Nikesh Arora(@nikesharora):Interesting take. The challenges today: 1. Most enterprises don't know how to make AI game changingly effective. As they embark on the journey, the use cases being addressed are "80%" single shot, semi deterministic use cases with multiple guardrails or humans in the middle. We are a little ways away from mass adoption of custom models. 2. More complex cases which require any multi agent orchestration and context retention are beginning to be conceived and tested. These cases will make model portability harder, requiring new evals and harnesses. One will have to commit to one structure and also commit to constantly updating and retraining your model.. 3. CIOs and CEOs aren't sure if the ultimate architecture is single stack, multi model - interoperable orchestration and context/harness/eval, or a custom model. Uncertainty causes slowdown on longer term decisions, which in this case is perhaps right. 4. Custom and Opensource come with the need to deploy on either your own GPUs or public cloud. "Interesting fact - if token prices fall as I hope - it will be cheaper to run frontier LLMs than open source on your own GPUs" 5. Generally horizontal solutions that can serve tens of thousands of customers make more money than vertical custom solutions, but maybe this time it's different? Even if we solve the model conondrum, the enterprises need to redefine workflows, collect more training data on each use case and rebuild the application in a simple UI flow, not everything will be done in a conversational window. But time will tell, this will continue to be a space to watch, lots of minds at work to solve this.

2026.07.16 22:35

Interesting take. The challenges today: 1. Most enterprises don't know how to make AI game changingly effective. As they embark on the journey, the use cases being addressed are "80%" single shot, semi deterministic use cases with multiple guardrails or humans in the middle. We are a little ways away from mass adoption of custom models. 2. More complex cases which require any multi agent orchestration and context retention are beginning to be conceived and tested. These cases will make model portability harder, requiring new evals and harnesses. One will have to commit to one structure and also commit to constantly updating and retraining your model.. 3. CIOs and CEOs aren't sure if the ultimate architecture is single stack, multi model - interoperable orchestration and context/harness/eval, or a custom model. Uncertainty causes slowdown on longer term decisions, which in this case is perhaps right. 4. Custom and Opensource come with the need to deploy on either your own GPUs or public cloud. "Interesting fact - if token prices fall as I hope - it will be cheaper to run frontier LLMs than open source on your own GPUs" 5. Generally horizontal solutions that can serve tens of thousands of customers make more money than vertical custom solutions, but maybe this time it's different? Even if we solve the model conondrum, the enterprises need to redefine workflows, collect more training data on each use case and rebuild the application in a simple UI flow, not everything will be done in a conversational window. But time will tell, this will continue to be a space to watch, lots of minds at work to solve this.

Xiaoyin Qu@quxiaoyin

2026.07.16 16:57

My bet: @thinkymachines will soon make more money than @AnthropicAI. Not by winning the race to build one standardized frontier model. By becoming the Palantir FDE for enterprise custom models. The playbook: 1. Release the best American open-weight model. 2. Drive widespread enterprise adoption. 3. Charge the largest companies 7–9 figures to post-train and run custom models behind their own firewall. The model rests on three bets: 1. Large enterprises will increasingly demand their own models with their own data, and this is how they differentiate and win. 2. Enterprises won’t need just one model. They’ll continuously need new models for different workflows, departments, and proprietary datasets. That creates extremely sticky, recurring revenue. 3. Autoresearch will make custom model development increasingly scalable. Tinker can become the interface enterprises use to post-train their own models—with @thinkymachines providing the expertise and infrastructure behind it. FDE, infra, everything, huge contracts. 4. Eventually, maybe everyone wants their OWN model, and autoresearch and training inside tinker on top of @thinkymachines's base model will make it happen. Meanwhile, Henry-ford-styled, standardized models will makes no margins. OpenAI and Anthropic will have their API margins squeezed by Deepseek/GLM/Grok/Meta etc, and their consumer subscriptions are loss centers. The fat margin will move to customization: proprietary data, post-training, evals, deployment, and infrastructure. If this thesis is right, @thinkymachines isn’t building just another frontier lab. It’s building the highest-value layer between frontier research and enterprise model ownership. Turns out, the best business model for enterprise is NOT to sell commodity API access. Sell them their own models. I’m extremely bullish on this approach. @miramurati may be the most commercially savvy frontier-lab leader. I have to admit it.

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