Most recruiters are still trying to convince themselves that the automation and AI wave won’t affect them. After all, they are the people people, and they will always have a job. But you’ve already done the math. Now what?

You know too much to feel comfortable, and not enough people above you know you know it.

Your current role feels safe. But a company that has frozen or eliminated internal growth and is quietly reducing headcount is not a safe harbor. The comfort you feel is the comfort of the known, not the comfort of the secure.

You’ve watched your ATS evolve from a filing cabinet into something that screens, scores, schedules, and sometimes interviews candidates without a human-in-the-loop. You’ve seen your company’s sourcing volume handled by tools that don’t sleep, don’t ask for headcount, and don’t need a LinkedIn Recruiter license. You’ve read the same data that everyone else in your field is ignoring: employers cut planned new hires by 35% in 2025. The companies still hiring are doing it with fewer recruiters, not more.

But here’s what separates you from most of your peers: you understand what actually survives the automation wave. Not resume parsing. Not pipeline management. Not scheduling, screening, or status updates because those are already gone or in progress.

What survives is the work that requires genuine human judgment, trust, and relationships. Advising hiring managers who don’t know what they really need. Building talent communities before a requisition opens. Managing the ecosystem of agencies, universities, and professional networks that no algorithm can fully own. Being the person a candidate trusts with a career decision, not just a job opening.

You can do that work. You’re already thinking at that level.

You are likely sitting at your desk right now, looking at a calendar of interviews or a pipeline of candidates, feeling a profound sense of "stuckness." You are excellent at what you do. You have the experience, the intuition, and the results to back up a promotion. Yet, you’ve hit a ceiling that feels less like glass and more like reinforced concrete. Above you, your managers are parked. They aren’t moving up because there is nowhere to go, and they aren’t moving out because they are just as terrified as you are.

So let’s talk about what you actually do with that.

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Last week, Meta laid off another round of employees. Second time this year. They hit Reality Labs, Facebook operations, sales, and recruiting. Recruiting was hit hardest.

Read that again. The company, with $200 billion in annual revenue and growing cloud revenue while posting record profits, cut its recruiting team first.

Not legal. Not finance. Not the layers of middle management that every CEO publicly claims to despise.

Recruiting.

The Pattern Is Hiding in Plain Sight
Here is how AI restructuring works at big companies.

First, the company spends 12 to 18 months investing heavily in AI infrastructure.

Second, leadership conducts an internal audit of which roles can be automated or reduced. They look at functions that are cost centers with no discernible strategic value.

Third, the layoffs arrive, blaming AI and framed as a competitive necessity.

Fourth, recruiting is among the first teams to be reduced because leadership has already concluded that hiring is slowing and that AI tools can absorb the rest.

From an executive perspective, if you are hiring fewer people, you need fewer recruiters. If AI is screening applications, you need fewer coordinators. If robotic agents are sourcing and doing outreach, you need fewer sourcers.

We have seen 45,000 to 60,000 tech jobs eliminated in Q1 2026 alone. That is a 51% increase over Q1 2025. Of those cuts, roughly 20% are being explicitly attributed to AI by the companies themselves. The other 80%? Cost restructuring, post-pandemic correction, and Wall Street pressure for margin improvement.

But recruiting gets cut in either case, because recruiting is seen as a cost function tied to hiring volume. When volume drops, the function does too.

The Uncomfortable Question Nobody Is Asking
We have spent years arguing for a seat at the table. We have done everything we can think of, from strategic workforce planning to developing quality-of-hire metrics to establishing a talent intelligence function. We talk about how indispensable we are, but executives don’t buy it. Recruiters are seen as transactional, easily replaced when needed.

Companies cut the recruiting team before the analytics team, before the marketing team, and in some cases before the teams that haven’t shipped anything in two years.

So, we have tried to build the case for strategic value, but executives still see it as a cost center.

While some TA leaders have genuinely transformed their functions into workforce intelligence operations that leadership cannot imagine operating without. Most have not.

Most are still measured on time to fill and requisition load, which are metrics that describe activity and throughput, not impact.

When the AI restructuring wave arrives, executives cut throughput functions first. They protect judgment functions.

What Is Actually Happening to the Talent Pipeline
There is a secondary crisis developing underneath the headline layoff numbers that recruiting leaders should be loudly raising right now. Entry-level job postings on platforms like Handshake are down 15% year over year. Applications per vacancy are up 30%. Companies are eliminating the roles that feed leadership pipelines, and nobody in the C-suite is modeling what the organization will look like in five years when there is no internal bench.

This is TA’s argument to make. Not “we need more budget for sourcing tools.” The argument is: the people being cut today are the senior leaders of 2030. Eliminating entry-level roles in the name of AI efficiency is placing a bet that AI will produce senior judgment when you need it. There is no evidence that is true.

73% of TA leaders in Korn Ferry’s 2026 data say critical thinking is the skill they need most. AI skills rank fifth. The judgment required to lead organizations does not come from a prompt. It comes from years of experiencing failure and getting feedback inside an organization. You cannot source it later. You have to grow it now.

What TA Leaders Should Do Differently
I have argued for years that our normal metrics are rubbish. Stop measuring yourself by hiring volume and activity metrics. Volume and speed metrics make your function look like a cost center.

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As I said in last week’s article, we are standing in the wreckage of the recruiting process. Artificial intelligence has increased the efficiency, scale, and speed of hiring processes, yet it has also led to the loss of meaningful human connection and increased candidate dissatisfaction.

The question is not whether to use AI in recruiting. That question has been answered.

The more important question is how to design a recruiting model in which AI handles scale and process optimization, while human interaction is available and used where it creates the greatest value.

This is the foundation of a high-touch, AI-enabled candidate experience.

The Core Problem: Efficiency Without Connection
No global recruiting function can operate efficiently without A)-based systems to increase speed and throughput. But these systems cannot make the process human. They cannot identify or reveal a candidate’s judgment, intent, adaptability, or cultural alignment.

As I said last week, at its core, recruiting is an information problem, but not all information can be quantified or measured. are often revealed only through human interaction. When we use AI, these interactions are minimized or eliminated, and recruiters and hiring managers risk making decisions based on incomplete or distorted data.

A New Design Principle: Human Interaction as a Strategic Resource
The solution is not to go back to fully manual processes. That would be both impractical and inefficient.

Instead, organizations must treat human interaction as a scarce and valuable resource, deploying it intentionally where it really counts in the candidate journey.

This requires a shift from a process-centric model to an experience architecture model. Rather than asking, “Where can we automate?” the more relevant question becomes, “Where does human interaction create the greatest value?”

There are three critical moments or times in a candidate’s journey that are critical:

1. Moments of uncertainty – where candidates need clarification, reassurance, or context

2. Moments of evaluation – where nuanced judgment is required from both sides

3. Moments of decision – where commitment, negotiation, and alignment occur

We need to design a high-touch experience at each of these points.

Practical Strategy 1: Humanize the First Meaningful Interaction
AI is highly effective at initial sourcing and outreach, but the transition from automated engagement to human contact is often poorly designed. Many candidates interact with bots or automated emails for extended periods before speaking with a recruiter.

This is a missed opportunity.

The first meaningful human interaction should occur early enough to establish credibility and trust, but not so early that it disrupts scale. A practical approach is to:

• Use AI for initial identification and qualification

• Trigger a human interaction once a candidate meets predefined relevance thresholds

• Ensure that the first human conversation is meaningful, not transactional

This interaction should focus on understanding the candidate’s motivations, constraints, and career context, rather than simply validating resume data. It sets the tone for the entire experience.

Practical Strategy 2: Redesign Screening as a Hybrid Process

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We are standing in the wreckage of the recruiting process while trying to pretend that what is happening is just an evolution. But AI, along with other factors, has wrought chaos.

What we are seeing is a classic technological arms race.

Candidates use AI to optimize applications. Employers deploy AI to detect or filter them. Candidates then refine their tools to evade those filters. Each cycle increases the game's sophistication while reducing the reliability of the signals produced.

Arms races rarely produce clarity. They produce escalation.

Here is where we are in 2026. Candidates are submitting AI-optimized resumes tailored to job descriptions in minutes. Cover letters are generated in seconds. Video interviews are being coached in real time by AI tools whispering answers through an earpiece. Work samples are assembled with AI assistance. And on the employer side, ATS systems are auto-rejecting candidates before a human ever looks at them, AI tools are scoring video interviews based on tone and word choice, and hiring managers are ghosting finalists who made it to the last round.

Hiring has always been an information problem.

Employers are trying to infer future performance from incomplete and imperfect signals: resumes, references, interviews, and credentials.

What AI has done is dramatically increase the noise in the system. Candidates can now generate signals that appear to demonstrate capability but are artifacts of software assistance. At the same time, employers have automated screening systems that amplify superficial indicators such as keywords or linguistic patterns.

The result is predictable: signal collapses, noise expands, and trust erodes

Both sides are gaming the other. Both sides know it. And the entire process has become a ritual that nobody believes in anymore.

So what does a recruiter do?

The Signal Problem
The honest answer is that most of what you receive in the top of the funnel is now close to useless as a measure of candidate quality. This is not the candidate’s fault. When you auto-reject someone for using the word “managed” instead of “led,” you trained them to stop being honest and start being strategic. You built the conditions for this. The resume they send you now is not a document about them. It is a document about what your ATS wants to see. Those are very different things.

The numbers bear this out. In the past three years, total applications per role on LinkedIn have doubled. At the same time, 66% of recruiters say finding quality candidates has gotten harder. More volume, less signal. That is what an arms race looks like when it matures.

The video interview is not much better. A candidate coaching tool called Final Round AI, which provides real-time AI assistance during interviews, reportedly had 300,000 users within months of launching. When nearly a third of hiring managers say they have encountered fake voices or AI-scripted answers in interviews, you must ask what you are evaluating. You are no longer assessing the candidate. You are assessing how well they have prepared their AI to impersonate them.

Emerging Adaptations
Smart recruiters are shrinking the funnel, not expanding it.

Instead of processing

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Note: This is a longer article than usual because it discusses where work is heading and how what has happened in the AI job marketplace foretells what is coming for all of us.

For more than a century, the job description has been the foundational unit of work organization. It was a model built on the assumption that work is stable enough to be described in advance. That work consisted of a set of duties, reporting relationships, qualifications, and expected outputs that remained consistent over time, and that this set of duties equaled what we call a “job.” That assumption is crumbling.

And if you want to see where it crumbles fastest, watch what is happening to the AI labor market right now.

Consider the title “AI Engineer.” It did not exist as a recognized role category five years ago. Today, it commands a median total compensation exceeding $245,000 in the United States, with top earners at frontier companies pulling $875,000 or more.

Netflix posted a single AI role with a salary ceiling of $900,000. Anthropic and OpenAI have structured packages for senior researchers that reach eight figures over multi-year terms. These are not anomalies in an otherwise stable compensation landscape. They are the leading edge of a structural rupture.

What makes the AI labor market instructive is not the numbers themselves but the logic behind them. Companies are not paying $800,000 for a job. They are paying for a specific, rare, time-sensitive capability to build and deploy systems that will reshape their entire cost structure. The capability is everything. And that same logic, once loosed from the frontier of AI, will migrate steadily into the rest of the economy.

A Tale of Two Labor Markets
The AI employment market has already split the world of work into two sharply diverging tracks. In 2025, tech companies laid off nearly 131,000 workers while simultaneously struggling to fill AI and machine learning positions. Microsoft eliminated thousands of jobs and raised AI engineer salaries past $300,000. Amazon cut positions across multiple business units and, within weeks, posted hundreds of openings for data scientists and ML specialists.

Human resources departments whose entire function is organized around job descriptions saw job postings decline by 16% as the sector was itself restructured. AI-related HR postings, by contrast, surged 25% in the first quarter of 2025 alone, with median salaries hitting $157,000.

This is not a contradiction. It is a preview. Companies are eliminating roles defined by stable, specifiable task sets that can be written into a job description, while hiring capabilities that are fluid, rare, and compounding in value. The job description, as a technology, was built for the first category of work. It has almost nothing useful to say about the second.

Younger workers are learning this lesson the hard way. Entry-level positions have declined 29% since January 2024. The unemployment rate for college graduates aged 22–27 reached 4.8% in mid-2025, meaningfully above the overall rate, as the traditional entry-level-to-executive career ladder built entirely on the logic of job-based progression fractured under the weight of AI-driven task automation. A degree in hand and a job description to match no longer adds up to a career path.

From Roles to Tasks, Skills, and Projects

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When I first wrote about humanoid robots and recruiting in mid-2025, the honest answer was that the technology was impressive but mostly just staged. The deployments were only pilots, heavily supervi…

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In 2022, we had the “Great Resignation.” In 2024, we had “Quiet Quitting.” In February 2026, we have entered what I’m calling the “Great Compliance.” According to recent survey data, only 7% of workers say they would quit over a return-to-office mandate today, down from 51% just a year ago.

But this isn’t because employees have suddenly rediscovered their love for fluorescent lighting and cubicles. It’s because they’re terrified of what we discussed last week: the hiring recession that has left job seekers competing for what few jobs are still available while corporate America rakes in millions in profits.

The leverage has shifted. It is no longer the candidate market that existed briefly. And companies know it.

Hybrid Creep: The Slow-Motion Retreat from Flexibility
The playbook is predictable. It starts with encouraging people to return to the office for two days a week, which seems reasonable. Then it moves to “three days required,” usually announced in a carefully worded email about rebuilding our culture.

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There is a famous photograph of General George S. Patton taken somewhere in France in the summer of 1944. He is standing in a field, jaw set, holding a whip, looking at something just beyond the fra…

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As we navigate the complexities of this “brittle” 2026 labor market and economy and the impact of AI, staying informed is more critical than ever.

To celebrate the season and our growing community, I…

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The official U.S. unemployment rate stood at 4.4% as of December 2025, the highest level since 2021, but still within what economists would consider a healthy labor market.

It is a number that feels almost absurd to anyone who has spent the past year watching their LinkedIn feed fill with “Open to Work” banners, watching peers go six months between interviews, or watching entire teams disappear from companies that are still posting record profits.

The disconnect is real and measurable. The official unemployment rate tells you how many people don’t have a job. It tells you nothing about how few jobs are being created or how frozen hiring has become. To understand the 2026 labor market, you have to look beyond the headline to the underlying data.

Start here: the U.S. economy added approximately 584,000 jobs in all of 2025. That is roughly 50,000 per month, which is the smallest annual job gain since COVID and the slimmest yearly increase since 2003. Meanwhile, employers announced 1.2 million planned job cuts in 2025, up 58% from 2024 and the highest annual total since the pandemic. Long-term unemployment is at a four-year high. Planned new hires announced by employers were down 35% from 2024, the lowest level since 2011.

This is not a recession by the traditional definition. But for white-collar workers, particularly in tech, software, marketing, and customer-facing corporate roles, the conditions are indistinguishable from one.

The Layoff Wave
The most visible driver is a wave of corporate downsizing led by some of the world's most profitable companies. Amazon eliminated approximately 30,000 corporate roles since October 2025, or about 9% of its corporate workforce. It linked those cuts publicly to AI-driven efficiency and management delayering.

Salesforce cut more than 5,000 positions across two rounds, with its CEO explicitly attributing the reductions to AI agents taking over customer support and sales functions. Meta, Block, HP, Microsoft, and Pinterest have all announced significant cuts in recent months, citing AI investment and efficiency as the rationale.

The tech sector alone announced 154,000 job cuts in 2025, up 15% from 2024. October 2025 was the worst single month for announced layoffs in more than two decades. The ripple effects have spread well beyond tech: UPS announced 30,000 cuts partly in response to reduced Amazon shipping volume. The cascade is real.

The AI Attribution Problem
Here is where the narrative gets more complicated. AI is unquestionably an active force in the current labor market. Consulting firm Challenger, Gray & Christmas tracked nearly 55,000 U.S. job cuts in 2025 that companies explicitly attributed to artificial intelligence, a figure that will grow in 2026.

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There is a rebellion brewing in the TA world. It started as a whisper in Slack channels and private roundtables, but last week, it spilled out into the open. A viral discussion on Reddit and LinkedIn raised a question I had only heard from a few leaders a couple of years ago.

“Is it time to drop LinkedIn Recruiter?”

For the better part of a decade, LinkedIn has been the king as the default operating system for sourcing. If you didn’t have a seat, you weren’t really in the game. It was our phone book, our background checker, and our primary communication channel.

But the mood has shifted. We are seeing the early stages of a “Tech Stack Rebellion,” driven by a phenomenon I call tool fatigue. The industry is waking up to a hard reality: paying a premium for access to a database that everyone else also has and that candidates are increasingly ignoring is no longer a strategy. It’s a tax.

Here is why the rebellion is happening, and more importantly, where the smart money is moving their sourcing strategies.

The Mathematics of Fatigue

Why are teams canceling licenses? It isn’t just budget tightening; it is a collapse in the signal-to-noise ratio.

We are currently witnessing a form of deadlock in digital communication.

1. The Automation Trap: Recruiters, armed with AI co-pilots and scheduling tools, are blasting out thousands of templated messages.

2. The “Workslop” Response: Candidates, overwhelmed by the spam, are using their own AI agents to filter inboxes or auto-apply to roles they haven’t read.

The result is silence. Response rates on traditional platforms have plummeted to single digits. When everyone has a megaphone, no one can hear anything.

Furthermore, as I have written about many times, if every recruiter uses the exact same tool to search the exact same keywords, we are all fishing in the same barrel. We aren’t sourcing; we are racing. And in a race where AI levels the playing field for speed, the only differentiator left is relationships, which a keyword search cannot find or buy.

The Shift: From “Access” to “Orchestration”

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Note: This story is not about real people or real situations. The people are my creations, but their stories reflect a reality many face.

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Why is the legal case against Eightfold novel, and what new questions does it pose?

Historically, human screening is limited by three structural features.

• First, while often biased or inconsistent, a human recruiter can usually explain why a candidate was shortlisted or rejected.

• Second, accountability is localized. A specific recruiter or hiring manager could be identified as responsible for the decision.

• Third, the scale of impact is limited. A single recruiter might screen hundreds or thousands of candidates, but not millions in real time across multiple employers.

AI-based screening systems change all three conditions.

• First: Modern AI systems infer patterns from historical data and apply correlations that may not be understood even by their designers. This creates a “black box” decision environment. A candidate rejected by an AI system cannot get a meaningful explanation beyond general statements about “fit” or “ranking scores.”

• Second: Responsibility. With human screening, responsibility is clear. With AI, it is spread across vendors, employers, data providers, and system integrators. This produces a responsibility gap. When bias or discrimination occurs, no single person or entity can easily be held accountable. The vendor claims it merely provides a tool. The employer claims it relied on a certified system. The algorithm itself is treated as neutral. This spread of responsibility is new and legally unresolved.

• Third: scale and automation. AI systems screen continuously across entire labor markets. Small biases in training data can be amplified. Human bias existed before, but it did not grow with this level of consistency, speed, and reach. Algorithmic bias is not just individual prejudice. It is systemic bias encoded into infrastructure.

• Fourth: Consent. This is the most novel dimension. Historically, candidates implicitly consented to human evaluation by applying for a job. They understood that a person would read their resume.
  
  What they did not consent to, and often are not informed about, is automated profiling using behavioral data, inferred attributes, external datasets, and predictive scoring models that may go far beyond the information they knowingly provided.

This raises legal issues related to data protection, informed consent, and automated decision rights, especially under GDPR, the EU AI Act, and emerging US state laws.

Does the case have merit?

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Recruitment process outsourcing is entering a structural transition. The question facing buyers and providers is no longer whether RPO will survive, but what form it will take and which strategies wi…

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BANI is a framework used to describe the conditions facing modern organizations and their functions as well as societies.

It stands for brittle, anxious, nonlinear, and incomprehensible. As opposed to…

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I don’t make magic ball predictions. I base my comments on data and clear trends. Last year, I was pretty much spot on (except for blockchain, which keeps eluding me and all of us). So here goes what…

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Looking back from December 2025, the landscape of work, technology, and human capital has shifted fundamentally. The “AI Hype Cycle” finally crashed into the wall of operational reality, and what eme…

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For the first time, I am offering a $20/year discount on an annual subscription.

Get a full year of this newsletter for only $30.

This offer is good until next Friday, December 5th.

Get access to all a…

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So many articles, podcasts, and videos champion the need to shift from traditional requisitions with endless lists of specific requirements to skills-based hiring. We are told to replace rigid job re…

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The United States is losing its entire innovation ecosystems, entrepreneurial networks, and the competitive advantages that have defined American business dominance for decades.

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