Cerebras CEO Andrew Feldman posits that recent tech layoffs are a delayed reaction to efficiency gains from mature SaaS tools, not yet AI. These tools increased managers' span of control, reducing the need for middle managers whose primary role was moving information up and down the chain.
AI automates the entry-level "grunt work" that traditionally formed the base of the corporate pyramid. This transforms organizations into diamond shapes, with fewer junior roles. This poses a new challenge: junior hires may know AI tools but lack the wisdom and judgment gained from that foundational experience.
Current layoffs are driven less by AI-driven automation and more by financial strategy. Companies are cutting labor costs to free up budget for necessary AI investments and to project an image of being technologically advanced to investors.
Recent tech layoffs, widely attributed to AI, are more likely driven by rising interest rates and a cultural shift for leaner operations. CEOs may be using AI efficiency as a convenient public justification for these cuts, even if the technology hasn't caused widespread displacement yet.
Firms are attributing job cuts to AI, but this may be a performative narrative for the stock market rather than a reflection of current technological displacement. Experts are skeptical that AI is mature enough to be the primary driver of large-scale layoffs, suggesting it's more likely a convenient cover for post-pandemic rebalancing.
The conversation around AI and job reduction has moved from hypothetical to operational. Leaders are being instructed by boards and investors to prepare for 10-20% workforce cuts, ready to be executed. This isn't a future possibility; it's an active, ongoing preparation phase within many large companies.
While companies cite AI when announcing layoffs, the data shows cuts are concentrated in industries that over-hired post-pandemic. Job losses in sectors like tech and professional services represent a "reversion to the mean" trendline, countering the narrative that AI is already replacing workers at scale.
Instead of immediate, widespread job cuts, the initial effect of AI on employment is a reduction in hiring for roles like entry-level software engineers. Companies realize AI tools boost existing staff productivity, thus slowing the need for new hires, which acts as a leading indicator of labor shifts.
While official unemployment rates remain low, a wave of "invisible unemployment" is hitting tech. Companies are achieving growth with flat headcount by leveraging AI, leading to a quiet squeeze on entry-level roles, mid-level performers, and senior executives with outdated skills who are leaving the workforce without being replaced.
Contrary to the popular narrative, AI is not yet a primary driver of white-collar layoffs. Instead of eliminating roles, it's changing the nature of work within them. For example, analysts now spend time on different, higher-value activities rather than manual tasks, suggesting a shift in job content rather than a reduction in headcount.
Firms might be publicly attributing job cuts to AI innovation as a cover for more conventional business reasons like restructuring or weak demand. This narrative frames a standard cost-cutting measure in a more forward-looking, strategic light, making it difficult to gauge AI's true, current impact on jobs.