Engineering jobs in the AI era: pay is rising, entry-level doors are closing

A question I keep seeing lately: "AI can write code now. Is it too late to switch into software engineering?"

Rather than answer from instinct, I read three recent releases from Hiring Lab, the research arm of the job site Indeed: the US Labor Market Snapshot for October 2026 (October 22), an analysis of AI exposure and advertised pay (September 17), and their quarterly survey of economists (September 24). One caveat up front: this is all US data. Other markets, including Taiwan where I work, won't move in lockstep, but the direction is worth paying attention to.

1. Software development is still the coldest category

In the October snapshot, Indeed's overall Job Postings Index was 103.8 on October 16, almost 4% above its February 2020 pre-pandemic level.

Software development is a different story. Its index sits at about 79, the lowest of the categories compared. The big tech hubs are cold too: Seattle (67) and San Francisco (72) are among the lowest local indexes in the country.

In the same snapshot, AI-related postings make up 7.3% of all postings, past their previous peak of 3.3% in 2022.

So the total hasn't come back, but the mix of demand is changing.

2. Pay is growing faster in AI-exposed roles

The September 17 analysis uses Indeed's own measure to sort occupations by how much generative AI could transform their skills. Software development, IT support, data and analytics, marketing and finance land in the high-exposure group.

The results run against a lot of people's intuition:

• Since 2021, advertised pay in the most AI-exposed occupations rose about 46%, versus 25% in the least exposed
• Controlling for occupation mix, there's a post-ChatGPT pay premium of 5.7% for the high-exposure group
• Postings in these occupations fell the most between 2022 and 2026, but saw the largest rebound over the past year

The author's conclusion: so far, AI looks more like a complement to skilled workers than a replacement.

3. But the entry-level door is narrower

For anyone changing careers, this is the number in that analysis that matters most: in the most AI-exposed occupations, the entry-level share of postings fell from 29% in 2021 to 10% in 2026, while the senior share rose from 22% to 47%.

The author also notes that part of the pay increase comes from exactly this shift toward fewer, more senior postings. Average pay is higher partly because the cheaper entry-level roles are gone.

4. Economists don't agree either

The September 24 survey polled 123 economists and experts between September 8 and 16. Asked which fields will see the largest AI-driven employment changes over the next 12 months, they put software development on both the biggest-losses list and the biggest-gains list.

One more signal: the index on whether AI will hit the wages of college-educated workers harder fell from 47.3 last quarter to 42.4, the largest quarter-over-quarter change in the whole survey.

5. What this means if you're switching careers

I came into engineering the long way, from food science and biomedical research, self-taught. Reading these numbers, here's what I'd tell someone considering the move now:

1. Don't compete on "I can write code" alone

An entry-level share falling from 29% to 10% tells you that turning a spec into a working feature is the part AI squeezes first. The market pays for the judgment upstream of that: breaking down problems, setting boundaries, and confirming the result is right.

2. Your previous field is leverage, not baggage

AI-related postings are growing, and AI projects that actually ship need people who understand the business. Healthcare, manufacturing, finance, food: any domain you know well, combined with engineering skills, is harder to replace than engineering alone.

3. Show delivery in your portfolio, not just features

One project that goes from requirements through architecture, tests, deployment and operations says more than ten tutorial projects. Being able to explain how you verify AI-written code is a plus.

4. Use AI to learn faster, not to skip the basics

AI speeds up self-teaching, and it also raises the bar at the hiring end. Both are true at once, which is why the fundamentals matter more, not less.

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The data says the path is still there, but the entrance is narrower and the bar is higher. If you're weighing a career change, or want to talk through how a cross-domain background can become an engineering advantage, book a 30-minute intro call.