Snippings

I cannot help but agree with Alon Jackson, CEO and Co-Founder of Astrix Security, when he says that companies at the forefront of the new era of agentic business will be those that understand that managing digital employees is not “a niche IT issue but as a strategic board-level imperative“.

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A real estate tokenisation project was launched in Dubai in March through a collaboration between the Dubai Land Department and the Dubai Virtual Assets Regulatory Authority. This set a regional precedent for regulatory backed tokenisation frameworks, as it was the first time a real estate registration authority in the Middle East had implemented tokenisation on property title deeds. The first property launched in May and achieved full subscription in under 24 hours.

In June, the KSA launched its own real estate tokenisation pilot under the Real Estate General Authority. The scheme aligns with the KSA’s Vision 2030 goals to modernise the property sector and attract global investment. The KSA’s RAFAL Real Estate Co. has partnered with droppRWA, a subsidiary of Web3 infrastructure provider droppGroup, to execute the country’s first tokenised real estate transaction. As part of the pilot, droppRWA will conduct a full feasibility study for property tokenisation across RAFAL’s portfolio in collaboration with leading consultancies.

The Qatar Financial Centre (QFC) is also actively piloting a real estate tokenisation through its Digital Assets Lab, with PropTech LLC leading efforts to fractionalise property ownership and enable secondary trading on compliant exchanges

From: Tokenised real estate gains ground in the GCC.

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Indeed, the shift is so pronounced that a word is being revived to describe it: “geoeconomics”. This was arguably coined in a 1990 essay by Edward Luttwak, the military strategist, to describe “the admixture of the logic of conflict with the methods of commerce”.

More recently Paul Tucker, the former deputy governor of the Bank of England, citing Robert Blackwill’s and Jennifer Harris’s 2016 definition of the term as “the use of economic instruments to promote and defend national interests”, notes in his book Global Discord that such instruments include tariffs, regulatory constraints, aggressive currency devaluations, foreign asset purchases, and controls over the export of energy and rare earths.

From: The new age of geoeconomics.

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The MCP specification has laid a solid foundation for how agents communicate with servers. But as we’ve worked with real-world use cases, one critical piece has stood out as missing: identity. Without a robust identity model, agents can’t truly act autonomously or securely. The MCP-I (I for Identity) specification addresses this gap—introducing a practical, interoperable approach to agentic identity. We’ve developed MCP-I in the spirit of collaboration, with deep respect for the groundwork laid by teams like Anthropic, and with the hope that this helps move the ecosystem forward.

From: Vouched ID Verification | AI Agents: Know Your Agent.

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As of mid-2025, the monthly stablecoin transacted value for retail (below $10,000, non-crypto) payments is around $250 billion. After falling February, the value of retail transactions has moved higher every month since. The average value of a retail transaction is around $1200.

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In a sense, tokenization simply represents the next stage of market evolution. Sixty years ago, purchasing a security or using it as collateral required a physical certificate. Interest payments involved literally clipping off pieces of the physical certificate (coupons). Transactions settled in five days. The 1960s “paperwork crisis” on the New York Stock Exchange, where trading volumes overwhelmed manual processing systems, forced the exchange to close on Wednesdays and shorten trading hours.

This led to electronic bookkeeping at central securities depositories and settlement across electronic books. The process first involved “immobilising” securities by securely storing the physical certificates in a central location and creating electronic records. They were then “dematerialised” meaning that physical certificates were eliminated, so that securities existed solely as accounting records.

From: Big Bang 2.0 – by Marc Rubinstein – Net Interest.

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But in recent months, a new class of agents has arrived on the scene: ones built using large language models. Operator, an agent from OpenAI, can autonomously navigate a browser to order groceries or make dinner reservations. Systems like Claude Code and Cursor’s Chat feature can modify entire code bases with a single command. Manus, a viral agent from the Chinese startup Butterfly Effect, can build and deploy websites with little human supervision. Any action that can be captured by text—from playing a video game using written commands to running a social media account—is potentially within the purview of this type of system.

Scholars, too, are taking agents seriously. “Agents are the next frontier,” says Dawn Song, a professor of electrical engineering and computer science at the University of California, Berkeley. But, she says, “in order for us to really benefit from AI, to actually [use it to] solve complex problems, we need to figure out how to make them work safely and securely.”

From: The problem with AI agents | MIT Technology Review.

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A digital employee is a complex AI-based software structure designed to autonomously perform tasks that are traditionally performed by trained people. Unlike traditional rule-based bots that follow simple instructions, well-designed AI employees are able to learn and adapt to a complex environment and make decisions similar to humans.

From: Digital Employees: The Invisible Workforce Of The Future.

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GenAI powered chatbots’ lack of reasoning can directly contribute to the nefarious effects of LLM grooming: the mass-production and duplication of false narratives online with the intent of manipulating LLM outputs.

From: (6) Bad Actors are Grooming LLMs to Produce Falsehoods.

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One of the methodologies to develop scenarios is the Oxford Scenario Planning Approach (OSPA). The OSPA approach starts with defining a very clear purpose and determining the exact intended use for the planning process. To develop scenarios, one might then establish a four-by-four grid along two axes, with each axis representing a particular driver of structural change (such as speed of technological adoption or increasing geopolitical fragmentation). Scenarios are then developed for each quadrant using the inherent characteristics of the axes and additional assumptions.

From: Once upon a time in the future: strategic foresight in central banks – Bank Underground.

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