Amazon prepares its first Swiss franc bond in a six-part AI-capex push



BNP Paribas, Deutsche Bank and JPMorgan have the mandate. Maturities run from three years to 25 years.

The trade follows Alphabet’s record Swiss issuance in February and Amazon’s $37bn dollar deal in March, and is the latest demonstration that hyperscalers are now multi-currency borrowers.


Amazon is preparing its first-ever Swiss franc bond issuance, Bloomberg reported on Monday, in a six-tranche deal that stretches across three-, five-, seven-, ten-, fifteen- and twenty-five-year maturities.

BNP Paribas, Deutsche Bank, and JPMorgan have been mandated to run the books. Amazon has not yet disclosed the size of the trade; pricing is expected later this week.

The trade is the most visible sign yet that the largest US hyperscalers have crossed a threshold in their funding strategy. A US dollar bond programme is no longer sufficient on its own.

The capital required to fund AI infrastructure has become large enough that Big Tech treasurers are now actively diversifying into euros, sterling, and Swiss francs, often within the same multi-currency programme, to broaden their investor base and to capture pockets of demand that the US market alone cannot satisfy at acceptable rates.

Amazon’s path into the Swiss market follows a well-trodden one. Alphabet sold more than CHF 2.75bn (roughly $3.6bn) across five maturities in February as part of a multi-currency drive that included sterling, euro, and a rare 100-year US dollar bond.

That Swiss tranche was the biggest-ever corporate bond sale in the Swiss market. Caterpillar and Thermo Fisher Scientific have both used the same market in the past eighteen months.

What Amazon adds to that list is scale: with roughly $200bn of capex planned for 2026 according to CEO Andy Jassy’s recent comments, the company’s incremental funding requirement runs to multiple tens of billions per year.

Six tranches across the Swiss curve is consistent with a treasurer trying to lock in long-duration capacity rather than to fund a specific project.

On 10 March, Amazon raised about $37bn across eleven tranches in the US bond market. That trade was followed shortly afterwards by a EUR 14.5bn deal split across multiple tenors.

The combined dollar-and-euro raise was, at the time, the largest single funding event in the company’s history. Demand on the dollar trade was reported to have run roughly four times the size sold.

Pricing on the long end came inside Treasury yields by margins that would have been inconceivable for the company a decade ago.

The Swiss franc issuance now extends that pattern into a third currency and a market structure where issuance costs typically run materially below dollar equivalents for similarly-rated borrowers.

The arithmetic behind the issuance is straightforward. Amazon Web Services is growing AI-related revenue at the high end of the hyperscaler range, but the capex required to support that growth is sufficiently lumpy that the company has chosen to pre-fund a significant share through long-duration debt rather than to draw down cash reserves.

That choice is being made simultaneously by Alphabet, Microsoft, Meta and Oracle. Combined hyperscaler debt issuance ran past $121bn in 2025 and is on pace to top that figure by mid-2026; the $650bn of combined Big Tech AI capex now planned for 2026 is the operating-budget number that explains the funding-side urgency.

Investor reception of these trades has been consistently strong. The four largest US hyperscalers all retain credit ratings in the AA range, which gives them access to the deepest pools of institutional fixed-income demand at margins that no private-market financing structure can match.

The largest 2025 trades were oversubscribed by margins that would have looked unusual in any other sector; Amazon’s March dollar trade ran roughly 4x covered.

The Swiss franc market is smaller in absolute terms (the all-currency corporate market clears around CHF 60-70bn a year by Refinitiv tracking), but the rate environment, with Swiss yields running materially below US dollar and euro equivalents, makes it commercially attractive for issuers whose absolute funding needs can be split across currencies.

The currency-strategy logic is genuinely diversification rather than yield optimisation. A multi-currency programme reduces dependence on any single investor base, gives a treasurer flexibility about which tranches to access in periods of regional volatility, and lengthens the average maturity profile by tapping markets where long-duration demand is particularly deep.

Amazon’s choice of a 25-year tranche at the long end of this Swiss deal is consistent with that strategy. Three, five, seven and ten-year tranches give the company belly-of-curve flexibility.

The fifteen and twenty-five-year pieces match insurance and pension demand that is harder to source in equivalent size in dollars.

The wider question, which the cleaner trades of the past three months have made more rather than less acute, is how long the supportive funding environment lasts.

Hyperscaler bond issuance has been running at a pace that even bullish analysts had not modelled at the start of 2025. Morgan Stanley and JPMorgan have estimated that the sector may need to issue as much as $1.5 trillion of additional debt over the coming several years to fund the AI build-out at planned pace.

That figure assumes capex continues to grow on its current trajectory; if AI revenue growth lags those expectations, the credit metrics underpinning the AA ratings could come under more scrutiny.

The cash-generation strength behind Alphabet’s market-cap rise is part of the story that has kept the credit picture intact so far, but it depends on operating cash flow continuing to scale with the build.

Amazon’s specific position remains comfortable. The company generated approximately $100bn of free cash flow in fiscal 2025 against group capex of about $80bn, with the gap funded from existing cash reserves and incremental debt.

AWS’s operating margins have stayed above 30%, the highest in the segment. The credit spread on Amazon’s recent dollar issuance was in line with that of higher-rated peers, and the Swiss franc trade is expected to price comfortably inside the broader US dollar curve.

That Alphabet’s earlier $10bn bond sale, then the company’s largest and cheapest, was, in its time, considered the standard-setting hyperscaler funding event.

Amazon’s current programme is, in dollar terms, several multiples of that size and is unlikely to be the largest such trade for very long.

What the Swiss issuance does not yet answer is whether AI revenue scaling will eventually justify the issuance pace.

Amazon’s bond investors are taking the company’s AWS-plus-retail combined cash-flow profile as collateral for the AI build, not the AI revenue itself, which remains too early in its monetisation curve to support credit metrics on a standalone basis.

That is the same bet Alphabet, Microsoft, and Meta are asking their bond books to take. The premise has worked through 2025 and into 2026.

Whether it works through to the back half of the decade depends on what AWS, Google Cloud, and the various large-language-model product lines deliver in revenue over the same window.

For now, the Swiss tranche prices when it prices, and Amazon adds a fourth jurisdiction to a treasury programme that increasingly looks more like that of a sovereign issuer than a corporate one.

The company has yet to issue in yen. On the current trajectory, that is a question of when rather than whether.



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  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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