AI Engineering Jobs Defy the Layoff Panic in New Data

The AI layoff story is missing the engineers who keep getting hired

AI engineering jobs were supposed to be first on the chopping block. SignalFire’s hiring data points the other way: engineers have gained share among new hires even as tech companies keep invoking AI in layoff explanations.

That tension is the real story. Tech layoffs hit their highest single-month total in years in May, and AI was the most-cited reason, according to Challenger, Gray & Christmas, as reported by TechCrunch. Software engineers looked exposed because AI coding tools are spreading fast. Yet the hiring data suggests companies still want builders.

“The rationale given for lots of layoffs is consistently AI, and specifically they’ll say AI with respect to code; they’ll say one engineer could do the job of however many engineers in the past,” said Asher Bantock, SignalFire’s head of research. “What we’re seeing on the ground is a little inconsistent with that.”

XOOMAR analysis: the evidence does not prove engineering hiring is booming everywhere. It does show that the broad “AI kills coding jobs” narrative is too blunt. The labor market appears to be separating software builders from other functions inside the tech reset.

SignalFire’s hiring data shows engineers gaining share during the AI reset

SignalFire tracked the careers of millions of employees across more than 80 million companies and focused on hiring rather than layoffs. The firm argues hiring is a cleaner real-time signal because laid-off workers may delay updating employment status.

The core numbers are hard to square with an engineering wipeout:

SignalFire’s “Tech Majors” group includes Alphabet, Meta, Apple, Amazon, Microsoft, Netflix, NVIDIA, Tesla, Uber, Airbnb, Block, and Stripe. Across those companies, engineers made up 55% of all new hires in 2025, up from 46% in 2019.

That matters because share-of-hires data captures company priorities. It does not say every engineer is safe. It does not say junior candidates have an easy market. It says that within a shrinking hiring pool, engineering is taking a larger slice.

For readers tracking adjacent labor stories outside this dataset, XOOMAR has also covered Lucid Motors layoffs and job cuts. The comparison is not evidence about SignalFire’s tech sample, but it shows why job-loss headlines can overpower more granular hiring signals.

AI coding tools are raising output, not erasing the engineering function

The cleanest interpretation is that AI coding tools are changing the unit economics of software work. They can make individual developers faster. But faster coding can also create more tests, more product ideas, more integrations, and more systems that need human judgment.

That is the Jevons paradox argument in this story. In plain terms, when a resource becomes more efficient, demand for it can rise because people find more uses for the cheaper capacity.

SignalFire’s Bantock put it directly:

“They’re suddenly a lot more productive, and there’s endless work for them to do.”

Nvidia CEO Jensen Huang made a similar point in an April interview at the Stanford Graduate School of Business. After rejecting the claim that AI will destroy software engineering jobs, he said Nvidia engineers are using agentic AI and are “busier than ever.” Agents may write code near instantaneously, but Huang said they push engineers toward “the next idea.”

The shift looks like this:

• Before AI coding tools: engineers spent more time writing routine code and boilerplate.
• After AI coding tools: engineers spend more time reviewing generated code, designing systems, guiding agents, integrating AI into products, and deciding what should be built next.

XOOMAR analysis: routine coding tasks face more pressure than the engineering function itself. The risk is concentrated where work is repetitive and easy to specify. The resilience appears strongest where engineers own architecture, product judgment, and production reliability.

The post-pandemic tech correction looks less like a pure AI purge

The strongest caution in the data is that layoffs and hiring are measuring different things. Layoff explanations can be political, financial, or strategic. Hiring behavior shows where companies still allocate scarce headcount.

SignalFire’s data says large tech hiring overall is 25% below 2019 levels, while engineering roles are down only 11%. That gap is the point. If AI were already replacing engineers at scale, engineering would likely be the first function to collapse inside a hiring contraction. SignalFire says it did not.

There is also broader labor-market caution. The Yale Budget Lab found that, across the economy, employment and unemployment measures do not yet show clear links to AI exposure, automation, or augmentation after the release of ChatGPT. Its analysis says the occupational mix is changing somewhat faster than in the past, but not dramatically, and that better data is still needed.

That aligns with Anthropic’s own split message. CEO Dario Amodei warned last year that AI could wipe out half of entry-level white-collar jobs and push unemployment as high as 20% within five years. But Anthropic’s head of economics, Peter McCrory, told TechCrunch in March:

“There’s at least no larger material difference in unemployment rates” between workers who use Claude for the “most central task of their job in automated ways” and workers in less AI-exposed jobs requiring “physical interaction and dexterity with the real world.”

The headline risk is real. The measured labor impact is still uneven.

Founders, engineers, investors, and laid-off workers are reading the same data differently

For founders, the SignalFire data supports a narrow but useful point: small teams can do more with AI, but early-stage startups still hired 7% more engineers in 2025 than in 2019. That suggests technical talent remains central when companies are building product rather than only managing growth.

For engineers, the message is mixed. Experienced developers who can direct AI tools may gain reach. Workers tied mainly to routine implementation may face harder comparisons against automated output.

For investors, the signal is that engineering talent is still where product velocity lives. SignalFire is a venture firm, so its lens is naturally close to startups and tech labor flows. That does not invalidate the data, but readers should keep the source perspective in mind.

For laid-off workers outside engineering, the data may feel cold. Resilience in AI engineering jobs does not soften losses in recruiting, support, marketing, operations, or other functions when companies cut costs. The story is not “AI layoffs are fake.” It is that AI-related layoff rhetoric can hide which roles companies are still trying to hire.

For another example of why technical execution remains a board-level issue, see XOOMAR’s coverage of the Xsolis data breach involving exposed SSNs. It is a separate story, but it underscores why software, systems, and controls still need accountable human owners.

The next AI hiring wave will test junior pipelines and hybrid technical roles

The market signal is not that software engineering is immune. It is that AI engineering jobs are being reshaped before they are being eliminated.

The pressure point is likely entry-level work. If companies expect AI tools to handle more routine coding, they still need a way to train junior engineers into people who can review, design, integrate, and own systems. Cutting the bottom rung too deeply would create a future talent problem that today’s productivity gains may hide.

The roles to watch are hybrid and applied: engineers who can work with AI agents, translate product needs into technical systems, and judge whether machine-generated output is safe enough for production. Evidence that would strengthen the resilience thesis would include continued growth in engineering share of hires and sustained startup engineering demand. Evidence that would weaken it would be a sharp drop in engineering hiring relative to other tech functions, especially at AI-heavy companies.

For now, the clean read is this: AI is changing tech employment, but the people who build, integrate, and govern software remain hard to replace.

The Bottom Line

• AI is being cited in layoffs, but engineering roles appear more resilient than the broader tech hiring market.
• SignalFire’s data suggests companies still prioritize software builders even as they cut elsewhere.
• The findings challenge the simple narrative that AI coding tools are eliminating engineering demand.