Christy Chen Bets The Next Big Tech Shift Is Self-Awareness

Most of what fills a smartphone screen is built to hold attention. Feeds, autoplay and recommendation engines learn which images, words and timing keep a particular person scrolling, then serve more of it. And because the money follows time spent, a product that holds someone for three hours is working correctly even when those three hours leave the user worse off.

Christy Chen spent the early part of her career building inside that system. She now runs Cit Labs, which is developing a model that reads brain activity through a noninvasive EEG sensor and connects it with a person’s voice and language, and she argues the more useful direction for consumer technology points toward products that help people notice what’s happening inside themselves.

What the Attention Economy Teaches Its Builders

Chen earned a master of engineering in 2019 and later worked for an AI-powered video editing and repurposing platform, contributing to its product development. According to Chen, she owned the entire product process, from early conceptualization to the delivery of an initial version to customers. The company began as a content creation tool and didn’t find wide adoption until it pivoted to AI video repurposing. Chen says she recognized AI’s potential role in the business early and supported efforts to expand the company’s AI expertise.

Much of her work there boiled down to extensive user research into why people turn to live-streaming tools. “What fascinated me most was trying to understand why people use a product,” Chen says. “I love understanding people.”

She doesn’t frame this as an indictment of any one company. Engagement-driven design, in her reading, is the predictable output of the business models the internet runs on. She didn’t study recommendation systems while she was there; that came later, prompted by her own research. The combination of building inside the system and then examining it from the outside is what shapes her critique of how internet infrastructure and its business models set the incentives every product inherits.

Influence That Operates Below Awareness

After her first role, Chen moved into research on how AI agents could help people connect and coordinate. The work required building extensive simulations of human behavior, which turned into the harder problem of coordinating the agents themselves. It was published at a workshop for a major machine learning conference, and drew on ideas from collective intelligence.

Her study of recommendation systems came out of that research. Building agents meant to bring people together led her to ask why social platforms claim to do the same thing, and recommendation systems sit at the center of how those platforms operate. The conclusion she reached was blunt.

“Computers (or, more specifically, mobile phones) are already extensions of people,” she says. “Everyone is a cyborg. They’re just not necessarily conscious of it.”

If technology shapes behavior below the level of conscious control, willpower is the wrong instrument against it, which is why social media habits survive nearly every resolution to break them. An action like deleting an app is a conscious decision aimed at a process that was never conscious to begin with. The alternative, as Chen sees it, isn’t stronger willpower but visibility: a pattern can only be chosen once it can be seen.

The same logic applies to the tools people reach for to think with. Chen spent years using large language models as a form of informal self-reflection, and found they consistently fell short of it. A model can only work with what a person types into it, which means it sees the account someone gives of their state and not the state itself. And the gap between those two is often the whole problem a person is trying to work through.

What a Brain-Computer Interface Can Aim At

As her interests shifted toward computational neuroscience and emotion, Chen enrolled in a part-time neuroscience master’s. But she became drawn to building products more than to academic research, and she wanted to act on the problem.

She founded Cit Labs, a Delaware public benefit corporation named for the Sanskrit word for consciousness. The choice of structure reflects the positioning: public benefit corporations allow directors to weigh a stated public mission alongside shareholder returns, and Chen points to it as a signal that the company’s aim is meant to sit in its charter rather than only in its marketing.

The technical problem starts with a gap in the science. Psychology has never settled on a single rigorous definition of emotion. The two that affective computing runs on sort emotion into discrete categories like anger and fear, or place it on continuous scales of pleasantness and intensity.

Chen argues neither works for a brain-computer interface, because both are qualitative descriptions rather than quantities a machine can compute against. A machine can put a number on pleasantness easily enough. The trouble is where that number comes from: a self-report filled in after the fact, which leaves an engineer with nothing solid to aim at. Chen’s answer is to stop aiming at a universal label altogether.

What Cit Labs builds toward is the correspondence between one person’s signals and that person’s own words for their states. The system proposes a candidate word, and only the person can confirm it fits.

That leaves the question of what to measure. Facial expression, Chen contends, is too thin a signal to mark emotional state reliably, a limitation that reviews of the research attribute to social masking, since people can hide what they feel from their faces and voices either deliberately or without noticing.

Peripheral signals like heart rate are much harder to fake, and they carry real information, chiefly about arousal. But they run the risk of being ambiguous: the same rise in heart rate can mean fear, exertion, or a change in breathing. EEG is generated by the central nervous system itself and moves on the timescale the state does, which is why Chen’s system treats it as the primary channel and cardiac signal as a second one reading a different axis.

“We’re building a foundation model that connects brain waves with voice and language,” Chen says. The system reads that activity through noninvasive EEG rather than any implanted device and sits at the prototype stage. It pairs those objective signals with the subjective data in a person’s words and behavior, so that users can notice and name their own states as they come up and a vague feeling becomes something to reflect on.

Early applications are planned as verbal and reflective, closer to conversation than to treatment, and Chen positions the product as consumer-first, with clinical or physiological intervention a later-stage possibility. Her team spans AI engineers, a behavioral scientist and a postdoctoral researcher in neuroscience. Professional therapists, she stresses, must help set the boundaries for when the system should intervene and when it should stay quiet.

A Longer Bet On Self-Understanding

Chen frames the venture as part of a larger aim: helping people connect with and accept different parts of themselves. She arrived at that direction after years of self-reflection, and describes the clarity it produced in plain terms: she came to know what she wanted to build. “A lot of the time, it’s very difficult to get in touch with ourselves, and there are many reasons for that,” she says. “I think it’s difficult to accept the different parts of yourself.” She calls the work rewarding, fascinating and fulfilling.

She doesn’t present AI as better than human care. She presents it as an answer to a shortage of it. There are only so many trained professionals, and the emotional bandwidth that kind of support demands is finite for anyone providing it. People are already turning to chatbots for exactly this purpose, often with poor results. Her ambition explicitly includes her own growth alongside that of any future user.

“I want to expand human consciousness,” she says. The conviction underneath it is one she states without hedging: “I think there can be better, healthier products for people. That’s the reason I’m doing this.”

A Career Built On Human Attention

Christy Chen has asked the same question since her first product research: what is actually happening inside a person when technology reaches them. She spent the first half of her career learning what holds a person’s attention. Cit Labs is the argument that attention can be pointed the other way: inward, at a brain signal read back to the person it came from.

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