Global Financial Markets Insights

Your first Q2 board meeting is this week. Data centres just entered their next phase. Here's what you need to know before you walk in. Q1 2026 is done. The signals I mapped in December are now consensus. If your board isn't talking about these shifts, you're already behind. 𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗛𝗮𝘃𝗲 𝗦𝗽𝗼𝗸𝗲𝗻 → JLL (Jan 2026): $3 trillion infrastructure supercycle. 100 GW of new capacity by 2030. Global data centre sector doubling to 200 GW. → Goldman Sachs (Mar 2026): 220% data centre power demand growth by 2030. AI workloads rising from 14% to 39% of total demand. → McKinsey: $6.7 trillion capex needed by 2030. $5.2 trillion for AI infrastructure alone. This isn't speculation. It's analyst consensus. 𝗧𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 𝗜𝘀 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗠𝗼𝘃𝗶𝗻𝗴 → Magnificent Seven lost $2 trillion in valuation → Oracle cut 30,000 jobs to fund AI data centres → Capital is shifting from headcount to compute Wall Street isn't panicking. It's repositioning. 𝗧𝗵𝗲 𝟴 𝗦𝗵𝗶𝗳𝘁𝘀 𝗬𝗼𝘂𝗿 𝗕𝗼𝗮𝗿𝗱 𝗡𝗲𝗲𝗱𝘀 𝗧𝗼 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 1. Cooling Evolution : Air → Liquid 2. Power Wars : Grid Dependent → Grid Optional 3. Capacity Unlocks : Assumed → Planned 4. Next-Gen Chips : Single Vendor → Multi-Platform 5. AI-Powered Ops : Humans Driving → Supervising 6. Sovereignty Wars : Compliance → Architecture-First 7. Talent Gambit : Fixed Roles → Reshuffled Skills 8. Sustainability Leadership : Green Screen → Green Scene Miss one shift → You fall behind. Miss three → You're out of the race. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Q1 was the warm-up. Q2 is where builders separate from bystanders. Save this image. Take it into your meeting. Be the person who reads the industry, not just works in it. 𝗬𝗼𝘂𝗿 𝗧𝘂𝗿𝗻: Which shift is your board focused on this quarter? ♻️ Repost to help someone prepare for their Q2 meeting ✅ Follow Guy Massey for infrastructure truths from 20+ years in the trenches

The AI buildout is accelerating into a physical infrastructure expansion, but the narrative on Wall Street is shifting quickly. In my conversations across the 1BusinessWorld ecosystem with executive leaders and institutional investors, a consistent theme keeps emerging: the friction between massive capital expenditure and actual operational returns. The recent margin pressure on “Little Leo” and his fund, Situational Awareness, proved that even German precision can’t hedge against bad market timing. We see this shift firsthand in how we run our own platform. At 1BusinessWorld, we continuously optimize technology costs by treating frontier and open-source models as dynamic commodities, switching capabilities as economics dictate. With enterprise buyers demanding ROI and squeezing software margins, hyperscalers can no longer self-fund their expansion out of cash flow, turning to bond markets instead for data center capital. As Fed Chair Kevin Warsh noted, rising Treasury yields across the curve reflect the market doing much of the tightening work itself. This pushes up long-term corporate borrowing costs, creating a much higher yield hurdle for capital to clear. In recent discussions with leaders in commercial real estate debt and credit servicing, one imperative stands out clearly: as data center infrastructure scales alongside higher borrowing costs, long-term resilience hinges on disciplined loan underwriting, proactive asset management, and a precise valuation of physical collateral. Bridging macro trends with grounded credit risk management will be essential for the sector moving forward. 📊 Visual via Torsten Slok & Apollo Global Management, Inc. #CommercialRealEstate #CREDebt #DataCenterInfrastructure #MacroEconomics

Everyone's chasing data center land. Almost everyone is missing the real constraint. It's not fiber. It's not even land. It's power. U.S. Interior Secretary Doug Burgum said at the Prologis conference: "To win the AI arms race against China, we've got to figure out how to build these artificial intelligence factories close to where the power is produced, and just skip the years of trying to get permitting for pipelines and transmission lines." Translation: The next generation of data centers won't be built where the land is cheap. They'll be built where the power is available. Three implications for dirt investors: 1. Nuclear Proximity = New Premium: Amazon already signed deals with Dominion Energy near the North Anna nuclear power station in Virginia and expanded partnerships with Talen Energy at the Susquehanna nuclear plant. Sites within transmission distance of existing nuclear facilities just became exponentially more valuable. 2. Warehouse Conversions Accelerate: If Prologis is eyeing their 6,000 buildings for data center conversion, every industrial site with surplus power capacity needs re-evaluation. What looks like a struggling warehouse today might be a data center tomorrow. 3. Grid Capacity > Geographic Desirability: Constellation Energy CEO Joseph Dominguez noted that data economy customers "want to run their systems 24-7" with "firm pricing so that they know the price for energy for 20 years". Long-term power contracts are becoming the new land entitlements. But here's what nobody's talking about: The same power constraints driving this opportunity are also creating massive project risks. According to a recent CoStar analysis, data centers will account for up to 60% of total power load growth through 2030. But there's a timing mismatch: data centers take 2-3 years to build, while power system upgrades take 8 years. That gap is forcing developers to either wait or find sites with existing capacity. The Community Resistance Factor Data Center Watch estimates $64 billion in data center projects were blocked or delayed over a recent two-year period. There are now 142 activist groups across 24 states organizing against data center development. Northern Virginia alone-the nation's largest data center market-has 42 activist groups fighting projects. Reasons cited: water consumption, higher utility bills, noise, decreased property values, loss of open space. Translation for land investors: Sites with existing power capacity + community support just became exponentially more valuable than sites with just land and zoning. The power infrastructure thesis isn't just about finding available capacity. It's about finding that capacity in counties that actually want data centers. Not every market will roll out the welcome mat. Are you evaluating community sentiment alongside power infrastructure access?

Data centers, compute, and energy have become a bottleneck and a cash cow. Companies that once discussed software margins in earnings calls now debate cooling technologies and power procurement. The numbers tell a story of infrastructure at an inflection point: Data centers consuming 460 TWh in 2022 (pre-ChatGPT) will exceed 1,000 TWh by 2026 and the global data center market size is projected to approach $1T within 7 years. Behind every earnings mention are two core realizations: 1) Data center infrastructure is foundational to everyone’s AI aspirations Meta increased CapEx by $5B to $72B, citing "substantial internal demand for GPU resources." Microsoft warns AI demand will exceed supply through 2025. Dell raised AI server guidance to $20B. Google is acquiring stakes in crypto miners for GPUs. Hyperscalers are going nuclear with Google signing with Kairos Power for 500MW, Amazon buying Talen Energy's 960MW campus, and Microsoft partnering with Constellation. Every tech giant's earnings call now reads like infrastructure procurement because when one GPT query burns 10x the energy of a Google search, training frontier models requires city-scale power, and AI ambitions die without compute. 2) There is a SH*T TON of money to be made across the data center value chain CyrusOne raised $9.7B specifically for AI infrastructure. Blackstone paid $1B for a Pennsylvania gas plant. Traditional utilities like PPL now build generation exclusively for data centers. Power isn't infrastructure anymore – it's the business model. Cooling specialists like Submer and Green Revolution tackle 300% power increases from new chips. Edge players like Armada can deploy modular centers anywhere. AI-native infrastructure companies like VAST Data ($9.1B valuation) rebuild the stack from scratch. Nscale raised $1.1B in another play from crypto miners turned infra provider. The gold rush extends everywhere, with NVIDIA projecting that the AI infrastructure market will hit $4 trillion by 2030 and a $1T+ buildout underway – every layer of the stack is capturing value. And... with that… coming soon… the full CB Insights’ data center value chain report.

Size Matters: Why Operational Scale Is Key in Crypto Fund Management In the current crypto hedge fund landscape, managers have yet to be truly rewarded for running a fully institutional operation. The amount of capital allocated to hedge fund–style strategies (beyond simple long-only investments) remains relatively small—often coming from individual allocators with higher risk tolerance. These allocators may be crypto-native high-net-worth individuals or investors who recognize the compelling risk-reward profile of certain crypto strategies. Consequently, many managers boast impressive track records compared to their TradFi counterparts, yet investors often overlook or underappreciate the operational risks involved. The reality is that most crypto hedge funds manage under $100M, which typically means they operate with lean teams focused on what directly impacts performance. Robust internal policies, comprehensive risk controls, and institutional-grade operational setups are time-consuming and costly. They may not improve the immediate track record, but are critical to avoiding catastrophic blow-ups. It’s the sort of thing you only realize you should have prepared for after a crisis has already struck—and by then, the damage might be irreversible, leaving you out of the game. Moreover, these “left-tail” or extreme risks tend to be more common than many believe. Exchange failures, for instance, have already happened with Mt. Gox, FTX, and Bitfinex. They will likely happen again. The top priority for any fund manager is survival—because no matter how impressive potential returns could be in the next bull market, you won’t be around to reap them if you fail to manage fundamental structural risks. This year, I believe, will draw a clear line between niche players and industry leaders. Institutional capital commanding trillions of dollars has lower performance targets but nearly zero tolerance for subpar operational setups. While smaller funds often cut corners to remain efficient, especially with limited budgets, this only increases the fat-tail risks. Ultimately, to attract and retain institutional flows, scale and robust operational practices are essential. In crypto fund management, “size matters” goes beyond assets under management—it encompasses a fund’s operational maturity and its ability to protect investor capital under even the most extreme market conditions.

Big Tech data centers are the hottest assets in the world… but is it hard for developers to cash out? Here’s the paradox reshaping capital markets: • Amazon, Microsoft, and Google pre-lease billions in new hyperscale capacity years before construction ends. • Developers deliver fully leased, mission-critical facilities… • Yet when it’s time to sell? Buyers vanish. Why? • Stabilized hyperscale data centers = massive $3B+ price tags • Locked into 10–15 year leases → limited upside for buyers • Only 7% of investors target stabilized “core” assets (CBRE) The result: Developers are reinventing exits with debt securitizations instead of equity sales. → $13.4B in ABS + SASB data center deals closed in H1 2025, double last year (JLL) → Blackstone/QTS → $1.5B CMBS refinance in Atlanta + Richmond → DataBank → $1B ABS backed by Atlanta, NY, Virginia facilities Meanwhile, creative equity plays are emerging: • Forward takeouts (buyers fund development + commit to buy at stabilization) • Hyperscaler purchase options (Amazon/Microsoft buying back facilities years into leases) • Minority stake sales (developers recycle capital while staying in the operator seat) The bigger shift? Data centers went from niche infrastructure to 13% of the SASB market in just 4 years (Goldman). What this signals: → Liquidity is flowing into bonds, not asset sales. → Core buyers are thin, but new funds (Blue Owl + Qatari SWF just raised $3B) are being built to fill the gap. → The future of data center finance may look more like Wall Street than Main Street. Are securitizations the permanent exit strategy for hyperscale developers, or will a new wave of core buyers finally step in? Full story: https://lnkd.in/gTJ-vupT

Manager Selection: The Hidden Alpha Engine “It’s not just the strategy. It’s who’s driving the car.” We obsess over strategies: macro vs long/short, private equity vs credit. But in alternatives, it’s often not what you buy—it’s who you back. Top-quartile managers can outperform by thousands of basis points. And yet, due diligence often gets treated like a checkbox. I’ve seen funds with dazzling decks and nothing under the hood. And I’ve seen quieter managers with airtight process, discipline, and skin in the game deliver decade-long outperformance. Manager selection isn’t always glamorous. But it’s your real edge. Don’t chase alpha. Allocate to it. #bealternative So how do you identify the right managers—and avoid the wrong ones? Here are five actionable principles backed by Hedge Fund Due Diligence, Due Diligence and Risk Assessment of an Alternative Investment Fund, and Private Equity Compliance: 1. Prioritize Behavioral Red Flags Over Marketing Shine Most blowups stem from behavioral warning signs—not poor returns. – Be alert to evasive answers, overpromising, and CV inconsistencies. – If the manager can’t clearly explain their worst drawdown, walk away. Operational risk often wears a smile. 2. Use a Layered Due Diligence Framework – Investment: strategy clarity, mandate discipline, leverage use. – Operational: NAV policies, service providers, valuation controls. – Manager: track record, co-investment, legal history. A strong fund passes all three layers—not just the first. 3. Move Beyond the Checklist Mentality – Ask how—not just what. – Request audit letters, compliance manuals, fund org charts. – Evaluate how quickly and how clearly information is shared. It’s not what’s disclosed. It’s how it’s delivered. 4. Re-underwrite Annually—Not Just at Allocation Diligence doesn’t stop once the subscription agreement is signed. – Monitor for style drift, team turnover, and audit delays. – Build an annual risk scorecard: manager alignment, NAV consistency, valuation transparency. Great managers stay great when they’re held accountable. 5. Investigate the “Why” Behind the Performance Outperformance isn’t always repeatable—but process is. – Ask: “What edge do you believe is durable?” – Review decision-making consistency, not just returns. – Confirm fee alignment, risk-adjusted mindset, and long-term incentive structure. Strong governance and repeatable process beat personality and narrative—every time. Alpha doesn’t live in the deck. It lives in the decisions behind it. What’s your non-negotiable when assessing a manager beyond performance? #bealternative

I was reminded of my visit to the Hitachi Energy facility in Karnataka, where Venu Nuguri briefly spoke about the HVDC opportunity & also flagged the emerging data centre theme. This was a time when there was no big buzz on the HVDC opportunity. The scale of the HVDC opportunity has since become evident. Hitachi Energy’s order book now stands at around ₹29,900 cr, nearly 4.7 times FY25 sales. The company is clearly ahead of the curve & remains best positioned to capitalise on HVDC-led grid expansion. What stood out equally was the data centre discussion. On the show today on CNBC-TV18 Mr. Venu highlighted that data centres are extremely energy intensive & more importantly, require flexibility. Capacity requirements can move rapidly from 100 megawatts to 250 megawatts within seconds. He also pointed out that a ChatGPT query consumes six to eight times more energy than a traditional Google search, underscoring how AI-driven workloads are fundamentally changing power demand dynamics. According to Mr. Venu, Hitachi Energy could potentially be best placed to address about 15% of total Data Centre capital expenditure. With data centre capex in India estimated at roughly USD 30 billion, this translates into an opportunity of about USD 4.5 billion. Any export-led data centre opportunities would be incremental to this. HVDC and data centres are no longer niche themes. Appears that they are fast becoming core pillars of the energy transition & digital infrastructure story. When Mr.Venu speaks about a theme, I think he is normally way ahead so we must take note.

Fund managers: Do you know if your GP commitment is in line with industry standards? Or if you're overspending on operating expenses? Or how often LPs in the industry are late to fulfilling capital calls? There has been a massive lack of data on the operational side of running a fund...until now. Carta's first-ever Fund Economics Report fills that gap. We leveraged data from 2,000 funds to generate benchmarks and insights on everything from capital calls to carried interest. The report launches Thursday, but here’s a sneak peek on operating expenses. As venture funds get larger, some expense categories scale up while others scale down. $100M+ funds tend to spend more than 1/5 of their opex in the first 5 years (i.e. the investment period) on legal fees - including fund syndication and structuring deals. Funds in the $1M-$10M category, by comparison, spend just 9% of their opex on legal costs. On the other hand, tax prep and filing becomes more efficient as funds get larger, going from 12% of opex for the smallest funds to just 6% for the largest funds. (Caveat: some expense categories aren't shown here, and some expenses are charged to the manco rather than individual funds.) Stay tuned for the full breakdown later this week!

The scale and financing of the global data-center build-out between 2025 and 2028 highlight a structural shift in how AI infrastructure is being funded. Morgan Stanley projects nearly $2.9 trillion in cumulative spending over this period, with tech companies themselves accounting for approximately $1.4 trillion. This level of self-funding is unprecedented and underscores both the strategic importance of AI infrastructure and the financial strain it is placing on balance sheets. What is equally notable is the growing reliance on external financing. Private credit is expected to provide roughly $800 billion, while private equity and related investors will contribute an additional $350 billion. Meanwhile, corporate bond markets remain a significant pillar, with about $200 billion in issuance, and asset-backed securitizations adding another $150 billion. This diversification of funding sources illustrates that the AI data-center arms race is no longer solely a big-tech undertaking—it is increasingly becoming a system-wide capital deployment channel involving the entire credit spectrum. This is reflected in recent bond-issuance patterns. According to Bank of America, the largest US AI companies—Alphabet, Amazon, Meta, Microsoft, and Oracle—have sharply accelerated issuance in 2025. While issuance over 2020–2024 remained relatively stable, 2025 has seen a surge, with the bulk of it concentrated between September and November. This aligns with rapidly rising capex budgets and the growing need to finance data-center expansions, power procurement, and accelerated hardware refresh cycles. The combined picture reveals a sector increasingly dependent on debt to sustain its growth trajectory. With interest rates still elevated and capex requirements compounding, the long-term sustainability of this funding model will depend heavily on whether AI-driven revenues scale fast enough to justify both the leverage and the pace of infrastructure expansion. Sources: Bank of America and Morgan Stanley