overheard

Overheard : On constant increase in expectations

Sam Altman’s June 10, 2025 post on achieving singularity captured something I’ve been thinking about lately. There’s a particular passage that perfectly describes how we’re constantly ratcheting up our expectations:

Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes.

This hits at something fundamental about human psychology. We have this remarkable ability to normalize the extraordinary, almost immediately.

I see this everywhere now. My kids casually ask AI to help with homework in ways that would have seemed like science fiction just three years ago. We’ve gone from “can AI write coherent sentences?” to “why can’t it write a perfect screenplay?” in what feels like months.

The progression Altman describes—paragraph to novel, diagnosis to cure, program to company—isn’t just about AI capabilities scaling up. It’s about how our mental models adjust. Each breakthrough becomes the new baseline, not the ceiling.

What struck me most is his phrase: “wonders become routine, and then table stakes.” That’s exactly it. The wonder doesn’t disappear because the technology got worse—it disappears because we got used to it. And then we need something even more impressive to feel that same sense of possibility.

June 10, 2025 by AI Humanity OverHeard Rantings Technology 0

Overheard : AI needs cloud

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.

January 13, 2025 by business Management OverHeard Technology 0

Overheard : Good business vs Bad Business

A simple visual of attributes of Good business vs Bad business based on a snippet Codie Sanchez shared in a podcast with Shane Parrish

✅ GOOD BUSINESS

  • 💰 Profitable + Cash flowing
  • 🤝 Get paid upfront
  • 📈 Long history of success
  • 👵 Easy to explain to grandma
  • ♻️ Sustainable model
  • 🎯 Predictable future

❌ BAD BUSINESS

  • 📉 Unprofitable
  • ⏳ Pay comes after service
  • 🌱 New/unproven model
  • 🤔 Complex to explain
  • 🎲 Uncertain future

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.

January 8, 2025 by business OverHeard 0

Overheard : Worthless friends vs Transactional friends

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

January 8, 2025 by Humanity OverHeard 0

Overheard : India 1-2-3

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.

November 22, 2024 by business Management OverHeard 0

On AI Agentic Workflows

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.

September 11, 2024 by Management Technology 0