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.

—

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.

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.

—

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.

After two decades of cloud architecture consulting, I see an unchanging pattern in enterprise deployments. Organizations approach me with unexpectedly high cloud bills, operational chaos, and architectures that look good on paper but cause headaches in production. The common thread is almost always the same. Fundamental concepts that should be foundational to any cloud deployment are treated as optional or ignored altogether.

I’m not talking about exotic requirements, bleeding-edge technologies, or vendor-specific best practices. Basic engineering principles are somehow getting lost amid the excitement of cloud adoption. I wish I could say it’s rare, but after working with organizations across industries and geographies for years, I can confirm that missing these fundamentals is more common than most people realize. The results are predictable. Bills grow faster than business value, architectures require constant firefighting, and teams are stretched too thin to optimize anything.

The good news? You don’t have to start over, but you do have to go back to basics. Here are three concepts most cloud architects overlook that will make your architecture dramatically more valuable and efficient.

Identifying common ground

When deploying heterogeneous architecture, especially in multicloud environments, organizations must aggressively reduce silos. This means establishing common control planes for security, governance, and operations. You won’t get there by relying on whatever proprietary technology each cloud provider offers out of the box. Each provider wants you locked into their way of managing things. That is fine for simple deployments, but when you are running across multiple clouds and on-premises systems, proprietary control planes introduce redundancy, complexity, and cost.

You need a single control layer that spans your entire environment. Instead of managing 10 different security solutions from 10 different providers, you have one. Instead of separate identity management systems for each cloud, you need one that works everywhere. This eliminates the need to change security parameters in five different consoles, maintain five different skill sets for five different operational models, and reconcile five different governance frameworks. The common control plane ties everything together.

This might sound hard, but it’s not as bad as you think. (I will cover the specific patterns in a future article.) The real issue is that most architects have never been trained to think this way. They were taught to select the best services from each provider rather than abstract away the differences. This fundamental gap costs organizations real money every single day.

Cost observability and optimization

Most architects treat cost visibility and optimization as afterthoughts, things that can be bolted on after the architecture is in place. That backward approach shows up in the results. Without cost observability and optimization baked into your architecture from day one, you cannot understand where your money is going, where waste is accumulating, or where you should make changes to align expenses with delivered value.

The more complex and heterogeneous your environment, the more critical this becomes. You need a unified cost observability layer that spans public and private clouds and your own infrastructure. This is about building a layer that aggregates cost data from everywhere, provides a single source of truth for spending, and delivers the insights needed to actively optimize. Without this, you are flying blind, making decisions based on incomplete data and discovering problems only after the invoice arrives.

Too many organizations fail to gain control of their cloud spending because their billing data is scattered across multiple consoles, with no way to correlate usage across providers. They cannot see which teams, projects, or services are driving costs. They miss opportunities to right-size, consolidate, or eliminate waste. You cannot improve what you cannot measure. Without a common cost observability and optimization layer built into your architecture, you will never achieve the efficiency the cloud was supposed to deliver.

Consider the human element

Here is an uncomfortable truth most architects do not want to discuss: The more complex your architecture is, the broader the range of skills you will need to keep it running. Complexity requires expertise, and expertise requires hiring, training, and retention. If your architecture demands 15 different skill sets to operate, you’d better have a plan for finding and retaining the people with those skills.

I have seen beautifully designed architectures fail because the organization could not meet hiring requirements. They compromised by hiring underqualified individuals, which led to operational failures, security gaps, and mounting technical debt. The architecture itself was sound. The human infrastructure around it was not. This is a solvable problem. It starts with acknowledging that you are not designing for yourself. You are designing for the team that will inherit this system after you have moved on to your next assignment or promotion.

The solution isn’t just simplifying architecture, though that should be a goal. The key is to consider human factors in your design. What skills are required? How can you find and train people? What cultural changes are necessary for effective operation? These questions are essential; ignoring them risks failure.

The simple bottom line

I understand why most cloud architects miss these basic principles. No single course or book brings all of this together in one place. Cloud architecture has become a collection of best practices, vendor recommendations, and conference talking points, rather than a disciplined engineering discipline focused on business value.

As a result, architectures end up optimized in the wrong places, if they are optimized at all. They are expensive to run, difficult to secure, and nearly impossible to operate at scale without constant intervention. The cloud promises efficiency, yet we are not delivering it because we have lost sight of the fundamentals.

It’s time to go back to basics. Commonality, human factors, and cost observability and optimization are the three things that separate architectures that create value from those that create cost. Incorporate these concepts into your architecture and you’ll get ahead of most production systems in use today.

I hope you’ll indulge me this week as I take a break from my usual rantings about agentic coding and meander down memory lane.

I learned to code BASIC in the mid-1970s at my progressive junior high school. In math class. We took Fridays to learn about line numbers, loops, statements, and the amazing GOSUB, which was as close as we got to a function call. It strikes me that it was pretty early in the whole tech world to be teaching seventh graders to program, and I feel lucky.

Coding faded into the background for me through high school, but soon afterward the arrival of Windows 3.0 and modems made writing software fun again. You could even earn a little profit. CompuServe, AOL, and bulletin board systems (BBS) created a market for shareware.

I bought a copy of Turbo Pascal for Windows 1.0 from Borland and a copy of Mastering Turbo Pascal 6 by the great Tom Swan, and I learned the Pascal syntax. I remember being delighted to discover that Swan had also written a book specifically for Turbo Pascal for Windows, and I devoured that too. 

In search of SYSBACK

I soon came up with an idea for a shareware project — System Backup. Any of you who can remember back to the DOS and Windows 3.x days will remember that there were four files — AUTOEXEC.BAT, CONFIG.SYS, WIN.INI, and SYSTEM.INI — that were crucial to one’s setup. You’ll also remember that it wasn’t uncommon to twiddle with these files to eke out an extra measure of performance from the system. Backing those files up was critical to the tweaking, so I wrote SYSBACK.EXE to do the job quickly and easily. 

Windows 3.1 introduced the amazing feature of *.wav files — sound through your tinny PC speaker! That led me to write WAVSHELL.EXE, which listed all your sound files and made them easy to play.

I guess I didn’t have much faith in the value of my shareware because I released it into the wild of the pre-internet shareware market for a measly $2. People soon started sending me checks in the mail — my address was in the About Box, with a plea to pay for the software. Soon I added a licensing scheme and a “nag screen” that would go away if the user entered a valid license key. I actually made a bit of money.

Eventually both programs became unnecessary as Windows grew more functional, and I abandoned things. I’m sure all that code is on a hard drive in a landfill somewhere. 

But that doesn’t mean that SYSBACK and WAVSHELL don’t live on!

Last week, on a whim, I went searching to see if I could find any remnants of my glory days. Although it took a while, I was delighted to discover that both programs had clung to life in the dark crevices of the internet. 

I found WAVSHELL.ZIP and SYSBACK.ZIP and downloaded them. Sure enough, there were the files — the 16-bit Windows executables, along with the accompanying READMEs formatted for Windows Write, the simple word processor that came with Windows 3.1.

Return to Windows 3.1

So, what to do? Naturally I fired up Claude and asked how to get all this running. It suggested I give Oracle VirtualBox a try. At first I tried an open-source DOS version, but soon ran into conflict with memory managers and Windows. Tweaking CONFIG.SYS like the old days didn’t seem to make a difference.

This led to downloading a set of *.img files of MS-DOS 6.22. I mounted the first disk in the A: drive of the virtual machine. (For you youngsters, the A: drive was a floppy disk — maybe you remember those?) I had to do a virtual version of the old disk shuffle for the three floppies, and then do the same to install Windows. Everything installed and ran like clockwork, and soon enough, Windows 3.1 was up and running. 

Then, of course, I had to figure out how to get the files from my modern system to this ancient OS. This led me to WinImage, a shareware program (!) that builds floppy disk images for you. I built an image, copied the files onto the “disk,” and mounted it in the A: drive in the VirtualBox machine. I copied the files to the virtual hard drive, and behold!

There they are, in all their glory. I have to say, it was quite a thrill. And they actually work!

Anyway, it was fun to remember all those halcyon days of CompuServe, 2400 BAUD modems, and getting checks from strangers in the mail. It seems strange to me that 35 years from now, some software developer will be writing a column about the deep dark past when people used to actually write code, argue about what programming languages were best, and, who knows, even had to type things out by hand.

After more than 30 years the cybersecurity field, Keith Jones recently realized that his role had changed, from being a single contributor to manager of a fairly large team. And this team was getting a lot accomplished — tasks that used to take up a huge chunk of his workday.

No, his company hadn’t hired a group of new employees to work under him. He simply accelerated his use of artificial intelligence tools. Now, instead of grinding through a lot of basic tasks, that work is done for him while he focuses on bigger-picture work.

“It really feels like I have a team behind the scenes, but what I have is Claude [Anthropic’s AI tool],” says Jones, who currently works as a cybersecurity researcher. “I’ve been thinking for the last several months about how much differently I work now than I did a year ago, when I would sit and write all the low-level stuff before I could get to the 10% of the good stuff I really wanted to focus on. Now I can sit back and say, ‘Give me three different ways to solve this problem.’”

Most people working in the technology field, like Jones, have had to figure out how best to work with AI. The technology has come on strong, with many companies making its use mandatory and actively evaluating whether employees are faster and more efficient because of it. And while it is boosting productivity and taking over the burden of repetitive, manual tasks, it’s also creating a new level of stress and a dizzying kind of mental exhaustion.

So what can tech workers do about the heavier mental load that comes with using AI, on top of escalating worries about the safety of their own jobs? AI users and industry analysts say there are specific ways to ease some of those burdens and prepare for a changing job market.

Combating the slop factor

When it comes to working with LLM tools, a well-known issue is dealing with AI workslop and hallucinations. The slop is AI-generated output that is low-quality, buzzword-heavy, and generic. It also can refer to bloated, boilerplate code. Hallucinations are inaccurate or completely made-up results. AI routinely offers this messy or incorrect information with total confidence, giving users a false sense of security.

Using this bad data can lead to anything from minor software bugs to severe liabilities. “Don’t believe the machine is infallible,” says Craig Shue, computer science professor and department head at Worcester Polytechnic Institute (WPI). “That’s when bugs will start working in. There’s a lot of misinformation on the internet, and that could be what the LLM is grabbing and using.”

Here are ways to combat the problem:

• Make AI show its work: Ask it to cite its sources or explain its reasoning. Example prompt: Explain the logic and show the steps before writing the code.
• Give the AI a source of truth: Instead of letting the AI search the internet for information, give it the exact source material (reports, transcripts, data sheets) to base its output on, telling it to use only the information provided.
• Validate. Validate. Validate: Never let AI publish code directly to the main project without first reviewing and running it locally. Similarly, never take AI output and simply move it on to the next person in the project. Every single AI output needs review.
• Don’t be fooled by confidence: As with managing a human, question the output. When you’re busy or tired, it’s easy to just go with the results it gives you. Don’t. Always analyze and question it.

Taking on AI-driven workload creep

Let’s face it: The great promise of AI is that it will take over repetitive, manual tasks, which will save you an incredible amount of time. What isn’t talked about as much is that it also can create a new workload — one that can be exhausting in a whole new way.

“Is AI saving people time? The short answer is yes,” says J.P. Gownder, vice president and principal analyst with Forrester Research. “But people also are being overwhelmed with overproduced things. Everyone wants to look busy and they’re producing more, but not necessarily better. Managers have to push back on that or it’s not really saving you time.”

In a multi-year study by Upwork, the largest online freelance marketplace, 77% of employees reported that AI had increased their workload. The report noted that a boost in productivity comes with a “significant emotional and relational cost,” with 88% of workers who saw the highest productivity gains also feeling burned out. And IDC’s Future of Work 2026 survey reported that 24% of IT workers report increased workload as a top AI concern.

Here are ways to combat the problem:

• Keep it short: Part of the prompt — always — should be to tell any AI tool to be concise.
• Build an anti-slop culture: Don’t simply accept and pass on workslop. It’s insulting for a co-worker to have to deal with pages of largely useless information.
• Filter the noise: When everyone on a team starts using AI, the volume of Slack messages, long-form memos, and data reports skyrockets. Don’t treat it all with equal importance. Practice radical prioritization.
• Manage the transcript deluge: Stop wading through 40-page meeting transcripts that bury action items. Instead, prompt the system to produce a concise summary focused strictly on deliverables or status updates.
• Reduce the AI blast radius: When asked to fix a bug, AI tools often rewrite hundreds of lines of unrelated code, multiplying your code review time. Prevent this by instructing the tool to isolate its changes only to the specific function or file in question.

Managing the AI mental tax

Using AI often necessitates a different kind of mental processing, changing what had been the natural pacing of your day and dramatically increasing context switching. Instead of simply building and testing, someone might be jumping back and forth between auditing, fact-checking, prompting, and re-prompting. To manage strain and protect your focus, new strategies are needed.

Published this past March in the Harvard Business Review, a study by Boston Consulting Group and the University of California, Riverside, surveyed 1,500 workers and coined the term “AI brain fry.” The researchers found that juggling multiple AI tools causes decision fatigue and increases errors.

How to combat the problem:

• Work in batches: Continuously reviewing AI output as it comes in can quickly lead to mental burnout. Dedicate blocks of time throughout your day to interact with your AI tools.
• Create analog islands: Your brain needs time to decompress, and that means taking a break from digital processing… and from screen time in general. Make time in your day to step away from screens, such as taking a 15-minute walk or reading a book instead of watching a video online.
• Know when to step in: Instead of endlessly tweaking prompts to get a perfect result, it is often faster and less mentally taxing to manually write or refactor the final 20% yourself.
• Create base prompts: Writing custom prompts that include everything from guardrails to tone instructions for every new project quickly drains your mental energy. Instead, build two or three reusable system prompts, such as one for refactoring legacy code and another for drafting API docs, and use them as templates.

Proving your human value in a new job market

With companies regularly using AI-based applicant tracking systems to filter resumes, and AI actively shifting job responsibilities and skills requirements, the strategy for how you apply for roles and handle interviews is changing.

Leo Freitas, an analyst and research manager at IDC Research, says it’s critical for job applicants to show potential employers what they can do that machines cannot. “You need demonstrable achievements,” he adds. “It’s good to show highly human skills.”

How to combat the problem:

For your resume:

• Mirror their language: Use the exact language from the job description. Traditional applicant tracking systems rely heavily on structured information and keyword matching, says Teresa Hill, founder and leader of Anchor GTM, a growth marketing consultancy. That means if a posting says “product marketing manager,” don’t just write “PMM.” Write both.
• Keep it simple: Use standard section headers, like “Experience” and “Education.” Creative alternatives and formatting can confuse parsers.
• Avoid AI writing: Use AI to help structure your thinking, then edit until the copy sounds natural and authentically human.
• Swap responsibilities for metrics: To stand out, especially with AI scanners, tie as many bullet points as possible from your work to business outcomes.

For the interview:

• So, how do you use AI? Every interviewer eventually asks some version of this question. The best answers focus on judgment vs. simply name-dropping tools. Explain what you automate, what you never automate, and why.
• Show that you’re a gatekeeper: AI tools can generate code or copy instantly, but they also introduce errors. To stand out, show that you know how to audit, verify, and safeguard quality.
• Focus on learning: Don’t emphasize your expertise with a specific AI tool. Focus on being someone who is adaptable and learns quickly, Hill says. 
• What sets you apart from a machine: Show, don’t tell. Give work examples that demonstrate that you know how to be creative, collaborative, and problem solve. Show metrics whenever possible.
• Demonstrate that you know the limits: Make it clear that you know when to use AI and what not to let it touch without heavy review. 

Future-proofing your career in a shifting tech market

The anxiety echoing through the tech industry is tangible as companies reallocate corporate capital toward automation. While both Gownder and Freitas emphasize that there is far more fear than actual AI-driven layoffs, the shift in corporate spending is undeniably stoking job insecurity.

“There’s this apocalyptical view that AI will take everyone’s job in a few years,” says Freitas. “I don’t see that happening, but many things will change in the nature of how we work. I don’t think the world is going to end tomorrow. But it’s always good to do a self-assessment and look at whether AI can do what you’re doing now.”

How to combat the problem:

• Use this technology to your advantage: Approach AI as a new tool, not a replacement. Use it to make yourself better and faster at your job. “I look at AI like it’s another new tool, and I’m going to learn it like I’ve learned any other tool,” says Jones.
• Don’t bury your head in the sand: Take a look at what you do and consider if it could be done by AI.If your role is highly automatable, think about switching to a more advanced position or to a role, like security, that more clearly needs a human in the loop.
• Take ownership of your career: Expand your knowledge and skills. Find courses and certifications (many are free online) and take advantage of employers’ training programs.
• Work with the business side: Make sure you understand the business — its long-term goals, competitive market, and jargon. Be the bridge between the technical and business sides of the company, giving presentations and solving business problems with technology.
• Highlight your in-house expertise: When you have institutional knowledge, make sure you are openly using it to benefit the business. Your knowledge can be your key differentiator.
• Push the business forward: Think about the next app or customer-facing system that will propel the business forward. Be the one who is advancing the company with tech.
• Continue to adapt: Don’t get stuck in your anxiety. Keep being curious and working your learning muscles. 

More on AI in the workplace:

• Burned out by bots: The rise of prompt fatigue in the workplace
• The AI tech job slaughter gets real
• Increased AI expectations without guidance leads to employee burnout
• ‘Botsitting’: The AI time-savings killer only governance can stop
• Here are the top AI certifications that will get you hired and promoted
• How to curb hallucinations in Copilot (and other genAI tools)

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.

“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”

The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles. 

OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.

The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.  

But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.

“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.

The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations. 

Numerous research firms have said in recent years that AI is driving short-term job losses, though tech industry experts and analysts have argued AI will also create new careers and jobs as agentic AI takes over low-skilled work. 

AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.

In the US, AI blamed for June job losses

According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.

“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”

Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.

AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.

“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”

While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.

“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.

That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.

That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.

OpenAI is selling its first hardware — without any help from Jony Ive. It describes the Codex Micro as a “command center for agentic work” but it’s really a 13-switch wireless keyboard customized to help developers keep tabs on what their Codex agents are doing. It costs $230.

The keyboard has 13 mechanical switches (one keycap covers two of them by default), a rotary encoder, joystick, and RGB backlighting around the whole keypad and individual keys. It comes with 32 customizable icon keycaps.

OpenAI claims that the Codex Micro is a serious business tool: The command keys enable Codex users accept changes, reject outputs, push-to-talk, start new chats, and trigger custom actions. The rotary encoder can be used to dial up the “brainpower” allocated to tasks — or in more conventional terms, how many tokens to allocate to reasoning on a task. And the RGB lighting can provide feedback on how various tasks are progressing. And the RGB lighting under the “agent” keys can provide feedback on how various tasks are progressing.

The Codex Micro’s manufacturer, Work Louder, already has a similar device on the market, the Creator Micro, which offers similar functionality, but without the colorful keys. It costs $56 less than the Codex Micro, however.

The Codex Micro will fit in snugly with OpenAI’s other merchandise, where using Codex is as much about a fashion statement as a technological choice.

This article first appeared on InfoWorld.

Linus Torvalds has a complicated relationship with AI, seeing both its good and bad points. But his latest remarks on the usefulness of AI may have raised a few eyebrows in open-source circles.

Just a few weeks after the Linux founder complained that a “continued flood” of AI-generated vulnerability reports had made the Linux kernel security mailing list “almost entirely unmanageable”, he has come to see the advantages of the technology.

“Linux is not one of those anti-AI projects,” Torvalds wrote in an email response to Linux Kernel senior engineer Roman Gushchin, archived at Kernel.org.

“It can also be a somewhat painful tool, both for maintainer workloads and just from a ‘it keeps finding embarrassing bugs’ standpoint,” he said of the use of AI in security scanning. “The solution is to make sure those LLM tools help maintainers instead of just causing them pain.”

Developers should be free to choose whether they use AI, he said. “We’re not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it.”

Torvalds’ measured support for AI does not come completely out of the blue: Around the same time that he was complaining about AI distorting security maintenance, he also spoke about its usefulness, claiming that it could improve programmer productivity by a factor of 10.

This article first appeared on InfoWorld.

Enterprises that use AI in hiring and firing decisions continue to be under scrutiny, and this time it’s Meta under the microscope.

A legal complaint filed on July 13 in a US District Court in California alleges that Meta used AI systems that unfairly and illegally selected workers for termination while they were out on protected leave.

More than two dozen anonymous plaintiffs are seeking a preliminary injunction that would prevent the company from finalizing their separations or altering their compensation, benefits, or protected leave status.

Meta has countered that the claims lack merit and that its workforce decisions were, and continue to be, made by people, not AI.

An important lesson

These allegations should serve as an important lesson to other businesses using AI in their HR decision-making, analysts note.

“Enterprises must begin by rejecting the convenient assumption that AI improves workforce decisions simply by touching them,” said Sanchit Vir Gogia, chief analyst at Greyhound Research.

There is “scant independent proof” that AI makes layoff choices more accurate or more lawful, he said. “It makes them faster, and faster has never been shown to be fairer.”

The claims against Meta

The complaint states that, on May 20, 2026, Meta began notifying roughly 10% of its workforce (around 8,000 employees) that they had been selected for termination. The company also announced that several thousand more would be reassigned to new AI initiatives. But this came even as Meta reported record revenues in Q1 2026 ($56.31 billion, a 33% year-over-year increase), and pledged to spend upwards of $100 billion on AI this year.

In addition to questioning the need for staff cuts, the filing alleges that Meta used a “constellation” of internal AI systems to score, rank, and select employees for termination. These tools included Meta’s internal AI coworker, “Metamate,” employee-trained “second-brain” agents that replicated their output, algorithms tracking keystrokes and other digital activity, and AI token usage dashboards.

“Meta did not assemble the termination list through the considered judgment of managers who knew the work,” the complaint claims.

The 26 plaintiffs, all current or former employees, requested, took, or were approved for “statutorily protected” leave within 24 months of the workforce reduction, and claim they were “disproportionately selected” for layoff based on scoring that essentially penalized them for exercising their legal right to take leave.

These practices are prohibited by federal and state law; The US Family and Medical Leave Act, for one, prohibits the use of protected leave as a “negative factor” in employment decisions. Further, the plaintiffs allege that Meta violated the US Worker Adjustment and Retraining Notification (WARN) Act that requires employers with 100 or more employees to provide written notice 60 calendar days in advance of mass layoffs.

This notice gives employees reasonable time to seek alternate employment; however, the complaint argues, an employee undergoing “significant medical treatment” or providing “around the clock care” for a “weeks old newborn” or other loved ones “cannot also be told that during this exact same time period they must look for new work.”

In one scenario, according to the filing, a scientist was identified for termination just two days before she gave birth while on pregnancy leave. In another, an engineer’s manager tied his performance rating to “broken time” when an injury prevented him from working. In a third, a researcher was called out after requesting time off following a medical diagnosis.

The plaintiffs are seeking a preliminary injunction pending an independent audit of the “algorithmically assisted selection process” and “resolution of the merits of their claims” in arbitration.

Once terminations are finalized, the harm to plaintiffs “cannot be undone by money damages alone,” the complaint states. For employees out on leave, “every day that goes by constitutes additional harm, in that Meta is taking away the entire purpose of a protected leave.”

Considerations for enterprises

Any system that materially influences who keeps a job is not an HR tool, Gogia noted. “It is high-risk enterprise infrastructure.”

An “AI-determined” process delegates the outcome to the system, while an “AI-assisted” one gives the system the ability to rank, recommend, and summarize, with a human formally making the final decision. Exposure arises in either model, Gogia pointed out, because the output has often been compressed and eliminates detail by the time of executive approval.

There must be one non-negotiable role in the process, Gogia said: A single executive with the authority to halt the process, suspend the model, and delay decisions when evidence does not hold. This person should be “a meaningful reviewer [who] understands the model’s limits, knows the actual work, and holds the authority to challenge the recommendation, with every override visible and reviewable,” he said. At the same time, the objective is to “govern the machine and the manager together,” since human judgement brings its own “risks, favoritism, and proximity” bias.

Gogia advised enterprises to retain fixed memory for auditing, determine who chose the auditor, what was excluded, and whether the result can be reproduced. They should also inventory every source feeding the model and its origins, and run adverse-impact analysis before making any firing decisions.

Leave details must never be identified as inactivity or weak adoption; a protected absence is not ordinary missing data, and the system has to be informed of this. Rather, these circumstances belong in an “independent review lane,” where human reviewers get enough context to “neutralize” the period without receiving specific leave details, Gogia said.

He pointed to another important question: What should the “second brain” AI agent that ingested the employee’s communications and documents to replicate the employee’s output be allowed to do when humans are away, and who owns that output?

Ultimately, said Gogia, “the safest position is not to ban AI from workforce planning. Used with discipline, it can expose duplicated work and inconsistent assessment, and it can challenge human bias rather than automate it.”

How employees can protect their rights

Employees, for their part, need a genuine window in which to challenge inaccurate data before separation becomes “irreversible,” and they should “fight the record, not the algorithm,” Gogia advised.

That means that, while the model cannot explain itself, documented evidence can. Employees should lawfully retain their own reviews, leave approvals, and severance documents, and build a chronology of events: When leave was requested, when performance language changed, when new metrics appeared, Gogia said.

Impacted workers should ask in writing which criteria were used in the decision, whether automated systems materially influenced it, how protected leave was treated, and what information about them influenced the result and how that information was verified.

Further, it’s important to take note of deadlines; the federal discrimination window is typically six months, although that is extended to 10 in many places, and internal processes are “not obliged to respect it,” said Gogia.

His ultimate advice for workers: “Preserve the lawful record, and protect the deadline.”

This article originally appeared on CIO.com.

The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.

Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.

Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.

This is Part 2 of a series on tech worker unionization. Also see Part 1: “A brewing battle: More IT workers want unions. The industry doesn’t.”

What unions can — and can’t — do for you

The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.

“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.

“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.

Beyond job security, unions can deliver concrete material gains. Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.

Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.

Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.

“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.

The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.

“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.

“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.

Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”

Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”

Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.

“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.

If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”

Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.

“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)

US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.

Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.

And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.

With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.

Step 1: Start a conversation with your co-workers

At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.

Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.

The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”

Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.

Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.

Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet

Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.

Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.

“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.

“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.

There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.

“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.

One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.

“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.

Step 3: Find the right union affiliation or go it alone

“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

Options include the Communications Workers of America (CWA), Service Employees International Union (SEIU), and the Office and Professional Employees International Union (OPEIU), among others. Another resource, the Tech Workers Coalition (TWC), provides training on organizing tactics, AI-in-workplace issues, and contract negotiation, and can match workers to the right unions for their needs.

The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.

Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.

For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.

Step 4: Choose your union model: majority vs. pre-majority or minority

Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.

The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.

A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.

The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.

A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.

For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”

Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.

Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”

Step 5: Who should — and should not — be in your union?

Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.

“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”

Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.

Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.

Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”

The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.

“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.

Step 6: You won the vote. Get ready for what comes next

Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.

But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.

Unfortunately, the legal framework that is supposed to protect workers during this process has been significantly weakened in the last few years. In a potentially more ominous development, SpaceX, Amazon, Trader Joe’s, Starbucks, and the University of Southern California have in separate legal actions challenged the constitutionality of the NLRB, arguing that the agency’s structure violates the separation of powers. The Fifth Circuit Court of Appeals upheld injunctions against the NLRB in SpaceX’s case in August 2025 — a serious challenge to the agency’s authority.

In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.

“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.

As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.

Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”

See Part 1: A brewing battle: More IT workers want unions. The industry doesn’t.

AI has created a tough job environment for entry-level workers and things aren’t getting better anytime soon — even those with AI capabilities now need “senior-level” skills to land a job.

“AI-exposed entry-level roles are seven times more likely to require traditionally senior-level skills such as judgement and leadership,” consulting firm PwC said in a study released this month.

That’s because AI is changing the traditional career ladder. Companies are increasingly looking for candidates that use the cutting-edge tools and services to amplify their performance and grow faster. “Organizations must rethink how they mentor and train junior staff, helping them step up to complex decision-making much earlier in their careers,” PwC said.

Entry-level job seekers with or without AI skills are already dealing with stagnant wages,  layoffs, and stalled hiring. 

(The PwC findings echo similar concerns raised late last year in McKinsey’s State of AI report. Many companies are reducing headcount by deploying AI agents to take over entry-level jobs.)

Early-career AI job postings “have flatlined in highly AI exposed sectors,” and listings for junior roles with mid-career or senior-level skills have grown 35% since 2019, PwC said.  The consulting firm largely discounted the notion that AI is taking jobs away, though other studies point in the opposite direction. 

By the end of May, AI-driven job cuts had reached 87,174 for 2026, already outpacing the total of around 54,836 in 2025, according to figures released by Challenger, Gray and Christmas earlier this month.

The AI-driven layoffs haven’t reached the “jobpocalypse” stage yet, and workers are more productive with it, said Andy Challenger, chief revenue officer at Challenger, Gray and Christmas. But companies are rethinking hiring and long-term operational strategies as AI becomes a routine component in daily workflows and processes, he said.

Businesses are “restructuring aggressively as they reposition for an AI-driven economy,” he said.

That’s putting downward pressure on entry-level hiring as AI tools absorb more routine work, said Kye Mitchell, head of Experis US, a part of ManpowerGroup. “That doesn’t remove opportunity, but it changes the expectations. Employers now expect candidates to come in with hands-on experience, AI familiarity, and the ability to contribute faster,” Mitchell said.

Compensation remains strong for specialized, in-demand skills, while more commoditized roles such as customer service, helpdesk, and some entry-level positions  are flattening. “The shift overall is toward skills-based hiring, where demonstrable capability matters more than credentials alone,” Mitchell said.

Graduates who combine technical fundamentals with practical experience, AI fluency and strong communication skills stand out quickly. Job candidates can’t rely solely on academic credentials. 

“Employers are moving away from ‘train-from-scratch’ hiring and looking for talent that can contribute earlier and continue to adapt,” Mitchell said.

The PwC study also focused on the productivity gap between companies that have invested heavily in AI and companies lagging in adoption.

Since ChatGPT showed up in 2022, AI-exposed companies have seen productivity gains of 40% versus other companies. “The companies achieving the biggest productivity gains from AI are not using it only to cut costs,” PwC said.

AI-forward firms are also raising headcounts and wages. “Far from being a job killer, AI may actually be a job expander when used to unlock growth and enter new markets,” PwC said. 

Workers who use their domain expertise to supplement AI tools can advance, with AI-exposed roles “2.5 times more likely to rely on skills like empathy, judgement, and creativity that become even more valuable as AI absorbs some routine work,” PwC said.