By Ricardo Lezama 
The writing is in the wall: any task that touches a computer is subject to automation. As a result, any individual without any depth of knowledge into the workings of that application code or mechanisms is exposed to an AI doing an equivalent or, at least, comparable quality job without significant overhead.
It’s not enough (or even wise) to memorize man pages as was once – not too long ago- deemed relevant as is evident with hundreds of scripted questions required for assessing technical prowess in big tech. Now, this personality template may be shown the door, and shrewd innovators who can use Generative AI for astute shortcuts could be those individuals that are not deemed redundant with respect to AI adoption.
Specifics Around Threat of Generative AI
Generative Artificial Intelligence, a sub-discipline of Artificial Intelligence, can take real data and synthesize the results into an output based on a zero-shot prompt that narrows the right set of contours for that output. This is faster than a search engine and a user paired for comparable output. Whether it’s better or not, companies would rather risk pivoting quickly for their own enterprise grade generative AI and LLM models than to wait around for the overhead to creep and be caught without this valuable tool.
| Year | Company | Jobs cut | AI angle |
|---|---|---|---|
| 2025 | Microsoft | 7 000 (≈ 3 %) | Reallocating $80 B to AI infrastructure; cuts span LinkedIn & Xbox. Finger Lakes 1 |
| Google (Alphabet) | 200 (May) on top of 12 000 (Jan 2023) | Ongoing “layer removal” to fund data-center & gen-AI roll-outs. Reuters | |
| Amazon | 14 000 managerial roles | Streamlining bureaucracy and investing in AI-driven logistics & AWS tools. broadcasticle.com | |
| Meta | ≈ 3 600 “low performers” (≈ 5 %) | Zuckerberg links trims to an “AI-first” product roadmap and efficiency drive. Gulf News |
Importance of A Personal Brand
Some companies continue to rapidly pivot away from the people that hold institutionalized knowledge of their corporate spaces, they now lean heavily into the tooling and results powered by AI infrastructure. If the tasks they assign can be remade by imaginatively designed Generative AI models, then they will simply cut out any individual who is either too expensive to maintain at a post or too antithetical to the cost savings they associate with this newer automation. Thus, individuals need for companies to know them the same way anyone else in their social world does, perhaps, even more due to economic concerns on both sides e.g. your friends don’t necessarily pay for your time.
In other words, individuals in the workforce may need to consider what is unique about the way they can specifically use generative AI and not try to compete with the technical abilities of a machine. Specifically, they should move away from memorizing static bodies of information and concentrate into innovating and concluding things that require their specific set of expertise. AI can provide us with interesting permutations and even synthetic data to train new AI’s but understand the nuance around both those processes and minute details that only an SME can ascertain are key.