IBM is tripling its US entry-level hiring in 2026. Not despite AI agents automating junior work, but because of it. The company’s CHRO, Nickle LaMoreaux, announced the move at Charter’s Leading with AI Summit in February, and her reasoning is the opposite of what you hear from most tech executives: “We are tripling our entry-level hiring, and yes, that is for software developers and all these jobs we’re being told AI can do.”
This is happening in a market where junior tech postings dropped 67% between 2023 and 2024 (Stanford Digital Economy Lab), new graduate unemployment hit a 37-year high of 13.3% in July 2025 (Fortune), and ServiceNow’s CEO warned that college graduate unemployment “could easily go into the mid-30s” within two years. IBM is not swimming against the current for sentimental reasons. They are making a calculated bet that most of their competitors are about to make a very expensive mistake.
The Old Entry-Level Job Is Dead. The New One Is Worth More.
LaMoreaux was blunt about why the old junior role no longer exists: “The entry-level jobs that you had two to three years ago, AI can do most of them.” IBM’s own AskHR chatbot handles 11.5 million interactions per year with a 94% containment rate. That means only 6% of employee questions reach a human HR partner. The routine answering, the data lookups, the FAQ responses that used to keep dozens of junior HR staffers busy are gone.
But IBM did not just eliminate those roles and pocket the savings. They redesigned them. Junior HR staffers now intervene when the chatbot underperforms, correct AI outputs, and handle the complex employee situations that require judgment and empathy. Junior software developers still write code, but they spend significantly more time working directly with clients, gathering customer feedback, and building first-of-a-kind products rather than maintaining existing ones.
The pattern: strip the rote work out of a junior role, replace it with higher-value tasks that an AI cannot do, and you end up with an entry-level position that justifies a bigger investment. IBM saved $3.5 billion annually through AI and automation by the end of 2024, on track for $4.5 billion by end of 2025. They reinvested those savings into roles that require humans to do what humans do best: talk to clients, exercise judgment, build relationships.
What Redesigned Roles Actually Look Like
Take IBM’s junior developers. The old version spent 70% of their time on boilerplate code, bug fixes, and test writing. The new version uses AI coding agents for those tasks and spends that recovered time on:
- Client interaction: sitting in customer meetings, understanding real pain points, translating business needs into technical requirements
- Cross-functional collaboration: working with marketing, sales, and product teams instead of sitting in a dev silo
- First-of-a-kind builds: prototyping new products rather than maintaining legacy systems
- AI oversight: reviewing and correcting what the coding agents produce, developing the judgment that turns a junior into a senior
This is not a cosmetic rename. It is a fundamentally different job with a steeper learning curve and a higher ceiling.
The Two-Speed Workforce: Who Wins and Who Loses
IBM’s move reveals a structural split forming across the tech industry. Call it the two-speed workforce.
Speed one: companies investing in AI-native entry-level talent. IBM is tripling hiring. McKinsey plans a 12% hiring increase for junior staff in North America. Cognizant is hiring 24,000 to 25,000 fresh graduates in 2026, up 20% from last year. AWS CEO Matt Garman called replacing juniors with AI “the dumbest thing I’ve ever heard” because it destroys the talent pipeline.
Speed two: companies cutting entry-level headcount and betting on AI agents. The Korn Ferry survey of 1,670+ talent leaders found 37% of organizations plan to replace entry-level roles with AI. Junior tech postings dropped 67%. UK tech graduate roles fell 46% in 2024. 35% of “entry-level” positions now require prior work experience, and 60% of entry-level tech jobs require three or more years of experience, which is not entry-level by any definition.
The speed-two companies are creating what LaMoreaux calls a “hollowing out” effect. Cut the juniors today, save money this quarter, and in three to five years you have no mid-level managers, no senior technical leaders, and no institutional knowledge. You have a barbell-shaped workforce: expensive seniors at one end, AI agents at the other, and nothing in between.
The Leadership Pipeline Problem
This is not a theoretical concern. LaMoreaux put a timeline on it: “I think the companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment.”
The logic is straightforward. Senior engineers do not appear from nowhere. They develop through 5 to 10 years of progressively complex work, starting with the junior tasks that AI now handles. If you eliminate junior roles, you eliminate the pipeline that creates your next generation of architects, tech leads, and engineering managers. You can try to hire them externally, but if every company hollowed out their pipeline at the same time, there will not be enough experienced talent to go around. The bidding war for senior engineers, already fierce, will become catastrophic.
BlackRock CEO Larry Fink called this a “crisis” in March 2026, committing $100 million to skilled-trade training programs targeting 50,000 workers over five years.
IBM’s Playbook: From Productivity Savings to Growth Investment
CEO Arvind Krishna framed the strategic pivot clearly: “This is the big pivot for 2026. I think the productivity story has probably played out. We need to move to this idea that AI is not just driving productivity, but how does it drive growth and value?”
IBM’s numbers support this. The company saved 3.9 million employee hours in 2024 through AI automation. Rather than converting those hours into headcount reductions, they redirected the savings into three areas:
- More entry-level hiring across software development, sales, and consulting
- AI infrastructure investment, including the Watson Orchestrate platform that powers agentic workflows
- Client-facing roles that generate revenue rather than just cut costs
The result: IBM’s total employment has remained stable or grown even as AI automated millions of hours of work. Krishna told CNN in October 2025: “Our total employment has actually gone up, because what [AI] does is it gives you more investment to put into other areas.”
This contrasts sharply with the approach at companies like Block (40% workforce reduction), Salesforce (4,000 customer service cuts), and Meta (planning 16,000 cuts to fund AI infrastructure). Those companies are treating AI as a cost-cutting tool. IBM is treating it as a growth multiplier.
What This Means for Hiring Managers and Job Seekers
The two-speed workforce creates different imperatives depending on which side of the hiring table you sit on.
For hiring managers: the question is no longer “can AI do this entry-level task?” The answer is usually yes. The question is “what should an AI-augmented entry-level employee do instead?” IBM’s answer is client work, cross-functional collaboration, AI oversight, and innovation. If your redesigned junior role looks like a watered-down version of the old one, you have not thought hard enough about what humans uniquely contribute.
For job seekers: the skills that get you hired into a redesigned entry-level role are different from what worked five years ago. Technical proficiency is table stakes. What separates candidates now is the ability to work with AI tools productively, communicate across functions, exercise judgment on ambiguous problems, and interact directly with clients or stakeholders. IBM’s CHRO said it plainly: “If you’re going to convince your business leaders that you need to make this investment, then you need to be able to show the real value these individuals can bring now.”
The World Economic Forum’s Future of Jobs Report 2025 projects 170 million new roles emerging by 2030 against 92 million displaced, a net gain of 78 million jobs. But those new roles require fundamentally different skills. The gap between what entry-level workers can do and what employers need them to do is where the real crisis lives.
Frequently Asked Questions
Why is IBM tripling entry-level hiring when AI can do those jobs?
IBM redesigned entry-level roles so AI handles the routine tasks (boilerplate code, FAQ responses, data lookups) while new hires focus on client interaction, cross-functional work, AI oversight, and first-of-a-kind product development. The new role is more valuable, not less, which justifies the larger investment.
What is the two-speed workforce?
The two-speed workforce describes a structural split in the labor market. Speed one companies (IBM, McKinsey, Cognizant) invest in AI-augmented entry-level talent. Speed two companies eliminate junior roles to cut costs, creating a hollowing-out effect that will lead to leadership and mid-level talent shortages in three to five years.
How many entry-level jobs has AI eliminated in 2025 and 2026?
Junior tech postings dropped 67% between 2023 and 2024 according to Stanford Digital Economy Lab data. UK tech graduate roles fell 46% in 2024. Recent college graduate unemployment hit 5.6% per the New York Federal Reserve, near levels not seen since 2013. The share of unemployed Americans who are new workforce entrants reached a 37-year high of 13.3% in July 2025.
Which companies are still hiring entry-level workers despite AI automation?
IBM is tripling US entry-level hiring in 2026. McKinsey plans a 12% increase in North American junior hiring. Cognizant is hiring 24,000 to 25,000 fresh graduates, up 20% from 2025. AWS CEO Matt Garman called replacing juniors with AI “the dumbest thing I’ve ever heard” because it destroys the talent pipeline.
What skills do entry-level workers need in an AI-augmented workplace?
Technical proficiency is now table stakes. IBM’s redesigned roles emphasize AI tool proficiency, client communication, cross-functional collaboration, judgment on ambiguous problems, and the ability to oversee and correct AI outputs. The World Economic Forum projects 170 million new roles by 2030, but they require fundamentally different skills than traditional entry-level positions.