Four months ago, a front-end engineer on my team looking to make upgrades to a product or feature would have filed a ticket and waited for the infrastructure group to unblock them. They might have lost a day, sometimes a week. Today, that same engineer makes changes themselves. The code is occasionally brittle, but it works, and the end result ships in a fraction of the time it used to take.

That small story is unfolding inside nearly every engineering organization right now. While some celebrate the sudden fact that one person can do the work of five, others are focused entirely on tech industry layoffs. Both perspectives skip the question I find more interesting: What happens to mastery when every engineer can suddenly do everything?

For two decades, we have described strong engineers as T-shaped. The horizontal bar represents breadth, which is a working familiarity across many areas. The vertical bar represents depth, the real command of one domain earned over years. Agent tooling has stretched that horizontal bar wider than ever before. My concern is the vertical bar, which is quietly getting shorter.

Consider what is now possible in a single quarter. With the right agent harness, one engineer can stand up a billing system, a data connector framework, regional and organizational tenancy infrastructure, or a consumption-based pricing implementation. The pull requests pass review. The tests are green. All looks good. But green tests cannot tell you if the person who shipped that work understood why the system needs redundancy in one specific place, where its failure modes are hiding, or which trade-offs the model made silently on their behalf. The pattern recognition that comes from watching systems fail over many years is suddenly weighted differently than it was even 12 months ago. Our industry has not caught up to that shift.

When the interview stops measuring judgment

The first place this surfaces is hiring, a trend that should concern anyone who has built a team. Traditional coding interviews were always an imperfect proxy for engineering judgment, but agent tooling completely obliterates them. If a candidate can produce a working solution in 20 minutes that would have taken two hours a year ago, the exercise no longer measures technical competence. It just measures how well a coder can prompt an agent.

At Thread AI, we have responded by widening what we look at when we interview job candidates. Our process moves across coding exercises, problem decomposition, system architecture, and behavioral components, with the weighting shifting by role. We allow AI assistance only in specific sections because our engineers still need to be able to operate without it. Some of our work happens in secure environments where you cannot lean on an agent to debug for you. What we’re really testing for now is judgment under ambiguity — the ability to notice when an agent’s output is confidently wrong — and the depth to predict where a system will break before it breaks.

Hiring teams that fail to make this shift will fill their rosters with false positives. They will hire people who interview like experts but struggle the first time a system behaves in a way the model did not anticipate. Ultimately, these companies will miss out on top-tier talent, and they won’t discover the misstep until it’s too late.

Confusing velocity with understanding

That brings me to a larger risk: the rise of false expertise as a category of its own. Historically, the reasoning has gone, “I built a proof of concept, therefore I am an expert.” I have watched versions of this appear in technical leadership, in investment decisions, and in policy conversations about AI. It travels well because the artifact looks real. A working demo is highly persuasive, even when the person behind it only half-understands how it functions.

The problem is bigger than one false expert shipping one bad product; an organization can absorb that. The real risk is a generation of consequential decisions being made by people who have confused velocity with understanding. You can already see the cultural scaffolding being built around this with the celebration of the “idea guy,” and the sudden fashion for invoking “taste” as a substitute for knowing how a system actually works. Those of us shaping tooling, hiring norms, and regulations have a narrow window to set better defaults before this pattern hardens into the way the industry operates.

Depth and breadth, held together

This is the path we’ve been walking at Thread AI since before the latest wave of tooling arrived.

To ground this in a specific example: I have spent 15 years building complex systems. I worked at Goldman Sachs through the financial crisis and the early Dodd-Frank infrastructure, moved critical payment systems into the cloud at The New York Times when that ground was still new, and built the system of record for model training and evaluation at Palantir across highly regulated environments in defense, health, and finance. This experience is the superpower that lets me course-correct an agent when it produces confident-but-wrong output.

I felt this sharply not long ago while scoping consumption-based pricing in a single quarter. The agents did an enormous amount of the heavy lifting, and at some point, I realized that my largest contributions to the effort were knowing where the system would fail and steering around it before we got there. I have also seen how organizations have built large teams around efforts like these in the past. Someone earlier in their career or relying purely on AI guidance would not have had that instinct to draw on. That is the part the public conversation keeps missing.

Every engineer at Thread AI, regardless of seniority, is expected to be a builder at heart. Our people own outcomes across compute, data, and the organizational and identity primitives that hold the product together, all without the traditional product-manager and project-manager scaffolding around them. Our model works only because the people in those seats have the depth to know what they do not know, and the breadth to fill the gaps with agents. They use the tooling instead of being used by it.

AI doesn’t replace deep understanding

The core issue facing organizations today is not whether AI will replace engineers. That question has been answered dozens of times this year with varying degrees of nuance, and it has stopped being interesting. The more useful question for anyone running a company is whether we are protecting the people who understand our systems deeply enough to recognize when the machines are wrong.

In our company, the answers have been to move toward explicit end-to-end ownership, and to hire for depth and breadth together rather than treating them as a trade-off. Layoffs dominate the headlines because they are an easy narrative, but they describe a symptom rather than the actual choice in front of us. The real choice is whether we let breadth quietly stand in for mastery, or whether we build organizations that keep human judgment in the loop on purpose.

In an era when software can be generated faster than it can be understood, the companies that hold onto what is human, verifiable, and deeply understood will be the ones still standing when the brittle parts give way.

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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

While the number of US jobs declined by 23,000 in July, employment in the tech sector rebounded as the AI revolution continues to gain steam.

The national unemployment numbers were reported Friday by the US Bureau of Labor Statistics. At the same time, research firms said July was a good month for IT hiring compared to June.

According to CompTIA, which analyzed the BLS data, tech sector jobs rose in July by 3,700. In June, the IT sector had lost 900 jobs.

Companies last month hired in the cloud, hosting, information processing, data, and semiconductor manufacturing sectors, CompTIA said in a statement. 

“We’re entering a labor market where opportunity is increasingly concentrated around specific skills, industries, and investments,” said Ger Doyle, regional president of North America at ManpowerGroup, a labor consulting firm.

For example, data center hiring was up 39% in July compared to the same period last year, Doyle said. Not surprisingly, the data-center growth has been fueled by AI infrastructure needs and demand for hardware. That has helped increase the number of jobs in ancillary sectors such as transportation.

July hiring data showed heavy truck driver demand, which surged by 181% from June, Doyle said.

According to the BLS data, employment went up by 2.4% in the “computing infrastructure providers, data processing, web hosting, and related services” sectors, which is listed under the information sector.

Jobs in the “computer and electronic product manufacturing” sector — which is bunched under manufacturing — rose by 2.9% from June to July.

But bad news continued for the telecommunications sector, where the number of jobs declined by 1.5% from June to July. That continued a downward slide in recent years.

Overall job layoffs slowed in July, with 33,429 cuts announced; that’s down from 45,849 cuts announced in June, and the lowest since July 2024, according to data from Challenger, Gray and Christmas.

“Hiring has also increased over last year by 25%, so while AI is shifting the labor market, it is not dismantling it,” said Andy Challenger, chief revenue officer for Challenger, Gray & Christmas.

The tech sector announced 9,867 cuts in July, bringing the year-to-date total to 149,023 so far in 2026, according to Challenger figures.

Tech sector hiring and job losses vary as organizations use different measurement techniques to gauge the overall hiring environment. 

The top-line US unemployment rate declined to 4.1% from 4.2%, in part because of  workers leaving the workforce or not looking for jobs, indicating a slow labor market.

Still, hiring demand exists across the US, despite declining labor force participation and longer job searches, Doyle said.

Because technology underpins many of the changes occurring across sectors such as healthcare and services, employers are rethinking hiring.  “That’s why the labor market many workers are experiencing doesn’t always match the one described by the headline numbers,” Doyle said.

ADP’s National Employment report said only 44,000 jobs were added to private payrolls in July, with choppiness across sectors.

But demand for AI skills continues to rise, according to research firms that track growth by analyzing job listings. CompTIA noted that about 14,000 new job listings in July sought AI and machine learning skills.

It’s not clear how the ongoing AI boom may be hurting or helping human jobs. Many recent job cuts have been attributed to AI, though some companies have been accused of using the technology as a rationale for cuts they made for other reasons.

A recent OECD report said that even manual jobs once thought immune to AI are at risk of being taken over by robots. The OECD report said creative jobs — typically management, arts, and social work — will be least threatened by AI.

There are also increasing indications that successful AI deployments will require humans in the loop. For example, more consulting firms are using forward-deployed engineers (FDEs) to implement agentic AI. And smaller consultancy firms are finding more productivity by automating mundane work with AI and spending more time meeting client requirements.