| Ticker | Direction | Speaker | Thesis | Time |
|---|---|---|---|---|
| WATCH |
Ali Ghodsi
CEO, Databricks |
Chinese models (like Kimi and DeepSeek) and open-source models are creating a price ceiling for US tech. These models are "good enough" (slightly behind US models) but significantly cheaper or free. This forces US hyperscalers to lower prices to compete, preventing them from maintaining massive margins on pure compute/token costs. Large Databricks customers are offloading high-volume tasks to Chinese models to save money. Geopolitical regulations could ban the use of Chinese models by Western enterprises. | — | |
| LONG |
Ali Ghodsi
CEO, Databricks |
Databricks raised $7B despite being cash flow positive. There are "2000 vibes" (Dot-com bubble) in the market. If the bubble bursts, capital markets will freeze for 3-4 years. Having a massive cash pile allows a company to survive a wipeout and acquire distressed assets while competitors conduct layoffs. Startups with zero revenue are raising capital at multi-billion dollar valuations, a classic bubble signal. If the bull market continues uninterrupted, holding excessive cash drags on returns (opportunity cost). | 2:26 | |
| AVOID |
Ali Ghodsi
CEO, Databricks |
Traditional SaaS companies rely on two moats: the User Interface (users are trained on it) and the Database (hard to migrate). Ghodsi argues both are evaporating. AI Agents interact with software via natural language, making the proprietary User Interface irrelevant. Furthermore, AI can easily restructure and migrate data, breaking the "lock-in" of the database. Companies that rely on "seat-based pricing" (charging per human user) will face a revenue collapse as one AI agent replaces 10,000 human users. Investors are privately questioning the efficiency of these SaaS companies. "Lazy" companies protecting existing revenue streams rather than innovating will be "wiped out." Some legacy companies may successfully pivot and integrate AI to lower their own costs, surviving the transition. | 3:07 | |
| SHORT |
Ali Ghodsi
CEO, Databricks |
Corporate clients are using AI to automate complex analysis and are subsequently pressuring human vendors to lower their prices. If an AI agent can read earnings calls, compare competitors, and generate a report in 15 minutes (work that used to take days), the value of the human service provider drops. Clients will no longer pay high fees for billable hours that AI has rendered obsolete. KPMG was pressured by a client to lower audit fees because AI made the work cheaper. Royal Bank of Canada (RBC) analysts are using agents to do equity research in minutes. Regulatory requirements may still mandate human oversight, maintaining a floor on pricing. | — | |
| LONG |
Ali Ghodsi
CEO, Databricks |
The future of enterprise AI is "Multi-Model," not winner-take-all. Just as financial institutions adopted a "Multi-Cloud" strategy (using AWS, Azure, and Google Cloud simultaneously) to reduce risk, enterprises are using multiple AI models. They switch between them based on performance and cost because the interface (English language) is universal. Ghodsi personally uses Claude for coding, ChatGPT for projects, and Gemini for speed. Databricks customers are demanding access to all major models. Intense price competition (race to the bottom) could compress margins for all providers. | — |