Companies pivot to cheaper AI models as open-source systems challenge industry giants

The AI race is shifting from bigger models to cheaper, smarter systems

Benchmark's Peter Fenton says open-weight models could soon handle most AI usage, putting pressure on the economics of the biggest model providers. For the past two years, the artificial intelligence race has been easy to score: bigger models, better benchmarks and whichever company could claim the lead, at least until the next launch. That scorecard is starting to look incomplete. As companies move from testing AI to using it in real products and workflows, it's not longer about tapping the best model, but accessing the one that's the best fit for a specific job, at the right cost, with the necessary data and in a chosen environment. That shift is opening the door for a new kind of AI competition, one focused less on model size and more on routing, cost, control and compute. "The model alone is no longer the product," Perplexity CEO Aravind Srinivas told CNBC. "It is the harness, the orchestration system that puts the model inside a very capable harness and pairs the model with a lot of tools." That means AI products are becoming systems that can decide which model to use, when to use it and what outside tools or company data sources are necessary. A customer service task might not need the most expensive model. A complex coding problem might. A routine internal workflow could run on a cheaper open model. A harder step could be escalated to a more powerful one. "The answer is always use whatever is the best for the task," Srinivas said. The emergence of alternative models comes as corporate America tightens its belt on AI spending, and presents another challenge for OpenAI and Anthropic, which have flourished over the past few years by selling the most cutting-edge technology. Perplexity this week previewed a new system for its computer-use product built around GLM 5.2, an open model from China's Z.ai. The system is designed to let a cheaper model handle more of the work while calling in a stronger model only when needed. That approach reflects a broader change in the market. Open-weight models, which can be downloaded, tuned and run by companies themselves, are becoming more capable. They are also cheaper to run than premium proprietary models from the biggest AI labs. Benchmark general partner Peter Fenton said the shift could be dramatic. "A maybe contrarian view that is becoming consensus is our belief that 90-plus percent of the tokens created will come out of open-weight models over the next 18 to 24 months, possibly even by the end of the year," Fenton told CNBC. Tokens are the units of data AI models process and generate. "The inference margins generated by the frontier model companies, I think, are going to come under pressure when you can run those without the markup that they're providing, when you have good enough models from open weights," Fenton said. Fenton said the move to open models is not only about saving money. In some cases, smaller models that are tuned for a specific task can be faster and perform better than larger general-purpose models. That is one reason Benchmark invested in Ollama, a company that makes it easier for developers and enterprises to download, run and manage open models. "One thing is where the model's from and where it was created and trained," Ollama CEO Jeff Morgan said. "But the more important thing to these businesses we speak to is where it runs and how it runs." Morgan said Ollama has been adopted by more than 85% of the Fortune 500, including companies in regulated industries such as aviation, insurance and health care. He said many companies start with smaller models running close to their own data, then expand to larger open models as they get more comfortable. The rise of open models also creates a strategic challenge for the U.S. Many of the most competitive open-weight models are coming from Chinese labs, including Z.ai and DeepSeek. That has made open-source AI a business issue, a policy issue and a national competitiveness issue. Srinivas said the U.S. should support open models because they make AI more affordable and accessible. "If you want the benefits of AI to be widely distributed to small businesses in America and American allied countries, then you really need AI to be a lot more affordable," Srinivas said. "And open source is the only way to do that." The shift could also affect the massive data center buildout underway across the tech industry. The current AI boom assumes demand will keep flowing to large cloud data centers filled with high-end chips. Srinivas says some AI work may eventually run locally instead, on devices owned by consumers or businesses. That wouldn't eliminate the need for data centers, but it could create a more hybrid AI system, with routine tasks run locally and the most difficult work getting sent to a more powerful model in the cloud. For investors, the question is whether the biggest AI labs can maintain their pricing power as open models get better and companies become more selective about what they use. Choose CNBC as your preferred source on Google and never miss a moment from the most trusted name in business news.

Companies are shifting toward cheaper open‑source AI models to rein in costs, Amazon CTO says | Fortune

Companies worried about mounting AI bills are increasingly shifting to cheaper, open-source models, according to Amazon's chief technology officer, Werner Vogels. "We see a shift happening between the cheaper open source models and the bigger expensive models," Vogels said in an interview on the sidelines of the UN's AI for Good summit. Stories of runaway AI bills have been making some executives skittish about building systems on the most advanced models from companies such as OpenAI, Anthropic, and Google DeepMind, that bill by the token. (A token is the basic unit of data an AI model processes, equivalent to about a word and a half of English language text.) Uber said it burned through its entire 2026 AI budget in four months, while company reportedly burned through half a billion dollars in a single month after failing to cap AI usage for employees have caused concern across industries. Fears of runaway spending are forcing companies to rethink how -- and where -- they deploy the most powerful frontier models. While large models from companies like OpenAI, Anthropic, and Google often deliver top-tier performance, they also come with significantly higher operating costs, particularly when deployed at scale. Open source models (also sometimes referred to as "open weight" models) can usually be downloaded for free but then users have to pay for their own cloud computing infrastructure on which to run the models. Still, this often works out to be cheaper than using the most advanced proprietary models. "Cost is a very important part of your architecture, you need to take that into account," Vogels said. "Do you really need to have the biggest, highest‑end model to solve this? The answer is no, you don't." The shift also reflects a broader maturation in how companies are thinking about AI adoption. After an initial wave of experimentation fueled by hype and rapid advances in large language models, many organizations are now entering a more pragmatic phase focused on return on investment. That means scrutinizing not just what AI can do, but what it costs to deploy and maintain over time. While customers may be shifting toward open‑source models as a cheaper option, Vogels also said companies were also putting a premium on transparency and trust in how models are trained and deployed "Transparency becomes extremely important," he said. "People want to know what is the data that goes into it." That demand is particularly acute in sectors like healthcare, government, and humanitarian work, where understanding how an AI system was trained -- and how it makes decisions -- can be as important as its performance. "If these people serve vulnerable communities. If they don't trust the system, they won't use it," Vogels said. Open-source models, which allow developers to inspect and modify code and more easily fine tune the model on their own data, are often seen as better aligned with those needs. But even most open weight model providers do not fully reveal all of the data on which the model was initially trained. At the Summit, Vogels also launched a new Amazon open-source AI tool designed to help researchers find relevant scientific datasets quickly. The system connects the AWS Registry of Open Data -- home to more than 1,100 datasets from organizations including NASA, NOAA, and the NIH -- to AI assistants, allowing users to search using natural language rather than navigating complex data catalogs. By enabling queries such as requests for satellite imagery or genomics datasets with specific licensing, the tool aims to replace processes that would have previously taken hours, lowering technical barriers for scientists, particularly those at under-resourced institutions, and accelerating research across fields such as climate science and public health.