Mustafa Abban

Stock vesting schedules have been morphing, as we all know. And Waymo just slipped a new experiment into the mix. Two recent Waymo offers shared on Levels.fyi tell the story: - An L3 software-engineer package that vests RSUs over two years, 50 percent each year. - An L5 package that keeps the classic four-year, evenly split design. That contrast hints at a deliberate split strategy: shorter horizons for early-career hires, longer runways for senior talent. The logic tracks. New grads often treat their first role as a springboard, so a two-year vest ensures most of their grant actually pays out before they move on. For seasoned engineers, who hold bigger grants and deeper domain knowledge, Waymo still wants the retention pull of a four-year schedule. Waymo isn’t alone. Across tech, equity timelines are turning into a tuning knob: - Lyft issues single-year new-hire grants, then refreshes annually. - Uber front-loads at 35 / 30 / 20 / 15. - NVIDIA moved to 40 / 30 / 20 / 10. - Pinterest compresses to roughly 50 / 33 / 17 over three years. - A handful of high-growth startups grant two-year blocks by default. What’s driving the shift? 1. Pay-for-performance. Shorter cycles let companies re-price grants and reward top performers without being locked into four-year promises. 2. Dilution math. Fewer unvested shares lower equity overhang, especially valuable when stock prices spike. 3. Flexibility. Refreshers become the main retention lever, not year-three and year-four vest cliffs. 4. Talent liquidity. For early-career hires, faster access to equity can feel more tangible than a promise that’s years away. If you’re negotiating, remember that earlier liquidity doesn’t always mean bigger upside. Always compare total grant size, ask how refreshers are determined, and run the tax math on concentrated vesting events. Have you seen a two-year or front-loaded schedule in your offer recently? Comment below with your thoughts and I’ll send over a vesting report we put together here at Levels.fyi. #vestingschedule

137

8 Comments

Why LLMs Matter Right Now? We’re at a turning point. Large-language models are no longer speculative toy —they’re the backbone of intelligent applications as we move toward 2026. Here’s why they matter—and why, as someone mentoring AI engineers in building real-world systems, now is the moment to lean in. 1️⃣ Maturity + Impact The ecosystem has shifted from demos to production-grade systems. Analysts highlight that infrastructure, tooling and adoption curves are aligned like never before. That means if you’re building AI-powered apps, you’re not just exploring you’re executing. 2️⃣ Breakthrough release changing the game Gemini 3 Pro from Google has hit the market with record-breaking benchmarks in reasoning, multimodal understanding and agentic tool use. When foundational models are this capable, your app isn’t constrained by what could be built—it’s constrained by what you choose to build. 3️⃣ A mentor’s view: Opportunity for developers As someone who guides AI engineers in building LLM apps, I see three major wins: 💡 Faster time-to-value because base capabilities are stronger. 💡 More specialization: choosing the right model and architecture for your use-case. 💡 Real-world differentiation: your AI app can now solve a domain problem rather than just show generative creativity. 4️⃣ Why exactly now matters Because the models, the deployment platforms and the business demand all intersect in this moment. If you wait, you’ll face higher competition and sharper expectations. If you build today, you ride the wave—not chase it. ♻️ Repost this to clarify someone's doubt about building with LLMs. ➕ Follow me, Pulkit Tyagi, for AI engineering that works — what will you build next as we head into 2026? #AI #LLM #GenerativeAI #AIFluency #AIEngineering #Development #TechTrends #BuildingWithAI #BuildingWithLLM

54

10 Comments