Every quarter, someone in tech quietly walks back a confident prediction they made not long ago.

In January 2026, Forrester published research showing that over half of all AI-attributed layoffs are likely to be reversed, because companies cut headcount based on AI promises that the technology was not yet ready to keep. The same month, Gartner forecast that generative AI in customer service would cost more than offshore human agents by 2030, prompting a pointed rebuttal from industry practitioners who argued Gartner was describing a vendor pricing failure, not an AI failure.

The retractions are usually healthier than the original predictions. They just get a fraction of the attention.

In October 2021, a year before ChatGPT, I delivered a talk called “The Future of Software Developers by 2040” to a developer conference. Looking back now is not about whether I got things right. The more useful question is which assumptions survived contact with a shock nobody saw coming.

A few stood up reasonably well. The talk argued the (Indian) IT hiring boom would not last. TCS, Infosys, Wipro and HCL were on record quarters then, with attrition above 20 per cent. The direction was right, though the mechanism I imagined, gradual automation and growth of the global capability centres, turned out to be the smaller story. The larger one was a foundation AI model that could write working code from a plain English comment.

One slide, titled “Going to be upside-down,” argued that the work mix in software would invert: more time on business understanding and customer needs, less on writing and testing code. That was framed as a gradual shift toward 2040. It is largely the reality in 2026 itself.

The line that aged the worst was the one I closed with: let machines be machines, and let humans be human. It earned a warm nod in the room. Five years on, it has the ring of a sentence printed over a stock photo of a sunrise. Tidy aphorisms assume the boundary they describe will hold still. This one has not.

Nobody in this industry predicts precisely. The people who navigate these shifts well are not the ones with the sharpest forecasts. They are the ones who revise quickly when the ground moves, before it costs them too much.

There is a Chinese proverb associated with Deng Xiaoping: crossing the river by feeling the stones. You do not map the riverbed in advance. You step, test your footing, adjust, and step again. The destination matters. But the only honest method is one stone at a time.

The next five years will not reward people who pick a single thesis and hold it. They will reward those who can hold a direction loosely, gather evidence weekly, and change their mind without ego when the facts demand it.

The cost of a wrong forecast held too long is now measured in quarters, not years. The cost of revising one in public is mostly ego. And ego is the cheapest thing to spend.

The forecasts that age worst are usually the ones that sound the wisest at the time.