Should You Apply for a PhD in AI (2025-26)? (yashbhalgat.github.io)

This is a candid, experience-based guide for people weighing PhD applications in AI for 2025–26. The core claim: frontier AI is increasingly driven by industrial labs and well-funded startups that own the compute, data flywheels, and deployment loops needed to iterate at scale—training billion‑parameter vision‑language or video/3D models often exceeds academic budgets. That said, academia still matters for work that doesn’t require hyperscale runs: theory and algorithms with compute‑light cores, evaluation/benchmarking, interpretability, alignment probing, privacy/regulatory research, medical or cross‑disciplinary projects, and any work where long, autonomous focus is essential. The piece notes examples (diffusion models, FlashAttention) where academia seeded ideas that industry scaled, and cites both bigtech (DeepMind, Meta, NVIDIA) and startups (Luma, Runway, Pika, Covariant, Figure) as leaders in fast iteration. Practical implications: choose a PhD only if your problem ages well and benefits from uninterrupted depth rather than raw scale—focus on data, evaluation, invariances, safety, interfaces, or compute‑light theory. Expect methodological shifts (many cascaded pipelines are collapsing into end‑to‑end pretrained backbones and task adaptation) and recognize the tradeoffs: academia yields autonomy and durable research skills; industry offers throughput, infrastructure, and faster feedback. The author advises getting 1–3 years of industry experience first to pressure‑test interests, then decide—pivoting during a PhD is feasible and adaptability is the most valuable asset.