A few months ago, a founder asked me to help him find a head of product. For 40 minutes I probed and he described the person he needed: someone who had built a business from nothing inside a larger company, had real product taste, could grow a team through ambiguity, and treated a production fire with the same seriousness as a revenue target. The person did not need to come from fintech. He was willing to give the right person two months in the seat before deciding.
Then he posted the role. The description read: 8+ years of product experience, B2C preferred, top-tier institute a plus. Everything he had described across those 40 minutes was gone. The resumes that came back carried as little of the people behind them. Between two humans who might have been exactly right for each other stood two documents, each one compressing away the things that would have made the match work.
This is the state of hiring today. We have changed how we work and how we build companies, while the way we represent people at work has stayed where it was.
The resume has had a long run. In 1482, Leonardo da Vinci wrote to the Duke of Milan describing what he could build for him, bridges and siege engines among them, in a letter often regarded as the first resume. The job description arrived much later, out of an industrial world that needed defined roles and standard requirements. Together they became the language through which people and organisations found each other, and software digitised that language without changing it. Resumes became profiles, job advertisements moved to platforms, screening became automated, and the exchange underneath stayed intact: professionals describe themselves through credentials, organisations describe roles through requirements, and technology looks for overlap.
That exchange is now under pressure from both sides, because AI has entered it. Professionals use AI to draft their resumes and organisations use AI to screen them, which means a document already designed to compress a person is now being written by one machine to be read by another. The compression compounds. What survives the pipeline is whatever was easiest to standardise, and what gets discarded is what the founder spent 40 minutes describing.
The more interesting shift is in what AI can understand. A resume records where someone worked and what they say they achieved. A long conversation reveals why they made a particular decision, how they approached a hard problem, what they took from a failure, and where their experience might translate into a context they have not worked in yet. For most of working history that understanding lived only inside relationships. A manager knew how someone performed under pressure, a colleague knew how they collaborated, and that knowledge disappeared when the relationship ended. AI makes it possible to carry that context forward, and once context can be carried, it can also be represented.
Representation is an old idea in other fields. Actors have agents and athletes have managers, people whose job is to know their client’s work well enough to advocate for it, while professionals have relied on documents. If AI can understand a professional through conversation, that understanding can become an active layer of representation: It can surface opportunities the person would not have thought to search for and people worth meeting, and it can evolve as their experience and ambitions change. The same holds for the other side of the table. A role can carry the context of the team, the problems to be solved and the contribution that will matter, in place of a list of qualifications. When both sides carry context, discovery moves from matching keywords to understanding fit.
For India the implications run deep. Professional opportunity here has historically travelled through networks; sifarish is one name for the dynamic. A trusted recommendation can explain a person’s capabilities in a way a resume cannot, which is why networks work and also why they decide who gets into the right conversations. Context that can be communicated and discovered without a prior connection changes that arithmetic. A professional in Indore can be understood by an organisation in Bengaluru, Singapore or London that has no reason to know them yet, and an organisation can find talent outside the networks it already trusts.
Serious questions come with this future: Who controls a person’s professional representation, how consent works, whether complex human qualities get flattened into scores, and whether automation widens access or hardens existing patterns. These questions will weigh as much as the technology, and the systems that answer them well will be the ones professionals trust with something as personal as their working identity.
The resume will keep its place, credentials will keep mattering, and human judgment will stay at the centre of hiring. But every era of technology has changed how work is represented, the way the spreadsheet did for finance and CAD did for engineering, and the professional profile has sat still through all of it, a stillness that is now ending. The useful question about a professional is no longer what a page says they have done; it is who they are, what they can do, and where they could create value next. The next generation of professional representation will be built in conversation and will carry the context a page of text discards. It will work alongside us rather than wait to be read.
(The views expressed are personal)
This article is authored by Anuj Rathi, co-founder & CEO, Profound.
