Startups become the main pathway to bigtech: how AI is reshaping hiring in IT

Hiring at major tech companies remains squeezed for the second year in a row: Google, Meta, Microsoft and Amazon have cut thousands of positions, while new openings have set a far higher bar. The main reason is the introduction of AI, which takes over tasks previously occupied by dozens of juniors and mid-level developers.

Big Tech Closes Its Doors

The wave of layoffs from 2023–2025 removed over 300,000 jobs in the tech sector. AI tools automated a significant portion of coding, testing and analytics tasks — and companies realized: you can do the same with a smaller team.

• Google cut 12,000 employees in 2023 and conducted another wave in 2024
• Meta eliminated over 21,000 positions over two years
• Amazon closed over 27,000 roles in AWS and retail tech
• Microsoft laid off 10,000 people as part of a reorganization around Azure AI

New vacancies at these same companies require experience with LLMs, fine-tuning, RAG systems and evaluation frameworks — something that only a handful were massively doing 18 months ago.

Startups in Talent Shortage

In contrast, startups in the AI and enterprise software space are experiencing an acute shortage of engineers. They need to build products quickly — usually within 6–12 months before their next funding round. Hiring an experienced specialist from FAANG is difficult: they value stability and high RSU grants. So startups look wider — at candidates with the right mindset, not just a specific tech stack.

According to LinkedIn Job Trends, in the first quarter of 2026, the number of openings tagged "generative AI" at companies with up to 200 employees grew 34% year over year. At large companies (over 1,000 employees), that same metric fell 11%. AI startups are particularly active in enterprise automation, legal tech and healthcare AI: they need engineers capable of implementing LLMs in regulated industries — and finding such people among recent graduates is not easy.

"I spent four months sending resumes to Google and OpenAI — with no result.

Then in three weeks I got two offers from AI startups with salaries higher than I expected," — a typical comment on Reddit/cscareerquestions.

Why a Startup is a Strategically Sound Move

Working at a startup is not a plan B, but for many professionals the best career path:

• Real AI experience from day one. At a startup, an engineer builds a RAG pipeline, selects embedding models and deploys them to production in a single sprint. At a large company this could take a year of approvals.
• Broad tech stack. One engineer often covers backend, ML inference and DevOps simultaneously — such experience is highly valued when transitioning to big tech.
• Equity. Pre-Series B startups often offer 0.1–0.5% stake in the company. With a successful exit, this can significantly exceed any FAANG RSU grant.
• Speed of growth. Senior position in 18 months — a real story. At Google, the median path to it takes 3.5–4 years.
• Visible results. A feature reaches users in a week — important both for your portfolio and professional motivation.

The main risk is instability: about 90% of startups don't survive five years. You should choose those with a working product, paying customers, and a confirmed funding round in the last 12 months.

What This Means

The IT hiring market has restructured: big tech became an elite club with tough filters, and startups became a live market where people are needed right now. For engineers, this means that the path to Google or Microsoft now often goes through 2–3 years of real AI experience at a startup — rather than starting directly in an entry-level position.

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