With new open models, Meta pitches another reboot of its struggling AI strategy
Meta has been trailing competitors. Zuckerberg thinks he's found a way forward.
“People’s diverse values represent different tradeoffs they would make on important issues. There is no technological solution that can align with everyone’s opposing interests and values at once,” the essay says. “Any singular superintelligence would have to prioritize some values over others and in the process would be incapable of being benevolent to everyone.”
Instead, Meta argues here that models should be personalized to the needs and values of individuals or groups of individuals. It also claims that decentralization will make everyone safer, because it will give the benefits and advantages of “superintelligence” to everyone equally, instead of privileging “a small number of individuals, businesses, governments, or AI itself.”
A public recalibration
Meta has been lagging behind other big tech companies and major frontier labs for foundation models. Its models haven’t seen the kind of adoption that those developed by OpenAI or Anthropic have.
OpenAI and Anthropic have aggressively targeted enterprise customers, releasing powerful models and harnesses for knowledge work tasks like software development, and they have made significant inroads and generated substantial revenue from this strategy. Meta has not seen the same level of success.
Meta also saw a total overhaul of its AI division last year, when former Meta AI chief scientist Yann LeCun was replaced by former Scale AI CEO Alexandr Wang. The reset led to a change in focus.
In recent months, the debate around open-weight models and distillation has increased in volume as recent Chinese models like Alibaba’s Qwen3.8-Max and Moonshot’s Kimi K3 have been shown to rival Anthropic and OpenAI at the frontier. Those models may perform slightly worse in coding benchmarks, for example, but they are generally cheaper to use.
In a sense, Meta seems to be positioning itself as a US alternative to Alibaba, Moonshot, or DeepSeek—not quite as frontier-facing as Anthropic or OpenAI, but more open, customizable, and affordable. It is also orienting itself—at least with these public statements—more toward personal use as opposed to large-scale enterprise deployments, at least for now.
That is a retreat from some of its earlier ambitions, in a way, as the company takes advantage of changing winds to try to plot a new course.