While Google, Microsoft, and Meta laid off workers by the thousands from the start of the year, Silicon Valley's developer community was flooded with a different kind of story: engineers saying they couldn't even land an interview anymore, pushed out by AI. The claim that AI was replacing software engineers circulated as something close to settled fact, backed by a steady stream of statistics showing tech hiring postings down sharply year over year. But when venture capital firm SignalFire analyzed actual hiring data across tens of thousands of companies, the numbers told a different story. Even at the peak of layoff anxiety, engineering's share of new hires didn't shrink — it grew.
When the story the public accepted and the data on the ground diverge this sharply, something is wrong with how we're talking about jobs in the AI era. And following that thread doesn't lead back to a story about engineers — it leads to a question about what kind of work people actually do.
The hiring surge hiding behind the layoff headlines
According to SignalFire's analysis, tech companies kept hiring engineers steadily even as the AI transition accelerated. Engineering's share of total new hires rose relative to other job functions. Demand was especially pronounced for developers, machine learning engineers, and infrastructure architects capable of designing and operating AI systems directly — while roles centered on routine tasks moved in the opposite direction.
There's a predictable logic behind this picture. The more companies adopt AI systems, the more people they need to build, maintain, and improve them. It's the same logic that applies on a factory floor: when automated equipment moves in, demand for the engineers who maintain that equipment rises with it. The same dynamic plays out in software. AI generating code doesn't eliminate demand for the code that builds and manages AI. The more complex AI systems become, the more specialized the role of the engineers responsible for them gets.
Look at where the layoffs actually concentrated, and a different pattern emerges. Cuts clustered in parts of recruiting and HR, in repetitive marketing operations, in high-volume content production roles, and in entry-level customer support. This looks less like the sweeping narrative of "AI eliminates jobs" and more like AI replacing certain kinds of repetitive work while simultaneously creating new kinds of work elsewhere. The dividing line isn't the job title — it's the nature of the task.
Why this data is hard to read as pure good news
Still, it would be a mistake to take this data as straightforward reassurance.
For one, SignalFire's sample skews heavily toward venture-backed tech companies. Whether engineering roles at mid-size manufacturers, retailers, and service businesses — which make up a large share of the Korean economy — follow the same pattern is a separate question that needs its own verification. There's no guarantee that hiring trends at global Big Tech map neatly onto Korea's small and mid-size business market. Hiring trends at Seoul-based IT firms have no particular reason to resemble the labor demand of manufacturers in smaller regional cities.
The more important counterargument sits inside the hiring-growth figure itself. The engineers in demand aren't primarily engineers who "use" AI — they're much closer to engineers who "build and evaluate" it. As AI coding tools spread, the role once held by junior developers doing straightforward, repetitive coding is shrinking, even as demand concentrates on engineers capable of designing and overseeing entire AI systems — a quiet polarization happening within the same job category. Engineering surviving as a category and every individual in that category being safe are two very different claims. Same job title, very different outcomes depending on what the work actually is.
It's also true that this data offers little comfort outside the technical field. Roles centered on routine work that AI can automate relatively easily — paralegal support, junior content planning, sales administrative support — are showing signs in multiple places of losing out in the hiring competition. SignalFire's data describes the situation for engineering. It doesn't support optimism across every job category.
What matters is the layer of work, not the name of the job
Read through the lens of Korea's solo entrepreneurs and freelancers, SignalFire's data points to something more specific than the job category itself: what someone actually does within that work is what matters.
Dig into why engineering survived, and it comes down to one common thread. Most of what the engineers who got hired actually did was break down ambiguous problems, verify AI-generated output, and reconcile the demands of multiple stakeholders within a single system. It wasn't a role of passing along whatever AI produced — it was a role of evaluating, correcting, and adapting AI's output to context. The people who survived weren't the ones using the tool called AI — they were the ones judging what the tool produced.
What happens when you carry this observation over to planners, content directors, and solo founders instead of engineers? Think through the list of things AI still doesn't do well, and a direction emerges. Building trust with clients who have complicated, competing interests. Making judgment calls in situations where the numbers and data don't fully back you up. Managing expectations that formed implicitly over a long relationship. Reading a need the other person hasn't put into words yet. These remain, for now, territory that people lead.
The competencies that forecasts of post-2030 workplaces keep pointing to point in the same direction. Not the ability to operate a tool, but the ability to judge the output that tool produces in context. Not the ability to efficiently churn out deliverables, but the ability to build trust between people. Not the ability to follow a fixed procedure quickly, but the ability to keep learning at speed in unfamiliar situations. These are competencies that work regardless of whether you run a café, plan content, or run a small consulting practice.
I don't think these competencies are new. They're strengths that people who've worked a long time already had before AI. What's changed after AI is that those strengths have become a far sharper point of differentiation than before.
A checklist worth running right now
From a solo entrepreneur's vantage point, there are a few questions worth pulling out of this data.
Start by taking a cold look at how much of what you do is routine work AI can already perform at a comparable level. If drafting reports, doing repetitive image editing, handling standardized customer responses, or organizing routine materials makes up a significant share of your time, now is the moment to think about where that time gets reallocated. The baseline is checking, firsthand, how well AI can already handle that work.
Whether you've used an AI tool matters less than how sharply you can review what it produces — and that's the competency shifting toward scarcity. The gap is already opening in the market between people who pass along AI-generated text or analysis as-is, and people who can catch the errors and bias inside it and refine it into something better. Reviewing the tool is becoming more valuable than operating it.
It's also worth revisiting how you're building relational trust. Long-standing clients, relationships that generate repeat business, referral networks — these are assets AI can't reproduce in any short span of time. As technology levels the playing field and AI tools become more accessible to everyone, the weight of these relationships only grows. Among competitors using similar tools, the differentiator stops being the tool and becomes the relationship.
What SignalFire's data shows is the survival of one particular job category. But following the reasons for that survival leads to an observation bigger than any job title: what matters more is the character of the work a person actually does. Rather than looking at layoff statistics and concluding "engineers are safe" or "my job is at risk," the real starting point for a career strategy is doing the concrete work of sorting, within what you do right now, what can be handed to AI and what a person needs to keep doing.
