Meta in Crisis: Internal AI Division Conflicts Threaten the Company Future

Hello HaWkers, if you follow the artificial intelligence universe, you have probably noticed that Meta has been in turmoil. What few people know is that behind the Llama releases and generative AI promises, there is an internal crisis that could redefine the future of Mark Zuckerberg's company.

Veteran researchers threatening to quit, four reorganizations in six months, and a research lab that many consider to be "dying a slow death." What is happening behind the scenes at Meta and how could this impact the job market for developers?

What Is Happening at Meta

Meta is facing an identity crisis in its AI division. After years of being known for FAIR (Facebook AI Research), one of the most respected research labs in the world, the company decided to make drastic changes that are causing friction with its best talents.

Crisis Numbers

Reorganizations in 2025:

January: Creation of Meta Superintelligence Labs
March: Merger of generative AI teams
June: Hiring of Alexandr Wang as Chief AI Officer
September: New structure with TBD Lab

Talent Departures:

Joelle Pineau (VP of AI Research) left the company
More than 50% of the original Llama paper authors left
At least 8 senior researchers left Meta in the past year
Ethan Knight returned to OpenAI after less than a month

Investment:

$65 billion allocated for AI initiatives
$14.3 billion paid for Scale AI stake acquisition

The Root of the Conflict

The conflict began when Zuckerberg decided to aggressively pivot to generative AI, prioritizing commercial products over fundamental research. FAIR, which always had autonomy to publish open research, now faces new layers of review before any publication.

Yann LeCun and the New Leadership

Yann LeCun, one of the fathers of deep learning and Chief AI Scientist at Meta, seems to have been marginalized in the new structures:

What Changed:

Alexandr Wang (28 years old, former Scale AI CEO) was named Chief AI Officer
Shengjia Zhao (ChatGPT co-creator) received the title of "Chief Scientist of Meta Superintelligence Labs"
Zhao's title was given to convince him not to return to OpenAI

💡 Context: LeCun remains at the company, but his influence on strategic decisions seems to have significantly diminished.

The "Slow Death" of FAIR

Employees and former employees describe FAIR as a lab that is "dying slowly":

Signs of Decline:

The latest Llama model was developed by the GenAI team, not FAIR
Researchers are pressured to focus on products instead of open research
Academic publications now go through corporate review
Budget redirected to commercial initiatives

Why This Matters for Developers

If you are a developer, especially if you work with AI or consider a career in Big Tech, these events have direct implications:

1. Big Tech Instability Is Real

The idea that working at a FAANG guarantees stability is being challenged. Four reorganizations in six months mean:

Constant team and leadership changes
Projects canceled or redirected
Uncertainty about the future of positions

2. The Value of Open Research Is in Question

FAIR was a pioneer in publishing open research, contributing enormously to the community. If Meta continues to prioritize closed products:

Potential Impact:

Fewer open papers on model architectures
Reduction in open source contributions
Concentration of knowledge in closed companies

3. Opportunities in AI Startups

With talents leaving Meta, many are founding or joining startups:

Observed Trend:

Former Meta researchers founding their own companies
AI startups competing for dissatisfied talents
High valuations for companies focused on fundamental AI

Career Lessons

Diversify Your Skills

Do not depend on a single company or technology:

In-Demand Skills:

Solid ML/DL fundamentals
Experience with multiple frameworks (PyTorch, JAX, TensorFlow)
Ability to adapt to rapid changes
Communication and technical leadership

Build Your Reputation Outside the Company

Researchers who leave Meta often have:

Career Assets:

Papers published at prestigious conferences
Recognized open source contributions
Industry contact network
Presence in technical communities

Consider Company Size

Each type of company has trade-offs:

Aspect	Big Tech	Startup
Stability	Variable	Low
Resources	Abundant	Limited
Bureaucracy	High	Low
Individual Impact	Diluted	Direct
Learning	Specialized	Generalist

What to Expect from Meta

Optimistic Scenario

Meta manages to integrate its teams and launches competitive products:

Llama continues evolving as an open source model
Meta Superintelligence Labs produces breakthroughs
FAIR finds new balance between research and product

Pessimistic Scenario

Conflicts continue and Meta loses ground:

More senior talents leave the company
Models fall behind OpenAI and Google
Open research practically ceases

Most Likely Scenario

Reality will probably be somewhere in between:

Predictions:

Meta will remain a relevant AI player
FAIR will be significantly reduced in scope
Focus will be on AI for products (Instagram, WhatsApp, Metaverse)
Llama models will continue, but with less fundamental innovation

Emerging Opportunities

For attentive developers, this turmoil creates opportunities:

In-Demand Skills

If you want to stand out in this scenario:

LLM Fine-tuning: Companies need to adapt open models
MLOps and Infrastructure: Model deployment and scale is critical
Model Evaluation: Benchmarking and quality are differentiators
AI Safety: Red teaming and safety are in high demand

Companies to Watch

Besides Meta, keep an eye on:

Anthropic: Growing rapidly with focus on safety
Mistral: European startup with competitive models
Cohere: Focused on enterprise
AI21 Labs: Models for specific applications

Conclusion

The Meta crisis illustrates a fundamental tension in the AI industry: how to balance open research with commercial competition? For developers, the main lesson is clear: build transferable skills, keep your network active, and do not depend exclusively on a single company for your career.

The AI market is in rapid transformation. Those who adapt and maintain continuous learning will be well positioned, regardless of which company wins the race.

If you want to understand more about the technologies shaping this scenario, I recommend checking out the article TypeScript in 2025: How the Language Became the Standard for Every Serious JavaScript Project to understand another important transformation in the development ecosystem.