Tether Invests in Humanoid Robots. When Will They Be Launched

RBK-cryptoPubblicato 2025-12-09Pubblicato ultima volta 2025-12-09

Introduzione

Tether, the company behind the USDT stablecoin, has invested in humanoid robotics through a €70 million funding round in Generative Bionics, a spin-off from the Italian Institute of Technology (IIT). The startup is developing "physical AI" robots for industrial use and human interaction, leveraging two decades of IIT research. Tether stated these investments support technologies that "expand human potential and reduce reliance on centralized systems." The first industrial deployment programs for these robots are planned for early 2026, targeting manufacturing, logistics, healthcare, and retail sectors. A full concept of the humanoid robot will be unveiled at the CES exhibition in Las Vegas. Tether investment portfolio also includes AI, financial services, energy, and biotech.

Tether, the company behind the largest stablecoin USDT, has announced investments in the development of humanoid robots through the Italian Institute of Technology's (IIT) spin-off startup Generative Bionics. The funding round raised €70 million.

Generative Bionics specializes in creating robots with "physical AI" designed for industrial use and human interaction. The startup utilizes two decades of IIT's robotics research, holds exclusive licenses to key technologies, and employs about 70 engineers and AI specialists from the Italian Institute of Technology, according to the statement.

Tether describes these investments as supporting new technologies that "expand human potential and reduce dependence on centralized systems controlled by large tech companies." The company noted it has previously invested in similar developments: Tether funds the creation of neural interfaces through Blackrock Neurotech, and together with Northern Data and Rumble, is deploying a global network for AI utilization.

Generative Bionics states that the first industrial deployment programs for robots are planned for early 2026. They will cover industries such as manufacturing, logistics, healthcare, and retail. The first full-concept humanoid robot developed by the company will be presented at the CES international exhibition in Las Vegas (held annually in January).

Tether CEO Paolo Ardoino stated that the company "is proud to support a team that turns Italy's scientific leadership into global industrial influence." Both Ardoino and another Tether founder, Giancarlo Devasini, are originally from Italy.

Beyond AI investments, Tether's portfolio includes investments in financial services, energy, biotechnology, education, and media across various countries. The company holds stakes in projects spanning commodities, money transfers, sports, and entertainment.

Only 7 native tokens have remained in profit since the beginning of the year. And it's not Bitcoin

Binance suspended an employee for insider trading. What happened

Strategy purchased the largest batch of Bitcoin since July

Domande pertinenti

QWhat is the name of the startup that Tether invested in for humanoid robot development?

ATether invested in the startup called Generative Bionics, a spinoff of the Italian Institute of Technology (IIT).

QHow much funding was raised in the investment round for Generative Bionics?

AThe funding round raised €70 million.

QWhat is the primary purpose of the 'physical AI' robots being developed by Generative Bionics?

AThe 'physical AI' robots are designed for industrial use and human interaction.

QWhen are the first industrial deployment programs for these robots planned, and which sectors will they cover?

AThe first industrial deployment programs are planned for early 2026 and will cover manufacturing, logistics, healthcare, and commerce.

QWhere and when will the first full concept of the humanoid robot developed by Generative Bionics be presented?

AThe first full concept of the humanoid robot will be presented at the international CES exhibition in Las Vegas, which is held annually in January.

Letture associate

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

marsbit2 h fa

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

marsbit2 h fa

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

marsbit3 h fa

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

marsbit3 h fa

Trading

Spot
Futures
活动图片