Niall Firth
Executive Editor
MIT Technology Review
About Me
Hear My Insights
How Can the Labor Market Keep Up with Tech Disruption?
Is the labor market evolving fast enough to keep up with our rapidly changing economy, or is it being left behind? As innovation and technology disrupt industries at unprecedented speeds, traditional career paths are giving way to new, unconventional trajectories. Around the globe, regulations aim to protect workers—both employees and freelancers—while striving to maintain economic agility, yet a persistent mismatch between job seekers and available roles remains. How are careers taking shape today, and where does this gap between supply and demand originate? How can we anticipate future trends and prepare the workforce accordingly? Who is adapting most successfully, and what are the secrets to their resilience?
Shared Progress: How Can Open-Sourcing Speed the AI Race?
Today, artificial intelligence has become a transformative economic force shaping industries, influencing policies, and driving global competition. Amid the rapid scaling of AI models across industries and borders, a key debate continues to divide: should AI be open-source or closed-source? Early 2025 saw a surge in open-source AI initiatives, with advocates emphasizing its cost efficiency and lower energy demands. But can open-source truly redefine the AI landscape? And after the breakthroughs of the first half of the year, what’s next for open-source AI? This session will explore open sourcing, first tackling its definition, before examining key trade-offs and exploring a path forward that fosters innovation and responsible AI development.
All-You-Can-Eat: Can You Balance AI’s Appetite for Data & Privacy?
The data privacy diet is a fluctuating one, that varies across time, regions, and adapts to (tech) disruptions. Data has proven to be one of our most valuable assets today – one whose protection varies from a legislation to another. But as regulations grow stricter, a potential data wall might emerge, threatening smaller startups' competitiveness and raising questions about shifting focus to application layers rather than LLMs. From the user perspective, building ethical AI is crucial, as current models may pose significant dangers. Understanding the origins of training data is equally important—where this data comes from directly impacts individual privacy rights. Parallels with the social media industry and its challenges around privacy offer interesting lessons. How can we reconcile increasingly strict privacy regulations with data-hungry business models while avoiding past pitfalls?