By late 2025, parts of Chamath’s thesis are partially playing out, but the overall claim is still too early to call.

1. Capability convergence of frontier models
Multiple independent benchmarks now show OpenAI, Anthropic, Google, Meta and leading Chinese/open‑source models clustered at the top with relatively small gaps, and different models winning on different tasks:

• Academic and industry evaluations of GPT‑4.1 / GPT‑4o, Claude 3/4, and Gemini 1.5/2.5 find that while one model may edge out others on a given benchmark (e.g., calculus problem‑solving, abuse detection), all are “very strong” and broadly competitive, with performance differences often in single‑digit percentage points. (arxiv.org)
• Community and meta-rankings (e.g., Kagi’s May 2025 LLM ranking) place GPT‑4.x, Claude 4, Gemini 2.x, Llama 3.1/4 and Qwen 3 in the same top tier rather than one clear runaway winner. (reddit.com)
• Newer releases (e.g., Claude Opus 4.5, Gemini 3 Pro, GPT‑5-class models) keep leapfrogging each other by a few benchmark points, reinforcing the picture of a tightly packed frontier rather than a single dominant, uniquely capable model. (itpro.com)
  This is consistent with Chamath’s qualitative point that “the models will basically be all really good,” though they are not literally identical and still have distinct strengths.

2. Economic value shifting to scaffolding vs. core models
Evidence for the stronger part of the claim—most monetization moving from core models to surrounding infrastructure, tools and applications—is mixed and not yet decisive:

• Core model providers remain extremely valuable and highly monetized in their own right. OpenAI is estimated at about $13B annualized revenue in 2025, largely from ChatGPT subscriptions and API usage. Anthropic targets roughly $9B by end‑2025 and $20–26B in 2026, with ~80% of revenue from enterprise products built directly on its models. (reuters.com) These are very large, direct model businesses, not yet commoditized utilities.
• At the same time, there is rapid growth in the “scaffolding” layer:
  • Developer tools like Cursor (Anysphere) have reached ~$500M ARR by 2025, selling an AI coding experience that can swap between multiple backend models. (en.wikipedia.org)
  • GitHub Copilot and Google’s Antigravity IDE act as multi‑model orchestration layers where models (GPT‑4.x, Claude, Gemini) are somewhat interchangeable components inside higher‑value workflows. (en.wikipedia.org)
  • Public markets and investors increasingly highlight enterprise software and infrastructure companies (Salesforce, ServiceNow, Snowflake, Microsoft, Meta) as the primary long‑term AI winners, where models are embedded as features inside much larger platforms. (kiplinger.com)
    This supports the direction of Chamath’s view—that a lot of value is accruing to application, tooling, and infrastructure layers—but we do not yet have clear quantitative evidence that most of the economic value has shifted away from model providers themselves. Frontier model training and API provision remain capital‑intensive, heavily moated, and highly profitable businesses.

3. Time horizon
The prediction was explicitly framed as an “over time” structural shift. As of November 30, 2025—about 18 months after the podcast—frontier models appear to be converging in capability, and scaffolding layers are clearly emerging as powerful, high‑revenue businesses. However, core model vendors still capture a substantial share of generative‑AI‑specific revenue, and we lack solid data showing a durable, majority share of economic value has moved to scaffolding.

Because the long‑term competitive and profit dynamics of models vs. scaffolding are still unfolding and the claim is inherently multi‑year, the fairest assessment today is “inconclusive (too early)” rather than clearly right or wrong.