibility at all.
These are governance problems, not engineering ones. And yet we remain unable to confront them as long as we keep treating high-quality machine interpreting as a kind of hocus-pocus, either impossible, suspect, or morally tainted, rather than acknowledging it for what it is: an emerging, functional technology that demands knowledge and, when necessary, regulation. Certainly not denial.
The Imitation Game Is Already On
Deepfakes are no longer science fiction. They work because they imitate reality well enough for us to accept them. Machine interpreting operates on a similar principle: not perfect equivalence of human performances, but sufficient resemblance. If we are already living in a world where synthetic voices and faces can convincingly pass as real, then we are also living in a world where machines will soon speak for us, across languages, in real time in a convincingly way. This opens up new and great opportunities, in which I firmly believe, but also a long series of challenges. The imitation game is not coming. It has already begun.
To close with a concrete example. Below is a simultaneous interpreting system developed by myself using a cloned version of my own voice. To my and my loves’ ones ears, this is quite astonishing. But voice is only the most visible layer. Giving my own voice to an artificial interpreter does not cause any harm. However, many other dimensions of interpreting can be faked or tuned with equal ease: how an immigrant’s words are rendered, whether they sound hesitant or confident, cooperative or evasive. Depending on who controls the machine — say, a state authority — such systems could quietly shape outcomes, including something as consequential as an asylum application.
- Umberto Eco, 2013, Dire quasi la stessa cosa: Esperienze di traduzione. Bompiani. ↩︎