AT&T routes 40% of AI workloads through open-weight models, cutting coding costs by 56%

AT&T now routes roughly 40% of its internal AI requests through open-weight models, cutting AI coding costs by 56% while absorbing only a 2% decline in output quality, the company said. The telecom giant processes approximately 45 billion tokens per day, up from around 8 billion a year earlier, and targets 60-70% open-model usage within the next year using an intelligent routing system built on LiteLLM and a custom AI gateway. AT&T also launched OTel 2.0, a post-trained open-weight model for telecom data developed with the GSMA's Open Telco AI initiative, trained on over 400 billion tokens and built on AMD GPUs and Microsoft Foundry.

AT&T routes 40% of AI workloads through open-weight models, cutting coding costs by 56% The telecom giant is processing 45 billion AI tokens daily and plans to push open-model usage to 70% as it rethinks spending on closed alternatives from OpenAI and Anthropic. AT&T is quietly pulling off one of the more aggressive enterprise AI pivots in recent memory. The telecom giant now routes roughly 40% of its internal AI requests through open-weight models, and it’s not slowing down. The company has set a target of 60-70% within the next year. The math behind the decision is hard to argue with. AT&T has slashed AI coding costs by 56% while absorbing only a 2% decline in output quality. For certain complex workloads, the savings are even more dramatic, with cost reductions hitting 80-90% compared to closed-model alternatives. The tokenomics of telecom-scale AI AT&T’s AI consumption has grown at a pace that would make any CFO nervous. The company now processes approximately 45 billion tokens per day, up from around 8 billion just a year ago. That’s roughly a 5.6x increase in twelve months. AT&T calls its approach “tokenomics,” and the logic is straightforward: not every AI request needs the most expensive model in the room. The company uses an intelligent routing system built on LiteLLM and a custom AI gateway to match tasks with the appropriate model. Simpler requests get directed to open-weight options like Nvidia https://cryptobriefing.com/markets/nvidia/ Nemotron, Meta https://cryptobriefing.com/markets/meta/ Llama, and Google https://cryptobriefing.com/markets/alphabet/ Gemma. More complex tasks can still tap into closed models when the quality threshold demands it. Open models closing the gap AT&T executives have stated that open-weight models have narrowed the performance gap with proprietary alternatives, which has prompted the company to rethink its investments in offerings from Anthropic https://cryptobriefing.com/markets/anthropic/ and OpenAI https://cryptobriefing.com/markets/openai/ . AI, tech, and the markets they move—in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. AT&T isn’t just consuming open models. It’s building them. The company launched OTel 2.0, a post-trained open-weight model specifically designed for telecom data. The model was developed in association with the GSMA’s Open Telco AI initiative, trained on over 400 billion tokens, and built using AMD https://cryptobriefing.com/markets/amd/ GPUs and Microsoft https://cryptobriefing.com/markets/microsoft/ Foundry. The broader enterprise shift AT&T isn’t operating in a vacuum. The move toward open-weight models reflects a growing trend across enterprise AI adoption, driven by three concerns: cost efficiency, customization, and data sovereignty. The company is also keeping an eye on Chinese open-weight models, though it hasn’t adopted any. Security and compliance concerns have kept those options on the evaluation bench rather than in production. Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy https://cryptobriefing.com/editorial-policy/ .