Microsoft shifts to in-house AI models to cut costs

Microsoft shifts to in-house AI models to cut costs">

As AI costs continue to rise, companies are looking for ways to cut back. The most recent example is Microsoft, which has reportedly begun to deploy a cost-savings strategy by relying less on software from OpenAI and Anthropic and instead deploying its own in-house models. Indeed, when it comes to two of its most widely used programs — Excel and Word — Microsoft has begun to use its homemade MAI models to respond to a certain percentage of user prompts, Bloomberg reported Tuesday.

In the past, the company had advertised the fact that large parts of Office 365 are powered by models from both OpenAI and Anthropic. While Microsoft still relies on those third-party models, it has also increasingly sought to stand up its own AI agents. Last month, at its annual Build conference, the company announced the launch of seven new MAI models, including an agentic coder and a text-to-image generator.

When reached for comment by TechCrunch, Microsoft said that it had nothing further to share. Microsoft’s apparent cutbacks are part of a broader trend. After a brief blitz of “tokenmaxxing” earlier this year, the last few months have seen a news cycle awash in stories about tech companies acting significantly more thrifty.

Other large companies — like Amazon, Uber, Meta, and Accenture — have also reportedly made moves to curb spending. The immense cost of providing and buying AI services has become a controversial part of the industry. The sticker shock has gotten so bad in some parts of Silicon Valley that some companies are reportedly looking to Chinese models for more affordable agentic solutions — despite some concerns over potential security issues.

Why this matters: Microsoft's move to rely more on its own AI models reflects a broader industry shift towards cost-cutting as AI expenses continue to soar. As companies look to reduce their dependence on pricey third-party models, they are investing heavily in developing their own in-house solutions. This trend has significant implications for the AI industry, as it may lead to a decline in demand for third-party models and a shift towards more proprietary solutions.

For developers and businesses, this means that they will need to adapt to a changing AI market, where cost and customization are becoming increasingly important. For consumers, this may lead to more efficient and cost-effective AI-powered services, but also raises concerns about the potential for reduced innovation and competition in the AI space. As the industry continues to evolve, it remains to be seen how companies will balance the need for cost-cutting with the need for innovation and progress in AI research.