Meta Platforms on Thursday released long-awaited developer access to its Muse Spark AI alongside an upgraded version, pushing the model deeper into the hands of developers while the company races to lock in its own AI stack, its own chips, and its own pricing power. The social media giant said Muse Spark 1.1 is its most capable model for real-world coding and agentic tasks, part of a broader mission it is pitching as delivering "personal superintelligence." The language is grand. The machinery is blunt.
The Corporate Stack Gets Bigger
Meta said the upgraded model can write and debug code, use software and external tools, understand text, images and video, and carry out complex multi-step tasks with less human intervention. In April, Meta debuted Muse Spark, the first text and reasoning AI model from the superintelligence team it assembled last year to close the gap with rivals in the heated competition for AI supremacy. That competition is not some airy innovation pageant. It is a race to build more concentrated corporate control over computing, data, and the tools people use to work.
Developers in the United States can now access Muse Spark in public preview on Meta Model API, where they can test prompts, compare outputs and prototype integrations. Those who sign up get $20 in free credits before switching to pay-as-you-go pricing. Meta CEO Mark Zuckerberg said in a post on X, "Our focus is on delivering strong agentic and multimodal models at very low cost." Low cost for whom, exactly, the company doesn’t say. The pricing is $1.25 per million input tokens and $4.25 per million output tokens, above OpenAI's entry-level GPT-5 mini and Anthropic's low-cost Claude Haiku 4.5, but below Anthropic's higher-end Claude Sonnet 4.6 model.
The new model is now available in Thinking mode in the Meta AI app and on the website. It is also expected to replace existing Llama models powering chatbots on WhatsApp, Instagram, Facebook and Meta's collection of smart glasses. One company. Many surfaces. Same grip.
The Data Center Hunger Game
Separately, an internal memo reviewed by Reuters said Meta plans to start manufacturing an artificial intelligence chip from September as part of its plan to boost overall computing power to 14 gigawatts next year. The tech firm's data center chip, code-named "Iris," is part of a four-generation project for Meta Training and Inference Accelerators that it will design in-house. The plan is to use custom-built silicon to improve the AI that powers its Facebook and Instagram social media platforms.
Testing the chip took only six weeks and found no major issues, the memo showed. Meta tailored the chip for its own needs and is working with Broadcom to help design it and Taiwan Semiconductor Manufacturing Co to manufacture it. The approach is likely to help the firm lower its massive computing costs and gain more independence from chip suppliers such as Nvidia and Advanced Micro Devices. In other words, the company wants fewer dependencies, more control, and a tighter grip on the infrastructure that keeps its platforms running.
The chip is meant to augment the large quantities of graphics processing units used for AI applications that Meta purchases from Nvidia and AMD. However, adopting the latest GPUs at a firm as large as Meta "has been a heavy lift, and it has cost us time," the memo showed. The memo doesn’t sound like a public-interest document. It sounds like a logistics report for a machine that eats electricity and capital.
Meta unveiled Iris under its technical name in March along with three other AI processors. It plans to launch a chip about every six months through 2027, whereas typically firms release AI chips at intervals of a year or more. Faster cycles. More hardware. Less pause.
This year, Meta plans to deploy seven gigawatts of computing infrastructure. To reach that total, Meta added 1 gigawatt in the first half of the year and forecasts adding another 5.5 gigawatts by the end of the year, the memo said. One gigawatt of energy is enough to power about 800,000 homes. The company plans to double capacity again next year to reach a total of 14 gigawatts in 2027. That’s the scale of the appetite. Homes are the comparison point because the numbers are too large to hide behind corporate jargon.
Who Pays for the Machine
Meta expects to spend as much as $145 billion on AI infrastructure this year, a significant portion of Big Tech's more than $700 billion projected outlay on the technology. To expand computing infrastructure, Meta has secured long-term, multi-year supply agreements with Samsung Electronics for memory chips, Sandisk for flash storage and Sumitomo Electric for fiber-optic equipment. Sandisk declined to comment. Samsung Electronics and Sumitomo Electric did not respond to requests for comment.
Components such as memory and AI chips have experienced a surge in demand as tech companies race to expand data centers to keep pace with AI's thirst for computing power. Memory and other chip prices have risen rapidly and substantially enough that "chipflation" has become a macroeconomic concern, Morgan Stanley analysts said. The costs ripple outward. The profits stay concentrated. The infrastructure gets bigger, and the public gets told this is progress.
Meta’s latest release shows how the AI race works in practice: a private company sets the terms, prices access, builds its own chips, expands its own data centers, and folds more of daily digital life into a system it owns outright. The company calls it superintelligence. The rest of us get the bill, the energy demand, and another layer of corporate dependency.