I had to stop after 6 months because my testosterone dropped so much (serious, blood test confirmed). Felt way better for the first few months, then slowly lower energy and not like myself. Eventually realized more vegetables is good, but nailing is protein wise on true vegan diet is very very hard.
It is absolutely not "very hard." It is challenging at first because like anything else, the startup costs of new habit formation can be high. But tofu, soy milk, seitan, tempeh, lentils, and many other plant protein sources are highly available and easy to cook with once you try. Been at it for a few years myself and at this point, it's extremely easy. I take protein shakes when working out, just like I did when I ate animal products.
Everyone is different, but if you weren't actually able to hit protein macros then I think that's a skill that isn't too hard to improve for most people (if they have a decent reason for wanting to go vegan in the first place).
I’ve been vegan for 14 years. Protein is not very very hard, unless you have multiple dietary restrictions on top of plant based. Which, if I can be frank, tend to be self imposed when people complain about this to me after dabbling in plant based.
Correlation is not causation, especially in a sample of 1, the plural of anecdote is not data etc etc. Protein is easy, just eat some beans and rice, tofu, tempeh etc. You can't just take the diet you had before and subtract things and expect it to work, you have to substitute. Make sure to get B12, D and maybe omega 3 and creatine either as supplements or fortified in foods, and get your levels checked every year or so.
I've been vegan for 15+ years, and I'm willing to say there are zero health benefits over a healthy omnivorous diet. The people who find one are misattributing it. It's not that vegan is healthier, just that what they were doing before was decidedly unhealthy. For a lot of people going vegan is the first time they actually think about what they eat, read labels etc, which is where most of the benefit comes from.
There is no gray area when it comes to veganism. Just because there is no word for that diet, does not mean you can just twist the definition of vegan for your convenience.
You could easily just say your a pescatarian who doesn’t eat dairy or eggs, and that would be more correct.
Sorry for not being perfect like you. For ethical reasons, I do not consume dairy or meat (except for fish). The vegan community should be more open to other perspectives, especially when someone is actively making an effort towards reducing harm.
Vegan Puritanism is totally antithetical to our collective goal of reducing animal suffering. Rather than being motivated by the prospect of claiming moral superiority, our goal should be to help the animals.
It's not really a moral issue, it's simply the definition of the word. Using an incorrect term to describe yourself knowing it is incorrect can be perceived as virtue-signaling or claiming your own moral superiority. For example, one could ask "why call yourself a vegan if you eat fish?".
There's no policing body here, but people are rightfully calling the double-standard out here.
Edit: Would also love to hear the ethics of why fish can be eaten when cows/chicken/pigs cannot. Doesn't sound ethically consistent to me at all.
I actually prefer not to call myself a vegan IRL both because of the issue you’re describing and because veganism is (rightfully) associated with having a toxic and intolerant culture. The only reason I said “vegan except fish” in my comment is because the title of the study used the word “vegan” and because the study was talking about veganism in a purely dietary/scientific lens, not an ethical one. I think this was a pretty reasonable thing to do.
If it doesn’t make sense for people to say “vegan except for chicken”, “vegan except for beef”, then why on earth would it make sense to say “vegan except fish”?
Fish aren’t lesser beings, they are living creatures like anything else.
Just stop saying “vegan except fish”. Say you eat plants and fish. That’s all you have to do.
> If it doesn’t make sense for people to say “vegan except for chicken”, “vegan except for beef”, then why on earth would it make sense to say “vegan except fish”?
I don't know why you take offense to someone saying "vegan except for beef" or "vegan except for fish." There is nothing inherently wrong with these statements. It just depends on whether you view veganism as a lifestyle or as a diet, which can vary depending on the context. In the context of a nutrition study, we are obviously talking about the diet, not the lifestyle.
Killing and eating animals is not a perspective vegans will be open to.
You can reduce harm, but you cannot call yourself vegan until you actually eliminate the possibility of eating animals or their products from your life. There is no reason you HAVE to eat fish.
It's not puritanism. You haven't grasped the ethical system if you don't understand that vegans view this as murder. Why should veganism have exceptions when we don't have exceptions for murder, rape, beating children? Most of us view animal exploitation at the same level.
If someone adopts a vegan diet purely for health reasons, are they not a true vegan? They haven’t accepted the moral framework that killing animals is wrong.
Should the study filter out people who eat entirely plant-based for reasons other than ethics. because according to you, that’s not a true vegan diet.
I think you are largely correct that veganism is a lifestyle, not a diet. But in the context in which my comment was made, it was perfectly reasonable for me to say “vegan except for fish,” because the study was analyzing veganism as a diet, not as an ethical lifestyle.
No, veganism is by definition an ethical framework, a term and movement created in the UK in the early 20th century, by the Vegan Society, which still exists, and has a very specific exact definition on their webpage that hasn't changed.
What you are talking about is the Plant Based Diet, which minimizes but doesn't fully exclude animal products, and is not an ethical framework.
You might as well say "I eat Kosher except for pork". It's nonsense.
I assume you mean that the study should not have said “vegan diet” and instead should have said “plant-based diet.” In that case, I agree with you. But don’t direct your anger towards me; direct it towards the authors of the study!
I'm not really angry at you, maybe slightly frustrated at the confusion everyone has about veganism. Appreciate your candor in any case. I'm definitely angry at some other people in this thread through.
People would provide significantly more global benefit for their cause, compared to their individual contribution, by not putting people off their cause wholesale.
I feel this way, so I shut my startup down. Not sure what I'm going to do next but I'm hoping it isn't so computer dependent. The reason I got into computers was unregulated opportunity and freedom and it's just the opposite today.
There is just no way all these data center investments in the trillions pay off. That has to be paid out of cashflow, like, real profit. The price to do useful things keeps falling, the payroll economy will crash long before there's actual trillions of dollars of cashflow for tokens.
They just need 5% of the worlds population to get $50-100 value per month out of them.
Even in my non-SWE job, paying $100/mo for my current $20/mo plan would still be a no-brainer.
I don't think there is much concern about open models either. Compute is constrained for the foreseeable future, and money is what will determine who gets it. Nevermind that the US will likely block Chinese model imports or China will block exports at some point. The cold war has already begun here.
betting on a compute bottleneck sounds like a recipe to get thrashed when the bottleneck relieves itself.
At the investment scales being discussed, CUDA/architecture and other advantages do not matter - you could spend 1 billion on building a new chip architecture. The ram/fab inputs have been a commodity market for years. Heck, even the model bottleneck doesn't seem real when it's only 1-4 billion or less to get a state of the art model.
At some point the compute bottleneck will be relieved, you can see NVidia hedging their strategy with both open models and on-device chips targeted for local inference. The 200 dollar a month plan will absolutely be taken over by local hardware in the future.
I highly doubt 415 million people will find enough reason to purchase $100 worth of Anthropic, especially when the price of intelligence keeps going down and smaller models become more and more capable to meet the average person's needs like drafting emails, customer support, basic RAG.
The cost of intelligence doesn't matter, it will just make margins wider.
Just look at software over the last 20 years. People pay based on the value they receive, not the cost the produce or serve it. That fact is literally is the backbone of tech, and why it has been an absolute money machine.
I think the worst case scenario for the labs is current (or next gen) SoTA models reaching a point where cheap consumer hardware can fully run them. But the labs practically have a monopsony on compute, and getting the kind of long context current models thrive on out of 16GB GDDR6 is gonna be a trick.
Tech has been a money machine because it has a marginal cost if approximately 0, so tech companies could literally give there products away and live off of the pennies they get from serving adds. Software is one of the hardest product classes to get people to pay for because the cost is anchored at 0.
Enterprises will pay tens of millions a month, millions of individuals will pay $100 a month and there will be a long tail as they offer cheaper pricing and perhaps ad-supported pricing.
The average American family won’t be willing to pay more than a Netflix subscription.
I think in five years, it will only be power users that use a model in its raw form - everyone else will mostly consume using wrapper apps.
Token cost is falling rapidly for a given quality. We don't think about this too much as newer models have made legacy apps obsolete - but its an interesting question what the cheapest text to sql or similar model would be. I still go by 10x cost decrease per 6 months for any given model quality.
Dario Amodei has apparently recently suggested that Anthropic might become only only private AI company in the entire world, which obviously it won't.
There is competition everywhere, and it is intensifying and catching up, not fading away. Open weight models are becoming more common, both within the US as well as elsewhere. Treasury secretary Scott Bessent just praised Meta's open weight models.
There is demand for AI at all different price points, and as all models at all price points become more capable, it seems that increasingly developers are seeing the most expensive ones as specialized tools, not daily drivers.
Compute/memory may be constrained for a few years until production capacity catches up, but this does not mean that demand for cheaper and open weight models will go away, else it would already be happening. Anthropic would like to sell an expensive Ferrari to everyone on the planet, but 99.99% of those people have no need for anything more than a Yugo.
No - but we already have cheap LLMs priced way below frontier models. This is not the housing market. There will always be someone willing to take a lower profit margin for a slice of the pie, and of course smaller models are cheaper to serve so can afford to be cheaper.
DeepSeek recently said that their super-low pricing let's them recoup the cost of the hardware it runs on in 10 months, so there is evidentially plenty of profit to be had over a projected 3+ year lifespan of a "GPU".
Some in the AI industry, or breathing the same air (Dwarkesh) project that limited GPUs will only be used to serve the most expensive models with the highest profit margins, but it is just not what we are seeing. If the only LLMs available were ones at Opus/Fable price points then the GPU scarcity would disappear since the demand at that price is just not there. It's remarkably like trying to fill all the seats on a plane - you can fill a few at 1st class prices, but most of the plane better be coach if you want to sell all the seats.
For a GPU, "selling all the seats", keeping it busy 24x7, is critical to profitability since the primary cost to serving is the GPU which has a limited lifespan.
There's a huge range of cost-per-task variation across models, and the capability of the smaller cheaper models keeps increasing.
For example, here we have Fable 5 at $3.14/task vs Kimi K3 at $0.84/task, with very little difference between them in coding capability (and this isn't even a coding/agentic fine tune of Kimi).
We now have models like Qwen 3.8 27B, small enough to run locally, with coding capability similar to Opus 4.5 based on challenging tasks like the Anthropic Kernel challenge.
I think we are rapidly getting to the "good enough" stage of LLMs, just like we did long ago with PCs. A cheap PC/LLM is all you need for 99.9% of normal use cases. Maybe nothing can touch whatever latest greatest models Anthropic and OpenAI have when it comes to solving Erdos problems, but most developers are working on problems more like the Anthropic Kernel challenge in complexity (or in fact typically way simpler ones).
> Look at housing, we've been waiting 20 years for "production capacity to catch up".
A large portion of the US construction capacity is now engaged in building data centers, priorities you know. Who knows what else will be a favorite tomorrow but it's unlikely to be properly built housing.
IMO this also clearly shows that the housing shortages happening in so many countries could have been fixed all along if those hording most resources wanted to, but why would they? The shortages are heavily tipping the scales in their favor.
There's 5B working age people world wide and only 50% of those make more than $500/mo where it's even conceivable to spend $50/mo. I did the napkin math, it can still sorta kinda work out.
I think more realistically we'll have something like the Google/social media US-vs-world profit split of 40-50% US vs rest of world combined. Even those numbers can work out but then I don't see tremendous growth.
But the sector valuation is already priced for wholesale workforce replacement or massively expanded productivity and AI platform providers taking a lot of that pie for themselves.
With corporate profits already near all-time highs with respect to GDP, who is going to buy all those new products (from expanded productivity ) if all the gains only go to OIA and Anthropic employees?
So thinking this through to come up with your numbers, total investment through 2027 is ~$2T and interest on debt is over 7%, which makes servicing this ~$140B/yr. However, failure rates on H100-B300 installed HW have been over 12%/yr even as the power and cost efficiency per token of the later builds has risen ~5x. So depreciation on the data centers is conservatively $240B/yr ignoring power costs (likely only $30B/yr at $0.05/kWh). Conveniently, this is $20B/month and if 5% of the population uses it, that's only 400million people so it's $50/mo or $600/yr, only if the AI-vendors make zero profit and $100/mo if they make 50% margins. Realistically, the OpenAI and Anthropic go to zero or it's $600/year.
I don't know where the 5% of world population came from, because that's clearly not just professionals or people making a lot of money. That's Uber drivers, and retirees in the developed world or tech workers in Asia making <$10000/year. Those don't look like great markets. This needs to be 2x higher value than their cell phone and internet that they might spend $300/year on today (that's a new iPhone every 3 years on an ATT plan). It's not like it can replace their plan, because they need that connectivity to use it!
Who's getting this value other than SWEs? There aren't 40 million SWEs and I don't see them spending over $6000/year. If their business does, it still has to pass on the cost to consumers and/or fire SWEs.
Why that many people per month? What timeframe were you considering for them to pay off their expenditure? For that matter what are you estimating their total expenditure to be?
Sure, they "just" need to exceed Netflix's global subscriber count, at a significant multiple of the monthly cost of Netflix in their most expensive markets, all in time for an IPO that is rumored to be happening later this year. No problem.
I would be curious to see if they ever publish detailed statistics on this. I'm sure as others have said the average family will not be paying much if anything for AI. Just within the HN bubble I have been paying a bit for it just for my hobbies and it's been fun, enlightening, incredibly useful for rewriting other peoples code and asking it all the dumb questions that I would get entirely roasted for here. Curious to know how many others are using it that way for hobbies, silly questions, rewriting other peoples code, finding and fixing vulnerabilities, debugging performance bottlenecks, etc... rather than strictly professional use cases.
I did some napkin math in a comment a little while ago, that if the whole shebang comes to a screeching hard stop where all these investments are written down to 0 and all AI revenue disappears completely, these trillions of debt could be repaid, with interest, by the hyperscalers with their pre-AI firehoses of cash flow in 6 - 8 years. It’s never going to be that simplistic, of course, but that doesn’t seem like a very dire situation.
The optimistic view is AI improves and generates significant value. You probably need it to generate of the order of 1% of world GDP for the investments to make sense.
I think current investment to date is ~$1tn and revenues are ~$100bn so you'd only need the growth to keep up a short while longer for the current lot to pan out.
It is the entire scam and many of those funding the data center build-out know this. Otherwise why are they hiding the trillions of debt under the rug?
There's a reason why a company like Stripe can stay private far longer than Anthropic or OpenAI can.
These AI companies have taken in all the capital from private investors and are still losing hundreds of billions and have no choice but to hype up the IPO and dump some of the stock at a purposefully inflated valuation to retail investors.
They are worth (on paper) so much that there are not enough retail money to buy them anymore. All these companies can do is put Uncle Sam on the hook to print money for them. There is no other way.
Yes it's a giant wire payment. Depending on your bank, it might have to be setup to send in daily large amounts (e.g. $999k every day for X days). It can actually cause problems for your local bank branch because one thing banks have to do at the regional level is show that their own local balance sheet is healthy. So a giant deposit moving in and out of the bank can cause compliance issues.
In my totally uneducated opinion as a founder working on winding down my software company that can't differentiate anymore and considering finding someone in hard tech to partner with, I think problem first is a mistake. Problem first is appropriate for a business, but for a lab, I think it should be organized around the art of possible, deep specialization and capabilities first. You never know what valuable things will come out of that process. Once you have something promising, you can match it to problems and make a business case working backwards from you. Generally startups are the opposite, you want to organize them around a customer and a problem, but fundamental R&D != startups.
How does R&D get revenue? I don't know how it is for hard tech, but from my understanding of these "researchy small team" labs in software, what ends up happening is that you find a bunch of small contracts to solve, throw really smart people at it, with the intent that you slowly build up internal core tools on the contract's dime. It's much less "hey someone gave us a bunch of money, let's explore this core thing" rather than almost "grifting" in a sense (well, it's not grifting because you provide value, you provide the service, but it's not the idea of "pure applied research" anymore on your end).
Gemma and Muse Glimmer are pretty sweet, I like my adaptiveness odds against their sloppiness. It seems like the new billionaire move is starting a company with your buddies to do your 'life's work' of automating scientific research. Sort of like science and enlightenment without human involvement. Key unsolved philosophy question of what the humans will do.
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