Crypto.com became the latest company to blame AI for workforce cuts, laying off 12% of staff on March 19. CEO Kris Marszalek didn’t sugarcoat it: companies that don’t pivot to AI “will fail.”
That announcement brings the first quarter’s AI-attributed layoffs to over 9,200, out of 45,000 total tech job cuts. The pattern is now unmistakable: one in five tech layoffs explicitly cites artificial intelligence as the reason.
The March Body Count
Crypto.com’s 180-person cut hit growth and customer relationship teams hardest. Marszalek framed it as existential: “Companies that move slowly will be left behind. Companies that move immediately and pair the best AI tools with top-performers will achieve a level of scale and precision that was previously impossible.”
The company recently spent $70 million acquiring the ai.com domain. The messaging is clear: they’re spending on AI assets while cutting the humans AI is meant to replace.
Crypto.com joins a growing list of Q1 cuts explicitly tied to AI:
- Block: 4,000 jobs (40% of workforce) - the largest single AI-attributed layoff in history
- Atlassian: 1,600 jobs (10% of workforce) on March 11
- WiseTech Global: 2,000 jobs in AI-driven restructuring
- Meta: 16,000 planned cuts as part of AI pivot
Amazon accounts for the largest absolute number, cutting approximately 30,000 positions in early 2026. Seattle has been hit hardest geographically, with over 16,500 affected workers, followed by San Francisco with nearly 9,400.
The Scale
Through early March, 45,363 tech jobs have been cut globally. Of those, 9,238 (20.4%) explicitly cited AI and automation as factors.
That AI attribution rate has nearly tripled from 2025, when AI was mentioned in fewer than 8% of layoff announcements.
If cuts continue at this pace, total reductions could reach 264,730 by year-end, exceeding 2025’s 245,000 layoffs. But analysts are divided on whether this represents genuine AI displacement or something else.
Real Displacement or Convenient Excuse?
A growing debate centers on whether companies are using AI as cover for broader cost-cutting.
The skeptics argue: If AI were truly driving productivity gains, we’d see it reflected in output metrics. Instead, many of the same companies citing AI are also dealing with post-pandemic overhiring, rising interest rates, and slowing revenue growth. AI makes a better headline than “we hired too many people in 2021.”
But the supporters counter: tasks traditionally handled by junior staff are measurably cheaper to automate now. Customer service, content generation, code review, data entry, and basic analysis are all areas where AI tools have dropped costs dramatically. Whether companies are cutting because AI is ready or because they believe AI will be ready soon, the effect on workers is the same.
The truth is probably both. AI is genuinely automating some tasks while also providing convenient framing for cuts companies would have made anyway. The distinction matters less than the outcome: fewer jobs exist.
The Junior Developer Crisis Deepens
While senior AI roles go unfilled, entry-level positions continue collapsing.
Entry-level tech job postings fell 46% in the UK in 2024, with projections hitting 53% by end of 2026. In the US, some datasets show a 67% decline in junior opportunities.
The mechanism is straightforward: junior developers historically learned by doing grunt work. Write boilerplate. Fix simple bugs. Format documents. Review pull requests for style issues. AI now handles much of this faster and cheaper than a trainee.
Most entry-level job postings now require two to five years of experience, according to hiring managers. The pathway that trained generations of developers is breaking down.
One consequence: developers trained primarily on AI assistance may lack fundamental debugging skills. When the AI generates incorrect code, they struggle to identify why. This creates a potential quality crisis down the road, as fewer developers gain deep understanding of systems.
The Compensation Split
For those who do break through, the rewards remain exceptional.
AI-related roles command a 67% salary premium over traditional software engineering positions. AI/ML engineers at the professional level earn 12% more than non-AI peers, and the gap widens at senior levels.
Average AI engineer compensation in the US ranges from $140,000 to $185,000 base, with total packages pushing past $200,000 for mid-career engineers and clearing $300,000 at senior levels.
The demand-supply imbalance is severe: AI talent shortage now sits at 3.2:1 across key roles. Companies need 3.2 qualified AI workers for every one available.
AI/ML hiring grew 88% year-over-year, with AI/ML Engineer representing 45% of all AI/ML job titles. LLM fine-tuning, deep learning, and NLP top the demand charts, while MLOps expertise is increasingly the bottleneck that determines whether AI investments deliver production value.
Who’s Actually Hiring
Despite the layoffs, certain companies are actively expanding AI teams:
- Hyperscalers: Google, Amazon, and Microsoft continue hiring AI specialists for cloud services
- AI labs: OpenAI, Anthropic, and newer players like Lambda and Reclaim.ai are scaling
- Enterprise software: SAP, Salesforce, and ServiceNow are building AI product teams
- Consulting firms: Accenture, Deloitte, and McKinsey report strong AI practice growth
The pattern: companies building AI are hiring. Companies using AI to cut costs are firing. The net effect depends on which group is larger.
What This Means
The labor market is stratifying along AI lines.
Senior engineers with AI expertise face a seller’s market. Multiple offers, strong compensation, reasonable job security. The talent shortage at this level is genuine and persistent.
Mid-career workers face a transition period. Those who develop AI skills have paths forward. Those who don’t are competing for a shrinking pool of traditional roles.
Entry-level workers face the toughest market in decades. The traditional training pathway is broken. Companies aren’t investing in developing talent they believe AI will make redundant. Breaking in requires demonstrable skills without the jobs that traditionally taught those skills.
The layoffs will continue. Whether AI is the real cause or a convenient justification, the result is the same: fewer traditional tech jobs, more pressure to develop AI competency, and a widening gap between those with the right skills and everyone else.
What You Can Do
If you’re job hunting:
- Target companies building AI, not just using it to cut costs
- Develop demonstrable AI skills through projects, not just credentials
- Consider adjacent roles (product, project management) that require human judgment
- Document everything in portfolios since traditional experience pathways are shrinking
If you’re employed:
- Build AI competency now while you have access to training resources
- Position yourself on the “augmented by AI” side rather than “replaced by AI”
- Watch for early warning signs: task automation, headcount discussions, restructuring talk
If you’re hiring:
- Consider the IBM approach: investing in entry-level talent creates future leadership
- Companies cutting all junior roles today will face talent pipeline problems tomorrow
- The talent shortage is real for senior roles; developing from within may be more reliable than competing for scarce external candidates
The Q1 numbers are in. The trend is clear. The question isn’t whether AI will reshape the job market. It’s whether you’ll be on the right side of that reshape.