And more than half the rise in the top 1% share since 1985 flowed through the pass-through businesses that was a "tax efficiency innovation" feature of the Reagan Revolution.
@AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. https://t.co/AKYm4cbVCU
@AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. https://t.co/AKYm4cbVCU
Everyone always asks me like with all this AI is anything still the same? And the answer is so far almost everything is exactly the same. The only weird The only weird new thing is that companies have these giant AI bills.
My favorite part of @Cloudflare is how easy it is to use them for your clanker They always had a great API and all you have to do is add an API token and you can do almost everything you'd do by hand
That was premised on humans doing the modifications. I think it is an open question to which degree this still matters. Now, it does matter, and the reason I say that, at least for the moment, is that tokens are still scarce. At this moment in time, we are all token limited.
If we don't do it ourselves, meaning hold ourselves to a high standard, build with efficiency, constantly be like trying to get better, if we don't do it ourselves, someone will come and do it to us.
I think one reason why um the the AIs have been scaled up less than you would have otherwise expected and like for example cost of of per token hasn't increased as much as you might have thought is because there's a benefit to doing more of your um work at small scale where you can run more training runs and get more cycles in
I mean, honestly, that's not so much a tech problem. It's more a token problem where we could do that today, but you wouldn't get very far with your subscription and not everyone is willing to spend so many tokens.
I believe current techniques are 4-6 orders of magnitude away from optimality in terms of data efficiency and test-time compute efficiency. But far future AI will be near-optimal.
And so it's like I mean getting into Eli Goldrat and the goal is like optimizing for utilization and efficiency of one node in your factory rather than the end to end goal of like how do we ship value and things that people like that are stable and like will last a long time. But that's my idea of token harder
I think that you can imagine at least in an year or two coming that the the burn rate of a strong engineer might be the same as their salary or their cost of employment.
And I think a good exposition you care a little bit less about like correctness on the way, but you can like deliberately craft things that are a little bit wrong that you correct along the way that gets like edited out in a crowd source environment.
In this sense, I think I was wrong to imply in Saying NO… that ecomodernism focuses on secondary managerialist goals like the efficiency of feedlots without speaking to the mysteries and passions of what animates life.
a more relevant metric in the long term is the cost, not the efficiency, of food production methods at sustainable levels of energetic and material throughput.
In 2026, long-context efficiency is king as more and more LLMs get plugged into agent harnesses (OpenClaw etc.), which requires working with longer and longer contexts.
Now, I post things like, "Hey, I'm adopting the zero token architecture." People like, "What's zero token architecture?" as like instead of burning tokens, you learn things and you think for yourself and just complete tasks.
Efficiency is what TV remote controls are good at, but whenever people try to maximize their productivity in the speediest way, things start to go south.
Pick your ASIC team where you can say I can bet the farm of I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it like I can count on the clock.
You know, our computer price is going up, but our token generation effectiveness is going up so much faster that token cost is coming down. It's just coming down an order of magnitude every year.
well the model the compute efficiency gains you get from research are so large you actually want most of your compute to go to research not to development because you know all these researchers are generating new ideas trying them out testing them and continuing to march along this and push the prao optimal curve of scaling laws further and further and further
What probably the single most important proxy metric that you can have it in a company today is token burn. Because what token burn says is your engineers are trying to do stuff or your non-engineers.
so so I think we're definitely going to see business models that that recognize that, you know, at some point we're going to see, you know, pay for results or you you know, in some in some form or we may see forms of compensation that are like labor. Um, uh you know, that that kind of work by the hour.
But then you actually sit down and do the maths, and then for most people if you just skip one burger per month, that compensates the, the CO2 output, or, like, the water use in equivalent of tokens.
The economics of orbital “datacenters” or essentially glorified Starlink satellites with a bunch of GPUs attached are likely to be even better than Starlink.
With AI, he's trying he's targeting 3 to five million in revenue per rep. 3 to 5 million. Honestly, if this was three or four years ago for a similar company, it would be 3 to 500K. That's an order of magnitude more efficiency.
Net net, we're going to need more sales and go to market professionals than ever because the winners are growing so quickly that even if they're more efficient, they will need more human beings than ever.
elements, JavaScript reigns supreme. SvelteKit's efficiency and reactivity make it my framework of choice, and TypeScript's type annotations bring a welcome layer of confidence to my codebase.
We don't really have much evolved experience in evaluating postal efficiency, do we? Okay, we have quart of a million half a million years of evolved experience in deciding who to like and trust because for most of our evolutionary, you know, existence, that was one of the most fi five most important questions to get right.
economists have this fantasy of the world of companies in direct competition driving down price and increasing efficiency while supplying the same thing which is based on a false premise that people know what they want to begin with. Now I would argue when you don't create differentiation, everybody suffers.
But when you pursue efficiency generally you start looking at numerical or mechanical factors and of course in the process you disregard psychological factors where the greater gains may be found and so you focus too heavily on cost reduction and too little on value creation.
While heat pumps can achieve higher efficiency, consumer uptake has been much lower than expected because fundamentally, a heat pump is a 20 year bet on future power prices that most homeowners are unwilling to make.
But even more important than efficiency, designed experiments can inform about causality, which is very difficult to determine from collected observed data.
If a bed is too deep, one has to use a very coarse grind. The result in the cup may or may not be good enough, but the extraction level will be low, which is inefficient, in the sense of wasting coffee material.
you can either see AI as an opportunity for your company to grow and do more or you can look at it as like cost cutting efficiency but I think the growth part is way more exciting.
So AI consumes approximately 0.04% of America’s freshwater if you include onsite and offsite use, and only 0.008% if you include just the water in data centers.
it's something like this that the brain is extremely parallel. uh it kind of has to be just out of just because of the biohysics. U but like it's even more parallel than your GPU.
They're relatively efficient compared to a lot of other things, and particularly compared to the herbivores, so that means they're probably looking at long distance rather than speed.
Seat based. Well, what is a seat? That doesn't make any sense. Consumption where it's like is it per message? Is it per token? Is it per conversation? And none of these things actually mapped very well to getting a job done and getting a job done well.
You can revisit a bunch of legacy industrial processes that are have been optimized for efficiency and say, well, what if we just like use twice as much power and we just want to do them faster and cheaper, right? Like less capex, less lead time, more power, right?
However, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment.
And that's a phase shift, because suddenly it makes sense when you write a paper to write it in Lean first, or through a conversation with AI, which is generally on the fly with you, and it becomes natural for journals to accept.
A unique lens on work in the late 90s that still resonated when I read it in 2018. It's a soulful exploration of how to maintain your humanity, creativity and inner fire in environments that often prioritize conformity and efficiency.
OpenAI has a fantastic margin. When they're doing inference, their gross margins are north of 75%. So that's a four to five X factor right there of the cost difference, is that OpenAI is just making crazy amounts of money because they're the only one with the capability.
But many of these gains have already been achieved: the best data centers already use just 10% of their electricity for cooling and other non-IT equipment.
I think this is one of the biggest things that physics can help with, and it’s an obvious kind of low-hanging fruit situation where the heat generation, the inefficiency, the waste of existing high-level computers is nowhere near the efficiency of our brains. It’s hilariously worse, and we haven’t tried to optimize that hard on that frontier.
Efficiency and invention are sort of at odds, because real invention, Not incremental improvement... Incremental improvement is so important in every endeavor, in everything you do, you have to work hard on also just making things a little bit better. But I'm talking about real invention, real lateral thinking that requires wandering, and you have to give yourself permission to wander.
I want to see more tools and fewer operated machines - we should be embracing our humanity instead of blindly improving efficiency. And that involves using our new AI technology in more deft ways than generating more content for humans to evaluate. I believe the real game changers are going to have very little to do with plain content generation.
This is because the language modeling objective used for many recent large LMs-predicting the next token on a webpage from the internet-is different from the objective "follow the user's instructions helpfully and safely" (Radford et al.,, 2019; Brown et al.,, 2020; Fedus et al.,, 2021; Rae et al.,, 2021; Thoppilan et al.,, 2022). Thus, we say that the language modeling objective is misaligned.
the electric technology used for cars only has to beat energy hogs that are below 10% in efficiency, our everyday car. But to qualify for aircraft use, the technology has to equal or beat what is used today, which is at the 30% to 40% efficiency level using modern turboprop or turbofan engines.
The US focus on efficiency has revealed the brittleness of its economy, which has neither the manufacturing capability to scale up domestic production of goods nor the logistics capacity to handle greater imports.
Now you have a carbon fuel burning aircraft that is more complex and heavier than the plane that it replaces, and if you do your sums carefully, you find that there are no fuel efficiency gains and, therefore, no environmental gains.
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