Nvidia's Suspicious "Round-Trip" Transactions

Quick Overview

Nvidia's massive revenue growth is highly suspicious because the company engages in complex "round-trip" financial transactions, such as the $100 billion investment into OpenAI, which mimics accounting manipulations used during the dot-com bubble by companies like AOL and Global Crossing to inflate demand artificially, suggesting the current AI boom's revenue recognition is not supported by true end-user spending.

Key Points: Nvidia's data center revenue skyrocketed since late 2023, resulting in $23 billion in AI-related GPU sales beyond its $3 billion quarterly baseline, driven primarily by hyperscalers purchasing hardware for AI startups. The top 20 AI startups generate an estimated annual run rate of only $15 billion in revenue, which is minuscule compared to the $116 billion in data center GPUs Nvidia sold in the first three quarters of 2025. OpenAI, the largest AI startup, expects to generate $12.7 billion in revenue in 2025 but anticipates losing money, with internal projections not seeing positive free cash flow until 2029, and even their most expensive Pro tier loses money. Nvidia engaged in deals like guaranteeing Coreweave excess capacity purchases up to $6.3 billion if demand falls short, and paying $1.5 billion to Lambda Labs to lease back GPUs it previously sold them, demonstrating self-stimulation of demand. The $100 billion investment into OpenAI, where the money flows from Nvidia to OpenAI and then back to Nvidia via GPU purchases, is cited as the biggest roundtrip deal, functionally moving money in circles to inflate revenue figures. The current AI boom mirrors the dot-com bubble, where infrastructure providers like Global Crossing inflated revenue through capacity swaps, though Nvidia's essential infrastructure role provides a stronger parallel to broadband companies than to failed dot-coms like Pets.com.

Context: The video analyzes the unprecedented surge in Nvidia's valuation, reaching $4.6 trillion, driven by the AI investment theme following the release of ChatGPT in November 2022, as its GPUs are critical for training large language models. The analysis focuses on whether the massive capital expenditures by hyperscalers like Microsoft and Google, and the subsequent revenue recognized by Nvidia, are sustainable given the current low revenue generation and high losses reported by the end-user AI startups, drawing direct parallels to historical financial bubbles.