Publication
Meta is laying off employees again 👥
Every time I read a headline about layoffs at Zuckerberg’s company, I subconsciously expect that the long-suffering VR/metaverse division has been put out in the cold again. This time the cuts are spread across the board: 8,000 people will be laid off, 7,000 reassigned to AI, and 6,000 roles left unfilled.
Meta is laying off employees again 👥
Every time I see a headline about layoffs at Zuckerberg’s company, I instinctively expect the long-suffering VR/metaverse division to have been put out in the cold again. But who cares about my childhood trauma and phantom pains? This time the cuts are spread across the board: 8,000 people will be laid off, 7,000 reassigned to serve artificial intelligence, and 6,000 positions left unfilled.
“As previously announced, we have decided to reduce headcount... to offset other investments we are making,” says a memo leaked to the media. Bravo, Meta. The company’s brave new bet on AI is becoming clearer by the day. The remaining 20% of staff will somehow muddle through 💁♂️
I started wondering: what share of Meta’s workforce will now work in AI? According to another memo leaked earlier, the company’s new Applied AI and Engineering division employs 2,000 people. Add the 7,000 being transferred, and the total comes to 9,000.
But is Meta on the right path? Progress in LLM development clearly depends not only on the number of specialists involved, but also on the effectiveness of their intellectual work. Computing power matters too, as does a company’s willingness to spend enormous amounts of time and money without any guarantee of success.
I believe AI adoption is moving from “let’s just try it” to “which role—or part of a role—can we replace right now?”
What programmers saw first—help with routine work, code, and rapid prototypes—is now spreading into marketing, analytics, sales, support, and content production. A production-ready prototype can now be assembled not only by a developer, but by an advanced user.
If your company wants to adopt AI, I would approach it like this:
- ✅ Map the roles and processes inside the business.
- ✅ Identify where AI genuinely reduces manual work, speeds up production, or improves outcomes.
- ✅ Find scenarios where you can build a virtual employee rather than just a “pipeline with a neural network.”
- ✅ Test it quickly in a pilot and calculate the impact.
- ✅ Scale only where the business case works—not where there is merely an attractive AI demo.
I write this so confidently that it sounds as though I am about to sell you an “AI in three days” course and access to a private club. But the point is different: I am watching this happen in real businesses right now. Often on a small scale, yes. But once it gains momentum in your field or role, catching up will be much harder.
When a routine role seems impossible to replace, the problem is often not its uniqueness but the fact that the role itself is poorly defined. For now.
This is exactly where I can help: map the business, identify roles and processes suited to automation, build a realistic AI pilot, and separate working scenarios from fashionable imitations of progress. Send me a DM and let’s discuss it 😎
Meta is designated an extremist organization in Russia.