According to @gensynai, Gensyn CEO @fenbielding stated that GPU capacity is not limited if there is a system to trust participating hardware, highlighting that scale depends on trust and verification rather than a finite GPU count; source: Gensyn (@gensynai) on X, Dec 18, 2025. For traders, this positions decentralized AI compute as a scale-out model contingent on verifiable trust systems when assessing adoption risk and potential network throughput; source: Gensyn (@gensynai) on X, Dec 18, 2025.

According to @karpathy, Tinker allows researchers and developers to retain roughly 90% of algorithmic creative control over data, loss functions, and training algorithms while offloading infrastructure, forward and backward passes, and distributed training to the framework. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, Tinker reduces the typical complexity of LLM post-training to well below 10%, positioning it as a lower-friction alternative to common “upload your data, we’ll train your LLM” services. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, this “slice” of the post-training workflow both delegates heavy lifting and preserves majority control of data and algorithmic choices, which he views as a more effective trade-off for practitioners. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, finetuning is less about stylistic changes and more about narrowing task scope, where fine-tuned smaller LLMs can outperform and run faster than large models prompted with giant few-shot prompts when ample training examples exist. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630 According to @karpathy, production LLM applications are increasingly DAG-based pipelines where some steps remain prompt-driven while many components work better as fine-tuned models, and Tinker makes these finetunes trivial for rapid experimentation. Source: @karpathy on X, Oct 1, 2025, https://twitter.com/karpathy/status/1973468610917179630; supporting reference: Thinky Machines post, https://x.com/thinkymachines/status/1973447428977336578