Salesforce regrets firing 4000 experienced staff and replacing them with AI

Decision-making, incentives, and “AI readiness”

• Commenters see the core failure as executives “estimating” and “assuming” AI maturity instead of running proper trials, phased rollouts, or realistic pilots.
• Many argue the move was driven by bonuses, branding, and Wall Street signaling (profit focus, “innovation” narrative, AI FOMO) rather than sober technical evaluation.
• There is strong sentiment that leadership will face no real consequences despite the scale of the error, contradicting the usual justification for huge executive pay.

Skepticism about the article and sourcing

• Multiple participants call the linked article likely AI-generated, pointing to style tells (odd bolding, generic tone), missing bylines, and no direct sourcing for claimed “internal discussions.”
• Others trace the story back to reporting from The Information and secondary summaries (Times of India, other outlets), but note that key sources are paywalled and not easily verifiable.
• Some question the central claim that 4,000 people were laid off because of AI, suggesting the macroeconomy and generic cost-cutting are more plausible drivers.

What Salesforce does and product perceptions

• Several comments express confusion about what Salesforce actually does, followed by explanations: complex, enterprise CRM glued into everything, heavy on consultants and vendor lock-in.
• UI/UX is widely criticized as convoluted and “non-intuitive,” compared unfavorably even to other enterprise tools (SAP, Jira, Teams). Slack’s acquisition and integration are also debated.

Limits of AI/LLMs in customer support

• Many argue current LLMs are good at plausible text but poor at high-stakes correctness, edge cases, and “unknown unknowns” typical of tier-2/3 support.
• Effective AI support would require exhaustive, constantly updated documentation and robust search; in reality, crucial know‑how lives in senior engineers’ heads, chats, and intuition.
• Verification is seen as harder than generation: for complex tasks, carefully checking AI output can take as long as doing the work yourself, erasing productivity gains.
• Discussion extends to theoretical AI (AGI/ASI, omniscience, formal verification), with consensus that intent specification, ambiguity, empathy, and real-world messiness remain unsolved.

Labor impact, accountability, and corporate behavior

• Several comments stress that Salesforce likely regrets the cost/benefit outcome, not the human impact of firing thousands.
• There are calls for strict liability for harms caused by black-box AI systems and criticism of “rightsizing” euphemisms.
• Some see this as a useful cautionary case employees can cite when management proposes mass replacement of humans with LLMs.

Meta: HN culture and AI hype

• A side thread critiques HN’s rising volume of AI “slop” and shilling; moderators respond by emphasizing community standards and tools (flagging, downvoting) to keep quality up.