Simultaneous interpretation is now a given in major international institutions, conferences, and multilingual events. Yet behind this practice, which has become commonplace, lies a rich history closely tied to the political and technological upheavals of the 20th century. From the booths of the Nuremberg Trials to contemporary neural algorithms, it reflects humanity’s ongoing struggle to understand and be understood.

This article traces the evolution of simultaneous interpretation, examines its current challenges, and explores its future in the age of artificial intelligence.

1. Origins: the shock of Nuremberg

Simultaneous interpretation as we know it officially emerged during the Nuremberg Trials in 1945. To prosecute Nazi war criminals, the Allied forces had to organize multilingual proceedings in English, French, German, and Russian. Until then, consecutive interpretation dominated, a speaker would talk, then an interpreter would reformulate. In a trial of such scale, this approach would have taken years.

IBM therefore developed a revolutionary system: soundproof booths where interpreters translated in real time, while listeners received the translation through multi-channel headsets. This innovation immediately became a reference model.

Reference: Gaiba, Francesca. The Origins of Simultaneous Interpretation: The Nuremberg Trial. University of Ottawa Press, 1998.

2. The golden age of human interpreters

From the 1950s onward, the United Nations and other international organizations widely adopted simultaneous interpretation. A new profession emerged: conference interpreting. Highly trained and often multilingual, interpreters became indispensable facilitators of diplomatic dialogue.

Their work requires exceptional skills: active listening, short-term memory, rapid reformulation, and mastery of cultural nuance. Between the 1960s and 1980s, the profession became institutionalized through specialized schools (ESIT in Paris, Geneva, Monterey), professional associations, and ethical charters.

Human interpreters became a discreet yet essential elite in global diplomacy.

3. Technological modernization (1990–2010)

The fall of the Berlin Wall and accelerating globalization increased demand. Multilateral organizations, and increasingly large corporations, had to manage events in dozens of languages.

Advances in digital audio simplified logistics: portable booths, digital consoles, and wireless headset systems. The profession itself remained unchanged, but the infrastructure became more efficient.

This period also saw the emergence of early machine translation software (such as Systran), which remained too imprecise to replace human interpreters.

4. The AI era: promises and limitations

Since around 2016, neural machine translation (Google Translate, DeepL) has shifted the landscape. AI no longer translates word by word; it learns structure, style, and context.

At the same time, speech recognition models such as Whisper (OpenAI) have reached unprecedented levels of accuracy. As a result, the pipeline “speech → text → translation → speech” has become technically viable, with latency measured in just a few seconds.

Reference: OpenAI, Introducing Whisper, 2022.

However, limitations remain:

• Errors in rare languages or dialects

• Difficulty handling humor, metaphors, and implicit meaning

• Data privacy concerns when information is transmitted to third-party servers

5. Cost: a major barrier

One of the strongest arguments in favor of automation is economic. A team of interpreters for a two-day conference with three languages can cost more than €15,000 (fees, travel, booths). In contrast, a scalable AI-based solution can reduce this cost by a factor of ten, while making multilingual access available to smaller events.

This opens the door to democratization: associations, SMEs, and local NGOs can now consider multilingual events without prohibitive budgets.

6. A hybrid future: humans + AI

The reality is that AI will not fully replace humans in the short term. Interpreters remain superior in high-stakes contexts such as legal proceedings, healthcare, and sensitive diplomacy. However, AI can effectively cover:

• Large-scale events

• Internal meetings

• Situations where speed matters more than stylistic perfection

The future will therefore be hybrid:

• AI for large-scale translation

• Humans for nuanced diplomacy and critical contexts

Conclusion

From Nuremberg to AI, simultaneous interpretation reflects humanity’s constant need for dialogue. Once reserved for major powers, it is now becoming accessible to all organizations thanks to technology. The future challenge is clear: making universal understanding accessible, reliable, and inclusive.

Far from disappearing, simultaneous interpretation is entering a new era, one in which collaboration between humans and machines will drive global understanding.