This thoughtful take by drfloyd51 from Hacker News on why we shouldn’t judge parents.

Before kids it was easy to judge bad parents. Then one day with child I found myself due to circumstances in a store way past my child’s bedtime. She was screaming and crying, because it was way past her bedtime.

Then I realized… I was now “the bad parent” I had so easily judged.

Then it was easy to judge parents with children younger than mine.

Until I learned that not all children have the same issues in the same order.

Then I learned it’s easier not to judge at all.

Jordi Visser talking about how AI has compressed time-to-market and in turn started rewarding metabolism over patience.

For more than a decade, equity markets were built around a simple premise: durable franchises deserved durable multiples. Investors weren’t just buying earnings. They were buying time. Time to compound. Time before meaningful competition arrived. Time protected by scale, distribution, switching costs, and capital intensity.

Time was the moat.

The entire architecture of modern markets reinforced that belief. Passive flows concentrated into the largest platforms. Growth indices tilted toward scalable digital economics. Valuation frameworks stretched duration assumptions further into the future. A narrow cohort absorbed more and more of the index because the math appeared rational.

Scale begot scale.

But something subtle has changed.

AI does not simply disrupt business models.

It compresses time.

When the replacement cost of competence collapses, when code can be generated instantly and iterated continuously, competitive cycles shrink. A product that once enjoyed a five- to ten-year window of defensibility may now face viable competition in months. Execution speed replaces installed base. Iteration cadence replaces headcount.

And when competitive half-lives shorten, equity changes character.

A share of stock used to represent ownership of a durable franchise with predictable cash flows. In the Age of Agents, it increasingly resembles a call option on execution velocity. Cash flows that once looked like fifteen-year streams begin to look like five-year bets.

When duration compresses, multiples reprice.

This is not simply a SaaS selloff. It is the repricing of time as an asset.

If the last cycle rewarded patience, buy scale, hold duration, let monetary expansion amplify returns, the next may reward adaptability. Velocity over size. Metabolism over moat.

This, kind of, conflicts with what Jason Fried shared on bespoke software.

Jason Fried shares his thoughts on bespoke software.

So when someone suggests that AI means everyone will build their own custom tools, ask who “everyone” is. The three-person accounting firm drowning in client paperwork? They want the paperwork gone, not a new system to maintain. The regional logistics company with 40 trucks? They want the routes optimized, not Joe spouting off about this new system he’s been messing around with. The law firm billing 70-hour weeks? They want leverage on their time, not a software project to design.

They don’t hate technology. But building and maintaining their own critical systems isn’t their wheelhouse, regardless of how much faster and easier it’s become. It’s another job on top of the job.

Dave Rupert explaing how the Gell-Mann Amnesia Effect is playing out in organizations with all the surrounding AI hoopla.

Sometimes I feel like there’s a palpable tension in the air as if we’re waiting to see whether AI will replace designers or engineers first. Designers empowered by AI might feel those pesky nay-saying, opinionated engineers aren’t needed anymore. Engineers empowered with AI might feel like AI creates designs that are good enough for most situations. Backend engineers feel like frontend engineering is a solved problem. Frontend engineers know scaffolding a CRUD app or an entire backend API is simple fodder for the agent. Meanwhile, management cackles in their leather chairs saying “Let them fight…”

Ha!

This comic by CommitStrip from 2016 feels way ahead of its time.

Terry Godier’s thought provoking post on how modern gadgets don’t stop talking.

This watch [Casio F-91W] costs twelve dollars. It weighs twenty-one grams. It has an alarm that sounds like a microwave in another room. It has told time the same way since 1989.

It doesn’t know my heart rate. It has no opinions about whether I’ve stood up enough today. It will never need a firmware update.

When the battery dies in seven years, I’ll press in a new one with a paperclip.

That will be the entirety of my obligation to it.

This watch [Applet Watch] costs four hundred dollars. It also tells time.

It also tracks my steps, monitors my blood oxygen, measures my sleep quality, logs my workouts, reminds me to breathe, reminds me to stand, nudges me to close my rings, alerts me to unusual heart rhythms, pings me with notifications from six apps, and dies every night.

One of these is a product. The other is a relationship.

And a very insightful take on screen time.

Screen Time gives you a report card. And if the grade is bad, the design makes one thing clear: That’s a YOU problem.

It measures YOUR usage. Tracks YOUR behavior. Gives YOU a weekly report card. If the numbers are too high?

You picked it up too much.
You spent too long.
You failed your limit.
Try again next week.
Try harder.

Screen Time is a blame shift dressed in a soft font.

Ha!

Ben Thompson talking about the impact of AI on seat-based business model.

[…] when Microsoft revealed how they will handle the potential business impact of AI reducing seats, which is a bit of a problem for their seat-based business model: the company is going to bundle AI into a new higher-tiered enterprise offering, E7, which is going to cost twice as much — $99 per seat per month — as the formerly top-of-the-line E5. That’s a big increase, which Microsoft needs to justify with AI that actually makes those seats more productive, and the product they launched with the new bundle was Copilot Cowork.

This never occurred to me. AI replaces humans. Organisations need less licenses for their enterprise software. Enterprise software makers double the price of their licenses. Organisations pay the same amount for—supposedly—increased productivity.

Sharif Shameem talks about the disadvantages of success and how willingness to look stupid helps countering it.

There’s this unfortunate pattern that happens when someone wins a Nobel Prize. They tend to stop doing great work. Richard Hamming talks about this in You and Your Research:

When you are famous it is hard to work on small problems. This is what did Shannon in. After information theory, what do you do for an encore? The great scientists often make this error. They fail to continue to plant the little acorns from which the mighty oak trees grow. They try to get the big thing right off. And that isn’t the way things go. So that is another reason why you find that when you get early recognition it seems to sterilize you. In fact I will give you my favorite quotation of many years. The Institute for Advanced Study in Princeton, in my opinion, has ruined more good scientists than any institution has created, judged by what they did before they came and judged by what they did after. Not that they weren’t good afterwards, but they were superb before they got there and were only good afterwards.

Before the Nobel Prize, nobody really cares who you are. But after the Nobel Prize, you’re a Nobel Prize winner, and Nobel Prize winners are supposed to have Good Ideas. Every idea, every paper, every talk at a conference is now being evaluated against the standard of your Nobel Prize-winning work. Everyone is asking, “is this worthy of a Nobel laureate?” It’s a high bar to clear. So instead of trying and occasionally failing, they just… stop trying. The fear of making something bad is worse than producing nothing at all.

This reminds me of Quality vs Quantity.

David Oks explaining how it was the iPhone, and not ATM, that killed the bank teller’s job. And then he goes on to theorise that it is paradigm shift that displaces workers and not automation. An insightful read.

The ATM tried to do the teller’s job better, faster, cheaper; it tried to fit capital into a labor-shaped hole; but the iPhone made the teller’s job irrelevant. One automated tasks within an existing paradigm, and the other created a new paradigm in which those tasks simply didn’t need to exist at all. And it is paradigm replacement, not task automation, that actually displaces workers—and, conversely, unlocks the latent productivity within any technology. That’s because as long as the old paradigm persists, there will be labor-shaped holes in which capital substitution will encounter constant frictions and bottlenecks. 

This has, I think, serious implications for how we’re thinking about AI.

People in AI frequently talk about the vision of AI being a “drop-in remote worker”: AI systems that can be inserted into a workflow, learn it, and eventually do it on the level of a competent human. And they see that as the point where you’ll start to see serious productivity gains and labor displacement.

I am not a “denier” on the question of technological job loss; Vance’s blithe optimism is not mine. But I’m skeptical that simply slotting AI into human-shaped jobs will have the results people seem to expect. The history of technology, even exceptionally powerful general-purpose technology, tells us that as long as you are trying to fit capital into labor-shaped holes you will find yourself confronted by endless frictions: just as with electricity, the productivity inherent in any technology is unleashed only when you figure out how to organize work around it, rather than slotting it into what already exists. We are still very much in the regime of slotting it in. And as long as we are in that regime, I expect disappointing productivity gains and relatively little real displacement.

Juan Cruz Martinez talking about how knowledge transfer worked with seniors and juniors. And how this ensured the institutional knowledge continued in the organisation.

There’s a cost that’s even harder to see from the planning meeting, and it’s the one that concerns me the most.

Every piece of institutional knowledge on your team lives in someone’s head. How the payment system actually works, not how the docs say it works. Why that service was split in 2021 and why you can never merge it back. The customer edge case that crashes the billing module every February.

This knowledge has always transferred through a specific mechanism: senior engineers teaching junior engineers by working alongside them. The junior asks a question that feels basic. The senior explains the answer. That explanation forces the senior to articulate something they’d never written down. The knowledge becomes shared. The bus factor drops.

When you stop hiring juniors, this mechanism stops. Not immediately. It degrades gradually, which is why it’s so easy to ignore. But three years from now, when your senior architect leaves for a role that doesn’t require them to review AI output twelve hours a day, they’re taking everything with them. And there’s nobody two levels down who absorbed even a fraction of it, because that person was never hired.

The activity in third paragraph is something that I have seen myself do as a senior. So this resonates with me and so does the concern that in the world of AI, with less number of juniors, how will the institutional knowledge be preserved.