A Boom That Doesn't Feel Like One for Everyone
Artificial intelligence is moving faster than anyone predicted — and for a growing number of workers, that speed feels less like progress and more like a freight train.
A new analysis from TechCrunch paints a divided picture of the AI economy: on one side, a small cluster of companies, founders, and investors raking in extraordinary returns; on the other, a much larger group of workers, mid-tier startups, and traditional businesses struggling to keep up or being quietly displaced.
The "vibes," as the piece puts it bluntly, aren't great — even among people who work in tech.
Who's Getting Rich
The winners are concentrated at the top of the stack. Nvidia, whose chips power virtually every major AI model, has seen its valuation balloon into the trillions. A handful of frontier model labs — OpenAI, Anthropic, Google DeepMind — are commanding eye-watering investment rounds. The founders and early employees at these companies are generationally wealthy.
Venture capital is flooding into anything with "AI-native" in the pitch deck, and a new class of AI infrastructure companies — data labelers, model evaluators, compute brokers — has quietly become essential plumbing for the whole ecosystem.
Who Isn't
For most everyone else, the picture is murkier.
Large language models are already reshaping white-collar work in ways that are difficult to measure but easy to feel. Copywriters, junior developers, customer support teams, paralegals, and graphic designers are all reporting that their workloads have shifted — not necessarily disappearing, but changing in ways that often mean fewer jobs, lower pay, or both.
Even within the tech industry, the gold rush is producing anxiety. Mid-sized software companies that aren't AI-native are scrambling to retool their products, often under pressure from investors who want an AI story. Many are burning cash to integrate tools that don't yet deliver the ROI they promised.
For workers at these companies, that translates into layoffs dressed up as "restructuring for the AI era."
The Structural Problem
What makes this moment different from previous tech cycles is the speed of the concentration. Past booms — cloud computing, mobile, social media — created disruption, but they also created enormous numbers of jobs along the way and distributed wealth relatively broadly across the ecosystem.
The AI boom, so far, is doing the opposite. The economic value is pooling faster and in fewer hands, while the displacement is spreading wider and faster than the new opportunities are materializing.
Economists who study technological transitions note that retraining programs and policy responses tend to lag behind the pace of change by years — sometimes decades. The worry is that by the time support systems catch up, a significant portion of the workforce will have been left behind in ways that are very hard to reverse.
What It Means
For Canada, and for cities like Ottawa with large public-sector workforces, significant post-secondary institutions, and a growing tech sector, these dynamics are worth watching closely. Federal government jobs — long considered relatively stable — increasingly involve the kind of knowledge work that AI tools are being designed to augment or replace.
The AI gold rush isn't over. If anything, it's accelerating. But the question of who gets to benefit, and who gets left holding the cost, is becoming harder to ignore — even for the people who built the thing.
Source: TechCrunch
