Career Intel with Dan 📊 | When the Algorithm Decides Who Gets Laid Off: A New Survey Finds the Legal Line Managers Are Crossing

DEFINING PATTERN: AI-cited layoffs kept accumulating this week, and Visa and Chime became the latest examples of AI flattening corporate structures, but the more consequential story may be how casually some of that displacement is being decided: a new ResumeTemplates.com survey found 59% of managers now use AI to help decide who gets laid off, and some are feeding the algorithm factors, like age and medical leave, that discrimination law treats very differently than performance. Revelio Labs' July tracker confirms the entry-level pipeline is genuinely thinner for AI-exposed roles even as AI-adopting firms grow headcount overall, a split that shows up again in how Gen Z describes the job hunt and in Gallup's data on collapsing youth optimism. Against that backdrop, unions are emerging as one of the few concrete levers workers have, Congress is asking for better data rather than new rules, and a handful of major employers, from Alphabet to Snap-on, quietly resumed hiring, a reminder that this remains a story of redistribution and selectivity, not collapse.

🔊 1. AI-Cited Layoffs Keep Piling Up, and "AI Washing" Skepticism Goes Mainstream

Monday.com became the latest company to name AI as a factor in job cuts, eliminating 630 positions, 20% of its global workforce, as part of a shift toward what it calls an AI Work Platform where employees and AI agents work side by side. Co-founder Eran Zinman told staff in a LinkedIn memo the move "was not made to reduce costs or replace people with AI," even as the company raised its 2026 operating margin forecast. TechCrunch's running tally of AI-cited tech layoffs added Monday.com to a list that now spans roughly 20 companies this year. Separately, a broader tracker covering all sectors counted 322 layoff events affecting 205,832 workers so far in 2026, with 54% of those events, 173 of 322, explicitly citing AI, automation, or ML as a factor, up sharply from just 7% in January. Market skepticism about that framing is now mainstream: OpenAI CEO Sam Altman said in February that "almost every company that does layoffs is blaming AI, whether or not it really is about AI," a dynamic that's become known as AI washing, and one industry count found 78% of companies now cite a need to refocus around AI as a reason for cuts.

🔹 Monday.com's 630 cuts carry $45-55 million in restructuring charges even as the company raised its non-GAAP margin forecast to 15%, a signal the cuts are about strategic repositioning, not financial distress

🔹 AI's share of cited layoff causes jumped from 7% of events in January to 54% by late July, the fastest change in the tracker's stated reason this year

🔹 Altman's own "AI washing" framing, from the CEO whose company sits at the center of the AI boom, is now shaping how reporters and researchers question every AI-cited layoff headline

💡 When the CEO most invested in AI's success is the one warning that "AI" has become a cover story for ordinary cost-cutting, that's worth taking seriously. Treat any single company's AI-layoff announcement as a claim to investigate against its financials, not a fact to accept at face value.

Impact: Immediate, and ongoing through the rest of 2026 as more Q3 earnings calls arrive.

🔊 2. Visa and Chime Cut Jobs as AI Flattens the Org Chart

Visa announced roughly 2,600 job cuts, about 7% of its workforce, concentrated in technology and product teams, as CEO Ryan McInerney said AI is helping "shape the way work gets done" at the company; a person with direct knowledge told CNBC AI was a significant factor but not the sole driver. Visa plans to reinvest in affluent-customer products, cross-border payments, and geographic expansion. Days later, fintech Chime said it would cut about 10% of its roughly 1,500-person workforce, around 150 employees. CEO Chris Britt wrote in a staff memo that "AI is changing what's possible but requires new skills," and that "smaller teams with fewer layers are moving faster than ever and getting more done."

🔹 Visa is redirecting savings toward affluent-customer products, cross-border payments, and international expansion, not simply shrinking the balance sheet

🔹 Chime's cuts land just after its 2026-2027 U.S. News Best Companies to Work For recognition, and its CEO frames the goal explicitly as fewer management layers, not fewer total functions

🔹 Both companies frame AI as reshaping org design, flatter teams, broader individual scope, rather than a single wave of pure automation

💡 Chime's CEO naming "requires new skills" in the same breath as "smaller teams" is the clearest signal yet that flattening management layers, not eliminating entire job categories, is becoming the default playbook, useful framing for anyone coaching mid-level professionals on how to stay necessary.

Impact: Immediate for affected employees at both companies; emerging as a template other mid-size fintech and payments firms may follow.

🔊 3. When AI Picks Who Gets Laid Off

A ResumeTemplates.com survey of 1,000 U.S. managers who use AI at work, reported by HR Dive, found 59% use AI when deciding who to lay off and 58% when deciding who to fire. Most keep a human in the loop: 57% said they would never let AI make a layoff call without supervision, and 91% said they'd override an AI recommendation they disagreed with. But 17% said they let AI decide "often or all the time" without review, and 1 in 4 managers use AI to help decide layoffs that often. Among managers who do lean on AI for layoffs, 80% have it weigh performance and productivity, 57% attendance, 42% salary or cost, and 32% tenure. Smaller but legally significant shares ask it to weigh factors that discrimination law treats differently than job performance: 31% direct AI to consider frequent sick days or medical leave, and 14% have it weigh age. Separately, 38% of managers said they'd never been trained on the ethical use of AI in HR decisions, and 58% couldn't confirm whether their company's AI tool had been tested for bias.

🔹 31% of managers who use AI for layoffs direct it to weigh sick days or medical leave, and 14% direct it to weigh age, both protected categories under the FMLA and ADEA

🔹 38% of managers using AI in HR decisions say they've never been trained on its ethical use, and 58% can't confirm their company ever tested the tool for bias

🔹 ResumeTemplates.com's chief career strategist, Julia Toothacre, named the exposure directly: "the biggest risks here are discrimination claims and wrongful-termination claims"

💡 The gap isn't between companies that use AI in layoffs and those that don't, most already do. It's between the 57% who keep a human checking the output and the meaningful minority letting it run unsupervised on inputs a court would treat very differently from a performance score.

Impact: Immediate; HR, legal, and compliance teams have a concrete new liability to audit, not a hypothetical one.

🔊 4. Entry-Level AI Exposure Is Real, But So Is Headcount Growth at AI Adopters

Revelio Labs' July 2026 AI Labor Market Tracker, the second monthly release of a new dataset benchmarked against academic research, found that demand for the most AI-exposed roles has fallen 42% relative to the least-exposed roles since October 2022, and that Computer Science and IT enrollment is down 28% over the same period. But the same tracker found companies that have adopted AI grew overall headcount 27% faster than non-adopters over that span, and postings-per-hire, a measure of hiring friction, rose 264% year over year to just over five postings needed per hire.

🔹 Demand for the most AI-exposed occupations has fallen 42% relative to the least-exposed since October 2022, the tracker's headline demand metric

🔹 Companies that have adopted AI grew headcount 27% faster than non-adopters over the same period, evidence this is redistribution across firms, not just contraction

🔹 Computer Science and IT college enrollment is down 28% since 2022, suggesting students are already reading the exposed-role data and steering away from it

💡 Two things can be true at once, and this tracker is built to show both: AI-exposed roles are measurably harder to get hired into, and AI-adopting companies are measurably growing. That argues for steering job seekers toward AI-adopting employers and AI-adjacent skills rather than away from the labor market altogether.

Impact: Emerging; Revelio updates this tracker monthly (next release mid-August), so this is now a trackable trendline, not a one-time data point.

🔊 5. Employers Still Aren't Training Workers for the AI Transition

A Conference Board report released July 28, based on interviews with 35 enterprise leaders and a global survey of roughly 1,300 workers, found that while 55% of knowledge workers now regularly use AI in their daily work, only 33% received employer-provided AI training in the past six months, and 28% received none at all. Just 48% of workers agreed their employer gives them sufficient time during work hours to build AI skills. The report's central finding is that most employer training still focuses on getting more out of an employee's current role rather than preparing them for how that role may change.

🔹 55% of knowledge workers use AI regularly, but only 33% got any employer-provided AI training in the last six months

🔹 28% of workers report receiving no AI training at all from their employer, despite majority daily use

🔹 Only 48% of workers say they get sufficient work-hours time to build AI skills, meaning much of the 55% adoption rate is happening on employees' own initiative and time

💡 Employers are getting the productivity upside of AI adoption without paying the training cost that would make it sustainable, a gap that shifts risk onto workers now and onto employers later, when the skills employees taught themselves don't match what the company actually needs.

Impact: Immediate; a direct input for any employer or workforce board building a 2026-2027 training budget.

🔊 6. Gen Z's Relationship With AI at Work Is Souring

New Gallup data, released with the Walton Family Foundation and GSV Ventures, found that 47% of adults 18 to 29 now believe AI does more harm than good, up from 36% a year ago, the largest one-year swing of any age group. Trust fell alongside it: only 20% of that age group now express at least some trust in AI, down from 30%, while 41% say they have none at all, up from 29%. Belief that AI will grow U.S. jobs over the next decade collapsed from 14% to just 5% among 18-to-29-year-olds, and young Americans' broader job-market optimism has fallen 27 points since 2023. Marketplace's reporting on the ground found the sentiment showing up in job-search behavior: one recent graduate described using ChatGPT to rewrite her resume specifically to beat employers' own AI screening tools after months of near-instant automated rejections. Ramp data cited in that reporting found 1 in 3 businesses it tracks now spend more than $10,000 a month on AI, money job seekers increasingly read as competing directly with entry-level hiring budgets. A separate Ramp study offers a genuine counterpoint, however: firms with the highest AI spending intensity grew total employment about 10% and entry-level employment about 12% after adopting AI, suggesting the picture is more mixed than young workers' souring sentiment alone would suggest.

🔹 47% of 18-to-29-year-olds now say AI does more harm than good, an 11-point jump in a single year, the largest of any age group measured

🔹 1 in 3 businesses Ramp tracks spend over $10,000 a month on AI, a figure young job seekers increasingly perceive as directly competing with entry-level hiring budgets

🔹 Counterpoint from the same data source: the heaviest AI-spending firms grew entry-level employment about 12% after adopting AI, the opposite of the displacement young workers report feeling

💡 The sentiment data and the spending data are telling two different stories. One is about how young workers feel navigating an AI-saturated hiring process; the other is about what's actually happening to entry-level headcount at the heaviest AI adopters. Workforce professionals should be careful not to collapse the two into a single narrative.

Impact: Immediate for job seekers' experience and morale; the underlying employment effect remains genuinely contested.

🔊 7. Unions Become the Clearest Practical Lever Against AI Job Cuts, Where They Exist

BLS's 2025 union data, released in February, shows 11.2% of U.S. wage and salary workers, 16.5 million people, are represented by a union, though the membership rate itself is 10.0%. Private-sector membership sits at just 5.9%, versus 32.9% in the public sector, and the industries with the thinnest coverage, finance (0.8%), insurance (1.2%), and professional and technical services (1.3%), overlap heavily with the white-collar functions most exposed to AI automation. Occupations at the other extreme, education, training, and library work (32.5% membership) and protective services (31.3%), have both the strongest coverage and, so far, the least AI-driven disruption. Against that backdrop, unions that do have coverage are moving fast on AI-specific contract language: the NewsGuild now has AI provisions in 85 to 90 contracts, Politico's union used arbitration to force the removal of an AI reporting tool rollout, and Microsoft's ZeniMax workers won contract language requiring the company to notify and negotiate with the union before introducing new AI systems.

🔹 Finance (0.8%), insurance (1.2%), and professional/technical services (1.3%) have the lowest union coverage of any industry group, and are also among the most AI-exposed

🔹 The NewsGuild has AI-specific language in 85 to 90 contracts, and Politico's union used arbitration, not legislation, to force an AI tool rollout to be pulled

🔹 ZeniMax (Microsoft) workers secured a specific mechanism, mandatory notify-and-negotiate before new AI systems are introduced, that non-union peers at the same company do not have

💡 The occupations with the least union coverage are, disproportionately, the ones white-collar AI is reaching first. That means collective bargaining, currently the most concrete tool workers have for negotiating notice, input, or restrictions on AI, is least available to exactly the workers who'd benefit from it most.

Impact: Immediate for unionized workers; a structural gap for everyone else that no pending federal legislation currently closes.

🔊 8. Legal AI Creates a New Occupation: "Legal Engineers"

Legal AI company Harvey now employs 160 "legal engineers," former practicing lawyers who train, test, and refine AI systems that handle research, document review, and drafting, a fivefold increase from a year earlier, with dozens more openings posted. The role sits between traditional associate work and product development: legal engineers use their courtroom and contract experience to judge whether the AI's output is actually usable, then feed that judgment back into the system.

🔹 Harvey's legal engineer headcount grew 5x year over year, to 160, with more openings still posted across offices, including a newly built-out EMEA team

🔹 The role is explicitly built for lawyers leaving traditional practice, not computer scientists, reusing legal judgment as the core skill rather than replacing it

🔹 Firms hiring into this category are pulling experienced associates and junior partners away from the billable-hour ladder that used to be the only path to legal seniority

💡 This is a real example of a new occupation appearing alongside AI-driven task automation rather than after it. But the tradeoff is that the traditional route, where junior lawyers learned judgment by doing the now-automated research and drafting work themselves, is thinning at the same time, a training-pipeline problem the profession hasn't solved yet.

Impact: Emerging; a concrete new career track worth naming for law students and career-changers, alongside a longer-term concern about how the next generation of lawyers builds judgment.

🔊 9. Bipartisan Senate Push for Better Federal Data on AI's Workforce Impact

Sens. Jim Banks (R-IN) and John Hickenlooper (D-CO), with co-sponsors Maggie Hassan (D-NH) and Jon Husted (R-OH), are pushing the AI Workforce PREPARE Act (S.3339), which would add AI-specific questions to federal labor surveys, authorize the Department of Labor to hire AI specialists, establish an AI Workforce Research Hub, and require employers to disclose when AI was a factor in a mass layoff. The bill also encourages, but doesn't mandate, that companies voluntarily share AI-adoption data. At a July hearing of the Senate HELP Committee's Employment and Workforce Safety subcommittee, Banks said better data is a first step toward helping workers build the skills needed "in the AI economy." The push comes as states continue to legislate unevenly on their own: a mid-2026 count found 109 state AI laws and 28 data center laws enacted as of July 1, with no comparable federal data-collection requirement yet in place.

🔹 The bill would require employers to disclose when AI was a factor in a mass layoff, the first federal disclosure requirement of its kind if it passes

🔹 It authorizes DOL to hire dedicated AI specialists and stand up an AI Workforce Research Hub, agency capacity that doesn't currently exist

🔹 It arrives against a backdrop of 109 state AI laws already enacted as of July 1, 2026, with no federal data layer to compare them against

💡 Right now, no federal survey asks how AI is actually used at work or which occupations are most affected, so policymakers, and organizations like RochesterWorks benchmarking local impact, are working from state-by-state anecdotes rather than comparable national data. This bill targets that specific gap rather than proposing new AI rules itself.

Impact: Long-term; the bill would need to pass and then data collection would need to begin before it changes anything measurable.

🔊 10. Not All Bad News: Select Employers Cautiously Resume Hiring

Not every recent signal points toward contraction. Alphabet, CSX, Booz Allen Hamilton, ServiceNow, and Snap-on all told investors or reporters in late July they expect to add headcount in specific areas even after months of hiring restraint. Alphabet is hiring into AI and cloud computing specifically. ServiceNow is building out its sales organization, including cybersecurity sales. Booz Allen's COO, Kristine Martin Anderson, said the government contractor needs to rebuild after cutting thousands of jobs last year and now needs cleared national-security talent it doesn't have. CSX plans a moderate increase in train and operations staff to meet demand, and Snap-on said it plans to expand its workforce.

🔹 Booz Allen's own COO tied the rehiring directly to last year's cuts going too far for current government-contracting demand, a rare public admission that a round of AI-era cuts overshot

🔹 The hiring is concentrated in specialized, cleared, or sales-facing roles (national-security clearance, cybersecurity sales, AI/cloud), not a broad return to pre-2025 headcount levels

🔹 Executives across all five companies frame the need as more people working alongside AI systems, not fewer, directly contradicting the pure-replacement narrative in this week's layoff headlines

💡 Read next to Visa, Chime, and Monday.com's cuts the same week, this isn't a contradiction, it's the same underlying shift showing up as both hiring and firing depending on whether a company's AI rollout increased or decreased its need for specialized human judgment.

Impact: Emerging; worth tracking whether this becomes a broader pattern into Q4 or stays limited to these five companies.

BOTTOM LINE THIS WEEK:

The throughline this week is less "AI is destroying jobs" and more "AI is changing who decides, and how carefully." Visa, Chime, and Monday.com all cut headcount citing AI, but the more consequential story may be the ResumeTemplates.com survey showing a meaningful share of managers now let AI make layoff calls with input, like age and medical leave, that discrimination law treats very differently than a performance score. Revelio Labs' tracker and Gallup's sentiment data both confirm real strain concentrated in entry-level and AI-exposed roles, even as AI-adopting firms grow overall and a handful of major employers quietly resumed hiring.

Unions, not new legislation, remain the most concrete tool available to the workers most exposed, and that tool is least available in exactly the industries, finance, professional services, tech, where the exposure is highest. Congress is asking for better data rather than new guardrails, which is a real start but not a fix. For job seekers and workforce professionals, the practical read holds from recent weeks: treat any single AI-layoff or AI-hiring headline as a data point to investigate against the company's own numbers, not a verdict, and pay closer attention to how, not just whether, AI is entering high-stakes decisions like layoffs and hiring.

Stay curious, stay current

Dan Lopez | danscareercorner.com