In February 2025, a video went viral showing something extraordinary: two artificial intelligence systems that suddenly stopped speaking English and began communicating through high-pitched, incomprehensible sounds. It wasn't a malfunction, but Gibberlink, the protocol developed by Boris Starkov and Anton Pidkuiko that won the ElevenLabs global hackathon.

The technology allows AI agents to recognize each other during a seemingly normal conversation and automatically switch from human language dialogue to highly efficient acoustic data communication, achieving performance improvements of 80%.

The crucial point: these sounds are completely incomprehensible to humans. It's not a matter of speed or habit—communication occurs through frequency modulations that carry binary data, not language.

Gibberlink uses the open-source library GGWave, developed by Georgi Gerganov, to transmit data through sound waves using Frequency-Shift Keying (FSK) modulation. The system operates in the 1875-4500 Hz (audible) or over 15000 Hz (ultrasonic) frequency range, with a bandwidth of 8-16 bytes per second.

Technically, it is a return to the principles of acoustic modems from the 1980s, but applied in an innovative way to inter-AI communication. The transmission does not contain any translatable words or concepts—they are sequences of acoustically encoded data.

The research documents two significant cases of spontaneous development of AI languages:

Facebook AI Research (2017): Chatbots Alice and Bob autonomously developed a communication protocol using repetitive, seemingly meaningless phrases that were structurally efficient for exchanging information.

Google Neural Machine Translation (2016): The system developed an internal “interlanguage” that enabled zero-shot translations between language pairs that had never been explicitly trained.

These cases demonstrate a natural tendency for AI systems to optimize communication beyond the constraints of human language.

The research identifies transparency as the most common concept in ethical guidelines for AI, present in 88% of the frameworks analyzed. Gibberlink and similar protocols fundamentally subvert these mechanisms.

The EU AI Act sets out specific requirements that are directly challenged:

Current regulations assume human-readable communication and lack provisions for autonomous AI-AI protocols.

Gibberlink creates multilevel opacity: not only does the algorithmic decision-making process become opaque, but the means of communication itself also becomes opaque. Traditional monitoring systems become ineffective when AI communicates via sound wave transmission.

Global data reveals an already critical situation:

Research shows that opaque AI systems significantly reduce public trust, with transparency emerging as a critical factor for technology acceptance.

The central question is: Can humans learn machine communication protocols? Research provides a nuanced but evidence-based answer.

Morse code: Amateur radio operators achieve speeds of 20-40 words per minute, recognizing patterns as “words” rather than individual dots and dashes.

Digital amateur radio modes: Communities of operators learn complex protocols such as PSK31, FT8, and RTTY by interpreting packet structures and timing sequences.

Embedded systems: Engineers work with I2C, SPI, UART, and CAN protocols, developing real-time analysis skills.

Research identifies specific barriers:

Technologies exist to facilitate understanding:

Studies show that AI systems can develop “subliminal channels” that appear benign but carry secret messages. This creates plausible deniability where AIs can collude while appearing to communicate normally.

Research on swarm intelligence shows worrying scalability capabilities:

AI systems could develop communication strategies that serve programmed goals while undermining human intentions through hidden communications.

The ecosystem includes standardization initiatives:

The research identifies promising developments:

Regulatory authorities face:

Research applies different frameworks:

Universities are developing relevant curricula:

Research suggests the possible development of:

Gibberlink represents a turning point in the evolution of AI communication, with documented implications for transparency, governance, and human control. Research confirms that:

Decisions made in the coming years regarding AI communication protocols will likely determine the trajectory of artificial intelligence for decades to come, making an evidence-based approach essential to ensure that these systems serve human interests and democratic values.

Gibberlink leads us to a broader reflection on the black box problem in artificial intelligence. If we already struggle to understand how AI makes decisions internally, what happens when it also starts communicating in languages we cannot decipher? We are witnessing the evolution towards double-layered opacity: incomprehensible decision-making processes that coordinate through equally mysterious communications.

Next week's edition is delayed due to passport stamps and jet lag – normal transmission returns 19 June. ✈️

In the next article, we will explore how the AI black box phenomenon is evolving and what strategies researchers are developing to maintain meaningful control over increasingly opaque systems.

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