This week Reddit r/LocalLLaMA user /u/eapache put forward a simple plan: use Kickstarter to crowdfund anywhere from several hundred thousand to several million dollars, specifically to incentivize labs to train a Qwen3.8 35B MoE open-source model. We noted that the original post directly addresses the Qwen team — since the infrastructure is already in place, the marginal cost of training one additional specific configuration should be manageable. Why not take the order?
What this is
The original post's logic isn't complicated: Chinese labs like Qwen already have hardware, data, and training pipelines. Training one additional open-source weights release at a specific scale (e.g., a 35B-parameter MoE — "Mixture of Experts" architecture: splitting the model into multiple sub-models that activate in turn, achieving larger-model performance with less compute) — the main costs are electricity and engineer time, not building infrastructure from scratch. The poster also "explicitly" hoped the Qwen team would see this — itself a reflection of the open-source community's shifting perception of Chinese labs' influence.
Industry view
Bulls argue that the open-source camp's evolution from "freeloading" to "willing to pay for specific configurations" shows the model layer is commoditizing (becoming infrastructure-like utilities); the "semi-open-source" strategies of Chinese labs like Qwen and DeepSeek are paying off — being both heavily used and explicitly named as needing service.
The opposing view cuts sharper: training cost is not marginal cost. Running a full training run is still in the tens to hundreds of millions of dollars; the crowdfunded money is just a drop in the bucket. A more realistic risk: once a lab accepts crowdfunding, it becomes accountable to funders, crowding out genuinely valuable frontier research directions. The more grounded advice in the comments: rather than wait for new models, just fork existing open-source weights and fine-tune — already daily operations on Hugging Face.
Impact on regular people
For enterprise IT: open-source options keep multiplying; the compliance and cost risks of private deployment (installing models on a company's own servers) continue to drop; mid-sized companies can run the numbers seriously.
For working professionals: solutions for using open-source models to build local knowledge bases and handle sensitive client data will mature — no longer needing to hand all data to cloud AI.
For consumer markets: future marketing gimmicks like "the model I supported" may emerge, but the impact on everyday users is limited; mainstream products remain dominated by a handful of big companies.