Here is a charitable perspective on what's happening:
- Nvidia has too much cash because of massive profits and has nowhere to reinvest them internally.
- Nvidia instead invests in other companies that use their gpus by providing them deals that must be spent on nvidia products.
- This accelerates the growth of these companies, drives further lock in to nvidia's platform, and gives nvidia an equity stake in these companies.
- Since growth for these companies is accelerated, future revenue will be brought forward for nvidia and since these investments must be spent on nvidia gpus it drives further lock in to their platform.
- Nvidia also benefits from growth due to the equity they own.
This is all dependent on token economics being or becoming profitable. Everything seems to indicate that once the models are trained, they are extremely profitable and that training is the big money drain. If these models become massively profitable (or at least break even) then I don't see how this doesn't benefit Nvidia massively.
> Nvidia has too much cash because of massive profits and has nowhere to reinvest them internally.
Here's an idea: they could make actual GPUs used for games affordable again, and not have Jensen Huang lie on stage about their performance to justify their astronomical prices. Sure, companies might want to buy them for ML/AI and crash the market again but I'm sure a company of their caliber could solve that if they _really_ wanted to.
I also just don’t understand, as someone with no business experience, how they aren’t just pouring all of that money into enhancing their production capacity. That’s very clearly their bottleneck here.
Yes, I’m certain they are spending an astronomical amount on that already, but why not more? Surely paying more money for construction of more facilities still nets gain even if you run into diminishing returns?
Instead they set up this whacko tax laundering scheme? Just seems like more corporate pocket filling to me, an idiot with no business knowledge.
The bottleneck is TSMC, who also make chips for almost every other hardware vendor.
TSMC is indeed increasing their production capability as fast as possible, but it's not easy... chip foundries are extremely expensive, complex, and take serious expertise to operate.
It’s called seeding the market. If they can accelerate the growth of potential customers, it will be more profitable than just increasing production to serve existing customers.
Think of exponential growth — would you rather increase the base or the exponent?
Hedging their bets against a potential sudden downturn in consumption of their product, e.g., an AI bubble exploding? If they invest heavily in production capacity only to find that there is not commensurate consumption, then they'll have lost badly.
> as someone with no business experience, how they aren’t just pouring all of that money into enhancing their production capacity. That’s very clearly their bottleneck here.
They know this madness can't go on forever. The last thing they need is to be left with billions of dollars of unused capacity when the bottom falls out of this very stupid bubble.
If they thought that, would they invest massively in the companies that own all these GPUs? They would not. They would invest in anything ELSE they could think of.
That might be the case if nvidia commonly invested in its customers - if they suddenly stopped doing that - but I don't think that was the case. On the contrary, these investments surprised because they were NOT the usual.
Why would they want to do that? The only sector that matter to nvidia is datacenter, its where 90%+ of their profits are. Making their consumer sector even less profitable just seems like a waste of time
How about positive mindshare? Regular people not growing up absolutely hating nvidia's guts and only begrudgingly buying their products. Also ensuring that a pretty big industry won't die from becoming too expensive.
Plus, diversification is good for when the bubble inevitably bursts.
But that's long-term thinking and we can't have that. People give Huang credit for having had a long-term vision on AI, but it feels like he definitely has blinders on right now.
Does anyone who can afford an nvidia card actually buy something else? Yes some people hate nvidia, but it's not like they have a hard time selling cards.
The consumer gaming card market is minuscule in comparison to their primary market now, to the point where worrying about diversifying there probably doesn’t make sense. Nor does it really matter whether consumer gamers hate them. That is likely to have zero effect on their core customer now.
Underestimating compounding and secondary effects, especially while rationalizing the abandonment of their core market and capability is one of the most famous ways that big companies provide evidence of their downward spiral. I can feel the MBA energy from here.
Can you name any companies that suffered by switching focus away from one market where they dominate in order to also dominate a market that is 10x the size of the first market already, and growing faster?
Your conclusion about training being the cost factor that will eventually align with profitability in the inference phases relies on training new models not being an endless arms race.
I'm just confused why people think token-based computing is going to be in such demand in the future. It's such a tiny slice of problems worth solving.
Yep. Same vibes as “ha ha who needs internet connected appliances” (pretty much all appliances are internet connected now). And the apocryphal “there is a worldwide market for maybe 5 computers”.
No-one "needs" or even wants appliances to be connected to the internet. You claim that "pretty much all" appliances are internet connected, while almost none of the appliances in my house are.
> - Nvidia also benefits from growth due to the equity they own.
aka this might be nvidia's next pivot. Contrary to gaming cards, the AI GPUs are productive assets. If nvidia feels that they will be very productive, then it makes sense that they invest broadly in the companies that are likely to make these profits. To share in the rewards while selling more GPUs.
Yup. Not just Nvidia. Just look at the quarterly results reported by Amazon, Google, Meta, Microsoft and Apple. Each one is reporting revenues never before seen in history. If you make 100 Billion a quarter you have to spend it on something.
These guys are running hyper optimized cash extraction mega machines. There is no comparison to previous bubbles, cause so no such companies ever existed in the past.
100 billion a quarter is Alphabet, right? Given how much click fraud there is, and that every org and business under the sun is held to ransom to feature on the SERP for their own name even — it’s tempting to say Google’s become a private tax on everything.
It's easy for the techies to see the problems. But advertising results have been very measurable for a very long time by now. Larger advertisers can leave the details to their techies and still be very clear as to their advertising's productivity post-cost of doing business.
What's shocking is the gulf between those companies and corporate 'normality'.
Eastern Airways, a UK airline, has just gone bust due to accumulated debts of £26 million. That's not even a rounding error for Google, yet was enough to put a 47-year-old company into bankruptcy and its staff out of work.
I think the only historical parallel to this disparity was the era of the East India Company.
They're "massively profitable" because they're laying off large portions of a major cost center - labor - and backloading uncoming data center construction costs. As those come due, and labor needs rise again, that profit disappears.
They have a track record of cornering a market and abusing their position, and also still somehow not being able to balance expenses and revenues to turn a profit that pleases shareholders. You get to decide if that's a problem with the company or the shareholders, I guess.
So many such profitable companies are the best possible evidence for the need for drastic antitrust intervention. The lack of competition and regulation is leading to a massive drain on every other sector.
This bubble is caused by excess competition. There are 4 large companies who believe that a large new market is being created so each is investing large amounts without any evidence that there will be a single winner that dominates the future market. None of these companies has anything remotely resembling a monopoly except for Amazon in online retail.
Right. As far as I can tell, OpenAI, Grok, etc sell me tokens at a loss.
But I am having a hard time figuring out how to turn tokens into money (i.e. increased productivity). I can justify $40-$200 per developer per month on tokens but not more than that.
There’s about 5M software devs in the US so even at $1000/year/person spend, that’s only $5B of revenue to go around. Theres plenty of other uses cases but focusing on pure tech usage, it’s hard to see how the net present value of that equates to multiple trillions of dollars across the ecosystem.
It's the first new way of interacting with computers since the iPhone. It's going to be massively valuable and OpenAI is essentially guaranteed to be one of the players.
In 2006, I foresaw that the smartphone was going to exist and be significant. But at the time I carried a Sharp Zaurus which just identified me as a gigantic nerd, and the clearest company to invest in was... HP/Compaq for the iPAQ, in terms of the most forward-looking device.
Then Apple came out with the iPhone but it was not clear they were going to be the market leader. They were certainly not the first mover.
With OpenAI, I see even less of a moat. Someone else comes along, makes a better foundational model, and they've got a gigantic advantage. What does OpenAI have?
It's not windows mobile because OpenAI was first and is the clear leader in the market. Windows mobile was late to the party and missed their window.
Palm is closer but it's a different world. It's established that Internet advertising companies are worth trillions. It's only in retrospect that what Palm could have been is obvious.
Barring something very unexpected OpenAI is coming out on top. They're prepaying for a good 5-10 years of compute. That means their inference and training for that time are "free" because they've been paid for. They're going to be able to bury their competition in money or buy them out.
Windows mobile by the time it looked like the iPhone was late to the party. But windows had been releasing a mobile os for a long time before that. Microsoft was first, they just didn’t make as good of a product as Apple despite their money.
OpenAI is also first, but it is absolutely not a given that they are the Apple in this situation. Microsoft too had money to bury the competition, they even staged a fake funeral when they shipped windows phone 7.
Yep. It would have to be something that dramatic to render all the technology and infrastructure OpenAI has obsolete. But if it's anything like massive data training on a huge number of GPUs then OpenAI is one of the winners.
This is where the money is. Anthropic just released claude for excel. If it replaces half of the spreadsheet pushers in the country theyre looking at massive revenue. They just started with coding because theres so much training data and the employees know a lot about coding
I'm not trying to be annoying, but surely if you'd justify spending $200/developer/month, you could afford $250/month...
The reason I wonder about that is because that also seems to be the dynamic with all these deals and valuations. Surely if OpenAI would pay $30 billion on data centers, they could pay $40 billion, right? I'm not exactly sure where the price escalations actually top out.
why would they sell you at a loss when they have been decreasing prices by 2x every year or so for the last 3 years? people wanted to purchase the product at price "X" in 2023 and now the same product costs X costs 10 times less over the years.. do you think they were always selling at a loss?
I can't read your hyperbolically titled paywalled medium post, so idk if it has data I'm not aware of or is just rehashing the same stats about OpenAI & co currently losing money (mostly due to training and free users) but here's a non paywalled blog post that I personally found convincing: https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
Nothing on infra costs, hardware throughput + capacity (accounting for hidden tokens) & depreciation, just a blind faith that pricing by providers "covers all costs and more". Naive estimate of 1000 tokens per search using some simplistic queries, exactly the kind of usage you don't need or want an LLM for. LLMs excel in complex queries with complex and long output. Doesn't account at all for chain-of-thought (hidden tokens) that count as output tokens by the providers but are not present in the output (surprise).
Completely skips the fact the vast majority of paid LLM users use fixed subscription pricing precisely because the API pay-per-use version would be multiples more expensive and therefore not economical.
> Nothing on infra costs, hardware throughput + capacity (accounting for hidden tokens) & depreciation
That's because it's coming at things from the other end: since we can't be sure exactly what companies are doing, we're just going to look at the actual market incentives and pricing available and try to work backwards from there. And to be fair, it also cites, for instance, deepseek's paper where they talk about what their power foot margins are on inference.
> just a blind faith that pricing by providers "covers all costs and more".
It's not blind faith. I think they make a really good argument for why the pricing by providers almost certainly does cover all the costs and more. Again, including citing white papers by some of those providers.
> Naive estimate of 1000 tokens per search using some simplistic queries, exactly the kind of usage you don't need or want an LLM for.
Those token estimates were for comparing to search pricing to establish whether — relative to other things on the market — LLMs were expensive, so obviously they wanted to choose something where the domain is similar to search. That wasn't for determining whether inference was profitable or not in itself, and has absolutely no bearing on that.
> Doesn't account at all for chain-of-thought (hidden tokens) that count as output tokens by the providers but are not present in the output (surprise).
Most open-source providers provide thinking tokens in the output. Just separated by some tokens so that UI and agent software can separate it out if they want to. I believe the number of thinking tokens that Claude and GPT-5 use can be known as well: https://www.augmentcode.com/blog/developers-are-choosing-old... typically, chain of thought tokens are also factored into API pricing in terms of what tokens you're charged for. So I have no idea what this point is supposed to mean.
> Completely skips the fact the vast majority of paid LLM users use fixed subscription pricing precisely because the API pay-per-use version would be multiples more expensive and therefore not economical.
That doesn't mean that selling inference by subscription isn't profitable either! This is a common misunderstanding of how subscriptions work. With these AI inference subscriptions, your usage is capped to ensure that the company doesn't lose too much money on you. And then the goal is with the subscriptions that most people who have a subscription will end up on average using less inference than they paid for in order to pay for those who use more so that it will equal out. And that's assuming that the upper limit on the subscription usage is actually more costly than the subscription being paid itself, and that's a pretty big assumption.
If you want something that factors in subscriptions and also does the sort of first principles analysis you want, this is a good article:
And in my opinion it seems pretty clear that basically everyone who does any kind of analysis whether black box or first principles on this comes to the conclusion that you can very easily make money on inference. The only people coming to any other conclusion are those that just look at the finances of U.S. AI companies and draw conclusions on that without doing any kind of more detailed breakdown — exactly like the article you linked me, which now I have finally been able to read, thanks to someone posting the archive link, which isn't actually making any kind of case about the subscription or unit economics of token inference whatsoever, but is instead just basing its case on the massive overinvestment of specifically open AI into gigantic hyperscale data centers, which is unrelated to the specific economics of AI itself.
Assuming they are playing 6 max with full tables 40% vpip is egregious and I do not see how they could have a winning strategy playing like that. (Looking at their results they are not winning).
Hmm… there are multiple variants of poker, at least one was weakly solved in 2015. I guess one could implement their algorithm. But I don’t know if the weakly solved variant of poker is popular?
The problem is specifically with unregulated sites that don't verify the identities of players. The bots aren't so much the problem as the fact that they can collude and share hole cards. But fyi, bots aren't actually good at playing poker outside of specific scenarios vs bad players or in scenarios where the decision tree is not large (ie short stack tournaments where the decisions are pre computed, you can imagine how massive your edge can be when you have a pair of 9s and you know there are already 3 dead aces and your decision is only all in or fold)
Collusion in live poker games in casinos is not a widespread problem. There is a problem with poker where people always think they are being cheated every time they lose. If you are playing in a casino in person it is very unlikely you are being cheated. If you are playing in a regulated website online that verifies the identities of the customers it is also unlikely you are being cheated.
The vast majority of people that play poker absolutely suck and think they are being cheated because they lose money very quickly. Most bad poker players would literally be better off playing blackjack.
Played thousands of hours in casinos. Saw some asshole show down cards to someone still in the hand, stuff like that, but never anything I thought was collusion.
A regulated online casino that verifies identity wouldn’t stop a bot. You’d just sign up under your name and use it. If your bot isn’t colluding it would just look to the casino like you’re good at poker.
The best bots are from cheating rings where the bots are colluding, not from bots that play poker perfectly (which don't exist in any real sense for full ring poker).
Just fyi 6max bots were destroying online $5/$10 games on Party Poker (back then the biggest site) in 2009. That was before public solvers, other algorithmic advancements and huge hardware progress.
I used to play poker back in the 2000s. The online game was getting harder then and I can only imagine it's gotten worse? Also GTO solvers are a thing now? I don't know what stakes you are referring to but I feel like the overall quality of poker play has never been higher.
What's true is that people aren't making as obvious mistakes, especially preflop, so you can't make hundreds of thousands just by knowing that Ace King is a good hand that you can go all in with. Anyone can find preflop poker charts and fix part of their preflop game.
Having a poker solver isn't enough. Let's say you play tournament poker, just having a basic understanding of concepts like ICM give you a massive edge. Let's say you take it a step further and understand concepts like "future game" and actually study them using tools, you're edge has expanded further.
There are a bunch of charts out there that tell you what hands to go all in with if you have 15bbs or fewer. None of those charts take into account ICM. Also how do you adjust the charts if your opponents are calling with too many hands? How do you adjust them if they call with too few hands?
Let's say we are just talking about cash game poker, it's not enough to have a solver, you need to understand how to actually study with the solver. People try to use them like a cheat sheet that tells you what to do, not understanding that a slight change to the inputs of the solver can drastically change the output. The purpose of a solver is to understand how different ranges interact at different stack depths on different boards.
ie: Playing 100bbs deep, on a KK3 flop with a flush draw, what hands should i check or bet as the preflop raiser? What happens if that 3 is a 7? What if it's a J? What if it's 33K instead of KK3? What if I'm 200bbs deep instead of 100? What if the opponent calls too much? What if they call too little?
>>There are a bunch of charts out there that tell you what hands to go all in with if you have 15bbs or fewer. None of those charts take into account ICM. Also how do you adjust the charts if your opponents are calling with too many hands? How do you adjust them if they call with too few hands?
There are multiple tools on the market that solve preflop all-in game for multiway pots with ICM and more advanced chip utility models. Those are very easy to solve you don't even need a chart (on the fly solving is fast enough on a laptop). You can also solve them with adjustments for certain players.
I'm a software engineer with 10+ years of experience. I'm also a poker player that has a very deep understanding of the game. Writing a poker bot that can beat the game is absolutely not trivial. There are "solvers" that use counterfactual regret minimization to solve a constrained version of the game for specific scenarios. These are useful for understanding the principles of the game but they are not the cheat sheet people think they are.
I think people fundamentally don't get that poker is not like chess. The vast majority of money I win is from identifying when players are too attached to their hand and never folding or when they just give up on their hand and fold to any bet.
I'm an ex online poker pro. You probably don't have the deep understanding of the game you think you have. Bots were already destroying the field up to mid-stakes 10 years ago.
I'm literally winning money playing online today playing 400nl (200nl with a straddle, in the US).
Please explain to me how you think these bots work? Do you think they are literally hooked into solvers and solving these hands in real time? If you actually understood poker you'd understand that the winrate from GTO is not good enough to make real money playing poker without a massive sample size, the game is all about exploiting players when they deviate from GTO. Explain to me how you program your poker bot to know intuitively that a player has too many bluff combinations when a flush arrives on the turn after they check back on the flop therefor you should call wider than standard? There are a billion little unique situations where people don't bluff enough, bluff too much, call too much or call too little and that is where the winrate from poker comes from.
This is the difference between having a 3 bb / 100 winrate and a 10-15 bb / 100 winrate. Maybe there are a bunch of shitty poker bots winning at 1 bb / 100 but if they are winning it's because some players suck really really bad, not because they are playing perfect poker.
I'm a current online poker pro but probably not for much longer. Bots are a serious and real problem and they do beat the games for a good winrate. But it's still possible to make money even in environments with some bots as long as you can find games with fish. And some games on geofenced sites (the OP said they play in Michigan) or other small pools don't appear to have bot problems.
If a sufficiently good bot exists it must be highly profitable and since its software it would be easy to port to every site. Surely you could just get an address in Michigan cheaply and would have financial incentive to.
I feel like there was (or will be, if it somehow hasn’t yet occurred) a very short gap between one site being unwinnable and all sites being so.
There's a lot of complexity here that you're overlooking. First of all, the sites have KYC and require geolocation software so it's not trivial to play. Especially not for the bot developers which have tended to live in Eastern Europe or Central Asia. They'll just go wherever is easiest to make money so it doesn't necessarily follow that every site would be overrun.
Second of all, poker is fairly capital intensive and whenever your bot account gets banned the site will confiscate your funds, so there's risk involved as well. And every time you get banned you need to create a new account with new KYC etc.
Third of all, bots play differently from humans and many of them are detected and caught by the players in addition to the site security. Further adding to the challenge is that the community of professional online players in the US is pretty small and everyone pretty much knows everyone else (we're all on Discord together, basically). So new names appearing at high stakes out of nowhere get scrutinized more.
Fourth, even if you're playing against a bot or cheater, you can still make money, since winrate is entirely driven by fish. You might lose a little against the cheater but as long as you're winning far more from the fish you'll still make money. This separates poker from other competitive games.
I don't mean to imply the bot and cheating issues don't exist, they're real and serious and existential, and every online pro these days spends a lot of time worrying about it, worrying if a certain opponent is cheating, etc. But I think the bigger issues facing online poker are actually regulatory (in the US, an unregulated market has sprung up since the pandemic that is now struggling with a lot of legal changes; Europe has a lot of anti gambling laws these days and more every year) as well as general game quality (fewer recreational players wanting to gamble large amounts of money online and more pros than ever trying to split that smaller pie).
Online poker is very much beatable. Poker isn't solved in the same way chess is. It also depends on the site and the rake. Some unregulated sites don't do KYC so collusion is possible.
What I meant by "poker isn't solved the same way chess is" is that if you take a solver and follow all the actions it tells you are "best" you will not make the most money. It isn't like stockfish where by using solver outputs you will automatically make more money than the best pros in the world. 99% of poker is understanding the unique ways your opponents are bad and adjusting your strategy to profit the most from them. Even the best pros in the world still make mistakes.
Let's say I'm building a small app that I'm hosting on some shared vps, if I think about the effort involved in setting up sqlite with litestream and just getting a $5 (or free) postgres provider I don't think sqlite makes my life easier.
Now if I'm building a local app then absolutely sqlite makes the most sense but I don't see it otherwise.
Litestream is dead simple to setup. You make an S3 bucket (or any compatible storage bucket), paste the access keys and the path to your db file in /etc/litestream, and then run
Effort of setting up litestream and sqlite is less time than you spend signing up for supabase. And you can have 100 apps with their own databases for almost free (just a few cents of storage) vs 5*100 for postgres.
I love postgres but in no way is it as simple to run as sqlite (pretty sure even postgres core team would agree that postgres is more complex than sqlite).
> but who decides how those resources are deployed
The current system selects people that have allocated resources effectively in the past by providing them more resources to allocate.
> We've ran high-risk R&D projects successfully before as public projects
And what is stopping countries from doing this today? This isn't an either or thing, public projects can still exist, there is no law of nature saying that massive companies are the sole source of innovations but for some reason people treat it like they are mutually exclusive. You bring up projects from decades ago but are there any modern examples?
Since when has throwing money at systems that haven't shown success worked? And you are suggesting that we take money from others to throw it at a system that doesn't work.
"Allocated resources effectively" is doing a lot of heavy lifting in your description. The current system also rewards large-scale cons and rent extraction with the political power to do more of the same.
It is classic circular reasoning. Why should they have all money? Well because they are the best ones at allocating the resources. How do we know they are best at allocating resources? Because they have all the money.
"To him that hath, more shall be given
and to him that hath not, more shall be taken away"
I suppose it was more popular in seemingly simpler times when the playing field was more even and the players more evenly matched and distributed. But here we are in the future, and that game seems to have been concluded.
I myself rather preferred the view from the shoulders of giants than the undersides of their feet.
There is a agent simulation paradigm where a large amount ofagents engage in one-on-one transactions and wealth is transferred. I haven't followed but decades ago there was a simulation where with rather simpler rules, in the end all the wealth concentrated to one agent.
In this model, it's the rules of the game, not the morality of the players that causes the effects.
It seems you're implying that since being a billionaire is not 100% heritable, it must be a result of individual merit (leap 1) which is purely synonymous with skillfully allocating resources (leap 2) more than any other type of person (leap 3) rather than any other explanation for how they got there, say, sociopathy, right place right time, etc.
> It seems you're implying that since being a billionaire is not 100% heritable, it must be a result of individual merit
Nope, another strawman, I never claimed it was 100% merit. You claimed that wealth is purely heritable which is easily proven false by observing reality.
> sociopathy
So your claim is that sociopathy is more important for getting rich than skill?
> right place right time
Choosing the right business to build given the state of the world is a skill. Business requires luck the same way poker requires luck, and let me tell you, if you play poker vs a skilled player you will lose a lot of money.
Yes exactly. Also, we should stop pretending that the money supply is fixed and that everyone exists on the same monetary playing field.
Arguments about efficient allocation are laughable when you consider that someone who is socially 6 steps removed from an institutional 'money printer' lives in a monetary environment where money is 10 times more scarce than it is at the source (due to taxation between each hop). Few people are so far removed in practice but the effects are still very powerful even with less distance. Taxation brings all economic activities closer to the government and banking sector.
In competitive industries were profits are paper thin, monetary asymmetry can fully determine business outcomes. The company receiving government contracts on the side has a massive upper hand over its competitors during a monetary contraction. Same can be said about companies which operate in environments where their customers have access to large amounts of credit by virtue of their highly valued collateral. Their success has little to do with optimal allocation and a lot to do with socio-economic positioning and monetary system design.
> The current system also rewards large-scale cons and rent extraction
I never claimed it was a perfect system and I am more than willing to admit rent extraction, scams, and monopoly power are massive issues with capitalism. It still hasn't been shown that replacing that with government is better.
> You bring up projects from decades ago but are there any modern examples?
There are no modern examples in the USA precisely because there is no political will to fund them. That political will is undermined because private companies are large and strong enough that they can influence politicians and prevent projects they they would have to compete with.
Your argument has the implication the wrong way around.
Meanwhile, look at China. The vast majority of China's cometlike economic success can be directly attributed to state funding, and many of its successful projects are directly state-run. That's because China still plays industrial politics that are concerned with economic growth and public welfare instead of rent-seeking.
> The current system selects people that have allocated resources effectively in the past by providing them more resources to allocate.
The current system selected people that have maximized shareholder value and financially engineered it into other financial assets that provide power under the capitalist system. This includes private equity services that have simply squeezed money out of consumers for no increase in quality of life, or companies that managed to avoid the consequences of the externalities of their economic activity.
> And what is stopping countries from doing this today? This isn't an either or thing, public projects can still exist, there is no law of nature saying that massive companies are the sole source of innovations but for some reason people treat it like they are mutually exclusive. You bring up projects from decades ago but are there any modern examples?
Regulatory capture and lobbying that attempts to force a profit motive behind every large government initiative when the profit motive substracts value away from society at large.
To be concrete, I guess in a different time and society someone like Sam Altman would have been a successful politician or perhaps like Hyman Rickover or Marcel Boiteux working within the government to cause colossal steps in progress.
reply