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Feb 09, 2026
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WATCH
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There is unprecedented capital expenditure ($50B–$100B) flowing into hardware, data centers, and energy. While the AI trend is real, the current build-out creates a risk of "overbuilding." The market is pricing in perfection, but physical constraints (energy) and ROI questions remain. The sheer volume of capital chasing hardware, combined with "circular" funding deals in the AI startup ecosystem, mirrors the pre-crash vibes of 2000. If AI adoption accelerates faster than hardware supply, these stocks will continue to run despite valuation concerns. |
CNBC
Under the hood of the AI economy: Databricks ...
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Feb 09, 2026
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SHORT
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Enterprise clients are using AI to aggressively squeeze costs out of vendors, auditors, and consultants. Tasks that used to justify high fees (e.g., analyzing earnings calls, auditing financial data) can now be done by AI agents in minutes. Clients are demanding fee reductions because they know the vendor's cost of labor has dropped, or they are bringing the work in-house. KPMG was pressured by clients to lower fees because AI made their auditing work cheaper. RBC analysts now use AI to synthesize earnings calls in 15 minutes, work that previously took days. High-end strategic consulting may remain insulated if it relies on human relationships and complex judgment rather than data processing. |
CNBC
Under the hood of the AI economy: Databricks ...
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Feb 09, 2026
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AVOID
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Traditional "System of Record" software companies (SaaS) are facing a "wipeout" scenario similar to the dot-com bust if they do not adapt immediately. These companies historically relied on two moats: a) The Interface Moat: Humans were trained on complex UIs, making switching costs high. AI Agents now use natural language, rendering the UI irrelevant. b) The Database Moat: Moving data was hard. New "Lakehouse" architectures allow AI agents to query data anywhere, breaking vendor lock-in. If a company charges based on "seats" (human users), their revenue will collapse as one AI agent replaces 10,000 human users. Databricks sees 80% of new databases being built by AI agents. Investors are privately questioning the efficiency and survival of traditional SaaS metrics behind closed doors. Incumbents with massive distribution might successfully pivot by integrating AI fast enough to protect their revenue base. |
CNBC
Under the hood of the AI economy: Databricks ...
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Feb 09, 2026
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WATCH
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Chinese models and open-source alternatives are catching up to US closed models rapidly. Models like "Kimi" and "DeepSeek" are performing nearly as well as top-tier US models but at a fraction of the cost (or free). This creates a "race to the bottom" for pricing power among US model providers. Databricks' largest customers are offloading workloads to cheaper Chinese/open models for cost efficiency. Geopolitical regulation or chip bans could stifle the progress of Chinese models. |
CNBC
Under the hood of the AI economy: Databricks ...
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Feb 09, 2026
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WATCH
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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. |
CNBC
Preparing for another tech wipeout: Databrick...
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Feb 09, 2026
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LONG
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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). |
CNBC
Preparing for another tech wipeout: Databrick...
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Feb 09, 2026
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AVOID
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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. |
CNBC
Preparing for another tech wipeout: Databrick...
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Feb 09, 2026
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SHORT
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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. |
CNBC
Preparing for another tech wipeout: Databrick...
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Feb 09, 2026
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LONG
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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. |
CNBC
Preparing for another tech wipeout: Databrick...
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Feb 09, 2026
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AVOID
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The market is waking up to the risk facing software companies that rely on complex user interfaces rather than proprietary data. (Anchor mentions Monday.com down 20% as an example). Historically, a software company's "moat" was that it was hard for humans to learn a new interface. AI destroys this moat because humans can now just "talk" to an AI agent to get tasks done, bypassing the specific software interface entirely. If a company doesn't own unique data (is not a system of record), it can be easily bypassed or replaced by an agent. The rapid shift to natural language interfaces (like Databricks' own "Genie") replacing complex technical dashboards. Oversold conditions; some vendors may successfully pivot to becoming "agent-first" platforms. |
CNBC
AI upends the software ecosystem...
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Feb 09, 2026
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LONG
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Despite the AI disruption narrative, legacy giants like SAP are insulated. SAP possesses a "Data Moat." They are the "system of record" for global enterprises. It is incredibly difficult to replace them because they hold the critical operational data. There is no viable alternative to rip and replace their infrastructure, and they are successfully integrating AI into their existing stack. Ghodsi asks rhetorically, "What would you replace SAP with?" noting there is no real alternative. Slower execution on AI integration compared to agile startups. |
CNBC
AI upends the software ecosystem...
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Feb 09, 2026
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LONG
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AI agents and "vibe coding" are taking over software creation. 80% of Databricks' new databases are created by AI, not humans. AI agents write software faster than humans. Every piece of software needs a database to store information. Therefore, the volume, usage, and optimization of databases will increase significantly over the next few years. Databricks internal data shows a massive spike in AI-generated database creation. The optimization must shift from catering to human administrators to catering to AI agents; legacy databases that cannot adapt to "agent-native" interaction may fail. |
CNBC
AI upends the software ecosystem...
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Feb 09, 2026
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WATCH
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Databricks is delaying its IPO despite being free cash flow positive. The company prefers private capitalization to weather potential public market volatility (fearing a repeat of 2022). They want to invest in long-term R&D (5-10 years) without the quarterly pressure to cut costs or layoff staff if the stock drops. Raised Series L funding specifically to stay private longer. Prolonged delay could fatigue early employees or investors seeking liquidity. |
CNBC
AI upends the software ecosystem...
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