Meta’s Superintelligence Lab could overtake Google within six months and become the strongest challenger behind OpenAI and Anthropic, according to a new projection from technology research firm SemiAnalysis, as Mark Zuckerberg’s company concentrates money, engineers, data, and computing power on a more centralized artificial-intelligence push.
The forecast marks a sharp shift in the competitive outlook for Meta, which has struggled to keep pace with the top frontier AI labs despite years of work on its Llama model family. SemiAnalysis said Meta’s position has changed because of three major developments: its $14.3 billion deal involving Scale AI, the reassignment of thousands of engineers into reinforcement learning data work, and a major expansion in data-center power capacity that could exceed 5 gigawatts of new commitments.
The finding does not mean Meta has already caught the leaders. SemiAnalysis said Meta’s latest released model, Muse Spark 1.1, still falls short of frontier performance in benchmark testing. A larger model, internally referred to as “Watermelon,” remains under training and is expected to be a key test of whether the company’s spending and internal restructuring can produce a measurable jump in model capability.
For now, OpenAI and Anthropic remain ahead in general-purpose AI performance, while Google is still widely viewed as a major force through Gemini and its extensive AI infrastructure. But SemiAnalysis argued that Meta’s newly concentrated approach could narrow the gap quickly, especially if Google continues to divide resources among cloud customers, external API services, and internal research.
Meta’s strategy is becoming more aggressive and more commercial. The company has begun offering paid access to its AI tools through a public portal, including an interface that can process up to one million tokens in a single context window. The move suggests Meta is moving beyond a strategy centered mainly on free or open software releases and toward direct competition in paid AI services for developers, businesses, and high-volume users.
Meta’s concentrated AI push
Zuckerberg reorganized Meta’s artificial-intelligence operations after setbacks tied to the Llama 4 project, creating a Superintelligence Lab designed to bring top talent, data generation, model training, and computing infrastructure under tighter control.
SemiAnalysis said this centralized structure could give Meta an advantage over rivals that spread resources across several business lines. While Google has enormous technical depth and infrastructure, the research firm said a large share of Google’s capacity is still tied to cloud operations and external customers. Meta, by contrast, is directing a growing share of new capacity toward a narrower goal: building and improving advanced AI systems.
That distinction matters because frontier AI development is increasingly shaped by three inputs: elite researchers, high-quality training data, and massive amounts of compute. Meta is attempting to build strength in all three at the same time.
The $14.3 billion transaction with Scale AI brought Scale founder Alexandr Wang into Meta’s orbit, along with the SEAL team, which specializes in safety, evaluation, and alignment. Those areas are critical because modern models are not judged only by raw knowledge or speed. They must also follow instructions accurately, avoid unsafe outputs, handle complex reasoning, and perform reliably across coding, research, workflow, and agentic tasks.
Reuters previously reported that Meta had offered compensation packages worth several hundred million dollars to some top AI engineers as part of the broader reorganization. Those offers reflected a heated talent war across the AI sector, where experienced researchers and infrastructure engineers have become among the most expensive employees in technology.
Scale AI deal adds data and evaluation strength
The Scale AI-related deal is especially important because AI progress increasingly depends on the quality of post-training data, not only on the size of the base model. Early large language models improved largely through bigger datasets and more computing power. The newest systems require more specialized data that teaches models how to reason through multi-step tasks, use tools, write and repair code, evaluate their own work, and complete complex workflows.
SemiAnalysis said Meta has reassigned roughly 3,000 engineers into full-time reinforcement learning task creation roles. These employees are producing data from real internal workflows, including code debugging, tool execution, testing cycles, and other process-based tasks.
That is a notable shift away from outsourced labeling and simple annotation. Instead of relying mostly on external workers to rate answers or label examples, Meta is generating more complex internal datasets built from the work of its own engineers. This approach can produce richer examples of how difficult tasks are actually solved, including mistakes, corrections, intermediate steps, and final validation.
Reinforcement learning has become central to the latest generation of AI models because it can help systems improve through feedback and repeated task attempts. For coding and agent-based work, the process is especially valuable: a model can attempt a task, run code, observe errors, revise its output, and eventually learn patterns that lead to successful completion.
If Meta can produce enough high-quality reinforcement learning data internally, it may be able to accelerate post-training performance even before its base models fully match those of OpenAI or Anthropic.
Computing capacity becomes the new battleground
The third pillar of Meta’s strategy is computing power. SemiAnalysis said Meta signed more than 5 gigawatts of new data-center power capacity in the first half of the year, bringing total contracted capacity since 2024 close to 10 gigawatts.
That figure is large even by the standards of the modern AI buildout. Data centers supporting frontier AI require enormous amounts of electricity for graphics processing units, networking equipment, cooling systems, storage, and other infrastructure. A multi-gigawatt commitment indicates that Meta is preparing for a prolonged period of heavy model training, post-training, and inference workloads.
Most of the newly contracted capacity is expected to support the Superintelligence Lab, according to SemiAnalysis. That includes advanced model training, post-training runs, and agent-based loops, where AI systems repeatedly perform tasks, receive feedback, and improve over time.
Meta’s infrastructure buildout also reflects a wider shift in the AI industry. The leading companies are not merely writing better algorithms; they are building industrial-scale computing networks. Access to power, chips, land, cooling, and data-center construction timelines has become a central competitive factor.
Wang reportedly told staff earlier this month that an upcoming Meta system would use 10 times the processing power of the current version. If accurate, that would underscore the scale of the company’s ambitions and the pressure it faces to turn infrastructure spending into stronger products.
Muse Spark 1.1 still trails the frontier
Despite the rapid expansion, Meta has not yet released a model that clearly matches OpenAI or Anthropic in broad capability. SemiAnalysis said Muse Spark 1.1 remains below frontier level in benchmark tests.
Meta’s company materials listed pricing for the Muse Spark 1.1 programming interface at $1.25 per million input tokens and $4.25 per million output tokens. That pricing places Meta in direct competition with other AI service providers seeking developers and business customers that need cost-efficient model access.
The company has also offered $20 in free testing credits to new accounts, a tactic designed to lower the barrier for developers to experiment with the platform. Such credits are common in cloud and software markets, but in the AI sector they can be especially important because developers often test several models before deciding which one to integrate into products or internal tools.
Meta’s large context window, reportedly capable of handling up to one million tokens, could appeal to users working with long documents, large codebases, legal materials, research archives, customer-support histories, or real-time market data. A larger context window allows a model to process far more information at once, reducing the need to break tasks into smaller pieces.
Still, price and context length are not enough on their own. For developers, model quality, reliability, latency, tool use, coding performance, and safety controls often matter more than headline specifications. Meta’s ability to win paid usage will depend on whether Muse Spark and future models can deliver consistent results in real workflows.
Watermelon becomes a key test
The most important near-term milestone is the larger model known as Watermelon, which remains in training. SemiAnalysis said its performance will help determine whether Meta’s concentration of talent, data, and power is translating into real progress.
If Watermelon shows a major improvement over Muse Spark 1.1, Meta could quickly gain credibility as a frontier contender. Strong results would also support Zuckerberg’s decision to restructure AI operations and commit vast resources to the Superintelligence Lab.
If the model falls short, the pressure will increase. Spending billions of dollars on talent, data infrastructure, and data-center power creates high expectations. Meta has the financial strength to absorb large outlays, but the AI market is becoming less forgiving as users compare models directly across coding, reasoning, document analysis, voice, image, and agentic tasks.
The challenge is not only to build a powerful model once. Leading AI labs are now expected to ship frequent improvements, support enterprise-grade reliability, and offer developer tools that make integration easier. That requires not just research breakthroughs, but disciplined product execution.
Google’s position faces a new challenge
Google remains a deeply capable AI competitor. It has decades of machine-learning research, custom AI chips, massive data-center experience, and a broad software ecosystem. Its Gemini models and cloud AI services give it a strong position across consumer and enterprise markets.
But SemiAnalysis said Google’s resource allocation may create an opening for Meta. Because Google serves cloud customers and external API users in addition to internal AI research, its newest computing capacity is spread across several priorities. That can support revenue and deepen customer relationships, but it may reduce the concentration of compute available for a single model-development effort.
Meta’s approach is different. Its Superintelligence Lab appears designed to push as much relevant capacity as possible toward frontier AI progress. That does not guarantee success, but it can shorten iteration cycles, especially if the company can rapidly train, test, evaluate, and refine new models.
The comparison highlights a broader divide in AI strategy. Some companies are balancing model research with cloud services, enterprise sales, and platform commitments. Others are prioritizing direct model advancement, betting that better AI systems will later generate commercial demand.
Crypto and automated markets may feel the impact
Meta’s AI expansion could also affect automated trading and digital-asset markets, though the impact is likely to come through developer tools rather than direct market exposure.
Cheaper and more powerful AI systems can help traders build, test, and revise code more quickly. Tools with large context windows can examine longer trading logs, monitor large volumes of social and market data, and assist with debugging automated strategies. Faster coding support can also reduce the time needed to deploy new market-monitoring tools.
That speed cuts both ways. When more participants can improve automation quickly, markets may become more reactive. Automated systems that parse news, sentiment, liquidity conditions, and order-book changes can intensify short-term price moves if many tools respond to similar signals at the same time.
For digital-asset traders, the practical issue is not whether Meta alone changes market structure overnight. It is whether lower-cost AI tools increase the number and speed of automated strategies across the market. As advanced models become easier to access, more trading teams may connect AI-assisted research, coding, risk testing, and execution systems into their workflows.
This could raise the value of stronger risk controls. Models can help identify errors in code, test strategies under stress scenarios, and scan for unusual market behavior. But they can also produce false confidence if their outputs are not checked carefully. In fast-moving markets, flawed automation can magnify losses just as quickly as improved tools can find opportunities.
Spending must turn into performance
SemiAnalysis concluded that the next six months will be a critical test for Meta. The company has assembled talent, expanded data pipelines, and committed to massive computing capacity. What it has not yet shown is a released model that clearly matches the strongest systems from OpenAI and Anthropic.
The stakes are high. If Meta’s next models narrow the gap, Google’s position as the third major AI powerhouse could be challenged. Meta would also strengthen its ability to sell paid AI tools directly to developers and businesses, reducing reliance on a strategy built mainly around open releases.
If the models fail to improve enough, the same spending could become a liability. The $14.3 billion Scale AI-related transaction, large compensation offers, and multi-gigawatt data-center commitments would face sharper scrutiny.
For now, Meta has changed the question around its AI ambitions. The issue is no longer whether it is willing to spend. It clearly is. The question is whether its Superintelligence Lab can convert that spending into models that perform at the frontier, attract paying users, and alter the balance of power in artificial intelligence.
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