Who Gets Cut In A Layoff?

We explain how companies decide who gets laid off and why, from the outside, the process often feels random and opaque.

- There is no universal rule for layoff selection, as decisions vary heavily by company, leadership style, and timing, making it impossible for individual contributors to reliably predict outcomes.
- Common factors considered include performance, the business importance of a team, seniority, and tenure, but each of these can cut both ways depending on company priorities and cost pressures.
- From an individual contributor’s perspective, mass layoffs often feel random because decisions are made quickly, by very few executives, and with incomplete information to avoid panic.
- At large companies, layoffs are often driven by shareholder or board pressure to improve profitability, while startups are guided by burn rate, runway, and investor feedback in a tighter funding environment.
- Layoffs can also be opportunistic, used to trim perceived excess, remove deprioritized teams, or reset perks, with additional randomness introduced by legal safeguards like manager quotas to reduce lawsuit risk.

Signs A Layoff Is Coming

We outline the practical signals that can help you tell when layoffs may be coming, especially in tech companies.

- For public companies, a prolonged and severe stock price decline is a strong warning sign, as companies generally can’t sustain operations when shares remain far below their highs.
- For startups and private companies, stagnant growth, hiring freezes, and an indefinite pause on recruiting often indicate belt-tightening ahead of potential layoffs.
- Companies preparing for layoffs frequently cut perks first, and leadership demeanor may shift noticeably through more guarded communication and changes that trickle down to managers.
- Gossip can surface through internal networks or platforms like Blind, but it’s unreliable and stressful, so the most consistent signal is the broader move into an “age of austerity,” where profitability replaces growth and visible reductions in spending become the norm.

The Effect of Interest Rates

We walk through how macroeconomic conditions—especially interest rates—directly affect tech companies and tech workers.

- The federal funds rate guides how expensive it is for banks (and ultimately companies) to borrow money, and while near-zero rates felt normal recently, historically higher interest rates are actually the norm.
- When interest rates rise, the cost of borrowing increases, which makes companies far less willing to invest in long-term bets that may not pay off for several years.
- Because the tech industry is heavily driven by growth funded through borrowing and investment, higher rates trigger spending pullbacks, reduced hiring, and ultimately the contraction and layoffs we’re seeing across the market.

Why Layoffs Happen

We explain why layoffs happen and why they’re a recurring reality in the tech industry.

Here are the core points from the lesson:

- Layoffs occur when companies face bad economics, often triggered by unforeseen macroeconomic shifts like the post-COVID slowdown after years of stimulus-fueled growth.
- Overambitious hiring is a major contributor, especially when overly positive economic conditions lead companies to scale headcount faster than the business can sustainably support.
- Fundamentally unsound business models and poor strategic direction also drive layoffs, and in practice, these factors usually overlap—making layoffs inevitable since executives can’t make perfect decisions all the time.

What Are Layoffs?

We break down what layoffs are, how they differ from firings, and why terminology matters in the tech industry.

Here are the core points from the lesson:

- Layoffs are ad hoc, unpredictable events that cut a large number of employees for economic reasons, unlike regular performance-based cuts where low performers are ranked out.
- Being laid off is generally not the employee’s fault and is different from being fired for incompetence, which is why layoffs—especially in tech—typically come with generous severance packages.
- Most recent “layoffs” in tech are technically RIFs (reductions in force), meaning the company has no intention of rehiring those employees, even though the industry commonly uses the term layoff.

Often Forgotten Team Considerations

We wrap up with two often-overlooked but highly consequential factors when selecting a team: on-call health and code review speed.

Here are the core points from the lesson:

- We explain that on-call quality can make or break your work-life balance, and many early-career engineers don’t evaluate it; the two key questions are how *frequently* you go on call and how *painful* it is (night pings, fire drills, number of incidents).
- We highlight that not all on-call is equal—middleware teams or teams responsible for triaging other teams’ outages often have disproportionately stressful rotations unless strong runbooks exist.
- We emphasize that code review speed is critical for learning and momentum: slow iterations (1–3 days per review) compound quickly, turning a small change into a week- or multiweek-long process and severely limiting growth for junior and mid-level engineers.
- We define healthy expectations: reviews returned within a day are ideal, &gt;1 day is a yellow flag, and &gt;2 days per revision is a red flag because it throttles how often you ship and how fast you improve.

Finding Scope To Grow

We walk through how to evaluate whether a team offers real *scope for growth*—one of the most important factors for long-term career progression.

Here are the core points from the lesson:

- We suggest assessing a team’s track record of major launches: frequent, meaningful wins signal room for impactful work, while long dry spells often indicate a team that is either too new (unstable) or too mature (no hard problems left to solve).
- We point out that very mature orgs like core Facebook Feed may be so optimized that it’s nearly impossible to produce meaningful results, making promotions extremely difficult; meanwhile, very new orgs offer big scope but come with chaos and uncertainty.
- We recommend examining the level distribution on the team: a healthy mix of engineers more senior *and* more junior than you enables both mentorship (learning) and leadership (scope creation), whereas being the most junior or most senior can severely limit growth.
- We advise deeply evaluating the engineering manager’s track record—whether they have successfully grown people from junior → mid-level → senior → staff—because a strong EM accelerates your development, while a newly minted or unproven EM introduces career risk.

Talk To Engineers

We explain how to get a real, unbiased understanding of a team by talking directly to engineers—not just the hiring manager.

Here are the core points from the lesson:

- We stress that managers have strong incentives to “sell” their team—they’re evaluated on hiring success and growth—so their perspective is inherently biased and overly positive.

- We recommend explicitly asking to meet 1-2 engineers on the team, ideally ICs who have been there for years and are leaving on good terms, as they provide the most honest, nuanced picture of day-to-day culture, challenges, and growth.

- We emphasize that speaking with the tech lead is especially valuable, since alongside the manager, the TL will influence our experience more than anyone else after joining.

Team Importance

We explain how to evaluate whether a specific *team* within a company is a strong place for growth, impact, and stability.

Here are the core points from the lesson:

- We show how to assess whether a team is core to the business by asking whether it’s a profit center or cost center; teams tied to revenue (like Google Ads) offer stability and long-term opportunity, while peripheral or internal-tool teams often limit growth.
- We illustrate how to categorize teams: mature core teams (stable, slower growth), big bets (high upside but real risk of shutdown, like Stadia), and low-impact internal teams (comfortable but slow-growth).
- We recommend questions that reveal real importance: What does success look like? What growth targets matter? Does the CEO mention this product in earnings calls? These signals show whether leadership actually prioritizes the work.
- We show how to “backdoor” the truth by asking leaders their top three priorities; if your team isn’t on that list, it’s unlikely to be a strong engine for your career unless you explicitly want a lower-pressure role.

Interviews Go Both Ways

We open this final section by explaining how to evaluate teams *within* a company—starting with the interview process itself.

Here are the core points from the lesson:

- We explain why “first principles thinking” about company financials is usually unhelpful for engineers: exponential growth makes predictions unreliable, and financial modeling without expertise leads to bad conclusions.
- We highlight that the interview stage is your first and most reliable chance to gather real signal about culture and team fit; you should “reverse interview” the company rather than defaulting to superficial questions.
- We point out common bad questions (e.g., “What’s your favorite thing about working here?” or “How’s the food?”) because they produce biased, obvious answers and don’t reveal anything meaningful about career growth or challenges.
- We recommend instead asking questions tied directly to your top priorities—work-life balance, growth, team culture, on-call load—so that you can evaluate whether the environment actually aligns with what you need.

Ineffective Evaluation Techniques

We close this section by calling out two common but misleading ways people try to evaluate companies and why we should avoid them.

Here are the core points from the lesson:

- We urge you not to judge a company based on how much you liked or disliked your recruiter or interviewers, because you’ll almost never work with them, and your network’s lived experience inside the company is a far better signal of culture and fit.
- We caution against spending lots of time analyzing financial statements and balance sheets unless you’re actually trained in that area; as engineers, we’re likely to misinterpret the numbers and waste time.
- We recommend instead focusing on conversations about the company’s vision, growth, and people, since fast-growing companies can change dramatically in just a few years and those qualitative signals tend to be much more predictive for your career.

General Career Direction Principles

We close with guidance for people who feel completely unsure about where to go next in their tech careers.

Here are the core points from the lesson:

- We suggest that early-career engineers, when possible, start with a big tech company for 1–2 years to gain stability, strong compensation, and a solid learning foundation—then use that platform to explore what they truly want.
- We recommend deliberately experiencing different company archetypes (startup, midsize, big tech) because each environment teaches different skills, exposes us to different cultures, and clarifies our long-term preferences.
- We emphasize that career needs change with age and life complexity—stability tends to matter more in our 30s and 40s—and that most big-tech engineers ultimately transition to smaller companies to find scope and growth.
- We add that when in doubt, choose a company where software engineering is core to the business, not a peripheral function; being in the “engine room” of a product-focused tech company dramatically increases learning, opportunity, and long-term career resilience.

Breaking Down Startups

We break down what it really means to join a VC-backed, high-growth startup and how the experience differs from big tech.

Here are the core points from the lesson:

- We outline the major upsides of early-stage startups: massive technical scope, the highest possible career ceiling, and deep fulfillment from directly shaping a company’s destiny—especially when joining at Series B or earlier.
- We explain the risks: early startups are unstable, resource-constrained, and extremely high-pressure, where production issues can threaten the company’s survival and where chaos is normal, not exceptional.
- We show why growth is the single most important signal when evaluating a startup: growth increases technical complexity, headcount, leadership opportunities, and the likelihood of exceptional financial upside; stagnation is a strong red flag.
- We contrast this with agencies/consultancies, noting they rarely offer strong long-term growth for early-career engineers unless someone is already a world-class expert—making startup or big-tech paths much more reliable career foundations.

Breaking Down Big Tech

We outline the real benefits and drawbacks of joining big tech, focusing on what actually matters for long-term career growth rather than the hype.

Here are the core points from the lesson:

- We explain that the biggest advantage of big tech is prestige. Having Google, Meta, Amazon, or Microsoft on our résumé dramatically increases future job opportunities, recruiter response rates, and long-term career mobility.
- We highlight additional strengths: top-tier compensation, strong and reliable learning environments, and overall consistency in job security, interview success, and career progress, all of which compound after a few years inside a major tech company.
- We walk through key drawbacks: massive company size makes it hard to find meaningful scope, many hard problems are already solved, work-life balance can be demanding, and advancement requires strong communication and leadership skills—not just coding ability.
- We stress that big tech isn’t ideal for everyone; engineers who prefer deep, heads-down coding or highly autonomous environments may struggle in the bureaucracy and stakeholder-heavy nature of these organizations.

How To Do Research

We walk through how to actually evaluate companies and teams, emphasizing that good information is hard to find and rarely lives in public reviews.

Here are the core points from the lesson:

- We stress that big companies aren’t monolithic—cultures vary widely across thousands of teams—so the most reliable way to assess a company or team is to tap into our network, where trusted people can share accurate, firsthand experiences.
- We highlight why building deep relationships in school and at work is invaluable: our network expands our access to real data over time, and meaningful connections (teammates, managers, peers) often shape our opportunities more than technical ability alone.
- We suggest complementary methods—reverse-interviewing during the hiring process, reading engineering blogs, following tech news, and checking curated communities—to gather signal, while warning that Glassdoor skews too positive and Blind skews too negative due to self-selection.
- We show that even venture capital, which allocates billions in tech, runs almost entirely on networks and backchannel references—underscoring that our own career decisions benefit from the same relationship-driven information flow.

Underrated Career Goals

We highlight the qualities engineers consistently underestimate and why these matter far more than most people realize when choosing a company or team.

Here are the core points from the lesson:

- We emphasize that the most underrated factor by far is a truly supportive team: a strong manager, teammates who challenge and care about us, and an environment where we grow—something incredibly rare, worth cherishing, and often more valuable than joining a prestigious brand.
- We explain that growth opportunities don’t come automatically from prestige; even at places like Meta or Google, you can land on a team with poor scope or weak culture, and real growth often requires intentional team selection and proactive effort.
- We stress that stability and sustainable work-life balance are often undervalued by early-career engineers, who assume they can power through long hours, even though sustained 60-hour weeks degrade learning, productivity, and mental health—and that the best creators historically do only a few hours of deep work daily.

Overrated Career Goals

We explain which attributes engineers tend to over-prioritize and why doing so often leads to bad career decisions.

Here are the core points from the lesson:

- We see many early-career engineers chase trendy product spaces (ML, AR/VR, blockchain) without genuine passion, ignoring the high risk that trends fizzle and leave teams stagnant; survivorship bias makes the rare success stories seem more common than they are.
- We highlight a similar trap with technical stacks: engineers often assume “more modern is better,” jumping between new technologies and hurting long-term growth, when stable, proven stacks at strong companies typically offer better learning and career durability.
- We show that over-prioritizing level/title or short-term compensation can backfire, since inflated titles at weak companies and high pay tied to terrible work-life balance rarely lead to long-term career success or well-being; growth opportunities and supportive teams tend to drive compensation naturally over time.
- We stress that compensation is multi-variable (base vs. equity vs. upside potential), and examples like Barnes & Noble illustrate how deceptively high salaries may underperform compared to equity-rich tech roles over a multi-year horizon.

Career Priorities List

We walk through the many different priorities that can shape which company and team are truly right for us, and why this makes the choice so hard.

Here are the core points from the lesson:

- We list 14+ possible priorities—compensation, work-life balance, company prestige, supportive team, growth opportunities, stability, maximum outcome (upside), remote work, product space, company ethics, technical space, title, benefits (like parental leave), and location—showing how many different axes there are to optimize for.
- We explain nuances within each priority: for example, early-stage startups can offer huge upside but high risk, big tech often offers more stability but lower ceiling, and team makeup/scope can make or break growth even inside a good company.
- We emphasize that the macro environment (economy, industry turbulence, company-specific churn) must be considered alongside our personal priorities when evaluating offers and career moves.
- We share that we have strong opinions and heuristics, but the goal is for each of us to examine and “stack-rank” our own priorities, test our assumptions, and disagree deliberately rather than blindly following default paths like “just go to FAANG.”

The First Thing You Must Figure Out

Choosing the right company or team is hard because what’s “great” truly depends on our individual priorities.

Here are the core points from the lesson:

- We emphasize that people differ in life stage, location, responsibilities, and goals, which means there is no universal “best” company or team, only what is best for our personal priorities.
- We caution against blindly chasing FAANG or big tech, noting that while it can be optimal for some, it can be a poor match for others depending on what they value and need at this stage of their career.
- We outline the key first step: listing all potential priorities and stack-ranking them to create a clear, personalized hierarchy that guides the next career move.

Why Team Selection Is So Critical

We open by explaining that choosing the right company or team is one of the highest-leverage decisions we can make in our software engineering careers.

Here are the core points from the lesson:

- We use the “bus on a highway” analogy to show that company choice determines how far we can go; our personal effort only determines where we sit on that bus.
- Even if we perform exceptionally, joining a stagnant or declining company limits our long-term trajectory, while joining a fast-moving, hyper-growth company can accelerate our career dramatically.
- Real-world examples like Marin Software versus Google illustrate how picking the right “bus” can either stall or amplify career outcomes, independent of our individual contributions.

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.