When Fable was pulled back and access to frontier systems stopped looking guaranteed, Lucas Atkins watched enterprises move to Chinese open models, not because they scored better but because availability could be counted on. That is his working definition of trust, and he separates it hard from safety: an open model is a directory of files you can inspect, running on code you can read, while the same claim about a closed API is unverifiable by construction. Arcee's response was to reorient the whole company and pretrain a 400 billion parameter model in six months, which he says plenty of people called impossible. The rest is control. Vincent Weisser describes a customer specializing an open model to automate finance work in a week or two and landing better results than Opus at a fraction of Haiku's cost. Closed terms of service bar you from training on outputs, so owning the model also means owning the traces, which is what makes a data flywheel possible at all; Nemotron and Trinity both adopted the open MDW license to put that permission in writing. Chris Alexiuk's framing is the mismanaged genius: a model tuned to be good across every harness is optimal for nobody's, and open weights let you fit it to the one or two things you actually do. The predictions land where you would expect from this panel, including open models reaching Fable level capability inside a year, and Atkins hoping the share of people who have ever run a model locally climbs from a rounding error to 10% to 15%. Speaker info: Carter Abdallah, moderator (NVIDIA): - https://x.com/Baxate - https://baxate.com Vincent Weisser (Prime Intellect): - https://www.linkedin.com/in/vincentweisser - https://www.primeintellect.ai/ Lucas Atkins (Arcee AI): - https://x.com/latkins - https://arcee.ai Chris Alexiuk (NVIDIA): - https://x.com/llm_wizard - https://www.alexi.uk/ Timestamps: 0:00 - Welcome and why this panel is the whole stack 1:14 - Prime Intellect: keeping the training stack open 2:15 - Arcee: why the west was losing the open model lead 3:22 - Pretraining a 400 billion parameter model in six months 4:23 - Nemotron: faster models are smarter models 6:35 - Is open source actually less trustworthy 7:40 - Trust is not safety 9:48 - When access stopped being guaranteed 10:56 - Releasing the data sets alongside the weights 11:59 - The open superintelligence stack 13:02 - Post training as the accessible layer 14:07 - Beating frontier models on a specific use case 15:11 - Making your costs predictable 16:11 - When the model and the harness blend together 18:15 - The mismanaged genius 19:19 - A call to action for builders 20:24 - Who owns the data you generate 21:27 - Owning your outputs, not just your weights 22:35 - The open MDW license 23:36 - Where post training unlocks new use cases 27:46 - You do not need frontier intelligence for most tasks 28:53 - Why efficiency has to happen in the open 29:55 - Closed models are not the enemy 33:05 - Predictions for the next year 38:22 - Running everything on your laptop 39:29 - Agent operating systems and the next Siri moment 41:36 - Closing