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AWS revenue grew 28% year-over-year, Azure grew 40%, and Google Cloud surged 63%, with Google experiencing a historic single-day market cap increase.
The updated Microsoft-OpenAI deal gives Microsoft free access to OpenAI models for five years and removes an AGI clause, while OpenAI can now sell through AWS and Google Cloud.
Dave Jones believes Apple's WWDC AI announcements, which will host models privately in Google Cloud, 'rug-pulled' big LLM providers and could end consumer use of ChatGPT if integrated Siri works well.
OpenAI’s capital strategy involves shifting capex to opex by partnering with multiple cloud providers. They now work with Oracle, CoreWeave, Microsoft Azure, Google Cloud, AWS, and smaller neoscalers.
Theo criticizes Google Cloud's reliability, citing a four-year history of issues and a recent incident where Google's algorithm mistakenly deleted Railway's entire account without human oversight.
Google Cloud revenue grew 63% year-over-year, with a $460 billion backlog in new orders, up from $240 billion in Q4. CEO Sundar Pichai said AI is now the cloud unit's largest growth driver, though compute constraints limited revenue.
Big Tech cloud earnings showed massive AI-driven growth: AWS up 28% year-over-year, Microsoft Azure up 40%, and Google Cloud up 63%, beating estimates and causing a historic market cap jump.
Microsoft and OpenAI restructured their deal, removing the AGI clause and granting Microsoft free access to OpenAI models for a half-decade. OpenAI is now free to sell models through AWS and Google Cloud.
Google launched an AI consulting group within Google Cloud hiring hundreds of forward-deployed engineers to help enterprise customers with agent development, mirroring moves by OpenAI and Anthropic.
Google Cloud revenue hit $20 billion with 63% growth, outpacing AWS and Azure, aided by AI demand and offering TPU capacity to other labs.
OpenAI ended its Azure exclusivity and now runs on AWS, Google Cloud, and Oracle, a move Alex Susskind Gross links to Microsoft's inability to meet OpenAI's voracious compute appetite.
Brad Gerstner points to hyperscaler revenue growth as proof of the AI boom's ROI: AWS grew 28% to a $150B run rate, Azure grew 39% to $108B, and Google Cloud grew 63% to $80B.
Google's $200B, five-year deal with Anthropic contributed to Google Cloud's $462B backlog. Google's stock rose 10% after the backlog announcement and gained further after the deal terms were reported.
Anthropic committed $200B to Google Cloud over five years, a deal representing over 40% of Google's reported $462B backlog of compute orders.
Nathaniel Whittemore cites Google Cloud's 63% year-over-year revenue growth as evidence against an AI bubble, citing similar strong growth from Azure (40%), Meta (33%), and AWS (28%).
Alex Wilhelm reports cloud capex surges continue, with Google Cloud revenue up 63%, AWS growth hitting 28% - its best in 15 quarters - and Microsoft, Amazon, and Meta all increasing planned spending for 2025.
Big Tech cloud earnings showed explosive AI-driven growth: AWS revenue was up 28% YoY, Microsoft Azure grew 40% YoY, and Google Cloud beat estimates with 63% YoY growth, triggering a record market cap jump.
Microsoft and OpenAI restructured their deal, granting Microsoft non-revenue-share access to OpenAI's models for five more years and removing the AGI clause. OpenAI is now free to sell models on AWS and Google Cloud.
Ben states Anthropic disallows cloud credits from Google Cloud or Azure to be used for Claude model inference, unlike OpenAI which permits it.
Theo reveals the OpenAI-Microsoft deal amendment ends Microsoft's exclusive hosting rights, opens OpenAI models to AWS and Google Cloud, and replaces a revenue share with a profit share capped through 2030.
Google Cloud unveiled its eighth generation of TPUs (8T for training, 8I for inference), offering three times faster training performance and 80% better performance per dollar.
Chris Lattner identifies Google's TPU as the biggest sleeper competitor to Nvidia, citing its seven-generation development and superior scale-out, but notes its adoption is limited by GCP-only access and lack of a developer community.
The reliance on hyperscalers (AWS, Google Cloud, Azure) for running AI models creates a centralized control point that governments could leverage to pressure companies and regulate AI, underscoring the critical need for powerful local AI and open models to maintain individual autonomy.