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Intro

In the recent article The State of Engineering Leadership in 2025, I closely analyzed the current state of Engineering Leadership which showed to have many interesting insights.

Especially, an insight into the increase of engineering leaders negatively perceiving AI, which got me thinking about what the reason for this might be.

In this article, I am sharing my view and my opinion on what is happening and what’s causing the negative trend.

Let’s get to 2 specific data points which we’ll be discussing in this article

Data point 1: 51% of engineering leaders perceive AI in a negative way

Based on the recent report done by LeadDev, which I’ve closely looked at, 51% of engineering leaders mentioned that they perceive AI in a negative way.

It’s an increase of 9% from the last year (42% → 51%), which is alarming.

The trend of perceiving AI negatively → then also spirals down to teams being less motivated. Let’s get to the second data point.

Data point 2: Maintaining motivation in teams is becoming a bigger challenge

We can see that engineering leaders overwhelmingly mentioned that teams are less motivated (38%) than they were 12 months ago. Which is a lot higher percentage than more motivated teams (14%).

Now, that we covered the data part, let’s get into my thoughts on why AI is perceived in a negative way by a lot of engineering leaders.

Here is my take on what’s happening

The main reason why I believe that is the case is the following:

1. It all starts with known public individuals giving sensationalistic over-optimistic takes.

2. Because of that → company leaders are experiencing FOMO when it comes to AI and are looking to enforce AI usage and AI feature development in their products.

3. And then the negative impact of AI is felt the most by people who actually do the work → engineering leaders and their teams who need to operate within unrealistic expectations.

This is a visual representation:

This vicious cycle is the main reason in my opinion for engineering leaders perceiving AI negatively and their teams feeling less motivated.

There are more reasons, but I wanted to explicitly highlight this, as I believe it’s crucial to understand what’s happening. I’ll be also sharing my advice for both company leaders and engineering leaders on what to do later in the article.

Let’s get more into specifics.

1. Sensationalistic predictions regarding AI from known public individuals

It all starts with over-optimistic predictions coming from individuals across different industries. And I get why they are doing this, they want to promote how great their AI products are so that more people would use them.

It’s a race between Big Tech companies and others on “who will have the best AI” and “who will replace the most people with AI” and this is creating the exact vicious cycle we mentioned above.

This then causes fear in people, especially when this is backed by layoffs.

1.1 Mark Zuckerberg saying that AI will replace mid-level engineers this year

There’s a reason why I call it a sensationalistic take, because it’s not even close to being true. Based on the data below, we can see that Big Tech has even increased the hiring of everyone with 2+ years of experience by 27.2% compared to 2023.

I’ve written my detailed thoughts on whether AI will replace mid-level engineers in 2025 here:

Will AI replace mid-level engineers in 2025? Gregor Ojstersek · Jan 15 Read full story

We do see a decrease in entry-level tech jobs (0-1 year of experience) in tech as mentioned in this article:

Decrease in Entry-Level Tech Jobs Gregor Ojstersek · Jun 4 Read full story

1.2 Anthropic’s CEO Dario Amodei mentioning AI could spike unemployment to 10-20% in the next one to five years

To quote the exact two statements from the article:

AI could wipe out half of all entry-level white-collar jobs.

AI companies and government need to stop "sugar-coating" what's coming: the possible mass elimination of jobs across technology, finance, law, consulting and other white-collar professions, especially entry-level gigs.

Such predictions are causing fear in everyone and stress in many people.

1.3 Amazon’s CEO Andy Jassy says AI will reduce its corporate workforce in the next few years

To quote from the article:

“We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs,” Jassy said in a message to employees. “It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.”

The executive said that Amazon has more than 1,000 generative AI services and applications in progress or built, but that figure is a “small fraction” of what it plans to build.

Jassy encouraged employees to get on board with the e-commerce company’s AI plans.

“As we go through this transformation together, be curious about AI, educate yourself, attend workshops and take trainings, use and experiment with AI whenever you can, participate in your team’s brainstorms to figure out how to invent for our customers more quickly and expansively, and how to get more done with scrappier teams,” he said.

The first part is to let everyone know that there are going to be fewer jobs available at Amazon and the second part → telling everyone that they should get better at their jobs.

This is the reason why people perceive AI in a negative way and why teams feel less motivated. Psychological safety is a big deal when it comes to high performance and without it → it’s hard to do good work long term.

2. Company leaders are experiencing FOMO when it comes to AI

Such predictions that we mentioned above then trickle down to companies experiencing FOMO and making radical decisions regarding AI. And I am hearing a lot of examples of this by talking to various engineering leaders across the industry.

Here are 2 prominent examples.

2.1 Shopify’s CEO enforcing the use of AI

Below is a snippet of the memo sent to all Shopify’s employees:

This memo is not a 100% representation of what’s happening across different companies, especially since it has claims such as:

• AI improves productivity by up to 100x

• Including AI usage as a KPI in the performance review

• Demonstrating that you can’t do what is needed with AI before asking for more Headcount

But it’s a good example of what FOMO does when it comes to AI.

You can find my full breakdown of the memo and my thoughts in this article:

Enforcing the use of AI in engineering teams - good or bad thing? Gregor Ojstersek · Apr 13 Read full story

2.2 Fiverr’s CEO "AI is coming for your jobs"

Below is a snippet of the memo sent to every Fiverr's employees:

Both of these are good examples of what FOMO does.

There are many more examples of company leaders making such requests similar to these 2, just they are not so public about it.

And some most prominent examples I have heard are:

• Companies doing complete re-branding to AI companies due to FOMO.

• Companies forcing to create AI features without them even being important for the product.

• Enforcing the use of specific AI tools and they made the process a lot worse.

3. Engineering leaders and teams operate within unrealistic expectations

Now, all of these above then trickle down to people who actually do the work. The expectations become unrealistic.

Here are some of the unrealistic expectations I have heard:

• Believing AI is a “Plug and Play” solution

• Expecting AI to eliminate all manual work

• Underestimating the cost and complexity of building internal LLMs

• Assuming AI will just magically understand the whole context of the specific domain

• Expecting AI to replace entire teams

• Assuming AI will just work without failures

• Thinking that there are no ethical, legal and security risks

• Assuming AI will exponentially increase the performance over time

So, such expectations automatically create a culture where people are less motivated and people have concerns regarding AI and how the future will look like.

My advice to company leaders and engineering leaders

1. Company leaders

• It all starts with setting the right goals and not letting FOMO get to you

Instead of: “We need AI or we’ll fall behind” → Define what actual business problems are or what actual inefficiencies in the process currently are there, then evaluate if AI is the best tool.

• Don’t try to do full-scale “AI transformation” all at once

I rather recommend doing small experiments on more low-risk parts of the business to test out how things are going. After you see it working well, move on to more important parts.

• Focus on creating a good culture

There is no better productivity hack than a good culture. No AI can replace that. If you try to lead with fear → it will backfire in the long term.

2. Engineering leaders

• Make sure to educate yourself on how others are utilizing AI tools

These two articles will help with that:

How to Use AI to Be a Great Engineering Leader Gregor Ojstersek · May 11 Read full story

How to use AI to increase Software Development productivity Gregor Ojstersek · Mar 9 Read full story

• Be confident and manage expectations correctly

Especially when leaders inside the company are experiencing FOMO regarding AI, it’s really important to stay composed and not get rattled.

Make sure to educate, provide pros and cons and don’t just go straight to saying yes to requests like “We need to start using this AI tool or we’ll fall behind”.

This article can help:

How to communicate with stakeholders the right way Gregor Ojstersek · Mar 5 Read full story

• Propose small experiments and then measure the impact it has on productivity

We have an article planned in the future on how to measure the impact of AI tools correctly, so definitely stay tuned for that. Until then, these two articles can help:

Setting Effective Targets for Developer Productivity Metrics in the Age of Gen AI Gregor Ojstersek and Laura Tacho · May 25 Read full story

How to use engineering metrics for the success of engineers and teams Gregor Ojstersek · October 20, 2024 Read full story

Here is what others are saying

There were a lot of interesting thoughts on yesterday’s LinkedIn Post, so I am sharing some of them here as well:

1. Owain Lewis mentioned too much hype regarding the optimism and pessimism when it comes to AI and very few people being in the middle (as that might actually be the reality).

2. Mikita Patskevich mentioned the loss of creative problem-solving due to AI can be the reason people feeling less motivated and negatively perceive AI.

3. Rahul Agarwal mentioned that tooling is still early and can be problematic to set up in real systems + the pressure to ship AI features is quite immense and prioritized over long-term work.

Last words

Let’s end this article with the following:

AI, like code, is a tool for solving problems. It's not "the best" solution for everything.

The key is to use all the different tools that are available and solve problems in the best way possible.

We are not over yet!

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