Meta is axing 600 roles across its AI division

Reaction to the “load‑bearing” / “fewer conversations” memo

• Many read the memo as: “we overhired, now remaining staff will do more for the same pay.”
• Others interpret it more charitably as a push to remove “too many cooks,” decision-by-committee, and gatekeepers that slow product velocity.
• Some think the wording is banal corporate-speak; others find it “wild” and dehumanizing to describe employees as “load‑bearing.”

Impact on trust, morale, and responsibility

• Several argue layoffs are especially harmful in research: fear and churn kill deep focus and long-term work; tenure exists partly to avoid this.
• Repeated internal reapplications and reorgs are seen as stressful and demoralizing; some affected prefer to take severance rather than gamble on another reshuffle.
• Many say leadership, not ICs, should bear consequences for overhiring (pay cuts, real accountability), but expect that managers remain insulated.
• Performance systems are described as favoring self‑promoters over quiet, strong engineers, worsening who gets rewarded and who gets cut.

Overhiring, bureaucracy, and politics

• Common view: Meta hired aggressively into hot areas (metaverse, then AI), then discovered bloated, slow orgs where headcount = status.
• People cite Pournelle’s law / “iron law of bureaucracy”: middle layers start serving themselves, not products.
• Several see this as a classic “new boss purge” and consolidation of power—replacing legacy FAIR/old‑guard people with the new leadership’s network.

Strategy shift: FAIR vs “superintelligence,” classic ML vs LLMs

• Multiple comments note cuts are concentrated in the foundational research group (FAIR) while hiring continues in the new “superintelligence” / product‑focused org.
• One narrative: “old” ML/vision/research work (even influential models like DINO, SAM) is being deprioritized in favor of LLM‑centric work and near‑term monetization.
• Others counter this is not “old AI”—these teams built up to Llama 4—so the move is more political than purely technical.

Meta’s AI position and AI bubble debate

• Several users say they barely think of Meta as an AI leader; MetaAI is perceived as notably worse than top models, even as Meta open-sources strong weights.
• Some think Meta is strategically flailing (metaverse, then AI) and “fumbling” against OpenAI, Google, and Chinese labs; others argue winning now is about applications, not just models.
• Broader thread: AI hiring was overextended across industry; many expect large percentages of AI roles with weak ROI to be cut as the hype cools.
• There’s tension between people whose work lives were genuinely transformed by LLMs and those who see clear plateauing, dubious business models, and a looming correction.