AI Funding

OpenAI and Anthropic now sit at the center of Big Tech’s AI cloud backlog

AI News AI Funding, Amazon Web Services, Anthropic, Big Tech, Cloud Infrastructure, Microsoft Azure, OpenAI

AI NewsMay 24, 2026·6 min read

OpenAI and Anthropic have become the fulcrum of a structural accounting dynamic where Big Tech giants simultaneously fund AI startups, book their cloud spending as revenue, and recognize paper gains on inflated equity stakes, raising hard questions about revenue quality and cash sustainability for institutional investors evaluating these cloud giants’ earnings. The $2 trillion in future cloud commitments tied to these two firms now represents the earnings visibility story that consensus valuations depend on, yet operates on a circular cash flow mechanism rather than genuine customer demand.

Microsoft’s $13 billion OpenAI investment consisted primarily of Azure credits that generated $60 billion annual cloud revenue for OpenAI, exceeding its $25 billion revenue twofold.
Anthropic spent $2.66 billion on AWS in nine months, nearly matching its total revenue in the same period, with Amazon recognizing $16.8 billion in mark-to-market gains on its stake.
OpenAI and Anthropic account for more than half of the nearly $2 trillion in forward cloud commitments recorded by Microsoft, Oracle, Alphabet, and Amazon combined.

$60B OpenAI annual cloud bill against $25B annual revenue, or 2.4x coverage ratio
$16.8B Amazon Anthropic equity gains versus $30.3B total Q1 2026 earnings, or 55% of profit
95% Amazon free cash flow decline to $1.2 billion, reflecting infrastructure investment velocity

The artificial intelligence investment cycle has crystallized around a structural problem that institutional investors have largely overlooked: the world’s largest cloud providers have engineered a self-reinforcing loop in which they fund AI startups, those startups deploy that funding exclusively on the same providers’ infrastructure, and the providers then recognize both the funding as customer cloud revenue and the startups’ rising valuations as unrealized gains on their balance sheets.

OpenAI and Anthropic sit at the center of this mechanism, with fresh regulatory filings revealing that these two companies alone account for more than half of the nearly $2 trillion in future cloud spending that Microsoft, Oracle, Alphabet, and Amazon have booked as committed revenue.

The arrangement is not illegal, but it operates on a circular cash flow model that obscures whether genuine economic demand exists or whether capital is simply moving through a series of related-party transactions disguised as separate business activities.

Microsoft Books $60 Billion OpenAI Cloud Revenue From Its Own $13 Billion Investment

The Microsoft-OpenAI relationship illustrates the mechanics of the structure with unusual clarity. Microsoft deployed approximately $13 billion into OpenAI through a funding agreement, yet the vast majority of that capital took the form of Azure compute credits rather than cash.

OpenAI has no choice but to consume those credits by purchasing cloud infrastructure from Microsoft to train and run its models. This requirement transformed what might appear as a capital investment into a pre-committed customer contract, where the “customer” is the very company receiving the funding.

The consequence is a doubling of OpenAI’s infrastructure costs relative to its revenue. OpenAI’s annual cloud bill has now climbed above $60 billion per year, while its total revenue remains around $25 billion, creating a cost-to-revenue ratio of 2.4 to 1.

In conventional enterprise software or SaaS contexts, such a ratio would signal unsustainable unit economics and trigger immediate strategic review. Within the AI financing narrative, however, Wall Street has reframed the same numbers as evidence of scaling and growth velocity.

Microsoft simultaneously benefits on two lines of its income statement. The Azure division recognizes $60 billion in annual cloud revenue attributed to OpenAI’s infrastructure consumption, bolstering cloud segment growth metrics that drive institutional valuations.

Independently, Microsoft’s corporate investments division marks up its equity stake in OpenAI as the startup’s valuation rises, creating unrealized paper gains that flow through non-operating income. Neither mechanism requires OpenAI to convert its cloud spending into actual product revenue or customer cash payments.

Anthropic Spends Nearly All Revenue Back to AWS Within Nine Months

Anthropic operates under an analogous arrangement with Amazon Web Services. Filings show Anthropic spent approximately $2.66 billion on AWS infrastructure during a nine-month period, nearly matching its total revenue for the same window.

This spending ratio means Anthropic is returning nearly 100 cents of every dollar earned directly back to AWS in the form of compute bills, creating a closed loop in which Amazon funds Anthropic, Anthropic purchases AWS capacity, and Amazon records the purchase as customer cloud revenue while simultaneously marking up its equity position in Anthropic as the company’s valuation inflates.

The equity gains have become material to Amazon’s reported profitability. In the first quarter of 2026, Amazon posted $30.3 billion in total earnings, of which Anthropic-related mark-to-market gains accounted for $16.8 billion, or 55 percent of consolidated profit.

That ratio signals a dangerous convergence: Amazon’s headline earnings increasingly depend on paper gains from AI startup equity stakes rather than on cash generated from actual customer relationships or products sold.

The mismatch between reported earnings and cash generation has begun to surface in Amazon’s cash flow metrics. The company’s free cash flow fell by 95 percent to $1.2 billion, a dramatic contraction that reflects the enormous capital outlays required to build the infrastructure that Anthropic and other AI firms are consuming.

The gap between earnings (boosted by equity mark-ups) and cash flow (pressured by real infrastructure investment) creates accounting asymmetry that institutional investors must now reconcile.

The $2 Trillion Cloud Backlog Depends Entirely on AI Startup Funding Cycles

The structural risk extends across the entire cloud sector. Microsoft, Oracle, Alphabet, and Amazon have collectively booked nearly $2 trillion in future cloud revenue commitments, with OpenAI and Anthropic accounting for more than half of that total.

These figures appear in quarterly disclosures as contracted future revenue, contributing directly to analyst models and consensus earnings forecasts that underpin institutional capital allocation decisions.

Yet the visibility on which those forecasts rest is not rooted in independent customer demand or market-tested pricing; it exists because two AI startups received funding from the same companies now counting that funding as customer contracts.

If OpenAI or Anthropic encounter valuation pressure, face regulatory restrictions on their operations, or experience slower-than-expected scaling in actual AI product adoption, the cloud spending commitments that justify $2 trillion in recorded future revenue would evaporate.

The companies would still own the infrastructure, but the revenue visibility that supports current cloud division valuations would disappear. This creates a hidden leverage point in the cloud sector’s investment thesis: institutional investors are implicitly betting not on cloud infrastructure demand broadly, but specifically on the continued growth and funding of two private AI startups.

The arrangement also creates a perverse incentive structure. Cloud providers benefit from funding AI startups regardless of whether those startups achieve profitable operations or reach genuine product-market fit. The funding itself generates immediate cloud revenue recognition and equity upside, independent of any external validation of the AI business model.

This decouples infrastructure investment from the underlying demand signals that normally govern capital allocation in competitive markets.

Mark-to-Market Gains Replace Cash Profits as the Driver of Cloud Giant Earnings

The shift from cash earnings to equity mark-ups as the primary source of reported profit represents a material change in how institutional investors should model these companies.

Alphabet reported $62.6 billion in earnings for the first quarter of 2026, with $28.7 billion attributed to gains on its Anthropic stake, meaning nearly 46 percent of consolidated earnings came from equity valuation increases rather than operations. Amazon’s situation was more extreme at 55 percent. These are not minority contributors; they are the dominant earnings drivers.

Equity mark-ups are inherently unstable. A downturn in AI sentiment, slower-than-expected model capability improvements, or competitive pressure on AI pricing would immediately compress AI startup valuations, erasing these paper gains. Unlike revenue from actual customer contracts, mark-up gains can reverse sharply and offer no accompanying cash benefit.

An institutional investor receiving earnings per share attributable primarily to mark-ups has less durable earnings than one whose profits derive from customer cash flows, yet the financial statements present both identically.

The accounting treatment also creates a visibility cliff. As long as AI startups remain in private markets

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DeepSeek is seeking funding at a valuation above $20 billion

Blockchain AI Funding, Alibaba, Chinese AI Startups, DeepSeek, Huawei Chips, Nvidia, Tencent

AI NewsApril 22, 2026·5 min read

DeepSeek’s valuation has more than doubled to above $20 billion in less than a week as Tencent and Alibaba enter funding discussions, signaling intensifying competition between U.S. and Chinese AI stacks at a moment when chip performance gaps remain wide but closing. For institutional crypto and blockchain investors, this mirrors broader geopolitical technology decoupling that could reshape compute infrastructure markets and alter which blockchain protocols and AI-native chains gain adoption in different regions.

DeepSeek’s valuation jumped from $10 billion to above $20 billion within days as Tencent and Alibaba initiated investment talks.
Nvidia CEO Jensen Huang warned that if DeepSeek optimizes AI models for Huawei chips instead of American hardware, it would be a “horrible outcome” for the U.S.
Huawei’s Ascend 910C currently delivers only 60% of the inference performance of Nvidia’s H100, with American chips estimated to be 5x more powerful today but potentially 17x by 2027.

$20B+ DeepSeek’s target valuation versus $10 billion floor announced Friday
60% Huawei Ascend 910C performance relative to Nvidia H100 benchmark
17x Projected U.S. chip performance advantage over Chinese rivals by 2027

DeepSeek’s valuation has surged past $20 billion in funding discussions with China’s largest tech firms, less than a week after the AI startup disclosed it was seeking at least $300 million at a $10 billion floor. Tencent Holdings and Alibaba Group are now in active talks to invest in the Chinese startup, according to reports on Wednesday citing four people familiar with the negotiations.

The rapid repricing reflects intensifying competitive pressure in AI infrastructure and a fundamental shift in how geopolitical technology competition plays out at the infrastructure layer, a concern with direct implications for how compute resources, and by extension blockchain networks, become distributed globally.

The speed of this valuation increase mirrors the broader urgency surrounding AI chip sovereignty. Within days of opening its first outside investor discussions, DeepSeek has attracted attention from two of China’s most valuable technology firms, each with incentives to reduce dependence on American semiconductor supply chains.

The talks remain ongoing, meaning the final valuation and funding amount could still shift, but the direction signals how quickly capital mobilizes around AI infrastructure bets perceived as strategically important to national technology ecosystems.

Nvidia CEO warns DeepSeek optimization for Huawei chips would harm U.S. competitiveness

Jensen Huang, Nvidia’s chief executive, articulated the core concern driving this funding competition during a Wednesday podcast appearance. He described it as a “horrible outcome” for the United States if DeepSeek’s AI models were optimized to run efficiently on Huawei chips rather than American processors.

Huang’s warning cuts to the heart of why institutional investors should track this funding round closely: the outcome determines whether future AI model development follows an American technology stack or a Chinese one, with cascading effects on which compute platforms, and which regional blockchain and protocol ecosystems, gain adoption in different markets.

If future AI models are optimised in a very different way than the American tech stack, and as AI diffuses out into the rest of the world with Chinese standards and technology, China will become superior to the United States.

Jensen Huang, Chief Executive, Nvidia

Huang framed the competition not as a near-term chip performance race, but as a standards-setting moment. Once large AI model developers optimize for a particular chip architecture, downstream applications, including blockchain infrastructure, AI-native protocols, and decentralized compute networks, tend to follow that same hardware assumption.

If DeepSeek V4 and future versions run optimally on Ascend silicon rather than H100s, that creates a self-reinforcing ecosystem where Chinese-standard hardware becomes the assumed baseline for global AI development, analogous to how x86 architecture became the assumed standard for decades of enterprise computing.

The strategic concern Huang raised extends beyond raw performance metrics. He noted that China possesses “abundant energy” for training and inference workloads and a “large pool of AI researchers,” meaning the country can overcome near-term chip deficits through scale and talent concentration.

DeepSeek’s own emergence as a credible alternative to OpenAI and Claude demonstrates this: the startup achieved competitive performance despite working under U.S. export controls on advanced semiconductors.

Huawei Ascend 910C delivers 60% of H100 performance but gap narrows as Chinese production scales

Current performance data suggests American chips still hold a decisive advantage. Huawei’s Ascend 910C, the chip preceding its newer 950PR model, delivers approximately 60% of the inference performance that Nvidia’s H100 achieves, a significant shortfall.

The H100 itself, however, is already two generations behind Nvidia’s current flagship processor, meaning the real-world gap between Huawei’s available silicon and Nvidia’s most advanced offerings is even wider.

The absolute performance spread remains substantial: American AI chips are estimated to be roughly five times more powerful than Chinese alternatives today.

However, that lead is projected to narrow considerably, with estimates suggesting American chips could hold only a 17-times performance advantage by 2027, a compression driven by both Chinese chip design improvements and anticipated slowdowns in American chipmaker gains as physical design limits tighten.

Production volume compounds the picture. Huawei is targeting 750,000 AI chip shipments in 2026, but that total production represents only 3% to 5% of the combined computing power Nvidia generates across all its customers. This means Chinese AI development remains fundamentally constrained by silicon availability, not just efficiency.

DeepSeek’s funding round therefore carries geopolitical weight precisely because capital flowing to the startup can accelerate model optimization techniques that extract maximum performance from scarce Huawei chips, reducing China’s dependence on incremental hardware improvements alone.

DeepSeek funding surge signals broader AI infrastructure competition reshaping compute markets

DeepSeek’s valuation jump coincides with a wider funding surge in AI infrastructure. Vast Data, a storage and compute company, announced a $1 billion funding round on the same day Huang made his remarks, indicating that capital markets are pricing in sustained competition for alternative AI stacks and infrastructure layers outside the dominant American ecosystem.

For institutional investors tracking blockchain and cryptocurrency infrastructure, this bifurcation matters directly.

If AI model training and inference optimizes around region-specific hardware stacks, blockchain protocols and decentralized compute networks will likely follow similar geographic and technical boundaries.

A Chinese-optimized AI stack built on Huawei chips creates natural incentives for blockchain protocols to optimize for deployment in that ecosystem, potentially fragmenting the global compute market into American-standard and Chinese-standard spheres.

Conversely, if Nvidia and American chipmakers maintain their advantage, the current concentration of AI infrastructure in U.S.-aligned jurisdictions will likely persist, benefiting blockchain platforms already embedded in that ecosystem.

The funding discussions involving Tencent and Alibaba also carry strategic weight because both firms operate blockchain or blockchain-adjacent platforms.

Tencent’s involvement in digital asset ecosystems and Alibaba’s blockchain infrastructure initiatives mean their investment in DeepSeek is not purely financial, but helps coordinate AI and blockchain infrastructure strategies at the firm level, accelerating the development of integrated Chinese technology stacks that reduce reliance on American components across multiple infrastructure layers.

The next decisive indicator is whether DeepSeek publicly discloses optimization metrics for Huawei Ascend chips versus Nvidia processors in model performance benchmarks. If inference latency and throughput on Ascend hardware approaches parity with H100 performance within the next two quarters, it signals that model optimization rather than raw chip performance is becoming the binding constraint on Chinese AI development, a turning point that would trigger accelerated adoption of Huawei chips by other Chinese AI firms and potentially force institutional investors to reprice both American chip export restrictions and regional blockchain infrastructure competition.

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