The amount of openness in any community or culture isn’t determined by the degree to which people in the “in” group exclude others. Instead, it is determined by the amount of effort that goes into including those who are on the “out.”
And it almost always shows up in the small things in conversation – in translating humor, picking common topics, showing curiosity.
Those small things are the big things.
The biggest challenge with a commitment to seeking the truth is that you will often not like what you find.
That’s because truth often goes against our biases and our identities and makes us uncomfortable in the process.
But it is in that discomfort that we find growth.
Showing someone the way doesn’t mean they’ll take it.
Sometimes, it even has the opposite effect.
That’s because people often need to arrive at the path forward themselves – in their own time, on their own terms.
The art of coaching isn’t in showing the way, but in creating conditions where the discovery of the path forward feels like theirs.
One of our kids’ teachers has been sharing this maxim in class – practice makes progress.
She’s replaced the old practice makes perfect with something more powerful — because the goal isn’t perfection, it’s progress.
Perfection is static and can get us stuck. Progress is alive and keeps us moving.
It’s a beautiful articulation of a growth mindset.
It resonated.
Very few stimuli are worthy of an immediate and urgent reaction – the kind that gets the fight or flight system going.
Breathe.
Then take the time you need to respond.
I was on a flight the other day and paused as the Captain’s briefing began with humor that made me look up.
It turns out the Captain had come to the passenger side to talk to us. Over the next 3 minutes, he humorously told us about his son’s Little League defeat, the rooster on his farm, and cracked jokes at the expense of the airport we were going to.
He ended with a lovely thank you for trusting him, his crew, and the airline with our safety. He believed the trust was well placed as he’d been doing it a long time but he never took it for granted.
When he finished, we all clapped.
I’ve been fortunate to take many flights over the years. This was the first time I experienced this reaction after the Captain’s briefing.
As I reflected on it, it was all just an illustration of the combined power of humor and gratitude that transformed something mundane to something unique and memorable.
Any effort that goes into creating the perception of competence almost always takes away from effort required to become more competent.
A simple test for effective leadership – the ability to act before something becomes a problem.
It requires the difficult work of thinking a few steps ahead and taking opinionated stances that prove to be right, recognizing and rewarding people who find ways to deliver exceptional value to customers, and not tolerating any signs of the bar being lowered.
All of this is hard to do. It’s much easier to choose the easier path.
But it is also the difference between relevance and irrelevance.
I’ve been running a small experiment with how I consume news.
Instead of reading daily emails, I’ve been using a simple prompt on ChatGPT to get concise updates on demand. No notifications, no constant drip.
So far, I’ve noticed two things:
Peace of mind. I don’t miss the sensationalist headlines.
Better timing. I now check the news every couple of days in the evening, not first thing in the morning. That shift alone makes the day feel lighter.
I don’t think I have missed much – the big stories still find their way through. But I’ve gained a calmer relationship with the flow of information.
It’s still an experiment, but it’s one I intend to keep running.
Cal Newport has taken the position of superintelligence skeptic and his latest article – What If AI Doesn’t Get Much Better Than This – made for interesting reading. A few notes –
I appreciated his quick description of pre-training vs. post-training to improve models.
A useful metaphor here is a car. Pre-training can be said to produce the vehicle; post-training soups it up. In the scaling-law paper, Kaplan and his co-authors predicted that as you expand the pre-training process you increase the power of the cars you produce; if GPT-3 was a sedan, GPT-4 was a sports car. Once this progression faltered, however, the industry turned its attention to helping the cars that they’d already built to perform better. Post-training techniques turned engineers into mechanics.
After delving into depth into relatively linear advances of more recent models like GPT-5 vs. GPT-4, he goes onto share a possible scenario.
If these moderate views of A.I. are right, then in the next few years A.I. tools will make steady but gradual advances. Many people will use A.I. on a regular but limited basis, whether to look up information or to speed up certain annoying tasks, such as summarizing a report or writing the rough draft of an event agenda. Certain fields, like programming and academia, will change dramatically. A minority of professions, such as voice acting and social-media copywriting, might essentially disappear. But A.I. may not massively disrupt the job market, and more hyperbolic ideas like superintelligence may come to seem unserious.
And he ends with a note that recommends less hubris and more care.
Even the figures we might call A.I. moderates, however, don’t think the public should let its guard down. Marcus believes that we were misguided to place so much emphasis on generative A.I., but he also thinks that, with new techniques, A.G.I. could still be attainable as early as the twenty-thirties. Even if language models never automate our jobs, the renewed interest and investment in A.I. might lead toward more complicated solutions, which could. In the meantime, we should use this reprieve to prepare for disruptions that might still loom—by crafting effective A.I. regulations, for example, and by developing the nascent field of digital ethics.
The appendices of the scaling-law paper, from 2020, included a section called “Caveats,” which subsequent coverage tended to miss. “At present we do not have a solid theoretical understanding for any of our proposed scaling laws,” the authors wrote. “The scaling relations with model size and compute are especially mysterious.” In practice, the scaling laws worked until they didn’t. The whole enterprise of teaching computers to think remains mysterious. We should proceed with less hubris and more care.
It is early days and there’s certainly a lot of unknowns. One idea I always reflect on when I read these sorts of takes is – everyone is talking their book, skeptics included.
The truth remains that AI, even in its current form, has a lot of potential to boost productivity and change workflows. Whether we see this journey end in Superintelligence in the short-term is unclear.
But, regardless, there’s enough disruption to go around and plenty of work to be done to use these changes for good and society for the changes ahead.
A Learning a Day 