The macro data will not tell you when AI hits your team
Goldman Sachs has put out a piece under the title Is AI Impacting Global Labor Markets?, and the part I keep returning to is the question mark. Two years into the loudest labour market prediction of my working life, one of the most quoted research desks in finance is still framing the answer as unsettled. That is not evasion. It is what honest macro work looks like when an effect is real but has not yet separated itself from the noise of an entire economy.
The reading most people miss is that a faint signal in the aggregate is not the same as a faint signal in your building. National labour statistics average across every sector, every seniority band, and every firm that has never touched the technology. They are built to describe an economy after the fact, not to warn a hiring manager in advance. If you are waiting for macro numbers to confirm what AI is doing to your roles, you are asking a rear view mirror to tell you what is coming.
The macro data will confirm what happened to your hiring long after you needed to know it.
What I see from where I sit is not a smaller number of jobs. It is a different shape of job. Briefs that used to ask for volume of output now ask for judgment about output. The work that automates first is the work that was already legible enough to specify, which is exactly the work junior hires were given to learn on. The roles that remain absorb more scope per person, which pushes required seniority up and required headcount sideways. Total employment can look flat while the composition underneath it changes completely, and composition is precisely what an aggregate is worst at showing.
For hiring leaders, the practical read is simple. Do not outsource this question to a research desk. Take your last twelve months of open roles and ask, honestly, which of those briefs you would write differently today. That list is your own labour market data. It is available now, and it is the only version specific enough to hire against.
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