“We’re not just cutting costs anymore—we’re recalibrating for a world where junior code gets written by AI and senior judgment becomes the only scarce resource.”

Having been through multiple reduction in force (RIF) events as both a participant and leader, I’ve seen the stark difference between well-executed and poorly managed layoffs. But today’s RIF wave feels fundamentally different. This isn’t just about economic pressure or overhiring corrections—it’s about a technological shift that’s forcing companies to confront decades of bad incentives and cultural rot.

Why This RIF Wave is Different

Let’s be honest about what’s driving today’s layoffs. Yes, there’s economic pressure and post-pandemic hiring corrections. But the elephant in the room is generative AI fundamentally changing what human labor is worth.

The Junior Dev Squeeze: Meta’s recent RIF targeting junior developers isn’t an anomaly—it’s a preview. GenAI can already handle the typical L4 SDE workload: writing straightforward functions, debugging simple issues, implementing well-defined features. What it can’t do yet is the L5+ work: architecting systems, making complex tradeoffs, orchestrating AI tools to solve ambiguous problems.

The Pandemic Hiring Bubble: During COVID’s tech surge, companies hired aggressively based on the assumption that all software engineering work was equally valuable. Coding bootcamp graduates who learned React in 12 weeks were getting $120K offers at major tech companies. It felt democratizing at the time—and it was genuine opportunity for many—but it also created a mismatch between skill levels and market needs that’s now being brutally corrected.

The Empire Building Reckoning: For years, companies incentivized managers to grow team sizes as a proxy for career advancement. More reports meant more influence, higher compensation, greater perceived impact. This created a perverse incentive to hire for the sake of hiring, optimize for team growth over output, and resist efficiency improvements that might reduce headcount needs.

The fundamental truth? This RIF wave isn’t just about buying time—it’s about rightsizing for a world where AI handles routine work and human judgment becomes the primary differentiator.

The Right Way: Transparent and Strategic

I’ve been fortunate to experience RIFs conducted by leadership teams who understood that how you handle these moments defines your culture for years to come.

Months of Planning: The best RIF I experienced involved months of preparation. Leadership war-gamed scenarios, modeled different cut depths, and carefully considered which teams and projects were truly essential versus nice-to-have.

Seeking Input: Before making final decisions, senior leadership sought input from department heads and key managers. “If we need to reduce headcount by 15%, what would you prioritize keeping?” These weren’t just pro-forma consultations - input genuinely influenced decisions.

Clear Criteria: The criteria for layoffs were transparent and consistently applied:

• Performance ratings over the past year
• Criticality of role to core business functions
• Skills transferability to priority projects
• Team redundancies where multiple people could do similar work
• Future-proofing against AI capabilities

Communication Strategy: Leadership over-communicated during this period. All-hands meetings explained the business context, the decision-making process, and timeline. Even before cuts were announced, the company was transparent about challenges and potential scenarios.

Generous Packages: Severance was meaningful - often 3-6 months plus extended healthcare. Career transition support, job placement assistance, and glowing references were standard. The message was clear: “This isn’t about you, it’s about us.”

The result? Even people who were laid off largely understood and respected the process. Alumni became advocates rather than detractors. The remaining team, while sad, trusted leadership and could focus on execution rather than wondering if they were next.

The Wrong Way: Opacity and Chaos

I’ve also experienced the polar opposite - a RIF so poorly managed it damaged the company’s reputation and culture for years.

No Warning Signs: Leadership maintained an optimistic facade right up until the day of cuts. All-hands meetings weeks prior focused on growth plans and new initiatives. The disconnect between messaging and reality was jarring.

Arbitrary Decisions: As a key leader, I wasn’t consulted about which roles or people were critical to our team’s success. Decisions seemed to come from spreadsheets rather than strategic thinking. High performers in essential roles were cut while underperformers in peripheral functions remained.

Poor Communication: The announcement came via a terse email at 6 AM. No explanation of business context, no rationale for the scope, no vision for the path forward. Managers learned about cuts to their teams at the same time as everyone else.

Inadequate Support: Severance was minimal. No job placement assistance. References were grudging at best. The message felt like “you’re a cost center we need to eliminate” rather than “we value you but can’t afford you.”

Information Vacuum: In the absence of clear communication, rumors filled the void. People spent more time speculating about future cuts than focusing on work. Productivity plummeted as everyone wondered if they were next.

The aftermath was predictable: talent flight, cultural breakdown, and a reputation that made hiring difficult for years. Even customers noticed the service impact from lost institutional knowledge and demoralized teams.

The Quota RIF Problem

Today’s most troubling trend isn’t poorly executed RIFs—it’s companies conducting RIFs just to keep up appearances. I’m seeing leadership teams scrambling to hit arbitrary “15% reduction targets” because that’s what Meta or Amazon announced, not because their business fundamentally requires it.

This cargo cult approach to layoffs misses the point entirely. The companies making smart cuts are doing so based on genuine strategic analysis: which roles add value in an AI-augmented world, which teams drive core business metrics, which skills will remain human-essential versus AI-replaceable.

Random percentage cuts are just empire building in reverse—making decisions based on optics rather than outcomes.

Culture is What You Punish and Reward

The RIF wave has exposed a fundamental truth about corporate culture: culture is simply what you punish and what you reward.

For years, tech companies rewarded:

• Team size growth over output efficiency
• Feature velocity over customer impact
• Individual contributor promotions based on complexity rather than business value
• Management advancement based on headcount rather than results

And they consistently failed to punish:

• Empire building that optimized for org chart position over business needs
• Hiring for hiring’s sake during boom periods
• Managers who couldn’t articulate their team’s business impact
• Processes that prioritized consensus over accountability

“But outputs can’t be defined!” Wrong. It takes good leadership to define and measure what matters. The companies thriving through this transition have leaders who can answer:

• How does this team’s work translate to business metrics?
• Which roles would we rehire immediately if budget allowed?
• What would we lose if this person/team disappeared tomorrow?
• How has AI changed what skills we actually need?

If you can’t answer these questions, you’re not leading—you’re just managing headcount.

The New Skills Hierarchy

GenAI is creating a stark new hierarchy of human value:

AI-Replaceable (Today): Writing boilerplate code, basic debugging, implementing well-specified features, routine testing, simple documentation.

AI-Augmented (Near Future): Complex system design with AI assistance, advanced debugging with AI tools, architectural decisions using AI analysis, strategic planning enhanced by AI insights.

Human-Essential (Foreseeable Future): Orchestrating AI tools to solve ambiguous problems, making complex business tradeoffs, building relationships and trust, creative problem-solving in novel domains, cultural and ethical judgment calls.

The companies getting RIFs right are using this framework. They’re keeping people who can work with AI effectively while cutting roles that AI will soon perform better and cheaper.

Lessons for Leaders

Measure Output, Not Empire Size: Stop promoting managers based on team growth. Start measuring business impact per employee, efficiency gains, and strategic value creation. The era of “more people = more important” is over.

Define Success Clearly: If you can’t articulate what success looks like for each role, you can’t make intelligent RIF decisions. Good leaders create clarity about what matters and why.

Culture Design is Strategic: What you reward shapes what you get. If you want efficiency and impact, reward those behaviors explicitly. If you want innovation and judgment, measure and incentivize those outcomes.

Plan for AI Integration: Don’t just cut the “AI-replaceable” roles—train your remaining team to work effectively with AI tools. The winners won’t be companies with the fewest humans, but companies with humans who are most effective with AI augmentation.

Process Matters as Much as Outcome: How you conduct a RIF shapes your company’s culture and reputation far more than the specific people affected. A well-managed RIF can actually strengthen trust in leadership, while a poorly managed one destroys it permanently.

Information Asymmetry is Toxic: When leadership has information that teams don’t, and that information affects people’s livelihoods, transparency becomes a moral imperative. The void of information gets filled with anxiety and rumors.

Generosity Pays Dividends: Generous severance and support isn’t just humane—it’s strategic. Alumni can become customers, partners, or even return as employees during better times. Reputation in talent markets is everything.

Final Thoughts

We’re in the middle of the most significant reshuffling of human economic value since the industrial revolution. GenAI isn’t just changing what work gets done—it’s forcing companies to finally confront what human judgment is actually worth.

The companies that handle this transition well will emerge stronger: leaner teams doing higher-value work, clearer accountability for business outcomes, and cultures that reward impact over empire building.

The companies that handle it poorly—those doing quota RIFs, cutting indiscriminately, or failing to integrate AI effectively—will find themselves with the worst of both worlds: reduced capability without improved efficiency.

RIFs are often inevitable in periods of technological transition. But they don’t have to be traumatic for culture and reputation. The difference between a RIF that strengthens your organization and one that devastates it comes down to leadership choices about process, measurement, and values.

In my experience, how leadership handles adversity—especially when it affects people’s livelihoods—is the truest test of company values. It’s easy to talk about “people first” when times are good. The real test comes when hard decisions must be made under technological and economic pressure.

The great recalibration is here. Choose wisely. Your future depends on it.