ClaimArticle
Anthropic and OpenAI are consolidating a closed-model duopoly that concentrates power and pricing leverage, and open weight models are the only real counterweight available to startups, schools, and enterprises.
The authors argue Anthropic and OpenAI's closed models are consolidating market power and pricing leverage, leaving open weight AI as the only viable alternative for smaller players. ✦ AI generated
Kevin Xu (Interconnected, co-author) · Interconnects · 2026-06-19 · original ↗
The duopoly of Anthropic and OpenAI are rapidly concentrating power between them with their closed, proprietary models. Anthropic, in particular, has flexed its monopolistic muscle recently by reducing its most advanced model's capability when it is being used to improve someone else's model. While the capabilities of their models are undeniable, so are their price tags and market concentration.
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Evidence · 4
The whole AI ecosystem — chip companies, developers, applications, enterprises — has an incentive for a competitive model layer, and the only companies that don't want that are Anthropic and OpenAI, who are pushing for a duopoly through regulatory capture.David Sacks · All-In Podcast · conf 95%Open datasets like The Stack v3 materially raise the floor for every lab that wants to build competitive code models without relying on closed ecosystems, and open weights are strategically important against calls to restrict distillation.AINews / Latent.Space · Latent Space · conf 90%Open-weights and open-source AI have become a real, decisive force — Jensen's first-ever post proves Nvidia is now fully behind them — and Nvidia, despite its dominance, is still a component manufacturer that must dance in both the frontier and open halls even though open models bypass CUDA, are cheaper, carry lower margins, and will bypass Nvidia itself.Jason · 20VC · conf 75%Frontier AI models are capturing the vast majority of economic value even though open-source models may account for the majority of raw tokens consumed.Gavin Baker · BG2 Pod · conf 60%
In practice · 3
Open weights are now Poolside's default approach, and the company will keep building toward the frontier while releasing increasingly capable models openly.Poolside · Interconnects · conf 60%Cosine can compete with US labs on a fraction of their budget because it licenses model weights for customers to run themselves rather than hosting inference, avoiding the massive data-center spend that consumes most of a frontier lab's capital.Alistair Pullen · Machine Learning Street Talk · conf 60%Inkling debuts at 41 on the Intelligence Index, making it the leading U.S. open-weights release, ahead of Nemotron 3 Ultra (38), Gemma 4 31B (29), and gpt-oss-120b (24).Artificial Analysis · Latent Space · conf 60%
This moment responds to
supports → Washington's current wave of AI regulatory activity risks spilling over into regulating or banning open source AI, and that would be a grave mistake.Kevin Xu (Interconnected, co-author) · Interconnectsrebuts → Despite 18 months of predictions that open source would kill the frontier labs, the share of economic value is actually increasing for frontier models while commodity tokens go to the rest—there is no evidence the intelligence gap is collapsing.Brad Gerstner · All-In Podcastextends → Closed models (like ChatGPT, Claude, Gemini) are fully controlled by the AI lab, analogous to dining at a restaurant, while open weight models are like having the recipe published so you can run them yourself.CJ · Syntaxsupports → Trying to slow or ban the open model ecosystem is futile, unsafe, and anti-freedom, because it would concentrate AI development among a select few and cut off outsiders' ability to adopt the technology.the author · Interconnectssupports → The US needs more domestic open-source AI models so companies aren't forced to choose only between OSS 12B and Chinese models.Andrew Feldman · All-In Podcast