Meta Delays Release of Flagship AI Model Behemoth, Sending Stock Down Over 3% Intraday

On May 15 local time, multiple foreign media outlets, including Bloomberg, cited informed sources as revealing that Meta Platforms is facing a severe technical and public opinion crisis. The company has been forced to postpone the public release of Behemoth, its flagship AI model, yet again, due to insurmountable technical bottlenecks. This news sent shockwaves through the capital markets, causing Meta's stock price to plummet by over 3% intraday. The decline also dragged down other AI - related stocks such as NVIDIA, bringing an end to the Nasdaq's six - day winning streak, which closed 0.8% lower.

As the next - generation product in the Llama series, Behemoth was envisioned by Meta as a means to overtake its competitors in the AI field from the very beginning of its development. It is reported that the model is expected to have a parameter scale of up to 1 trillion, aiming directly at OpenAI's GPT - 4. However, the vast gap between aspiration and reality has plunged Meta into a difficult situation. Insiders revealed that the engineering team has encountered multiple obstacles in optimizing the model training algorithm. On one hand, it is extremely difficult to completely eliminate biased or incorrect data during the data cleaning process, leading to frequent logical deviations in the model's reasoning tasks. On the other hand, the computing power requirements of the trillion - parameter model far exceed the capabilities of the existing computing clusters. Even with the help of computing resources from AWS and Google Cloud, a single complete training cycle still takes several months.

"The team tries new optimization solutions almost every day, but the test results are always disappointing," an anonymous engineer admitted. There have been growing voices of doubt within the company, with some arguing that senior management was overly optimistic about the project, blindly pursuing a large parameter scale while neglecting the adaptability of the underlying infrastructure. More worryingly, Meta's senior management has blamed the slow progress of Behemoth on the Llama 4 model development team. This internal conflict is likely to further hamper the R & D efficiency.

This delay is not an isolated incident but rather a reflection of the collective challenges faced by global AI giants in cutting - edge technology research and development. OpenAI has remained tight - lipped about the release time of GPT - 5, and Google has also adjusted the launch plan for Gemini 2.0 several times due to difficulties in multi - modal integration. Industry analysts point out that the current AI technology has entered a deep - water phase, and improving model performance no longer depends solely on increasing parameter counts. Instead, it requires multi - dimensional breakthroughs in algorithm innovation, data quality, and computing power scheduling.

Looking back at Meta's AI strategy, although the Llama series has gained wide adoption in academic circles and start - ups after being open - sourced, its commercial monetization capabilities have always been controversial. Take Llama 3 as an example. Despite having 70 billion parameters and ranking high in the open - source model rankings, its long - text processing ability and multi - turn dialogue fluency are still inferior to GPT - 4o in enterprise application scenarios. The delay of Behemoth has undoubtedly heightened market concerns about the commercial prospects of Meta's AI initiatives.

The reaction of the capital markets has been immediate. In a research report, Dan Ives, an analyst at Wedbush, pointed out, "Competition in the AI field has entered a 'race against time' phase. Every product release delay could put Meta at a disadvantage in the battle for market share." Investors' doubts about Meta stem not only from technical bottlenecks but also from its persistently high R & D investment and lagging profit expectations. Financial reports show that Meta's AI R & D investment surged by 65% year - on - year in 2024, yet AI - related business revenue accounted for less than 15% of its total revenue.

In terms of industry competition, ByteDance, the parent company of TikTok, has announced that it will launch a multi - modal model with a parameter scale of 2 trillion within this year. Amazon's Bedrock platform has also attracted a large number of corporate customers thanks to the computing power advantage of AWS. If Behemoth fails to be launched on time in the autumn, Meta will not only miss the critical window for AI technology iteration but also risk losing users and experiencing a shrinkage of its developer ecosystem.

However, Meta still has opportunities to turn the situation around. With over 3 billion monthly active users globally, Meta has an enormous data resource that, if utilized properly, could provide high - quality data for model training. In addition, the hardware and algorithm experience accumulated by Meta in the VR/AR field may open up new avenues for the implementation of AI technology. But time is running out for Meta. How to adjust its R & D strategy and reassure investors will be crucial to the success of its AI strategy. In this "marathon" of the AI field, every decision made by Meta will profoundly shape the future landscape of the technology industry.