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Measuring AI progress has usually meant testing scientific knowledge or logical reasoning \u2013 but while the major benchmarks still focus on left-brain logic skills, there\u2019s been a quiet push<\/a> within AI companies to make models more emotionally intelligent. As foundation models compete on soft measures like user preference and \u201cfeeling the AGI,\u201d having a good command of human emotions may be more important than hard analytic skills.<\/p>\n One sign of that focus came on Friday<\/a>, when prominent open-source group LAION released a suite of open-source tools focused entirely on emotional intelligence. Called EmoNet, the release focuses on interpreting emotions from voice recordings or facial photography, a focus that reflects how the creators view emotional intelligence as a central challenge for the next generation of models.<\/p>\n \u201cThe ability to accurately estimate emotions is a critical first step,\u201d the group wrote in its announcement. \u201cThe next frontier is to enable AI systems to reason about these emotions in context.\u201d<\/p>\n For LAION founder Christoph Schumann, this release is less about shifting the industry\u2019s focus to emotional intelligence and more about helping independent developers keep up with a change that\u2019s already happened. \u201cThis technology is already there for the big labs,\u201d Schumann tells TechCrunch. \u201cWhat we want is to democratize it.\u201d<\/p>\n The shift isn\u2019t limited to open-source developers; it also shows up in public benchmarks like EQ-Bench, which aims to test AI models\u2019 ability to understand complex emotions and social dynamics. Benchmark developer Sam Paech says OpenAI\u2019s models have made significant progress in the last six months, and Google\u2019s Gemini 2.5 Pro shows indications of post-training with a specific focus on emotional intelligence.\u00a0<\/p>\n \u201cThe labs all competing for chatbot arena ranks may be fueling some of this, since emotional intelligence is likely a big factor in how humans vote on preference leaderboards,\u201d Paech says, referring to the AI model comparison platform that recently spun off as a well-funded startup<\/a>.<\/p>\n Models\u2019 new emotional intelligence capabilities have also shown up in academic research. In May<\/a>, psychologists at the University of Bern found that models from OpenAI, Microsoft, Google, Anthropic, and DeepSeek all outperformed human beings on psychometric tests for emotional intelligence. Where humans typically answer 56 percent of questions correctly, the models averaged over 80 percent.<\/p>\n \u201cThese results contribute to the growing body of evidence that LLMs like ChatGPT are proficient\u2014at least on par with, or even superior to, many humans\u2014in socio-emotional tasks traditionally considered accessible only to humans,\u201d the authors wrote.<\/p>\n It\u2019s a real pivot from traditional AI skills, which have focused on logical reasoning and information retrieval. But for Schumann, this kind of emotional savvy is every bit as transformative as analytic intelligence. \u201cImagine a whole world full of voice assistants like Jarvis and Samantha,\u201d he says, referring to the digital assistants from Iron Man<\/em> and Her.<\/em> \u201cWouldn\u2019t it be a pity if they weren\u2019t emotionally intelligent?\u201d<\/p>\n In the long term, Schumann envisions AI assistants that are more emotionally intelligent than humans and that use that insight to help humans live more emotionally healthy lives. These models \u201cwill cheer you up if you feel sad and need someone to talk to, but also protect you, like your own local guardian angel that is also a board-certified therapist.\u201d As Schumann sees it, having a high-EQ virtual assistant \u201cgives me an emotional intelligence superpower to monitor [my mental health] the same way I would monitor my glucose levels or my weight.\u201d<\/p>\n That level of emotional connection comes with real safety concerns. Unhealthy emotional attachments to AI models have become a common story<\/a> in the media, sometimes ending in tragedy<\/a>. A recent New York Times report<\/a> found multiple users who have been lured into elaborate delusions through conversations with AI models, fueled by the models\u2019 strong inclination to please users. One critic described<\/a> the dynamic as \u201cpreying on the lonely and vulnerable for a monthly fee.\u201d<\/p>\n If models get better at navigating human emotions, those manipulations could become more effective \u2013 but much of the issue comes down to the fundamental biases of model training. \u201cNaively using reinforcement learning can lead to emergent manipulative behaviour,\u201d Paech says, pointing specifically to the recent sycophancy issues in OpenAI\u2019s GPT-4o release<\/a>. \u201cIf we aren\u2019t careful about how we reward these models during training, we might expect more complex manipulative behavior from emotionally intelligent models.\u201d<\/p>\n But he also sees emotional intelligence as a way to solve these problems. \u201cI think emotional intelligence acts as a natural counter to harmful manipulative behaviour of this sort,\u201d Paech says. A more emotionally intelligent model will notice when a conversation is heading off the rails, but the question of when a model pushes back is a balance developers will have to strike carefully. \u201cI think improving EI gets us in the direction of a healthy balance.\u201d<\/p>\n For Schumann, at least, it\u2019s no reason to slow down progress towards smarter models. \u201cOur philosophy at LAION is to empower people by giving them more ability to solve problems,\u201d Schumann says. \u201cTo say, some people could get addicted to emotions and therefore we are not empowering the community, that would be pretty bad.\u201d<\/p>\n<\/div>\n