Technology

Alphabet, Google’s parent company, is actively tapping into debt markets, signaling a strategic move to fund its ambitious investments, particularly in artificial intelligence. The tech giant recently completed a substantial $20 billion bond offering and is now exploring opportunities to issue rare, long-dated bonds, including a potential 100-year sterling bond, as it diversifies its funding sources across multiple currencies.

This foray into the bond market comes at a time of increased scrutiny regarding the financial implications of the AI race. Despite concerns about potential bubbles and the heavy capital expenditure required for AI development, investors have demonstrated strong appetite for Alphabet’s debt, with the initial $20 billion offering seeing demand exceeding $100 billion. This robust demand allowed Alphabet to increase the offering from an initial $15 billion.

The company’s strategy extends beyond the U.S. Dollar, with plans to issue bonds in both British pounds and Swiss francs. A 100-year bond in sterling would be a particularly noteworthy event, representing the first such issuance by a technology company in nearly three decades – the last being Motorola in 1997. This move underscores Alphabet’s confidence in its long-term financial stability and its commitment to securing funding for sustained innovation.

Alphabet’s Recent Bond Offerings: A Breakdown

The $20 billion bond offering in the U.S. Was divided into seven tranches, with maturities ranging up to 40 years, expiring in 2066. Initially, the 40-year bonds were expected to yield 1.2% above U.S. Treasury bonds, but that margin is now expected to tighten to around 0.95% due to high investor demand. The strongest demand was seen for shorter-term bonds, with three-year notes offered with a yield premium of only 0.27% over Treasuries. JPMorgan, Goldman Sachs, and Bank of America are leading the bond issuances across the three currencies.

Beyond the U.S. Market, Alphabet also issued €6.75 billion in bonds in 2025, split into five tranches with varying maturities, marking its largest inaugural euro bond issuance. A €1.5 billion tranche with a 4-year maturity carried a coupon of 2.5%, while another €1.5 billion tranche with a 9-year maturity offered a 3% yield.

AI Investments Fueling Debt Issuance

The surge in debt issuance is directly linked to Alphabet’s substantial investments in artificial intelligence. The company has already raised nearly $32 billion in the past 24 hours through these various debt offerings, demonstrating a clear commitment to funding its AI initiatives. This move comes as other tech giants also face increasing pressure to invest heavily in AI to maintain their competitive edge.

Despite growing concerns about tech company debt levels related to AI development, Alphabet’s bond sales have been met with strong investor interest. This suggests that the market remains confident in the company’s ability to generate returns on its AI investments. The company’s first venture into the franc bond market and the potential 100-year sterling bond further highlight its innovative approach to financing its growth.

The success of these bond offerings positions Alphabet to continue its aggressive expansion in the AI space, potentially reshaping the technological landscape in the years to reach. The company’s ability to secure funding across multiple currencies and with varying maturities provides it with financial flexibility and resilience in a rapidly evolving market.

Looking ahead, the market will be closely watching Alphabet’s progress in deploying its AI investments and the impact of these investments on its financial performance. The issuance of the potential 100-year sterling bond will be a key indicator of investor confidence in the company’s long-term prospects.

What are your thoughts on Alphabet’s debt strategy? Share your insights in the comments below.

The global financial landscape is undergoing a significant shift as SWIFT, the dominant force in international payments, increasingly integrates with blockchain technology. Although numerous blockchain networks are vying for a role in this new era, Ripple’s XRP and Stellar’s XLM have emerged as frontrunners, actively participating in SWIFT’s initiatives and gaining recognition for their potential to revolutionize cross-border transactions. This growing alignment with SWIFT positions both cryptocurrencies to potentially disrupt traditional correspondent banking systems, offering faster, more transparent, and cost-effective solutions.

SWIFT’s move towards adopting the ISO 20022 messaging standard is a key driver of this change. By early 2026, SWIFT anticipates that 90% of all transactions will transition to ISO 20022, a modern standard designed to improve data richness and interoperability. Ripple and Stellar are both actively involved in the Registration Management Group (RMG), which oversees ISO 20022 compliance, alongside Algorand and Hedera Hashgraph. This participation signals a clear indication of SWIFT’s willingness to embrace blockchain-based solutions.

Early Blockchain Experimentation at SWIFT

The collaboration between SWIFT and blockchain companies isn’t a recent development. A recent SWIFT innovation webinar revealed that the organization began exploring distributed ledger technologies, including Ripple and Stellar, as early as the mid-2010s. As crypto researcher SMQKE highlighted, these early experiments focused on streamlining account reconciliation and liquidity management across borders, demonstrating a long-standing institutional curiosity in the potential of blockchain.

During this initial phase, global banks were actively searching for ways to modernize correspondent banking, reduce delays, and enhance transparency in international payments. SWIFT participated in proofs of concept to evaluate whether distributed ledgers could deliver these improvements. This early exploration laid the groundwork for the current integration efforts and underscores the evolving relationship between traditional finance and the digital asset space.

XRP and XLM: Distinct Strengths in the New Landscape

While both XRP and XLM are benefiting from SWIFT’s embrace of blockchain technology, they offer distinct strengths. Ripple (XRP) has established partnerships with over 300 financial institutions and consistently experiences high trading volumes, particularly in futures markets. This extensive network and robust trading activity position XRP as a strong contender in the evolving payments landscape.

Stellar (XLM) as well boasts significant partnerships, including collaborations with companies like MoneyGram and IBM World Wire. Although, its trading volume remains lower than XRP’s, with fewer daily transactions processed. Despite this difference, Stellar’s focus on financial inclusion and its low transaction fees make it a valuable asset in the broader blockchain ecosystem.

Market Signals and Potential Price Impact

The increasing recognition from SWIFT is already impacting market sentiment surrounding XRP. Market analyst Ali Martinez has identified a bullish symmetrical triangle forming in XRP’s price chart, suggesting a potential breakout. Martinez notes that a break above $3.26 could send XRP to $3.90, a significant increase from its current trading price of around $3.11. This technical setup is further supported by recent whale activity, with large XRP holders accumulating 120 million tokens, signaling strong institutional confidence.

SWIFT’s highlighting of Ripple and Stellar as potential disruptors in cross-border payments also comes as the organization explores blockchain-based solutions as alternatives to traditional correspondent banking systems. This development underscores the growing interest in leveraging blockchain technology to address the inefficiencies and complexities of international finance.

What to Watch Next

As SWIFT continues its ISO 20022 migration and further integrates blockchain technology, the roles of XRP and XLM will likely become even more prominent. The ongoing experimentation and collaboration between traditional financial institutions and blockchain companies will shape the future of cross-border payments. Investors and industry observers will be closely watching the adoption rates of ISO 20022, the development of new blockchain-based solutions, and the evolving regulatory landscape surrounding digital assets.

What are your thoughts on SWIFT’s blockchain initiatives? Share your insights and predictions in the comments below.

Researchers are refining methods to predict how chemicals interact with human plasma proteins, a critical step in assessing the safety and efficacy of new compounds. A new approach leverages machine learning and a technique called “read-across” to estimate the fraction of a chemical unbound in plasma – a key pharmacokinetic parameter – with improved accuracy and interpretability. This advancement offers a potential alternative to traditional animal testing and could accelerate the development of safer pharmaceuticals and industrial chemicals.

Understanding plasma protein binding is crucial because only the unbound fraction of a chemical can exert pharmacological activity or cause toxic effects. High protein binding can reduce the amount of free drug available, potentially diminishing therapeutic benefits. Conversely, a high unbound fraction could increase the risk of adverse reactions. Accurately predicting this binding affinity, known as the fraction unbound (fu), is therefore a cornerstone of drug development and chemical safety assessment. The focus on predictive modelling of human plasma fraction unbound (fu) represents a significant step towards more efficient and ethical chemical evaluation.

Read-Across and Machine Learning: A Synergistic Approach

The research, detailed in recent publications, centers on combining read-across methodology with machine learning algorithms. Read-across involves using data from structurally similar chemicals to predict the properties of a target chemical. This is particularly useful when experimental data for the target compound is limited or unavailable. The study highlights the power of using a similarity-based read-across (RA) method to generate predictive models. Researchers are focusing on creating Physiologically-Based Kinetic (PBK) modeling using this approach.

Machine learning algorithms are then employed to analyze the relationships between chemical structure and the fraction unbound in plasma. The team at Jadavpur University, as detailed in their published abstract, developed models using computational resources to generate predictions as an alternative to animal experimentation. These models aim to be interpretable, meaning scientists can understand *why* a particular prediction is made, rather than simply receiving a black-box output. This interpretability is vital for building confidence in the predictions and identifying potential areas for further investigation.

Improving Predictive Accuracy and Reducing Reliance on Animal Testing

Traditional methods for determining plasma protein binding often involve laboratory experiments, which can be time-consuming, expensive, and raise ethical concerns regarding animal welfare. The development of accurate in silico (computer-based) models offers a compelling alternative. Recent advancements, including transfer learning strategies – where a model trained on a broad chemical library is fine-tuned for specific applications – are further enhancing predictive capabilities. A study on per- and polyfluoroalkyl substances (PFAS) demonstrates the potential of these techniques.

The European Food Safety Authority (EFSA) is also actively exploring the employ of read-across for chemical safety assessment, recognizing its potential to streamline the evaluation process. According to EFSA guidance, Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) models can predict Absorption, Distribution, Metabolism, and Excretion (ADME) parameters, including the fraction absorbed and the fraction unbound.

Applications in Pharmaceutical Development and Beyond

The implications of this research extend beyond pharmaceutical development. Accurate prediction of plasma protein binding is also crucial for assessing the environmental fate and toxicity of industrial chemicals, pesticides, and other substances. For example, a case study involving Valproic acid and 2-Ethylhexanoic acid utilized PBK read-across to model exposure in pregnant individuals and fetuses, incorporating plasma protein binding data (see parameters incorporated for PBK modelling).

As computational resources continue to expand and machine learning algorithms develop into more sophisticated, these predictive models are poised to play an increasingly important role in chemical safety assessment and drug discovery. The ongoing refinement of these techniques promises a future where chemical development is both more efficient and more protective of human health and the environment.

The continued development and validation of these models will be critical to their widespread adoption. Further research will likely focus on expanding the chemical space covered by these models and improving their ability to handle complex chemical structures. What comes next will be the integration of these predictive tools into regulatory frameworks and industry workflows.

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