Perception of time-tracking is shifting. Initially seen as invasive, remote employees now ask for it to prove their contributions, especially with non-traditional schedules. It has become a tool for employee empowerment and validation rather than corporate surveillance.
Leading in an AI era is less about managing people and more about designing systems of agents, workflows, and data. The focus shifts from interpersonal skills to architectural thinking, making leadership a builder role again. People who enjoy 'doing the thing' will thrive.
Don't underestimate very junior talent who are native to new AI tools. A recent Stanford grad at Laurel built a 'chief of staff' agent for the sales team, automating call prep by scraping internal and external data. This highlights a new source of high-leverage innovation.
The traditional trade-off between scope, quality, and speed is breaking. Because AI tools can turn a design mock into a working feature over a weekend, teams no longer have to cut scope to maintain speed and quality. Instead, they can ask, 'can we increase scope?'
Laurel's CPO hires at the extremes: very senior, autonomous PMs who act like GMs and very junior, curious builders. This model eliminates mid-level roles focused on internal coordination and politics, which are too slow for the pace of AI development.
Laurel's CPO has stopped hiring user researchers, arguing that AI tools now empower PMs and designers to conduct and synthesize research directly. This removes the 'translation layer' of a separate research function, making the team more integrated and faster.
The PM role is shifting to that of a 'product builder.' Instead of manually sifting through data, they can use AI agents to scrape sources like Gong, Slack, and Intercom. This provides an aggregated 'voice of the customer' and a data-backed strategy in minutes, not weeks.
While AI dramatically increases development speed, it's a double-edged sword. Without a solid product foundation, user understanding, and clear principles, teams will simply accelerate the shipment of low-value features. AI amplifies both good and bad practices.