A couple weeks ago I was talking with a friend on mine who is an executive at a major HR software company. He told me about some disturbing trends in terms of the AI takeover and what that means for people's jobs.

Normally his company hires as many as 50 new people each year as the company grows. In the past year, there have been no net new hirings across the company. He said that he doesn't see that changing anytime soon. There has been some re-positioning among current employees, with some promotions being made to fill new positions that have arisen based on the evolution of AI tool use.

This company is using Anthropic to automate what had been done by a team of people. So, while the company  itself continues to grow, it doesn't really create new opportunities for new people to benefit from it. Instead, there is always a chance that the jobs that current employees are filling could disappear, leaving some of those who have helped the company get to where it is get left out in the next phase.

I've been thinking about what that trend (which is happening in most segments of the economy) means for the general population. It seems like innovative tools like AI should make life less stressful for the general population as it takes over some of the mundane aspects of our daily work demands, but instead they are making life more stressful, as most of the population are now required to compete against tools that end up being superior to their skill sets.

This churn of opportunity and the need to re-tool has been around as long as society has been industrialized. However, this latest phase of churn feels different.

Here are some of the major issues I see with society attempting to stabilize itself in terms of realigning with what AI is doing.

• Skill Specialization
  Many people spend years mastering skills that are highly specific to their industry. When AI automates those tasks, the deep but narrow expertise they developed doesn't easily transfer to emerging fields.

• Educational & Training Gaps
  Transitioning into new careers often requires advanced technical training, certifications, or higher education. This retraining is not only expensive but also time-consuming, making it harder for mid-career workers with financial and family obligations.

• Age & Career Stage Factors
  Workers later in their careers usually find it more challenging to learn entirely new skill sets. This older workforce contingent also tend to face bias when competing with younger applicants when transitioning into new industries.

• Mismatch Between Old and New Job Markets
  The types of jobs being displaced often don't map neatly to the jobs being created. For example, a truck driver cannot directly pivot into a data analyst role without years of training.

• Emotional & Psychological Barriers
  Identity is often tied to work. Losing a role that a person has done for decades, then being told to "just learn to code" or "re-skill", creates not only practical but also emotional resistance.

As a business owner, I haven't necessarily had to deal with the possibility of getting let go from a company, but I have had to learn to change my attitude towards how I approach my businesses. My cheese has been moved a lot over the past several years, and I'm learning that I have to get back into startup and learning mode to be able to compete with what AI is doing.