Meta just dropped its latest salvo in the AI arms race with the launch of the Llama 4 family, and the industry is taking notice. The new lineup includes Scout, Maverick, and the still-in-training Behemoth—each designed to handle everything from text and code to images and video with greater efficiency and intelligence than ever before.
Here’s what makes Llama 4 a big leap forward, why it’s not without controversy, and how it fits into the broader economic and geopolitical picture shaping the future of AI.
Smarter, Leaner, and Built to Scale
The standout feature of Llama 4 is its use of a Mixture of Experts (MoE) architecture. Instead of relying on a single massive model to do everything, MoE splits tasks across specialized “expert” sub-models. That means more power when needed—and more efficiency when it’s not.
Take Maverick: it has 400 billion parameters in total but only 17 billion actively in use at any given time, distributed across 128 expert models. Scout is much smaller but still very powerful, with 17 billion active parameters and 109 billion in total.
This architecture helps Meta make models that are high-performing and low-cost, important for businesses deploying AI at scale.
Meta says Maverick shines as a general-purpose assistant, excelling at creative writing, multilingual conversations, programming, and reasoning. Internal benchmarks suggest it performs better than OpenAI’s GPT-4o and Google’s Gemini 2.0 in several areas, although it still trails top-tier competitors like Gemini 2.5 Pro, Claude 3.7 Sonnet, and GPT-4.5.
The largest model, Behemoth, is still in training, but early results are impressive. With 2 trillion total parameters, Meta claims it already outperforms leading models from OpenAI, Google, and Anthropic on multiple STEM-related benchmarks.
The Licensing Catch
Not everything about Llama 4 is open and accessible. Developers have been quick to point out restrictive licensing terms. Meta is currently prohibiting the use or distribution of the models by users or companies based in the EU, a move widely seen as a reaction to the region’s stringent AI and data privacy regulations.
Besides, companies that host over 700 million monthly active users will need permission to use the models, a point that gives Meta considerable gatekeeping power. Notably, such restrictions have not left anyone on the fence, especially on issues of accessibility and transparency.
DeepSeek and the China Factor
Meta’s scramble over Llama 4 wasn’t solely about creating the next thing—it was also about keeping up with the competition. Chinese AI company DeepSeek has been building steam quickly. Its latest R1 and V3 models equaled or surpassed previous versions of Llama, particularly in performance-to-cost ratio. Insiders say Meta acted by putting together internal “war rooms” to determine how DeepSeek managed to keep deployment so low.
That is, this announcement isn’t merely about driving innovation—it’s about keeping ahead of a global AI race that’s filling up with new competitors every month.
Economic Pressure: Recession as a Catalyst
Aside from the tech, there are also broader economic forces, like any downturn, that could change the trajectory of AI. History indicates that downturns tend to accelerate technology adoption. Economist Shingo Watanabe points out that following the initial shock of the Great Depression, the 1930s became the fastest decade for technology adoption before WWII, with an impetus in automation.
We’ve seen similar patterns more recently: the 2008 financial crisis and the COVID-19 pandemic both drove massive investment in automation and digital infrastructure, particularly in the U.S.
The same could happen again. If a tariff-driven recession hits, many expect U.S. businesses to double down on AI as a way to cut costs and boost productivity. As Fortune’s Jeremy Kahn put it, “there’s ample reason to believe that a tariff-induced recession could be a boon to AI adoption.”
In contrast, regions with more rigid labor laws, like Europe, and countries with less flexible fiscal policies, like parts of East Asia, have historically seen slower tech rollouts during downturns.
Proceed with Optimism—and Caution
Things are going to change dramatically with Meta’s Llama 4. There comes a phase where bigger models, more efficient design, and the intensification of a global competition set the stage for rapid innovation. But here, for investors, founders, and developers, the point to be remembered is that not all that glitters is gold.
As the investment platform aVenture wisely points out: “Past performance is not a guarantee of future results, and venture capital and private assets must be a contributing component to a diversified portfolio.” Where the field is developing as quickly as AI, diligence and diversification continue to be vital.
Meta’s release of Llama 4 is a clear signal: the pace of AI advancement isn’t slowing down anytime soon. Whether you’re building the next AI app, navigating regulations, or figuring out where to invest, one thing is certain—the future is arriving faster than anyone expected.
