저자: ChamathPalihapitiya · 외국인
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AI 버블 우려 속에서 AI 기업 수익화 과정은 정리(sorting) 단계이며, 백오피스 자동화 등 실사용처는 정확도가 높다고 봄. 과거 인터넷·소셜·SaaS 사례처럼 소수 승자만 남는 구조가 반복될 것이라 주장. 과잉투자는 단기 고통을 주나 장기 기반을 만든다는 논지. 대상 자산 개별 가격 방향 예측은 없음.
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and it may take advantage of what Freeberg said. He uses an LLM as a jumping off point, but then builds an entirely different approach, is able to generate entirely different and cheap silicon. That hasn't happened yet. And the reason it hasn't happened is not because it doesn't make sense, but it's because we're so early into it.
Yeah. And then what do these big guys do? The big guys have spent tens of billions of dollars. Yeah. On all kinds of stuff. human capital, opex, capex, that the AI boogeyman that has been created doesn't have some negative ramifications over the next couple of years as well as this stuff gets sorted out.
Hey guys, welcome Everyday Finance. The last two years have been very exciting for artificial intelligence. When chat GPT came out in late 2022 and generative AI took off in 2023, it was seen as both new and disruptive technology. Startups got a lot of money very quickly. Big companies like Microsoft and Meta put a lot of money into AI.
However, with any wave of excitement, questions are starting to show. Is AI really going to grow at an unstoppable rate? Or are we seeing the start of an AI bubble? These worries were harder to ignore this past week. Sam Alman, CEO of Open IIA, compared the current state of AI to the dotcom boom in the late 1990s, pointing out that bubbles form when people get too excited about a fact.
He said that even though AI is the most important new technology in decades, investors may be getting too excited about it faster than the tools can really do. To back up this warning, an MIT study found that most AI projects aren't even close to being finished. Public AI stocks dropped about 10%. And Mark Zuckerberg of Meta stopped hiring AI experts after the company had grown quickly.
Chamath Palahapisha, David Friedberg, and David Saxs all spoke out on the most recent Olan show. They called it a reality check moment and talked about whether the AI bubble is beginning to pop. It sharp ideas about what would happen next and what is still one of the most important changes in our time.
I think the first wave was just a lot of boards who read the words AI somewhere in an article and then went to a board meeting and turned to the CEO and said, "What's your AI strategy?" Mhm. And then the CEO turns around and sends that down into their or eventually hits the CTO's desk. And I think the first wave is mostly people just spending money because they had large existing budgets and so they were like, "Let's just go and try a bunch of different things." And I think now we're going through the sorting function of realizing that there's a big difference between probabilistic software and deterministic software.
That's probably the biggest reason why you're seeing so many failure modes in sales and marketing. It's very hard to codify sales and marketing into a set of heruristics that never change. But back office processes, why they're so good and a great target for AI is ultimately you have so many people that have been hired to deal with edge cases, right?
I think like that's what people do in most companies is they're in charge of a process and they're they're dealing with edge cases. And I think that you can get extremely high rates of accuracy if you implement AI correctly in back office tasks. I think the real question is like what happens to all of this revenue that has been generated?
You're seeing companies generating $50 million of ARR in a matter of months and then raising huge rounds. I think what we haven't seen is whether there'll be any sort of either logo churn or dollar churn as new companies come in with even cheaper solutions, the foundational models move up the stack and just absorb capability or things just don't work and they get abandoned.
All of that churn happened in social. I remember when I was in the middle of helping build Facebook, we went through that whole cycle. There was seven or 8,000 social companies and within 6 years there was five of us left. It happened in SAS when I was investing in SAS. There's a couple of very early and important successes like Yammer which Sax started which I was very lucky to be an investor of but then it took many years for the handful of winners to get really sorted out and I suspect we're about to go through that same cycle in AI.
So I think that article basically paints a very accurate picture. There's been a lot of triing and experimentation. We now need to go through a sorting and a cleansing and then we'll rebuild from first principles around the and not surprising. It's concerning when models successively don't get meaningfully better especially because there's a tendency for incremental models to get overfit to benchmarks.
That's another concerning thing that I think people were like scratching their heads about. And if you look at the last big set of model updates, X of Grock, they didn't really meaningfully improve their separation from the last generation model on the big benchmarks that matter. And that's also very concerning because they tend to overfit to begin with that I have.
You know how at some point like you get so invested in a way of doing things that when things change, it's almost too hard to change the way your company orients. And you see it in every single phase of these big innovation cycles. The question is, is there a point at which these foundational model companies have invested too much money in LLM where so many of their employees only understand that and all of a sudden there are completely different ways of building these models.
State space is one example. But then you know some new entrant decides to build a completely different kind of representation. They may also then build custom silicon for it. All of those things have yet to happen. And the reason is we're really only a few years into what should be a multi-deade innovation cycle.
So those are like some really important questions because then you have this problem of the sunk cost fallacy of well we spent all this money on this LLM based approach and now all of a sudden there's this thing that comes around the corner. That's the thing that I'm really curious about and waiting for is will there be these small teams and it may take advantage of what Freeberg said.
uses an LLM as a jumping off point, but then builds an entirely different approach, is able to generate entirely different and cheap silicon. That hasn't happened yet. And the reason it hasn't happened is not because it doesn't make sense, but it's because we're so early into it. Yeah. And then what do these big guys do?
The big guys have spent tens of billions of dollars. Yeah. On all kinds of stuff. Human capital, OPEX, capex. There's also all this other stuff that people never talk about that are very practical realities of building a so for example we've been selling into the enterprise at 8090 we'll do this year this is our first full year of business we'll do about $40 million of bookings
in the first year that's crazy and it's all back office type stuff but our first customer where we delivered enormous gains we were fired and the reason we were fired was not because the technology wasn't working but it's because as it goes lower and lower from the board and the CEO into the bowels of the organization, you actually run into huge push back.
And I think that there's going to be that cycle that we have to deal with as well, which is in all of these AI businesses, there's all kinds of stuff that scare people. And there are many people in decision-m roles at many of these companies that will have to sort out where's their comfort level. And it was like one of these things where I was like, well, what are we supposed to do?
We escalated and blah blah blah and it got resolved. But my point is that I think is a cycle that has yet to play itself out. In all of the Fortune 2000, global 2000, I would not expect that the AI boogeyman that has been created doesn't have some negative ramifications over the next couple of years as well as this stuff gets sorted out.
Chimath sees a lot of similarities between the current AI wave and tech waves that came before it such as social media and SAS. There were thousands of people who entered each one at first, but only a few made it to the end. He says that AI is now hitting the stage called winnowing or companies that are based on hype and spending too much without a clear business plan will go away, leaving behind companies that will last and have real world uses.
In response, David Sax says that he agrees the sector is going through a shakeout, but he doesn't see it as a collapse. Instead, he sees it as a good correction. Says that false claims about a GI, like saying that computers will be smarter than humans in just a few years feed both utopian dreams and fears of the end of the world.
He makes it clear that the field is actually moving forward through slow, steady progress, not big sudden jumps. His main point was that AI is still very popular, but it looks more like a normal race for technology than a magical race to become super intelligent. I'll make the bull case. I'll make the bull case.
Yeah, please. Yeah. I think the simplest way to make the bull case is if you look at the kager on their mouse and the conversion from ma to Dows, you essentially take a small minor percentage of the Facebook or Google terminal arpoo and apply it to some number of ma dows 3 years from now. So if I had to guess, if you take 4 500 million MADOW growing at I'm guessing, let's just be conservative 50%
500 million weekly active users right now. 750. Okay. So then, so then I would probably put that at like 500 DAO to be conservative. It's probably doubling every two years. So 500 goes to a billion, billion goes to two in four years. And at 2 billion DAO, they generate a tenth of Facebook's revenue.
just to be very conservative and you probably get to a trillion five valuation then I think the midterms are going to be about the economy but the economy is going to be a window into a lot of these other issues the safety security issue the immigration issue the tariff issue rates I think rates are going to be an enormous swing factor here I think Powell is really holding on white knuckled to a position that probably doesn't underly the the data So I think in the short term it's just going to be more of an economic up or down vote for the Trump administration.
Many things I think are positive and in their control, but there are a few things that are not in their control. In 2028, I don't know. The one thing I would say about the candidacy though is like I don't know if you guys remember, but when the Republican primary process started, the person at the top of the polls at the time was Ronda Santis.
And I said, "You do not want to be ahead early. These people never win. They never win. They take all the arrows and peak too early. No, it's it's not even that. It's just like they never win. So, if I had to apply that rubric here, what I would say is the practical choice that the Democratic voters will have is to actually ask whether they want a replay of California on an American 50-state scale.
And if you look at it through that lens, I think Gavin Newsome is going to have a very difficult path because of what has transpired over his tenure as the governor. And he's going to have to explain away a lot. So I think that in the absence of real choices, I think that he's the clear winner. What is that phrase?
In the land of the blind, the oneeyed man is king sort of a thing. I think when you actually go into the election cycle starting after 26 in 27 honestly it's going to be up in the air. What I will tell you is that the way that the electoral college is set up and the voting dynamics for the presidency is set up a socialist cannot win.
But the way that the democratic primaries are set up a socialist can absolutely win. So this is the sort of Saxis point which is I think for the Republican side the best outcome is a very radical agenda that isn't really supported by substantive facts and is more supported by vibes. This is what I feel.
I just feel it should be right. And I think that those things may win a primary but will just get destroyed in a federal election. Put into Grock the following question. And the reason I asked the question this way is I think that the three most credible candidates that have a record to run on on the Democratic side are Gavin Newsome, Gretchian Whitmer, and Wes Moore.
So I asked Grock, rank Michigan, California, and Maryland. So three Democratic states run by three very popular, well-known Democratic governors on three metrics, quality of life, cost of living, and crime, and summarize that into a ranked overall list. Right? So, pretty standard plain vanilla question.
And at the end, I I can publish this later if you want, but at the end, the final overall rankings were Maryland first, excels in quality of life and has the lowest estimated crime rates with a moderately high but manageable cost of living. Michigan, most affordable with decent quality of life and moderate crime, making it a balanced choice.
California, high quality of life in terms of opportunities and lifestyle, but severely hampered by high cost and elevated crime rates. And there's all kinds of detail, but I was just giving you the final summary. So, I think that when you get into a Democratic primary, those are three people that will run.
And I think Democratic voters will have to choose what record do I want to put up to vote for the broad voting American public. Is it California's? Is it Maryland's or is it Michigan's? And if you just do a quick AIdriven analysis, Wes Moore, then Gretchen Whitmer, then Gavin Newsome just on the facts.
I was pretty curious about the historical pattern of this. So I just went back and here's what I learned. What's interesting to note is that since 1945, so not assuming, you know, conflicts in Africa because they are not really quite part of the western political global order per se, it's important to ask the question like how have other territorial disputes panned out?
So there are disputes that end in stalemates and when you end up in a stalemate, you basically arrange a DMZ demilitarized zone type arrangement. So there's one in Korea, right? 1953 to now. There's one in Cyprus 1974 to right now. And there's one in Kashmir 1947 to today. And then there's this kind of thing in between Israel and Lebanon.
And then second, there are disputes where there's some action taken by one country to take something by force, but then there's no consensus internationally as to who owns a territory. So Crimea as Saxs mentioned 2014 to present and then Israel and West uh East Jerusalem sorry. So I think the point is that unless President Trump figures out a way to do something that's frankly truly unprecedented.
The historical pattern is that it's been nearly impossible for even the most powerful and influential person in the world to frankly draw deals in these kinds of situations, especially when they become protracted. And I think that's what he's getting to when he reinforces these things never would have started or gotten this far had I been president.
You know, that's what he says in quotes. And I think this is what he's referring to, which is that the longer these things get drawn out and particularly with Russia, Ukraine, and Israel, Gaza, you're talking about conflicts and historical issues that have been simmering for hundreds of years and in some cases thousands of years all the way back to the antiquity.
So, I don't know. I I just think that it's an incredibly incredibly difficult position and I just don't know what's going to happen. But I think that for modern progress to happen, I think it's important for both of these two issues to get put to bed as quickly as possible. In the late 1990s, the internet's technology grew much faster than people's use of it.
That whole caused the dot crash, which erased billions of dollars in wealth and left behind empty buildings and networks that were too busy. But in the years that followed, those same networks led to a wave of growth and innovation that no one saw coming. At first glance, what seemed like a bubble turned out to be the start of a change.
Okay, the cycle showed a trend that keeps happening. Markets often overshoot in the short term, but the extra capital still builds the systems that allow long-term change. The internet story wasn't just guesswork. It was about how investments that were put in the wrong places built the digital economy.
The idea itself wasn't what went wrong. Investors weren't patient enough. People who look past the crash saw the real good. The dotcom era, as Chimath and others point out, showed how overbuilding can have painful short-term effects, but build the foundation for the next wave of innovation. The real lesson is that investors don't usually get to see how quickly new technologies are adopted.
History showed that the crash wasn't the end of the internet. Instead, it was the shaky start of a change that changed business, communication, and society around the world. That brings up a bigger question for us now. Are new tools changing in the same way? Are the platforms that seem too full today planting the seeds for tomorrow's big breakthroughs?
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