Month: November 2024

I asked someone for something the other day. They said (with a lot of confidence) – “No, we don’t have that anywhere close by.”

I walked a bit further and found what I was looking for.

The truth is that I’ve done the same on occasion in the past. I’ve said a no just because it was easier. Laziness is a powerful motivator.

Two reflections.

First, it is important to reflect on whether we’re saying a no with confidence to someone simply because we don’t want to do the work. Rather than dissuade them, we should choose honesty – “I don’t know” is a far better answer.

Second, don’t take every “no” we get too seriously. Trust but verify.

Below is a chart about how couples meet in the US. It is always fascinating to see a chart like this. It is wild to think that one in three couples used to meet each other through friends. Now, it is less than 1 in 10.

Similarly, 1 in 5 couples used to meet at a bar. That’s dipped closer to 1 in 20. 1 in 4 met through family. That’s also closer to 1 in 20.

It made me wonder about the breadth and depth of our relationships.

It also made me wonder about the ripple effects of this. For example, below is an excerpt from a paper about the impact of assortative mating and income inequality.

“We find that the observed increase in assortativeness accounts for approximately half of the increase in household income inequality between 1980 and 2020,” a paper from the Federal Reserve Bank of St. Louis states. “The most important factors contributing to household income inequality through mate selection are selection on education (35%) and skill (30%), with selection on income (15%) and age (15%) trailing significantly, while selection by race (5%) plays a relatively inconsequential role.” (ref)

There are many such unknowns that I’m sure will be studied in time. The impacts, as always, with big societal changes, will be far reaching.

Actor Matt Damon had a great story of the vacation he promised his wife after a series of movies.

Except they had to cancel their long-planned vacation because Chrisopher Nolan called to offer him a role in Oppenheimer. In Damon’s words –

“I had — not to get too personal — negotiated extensively with my wife that I was taking time off. I had been in Interstellar, and then Chris put me on ice for a couple of movies, so I wasn’t in the rotation, but I actually negotiated in couples therapy ­— this is a true story — the one caveat to my taking time off was if Chris Nolan called. This is without knowing whether or not he was working on anything, because he never tells you. He just calls you out of the blue. And so, it was a moment in my household.”

He had an agreement with his wife that he’d hold the vacation sacred. The only exception was to answer a Christopher Nolan call.

His other colleagues agreed. The Nolan call was always special.

I thought it was such a lovely testament to Nolan’s leadership and impact on his team.

Inspirational.

I spent many years in awe of Roger Federer’s artistry and Rafael Nadal’s grit. As a Roger Federer fan, I used to hate Roger-Rafa games. He had a way of making Roger, even in his prime, seem human.

He did so with the kind of tenacity that I thought made him the greatest sporting role model I know.

Seeing Roger’s note to Rafa on his retirement brought all those memories and emotions back. As is the case with any exchange between these two, it oozed class. (H/T: Dan’s blog for sharing)

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As you get ready to graduate from tennis, I’ve got a few things to share before I maybe get emotional.

Let’s start with the obvious: you beat me—a lot. More than I managed to beat you. You challenged me in ways no one else could. On clay, it felt like I was stepping into your backyard, and you made me work harder than I ever thought I could just to hold my ground. You made me reimagine my game—even going so far as to change the size of my racquet head, hoping for any edge.

I’m not a very superstitious person, but you took it to the next level. Your whole process. All those rituals. Assembling your water bottles like toy soldiers in formation, fixing your hair, adjusting your underwear… All of it with the highest intensity. Secretly, I kind of loved the whole thing. Because it was so unique—it was so you.

And you know what, Rafa, you made me enjoy the game even more.

OK, maybe not at first. After the 2004 Australian Open, I achieved the #1 ranking for the first time. I thought I was on top of the world. And I was—until two months later, when you walked on the court in Miami in your red sleeveless shirt, showing off those biceps, and you beat me convincingly. All that buzz I’d been hearing about you—about this amazing young player from Mallorca, a generational talent, probably going to win a major someday—it wasn’t just hype.

We were both at the start of our journey and it’s one we ended up taking together. Twenty years later, Rafa, I have to say: What an incredible run you’ve had. Including 14 French Opens—historic! You made Spain proud… you made the whole tennis world proud.

I keep thinking about the memories we’ve shared. Promoting the sport together. Playing that match on half-grass, half-clay. Breaking the all-time attendance record by playing in front of more than 50,000 fans in Cape Town, South Africa. Always cracking each other up. Wearing each other out on the court and then, sometimes, almost literally having to hold each other up during trophy ceremonies.

I’m still grateful you invited me to Mallorca to help launch the Rafa Nadal Academy in 2016. Actually, I kind of invited myself. I knew you were too polite to insist on me being there, but I didn’t want to miss it. You have always been a role model for kids around the world, and Mirka and I are so glad that our children have all trained at your academies. They had a blast and learned so much—like thousands of other young players. Although I always worried my kids would come home playing tennis as lefties.

And then there was London—the Laver Cup in 2022. My final match. It meant everything to me that you were there by my side—not as my rival but as my doubles partner. Sharing the court with you that night, and sharing those tears, will forever be one of the most special moments of my career.

Rafa, I know you’re focused on the last stretch of your epic career. We will talk when it’s done. For now, I just want to congratulate your family and team, who all played a massive role in your success. And I want you to know that your old friend is always cheering for you, and will be cheering just as loud for everything you do next.

Rafa that!

Best always, your fan,

Roger

Prof Ethan Mollick has a helpful post on “Thinking like an AI.” 3 salient notes:

(1) Large Language Models do “next token” prediction. They predict the next word that follows our prompt (and so on). That doesn’t mean they’re dumb autocomplete systems. But it does help develop the intuition for why the prompt matters. The pattern of words in a prompt

For example, if I write “The best type of pet is a” the LLM predicts that the most likely tokens to come next, based on its model of human language, are either “dog”, “personal,” “subjective,” or “cat.”

If I change the word “type” to “kind” in the sentence, the probabilities of all the top tokens drop and I am much more likely to get an exotic answer like “calm” or “bunny.” If I add an extra space after the word “pet,” then “dog” isn’t even in the top three predicted tokens!

(2) LLMs make predictions based on their training data. So, the better the training data, the better the predictions.

Contrary to some people’s beliefs, the AI is rarely producing substantial text from its training data verbatim. The sentences the AI provides are usually entirely novel, extrapolated from the language patterns it learned. Occasionally, the model might reproduce a specific fact or phrase it memorized from its training data, but more often, it’s generalizing from learned patterns to produce new content.

(3) LLMs have a limited memory.

Prof Mollick ends his post with a useful caveat.

Understanding token prediction, training data, and memory constraints gives us a peek behind the curtain, but it doesn’t fully explain the magic happening on stage. That said, this knowledge can help you push AI in more interesting directions. Want more original outputs? Try prompts that veer into less common territory in the training data. Stuck in a conversational rut? Remember the context window and start fresh.

But the real way to understand AI is to use it. A lot. For about 10 hours, just do stuff with AI that you do for work or fun. Poke it, prod it, ask it weird questions. See where it shines and where it stumbles. Your hands-on experience will teach you more than any article ever could (even this long one). You’ll figure out a remarkable amount about how to use AI effectively, and you might even surprise yourself with what you discover.

While true, I think his post is an excellent starting point.

When I first heard the term “metabolic syndrome”, my reaction was that there was no way I have that.

Casey Means, in her book, Good Energy explained that Metabolic syndrome means our cells are struggling to get their jobs done because of problems in their energy production system. It is clinically defined as having three or more of the following traits:

1. Fasting glucose of 100 mg/DL or higher
2. A waistline of more than 35 inches for women and 40 inches for men
3. HDL cholesterol less than 40 mg/DL for men and 50 mg/DL for women
4. Triglycerides of 150 mg/dL or higher
5. Blood pressure of 130/85 mmHg or higher

3 and 4 are definitely suspect from the last time I did my blood work.

This realization was part of what inspired so much action after reading her book.

This blog has been a vehicle to read and share what I learn about interesting technology. Over the years, I’ve gravitated toward energy – following the exponential changes in solar and wind adoption has been incredible to watch. One area I haven’t written enough (or at all) about is autonomous cars.

On reflection, I think the reason for this reluctance was largely an acceptance of the challenges involved coupled with a lack of imagination. Every time I thought about autonomy, I found myself wondering whether autonomous cards would ever work in a country like India. The answer to that felt self-evident. So I moved on.

2 recent experiences changed this –

(1) I took a Waymo x Uber taxi. It was a smooth ride without any of the pressure to make conversation (which I appreciate).

(2) I tried Tesla’s FSD or Full Self Driving capability. It has gotten very good.

Both of these provide interesting visions of the future of transportation. They’ve inspired me to spend more time thinking about it.

“Dealing with challenges is the living of life.” | From Shripriya’s post that reminds us that we don’t remove challenges. We just trade them for different ones.

This reminded me of two ideas I think about from time to time. The first is “the obstacle is the way.”

The second is that “it never gets easier. You just learn to go faster.”

As an energy enthusiast who has been following the deployment of solar and wind energy over the past 7 years, there was always a big question – when will China ramp up installation?

Climate is influenced by global emissions. And there’s a limit to how much we’d be able to achieve if China didn’t take massive steps toward decarbonization.

The answer to that question, it seems, was over the past couple of years. Yale’s School of Environment had a fascinating in-depth article. A few salient notes:

In 2022, China installed roughly as much solar capacity as the rest of the world combined, then doubled additional solar in 2023.

Massive wind farms were already operating in northern China, and now a series of utility-scale clean energy bases involving many of China’s massive state-owned utility companies were planned for the relatively empty western desert regions. These bases, a combination of vast solar arrays and wind farms, are to be connected to markets in eastern China through high-speed transmission lines. The projects take advantage both of high solar radiation in the desert and large amounts of cheap, available land. China aims to build more than 200 such bases to help to raise its renewables capacity to about 3.9 terawatts by 2030, more than three times its 2022 total.

This chart tells the story.

Installation is one half of the battle. The next part is deployment and there are still challenges to be overcome.

Renewables now account for half of China’s installed capacity, but there has also been a surge in permits for new coal-fired power plants, and China still generates about 70 percent of its electricity from fossil fuels. This means actual renewable energy use is lagging behind installed capacity.

This is largely due to problems with China’s giant grid, which prefers high-speed transmission from reliable sources to the challenge of integrating variable renewable power and the associated challenge of matching intermittent supply to demand. For the grid companies, China’s coal-fired power plants are steady and predictable, and they are allowed many more hours of grid access than renewables. In addition, anxieties about energy security are now high on the policy agenda, reinforced by geopolitical tensions and recent droughts that affected hydropower output and resulted in power cuts. In China, energy security still means coal.

They’ve identified grid unification as the key lever here.

To use its renewables capacity efficiently however, China has recognized that power system reforms are long overdue. The National Development and Reform Commission recently announced plans to create a unified national power market by 2030, merging its six regional grids into one nationwide electricity market to better manage fluctuations in supply and demand. If that can be achieved, China could not only enhance its position as the global leader in installed capacity for renewable but might also make better use of the clean energy it produces.

The end outcome is inevitable – cheap renewable energy is going to win. The journey will be fascinating to watch.

The most important lever a leader has to create focus is to simplify the priority list.