Young people are ‘growing up fluent in AI’ and that’s helping them stand apart from their older peers, says Gen Z founder Kiara Nirghin

At Fortune Brainstorm AI, Stanford technologist and Chima co‑founder Kiara Nirghin argued that Gen Z’s comfort with AI—treating coding and day‑to‑day tasks as collaborative with AI agents rather than doing them from scratch—gives younger workers an advantage to pioneer new use cases and extract deeper insights from complex research; she pushed back on a 2025 MIT Media Lab study that found ChatGPT users underperformed, saying intelligent Gen Zs use AI to think more deeply. Nirghin warned that recent model releases have materially raised benchmarks and urged workers at all levels to adopt leading models such as ChatGPT and Gemini as “co‑pilots” to avoid falling behind as capabilities accelerate, implying potential productivity and skills gaps across generations and firms that move slowly on AI adoption.

Analysis

At Fortune Brainstorm AI Stanford technologist and Chima co‑founder Kiara Nirghin argued that Gen Z is not merely adopting AI but growing up “fluent in AI,” treating coding and everyday tasks as collaborative work with AI agents rather than building from scratch; she cited that this fluency changes how people write, take tests and apply for jobs and positions younger workers to pioneer novel use cases. A 2025 MIT Media Lab study is cited in the article showing ChatGPT users “consistently underperformed at neural, linguistic, and behavioral levels,” but Nirghin countered that many intelligent Gen Z users leverage AI to generate deeper insights—she pointed to running complex research through models to surface perspectives users might not otherwise see. Nirghin highlighted that two recent model releases in the prior weeks have “engulfed the benchmarks” and can make prior uses roughly 10x more effective, and she urged workers at all career levels to adopt main model players such as ChatGPT and Gemini as co‑pilots to avoid falling behind. The combination of rapid model improvement, generational fluency, and the potential for material productivity upside implies a structural advantage for firms and teams that continuously integrate new models, while the MIT findings and fast obsolescence of tools underscore the risk of overreliance and skill gaps for laggards.

Key Decisions for Investors

• Increase exposure to companies enabling AI integration (enterprise co‑pilot tools, model access layers and workforce upskilling platforms) given Nirghin’s argument that AI fluency can drive outsized productivity gains, monitor adoption metrics and product integrations with ChatGPT/Gemini
• Prioritize investments in firms that have public roadmaps for continuous model upgrades and employee reskilling initiatives, and consider underweighting businesses that show slow or no plan to integrate leading models
• Watch objective signals cited in the article — frequency of new model releases and benchmark performance, hiring of AI roles, and use of external co‑pilot tools — as triggers to adjust position sizing because capabilities are evolving rapidly
• Maintain risk controls for execution and cognitive‑quality concerns highlighted by the 2025 MIT study (e.g., favor firms with governance around AI outputs and validation processes)