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... But who cares about my childhood traumas and phantom pains? This time, all the sisters were given earrings: 8,000 people ...
Meta fires 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... But who cares about my childhood traumas and phantom pains? This time, all the sisters were given earrings: 8,000 people were fired, 7,000 were transferred into slavery to serve artificial intelligence, 6,000 vacancies would remain unfilled.
“As previously announced, we have decided to reduce headcount... to offset other investments we are making,” the memo, leaked to the media, said. Bravo, Meta! The company’s marvelous new bet on AI is becoming increasingly clear. And 20% of the staff somehow change their minds 💁♂️
I was wondering what the total percentage of staff at Meta is in the AI field? According to another memo leaked to the media earlier, the new division of Applied AI and Engineering employs two thousand employees. We add 7,000 transferred people and get 9,000 people at the end!
But did Meta follow that path? After all, it is obvious that progress in the development of LLM depends not only on the number of specialists involved, but also on the effectiveness of their intellectual work. Computing power and the willingness of companies to spend enormous resources and time without guaranteeing a successful result also play an important role.
I believe that AI adoption is moving from the “let's just try it” stage to the “what role or part of the role can we already replace right now?” stage.
What was previously well seen among programmers - assistance with routines, code and rapid prototypes - now flows into marketing, analytics, sales, support and content production. Moreover, today a combat prototype can be assembled not only by a developer, but simply by an advanced user.
If you are a company that wants to implement AI, then I would look at it like this: ✅ analyze roles and processes within the business; ✅ understand where AI really reduces manual labor, speeds up production or improves results; ✅ find scenarios where you can already assemble a virtual employee, and not just a “pipeline with a neural network”; ✅ quickly test this on a pilot and calculate the effect; ✅ scale only where there is an economy, and not just a beautiful AI demo.
I’m writing this as confidently as if I’m about to start selling you an “AI in three days” course and a link to a closed club. But the point is different - I literally see in living examples how this is happening now. Yes, so far often on a small scale. But when it gains momentum in your field or role, it will be more difficult to catch up.
And if it seems that some routine role cannot be replaced, then often the problem is not uniqueness, but that the role itself is poorly described. Still poorly described.
And this is exactly what you can help with: disassemble the business, find roles and processes for automation, assemble a realistic AI pilot and separate working scenarios from fashionable imitation of progress. Contact us in pm, we’ll discuss 😎