Will open source pop the AI bubble?

19 Dec, 2025

I enjoyed reading the president of the Mozilla Foundation's op-ed in today's FT: 'Open source could pop the AI bubble — and soon'.

I don't think open source software will be the catalyst for popping the AI bubble¹, however I do think (a) it is one of the challenges to the current narrative that is fueling AI valuations, (b) the capital cycle will turn, and ( c) we're closer to the peak of the bubble than the beginning of it.

A non-exhaustive list:

• Limits to productive capital absorption

• Peak scarcity pricing for GPUs (supply catches up to demand)

• Technology companies transitioning from asset-light businesses to capex-heavy businesses

• Grid access constraints driving datacenter operators toward behind-the-meter generation, where turbine and transformer supply are severely constrained

A few random thoughts/prognostications:

• Hardware/infrastructure layer: NVIDIA GPUs and Google TPUs become commoditized; hyperscaler compute (Azure, AWS) faces margin compression but enjoys enterprise lock-in with high switching costs

• Systems software: CUDA (NVIDIA) lock on the market weakens as hardware-agnostic options mature; potentially 18-36 months before the marginal buyer has options and the ability to deploy across manufacturers

• Data layer: Strategic / trapped value lies in companies' proprietary unstructured data lakes; question: does being a custodian of data (e.g., Box, Snowflake) translate to capture rights for AI training / inference, or do enterprises maintain separation b/w storage and intelligence layers?

• Frameworks / tooling: Have no thesis

• Model layer: OpenAI and Anthropic are very strong, but will face stiff competition from open-source models per the FT piece; my core thesis is that focus shifts from prioritization of compute to inference optimization (cost, latency, energy efficiency), and that open source models deliver 'good-enough' performance for general use cases within 18 months; as model quality converges, switching costs drop; OpenAI / Anthropic don't capture full TAM, particularly ex-US where local models and data sovereignty concerns matter ²

• Serving / APIs: Solid moat for serving the latest models to technology-forward enterprises and startups willing to pay for API access and managed infrastructure

• Application layer: Opportunity for differentiation through brand affinity and UX, but unclear whether application-layer companies can capture value or will be marginalized as commoditized front-ends to model APIs

I am pondering three questions (not financial advice!):

1. Do I own enough Alphabet? (I don't think I do.)

2. Is Apple the dark horse in the competition? I think so but haven't acted on it. (Apple Silicon enables on-device inference, privacy positioning, and ecosystem lock-in, potentially inverting the centralized API model; retains flexibility on model layer, can partner with different providers or deploy proprietary models with each OS release)

3. When do training datasets get exhausted? I am over my skis on this, but am enjoying the Dwarkesh discussion with Andrej Karpathy

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