# Tech Workers Invest Nights Learning New AI Tools > Source: > Published: 2026-06-21 12:08:05.650121+00:00 # Tech Workers Invest Nights Learning New AI Tools Business Insider reports that many tech workers are spending nights and weekends learning and experimenting with new AI tools to keep pace with rapid change. The article profiles Maahir Sharma, a 24-year-old software engineer at a Big Tech company, who says AI has sped some tasks and that he spends about **20 hours a week** outside work experimenting with tools such as **Cursor**, which he pays for out of pocket. Business Insider also cites an **Ernst & Young** survey of more than 1,000 US desk workers across six industries that found **85%** were learning how to use AI outside work. The story notes broader hiring trends, reporting that LinkedIn hiring for AI engineers has surged since 2022. ### What happened Business Insider reports that many tech workers are spending nights and weekends learning and experimenting with new AI tools to avoid falling behind. Business Insider profiles **Maahir Sharma**, a 24-year-old software engineer at a Big Tech company, who says AI has dramatically sped some work and that he spends about **20 hours a week** outside work experimenting with tools such as **Cursor**, which he pays for out of pocket. Business Insider cites an **Ernst & Young** survey of more than 1,000 US desk workers across six industries that found **85%** were learning how to use AI outside work. Business Insider also reports LinkedIn hiring for AI engineers has surged since 2022. ### Editorial analysis - technical context The article highlights hands-on experimentation with tools such as coding assistants and agent frameworks as the practical route many practitioners choose to learn. Industry-pattern observations show that interactive, project-based learning tends to accelerate skill transfer for engineers compared with passive reading or lectures. For practitioners, this implies that building small end-to-end projects and integrating assistants into real workflows is a common learning vector used to internalize tool behavior and API limitations. ### Industry context Observers have noted a broader labor-market shift toward AI-specialized roles, which corresponds to reported hiring increases for AI engineers on platforms like LinkedIn. Industry-pattern observations suggest that when employers signal demand for AI skills through hiring and compensation, workers commonly absorb the cost in unpaid hours to upskill quickly. This dynamic raises questions about access, equity, and burnout across technical teams. ### What to watch Indicators an observer might follow include repeated, representative survey data on after-hours upskilling rates; job-posting composition on major platforms for AI-specialized roles; and adoption metrics for category-defining tools such as coding assistants and agent platforms. Industry-pattern observations suggest tracking third-party tooling costs and employer-provided access to training, since those factors materially affect who can participate in rapid upskilling. ## Scoring Rationale The story matters to practitioners because it documents widespread, unpaid upskilling and links that behavior to hiring trends, which affects career planning and team resourcing. It is notable but not a technical breakthrough. Practice with real Logistics & Shipping data 90 SQL & Python problems · 15 industry datasets [High-Value Overnight OrdersEasy](/problems/sql/high-value-overnight-orders) [Delivered International ShipmentsMedium](/problems/sql/delivered-international-shipments) [On-Time Delivery Rate by CarrierHard](/problems/sql/on-time-delivery-rate-by-carrier) 250 free problems · No credit card [See all Logistics & Shipping problems](/problems/datasets/logistics)