The dumber the disagreements, the better the world actually is.
Another great post by Morgan Housel on the dichotomy of life. We will always be stressed, but what we stress about will be different based on the quality of life. It was, it is and it will be 😊.
On The Verge‘s Decoder podcast, Matt Garman, CEO of AWS, explained why AI’s potential is intrinsically tied to the cloud. The scale and complexity of modern AI models demand infrastructure that only major cloud providers can deliver
You’re not going to be able to get a lot of the value that’s promised from AI from a server running in your basement, it’s just not possible. The technology won’t be there, the hardware won’t be there, the models won’t live there, et cetera. And so, in many ways, I think it’s a tailwind to that cloud migration because we see with customers, forget proof of concepts … You can run a proof of concept anywhere. I think the world has proven over the last couple of years you can run lots and lots and lots of proof of concepts, but as soon as you start to think about production, and integrating into your production data, you need that data in the cloud so the models can interact with it and you can have it as part of your system.
A simple visual of attributes of Good business vs Bad business based on a snippet Codie Sanchez shared in a podcast with Shane Parrish
Codie said
In my definition, good business equals profitable, cash flowing, what I call a cash-flow versus cash-suck business (so you get paid upfront for a service, not after you provide a service), sustainable (it can exist for a long time), historical (it has existed for a long time), understandable (you can explain it to grandma really easily), and you have what’s called the Lindy effect, the likelihood of the future continuing to cash-flow just as it did in the past. Those are my parameters for a good business. A bad business would be a business that is unprofitable, hard to understand, hasn’t been around for very long, and you have to provide the service before you get paid for the service. That is a business that is just much harder. That’s a harder game to win.
Codie Sanchez quoting Prof. Arthur Brooks on different types of friendship in a conversation with Shane Parrish.
Worthless friends are the friends that have no transactional value. You don’t want anything from them. They don’t want anything from you. They want to hang out with you. They want to go on a walk with you. They don’t want your email list. They don’t want access to your money. They just want to have a beer on a Friday night. And these friendships end up materially increasing, our happiness, these worthless friends, whereas these transactional friendships actually end up, in many ways, decreasing our happiness
Don’t ever forget where you came from, but always be looking where you’re going.
Micky Malka (Capital Herder and Team Finder) on a recent conversation with Patrick O’Schaughnessy on Invest Like the Best podcast.
Great discussion between Jim O’Shaughnessy and Sajith Pai on the India as a market in the Infinite Loops Podcast. Sajith did a great job describing India as a combination of three markets and not a monolithic market of 1.5 billion people.
India is not a 1.5-billion-person market that many Westerners believe. Instead, it’s three distinct “countries” hiding in plain sight. There’s India One: 120 million affluent, English-speaking urbanites (think the population of Germany) who love their iPhones and Starbucks. Then comes India Two: 300 million aspiring middle-class citizens who inhabit the digital economy but not yet the consumption economy. Finally, there’s India Three: a massive population with a similar demographic profile to Sub-Saharan Africa, that’s still waiting for its invitation to join India’s bright future.
Highly recommend checking out the podcast and this report (on Indus Valley – a play on words comparing the market in India to the tech market in Silicon valley) that Sajith and team put together.
Amazing conversation with Bret Taylor on agentic workflows leveraging AI in the enterprises. The whole conversation is worth listening to multiple times, but this specific segment where Bret speaks about the difference between traditional software engineering and AI driven solutions was thought provoking on how much change management organizations have to go through to adopt to these new solutions.
Now if you have parts of your system that are built on large language models, those parts are really different than most of the software that we’ve built on in the past. Number one is they’re relatively slow compared — to generate a page view on a website takes nanoseconds at this point, might be slightly exaggerating, down to milliseconds, even with the fastest models, it’s quite slow in the way tokens are emitted.
Number two is it can be relatively expensive. And again, it really varies based on the number of parameters in the model. But again, the marginal cost of that page view is almost zero at this point. You don’t think about it. Your cost as a software platform is almost exclusively in your head count. With AI, you can see the margin pressure that a lot of companies face, particularly of their training models or even doing inference with high-parameter-count models.
Number three is they’re nondeterministic fundamentally, and you can tune certain models to more reliably have the same output for the same input. But by and large, it’s hard to reproduce behaviors on these systems. What gives them creativity also leads to non-determinism.
And so this combination of it, we’ve gone from cheap, deterministic, reliable systems to relatively slow, relatively expensive but very creative systems. And I think it violates a lot of the conventions that software engineers think about — have grown to think about when producing software, and it becomes almost a statistical problem rather than just a methodological problem.
AI will not replace humans but humans who use AI will replace humans that don’t – Dr. Fei-Fei Li
Leadership isn’t about being the hero. It’s about empowering your team to become heroes themselves.
Google Gemini
For folks that are driven, wired to see an issue and tackle it head-on, it is difficult to not jump in and “try” to help your team whenever they run into an issue. But the reality is that most folks are capable, creative individuals. They just need the space to flex their own problem-solving muscles.
If you team has the skills and experience, let them handle it :-).
