Categories
AI AI: Large Language Models

The Texture of Autonomy

There is a distinct texture to working with a truly capable person. It is a feeling of relief, specific and profound.

When you hand a project to a junior employee who “gets it,” the mental load doesn’t just decrease; it vanishes. You don’t have to map the territory for them. You don’t have to pre-visualize every stumble or correct every navigational error. You simply point to the destination, and they find their way.

I was thinking about this feeling—this specific brand of professional trust—when I read a recent observation from two partners at Sequoia regarding the current state of Artificial Intelligence:

“Generally intelligent people can work autonomously for hours at a time, making and fixing their mistakes and figuring out what to do next without being told. Generally intelligent agents can do the same thing. This is new.”

The phrase that sticks with me is “without being told.”

For the last forty years, our relationship with computers has been strictly transactional. The computer waits. We command. It executes. Even the most sophisticated algorithms have essentially been waiting for us to hit “Enter.” They are tools, no different in spirit than a very fast abacus or a hyper-efficient typewriter.

But we are crossing a threshold where the software stops waiting.

The definition of intelligence in a workspace isn’t just raw processing power; it is the ability to recover from failure without supervision. It is the capacity to run into a wall, realize you have hit a wall, back up, and look for a door—all while the manager is asleep or working on something else.

When Sequoia notes that “this is new,” they aren’t talking about a feature update. They are talking about a shift in the ontology of our tools. We are moving from an era of leverage (tools that make us faster) to an era of agency (tools that act on our behalf).

This changes the psychological contract between human and machine. If an agent can “figure out what to do next,” we are no longer operators; we are managers. And as anyone who has transitioned from individual contributor to management knows, that is a fundamentally different skill set. It requires clearer intent, better goal-setting, and the ability to trust a process you cannot entirely see.

We are about to find out what it feels like to have a digital colleague that doesn’t just listen, but actually thinks about the next step.

Categories
AI Leadership

The Power of Two

I recently watched and thoroughly enjoyed Harry Stebbings’ interview with OpenAI’s Sam Altman (CEO) and Brad Lightcap (COO). In addition to gaining new insights into OpenAI’s evolution, their conversation covered a wide range of topics regarding the future of AI and its implications for society and new ventures.

One of the most fascinating aspects was the dynamic between Altman and Lightcap — hearing them discuss their respective strengths, weaknesses, and how those translate into their roles at OpenAI. It’s uncommon to witness a dual interview like this, with two colleagues who have clearly worked together for years and have complete confidence and trust in each other’s judgment and insights.

Throughout my involvement with various small companies, I wish I could have experienced such a powerful duo! In my experience, it’s not uncommon for the CEO to dominate the senior management team’s dynamics. While this sometimes works well, I’ve also seen it lead to reduced performance or frustration among senior managers due to the CEO’s actions.

Altman and Lightcap (and OpenAI by extension) appear to have a much more synergistic working relationship — effectively amounting to a co-equal division of responsibilities. I highly recommend watching this conversation for anyone involved in a startup aiming to scale quickly and effectively! Congratulations to Harry Stebbings for his hosting this excellent conversation with two key individuals leading the evolution of AI!