Syz the moment

Q1 FY2027 delivered a major datapoint for the AI bull case: Oracle’s trailing 12-month operating cash flow reached $46.9 billion. • +118% year-on-year • +47% quarter-on-quarter • Nearly $15 billion added in one quarter Yes, Oracle’s capital expenditure remains enormous. But that is precisely the strategy: build the computing and data-center capacity needed to serve one of the industry’s largest contracted AI backlogs. The key question was whether this massive infrastructure investment would eventually translate into revenue and cash generation. As capacity comes online and remaining performance obligations convert into sales, the answer is becoming clearer. AI infrastructure is moving from capital expenditure… …to cash flow. $ORCL Source: Steven Fiorillo

Q1 Adj. EPS: $1.92 vs $1.74 est Q1 Sales: $19.300B vs $19.144B est Raised FY2027 Adj EPS Guidance Raised FY2027 Sales Guidance Stock was up+7.85% during after-hours trading (The stock still needs to rally 112% to see its all-time high again...) Here are the details: ORACLE $ORCL Q1’27 EARNINGS HIGHLIGHTS 🔹 Revenue: $19.3B (Est. $19.14B) ; +30% YoY 🔹 Adj. EPS: $1.92 (Est. $1.74) ; +30% YoY 🔹 RPO: $664B; +$209B YoY FY Guide: 🔹 Revenue: At least $90B (Est. $89.79B) 🔹 Adj. EPS: $8.10 (Est. $8.07) Q2 Guide: 🔹 Adj. EPS: $1.85-$1.93 USD (Est. $1.89) 🔹 Revenue Growth: 30% to 34% USD/CC 🔹 Cloud Revenue Growth: 65% to 71% USD; 64% to 70% CC Segment Net Revenue: 🔹 Cloud: $11.6B; +62% YoY 🔹 Cloud Infrastructure: $7.4B; +121% YoY 🔹 Cloud Applications: $4.2B; +10% YoY 🔹 Software: $5.6B; -3% YoY 🔹 Hardware: $774M; +15% YoY 🔹 Services: $1.4B; +5% YoY Other Q1 Metrics: 🔹 Non-GAAP Operating Income: $8.2B; +31% YoY 🔹 Non-GAAP Operating Margin: 42% 🔹 Operating Cash Flow: $23.1B; +184% YoY 🔹 Free Cash Flow: -$5.4B 🔹 CapEx: $28.5B Key Updates: 🔹 AI Cloud Contracts: More than $30B booked in Q1, increasing RPO to $664B 🔹 ATM Equity Program: $20B of common stock sold before commissions Source: Trend Spider, Wall St Engine

As highlighted by Goldman, S&P 500 earnings grew by approximately 30% year-on-year in Q2 2026, excluding “other income” linked to private investment stakes. Hyperscalers and the AI infrastructure companies benefiting from their capex boom delivered an impressive 54% increase in earnings, contributing roughly half of the index’s total EPS growth. But the earnings strength extended well beyond AI. Excluding the Energy sector—where profits were boosted by higher oil prices—the rest of the S&P 500 still recorded solid and accelerating EPS growth of 14% year-on-year. Source: GS

He says both companies are racing toward self-improving superintelligence and "gambling with our lives." Source: Amrith Ramkumar

That is Evercore’s base case—more than 20× today’s consumption. Goldman Sachs projects a similar 24× increase. The biggest driver? Agentic AI. AI agents could become the largest source of token demand by 2030. They operate continuously—and a single agentic task can consume 10–100× more tokens than a chatbot response. But the forecast excludes one potentially enormous category: physical AI—robots, autonomous machines and intelligent industrial systems. Every token ultimately requires physical infrastructure: 🔹 GPUs 🔹 Memory 🔹 Networking 🔹 Electricity 🔹 Data centres That infrastructure largely does not exist yet. AI-related stocks will experience corrections. But forecasts for underlying demand continue to be revised higher—not lower. The AI infrastructure cycle may have much further to run than markets currently assume. Source: Kyle Reidhead | Milk Road

Since mid-2023, AI has generated roughly 1 million jobs in America—more than offsetting an estimated 200,000 AI-related losses, according to The Economist. Around 1% of US professional roles are now “AI jobs,” rising to 4–5% in computing and life sciences. But the biggest surprise may be outside software. AI infrastructure spending has increased by about $500 billion annually since 2022. Data-centre construction alone is running above $75 billion a year—creating demand for electricians, HVAC specialists, grid engineers and technicians. Meanwhile, supposedly vulnerable professions are still expanding: Paralegal employment: +11% Market-research analysts: +6% US employment overall: +2.5% The emerging picture is more nuanced than “AI destroys jobs.” For now, AI appears to be transforming work faster than it is eliminating it. Source: Bearly AI

Remember, this is inference token traffic. Every token lands in the KV cache which is a memory problem. Will supply be able to catch up? Source: Trade Whisperer

That's how OpenAI's Greg Brockman closed the GPT-6 Astra launch. The headlines: → Trained on 100,000+ GPUs at Stargate, Texas → Claims the lead in coding, science, reasoning and cybersecurity → Works inside software: spreadsheets, 3D worlds, circuit boards, tax forms → First model to hit OpenAI's "Critical" cyber threshold — access restricted via Daybreak → Priced at 2.5x GPT-5.6 Why this matters for AI hardware & memory chips: 1) 100k GPUs for one model = the compute race is accelerating, not plateauing. 2) Agentic models that build, reason and act burn far more inference tokens. Inference = sustained demand for HBM and DRAM, not one-off training spend. 3) 2.5x pricing shows customers will pay for capability → hyperscaler capex stays funded. The picks-and-shovels trade remains alive and well.