On Tuesday, Meta is hosting its first-ever LlamaCon AI developer conference at its Menlo Park headquarters. This event is more than just a gathering of developers; it is a pivotal moment for Meta in its quest to build a sprawling Llama ecosystem. The company aims to pitch developers on building applications with its open Llama AI models, a task that has become increasingly challenging in the rapidly evolving AI landscape.

The Changing Dynamics of the AI Landscape

Just a year ago, Meta's Llama models were considered groundbreaking. The launch of Llama 3.1 405B was hailed as a significant victory, with Meta's CEO, Mark Zuckerberg, touting it as the "most capable openly available foundation model." At the time, Llama 3.1 405B was on par with OpenAI's GPT-4o, making it a darling among AI developers. The Llama 3 family delivered cutting-edge performance with the added freedom to host the models wherever developers chose. Jeremy Nixon, who has hosted hackathons at San Francisco’s AGI House, described the Llama 3 launches as "historic moments."

However, the landscape has changed dramatically in recent months. Meta has struggled to keep up with both "open" AI labs like DeepSeek and closed commercial competitors such as OpenAI. The launch of Llama 4 earlier this month was a stark contrast to the earlier success of Llama 3. Developers were underwhelmed by Llama 4, with benchmark scores falling below models like DeepSeek’s R1 and V3. This was a far cry from the boundary-pushing model lineup that Llama once represented.

The Controversial Launch of Llama 4

The reception to Llama 4 has been mixed, to say the least. One of the most controversial aspects was Meta's optimization of a version of Llama 4, known as Llama 4 Maverick, for "conversationality." This optimization helped it secure a top spot on the crowdsourced benchmark LM Arena. However, Meta never released this optimized version. Instead, the version of Maverick that was broadly rolled out performed much worse on LM Arena.

This discrepancy raised concerns among the developer community. Ion Stoica, an LM Arena co-founder and UC Berkeley professor, told that Meta should have been "clearer" about the differences between the models. He emphasized that such incidents erode trust within the developer community. "When this happens, it’s a little bit of a loss of trust with the community," Stoica said. "Of course, they can recover that by releasing better models."

The Missing Reasoning Model

Another glaring omission from the Llama 4 family was an AI reasoning model. Reasoning models are designed to work through questions carefully before providing answers, often resulting in better performance on specific benchmarks. In the past year, much of the AI industry has released reasoning models, making Meta's absence in this area notable.

Nathan Lambert, a researcher with Ai2, suggested that Meta may have rushed the launch of Llama 4. "Everyone’s releasing a reasoning model, and it makes their models look so good," Lambert said. "Why couldn’t [Meta] wait to do that? I don’t have the answer to that question. It seems like normal company weirdness."

This omission is particularly significant given the increasing competition in the AI space. For example, Alibaba recently released a collection of models, Qwen3, which allegedly outperform some of OpenAI’s and Google’s best coding models on Codeforces, a programming benchmark. This development further intensifies the pressure on Meta to deliver superior models.

The Path to Recovery

To regain its position as a leader in the open model space, Meta needs to deliver superior models that can compete with the best in the industry. According to Ravid Shwartz-Ziv, an AI researcher at NYU’s Center for Data Science, this may involve taking more risks and employing new techniques. "To regain the open model lead, Meta simply needs to deliver superior models," Shwartz-Ziv said. "That may involve taking more risks, like employing new techniques."

However, Meta's ability to take big risks is uncertain. Current and former employees have previously told Fortune that Meta’s AI research lab is "dying a slow death." The departure of the company’s VP of AI Research, Joelle Pineau, this month further complicates the situation.

The Importance of LlamaCon

LlamaCon is Meta’s opportunity to showcase its latest innovations and regain the trust of the developer community. This event is crucial for Meta to demonstrate that it can still compete with upcoming releases from AI labs like OpenAI, Google, xAI, and others. If Meta fails to deliver at LlamaCon, it risks falling even further behind in the ultra-competitive AI space.

A Turning Point for Meta

Meta's LlamaCon is more than just a conference; it is a critical turning point in the company's AI journey. The success of Llama 4 and the reception from the developer community highlight the challenges Meta faces in maintaining its leadership in the AI space. To regain its position, Meta must deliver superior models that address the needs and expectations of developers.

The AI landscape is rapidly evolving, and competition is fiercer than ever. Meta's ability to innovate and take risks will be crucial in determining its future success. LlamaCon offers a glimpse into Meta's plans and capabilities, but the real test will be in the execution and delivery of its AI models.

As Meta navigates this critical moment, the developer community and the broader AI industry will be watching closely. The outcome of LlamaCon could shape the future of Meta's AI efforts and its ability to remain a leader in this transformative field.