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Companies, industries and independent research

Research topics

Start with a company or an industry, or go straight to a specific question. Each collection brings related research together; every study keeps its own evidence and update history.

Company research

Company research →
Semiconductors and computing platforms

NVIDIA

Inference efficiency, equipment demand, profit and cash: connect technical progress to NVIDIA’s business.

Will cheaper AI inference weaken NVIDIA or expand its market?
Explore the topic →Semiconductors and computing platforms

Marvell

Track Marvell growth through custom silicon, connectivity and reported financial results.

Why Marvell Expects Supporting Chips to Have Higher Gross Margins Than Full-Custom Accelerators
Explore the topic →Semiconductors and computing platforms

Supermicro

Track Supermicro's competitive position in the AI compute supply chain through its server-integration business model, gross margin trajectory, customer mix, and capital responsibility.

Supermicro's Low-Price Moat Hypothesis: What Margins and Cash Flow Can Establish
Explore the topic →AI Data Platform Economics

Reddit AI Data Economics

Research Reddit's cost structure, operating leverage, and shareholder-return transmission as an AI data licensor, extracting actual expense composition and revenue relationships from financial disclosures and earnings calls

Reddit's Cost Leverage: Separating R&D, Stock Compensation and Compute Spending
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Industry research

Industry research →
Compute and infrastructure

Capital and commitments behind AI compute

After equipment is delivered, who waits for cash and who carries future capacity commitments? Follow contracts into the financing of AI compute.

After GPUs are sold, who still carries capital for AI compute?
Explore the topic →Enterprise AI Coding Costs

Enterprise AI Coding Costs

Research on budget allocation, permissions, cost attribution, and mid-month recovery for enterprise AI coding tools, starting with a complete case based on documented Claude rules.

Enterprise AI Coding Budgets: Who Can Use How Much, and What Happens at a Mid-Month Limit
Explore the topic →AI Agent Operations

AI Agent Execution Control

Research documented mechanisms for pausing, clarifying, resuming and persisting state during AI agent execution, comparing controls across frameworks.

How an AI Agent Can Stop and Ask: Four Interaction Paths and Their Conditions
Explore the topic →Cloud GPU Compute Access and Limits

Colab Runtime Limits and Diagnosis

Research the gap between Google Colab's documented runtime commitments for paid tiers and observed termination behavior, including compute unit mechanics, termination conditions and user diagnostic methodology.

Why can a paid Colab runtime end before 24 hours? What an L4 user report can establish
Explore the topic →Enterprise AI Coding Costs

Enterprise AI Agent Deployment and Outcomes

Research how enterprises extend AI agents from pilots into knowledge-worker daily work: actual deployment patterns, measurement scope, governance frameworks, and the evidence required before scaling autonomy.

Enterprise AI Agent Deployment: Which of BBVA’s Seven Metrics Actually Measure Agent Outcomes
Explore the topic →AI Agent Operations

MCP Agent Authentication Architecture

Research the normative requirements and implementation decisions for credential boundaries between AI agents and remote MCP servers, including token audience validation, multi-user consent, and downstream API credential isolation.

Remote MCP multi-user authentication: token audiences, delegation and proxy consent
Explore the topic →Valuation Methodology

AI Investment Valuation Methods

Research the applicability boundaries, assumption construction and failure conditions of valuation methods in the AI industry, using concrete company cases to develop the method

AMD in a DCF: separating AI revenue, margin and capital-spending assumptions
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All research

Ordered by substantive updates

Standalone research

Will cheaper AI inference weaken NVIDIA or expand its market?

NVIDIA’s operating profit is still growing; Amazon says its own Trainium chips already run most Bedrock inference. More inference use does not mean every new order goes to NVIDIA.

Read the full analysis Standalone research

After GPUs are sold, who still carries capital for AI compute?

The NVIDIA case shows collection waiting, conditional capacity buying and customer advances. These reveal contractual capital responsibility—not a verified industry cash loop.

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When could NVIDIA have to buy unsold AI-cloud capacity?

One type of agreement leaves unsold committed capacity for NVIDIA to purchase. Signing, triggering, execution and economic loss are separate states; aggregate commitments cannot collapse them.

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NVIDIA receivables outgrew sales. Does that establish customer trouble?

Receivables rose 54.9% sequentially against 17.9% revenue growth; five direct customers hold about 70% of the balance. These reveal capital absorption and concentration, not default.

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Why can’t NVIDIA’s cash decline be assigned entirely to collections?

Prior-quarter liability support, prepayments and federal-payment timing provide competing explanations. Separate tax expense, payable and actual payments before assigning causes.

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NVIDIA profit barely changed. Why did operating cash fall by half?

In fiscal 2027 Q2, net income rose 2.3% from Q1 while operating cash fell 52.2%. A line-by-line reconciliation points to receivables, prepayments and payment timing. Extended terms for certain customers also complicate the idea that selling equipment means collecting cash first.

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