Why do we have so many meetings? Few workplace features are so scorned, yet seemingly so necessary. This paper provides the first large-scale economic evidence on workplace meetings using an original survey of more than 9,000 workers linked to matched employer–employee administrative data from Norway. We show that meetings are both common and costly, consuming an average of 12 percent of work hours and 14 percent of firm wage bills. Planning, problem solving, information sharing, and project coordination account for the majority of meeting activity. High-paying and high-revenue firms devote more resources to meetings despite facing a substantially higher opportunity cost of employee time. Meeting frequency and intensity are positively related to worker wage growth. Workers in meeting-intensive firms report greater on-the-job learning, and interactions with more senior colleagues are associated with stronger wage growth, suggesting that knowledge transmission within firms is an important mechanism. Meetings are the broccoli of work – widely disliked, but probably good for us anyway.
Why? An argument stands on its own, regardless of origin. If it's poorly written, by someone who is say a non-native speaker, it can still be a VALUABLE and VALID argument. It would be racist to dismiss an argument because it was poorly written by a non-native speaker. "Racism" against a computer is just as ignorant and petty.
If carbon taxes are already a lethal policy for an political campaign, it's absurd to think that fears of ASI will create any real movement around pausing AI.
If there is any movement to pause AI development, it will come from the general public's dislike of these companies. Not from the AI safety angle.
If that is true and one cares about a moratorium on progress in the US then it seems like the number one way is to meet people where they are: so water use misinformation, degrowth, power supply constraints. That does place all the people who push for these things in a different light. They may well be attempting to do what the AI safety labs are ostensibly trying to do.
As an AI safetyist, one’s closest ally (in a distributed coordinated way) is the populist misinformer. Fascinating.
If there's going to be any pause, I'm sure it will come from a populist movement. I just can't imagine misplaced worries about AI water use will translate into the kinds of policy the authors want to see.
Yeah it’s like shoving the top of a double pendulum. You will get some movement in one direction, but where it will precisely land is hard to predict. The water-use argument is already earning refinement by differentiating “AI datacenters” from “normal datacenters” in an effort to control the movement.
I imagine any populism movement will require rampant fearmongering to get a result. Considering the rough present alignments, presumably blue tribe focused propaganda will involve climate and inequality focused fear and red tribe focused propaganda will involve job loss. Grey tribe positioning is the P(doom) meme where everyone is rewarded for a high-P(doom) estimate.
It's possible the general public wouldn't care enough one way or the other such that elected representatives could just do it without any change to their electoral fortunes.
I don't think it's a gotcha at all, they openly said it and were pondering how that might help them. Misinformation plays more to sides that are wrong about more things, and I don't consider misinformation my ally in anything.
If model performance continues to scale with model size, I have a hard time seeing how local models will have any chance of competing with models hosted on datacenter hardware.
1. There are strong economies of scale in hosting inference (batched prompts, high uptime, shared infrastructure).
2. There are physical limits on how much memory we will be able to produce over the next few years. Demand will probably scale at least as fast as production does, so we won't be saved by falling prices.
For your first point- You've just repeated "shared tenant." A scaling factor that's been used since before the turn of the milenium. Uptime is, as always, an irrelevancy for personal/homelab vs cloud. It shifts from uptime to pure financial (capex first, then how you account for "wasted" time).
2) The current memory crunch is more political than cyclical. The only reason we have fabs as far intro construction as we do is CHIPS Act. Which, predates LLMs public existance by more than 6mo. the horrific silicon prices are a direct result of openAI's openly Illegal dealings. Their pretense of needing it for stargate gets sundered further with each missed or cancelled deadline.
They predicted the political and regulatory outcome superbly.
The big commercial models seem to gain far more from pre-processing than they do from size, and you can already run pretty useful models on desktop hardware.+
Check out this video about how DeepMind significantly improved performance: https://youtu.be/Dkqzqw8rxXI They basically ran the LLM tuning through an old-school genetic or annealing style algorithm and trounced what a larger model could do alone.
There's a value for many people and organizations with running a model locally on hardware they fully own and control (or pay to colocate in a datacenter somewhere) vs running a model on something owned/controlled by any third party. For highly privacy sensitive, medical applications, etc. It's not just a question of raw efficiency in dollar per tokens per day or tokens/second.
Cloud models will always be ahead, but not every task needs Fable-level intelligence. The number of usable situations for local models will increase as hardware and open-weight models improve.
Which is why I pause when they say they're not looking for investor money – in medicine you'd at least have to phrase things in terms of "what already exists, and what's our contribution"? From that lens, I'm not sure what they're trying to contribute: instead of increasing the predictive value of full-body imaging, they're just making it cheaper?
Their goal is to downgrade people who are violating their TOS, so I think they'd have some argument there. I have no idea how they'll deal with inevitable false positives, especially given how oversensitive most of the other triggers are.
The challenge is the examples they’ve mentioned (distributed training infra? ML acceleration techniques?) go beyond what’s prohibited by their ToS and is like a catch net.
I would wager the majority of ML and data science work in the world aren’t frontier LLM development.
Look at real-life stuff like laws, company policies, or school rules. Humans have to enforce them, and we constantly see crazy cases in the news. There’s no way simple rules can ever make speech completely 'safe.' I can't prove it with math or logic yet, but I have a feeling that it’ll never happen. Even humans can't do it.
We can run a simple thought experiment here. Say Case A violates rule B, so we add rule C. Then Case D violates rule B but follows rule C, so we add an exception... and it just goes on and on like that forever. It never ends. In the end, you just get a massive pile of rules that makes it impossible to get anything done.
Ultimately, we will have to face the truth that knowledge is dangerous.
Giving knowledge directly to people who cannot actually understand it and allowing them to just use it blindly can be extremely unsafe.
To use a real-world analogy, the problem we are facing with weak AI right now is just like the debate over gun legalization. Do we want to risk the abuse of guns or knowledge just to protect the freedom to own them?
> I can't prove it with math or logic yet, but I have a feeling that it’ll never happen.
It's not really that hard to actually prove it with math.
It's a computer, so to produce the boolean result (safe or unsafe) there has to be a mathematical formula. This formula will inherently be extremely complex, but even a very simple formula has a huge problem. Suppose "unsafe" is true if X - Y > 0. Make X and Y themselves as simple or complicated as you like but even in the simplest version it's already impossible to calculate unless the model has perfect information.
You can't calculate "X - Y" if you don't know the value of X. And it's indisputable that there is information it doesn't have. Case in point, telling you about a vulnerability in some piece of code is safe (and indeed not telling you is unsafe) if you're the developer and you want to patch it or an administrator and want to mitigate it, but the opposite if you're the attacker and want to exploit it. The model does not know which one you are, therefore it cannot make the correct determination any more than it can solve one equation with two unknowns.
This is why we have courts and juries. Creating laws that cover all cases and contexts is effectively impossible, so we have humans decide what a fair outcome would be in this specific situation.
To make an analogy:
Imagine a patron gets banned from ordering alcohol at a particular establishment, because they got too drunk one time.
It's completely reasonable for the establishment to reject a request for an alcoholic drink, and suggest something alcohol-free instead.
It is not reasonable for them to say "sure, here's your alcoholic drink as you requested" and give them an alcohol-free substitute without telling them.
The fact that the patron broke the rules has nothing to do with it.
> It is not reasonable for them to say "sure, here's your alcoholic drink as you requested" and give them an alcohol-free substitute without telling them.
Your analogy doesn't work because:
- they tell you the rules at the entrance of the bar
- they totally tell you when they give you a substitute
The only issue is the bartender asking you for your money before serving you the drink really but again, this is known since day 1 by the customers.
Your rebuttle seems to be arguing it's okay for a bartender to simultaneously say:
"This is alcohol"
And
"Or maybe it isn't alcohol."
Or to rephrase it, "They tell you the rules at the entrance, they then tell you they don't follow those rules and they are totally serving alcohol even if they are not."
No they tell you at the entrance that at any point they may unilaterally decide to replace the alcoholic drink you ordered by a non alcoholic one.
You can decide you are okay with that or not but they aren't dishonest. I wouldn't enter that bar personally but if you do you cannot really complain. It is like complaining because you haven't won at the casino.
Their detection is too aggressive. Just today I'm trying to build a kernel for some SBC and I hit that downgrade. I just asked some things about `make menuconfig` items. I suppose it just flags everything related to linux kernel as cyber attacks.
You know, I'm not saying I don't understand what they are doing from a business perspective, but I'm just saying: DeepSeek V4 doesn't silently sabotage you because it thinks you are trying to violate a ToS. Anthropic's clawing back a bit of a moat perhaps, with Fable being an actual improvement of sorts, but now with torching user trust they are really banking on open weight models not catching up to where they are now. I wonder if they have a good reason to believe that they won't, or are hoping for something entirely different to save them.
(P.S. Yes of course I know about model censorship, a different problem, but all of the models are censored to some degree. It happens to be less of a problem for open weight models anyhow, but I figured I'd just preempt this since it's inevitable.)
I actually kinda like DSv4 over Opus 4.7 for some tasks, although I have not figured out what the deciding factor is. (Opus 4.8 so far has not worked very well for me at all, no idea why.)
They will give you s*t output, that’s how they deal with it. And say that less than 1% of the requests were affected. Think of this like a kind of shadow ban while you still pay top $.
The public communication around research like this is terrible.
> "2 to 3 Cups of Coffee a Day May Reduce Dementia Risk. But Not if It’s Decaf." - NYT
> "Daily cups of caffeinated coffee or mugs of tea may lower dementia risk." - Science News
"Reduce," "Lower" - this is all causal language for a study that is purely observational. The authors do a good job keeping causal language out of the paper, so why can't media do the same?
This leads to an environment where everyone knows that "correlation != causation," but almost nobody understands why.
The most interesting finding is that the non-DHA effect is much stronger than the DHA effect. This doesn't align with the mechanistic explanation. Either this this is a novel and interesting result, or it's more evidence that we're just measuring wealth and health consciousness.
Observational studies like these are useful for guiding future research, but, on their own, they're essentially useless for informing lifestyle changes.
The non-DHA omega-3 EPA are good at preventing perivascular fibrosis and thus a better glymphatic system for the removal of beta-amyloid proteins. EPA also helps produce melatonin which kick off sleep and this whole process.
Natto-serrazime is probably an excellent complement as it is on the other side and is a dissolver. (Noteworthy: Pterostilbene + Glucosamine similar to EPA reduces fibrosis)
The interesting connection is how this is needed when we are older, but not younger. When younger ERa activates more which does this all on its own. This is the connection to why 2/3 of alzheimer's are post-menopausal women and why HRT is important.
Edit: and to tie this to APOE as it is the gene most associated with Alzheimer's. e4/e4 requires more choline so someone with e4/e4 is more likely to be choline deficient. EPA/DHA usually attach to Phosphatidylcholine (PC) when in the blood/brain. PEMT is a gene controlled by ERa to make choline, but from the above less ERa activation and we make less PEMT so less choline and less PC. Choline is the precursor to Acetylcholine (primary neurotransmitter for memory and focus and essential for REM sleep). This is why Choline is known to help with Alzheimer's.
I did a job for some neuroscientists years ago and we found a very strong correlation between microplastics exposure and elevated acetylcholine in a very young sample. They all thought there should be no effect or the effect should be inverted because of oxidative stress. We never resolved the phenomenon though. From what I understand, Acetylcholine elevation in the lipidome is either neuroprotective or neutral. Is there any reason why microplastics exposure would tend to increase acetylcholine?
Depends on the microplastics, but many act as endocrine disruptors that "mimic" estrogen, tricking the body into over-activating ERa and upregulates the PEMT gene and the higher acetylcholine. It could also be that the microplastics can physically bind to or chemically inhibit acetylcholinesterase and that is the reason for the higher acetylcholine. Depending on the cause this is only a short term good thing, but could be downregulating genes.
Yeah, we put an awful lot of work into such research and find nothing that doesn't look like either measuring health consciousness or measuring health. (ie, is going to church weekly actually a benefit, or is the ability to attend a weekly social event what's actually being measured.)
Why do we have so many meetings? Few workplace features are so scorned, yet seemingly so necessary. This paper provides the first large-scale economic evidence on workplace meetings using an original survey of more than 9,000 workers linked to matched employer–employee administrative data from Norway. We show that meetings are both common and costly, consuming an average of 12 percent of work hours and 14 percent of firm wage bills. Planning, problem solving, information sharing, and project coordination account for the majority of meeting activity. High-paying and high-revenue firms devote more resources to meetings despite facing a substantially higher opportunity cost of employee time. Meeting frequency and intensity are positively related to worker wage growth. Workers in meeting-intensive firms report greater on-the-job learning, and interactions with more senior colleagues are associated with stronger wage growth, suggesting that knowledge transmission within firms is an important mechanism. Meetings are the broccoli of work – widely disliked, but probably good for us anyway.
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