Institutional-grade equity research on AI infrastructure, power, and the capital cycle. Synthesized from the same data pipeline that feeds the MCP endpoint.
DeepSeek V4 Flash at $0.14 per million tokens makes $8.69 per GPU hour at sparse attention speed, but loses money at normal reasoning speed. Verified revenue is $500M annualized, not $1.5B. Chinese private labs combined do $2.6B versus OpenAI alone at $25B.
Morgan Stanley's Aug 3 note sparked the open vs closed debate again. Enterprise production data says closed still wins spend, open wins volume. Meta's Llama stumble changes the open-weight map.
The largest open-weight model yet claims second only to Anthropic's Fable 5. BABA popped 4.5% but US AI stocks rallied too — the market sees cost pressure, not a Sputnik moment.
Capability is accelerating on longer-horizon tasks, but options flow and compute markets show no recursive AGI loop yet
The problems are real, the proofs are Lean-verified, and the $2,000 price tag is not what it looks like. But this lands at a moment when OpenAI needed a win.
The Indian IT industry built a $300 billion empire on human labor arbitrage. AI coding agents are the GLP-1 of that model. The difference: Infosys still has time to pivot.
A pure post-training overhaul pushed DeepSeek's cheapest model within one point of GPT-5.6 Luna. But OpenAI cut Luna's price 80% the day before — the inference price war is on.
Two hyperscaler prints, 24 hours apart, drew opposite verdicts from the market. The difference isn't about who spends more. It's about who can point to the revenue.
The market just priced in a memory-cycle peak. The physical data says the opposite: HBM is sold out through 2027, conventional DRAM is in outright shortage, and the two leading memory stocks trade at 4x to 5x forward earnings while growing revenue 200% to 300% year over year.
At 9x forward earnings and a 9.8% FCF yield, Adobe is the cheapest large-cap software stock in the market. The AI disruption thesis is unproven — but the street is split down the middle.
The buildout is now 75 percent borrowed money. That means the bond market, not the stock market, decides what happens next.
The world's largest private capital firm is treating GPU clusters like office buildings. The data says they're right.
40% of announced datacenter projects are dying. Bloom Energy just printed its first billion-dollar quarter. TSMC raised capex because tool prices are inflating. And the semicap selloff is fighting its biggest customer.
First GAAP profit. $1.2 billion in net cash. AI in 16 of 20 top deals. And a stock price that still trades like a melting ice cube.
AWS is growing at 28%, the AI buildout is accelerating, and the 2027 capex signal could be the real shocker. The Bargo 2.5x model explains why.
The Bargo 2.5x tokenomics model says capex must rise. The model says the spending is clearing. Here's what the numbers say before the print.
The pass-through gap is an accounting problem, not a demand collapse. At 3.3x forward earnings, the market is pricing in a recession the physical data doesn't show.
Five machines in 2026 against ASML's 130. Five generations behind. Zero precedent of Chinese lithography running production. The supply story hasn't changed.
Positioning data says the unwind is fading. SK Hynix reports tomorrow and the Street still forecasts a record ₩64 trillion operating profit.
The Q3 guide missed by a mile, but the weakness is a factory move, not demand destruction. Computing is surging, margins are expanding, and the sell-off from $96 to $46 looks like an overreaction to the wrong story.
MSFT reports FQ4 Tuesday with $37B of AI revenue already booked, Azure growing 40%, and the Street calling for 46% upside. The stock needs one thing: proof the capex is working.
The Liang Wenfeng transcript reveals a 10-month GPU payback at 85% margins — and open-source just captured three-quarters of all AI token volume
The Bargo disruption model runs 32 software companies through three guardrail tests. The AI-vs-fundamentals correlation collapses to zero under basic controls. The seat-subscription model is under genuine pressure — but AI exposure doesn't predict which names struggle.
The per-token price debate misses the point. K3 is a 2.8T-parameter brute with an immature inference stack that burns three times the tokens of its peers. The open-weight release today is a compute-demand story.
Both are right about their champion. Neither addressed that China is winning.