GuShiio.com The winners of AI at this stage are all users, and the losers are companies with proprietary models.
Author: MD
Produced by: Bright Company
Recently, the well-known American podcast Invest Like the Best once again interviewed Marc Andreessen, co-founder of Andreessen Horowitz. In the interview, Marc and anchor Patrick discussed in depth the major changes AI is reshaping technology and geopolitics, and discussed DeepSeek’s open source artificial intelligence and its significance in technological competition among major powers. In addition, they also shared their views on the evolution of global power structures and the overall transformation of the venture capital industry.
“Bright Company” used AI tools to sort out the core content of the interview as soon as possible. For the full text, please refer to the original link at the end of the article.
The following is the interview content (abridged):
Talk about DeepSeek, AI winners and losers
Patrick: Marc, I think we have to start with the core issue. Can you share your thoughts on DeepSeek’s R1?
Marc: There are many dimensions here. (I think) the United States remains a recognized scientific and technological leader in artificial intelligence. Most of the ideas in DeepSeek originate from the past 20 years, and even surprisingly 80 years ago, work done in the United States or Europe. The first research on neural networks began in research universities in the United States and Europe in the 1940s.
Therefore, from the perspective of knowledge development, the United States is still far ahead.
But DeepSeek has made a very good use of this knowledge. They also did something amazing., that is to make it available to the world in open source form. This is actually quite amazing because there is a reversal of this phenomenon.You have American companies like OpenAI that are basically completely closed down.
Part of Elon Musk’s lawsuit against OpenAI requires them to change the company’s name from OpenAI to Closed AI. OpenAI originally envisioned that all content would be open source, but now everything is closed. Other large AI laboratories, such as Anthropic, are also completely closed. In fact, they have even stopped publishing research papers, treating everything as exclusive property.
And the DeepSeek team, for their own reasons, actually fulfilled its promise of true open source. They released the code for their LLM (called V3) and their reasoner (called R1), and released a detailed technical paper explaining how they built it, which basically provides a roadmap for anyone else who wants to do similar work.
So it’s made public. There is a false argument in the outside world that if you use DeepSeek, you will give all the data to the China. If you use the service on the DeepSeek website, this is true. But you can download the code and run it yourself. But let me give you an example: Perplexity is an American company, and you can use DeepSeek R1 on Perplexity, fully hosted in the United States. Both Microsoft and Amazon now have cloud versions of DeepSeek, and you can run it on their cloud platforms, and obviously both companies are American companies that use American data centers.
This is very important. You can download this system now,And you can actually run it on $6000 worth of hardware at home or at work.Its capabilities are comparable to cutting-edge systems from companies such as OpenAI and Anthropic.
These companies have invested a lot of money in building their systems. Today, you can buy it for $6000 and have complete control. If you run it yourself, you have complete control. You can understand with complete transparency what it is doing, you can modify it, you can do all kinds of things with it.
It also has a very excellent property called distillation. You can compress a large model that requires $6000 in hardware and create a smaller version. Someone online has created smaller versions of the models and optimized them so you can run them on a MacBook or iPhone. These versions are not as smart as the full version, but they are still quite smart. You can create customized, domain-specific, distilled versions that perform well in specific areas.
This is a huge advance in making large-model reasoning and R1 model reasoning more popular in programming and science. Six months ago, these were very esoteric, extremely expensive and proprietary. Today, it has become free and always available to everyone.
For every major technology company, Internet company, every startup, we have dozens or even hundreds of startups this week that are either rebuilding on DeepSeek, integrating it into their products, or studying the technology they use and using it to improve existing AI systems.
Mark Zuckerberg of the Meta team recently talked aboutThe Meta team is dismantling DeepSeek and borrowing the ideas completely legally because it is open source,And make sure that the next version of Llama is at least comparable to, or better than, DeepSeek in reasoning capabilities. This has really promoted the development of the world.
The two main points we can learn from this are: AI will be everywhere. There are a lot of AI risk control people, security people, regulators, officials, governments, EU, British people, all of these people who want to restrict and control AI, and this basically ensures that none of this will happen, which I think is great. It is very consistent with the free tradition of the Internet. This then achieves a 30-fold cost reduction in reasoning capabilities.
Perhaps finally, this suggests that reasoning will work. Reasoning will work in any area of human activity, as long as you can generate answers that can be checked for correctness by technical experts after the fact.
We will have AI capable of human and superhuman level reasoning, which will work in areas that really matter: coding, mathematics, physics, chemistry, biology, economics, finance, law and medicine.
This basically ensures that within five years, everyone on earth will have a superhuman AI lawyer and AI doctor who are on call at any time. This is just a standard feature on mobile phones. This will make the world a better, healthier and wonderful place.
Patrick: But this is alsoThis is the most unstable, and the model becomes obsolete within two months. A lot of innovation is happening at every technology level. But just at this point in time, entering this new paradigm, if you’re writing a column about winners and losers for all stakeholders, whether it’s new application developers, existing software developers, infrastructure providers like Nvidia, open source and closed-source model companies. Who do you think will be the winners and losers after the release of R1?
Marc：If you take a snapshot today,So from the perspective of zero-sum games, for winners and losers at a point in time, the winner is all users,All consumers, every individual, and every business that uses AI.
There are some startups, such as companies that do AI legal services, whose cost of using AI last week was 30 times higher than it is now.
For example, for a company that is building an AI lawyer, if the cost of its key inputs drops 30 times, it’s like the cost of gasoline when driving a car drops 30 times. Suddenly, you can drive 30 times farther with the same dollar, or you can use the extra spending power to buy more. All of these companies will either greatly expand their ability to use AI in all these areas, or they will be able to provide services cheaper or free. So for users and the world, this is an excellent result based on a fixed-size plate.
Losers are companies with proprietary models,Such as OpenAI, Anthropic, etc. You’ll notice that OpenAI and Anthropic have both sent out pretty tough but provocative messages over the past week explaining why this is not the end for them. There is an old saying in business and politics thatWhen you explain, you lose.
Then the other one is Nvidia. There are many comments on this, but Nvidia makes standard AI chips that people use. There are some other options, but Nvidia is what most people use. The profit margin on their chips is as high as 90%, and the company’s stock price reflects this. (NVIDIA) is one of the most valuable companies in the world. One of the things the DeepSeek team did in their paper was that they figured out how to use cheaper chips,In fact, Nvidia’s chips are still used, but they use them more efficiently.
Part of the 30-fold cost reduction is that you only need fewer chips. By the way, China is building its own chip supply chain, and some companies are starting to use chips derived from China, which is certainly a more fundamental threat to Nvidia. So this is a snapshot at some point in time.But the problem is, your question suggests another way of looking at it, which is that over time, over time, what you want to see is the elasticity effect.Satyanadella used this phrase called Jevons ‘Paradox.
Imagine gasoline. If the price of gasoline drops significantly, then suddenly people will drive more cars. This often occurs in transportation planning. So you have a city like Austin, where traffic is jammed and someone will have the idea of building a new highway next to an existing highway. In just two years, new highways will be jammed, and it may even be more difficult to get from one place to another. The reason is that lower prices of key inputs can induce demand.
If AI suddenly became 30 times cheaper, people might use it 30 times, or by the way, they might use it 100 times or even 1000 times. This economic term is called elasticity.
soFalling prices equals explosive growth in demand.I think there’s a very reasonable scenario here, where on the other side, as usage explodes, DeepSeek will do well. By the way, OpenAI and Anthropic will also do well, Nvidia will also do well, and China chip manufacturers will also do well.
Then you will see a tidal effect and the entire industry will explode. We’re really just beginning to figure out how to use these technologies。Reasoning has only begun to work in the past four months。OpenAI only released their o1 inference model a few months ago.It’s like taking fire from a mountain and giving it to all mankind。Most humans haven’t used fire yet, but they will.
Then, frankly, it’s also an old idea, which is creativity, which means, well, if you’re OpenAI or some similar company, what you did last week is not good enough. But then again, this is the way the world works. You have to get better. These things are all competitions. You have to evolve. So it’s also a very powerful catalyst for many existing companies to really improve their skills and become more aggressive.
……
Patrick：……, if a China company uses models developed in the United States that invest a lot of money in these models and then lead to this kind of technology that brings wealth to the world, it is difficult to understand. I would love to hear your reaction from these two perspectives.
Marc：Yes, so there are some real issues here. There is an irony to this argument, and you do hear this argument. The irony, of course, is that OpenAI did not invent Transformer. The core algorithm of a large language model is called Transformer.
It wasn’t invented at OpenAI, it was invented at Google. Google invented it and published a paper about it, and then, by the way, they didn’t commercialize it. They continued to study it but did not commercialize it because they believed it might be unsafe for safety reasons. So they left it on the shelves for five years, and then the OpenAI team understood it, picked it up and moved forward.
Anthropic is a branch of OpenAI. Anthropic did not invent the Transformer. So whether it’s these two companies, or every other U.S. laboratory that is working on large language models, every other open source project is built on something they haven’t created and developed themselves.
By the way, Google invented Transformer in 2017, but Transformer itself is based on the concept of neural networks. The idea of neural networks dates back to 1943. So 82 years ago was actually the time when the original neural network paper was published, and Transformer was built on the basis of 70 years of research and development, most of which was funded by the federal and European governments in research universities.
So this is a very long spectrum of intellectual ideas and developments, and most of the ideas that go into all of these systems are not developed by the companies currently building them. No company sits here, including our own, with any special moral proposition that we were built from scratch and that we should have complete control. This is not the truth at all.
So, I would say that arguments like this are based on contemporary frustration. By the way, these arguments are also meaningless because China has done it, it has come out, and things have already happened. There is now a controversy over copyright. If you talk to experts in the field, many of them have been trying to understand why DeepSeek is so good. One theory, which is an unproven theory, but one theory experts believe is that China companies may have used data for training that American companies did not use.
What is particularly surprising is that DeepSeek is very good at creative writing. DeepSeek is probably the best AI in the world for creative writing in English. This is a bit strange because the official language of China is Chinese. Although there are some very good China English-language novelists, in general, you might think that the best creative writing should come from the West. And DeepSeek may be the best right now, which is shocking.
So one theory is that DeepSeek may have trained. For example, there are websites called Libgen, which are basically huge Internet repositories full of pirated books.I certainly don’t use Libgen myself, but I have a friend who uses it often.It’s like a super collection of the Kindle store. It has every digital book in PDF format that you can download for free. It’s like a movie version of the Pirate Bay.
American laboratories may not feel that they can simply download all the books from Libgen and train, but perhaps China laboratories feel that they can. Therefore, there may be such differential advantages. That being said, there is still an unresolved copyright battle here. People need to approach this issue carefully because there is an unresolved copyright battle here, with some publishing companies basically wanting to prevent generative AI companies like OpenAI, Anthropic and DeepSeek from being able to use their content.
There is an argument that these materials are copyrighted and cannot be used at will. There is another argument, basically saying that AI trains books, you are not copying books, you are reading books. AI reading books is legal.
You and I are allowed to read books, by the way. We can borrow books from the library. We can pick up friends ‘books. These actions are all legal. We are allowed to read books, we are allowed to learn from books, and then we can move on with our daily lives and talk about the ideas we learn in books. Another argument is that training AI is more like humans reading books than stealing.
Then there is the practical reality that if their AI can be trained in all books, and if U.S. companies are ultimately prohibited by law from training books, then the U.S. may lose the race in the AI field.
From a practical perspective, it could be a fatal blow, like they won and we lost. There may be some entanglement in the entire debate. DeepSeek did not disclose the data they used for training. So when you download DeepSeek, you don’t get training data, you get so-called weights. So what you get is a neural network trained with training material. But it is difficult or even impossible to view and deduce training data from there.
By the way, Anthropic and OpenAI also didn’t disclose the data they used for training. Then there is fierce speculation in the field about what is and what is not in the OpenAI training data. They believe it is a trade secret. They will not disclose this content. Therefore, China DeepSeek may or may not be different from these companies. They may differ in training methods from China companies. We don’t know.
We don’t know what OpenAI and Anthropic’s algorithms are because they are not open source. We don’t know how much better or worse they are than the publicly available DeepSeek algorithm.
Talking about closed source and open source
Patrick：Do you think those closed-source models that enter the competition, like OpenAI and Anthropic, will eventually become more like Apple and Google’s Android?
Marc: I support maximizing competition.By the way, that fits my status as a venture capitalist. So, if you are a company founder, if I run an AI company, I need to have a very specific strategy that has pros and cons that requires trade-offs.
As a venture capitalist, I don’t need to do this. I can make a variety of contradictory bets。This is what Peter Thiel calls deterministic optimism and uncertain optimism. Company founders and CEOs must be deterministic optimists. They must have a plan and must make difficult trade-offs to achieve that plan. Venture capitalists are non-deterministic optimists.We can fund a hundred companies with 100 different plans and conflicting assumptions.
The nature of my job is that I don’t have to make the choices you just described. Then, it made it easy for me to make a philosophical point, which I personally sincerely agree with, that I support maximum competition. So, further down, it means I support free markets, maximum competition and maximum freedom.
Essentially,If you could have as many smart people as possible come up with as many different methods as possible and compete with each other in the free market, see what would happen.Specifically when it comes to AI, this means that I support large laboratories growing as quickly as possible.
I support OpenAI and Anthropic 100% doing whatever they want to do, launching whatever product they want to launch, and working hard to develop as hard as possible. As long as they do not receive preferential policy treatment, subsidies or support from the government, they should be able to do whatever they want as a company.
Of course, I also support startups. We are certainly actively funding AI startups of all sizes and types. So, I hope they can grow, and then I hope open source can grow, in part because I think if something comes in open source, even if it means companies with some business models can’t work, the benefits to the world and the industry as a whole are so great, we’ll find other ways to make money. AI will become more common, cheaper, and easier to obtain. I think this will be a good result.
Then, another very critical reason for open source is that without open source, everything becomes a black box. Without open source, everything becomes a black box owned and controlled by a few companies that may end up colluding with the government, and we can discuss that. But you need to open source so you can see what’s happening inside the box.
By the way, you also need open source for academic research, so you need open source for teaching. So, the problem before open source was, back to two years ago, when there was no basic open source LLM, Meta released Llama, then Mistral in France, and now DeepSeek.
But before these open source models emerged, the university system was going through a crisis.That is, university researchers in places such as Stanford, MIT and Berkeley do not have enough funds to buy billion-dollar Nvidia chips in order to truly compete in the AI field.
So if you talked to computer science professors two years ago, they would have been very worried. The first concern is that my university does not have enough funds to compete in the AI field and remain relevant. Then another concern is that all universities together don’t have enough money to compete because no one can keep up with the fundraising power of these large companies.
Open source puts universities back into competition。This means that if I were a professor at Stanford, MIT, Berkeley, or any state school, whether at the University of Washington or elsewhere, I could now teach using Llama code, Mistral code, or DeepSeek code. I can do research, I can actually make breakthroughs. I can publish my research and let people truly understand what happened.
Then, every new generation of kids comes to college and takes computer science courses, and they will be able to learn how to do this, which they wouldn’t be able to do if it were a black box. We need open source just as we need freedom of speech, academic freedom, and research freedom.
So my model is basically, you have big companies, small companies and open source competing against each other. This is what happens in the computer industry. This works well. This is what happens in the Internet industry. It works well. I believe this will happen in the AI space, and I think it will work well.
Patrick: Is there a limit that you want to maximize the speed of evolution and the greatest degree of competition? Maybe there is. If I say that the best thing we know is made by China, is there a situation where you say, yes?I want maximum evolution and competition, but national interests somehow outweigh the desire for maximum evolution speed and development?
Marc: This argument is a very real argument. It is frequently proposed in the AI field. In fact, as we sit here today, there are two things. First, there are actually restrictions on Western and American companies selling cutting-edge AI chips to China. For example, Nvidia cannot actually legally sell its cutting-edge AI chips to China today. We live in a world where such decisions have been made and policies have been implemented.
Then the Biden administration issued an executive order that I think has been revoked now, but they issued an executive order that would impose similar restrictions on software. This is a very lively debate. With the DeepSeek incident, another round of such debate is underway in Washington, D.C.
And then basically, when you get into a policy debate,You encounter a classic situation where you have a rational version of the debate about what is in the national interest from a theoretical perspective.Then you have a political version of the argument, which is well, what does the political process actually do to rational arguments? Let me put it this way. We all have a lot of experience watching rational arguments meet the political process, and it’s usually not the rational arguments that win. After processing by the political machine, the results are usually not what you initially thought you would get.
Then there is the third factor that we always need to discuss，namely, the impact of corruption, especially in large companies。If you are a large company and you see the changes that are happening to China companies (more competitive), the threats of what is happening to open source,Of course you will try to use the U.S. government to protect yourself.Maybe this is in the national interest, maybe it is not. But you will definitely push for this, whether it is in the national interest or not. This is what complicates the debate.
You cannot sell cutting-edge AI chips to China. This must hinder them in some way. There are some things they will not be able to do. Maybe that’s a good thing because you’ve decided it’s in the national interest. But let’s take a look at three other interesting consequences.
So one of the consequences is to provide huge impetus to China companies.To design how to do things on cheaper chips. This was an important part of DeepSeek’s breakthrough, which was that they figured out how to use legalcomplianceto do what American companies can do with larger chips.This is one reason why it is so cheap. One of the reasons is that you can run it on $6000 worth of hardware is because they put a lot of time and energy into optimizing the code so that it can run efficiently on cheaper chips without sanctions. You forced an evolutionary response.
So this is the first reaction, and maybe it has backfired to some extent. The second consequence is that you inspire China’s state-owned and private sectors to develop a parallel chip industry.So if they know they can’t get American chips, then they will develop. They’re doing that now。They have a national plan to build their own chip industry so they don’t rely on American chips.
So from a counterfactual perspective, maybe they will buy American chips.Now they will go figure out how to make it themselves.Maybe they can do this in five years. But once they reach a position where they can manufacture themselves, then we will have a direct competitor that we would not have in the global market if we just sold them chips. And by the way, at that time, we have no control over their chips. They have complete control. They can sell at below cost, and they can do whatever they want.
How AI reasoning capabilities are changing the VC and investment industries
Patrick: How do you think all this will affect capital allocation? What I am most interested in is how your company, Andreessen Horowitz (A16Z), will be affected, maybe five years from now. If I think investment companies are a combination of the ability to raise capital, do excellent analytical work, and be able to judge people, especially in the early stages, how do you think this functionality will change with the advent of o7(AI reasoning capabilities)?
Marc: I hope that the analytical part can change dramatically. Let’s assume that the best investment companies in the world will be very good at using this technology for the analytical work they do.
Having said that, there is a saying that the shoemaker’s son has no shoes,Perhaps the venture capital companies that are investing most aggressively in AI may be among those that are not aggressive enough in practical applications.But there are multiple efforts underway within our company, and I am very excited about it. But companies like ours need to keep up, so we have to really do it.
Are some work already underway within the industry? Probably not yet. Maybe not enough. That being said, many of the people we talked to had a very analytical perspective on late-stage investing or public market investing. There are even great investors, I think Warren Buffett. I don’t know if that’s true, but I’ve always heard that Warren never meets with the CEO.
Patrick: He wants the ham sandwich company.
Marc: Yes, yes, he wants the company to be as simple as a ham sandwich. And I think he was a little worried that he would be attracted to a good story. You know, many CEOs are very attractive people. They are always described as having fine hair, white teeth, polished shoes, and straight suits. They are very good at sales. You know, one of the things CEOs are good at is selling, especially selling their own stocks.
So if you’re Buffett and you’re sitting in Omaha, all you do is read the annual report. The company lists everything in its annual report and is bound by federal law to ensure that its content is truthful. So this is your analysis. So, do reasoning models such as o1, o3, o7, or R4 do a better job of manually analyzing annual reports than most investors? Maybe so.
As you know, investment is an arms race, just like everything else. So if it works for one person, it will work for everyone. It will become an arbitrage opportunity for a while, then it will close and become the standard. So I expect the investment management industry to adopt this technology in this way. This will become a standard way of operating.
I think the situation is a little different for early-stage venture capital.What I am going to say next may be just my personal wishful thinking. I might be the last Japanese soldier on a remote island in 1948, saying what I have to say next. I’m going to take a chance. But what I want to say is, look, in the early stages, IA lot of what we do in the first five years is actually really in-depth evaluation of individuals，Then we have a very in-depth collaboration with these people.
This is why venture capital is difficult to scale, especially across geography.Geographic-scale experiments often fail. The reason is that you ultimately need to spend a lot of time face-to-face with these people, not only during the evaluation process, but also during the construction process. Because in the first five years, these companies have usually not entered autonomous driving.
You actually need to work closely with them to ensure they can achieve everything they need to succeed. There are very in-depth interpersonal relationships, conversations, interactions, guidance, and by the way, we learn from them and they learn from us. This is a two-way communication.
We don’t have all the answers, but we have a perspective because we see a broader panoramic view and they focus more on specific details. As a result, there is a lot of two-way interactions. Tyler Cowen talked about this, and I think he called it project selection.
Of course, talent mining is another version, which is basically, if you look back at any new area in human history, you can almost always find this phenomenon,That is, there are some people with unique personalities who try to do something new, and then there are some professional support layers who fund and support them.In the music industry, David Geffen discovered all the early folk artists and turned them into rock stars. Or in the film industry, David O. Selznick Dr. Selznick discovered early film actors and turned them into movie stars. Or 500 years ago in a cafe or pub in Maine, someone was discussing which whaling captain could go and catch whales.
You know, this is Queen Isabella listening to Columbus ‘proposal in the palace and saying: Sounds reasonable. Why not?& rdquo; This alchemy that has been developed over time, between people who do new things and the professional support layers that support and fund them, has existed for hundreds, even thousands of years.
You may have seen tribal leaders thousands of years ago, sitting around the fire, and young soldiers came over and said: I want to lead a hunting party to the area over there and see if there is better game there.” rdquo; The chief sat by the fire and tried to decide whether to agree.So this is a very human interaction. My guess is that this interaction will continue. Of course, then again, if I came across an algorithm that was better at doing this than I was, I would retire immediately.We will wait and see.
Patrick：You are building one of the largest companies in the field. How to adjust the company’s development strategy to deal with this new technology? Whether it is actual operation or strategic direction, have you made adjustments? How do you adjust the company’s direction to respond to this new technology?
Marc: An important part of running a venture capital company, in our view, is that there is a set of values and behaviors that you must have that we call immutable. For example, respect for entrepreneurs. You need to show great respect for entrepreneurs and the journey they go through. You need to have a deep understanding of what they do. You can’t just take a quick look.
You need to build a deep relationship. You have to work with these people for a long time, and by the way, these companies take a long time to build. We don’t believe in overnight success. Most great companies are built over a span of 10, 20, 30 years. Nvidia is a good example.Nvidia is about to celebrate its 40th anniversary, and I think one of Nvidia’s original VCs is actually still on the board of directors. This is a good example of long-term construction.
So, there is a core set of beliefs, opinions and behaviors that we will not change, and these are related to what we just mentioned. The other is face-to-face communication. You know, these things can’t be done remotely, that’s one thing. But on the other hand, you need to keep up with the times because technology changes so fast, business models change so fast, and competitive dynamics change so fast.
If anything, the environment has become more complex because you have a lot of countries now and all these political issues now, which also makes things more complex.We never really worried about the political system putting pressure on our investments until about eight years ago.Then about five years ago, the pressure really intensified. But in the first ten years of our company’s existence, and the first 60 years of venture capital, it was never a big deal, but now it is.
Therefore, we need to adapt. We need to engage in politics, something we haven’t done before. Now we need to adapt, and we need to figure out that maybe AI companies will be very fundamentally different. Perhaps their organizational structures will be completely different. Or as you said, maybe software companies will operate completely differently.
There is a question we often ask ourselves, for example, what is the organizational structure of a company that truly makes full use of AI? Will it be similar to the existing organizational structure, or will it actually be very different? There is no single answer to this, but we are thinking about it seriously.
So, one of the delicate balancing tasks we do every day is to try to figure out what is eternal and what is contemporary. This is conceptually an important part of my thinking about companies, which is that we need to navigate between the two and make sure we can distinguish them.
Patrick: Your company is now very big, and it is somewhat similar to companies like KKR or Blackstone Group. You and Ben Horowitz, as founders, were experienced founders when you founded this company. Similar to Blackstone, Schwarzman never really invested before founding Blackstone. Look at its current development.
It seems that this founder-led approach to building asset management investment companies will eventually develop into truly large and ubiquitous platforms. You have vertical businesses that cover most of the exciting cutting-edge areas of technology. Do you think this view has some truth? Will the best capital allocation platforms be created more by founders than investors?
Marc: Yes, so there are a few points. First of all, I think this observation is reasonable. In the industry, people often talk about it that many investment operations are often called partnerships. Many venture capital firms operate this way. Historically, it was a small team of people sitting in a room, exchanging ideas with each other, and then investing. By the way, they don’t have a balance sheet. This is a private partnership. They pay the money in the form of compensation at the end of each year. This is the traditional venture capital model.
A traditional venture capital model has six general partners (GPs) sitting around a table doing this. They have their own assistant and several assistants. But the point is, it’s entirely based on people. By the way, you’ll actually find that in most cases, people don’t like each other very much.
Mad Men shows this well. Remember in “Mad Men”, in the third or fourth season, members left to start their own company, and they didn’t actually like each other. They knew they needed to get together and start a company. This is how many companies operate. So, it’s a private partnership, and it’s what it stands for.
But then what you see is that these companies are having a hard time sustaining. They have no brand value. They have no potential corporate value. They are not a business. You see this model of companies where when the original partners are ready to retire or do something else, they hand them over to the next generation. Most of the time, the next generation cannot continue. Even if they can survive, there is no potential asset value. The next generation will have to hand it over to the third generation. It may fail in the third generation, and then it will eventually appear on Wikipedia. It would be like this, yes, this company existed, then it disappeared, and other companies replaced it, passing by like ships at night.” rdquo;
So this is the traditional way of operation. By the way, if you are trained in traditional investment, you are trained in the investment part, but you have never been trained in how to build a business. So, it’s not your natural strength, you don’t have the skills or experience, so you won’t do it. Many investors have operated this way as investors for a long time and made a lot of money. So, it works well.
Another way is to build a company, build a business, and build something with lasting brand value. You mentioned companies like Blackstone and KKR, these huge public companies. The same goes for Apollo, these huge companies, as you may know, the original banks were actually private partnerships. Goldman Sachs and JPMorgan Chase 100 years ago were more like today’s small venture capital firms than what they appear to be today. But then, over time, their leaders transformed them into these huge businesses. They are also large listed companies.
So, this is another way, which is to create a franchise. Now, to do this, you need a theory as to why a franchise should exist. You need a conceptual theory of why this makes sense. And then, yes, you need business skills. Then, at that point, you are running a business, and it’s like running any other business, which means, well, I have a company. It has an operating model, it has an operating rhythm, it has management capabilities, it has employees, it has multiple levels, and it has internal professional division of labor and specialization.
Then you start thinking about expansion, and then over time, you start thinking about the potential asset value, which means that the value of this thing is not just in the people who are there right now. It’s not as eager as we are to distribute profits, or anything else. But one of the big things we’re trying to do is build something that’s durable.
By the way, we’re not rushing to go public, or anything else, but one of the big things we’re trying to do is build something that has this durability.
Patrick: What new differences do you want to make in the next 10 years that don’t exist yet? Are there some uncompromising ways you hope the company will never evolve like traditional large asset managers?
Marc: We are rapidly evolving in terms of what we invest in, what the company does, models, and background of the founders, and these are constantly changing. For example, there has been a consensus in the venture capital community for 60 years that you would never support researchers starting companies to conduct research. He just does research, runs out of money, and you end up getting nothing.
However, many of today’s top AI companies are founded by researchers. This is an example of some so-called eternal values that need to be adjusted according to changes in the times. We need to maintain a high degree of flexibility with regard to these changes. So with these changes, so will the help and support companies need to succeed.
One of the most significant changes in our company, which I mentioned before, is that we now have a large and increasingly complex political operation. Four years ago, we were still blank in the political field. Today, this has become an important part of our business in ways we never expected before.
I am sure that in another 10 years, we will not only invest in areas that are currently unimaginable, but we will also have operating models that are currently unimaginable. Therefore, we are completely open to changes in these aspects. However, there are some core values that I hope to remain the same for the next 10 years because these values have been thought out and are the cornerstone of our company.
But what I have always emphasized to our team members and limited partners is thatWe are not pursuing scale for the sake of scale. After many investment companies reach a certain size, they will give priority to expanding the scale of asset management, from billions to hundreds of billions or even trillions of dollars.This approach is often criticized as focusing more on collecting management fees than on achieving excellent investment performance. This is not our goal.
The only reason we are expanding is to support the companies we want to help founders build. When we scale up, it’s because we believe it will help us achieve this goal.
However, I must emphasize that the core of our company has always been early-stage venture capital. No matter how big we get, even if we set up growth funds, some AI companies do need a lot of money to be able to write bigger checks. We did not establish growth funds from the beginning, but gradually established them with market demand and company development.
But the core business is always early stage venture capital. This can be confusing because from the outside, we manage a lot of money. Why, as the founder of an early stage startup, would I trust you to spend your time with me? Because you Andreessen Horowitz invested hundreds of millions of dollars in late-stage investments, and you only invested $5 million in my Series A financing. Will you still spend time paying attention to me?
The reason is that the core business of our company has always been early stage venture capital. From a financial perspective,The return opportunities on early investments are comparable to those of later companies, which is a characteristic of startups.But more importantly, all of our knowledge, networks, and what makes our company different come from our deep insights and connections in the early stages.
So I always tell people that if circumstances force the world to be in trouble and we have to make sacrifices, then early stage venture capital business will never be sacrificed. This will always be the core of the company. That’s why I spend a lot of time working with early founders. On the one hand, it’s very interesting; on the other hand, it’s also the place where you learn the most.
The transformation of global power structures: Elites and anti-Elites
Patrick: If we consider changes in the global power structure, which power centers are you most concerned about, whether they are gaining power or losing power?
Marc:”The Machiavellian”. I’m sure you’ve probably had a dozen people recommend this book on your show. This is one of the greatest books of the 20th century. It elaborates on theories of political power, social and cultural power. There is a key point in this book that I see everywhere right now,That is, the concepts of elite and anti-elite.
The view goes like this: Basically, democracy itself is a myth. You will never have a fully democratic society. By the way, the United States is certainly not a democracy, it is a republic. But even well-functioning democracies tend to have a republican nature, a lower-case r republic. They tend to have a parliament, or a House and Senate, or some kind of representative body. They tend to have a representative body.
The reason is that a phenomenon described in this book, called the Iron Law of Oligards, is basically this: the problem with direct democracy is that the masses cannot organize. You can’t really get 350 million people to organize and do anything. There are too many people.
So, in basically every political system in human history, you have a small, organized elite class governing a large, unorganized mass class. You start with the original hunter-gatherer tribes and go all the way to every other political system in the United States and the modern era, whether it be the Greeks or the Romans, or every empire and every country in history.
So,A small, organized elite class governs a large, unorganized mass class. This relationship is fraught with danger, because the unorganized public will obey the elite for a while, but not necessarily forever.If the elite becomes oppressive to the masses, the masses far outnumber the elite. At some point, they may show up with torches and spears. So there are tensions in this relationship. Many revolutions occur because the masses decide that the elite no longer represents them.
Our society is no exception. We have a large, unorganized public class. We have a very small, organized elite class. The United States has built a system where we have two elite classes. We have an elite class of Democrats and an elite class of Republicans. By the way, there is a huge overlap between these two elite classes, and some actually call it a single party. Perhaps these elite classes have more in common than they have with the general public.
For a long time, we have had a Republican elite whose policies are ultimately represented by the Bush family. We have a Democratic elite whose policies are ultimately represented by Obama. Over the past decade,There is basically an insurgency within the elite on both sides of the United States.This is actually the key point in “The Machiavellian” that change is usually not a direct confrontation of the masses against the elite. What happened was the emergence of a new anti-elite class.
You will have a new anti-elite class emerging, trying to replace the current elite class. My interpretation of current affairs is that in general,The current elite class of the world that runs the world has been found to be doing poorly.We can discuss why later. But in general, if you look at the approval ratings of (Western) political leaders, the approval ratings of institutions, all of these are declining. What’s happening all over the world is, if you’re an incumbent institution, if you’re an incumbent newspaper, if you’re an incumbent television network, if you’re an incumbent university, if you’re an incumbent government, generally speaking, your poll ratings are a disaster. This is what people are basically saying that the elite in power is failing us.
And then these anti-elite classes came along, and they said: Oh, I know I have a better way to represent the public, I have a better way to take over.” rdquo; My new anti-elite movement should replace the current elite movement, as is the case with the Democratic Party. This was Bernie Sanders in 2016, this was Ocasio-Cortez (AOC) and the entire progressive wave. And on the Republican side, this is clearly Trump and his Make America Great Again (MAGA) movement and everything it stands for.
But by the way, this dynamic is also happening in the UK. The Conservative Party has collapsed, and now you have this Reform Party, Nigel Farage, which is very threatening. You have Jeremy Corbyn, who is also an anti-elite class from the left.
The same is true in Germany. In fact, just this week, something very dramatic happened in Germany, namely the rapid rise of the so-called far-right party AfD. There was a leader named Alice Weidel, and for the first time in German political history, in 50 years or more, the Christian Democratic Union of Germany (CDU) actually worked with the AfD on something. Suddenly, AfD became a viable competitor. They are an anti-elite class trying to take over the right wing of the German political system.
So basically, no matter where you go in the world, there is always an anti-elite class that shows up and says: Oh, I can do better.& rdquo; This is a struggle between elite classes. The public is aware of this, they are watching democratic societies, and they will ultimately make a decision because they will decide who they will vote for.
That’s why Republican voters decided they would vote for Trump over Jeb Bush. This is how the anti-elite class defeats the elite class. This is actually related to the criticism of Trump, which is very interesting, that Trump is criticized by the existing elite as saying: Oh, he is not one of the people. He is a super rich billionaire who lives in a golden attic and is driven by people everywhere. If you are a rural farmer in Kentucky or Wisconsin, you shouldn’t think he’s one of you.& rdquo;
The point has never been that Trump is one of the people。The point is that Trump is an anti-elite class who can better represent the people.This was the basis of his entire movement. By the way, the same goes for the media world. Everything you are describing is exactly what is happening in the media world. The elite media has ruled for 50 years, and it is TV news, cable news, newspapers and these well-known magazines. Now you have an anti-elite class. The anti-elite class is Patrick you and (well-known podcast anchor) Joe Rogan. There are many more people.
By the way, if you look at the numbers, it’s very clear.The public, audiences, and readers are leaving old media and turning to new media.The existing elite is very angry about this. They angrily write all the negative articles about you guys, saying you’re all a bunch of white supremacists and the whole thing sucks. Like, this is the way the world works. So we are in the middle of all this. I don’t know if transition is the right term. It is more like a fierce battle between the old elite and the new elite.
Patrick: What were the first seeds of the decline of the previous generation’s elite that led to those 11% approval ratings? What do you think this is mainly due to?
Marc: There are two theories. One theory is that these approval ratings are wrong, and another theory is that these approval ratings are correct. By error, I mean that these approval ratings are measured correctly, but people are giving the wrong answers.
If you’re the head of CNN or Harvard University, or you’re in charge of any similar organization and your approval rating is only 11%, by the way, Gallup has been conducting a very remarkable survey for 50 years called Institutional Trust. You can Google the 2024 Gallup Institutional Trust Survey, and you’ll see some very spectacular charts, and you’ll find that institutional trust basically peaked in the late 1960s and early 1970s, and then kept declining.
By the way, this phenomenon predates the emergence of the Internet. Interestingly, it was blamed on the Internet, but it predated the Internet. So, this is a phenomenon that has been developing since the 1970s and has been accelerating. By the way, these support rates have fallen faster since 2020.
They just slide like this, and then plummet after 2020. TV and Internet news, I don’t know what the specific number is. It’s in single digits, and people no longer believe it at all. They no longer believe what is said on the TV news. By the way, audience ratings are falling in the same way.
So, one theory is that if you’re the head of NBC News or CNN or Harvard University, your theory might be: Oh, people are wrong. People are misled, they are deceived, they are deceived by populists and demagogues, they are deceived by false information.” rdquo; That’s why the concept of false information has become so popular. hellip; it’s only a matter of time before people are deceived by malicious actors, populists and demagogues until we explain to people that they have been deceived. They will believe in us again.
So, this is a theory. Another theory is that the elite is corrupt. They are corrupt, dysfunctional, corrupt, and they no longer provide services. Under this theory, these numbers, the decline in approval ratings are correct, because every time you look at Congress, they are spending your money on all kinds of crazy things without hesitation. If you go to CNN or NBC News, they always lie to you about a thousand different things. If you go to Harvard, they will teach you racial communism, America is evil, etc., these crazy things.
Under this theory, people are right, people have seen through these elite classes.These elites have basically been in power for too long, they have too much power, they have not been subject to enough censorship, they have not been subject to enough competitive pressure, and they have been corrupted in place.They no longer provide services. The reality may be that both situations occur. It’s easy to let the next instigator appear and just start throwing stones at those in power and saying anything.
If you are someone who has no political power today but wants it, the easiest thing to do is to show up and start shouting that the current elite is corrupt. Maybe that’s a bit true, and sedition has a role, or whatever it is, but I think most of the reason is that the elite has become corrupt.
My version is very straightforward, and Burnham talks about this in his book. He talked about the cycle of elites. He said that in order for an elite to truly remain healthy, authentic, productive and not corrupt, it needs to be constantly injected with new talents. It does this through an elite cycle process.
So what it will do is it will identify promising young talents and invite them to join the elite class. It does this for two reasons. The first is for self-renewal. Another is that those people are most likely to become anti-elite. Therefore, this is also to prevent future competition. So, my experience started when I was 22, and it was, Oh, hey Mark, we really want you to come to Davos. We really hope you can come to Aspen. We very much hope that you will come to New York for this large conference. We would love to have you at the New York Times dinner party. We hope you can hang out with reporters for 25 years.& rdquo;That’s what I did, and it was like, oh, that sounds great. These are the best people in the world. They control everything.They have the best degrees and they graduate from the best schools. They have all positions of power. They like me. They think I’m great.& rdquo;
They kept praising me, I come from cornfields in Wisconsin. Here I am, I am in the elite class.
All I have to do is never argue with anything. All I have to do is agree with everything that is said in the New York Times, agree with everything that is said in Davos, vote for the candidates you should vote for, donate to the candidates you should donate, and never, never go off track. Then you will become part of the elite class.
I have a lot of people my age who have done this. Some people are now the largest Democratic donors in the world, they are fully integrated into the elite, they are there, they have a good time, and they think it’s all great, which is great. Some people think this is great, and maybe it’s the right thing to do.
Then some people come to a certain point and they look around. It’s like J.D. The story of Vance. He grew up in rural Kentucky, or in the Appalachian region of Ohio. He eventually entered Yale University. He was eventually invited into all these inner circles.
Then he finally looked around, and he just said:“Wow, these people are not what I imagined they were at all. These people are selfish and corrupt, they lie about everything, they are engaging in speech suppression, they are very authoritarian, and they are plunging public finances.Oh, my God, I’ve been deceived my whole life. These people don’t deserve the respect they have, and maybe there should be a new elite class to take power.& rdquo; So, this is a lot of the debate that is going on right now. Yes, I am a case study.
Optimism and pessimism: Will the world be better?
Patrick: If we put on optimistic glasses, you emphasize early risk investing. You will meet all these young, smart people who are about to build the future. Let’s put on optimistic glasses and assume that AI has the most positive impact in all areas where we can verify results. Reasoning becomes so powerful.
So, what other related bottlenecks will hinder the eruption of the technological revolution we expect? That could be a clinical trial in medicine, or something is progressing slower than AI, and AI is not a problem. We will be eager to make progress.
But the atomic world, the world of surveillance or the world of clinical trials, etc., may become limiting factors rather than intelligence and knowledge. What bottlenecks are you most interested in?
Marc: The way I have always thought about technological change is that there used to be three lines on the chart, but now there are four lines. So,One is the speed of technological change, this is a line, and everything is usually getting better and better.Then every once in a while, you’ll see these discontinuous jumps, or something getting dramatically better, like what happened to AI last week.
Then you have another thread on it, which is social change, which is basically, when the world is ready to accept new things. Sometimes you see the phenomenon that the new thing actually existed before the world was ready, and for some reason, it was not adopted. Then five or fifty years later, it suddenly took off and developed rapidly. So, there is a social dimension, and then there is a financial dimension above it, which is whether the capital market is willing to fund it. Can it produce rewards?
I think the art of being an entrepreneur or technology investor is trying to transcend all three.
So, you try to support something where the technology is really ready and society is ready to adopt it, and you can actually get funding for it or go public and make it public.。
So, you have to align these three curves.
A lot of what we do in our daily work is align these three curves. The fourth line has now emerged in the past five years. Over the past four years, the overwhelming answer has been government. It was very strange and disturbing to me when I first encountered it because I wasn’t used to it. And I never saw us as involved in politics or partisan, or that we were really trying to go to Washington for favor. We are not trying to get subsidies. But we also don’t think we need to do anything to avoid being trampled on. Then this happened suddenly.
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Patrick：What can you most feel the way this elite class wants to destroy you? How does it manifest?
Marc：This roughly coincides with a nationwide shift in sentiment, probably between 2013 and 2017. I grew up in the 1990s and, politically speaking, I am a default Democrat for Clinton and Gore. There was a deal (The Deal), with a capital D, which means, yes, you are a Democrat, but Democrats are pro-business, they love technology, they love startups. Clinton and Gore love Silicon Valley. They love new technology. They are always excited about what we do. They are always willing to help us if other countries come against us, or something else. They are always trying to help us and support us.
Yes, you can be a pro-business, pro-tech Democrat. This is great. You can make a lot of money. People will write a lot of great articles about you, and then you donate all the money and you become a philanthropist, which is great.
You die, and your obituary will say he was a great entrepreneur and a great philanthropist, and everything was fine. Basically, starting in 2013, every aspect of the deal fell apart. This is manifested in many ways, but first and foremost it is media reports. The official bodies of mainstream media are starting to turn to us and everything we do is evil. This is actually quite surprising. In 2012, social media was regarded by mainstream media as an absolute, purely good thing because it helped Obama’s re-election.
Everyone knows that it will only elect the right political candidates. Then in 2016, the narrative had completely reversed. Social media as well as the Internet and technology are destroying democracy, everything is being destroyed. Therefore, media coverage is like a canary in a coal mine.
Part of the reason is that the employee community has been radicalized, by the way. There was a strange situation where these large investment managers came up and asked you to take a radical political stance in the company, which was completely ridiculous at the time. Then eventually, the government itself emerged, and the Trump administration bureaucracy began to do this, which was beyond his direct control.
But under the Biden administration, this became an organized movement that I would describe as sabotage,Accompanied by endless prosecutions, investigations, Wells notices, debankings, censorship, attacks, attempts to completely destroy the entire industry。Of course, that’s ultimately why we responded. My hope is that this is over. That said, the new government is taking a very different approach and no longer doing all these things.
Then my hope is that the next Democratic administration will realize that attacking technology and attacking startups is not actually necessary. In fact, it could be anti-productive, because if you push Elon Musk out of your camp, there are consequences. I talk to a lot of Democrats, we support a lot of Democrats at the company, a lot of members of Congress and senators, and I will go and talk to them again next week.
Basically, what they told me was, look, there’s a civil war within the Democratic Party, and on one side are those of us who think the party should go back to the center, stop attacking capitalism, attacking business and attacking technology, and just win the election again.
Then there are some who argue that the party actually needs to become more radical, we need to be more differentiated from the other side, and we need to become more extreme in economic policy, science and technology policy, and social policy. They are fighting for this. My hope is that they will return to the middle so we never have to go through all this again. We can maintain a positive relationship with both parties, but we will see what happens.
Patrick: Like many others, I am very interested in the nature and state of global supply chains. When you delve into the ingredients of medicines, or the ingredients of many other things, you see how interdependent the world is, especially the United States ‘dependence on the outside world, for general supply chains.
I’m curious how you think and hope that this state will evolve over the next decade or so, because obviously there’s a reason why we’re going global. But now, global supply chains do have many fragile links. How do you view the evolution of the economy and this part of the economic story? Going back to what you mentioned earlier, you want the United States to win supply chain manufacturing, how the United States will win this competition, and all these exciting ideas you hear today.
Marc: Yes, it’s really important, it’s very different from the past.& hellip; as you know, the complexity of supply chains. Take the iPhone as an example, which is a typical product. There is a file you can download online that may be a bit outdated. But it lists the components that make up the iPhone and where those components come from. A document I read ten years ago may now have an updated version, but the document I read ten years ago showed that at least then, iPhone parts came from 40 different countries.
So when the iPhone is assembled at the Foxconn factory in China, 39 countries have actually sent parts over, and these parts are assembled into subassemblies of subassemblies, and then become assemblies. The same is true for cars, and the same will be true for robots, anything complex, anything computerized or mechanical will have this property. By the way, that’s actually hard to get from trade numbers because I believe it’s true.
China actually gets all of the Credit for the entire iPhone export value in the export figures, although the economic value added occurring in China is actually a single-digit percentage. Because most of the stuff in the iPhone comes from 39 other countries. The analysis you really want to do is so-called economic value-added analysis. You basically want to say, well, of the $1000 that goes into the iPhone, where does the value of these things come from, in dollars? The answer comes from all over the world.
This is the debate about simply outsourcing offshoring or reversing globalization. We are not talking about bringing steel mills back from China to the United States. We’re talking about unlocking a supply chain involving 40 countries where things are shuttling back and forth because everything is being built and assembled. By the way, this is also a problem with the modern economy, and it conflicts with reality in multiple ways.
……
Then there is political and economic pressure. The U.S. political system assumes that for 30 years, you can offshore manufacturing from the U.S., and the communities in the Midwest and South that see all factories close will just sit back and they will come up with something else. In many parts of the United States, they never came up with a new solution. It turns out that they can still vote.
Part of the reason is that in my country (the United States), many people are radicalized because governments and businesses seem to think it’s okay to hollow out the economy and send everything overseas.
So, part of the reason that happens in the American political system is that they decide they no longer accept this approach, they will vote for something different. This view was put forward at the time, but the argument of economic efficiency prevailed and brought benefits. It paid off in some ways. But many people in the United States have been radicalized. I come from a place where a lot of people are radicalized because governments and businesses seem to think it’s okay to hollow out the economy and send everything overseas.
So even if you get rewards from economic efficiency, your political system may not be able to afford it. You may regret it very much. I don’t think there is an easy answer here. Anyone, my point is, who says there are simple answers here is wrong. It’s complicated.
The possibility is that the world will remain highly interdependent, with great pressures and fluctuations back and forth. This dynamic will continue alongside tariff and trade negotiations. It will be an ongoing process with twists and turns along the way, but fundamentally the world will remain interconnected in many ways and we will try to cope with it.
The problem is that if there is a war or a more serious epidemic at some point, or something like that, this interdependence can be severely pressured to the point of rupture.I hope this will not happen. But to some extent, the more interconnected the world, the more resilient it becomes, because there are more ways to do things, more ways people can adapt, and everything can change.。Then in some ways, the more interconnected the world, the more dangerous things become, because if one part breaks down, the entire system breaks down. So, there is a real tug of war here.
Tan Yushu and China’s robot industry: “This specific environment is called Shenzhen”
Patrick: There is another area lurking at the forefront of technology that I haven’t seen you talk about much, and that is robotics. Everyone is very excited about its potential. It’s easy to imagine a humanoid robot that can do all the things around it that humans no longer need to do. Making this world a reality requires a lot of technological breakthroughs. What do you think will happen in the field of robotics? What is overrated? What is underestimated? How do you view it?
Marc: I will list four things. So, I would say mobile phones, drones, cars and robots. Basically, this is the ladder China is climbing。By the way, it’s not just a product, it’s a ladder across the supply chain.So, China became the place where all phones were assembled and manufactured. So, as you know, they have built a complete ecosystem in China with thousands of specialized companies that basically make all kinds of electronics and hardware, machinery and computer-related things.
This particular environment is called Shenzhen, and it’s a cluster of thousands of companies that basically make all kinds of electronics and hardware, machinery and computer-related things. So, their mobile phones (supply chain), and then they used this supply chain to win the drone market for China. Consumer-grade drones, like DJI drones. Basically, China has won the global drone market, with their market share exceeding 99%.
……
In many ways, a drone is like a flying mobile phone. It has a lot of the same equipment, and then it has something new, but they want to get into this space, at least until recently. Now they are entering the automotive sector.The reason is that a modern self-driving electric car is more like a car on wheelsrunA laptop, or more like a smartphone running on wheels, rather than like a traditional internal combustion engine car.
Tesla in the United States is an example, in which Tesla is a computer and many batteries wrapped in a frame with some tires on the outside. A good indication of change is if you go to the service area of a traditional car dealer, compared to the service area of a Tesla dealer. The service area of the traditional automobile industry is full of oil and dirt, everyone has work clothes, and they wipe their hands with a dirty cloth all day long.
You go to the service area of a Tesla dealer, and it’s like an operating room. Everything is clean because it is an electric car and has no internal combustion engine. All this oil and dirt is gone, it’s just a computer. China are basically doing what they used to do in the automotive sector with drones and smartphones, which is that they have built a complete ecosystem that leverages these other supply chains. They have built a complete ecosystem with all the parts needed to build self-driving electric vehicles. Now they are bringing these cars to market. Suddenly, they became very good, just as good as mobile phones and drones in China, they were completely modern, very advanced, very cheap, and at the forefront of technology. Cars have also become very good, and they cost only a third or a quarter of the price of similar cars in the United States.
The fourth stage is robots. If you have a supply chain of phones, drones and cars, you have almost everything you need to make robots. This is the next stage. They are doing this. Of course, Elon and other companies in the United States are building humanoid robots. I hope and expect that they will do well. But China is definitely doing the same.
The company I pay most attention to is a national champion company in China called Unitree.We weren’t involved, but Unitree sells robotic dogs that are comparable to those from Boston Dynamics. Boston Dynamics ‘robotic dogs sell for between $50,000 and $100,000, which is why you rarely see them. Unitree dogs start at $1500, by the way.
We have two and they are great. They can do back flips, they can climb stairs, they can talk to you, they have large language models built in, and they can teach you quantum physics while running around in your yard, which is great. And then they are now launching humanoid robots at a much lower price. They are definitely moving towards robots.
It’s going to be a real tug-of-war, if you believe that humanoid robots will emerge, and I do believe, and on a large scale, if China is willing to make them for 10,000 or 20,000 dollars, we can buy a billion of them, and suddenly we have robots that build houses, do gardening, do everything you want robots to do, and wait to serve you, then China makes them and sells them to you, and they are very cheap and work well. This is great.
……
Phones and drones are already a fierce issue, but cars and robots will become even more intense. This hasn’t fully happened yet because the robotics field hasn’t fully exploded yet, but I think the robotics field will explode in the next few years.
Patrick: It was interesting to watch the competition to build bodies and brains for robots. American companies like Physical Intelligence are working hard to build datasets that we don’t yet have, just like the open networks we once had to train AI. Have you seen some exciting areas where many young people and companies excite you, but do you feel the market is not aware of what is happening and the possible potential?
Marc：I think it might be Biotech.The good news is that in the modern world, many people are interested in new technology and many people talk about it. When I was a child, the early adoption market was very small. So, there are only a handful of people who want their first personal computer or something else.
Now you have 50 million or 100 million early adopters who just want the latest thing and keep talking about it online. So I’m not sure if there’s still too much delay right now, but maybe in the field of biotechnology, everything, such as life extension, embryo selection, possible reproductive technology, getting embryos from stem cells, for example.
Getting embryos from stem cells, you know, you probably know a lot of people who have situations where people have fertility problems when they are young, or they get to a certain age and have fertility problems, but they want more children, and then they are forced to make some difficult choices involving in vitro fertilization (IVF) or different types of donors.
It looks like we will be able to get embryos from stem cells, so you can have truly biological children at a later age. External pregnancy is still some time away, but maybe it will be a big problem at some point. People often talk about birth rates. Well, if you could continue to have children in your 60s, if you could have a dozen children through external pregnancy, would more people choose to do so? Maybe.
So that’s one aspect. Another possibility is genetic optimization. So, an endless hot topic is intellectual enhancement. Now we have CRISPR, we have gene editing technology.
Scientists are then identifying hundreds of genes that correspond to IQ. So, you should have the ability to improve your IQ, which raises a series of downstream problems.
……
Patrick: Very interesting.
Marc: Everything I have just described is becoming possible. They have incredible implications in terms of health, society and more that will emerge in the next few hundred years. So, I think people may start to realize more that there is more discussion to do in these areas than what we are doing right now.
……
Patrick: A quick last question.& hellip;, which book would you choose other than the aforementioned “Machiavellian”?
Marc: I still strongly agree with a book called “The Weirdest People in the World” by Joseph Henrich. This book may be ten years old, but I don’t think it has received much attention. This book is very insightful for understanding the nature of culture, especially the nature of different cultures.
As you know, there is so much about Western culture in our politics right now, and immigration, the meaning of all these different debates, etc. For me, this is the most informative book, trying to understand how to think about culture.
Patrick: Marc, thank you so much for taking the time.
Marc: Okay. Thank you, Patrick.
Original link:https://joincolossus.com/episode/the-battle-for-tech-supremacy/
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