Author: alearningaday

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.

Imagine you’ve decided to change a habit – for instance, you’ve decided you’re going to replace time watching Netflix with time reading a book.

For most people, the next step is encountering a familiar foe I call habit change frustration. You’ll find yourself struggling to break old habits easily and will get frustrated.

And that frustration will likely build over time. If you have some form of weekly review system, you’ll realize you’re performing woefully against your goal.

That’s when you’ll find yourself with two choices –

(1) The default choice will be to simply give up on the goal. You’ll find some justification for that decision and move on.

(2) The non-obvious choice will be to continue living with the habit change frustration.

The benefit of doing so is that you keep the tension. That tension will keep building till it hits a point when you’ll decide to make the change.

I’ve had experiences with some habit changes that took months before the switch flipped.

The tension that causes habit change frustration is a feature, not a bug.

We use a travel checklist in our phone’s notes and run through the checklist every time we take a trip.

We’ve been using this system for the past 4 years and the difference in the packing experience pre- and post- checklist is night and day. We used to leave for a trip asking each other random questions about whether we remembered x or y.

And we’d inevitably realize we forgot something.

Over time, we’ve continued to iterate on the checklist of course. It is definitely not perfect. But if we forget something this time around, we just add it in and know it’ll be better next time.

This is a simple example of an adaptive system. The lesson to take away is that the best processes and systems are adaptive. They’re continuously learning from previous attempts and getting better.

That’s applicable to the most productive humans as well. They’ve built in learning loops that help them continue to adapt.

That makes it impossible to bet against adaptive systems.

As organizations, every dollar that doesn’t feed our ego will feed our growth.

As individuals, every dollar that doesn’t feed our ego will feed our joy and peace of mind.

(H/T: John Bragg on Farnam Street for the first part)

“Success is being excited to go to work and being excited to come home.” | Will Ahmed

Simple and profound at once.

It resonated.

A professional fighter we know shared – “Sleep is the most effective legal performance enhancing drug.”

I’ve noticed significant lifts in my energy and productivity when my average during a work week is closer to 8 hours vs. closer to 7.

True in fighting, and true in life.

Marcus Rashford, a former Manchester United player who was transferred to Barcelona, recently shared in an interview that he felt the club was stuck in no man’s land.

It’s easy to dismiss criticism from a player who grew up at the club and was eventually forced out by the latest in the string of managers he’s played under. But there was truth in what he said.

The real issue he pointed to is one of instability. Managers have been cycled in and out so quickly that none have been given the 4–5 years typically needed to mould a team in their philosophy. No manager in the past decade has lasted longer than three years.

In retrospect, the club lurched from one short-term and ill thought out managerial decision to the next.

That’s why Rashford’s point resonates.

Too much change creates chaos and prevents long-term progress.

Too little change – especially when results are consistently poor with no line of sight to improvement – is equally ruinous.

Sustainable success, whether in football or in organizations, lies in knowing when to persist and when to pivot.

As always, the magic is in balance.