Speaking to the US Senate Banking Committee on Wednesday to give his semiannual monetary policy report, Powell told elected officials that AI’s effect on the economy to date is “probably not great” yet, but it has “enormous capabilities to make really significant changes in the economy and labor force.”
No timeline given, but another signal that labor disruption is on the horizon. And fiddling with interest rates isn’t going to fix this one.
As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs. It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.
Programming, at its essence, is conversation with computers. It’s how we translate human intention into machine action. Throughout computing history, we’ve continuously built better translation layers between human thought and machine execution—from physical wiring to assembly language to high-level languages to the World Wide Web, which embedded calls to backend systems into a frontend made up of human-readable documents. LLMs are simply the next evolution in this conversation, making access to computer power more natural and accessible than ever before.
Returning to a measure we introduced in 2023, we examine American data on employment by occupation, singling out the type of workers that are often believed to be vulnerable to ai. These are white-collar employees, describing people in back-office support, financial operations, sales and much more besides. There is a similar pattern here: we find no evidence of an ai hit (see chart 2). Quite the opposite, in fact. In the past year the share of employment in white-collar work has risen very slightly.
My exact words to a small group of our finance, legal and talent colleagues last week: ‘You are committing career suicide if you’re not aggressively experimenting with AI.’
The reality is, we don’t know today how much AI will do in the future. But lots of companies are betting on a lot. Even if AI doesn’t take your job, someone who is using AI will. Is the mantra I hear most repeated.
And then, almost overnight, business leaders see the savings of replacing humans with AI — and do this en masse. They stop opening up new jobs, stop backfilling existing ones, and then replace human workers with agents or related automated alternatives.
Be it automation, off shoring, or just plain ‘do more with less’ AI is accelerating automation of many tasks. I know the long view is that technology results in more jobs. But when will they come?
Anthropic expects AI-powered virtual employees to begin roaming corporate networks in the next year, the company’s top security leader told Axios in an interview this week.
Remind me to check in on Anthropic, who currently has over 100 open positions on their careers page how this is going.
Quote Citation: Sam Sabin, “Exclusive: Anthropic warns fully AI employees are a year away”, Apr 22, 2025, https://www.axios.com/2025/04/22/ai-anthropic-virtual-employees-security
Whenever a new technology is invented, the first tools built with it inevitably fail because they mimic the old way of doing things.
I think I read elsewhere, that the true power of AI will be when it finds its application niche. Not in writing emails.
Quote Citation: Pete Koomen, “AI Horseless Carriages”, April 2025, https://koomen.dev/essays/horseless-carriages/
Really much too long to quote only one or two sections, but it is a line in the sand of 5-10 years to AI driven economies.
Quote Citation: Dario Amodei, “Machines of Loving Grace”, October 2024, https://www.darioamodei.com/essay/machines-of-loving-grace
Industry leaders don’t have a good track record of predicting AI developments. … As an example, Sutskever had an incentive to talk up scaling when he was at OpenAI and the company needed to raise money. But now that he heads the startup Safe Superintelligence, he needs to convince investors that it can compete with OpenAI, Anthropic, Google, and others, despite having access to much less capital. Perhaps that is why he is now talking about running out of data for pre-training, as if it were some epiphany and not an endlessly repeated point.
