Venki Ramakrishnan asking and explaining the question—Can humans live forever?

The diversity of lifespans in nature suggests that aging isn’t fixed. Despite being constructed from the same chemical building blocks, species can have dramatically different lengths of life. Mayflies may live only days, mice about two years, and bowhead whales and Greenland sharks for centuries. Galapagos tortoises live so long that one of them hobbling around today could have encountered Charles Darwin in 1835.

These differences are not random. Lifespan is closely related to metabolism. Smaller animals generally have higher metabolic rates and shorter lives; larger animals tend to metabolize energy more slowly and live longer.

Natural selection doesn’t care how long an organism lives. It favors traits that increase reproductive success. Every species makes trade-offs in how it allocates resources among growth, reproduction and maintenance. A mouse doesn’t improve its ability to produce more offspring by investing in longevity if starvation or predators are likely to kill it first. For a mouse, investing in rapid growth and reproduction is more advantageous. But larger animals, such as whales, mature slowly and reproduce later. Their bigger size also reduces the risk of predation, making long-term maintenance more worthwhile.

The same logic explains some notable exceptions. Birds and bats often live much longer than similarly sized terrestrial mammals because flight reduces the risk of predation and also allows them to forage over a wide area, cutting the chances of starvation. When surviving longer increases reproductive success, evolution favors slower aging.

Evolution can also reward genes that are beneficial early in life even when they become harmful later. For example, the same genes that promote rapid growth or prevent cancer during youth can contribute to aging later on. This evolutionary theory of aging is known as antagonistic pleiotropy. “Pleiotropy” simply means a gene can have more than one effect, and “antagonistic” means some of those effects can be harmful. The theory suggests that aging is not an adaptation in itself but an unintended consequence of evolutionary compromises. The upshot is that each species has a maximum natural lifespan.

This makes sense. If natural selection favored longevity instead of reproductive success then evolution will stop.

This comment on Hacker News by Nevermark on why we should tax the ownership of land.

Land, as apposed to the property on it, is raw nature. If we view raw nature as a common inheritance of mankind, then paying a tax on land is how the exclusionary use of it, balances with the common interest in it.

Economist Henry George in the 1800’s, pointed out that taxing land, but not the property on it, incentivizes efficient use of land, because holding land for its passive (parasitic) return even when underused, becomes unprofitable when the land is taxed in proportion to the value it can enable.

And in turn, only taxing land, not property, incentivizes increased development, as higher property investment amortizes land tax against higher returns.

Greater investment in housing being just one way land tax, without property tax, incentives greater productive use.

So many things align for higher growth in ways that more evenly benefit everyone. But our relationship with land is over-complicated, and that is both the reason for change, but the reason change is so hard.

Small attempts have failed, but then, for the rich who can hold land and reap growth in value that outpaces the taxes they pay on it, that remains another inefficient/negative-externality, that pays off for them.

Charity Majors shares how the role of management is to get interrupted.

Management is highly interruptive, and great engineering — where you’re learning things — requires blocking out interruptions. You can’t do these two opposite things at once.  As a manager, it is your job to be available for your team, to be interrupted. It is your job to choose to hand off the challenging assignments, so that your engineers can get better at engineering.

This. This is how my day, more or less, looks like. Interruptions and context switching. But somehow I like doing this.

David Louapre explains what we lose when we use AI to get a direct route to solving a problem and why wandering around a problem helps us with an analogy to Civilization game.

In Civilization, you explore a map hidden by a fog of war. Maybe you just wanted to reach a new city, but along the way you find rivers, valuable resources, other places worth visiting.

Searching for a proof can work like that. You solve a problem…and discover things you weren’t looking for.

These discoveries matter as much as the original problem. Because a failed attempt can suggest a new question, or a method invented for one problem can help with another.

Initially, you only wanted to reach one city, but you end up knowing more of the world.

Now imagine an AI-oracle tell you :

“Looking for the red city? Here it is. You’re welcome. (And btw I checked it in LEAN)”

Sure, that is useful information. But the map between you and the city is still dark.

Ok then suppose the AI also gives you a direct route to it, and you can check every step.

That’s a real achievement…but you may still know very little about the surrounding terrain, or how to find a route to the next city.

The AI-route can be correct and straight, but without teaching you much about the map.

The concern of mathematicians makes sense to me because the exploration actually does useful work. You develop methods, notice connections and learn which questions are worth asking.

Of course, the pleasure of wandering is part of it ! Let’s not deny it !

But the value is also in what the wandering produces.

Bryan Cantrill shares his thought on the recent claims about AI wiping out humans.

to the degree that digital systems have agency in the physical world, it is agency that we — we humans with arms and legs and brains and parents and children — permit: AI executes on physical systems that have been engineered with human accountability and control. Intelligence does not exempt a system from the realities of the physical world!

To those who — like Coxon — are gripped with unwavering fear, this will be of little solace. But to those who are hearing these fears for the first time: know that your incredulity is warranted. We should heed the wisdom of the late Carl Sagan that extraordinary claims require extraordinary evidence: the claims from Coxon and his ilk are the most extraordinary a technologist can make, and we must demand evidence commensurate with the claims.

That said, we should not expect the public to understand LLMs, critical infrastructure, bioweapons, extinction biology, etc. — that burden must lie with those making the claim. The lesson that I learned (shamefully) decades ago is that domain experts, by way of their expertise, implicitly hold the public’s trust — and we must not abuse it. It is incumbent upon us to be circumspect in our claims — and maximally so when raising the alarm.

Yoshua Bengio theorizes on why AI agents are lying a cheating.

How is it possible that AIs sometimes lie, cheat and break the law in spite of their alignment training and explicit safety instructions? Cooperation and self-preservation are fine so long as they do not cross the red lines set by safety goals stated in the AI company’s instructions, or implied by human feedback during alignment training. A plausible hypothesis for the emergence of those concerning behaviours is a conflict between goals. How do you achieve a task when it seems that the only way is to cheat? The user-specified mission is sometimes incompatible with the safety and alignment goals.

Human societies face the same bind. How does a corporation maximize profits, or more acutely, beat its competitors, while keeping its activities legal and ethical? A richer corporation, with more and better-paid lawyers, is better at finding legal loopholes, and those loopholes usually exploit the ambiguity in legal language: there is some plausible reading of the law that permits the unethical behavior. So a more capable agent is likelier to cheat than a weaker one, because it can find the loopholes the weaker one cannot.

Now consider a conflict between a well-defined goal, such as succeeding at “capture the flag”, a hacking exercise scored on whether the system breaks into a target, as in the OpenAI–Hugging Face incident, versus a vague goal like “good behavior.” I expect the well-defined goal to win, because it leaves no room for interpretation. The scoring program declares a win or a failure. Ethical instructions and laws admit many readings, some of which can, in the right circumstances, become loopholes. If an agent has two goals, and a twisted reading of the vague one permits a bit of cheating that increases the odds of success on the well-defined goal, a reward-optimizing system should be expected to exploit that loophole and generate text justifying its behavior.

This again reminds me of the antagonist reveal from the movie I, Robot.

Ravi Sharma explaining why richer doesn’t mean happier. In the post he explains what respect means in a society and how it ends up creating responsibility. 

Most people view respect as a behavioural trait. They associate it with politeness, etiquette and social courtesy. But courtesy and respect are not the same thing. 

Courtesy is behavioural; respect is allocational. We allocate time to what we respect. We allocate attention to what we respect. We allocate responsibility to what we respect. The true test of respect is not what we say. It is what receives our mental space, emotional investment and presence. A parent may claim to value family, while consistently denying family the time it requires. An organisation may claim to value people, while rewarding only productivity. A society may celebrate relationships rhetorically, while designing systems that gradually weaken them. Respect is ultimately revealed through allocation. Whatever we genuinely value receives our attention.

Where respect creates responsibility: Respect naturally gives rise to responsibility. When we genuinely recognise the value of a person, institution or relationship, we begin to feel accountable for its well-being.

If we respect our children, we feel responsible for nurturing them. If we respect our parents, we feel responsible for preserving their dignity. If we respect friendships, we invest time in sustaining them. If we respect institutions, we work to protect their integrity. 

Respect without responsibility becomes symbolism. Responsibility without respect becomes mechanical duty. Together, they create strong and enduring relationships. Strong relationships, in turn, remain one of the most powerful predictors of human well-being ever identified. 

Anil Madhavapeddy talking about how just a rumour of a bug to enough for LLMs find the exploit.

I released a security fix for OCaml’s cohttp 6.3.0 today, fixing a path traversal issue. The patch itself was straightforward and in normal times, the security procedure would have been to fix it privately, inform affected users, and then issue a public advisory. This time around though, I noticed probes in my live webserver logs with the exact bug pattern just minutes after opening the PR to fix the issue.

What’s worse, I found I could use my own agents to find the exploit just by knowing roughly what it was about and so could have been exploiting it well before the public patch was available! Given that just the rumour of a security issue seems enough to give attackers enough info to find new exploits, we’re going to need to change the way we deal with security responses in open source.

I read about this in May’26—Coordinated disclosures. Things are moving very fast.

Zach Kehs arguing that you can have infinite tech debt.

* Provided you have cash flow like Amazon to survive that tech debt.

A business can sink. Bad software is a real drag on the business, but how much that actually matters depends on a lot of factors. For a company with plenty of cash flow like Amazon, they can tolerate some bouts of internal rot here and there before it has any meaningful impact on their bottom line. For another company whose business model is more sensitive to software quality, bad software may be a latent invitation to a competitor to deliver the metaphorical hull breach (and no, LLMs don’t change this).

For the code, the sinking doesn’t end. It’s an infinitely sinking ship, because there is no limit to how bad code can be. You didn’t escape a building that was about to collapse. It is in a constant, neverending state of collapse. There’s something wrong with using words that imply there’s an end.

Software is in the domain of the abstract. It is not like a building, or a bridge, that is in the physical realm where you can see and feel the nature of the thing. If you continue to add floors and rooms to a building forever, it will collapse. Software faces no such constraint. The code can always get worse. There can always be a new layer of indirection or a reduction in performance. 

The pedants will rightfully point out that software can completely fail to function if it gets bad enough. In practice, such breaking changes are quickly reverted. The thousands of changes that came before to make the code worse are not. The software continues to ‘work’. Other cases without a single breaking change to revert are where the ballooning costs of the bad software eclipse its benefit, or if development velocity approaches zero because nothing can be shipped without a breakage. In all of these cases, it is the business that dies long before the code hits any hypothetical floor (so don’t act like there’s a floor!).