By Aravind Putrevu
Are we ready for a future where AI decides what we see and buy? The honest answer is that this future is already here. For more than a decade, feeds and ads on YouTube, Instagram, Facebook, and almost every marketplace have been ranked by machine learning with very little human handholding. Search for a mixer grinder once, and your internet starts to look like an appliance showroom.
That pattern is not new.
What is new is that the system no longer only chooses from a shelf of existing items. It can now invent the shelf. Images of a dress, a phone, or a piece of furniture may be generated. A chat assistant may describe features and prices that sound authoritative but are not attached to a real SKU. Influencer faces, reviews, and product photos can be synthetic.
The result is a power shift from simple ranking to full content creation, which introduces a trust gap that users must learn to cross.
Automation Saves Time, Brings Out Better Prices
Is that bad by default? Not really. Automation has also enabled discovery to be quicker and buying to be less complex for busy households in India. It saves time, brings out better prices and assists small sellers to get the right buyers. The moral concern is on authority and transparency.
When systems are trained to maximise attention and conversion, they tend to push us toward what keeps us scrolling or spending, not what serves our interests. The right response is to raise the bar on proof, choice, and accountability.
First, provenance has to be visible. Platforms should label generated images and synthetic reviews, and attach verifiable source data to specs and prices.
Second, users need real controls that are easy to find. A simple panel that lets you tune or pause personalisation, clear history, and see why you are seeing a recommendation should be standard.
Third, outcomes should be measured and reported, not only click-through but also complaint rates, return rates, and seller authenticity. If we can see the scorecard, we can judge whether the system works for people, not only for ads.
Trust, But Verify
There is also a personal playbook: Trust, but verify. Cross-check a product across two or three platforms. Look for buyer photos taken in real homes, not perfect studio shots.
Read the one-star reviews first. Prefer sellers with a history of fulfilled orders, not just a polished storefront. Start with a small purchase before you commit to a big-ticket item from a new brand.
Use return windows and payment protection that your bank or UPI app offers. When you are buying in a category for the first time, add a human anchor. Ask a friend who has used the product, call a store, or consult a trusted community. Note the exact model number and verify it on the manufacturer’s site. If a chat agent recommends something, ask it to show the source page and to list alternatives with pros and cons in plain language.
Here is the blunt truth. Machines already shape a large slice of our online life, easily 40 per cent or more of what we notice and what nudges us to act. Human teams then scale it with creative, logistics, and policy.
That is not a doom story. It is a design problem. If platforms are built for transparency and user agency, the net effect can be positive. We discover more, waste less time, and avoid buyer’s remorse. If they hide how the system works and flood the feed with synthetic gloss, we lose the ability to tell signals from noise, and the market becomes a maze.
So are we ready? We are ready enough if we accept reality and act like informed participants. Treat recommendations as suggestions, not orders. Set a budget before you browse.
Compare at least two services, including a local store if you have access. Ask for receipts and warranties that name the actual manufacturer. Support rules that mandate clear labels for generated media and simple privacy controls. The technology will keep deciding what to show.
Our job is to decide what to trust and what to buy. Let machines recommend. Let people decide.
(The author is the Director of Developer Marketing, Coderabbit)
Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.
