The AI Layoffs No One Is Explaining Properly

The recent AI layoffs feel unsettling at first. Look closer, and it’s executional work being removed. Is this the new norm?

If you look at layoffs across the tech industry, SaaS, fintech, retail, DTC, and global digital-first businesses, it can feel scattered. But when you look more closely a clear pattern emerges. Over the past 12 to 18 months, and especially into Q1 2026, companies have moved beyond experimenting with AI and are now restructuring around it.

In January 2026, Block Inc., led by Jack Dorsey, cut roughly 4,000 roles, close to 40% of its workforce. Revenue was strong. This was not driven by economic pressure, but a structural decision tied to AI productivity and a shift toward leaner, higher-output teams.

A similar shift has played out at Salesforce, where thousands of roles were reduced as AI systems began handling close to half of all customer support interactions. At Klarna, approximately 700 roles were impacted as AI replaced large portions of support workflows, with some human roles later reintroduced when limitations surfaced in more complex cases.

The same pattern is showing up across IBM, HP, Google, Amazon and others. Across SaaS companies, fintech platforms, retail and DTC brands, CPG organizations, and high-growth digital businesses, leadership teams are reducing execution-heavy roles while increasing investment in AI and high-leverage talent.

What connects these decisions is not a broad replacement of knowledge workers, but a targeted removal of predictable work.

Inside these organizations, the impact is specific and consistent across functions.

At Salesforce, the reductions were concentrated in customer support roles, particularly tier 1 and tier 2 service agents, as AI systems began handling a significant share of customer inquiries.

At Klarna, approximately 700 customer service roles were impacted as AI chatbots replaced large portions of support workflows, although the company later reintroduced some human roles after limitations surfaced in more complex cases.

At IBM, the shift has focused on HR and back-office functions, including recruiting coordinators, HR administrators, and roles tied to payroll, scheduling, and internal process management, with a plan to replace a meaningful portion of these roles through automation over time.

At Block Inc., while the company did not isolate one function publicly, the scale of the reduction reflects a broad restructuring across teams as AI enables higher output with fewer people.

At Duolingo, the company reduced reliance on contract translators and language content contributors, as AI systems began generating large volumes of language-based content.

Across Google and Amazon, workforce reductions have included operational support roles, program coordination, and content operations, particularly in areas where work is structured, repeatable, and executed at scale.

More recently, the pressure has begun to extend into execution-focused technical and creative roles. This includes junior developers and QA testers working on repetitive code and testing scripts, web developers focused on templated builds and CMS-driven environments, content writers producing SEO-driven articles and product copy, marketing coordinators executing campaigns, and data analysts primarily focused on reporting.

These roles are not disappearing entirely, but the number of people required to perform them is shrinking as AI takes on more of the execution.

This is the shift shaping hiring across startups, scaleups, and global organizations.

Companies are removing layers that translate decisions into execution and replacing them with systems that can execute instantly. The roles most affected are those where the process is defined, the inputs are predictable, and the output is repeatable.

At the same time, the value of leadership, creativity, and decision-making is increasing. Founders, CEOs, and executive teams are under pressure to rethink how they build their organizations, particularly across marketing, product, operations, and technology leadership.

For HR Leaders, Chief People Officers, and executive hiring teams, this is where the shift becomes more complex. The mandate is no longer to fill roles as defined, but to rethink what those roles should be. Traditional hiring frameworks are not built for this level of change, particularly across executive search mandates for CEO, CMO, CTO, COO, CFO, CDO, CIO, and CGO roles in high-growth environments.

AI does not generate direction on its own. It responds to inputs. When those inputs are thoughtful and informed, the output improves. When they are not, it simply scales the same limitations.

For organizations operating in digital transformation, innovation, and high-growth environments, this creates a new lens on executive search. The focus shifts from experience and functional expertise alone to leadership capability, adaptability, and the ability to operate in ambiguity.

Many companies are still hiring based on outdated role definitions, building teams around execution capacity rather than leadership capability. Job descriptions often reflect work that is already being automated, and hiring decisions are still being made based on output rather than impact.

What Q1 2026 highlights is a structural shift in how companies scale. Teams are becoming smaller, expectations are increasing, and the gap between execution and value creation is widening.

Founder & Executive Takeaways

• AI is not removing entire functions. It is removing the predictable work inside them. Roles built on repeatable tasks are already under pressure.

• The biggest exposure in your organization is not performance. It is where work is structured around execution instead of judgment.

• Hiring for output is becoming less effective. Hiring for leadership, thinking, and decision-making is becoming critical across marketing, product, operations, and technology roles.

• Many job descriptions across startups, scaleups, and enterprise organizations are already outdated. If the role can be defined step by step, AI will likely replace part of it.

• The middle layer of organizations, including coordination-heavy and process-driven roles, will continue to compress.

• Developing junior talent will become more complex. If AI handles repetitive work, leaders need to rethink how teams learn and grow.

• AI amplifies the quality of thinking behind it. Strong inputs create leverage. Weak inputs scale problems.

• The key question for founders, CEOs, and Chief People Officers is simple. Is this person shaping the work or executing it? That answer determines how durable the role is.