These are realistic (IMHO, of course) projections based on studies I've helped with and conversations I've had with my network. Naturally, the impact will vary enormously based on roles, and the timelines won't be evenly distributed.
But these kinds of projections aren't unusual at all — if you use the Deep Research capabilities of modern models to build a list of public projections for your own research, you'll see similar estimates. These reports will generally use the framing of "efficiency gains", where AI will "free-up employees from drudgery to focus on higher-value work", but my intuition is that a future where all individual contributors are elevated to Director of Agentic Workflows is probably not the most likely outcome.
What studies? MIT estimates only 5% of the workforce can be replaced long term. What tasks are you employees using AI on, CMU shows the best llm only has a ~30% success rate for basic business tasks. Are you a vibe coding start up or something?
I see and are these studies public ? Could we see the data and the methodology here ? Thing is there are benchmarks to judge software engineering capability of AI. I am more interested in how the jobless predictions made ?
I understand all the theory but it can largely be condensed into - AI makes workforce more efficient so you need less people. But there are no good studies afaik that measure AI powered efficiency and surely nothing about how to model workforce reduction due to AI. I am curious what the science is behind these opinions.
> These reports will generally use the framing of "efficiency gains", where AI will "free-up employees from drudgery to focus on higher-value work"
Okay, but what are these reports _based_ on? Everything I've seen along these lines has been, essentially, marketing material; there seems to be very little hard data suggesting this kind of outcome.
