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Large companies are moving away from traditional surveys and turning to AI-generated replicas of real people, a shift that offers faster insights but also sparks concerns about employment and data privacy.
A viral TikTok can make a brand famous in hours, but many companies still rely on twelve-week research cycles.
By the time results arrive, the data is often obsolete.
There’s often a delay between getting feedback and understanding what it means. Because of this, big companies can struggle to respond quickly when trends change fast.
Many companies believe that digital twins are the solution.
These are digital copies of real things, systems, or even people. Companies use them to try out ideas and see what might happen before doing it in real life.
Major banks and pharmaceutical companies are already utilizing this technology to predict how people would react to important events or freshly released items.
Testing happens in seconds instead of weeks
The technology is currently gaining momentum in high-tech businesses.
Researchers at the University of Glasgow built a digital twin system that uses machine learning to check computer networks.
Their new method can measure how well a network is working in just 4.78 seconds. Older methods took about 33 hours to do the same job.
Because it is so much faster, engineers can test many more situations, especially as networks become more complex.
The same demand for quick information is altering consumer research.
A startup named Brox has generated 60,000 digital duplicates of actual individuals.
These are not simply estimates, but highly detailed profiles based on extensive interviews, with some comprising up to 300 pages of material about a single person.
Instead of depending primarily on traditional statistical models, firms may now run multiple simulations in hours rather than months.
Hamish Brocklebank, who runs Brox, explained the difference.
“You can create 10,000 truly synthetic digital twins [using LLMs], but the answers will still normalize into a very tight distribution, which is not realistic when you’re actually asking real people,” he said.
Because Brox already has these twins ready to go, a major pharmaceutical company can ask the digital crowd questions and get reliable results in hours, skipping the entire step of finding real people to interview.
Automation targets higher-paid workers
The rapid push toward automation has a disadvantage.
According to MIT economist Daron Acemoglu, many businesses utilize automation primarily to save money rather than to increase efficiency.
According to his research, employers are more willing to replace people with higher compensation.
The study also demonstrated a significant impact on income inequality.
Automation accounted for 52% of the increase in income disparity between 1980 and 2016.
Acemoglu noted that the higher a worker’s pay, the more corporations are incentivized to automate that position.
He also argued that this focus on cutting labor costs has reduced many of automation’s potential benefits.
According to the research, efforts to lower wages erased 60% to 90% of the productivity gains automation was supposed to create, resulting in what he described as relatively weak productivity growth.
Privacy is also becoming an important problem.
A team at IMDEA Networks Institute uncovered that prominent AI systems, including ChatGPT, Claude, and Perplexity AI, use tracking techniques developed by Google and TikTok.
These trackers may collect information about what users talk about, such as chat titles and web addresses.
Digital twins are formed utilizing highly personal information, such as childhood experiences, behaviors, and relationships.
When paired with third-party tracking, these technologies can gather and handle massive volumes of sensitive data.
The AI simulation and digital twin industry is expected to reach $21.33 billion by 2030.
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