- 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
…
- 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!):
Do I own enough Alphabet? (I don’t think I do.) 1.
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) 1.
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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I am speaking to the hardware and model layers. I think there will be many valuable businesses built on AI tooling that solve acute pain points within verticals. I also think there will be an explosion of software necessitating a greater variety of cybersecurity solutions.↩ 1.
I’ve run multiple local models on a Mac; they’re slower and less refined than frontier models, but improving rapidly. I can envision free local access to today’s best-in-class capabilities by year-end 2027.↩