I'll offer two counter-points. Weak but worth mentioning. wrt China there's no value to extract by on-shoring manufacturing -- many verticals are simply uninvestable in the US because of labor costs and the gap of cost to manufacture is so large it's not even worth considering. I think there's a level of introspection the US needs to contend with, but that ship has sailed. We should be forward looking in what we can do outside of manufacturing.
For AI, the pivot to profitability was indeed quick, but I don't think it's as bad as you may think. We're building the software infrastructure to accomodate LLMs into our work streams which makes everyone more efficient and productive. As foundational models progress, the infrastructure will reap the benefits a-la moore's law.
I acknowledge that this is a bullish thesis but I'll tell you why I'm bullish: I'm basically a high-tech ludite -- the last piece of technology I adopted was google in 1996. I converted from vim to vscode + copilot (and now cursor.) because of LLMs -- that's how transformative this technology is.
> which makes everyone more efficient and productive
There is something bizarre about an economic system that pursues productivity for the sake of productivity even as it lays off the actual participants in the economic system
An echo of another commenter who said that its amazing that AI is now writing comments on the internet
Which is great, but it actively makes the internet a worse place for everyone and eventually causes people to simply stop using your site
Somewhat similar to AI making companies more productive - you can produce more than ever, but because you’re more productive, you don’t hire enough and ultimately there aren’t enough people to consume what you produce
> There is something bizarre about an economic system that pursues productivity for the sake of productivity even as it lays off the actual participants in the economic system.
Not only does it lay off many of them, but it expects the rest to work longer hours with fewer raises in many cases.
> many verticals are simply uninvestable in the US because of labor costs and the gap of cost to manufacture is so large it's not even worth considering.
I think this is covered in a number of papers from think tanks related to the current administration.
The overall plan, as I understood it, is to devalue the dollar while keeping the monetary reserve status. A weaker dollar will make it competitive for foreign countries to manufacture in the US. The problem is that if the dollar weakens, investors will fly away. But the AI boom offsets that.
For now it seems to work: the dollar lost more than 10% year to date, but the AI boom kept investors in the US stock market. The trade agreements will protect the US for a couple years as well. But ultimately it's a time bomb for the population, that will wake up in 10 years with half their present purchasing power, in non dollar terms.
There are no think tanks mentioned in that article though.
The accord also appears to contradict the stated goals:
> Miran proposes a modern equivalent of the 1985 Plaza Accord, which he refers to as the Mar-a-Lago Accord. The goal would be coordinated currency appreciation among U.S. trading partners to address the dollar's overvaluation
> For now it seems to work: the dollar lost more than 10% year to date
...and I thought American Labor was having something of a moment in 2024-2025. The law of unintended consequences may have surprises in store for the planners in the coming years.
I think an interesting way to measure the value is to argue "what would we do without it?"
If we removed "modern search" (Google) and had to go back to say 1995-era AltaVista search performance, we'd probably see major productivity drops across huge parts of the economy, and significant business failures.
If we removed the LLMs, developers would go back to Less Spicy Autocomplete and it might take a few hours longer to deliver some projects. Trolls might have to hand-photoshop Joe Biden's face onto an opossum's body like their forefathers did. But the world would keep spinning.
It's not just that we've had 20 years more to grow accustomed to Google than LLMs, it's that having a low-confidence answer or an excessively florid summary of a document are not really that useful.
Chatting with Claude about a topic is in another universe to google search.
I default to Claude for almost everything where I want to know something. I don’t trust Google’s results because of how weighted they are to SEO. Being good at SEO is a separate skill set.
The answers are not low confidence, cite sources, and can do things that Google cannot. For example: I used Claude to design a syllabus to learn about a technical domain along with quizzes and test suites for verification. It linked to video series, books, and articles grouped by an increasingly complex knowledge set.
Is this really true re: "modern search"? Genuine question because this is probably outside of my domain. I'm just trying to think of industries that would critically affected it we went from modern search to e.g. AltaVista/Yahoo/DogPile and kind of coming up empty except in that it might be more difficult for companies that have perfected modern SEO/advertising to maintain the same level of reach, but I don't think that's what you're alluding to?
I think there's a bubble around AI, but I don't think I agree with this argument. Google search launched in 1998, and ChatGPT launched in 2022.
In 2001, if Google had gone under like a lot of .com bubble companies, I think the economic impact visible to people of the time would have been marginal. There was no Google News, Gmail, Android, and the alternatives (AltaVista, Ask Jeeves, MSN Search) would have been enough. Google was a forcing function for the others to compete with the new paradigm or die trying. It wasn't itself an economic behemoth the way it is today.
I think if OpenAI folded today, you'd still have several companies in the generative AI space. To me, OpenAI's reminiscent of Google in the late 90s in its impact, although culturally it's very different. It's a general purpose website anyone with an internet connection can visit, deep industry competitors are having to adapt to its model to stay alive, and we're seeing signs of a frothy tech bubble a few years after its founding. People across industry verticals, government, law, and NGOs are using it, and students are learning with it.
One counterpoint to this would be that companies like Google reacted to the rise of social media with stuff like Google+, but to me the level to which "AI" is baked into every product at Google exceeds that play by a great margin. At most I remember a "post to plus" link at the top of GMail and a few hooks within the contact/email management views. In contrast, they are injecting AI results into almost every search I make and across almost every product of theirs I use today.
If you fast forward 20 years, I would be surprised if companies specializing in LLMs were not major players the way today's tech giants are. Some of the companies might have the same names, but they'll have changed.
> At most I remember a "post to plus" link at the top of GMail and a few hooks within the contact/email management views.
Google probably could have been whatsapp but to push Google+ scrapped a successful gmail chat for hangouts, which you had to visit Google+ feed each time to open at first.
Another thing to note about China: while people love pointing to their public transit as an example of a country that's done so much right, their (over)investment in this domain has led to a concerning explosion of local government debt obligations which isn't usually well-represented in their overall debt to GDP ratios many people quote. I only state that to state that things are not all the propaganda suggests it might be in China. The big question everyone is asking is, what happens after Xi. Even the most educated experts on the matter do not have an answer.
I, too, don't understand the OP's point of quickly pivoting to value extraction. Every technology we've ever invented was immediately followed by capitalists asking "how can I use this to make more money". LLMs are an extremely valuable technology. I'm not going to sit here and pretend that anyone can correctly guess exactly how much we should be investing into this right now in order to properly price how much value they'll be generating in five years. Except, its so critical to point out that the "data center capex" numbers everyone keeps quoting are, in a very real (and, sure, potentially scary) sense, quadruple-counting the same hundred-billion dollars. We're not actually spending $400B on new data centers; Oracle is spending $nnB on Nvidia, who is spending $nnB to invest in OpenAI, who is spending $nnB to invest in AMD, who Coreweave will also be spending $nnB with, who Nvidia has an $nnB investment in... and so forth. There's a ton of duplicate-accounting going on when people report these numbers.
It doesn't grab the same headlines, but I'm very strongly of the opinion that there will be more market corrections in the next 24 months, overall stock market growth will be pretty flat, and by the end of 2027 people will still be opining on whether OpenAI's $400B annual revenue justifies a trillion dollars in capex on new graphics cards. There's no catastrophic bubble burst. AGI is still only a few years away. But AI eats the world none-the-less.
My point is not that value extraction wouldn't happen, my point is simply that in addition to the value extraction we also made other huge shifts in economic policy that taken together really seem to put us on a path towards an "AGI or bust" situation in the future.
Is that a bit hyperbolic? isn't this just the same as dotcom and housing bubbles before where we pivoted a bit too hard into a specific industry? maybe... but I also am not sure it would be wise to assume past results will indicate future returns with this one.
AI is appealing to the investors not because it solves human problems, but because it solves some of the problems of previous bubbles.
When we wired the world for the Internet in the 1990s, or built railways across the continent in the 1800s, we eventually reached a point where even the starriest-eyed investors could see they've covered effectively the entire addressable market. Eventually AOL ran out of new customers no matter how many CDs they mailed out, or we had connected every city of more than 50 people with steel rail, and you could hear the music was slowing down.
By dangling the AGI brass ring out there, they can keep justifying the expenditure past many points of diminishing returns, because the first thing we'll ask the Omnipotent AGI is how to earn the quadrillions spent back, with interest.
It also has the benefit of being a high-churn business. The rails laid in 1880, or the fiber pulled in 2000, were usable for decades, but in the AI bubble, the models are obsolete in months and the GPUs in years. It generates huge looking commercial numbers just to tread water.
Core R&D, training, public education at all levels, health care, small businesses of all kinds, arts and entertainment, and many more.
Basically IP generation at all levels, and high-touch face-to-face service businesses.
The US already had the foundations for a post-industrial economy, but the cancer of extractive financialisation ate it alive. Instead of expanding into diversity it contracted into a startup culture that's always been a thin front for Wall St.
So the result is that it's very hard to start a mid-rank IP or service business. You have small-scale cottage-level producers like authors, musicians, video creators, and indie app developers relying on huge monopolies like Amazon, Spotify, Google, and Apple, and you have bigger projects playing the startup game and scrambling for funding rounds, where anyone who doesn't become a unicorn is a loser.
There's plenty of space for businesses between those extremes, but the economy isn't set up to support them. The space isn't dead yet, but it could be much, much bigger.
Universities and corporate R&D have similar issues. Metrics support conformist publishing and resume-development, not risky original talent and exploration.
Tariffs and brain drains are absolutely toxic and are going to nuke all of these spaces.
You can’t have any of this, literally, without manufacturing at home. You can’t do R&D of any sort if you don’t have the capability to produce things, to send an email or walk over to the company making it, speak with someone and have a consultation around what it is you hope to achieve because it’s the people who machine, who fabricate, who apply chemical treatments and layers of paint, who bend who etch and understand tool clearance requirements and so on that know this stuff. Manufacturing is a broad term.
I’m reminded of recent-grad architects whose proposed ideas are bereft of consideration of material properties and pitfalls.
I’m also reminded of Air Force aircraft engineers needed to be told their parts have to be adjusted because they can’t be machined. And the person who knows what needs to be done is Bob whose hands are covered in grease and oil because there’s not enough orange Zep on this planet to clean that guy.
To use China as an example: their entire pipeline from conception of idea to the end-customer is in China. They don’t even need to sell it externally (and frequently they don’t).
The West is fucked because they think pushing things around is being productive. GDP is a reflection of this failure because it’s a flawed, abused metric that’s devoid of where money actually ends up (most often it’s Chinese firms doing the work), so your GDP is looking great meanwhile you are incredibly unproductive over all. It’s a meme tool used for nonsensical political posturing.
Simply put: you can’t start R&D in the middle and not take into consideration a pipeline. It doesn’t work that way. People don’t have the experience or knowledge.
For AI, the pivot to profitability was indeed quick, but I don't think it's as bad as you may think. We're building the software infrastructure to accomodate LLMs into our work streams which makes everyone more efficient and productive. As foundational models progress, the infrastructure will reap the benefits a-la moore's law.
I acknowledge that this is a bullish thesis but I'll tell you why I'm bullish: I'm basically a high-tech ludite -- the last piece of technology I adopted was google in 1996. I converted from vim to vscode + copilot (and now cursor.) because of LLMs -- that's how transformative this technology is.