Which is no shade on Claude Code, but given everything CC has already done, this seems pretty normal.

"Theory" and "hypothesis" are pretty interchangeable in colloquial usage, and the post's tone is very colloquial. On top of that, there are things referred to as hypotheses that have empirical evidence, like the amyloid beta hypothesis. There is empirical evidence that supports it, but there is also evidence that doesn't, and it's an open question (depending on who you ask).

I don't think it shows that the author lacks the ability to discern state of the art research or is making wildly unsupported statements, I think they were using plain-English terms to describe a state where there's a lot of uncertainty about the physical mechanism as opposed to say, how a car engine works (which at a certain point relies on theories of physics, but they're theories that are almost universally agreed upon).

I think this approach is particularly good for docs where the assumption is the audience wants to understand why you reached the conclusions you came to, and the doc is sort of a persuasive argument. I think this is a valuable doc (and how I like writing and reading), but it is not always the case.

I think often you do want to start with the conclusion, the "end" so to speak, to orient the reader. And also to address the reader who trusts your judgement, and just wants to get up to speed. I've seen a lot of cases where the audience might not be ready/want to follow along w/ a train of reasoning, they want to know the punchline. And once they do, then they might want to follow up.

> Place critical appointment/hiring processes into the hands of randomly selected oversight boards. These boards manage appointments, evaluations, and dismissals, mitigating biases and discouraging the formation of insular power groups.

This has the same issue elections have, just at a smaller scale. A better analog is juries, and charisma/storytelling definitely matters when you're talking to a jury.

> Directly select candidates at random for positions from an eligibility pool. Set and maintain the eligibility standard (such as an exam) by randomly selected oversight board to keep it updated and prevent the standard from being manipulated or gamed.

This is somewhat analogous to college admissions, and the gaming is alive and well there too. You get rid of politics, but you're back to optimizing for KPIs and things. I'm not sure why randomly picking from the top 5% of KPI optimizers is going to be better than picking the top one.

> Firms could randomly select employees or shareholders to serve on their boards. These members can significantly dilute insider collusion and introduce perspectives often overlooked by traditionally selected executives.

Same issue as juries, plus the random picks probably won't know the material well. Although I don't know much about traditional board selections, maybe that's true regardless. If you weight based on % ownership for shareholders, you're de facto giving the seats to big funds, if not, it can quickly become a lottery of like, any random person in the states.

> Use stratified sampling to select committees, ensuring diverse representation of viewpoints, backgrounds, and expertise, contributing to balanced decision-making.

This is the jury thing again? It seems like the solution "randomly pick oversight/approval boards" was listed three times.

> Create randomly composed auditing and oversight committees, deterring corrupt practices through constant unpredictability in oversight.

Constant unpredictability in oversight sounds terrible. The reason we have judges and case law and things in the legal system is that there are tons of edge cases, where reasonable minds will differ. You want to build up a consistent set of guidelines people can follow. A lot of people who are on the edge of rules aren't trying to be corrupt, they're just not sure what they are/aren't allowed to do.

As the above comment says, in my experience bugs introduced from messy code are way more likely than the time savings of not cleaning up code.

The usual exception I'd make are things that like, mostly the same but not quite (e.g. a function to fade out a light over time vs a function to fade out a color over time). Often I find requirements for those diverge over time, so I'll just leave some repeated patterns across both.

To make sure I understand right: you're saying a good way to run things is: publish a list of a bunch of things that could be true or false, and then if industry cares enough, they should spend time/money debunking it?

I think that would be an extremely slow/conservative way to run just about anything, and is not the way we handle basically any claim. I can see an argument for "don't do something until you prove it's safe", useful in some very high-risk situations, but "warn that all kinds of commonplace things could cause cancer until somebody proves it doesn't" is misleading, not just conservative.

And it doesn't even work -- lots of places have spent time/money debunking e.g. negative claims about aspartame, but claims about how unsafe it is persist. And it all comes back to dosage. There is no good evidence that aspartame, at the levels found in a normal soda, cause any issues for humans, but this gets drowned out by studies either showing effects from massive doses on rodents, or indirect effects (e.g. it makes you hungrier, so if you eat more refined sugar as a result of that hunger, then yes it's bad for you, just like more refined sugar is almost always bad for you).

However, Prop 65 is much broader than that. To qualify, a chemical just needs to show up on one of maybe half a dozen lists that show the chemical has some association w/ cancer, but all these show is that in some study, at some quantity, the association existed. The amount that was linked to cancer could be far beyond what is ever present in a consumer good, and the links could have only been shown in non-humans.

The lists aren't the ones gov't agencies like the FDA use to regulate product safety, they're lists far upstream of that that research institutions use to inform further study. The typical starting point is a mouse study with a huge dosage. It's not a useless study, but it's not meant to inform what a human should/should not consume, it's just the start of an investigation.

I don't think this actually has any bearing on the substance of the broader argument, but Prop 65 is not the best example.

I've heard of many companies encouraging their engineers to use LLM-backed tools like Cursor or just Copilot, a (small!) number that have made these kinds of tools mandatory (what "mandatory" means is unclear), and many companies laying people off because money is tight.

But I haven't heard of anybody who was laid off b/c the other engineers were so much more productive w/ AI that they decided to downsize the team, let alone replace a team entirely.

Is this just my bubble? Mostly Bay Area companies, mostly in the small-to-mid range w/ a couple FAANG.

On top of that, people have genuinely different preferences so what seems "better" for a user to one person might not to another.

And then on top of that, yeah, some people don't care. But in my experience w/ software engineers at least, the engineers cared a lot, and wanted to take a lot of pride in what they built, and often the people pushing against that are the mgmt. Sometimes for good reason, sometimes not, that whole thing can get very debateable.

There still needs to be some entity who says "here is when we'll listen to the AI, here are the roles the AI will fill, etc", and that entity IMO is effectively the CEO.

I suppose you could say that entity is the board, and the AI is the CEO, but in practice I think you'd want a person who's involved day-to-day.

The article quotes:

> "...But I thought more deeply and would say 80 percent of the work that a C.E.O. does can be replaced by A.I.”...That includes writing, synthesizing, exhorting the employees.

If AI replaces those things, it has not replaced the CEO. It has just provided the CEO leverage.

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My point is just that I don't think "wages should keep pace with productivity" is true. If wages always rose with productivity, we'd be focusing all the gains on the people in the sectors where productivity is growing, and not lowering the cost of goods for everybody else.

Often that incentive is making goods cheaper, so they're more competitive. That's a huge generalization, but it makes the point that there's nothing wrong with productivity gains outpacing wages.

I'd argue the whole point of productivity gains is that they do outpace pay. The idea is the same work generates more value. Some of that extra value can be passed back to the employee, but if all of it is passed back to the employee, then the goods produced don't actually get cheaper. If nothing gets cheaper, there's no incentive for a business to invest in tech that makes employees more productive.

The data in the piece has a lot of problems with it too, but I think the core assumption is fundamentally off-base.

As far as sweeping generalizations go, I think that's a pretty reasonable one :). I'd imagine that almost all of our users (including most lab admins who assign permissions) don't want to keep a complex permission system in their head.

What we've seen is that this system ends up leading to a small number of well-designed and well-named roles. Most users see the roles themselves ("DNA Designer"), but don't need to worry about exactly what the configuration behind it is.

Somebody needs to be aware of the powerful (although not quite turing-complete) configuration system, but what we've seen in practice is that it's usually one or two technical admins whose job it is to gather requirements from the different teams and figure out how to translate those into a few digestible policies that everybody else can assign.

We certainly didn't invent this model (it's basically RBAC), but we've found it's a good way to address the often-complex demands of a big pharma (where IP is crazy regulated from like, 3-4 angles) without taxing the individual scientists too much.

The issue is genre. To take an extreme example, I might like the brutal open-world genre a la Dark Souls and complain that the Forza games are all garbage because they aren't that. Most would recognize this complaint as silly, because if I want a brutal open world with no hits I should find a game that purports to contain that, not go bashing racing games because I don't like that genre as much.

What the author's doing to Zelda obviously isn't as extreme, and it is a bit more grounded (Zelda games did used to be more like what the author wanted). But it's the same type of argument: back when there were no open world games, the author played one called Zelda and really liked it. Since then (starting with the third), almost /every single title in the series/ has been an extremely dungeon-focused, puzzle-focused game. The overworld has always been a big part, and there have always been some secrets, but the defining aspect of the genre has become these dungeons and puzzles.

This isn't to say the complaints are invalid; there's nothing wrong with wanting a game that is more hidden, less hand-holding, more focused on the action and less on the gimmicks that let you solve a puzzle. But that's not asking for a better version of Zelda, that's asking for a different genre altogether. Focus on asking for new titles in that genre, and leave other genres in peace.

If this data is present (data showing that present hiring methodologies don't predict successful business outcomes), do you have data showing that hiring people who need jobs is any better? We /are/ different from each other -- as an example so obvious it borders on the ridiculous, people who have been programming for ten years will be much faster at it than people who haven't. At what point do you draw the line to state that people stop being different? If so, what data did you use to draw that line?

[1] http://blog.alinelerner.com/resumes-suck-heres-the-data/

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