Friedberg predicted that the ability to run LLMs and build web‑data–based alternatives would commoditize faster than most early‑2023 observers expected, enabling many new competitors beyond OpenAI/Google/Microsoft. Evidence through late 2025 strongly supports this.

1. Rapid commoditization of LLM capabilities
   • Hugging Face’s own 2023 retrospective calls 2023 the “year of open LLMs,” documenting a wave of open‑weight releases (LLaMA, Llama 2, StableLM, MPT, Falcon, Qwen, Mistral, Yi, etc.) and emphasizing that smaller models plus quantization made LLMs “really accessible to many more people.” (huggingface.co)
   • A July 2023 study already identified ~15,821 LLMs on Hugging Face, just months after ChatGPT’s breakout, showing extremely fast proliferation. (arxiv.org)
   • By July 2024, there were ~784,000 open‑weight models on Hugging Face, and by June 2025 more than 1.8 million models total on the platform—an ecosystem far beyond a handful of proprietary systems. (gretel.ai)

2. Ability to run LLMs spread to commodity and edge devices
   • Google’s Pixel 9/9a phones include the on‑device Gemini Nano model for real‑time summarization, translation, and captioning, demonstrating LLM‑style inference running locally on consumer smartphones. (en.wikipedia.org)
   • Apple Intelligence, enabled by default starting with iOS 18.3 on Apple Silicon devices, provides systemwide generative features using small on‑device models; Apple also brings these AI capabilities to a $599 iPhone 16e, broadening access. (theverge.com)
   These shifts show that “the ability to run LLMs” is no longer confined to specialized data centers; it’s increasingly a standard capability of mass‑market hardware.

3. New competitors vs the early incumbents
   • The 2023 Hugging Face review lists strong open models from Meta, Stability AI, Mosaic, Salesforce, Falcon (TII), Alibaba’s Qwen, Mistral, Yi, Deci, and others, many with open or open‑weight licenses and strong benchmark performance—direct alternatives to closed systems. (huggingface.co)
   • Databricks released DBRX in March 2024 as an open model that outperformed earlier open models from Meta and Mistral on a range of benchmarks, showing sophisticated models from data‑platform players, not just hyperscalers. (en.wikipedia.org)
   • Meta’s Llama 3 / 3.1 family (up to 405B parameters) is positioned as competitive with leading proprietary models like GPT‑4o and Claude 3.5 Sonnet, and is made widely downloadable and deployable across major clouds—further reducing dependence on a few closed providers. (theverge.com)
   • A 2025 survey of major AI companies lists Anthropic, Meta, DeepSeek, Databricks, Hugging Face, Amazon, Nvidia and others alongside OpenAI, Google and Microsoft as core generative‑AI players, reflecting a multi‑vendor landscape. (kiplinger.com)
   • Microsoft’s own Copilot ecosystem now integrates models from Anthropic and also supports Meta, xAI and DeepSeek models, explicitly moving beyond exclusive reliance on OpenAI. (reuters.com)

4. Speed relative to early‑2023 expectations
   • By mid‑2023, there were already tens of thousands of LLMs on Hugging Face, and the 2023 retrospective notes that new open models were arriving monthly, then weekly, and eventually even daily, including strong entries from new players and from China. (arxiv.org)
   • Within roughly two years of the February 2023 prediction, high‑quality open models (e.g., Llama 3.1, DBRX, Mistral’s Mixtral) and on‑device models from Apple and Google made it routine for organizations and even individuals to run capable LLMs without depending solely on a small trio of incumbents.

Taken together, the explosive growth of open and downloadable models, their deployment on commodity and edge hardware, and the emergence of multiple strong LLM providers show that LLM capability has indeed commoditized, and it happened on a faster timeline than the early‑2023 narrative of a tightly concentrated, hyperscaler‑only future suggested. This matches Friedberg’s prediction well.