Unlike COVID, which universally and immediately affected everyone, AI's disruption is gradual and highly sector-specific. A surgeon's job isn't changing this month, but a software engineer's is. The comparison creates misplaced urgency for many outside of tech.
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Unlike past industrial shifts, AI's impact won't be contained to specific industries. Once AI can perfectly replicate a human worker behind a keyboard, video, and mouse, it will trigger a simultaneous displacement wave across all remote-capable jobs.
Contrary to expectations, professions that are typically slow to adopt new technology (medicine, law) are showing massive enthusiasm for AI. This is because it directly addresses their core need to reason with and manage large volumes of unstructured data, improving their daily work.
The current disruptions and workflow changes in software engineering, such as AI's impact on business models like Tailwind's and tools like Stack Overflow, offer a direct preview of what other knowledge-based professions will experience. Observing how developers adapt provides a six-month glimpse into the future of AI-driven change across all industries.
AI lowers the economic bar for building software, increasing the total market for development. Companies will need more high-leverage engineers to compete, creating a schism between those who adopt AI tools and those who fall behind and become obsolete.
A new technology's adoption depends on its fit with a profession's core tasks. Spreadsheets were an immediate revolution for accountants but a minor tool for lawyers. Similarly, generative AI is transformative for coders and marketers but struggles to find a daily use case in many other professions.
AI is expected to disproportionately impact white-collar professions by creating a skills divide. The top 25% of workers will leverage AI to become superhumanly productive, while the median worker will struggle to compete, effectively bifurcating the workforce.
Instead of immediate, widespread job cuts, the initial effect of AI on employment is a reduction in hiring for roles like entry-level software engineers. Companies realize AI tools boost existing staff productivity, thus slowing the need for new hires, which acts as a leading indicator of labor shifts.
Industries with fixed demand (accounting) will see job losses as AI handles the necessary workload. Sectors with expandable demand (software engineering) may absorb AI's productivity gains by creating vastly more output, thus preserving jobs for a longer period.
The immediate threat of AI is to entry-level white-collar jobs, not senior roles. Senior staff can now use AI to perform the "grunt work" of research and drafting previously assigned to apprentices. This automates the traditional career ladder, making it harder for new talent to enter professions like law, finance, and consulting.
Contrary to the popular narrative, AI is not yet a primary driver of white-collar layoffs. Instead of eliminating roles, it's changing the nature of work within them. For example, analysts now spend time on different, higher-value activities rather than manual tasks, suggesting a shift in job content rather than a reduction in headcount.
