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Former Google CEO Eric Schmidt (Image source: medium)

Google's former CEO retracted his previous remarks criticizing his former employer for not being competitive enough and slamming remote work for ruining Google.

Recently, Eric Schmidt, former CEO and executive chairman of Google, said in a speech at Stanford University that Google lost to startups such as OpenAI in the AI ​​(artificial intelligence) competition because Google's remote work policy caused employees to not work hard enough.

"Google believes that work-life balance, leaving get off work early, and working from home are more important than winning. But startups succeed because people work really hard." Schmidt bluntly stated that Google employees now "enjoy" work-life balance too much, and as a result, the company has lost its fighting spirit and lost to OpenAI. Google's current remote work style is to blame, and employees believe that going home early to be with their families is more important than achieving success at work. "I'm sorry to be so blunt, but when you start a business after graduating from college, you'll understand that you won't allow employees to come to the company only one day a week."

Schmidt emphasized that AI start-ups can first "copy" other people's mature works, and then "hire a large number of lawyers to clean up the mess" after the product becomes popular.

However, one day after the video was released, Schmidt apologized for his remarks.

On August 15, Schmidt said in an email to The Wall Street Journal that “I made an error in my comments about Google and its hours, and I regret my mistake.”

Public information shows that Schmidt served as CEO of Google from 2001 to 2011 and served on the board of directors until 2019, after which Schmidt left the board of directors of Google's parent company Alphabet.

But so far,Schmidt is still a shareholder of Alphabet, holding about 147 million shares of Alphabet, close to 1% of the shares, worth about $24 billion. Schmidt's net worth has reached $31.4 billion.

Schmidt's overly straightforward remarks were widely controversial, with some welcoming them and others criticizing that Google's management is the company's biggest problem. The Alphabet Workers Union posted on X (formerly Twitter): "Flexible work arrangements do not slow us down. Understaffing, frequent changes in priorities, constant layoffs, no salary growth, and management's lack of execution on projects are what slow down Google employees every day."

Just one day after Schmidt made this statement, he quickly apologized publicly and the relevant video was set to private by Stanford University.Prior to this, Schmidt's video had been viewed more than 400,000 times.

It is worth noting that many well-known American entrepreneurs actually hold similar views to Schmidt. For example, Tesla CEO Elon Musk and JPMorgan Chase CEO James Dimon have publicly complained about remote work policies. Dimon once wrote in his annual shareholder letter that it is impossible for company management to sit in front of a screen and lead the team.

"Look at Musk and TSMC. The reason these companies are successful is because they are able to drive their employees to work. You have to push your employees hard enough to win. TSMC will let a physics PhD work in the factory in the first year. Can you imagine American PhD students working on the assembly line?" Schmidt said.

In addition, Schmidt also shared some investment advice about Nvidia, saying that he saw a clear trend in the stock market that large technology companies are planning to make larger and larger investments in Nvidia. "I'm talking to big companies, and they're telling me that they need $20 billion, $50 billion, $100 billion - very, very urgently." Schmidt added that he is a "close friend" of OpenAI CEO Sam Altman.

Schmidt believes that although Nvidia will not be the only winner in the field of AI, there are not many other options. In his view, large companies that can invest more money in Nvidia chips and data centers will be technologically ahead of smaller competitors.

"There are only three cutting-edge models, and the gap between them and all the others seems to be getting bigger and bigger. Six months ago, I was sure the gap was narrowing, so I invested a lot of money in small companies. Now, I'm not so sure." Schmidt pointed out. "I used to think that Nvidia's CUDA was a stupid programming language, but now CUDA is Nvidia's most powerful moat. All large models must run on CUDA, and only Nvidia's GPU supports CUDA. This is a combination that other chips cannot shake."

Schmidt stressed that dominant technology companies often find it difficult to adapt to new industry waves, and that innovative ideas and a strong office presence are very important to Silicon Valley.

Schmidt’s point therefore raises a broader question: How do we balance the need for innovation with the well-being of our employees in the rapidly evolving era of AI?

Schmidt's words may represent the challenges facing the entire technology industry. With the rapid development of AI technology, how can large companies find a balance between maintaining competitiveness and maintaining employee satisfaction? This is not only related to the success of the company, but also to the future development direction of the entire industry.

As of press time, Google-A (NASDAQ: GOOGL) stock price rose 1.54% to $163.77 per share, with a total market value of $2.02 trillion. Since the beginning of 2024, Google's stock price has risen by about 16%.

The following is a condensed transcript of Schmidt’s speech:

Moderator: Today's guest needs no introduction. I first met Eric about 25 years ago when he came to Stanford Business School as the CEO of Novell. Since then, he has held important positions at Google, starting in 2001, and joined Schmidt Futures in 2017. In addition, he has been involved in many other projects, and you can check out the relevant information. So Eric, if I can, I will start with that. First, where do you think artificial intelligence is going in the short term? I guess you define it as the next one to two years.

Schmidt: Things are changing so fast that I feel like every 6 months I need to give a new talk about what's going on. There are a bunch of computer science students here, can anyone explain to the rest of the class what a "million token context window" is? Please name yours and tell us what it does.

Student: Basically, it allows you to prompt with a million tokens or a million words.

Schmidt: So you can ask a million-word question. Anthropic is 200,000 tokens, going to a million, and so on. You can imagine OpenAI has a similar goal. Can anyone here give a technical definition of an AI agent? Again, computer science.

Student: An AI agent could be something that acts in a certain way. It could be calling something on the web to look up information on your behalf. It could be a number of different things along those lines. There are all sorts of things going on in one process.

Schmidt: So an agent is something that performs some kind of task. Another definition is that it is an LLM, state, and memory. So, computer scientists, can any of you define "text-to-action"?

Student: Rather than converting text into more text, you have the AI ​​trigger actions based on that.

Schmidt: Another definition is the Python language. I never want to see a programming language survive. Everything in AI is done in Python. There's a new language just came out called Mojo that looks like they've finally solved AI programming, but we'll see if it can really survive Python's dominance. There's also a technical question. Why is NVIDIA worth $2 trillion while other companies are struggling?

Student: The technical answer is that most code needs to run using CUDA optimizations which are currently only supported on NVIDIA GPUs, so other companies can do whatever they want, but unless they have 10 years of experience in software, you won't have machine learning optimizations.

Schmidt: I like to think of CUDA as the C programming language for GPUs, and I find that idea satisfying. CUDA was founded in 2008. Even though I always thought it was a terrible language, it became dominant. There was also a noteworthy insight: there was a set of open source libraries that were highly optimized for CUDA that were not done by any other library. This was completely missed in the discussion by all the people building these technology stacks. These libraries are technically called VLLMs, and there are many similar libraries that are also highly optimized for CUDA, which makes it very difficult for competitors to copy.

So what does all this mean? In the next year, you will see very large context windows, agents, and text-to-action applications. When these technologies are delivered at scale, they will have a huge impact on the world, far beyond the impact of social media. Here’s why: In the context window, you can use it as short-term memory, and I was shocked by the length of the context window. The technical reason has to do with the difficulty of serving and computing. The interesting thing about short-term memory is that when you input information and ask questions, like read 20 books and use the text of the book as a query, and then ask them what they are about, it forgets the middle parts, which is similar to how the human brain works.

With regard to agents, there are people now building LLM agents where they read and understand knowledge in areas like chemistry, then test it and add it back into their understanding. That's very powerful. The third aspect is text to action. I'll give you an example: let's say the government is trying to ban TikTok. If TikTok is banned, I suggest you say the following to your LLM: copy TikTok for me, put my preferences in it, make this app and publish it in the next 30 seconds, and then within an hour, if it doesn't go viral, do something similar. That's command. You can see how powerful this is.

If you can convert from an arbitrary language to an arbitrary numeric command, that's essentially what Python is in this scenario. Imagine that everyone on Earth had their own programmer who actually did what they wanted to do, instead of programmers who didn't work as required. The programmers here know what I'm talking about. Imagine a programmer who wasn't arrogant and actually did what you wanted to do, without you having to pay a high price. And the supply of these programmers is unlimited.

Host: All this will happen in the next one or two years?

Schmidt: Very soon. All three of these things, I’m sure, will happen simultaneously in the next wave. So you ask what else will happen. I have a swing every six months, so we are in an even-odds oscillation. Right now, the gap between the leading edge models (there are only three now) and the rest seems to be getting bigger. Six months ago, I was sure that the gap was closing, so I invested heavily in some small companies. However, now I’m not so sure about that.

I'm in conversations with some of the big companies who are telling me they're going to need $10, $20, $50, or even $100 billion. The Stargate project is going to be $100 billion, and it's going to be very difficult. My good friend Sam Altman thinks it's going to be about $300 billion, maybe more. I pointed out to him that I've calculated the amount of energy that's going to be needed.

In the interest of full disclosure, I went to the White House on Friday and told them that we need to be best friends with Canada. Because the Canadian people are really nice, they helped invent AI, and they have a lot of hydroelectric resources. Because we as a country don't have the power to accomplish this. The alternative is to have an Arab country fund this project. I personally like the Arabs, I've spent a lot of time there, but they probably won't play by our national security rules. And Canada and the United States are one of the big three that we can all agree on.

So in these $100 billion to $300 billion data centers, power starts to become a scarce resource.

By the way, if you follow this line of reasoning, you might ask why am I discussing CUDA and NVIDIA? If all $300 billion went to NVIDIA, you know what to do in the stock market. However, this is not a stock recommendation and I am not a licensor.

Moderator: Part of the reason is that we're going to need more chips, but Intel gets a lot of money from the US government. AMD is trying to build a fab in South Korea.

Schmidt: Raise your hand if you have an Intel chip in any of your computing devices. So much for the monopoly.

Professor: But that's the point. They did have a monopoly, and now NVIDIA has a monopoly. So are these barriers to entry? When it comes to CUDA, are there other options? I was talking to Percy Lange the other day. He's switching between TPUs and NVIDIA chips, depending on what he can get his hands on. It's because he has no choice.

Schmidt: If he had unlimited money, today he would choose NVIDIA's B200 architecture because it's faster. I'm not implying that -- it's good to have competition. I've talked to Lisa Su at AMD at length. They've built something that can convert the CUDA architecture to their own architecture, called Rokam. It's not fully functional yet, but they're working on it.

Moderator: You worked at Google for a long time, and they invented the Transformer architecture. Thanks to the great people there, like Peter, Jeff Dean, and everyone. At the moment, OpenAI seems to have lost the initiative. In the latest leaderboard I saw, Claude from Anthropic is at the top. I asked Sundar about this, but he didn't give me a very clear answer. Maybe you have a more pointed or objective explanation of the situation there.

Schmidt: I am no longer an employee of Google. Google's work-life balance is more about letting employees go home early and work from home, rather than pursuing victory blindly. In contrast, startups succeed because employees work hard. Although it may be a bit blunt to say this, the fact is that if you start a company out of college and want to compete with other startups, you can't let employees only come in one day a week.

Host: The same thing happened with Microsoft in the early days of Google.

Schmidt: Now, it seems that in our industry, for a long time, companies have always won by being really innovative and dominating a space rather than making the next transformation. This is well documented. I think founders are special and they need to be in control, even though they can be difficult to work with because they put a lot of pressure on their employees. As much as we don’t like Elon’s personal behavior, look at what he gets from his employees. I had dinner with him once and he was flying. I was in Montana and he had to fly at 10 o’clock that night for a midnight meeting with x.ai. Think about it.

Different places have different cultures. I was impressed by TSMC. They had a rule that freshly graduated PhD students, excellent physicists, had to work in the basement of the factory. Can you imagine having a physics PhD in the United States do that? Not likely. It's a different work ethic.

The reason I work so hard is because these systems have network effects, so time is very important. In most businesses, time is not that important, you have a lot of time. Coke and Pepsi will still exist, they will still compete with each other, everything is cold and icy. When I was dealing with telecom companies, the typical deal took 18 months to sign. There is no reason to take 18 months to do anything, it should be done as soon as possible. We are in a period of growth and maximizing revenue, but it also requires crazy ideas.

For example, when Microsoft made the deal with OpenAI, I thought that was the dumbest idea I had ever heard. Outsourcing your AI leadership to OpenAI and Sam and his team? That's crazy. No one would do that at Microsoft or anywhere else. Yet today, they are becoming the most valuable company, certainly going toe-to-toe with Apple. Apple didn't have a good AI solution, but it looks like they made it work.

Sponsor: How will AI play out in terms of national security or geopolitical interests, especially in competition with China?

Schmidt: As the chairman of an AI commission, I looked at this in depth. We wrote a report of about 752 pages, and the conclusion was that we were ahead and needed to maintain that lead, and that required a lot of funding. Our main clients were the Senate and the House, and that led to the CHIPS Act and other similar policies.

If the frontier model and some open source models continue to develop, there may be only a few companies that can compete in this field. Which countries have such capabilities? These countries need to have sufficient funds, abundant talents, strong education systems, and the will to win. The United States and China are two of the main countries. As for whether other countries can participate, I am not sure. But what is certain is that in the future, the competition between the United States and China for intellectual hegemony will be a major struggle.

The U.S. government has essentially banned Nvidia chip exports to China, even though they won't admit it publicly. We have about a 10-year technology advantage in DUV chips, 5-nanometer chips. That advantage puts us several years ahead of China, which makes China very unhappy. This policy was set by the Trump administration and supported by the Biden administration.

Moderator: Did Congress listen to your advice and make large investments, obviously the CHIPS Act is an example of that.

Schmidt: In addition, we need to build a massive AI system. I lead an informal, ad hoc, non-legal group that includes the usual industry players. Last year, these members made the case for what became the Biden administration’s AI bill, the longest presidential directive in history.

We talked about a core problem: How do you detect danger in a system that has learned something but you don't know what to ask? In other words, the system might have learned something bad, but you don't know how to ask it. For example, it might have learned how to mix chemicals in some new way, but you don't know how to ask it. To solve this problem, we proposed in our memo to the government to set a threshold that we called 10 to the 26th power, which is a technical computing measure. Above this threshold, companies must report their activities to the government. The European Union, to make sure they are different, put 10 to the 25th power of 10. But these numbers are close enough. I think all of these distinctions will disappear because the existing technology will be obsolete. The technical term is called co-training, which basically means that the parts can be merged together. So we may not be able to protect people from these new things.

Moderator: Next, I want to discuss a somewhat philosophical question. Last year, you co-authored an article with Henry Kissinger and Dan Hertenloch about the nature of knowledge and how it develops. I discussed this question the other night as well. For most of history, humanity's understanding of the universe was mysterious until the scientific revolution and the advent of the Enlightenment. In your article, you mentioned that models are now becoming so complex and incomprehensible that we don't really know what's going on inside them. I'm quoting Richard Feynman: "What I can't create, I don't understand." I saw this quote the other day. But now people are creating things that they can create without really understanding their inner workings. Has the nature of knowledge changed in some way? Do we have to start taking these models at face value and not explaining them to us?

Schmidt: I want to give you the example of a teenager. If you have a teenager, you know they're human, but you can't quite figure out what they're thinking. And yet we've managed to accommodate teenagers in society, and they'll eventually grow out of that. It's a serious problem. So we might have knowledge systems that we can't fully describe, but we understand their boundaries and the limits of what they can do, and that's probably the best we can get. Do you think we'll ever understand those limits? If we can do that, that's great.

The consensus of my group in weekly meetings is that eventually there will be what is called adversarial AI, where there will actually be companies that hire you and pay you to break your AI system. Just like red teams. Instead of today’s human red teams, you’re going to have entire companies and an entire industry of AI systems whose job is to break existing AI systems and find vulnerabilities in them, especially the ones that we can’t figure out. That makes sense to me. It’s also a great project for Stanford. If you have a grad student who has to figure out how to attack one of these large models and understand what it does, that’s going to be an important skill to build the next generation. So it makes sense to combine the two.

Moderator: Now, let's answer some questions from the students. There is a student in the back, please tell me your name.

Student: You mentioned earlier, and this is related to the current comment, about getting AI to actually do what you want. You mentioned adversarial AI a little while ago, and I wonder if you could elaborate on that a little bit more. It seems like, aside from the fact that computing power obviously increases, you get higher performance models, but the question of getting them to do what you want, seems to be partially unanswered.

Schmidt: Well, you have to assume that the current hallucination problem will decrease as the technology improves and so on. I'm not saying it will go away. And then you also have to assume that there's efficacy testing, so there has to be a way to know if this thing is successful. In the example that I mentioned about the TikTok competitor, I wasn't suggesting illegally stealing other people's music. What would you do if you were a Silicon Valley entrepreneur? Hopefully, you're all Silicon Valley entrepreneurs. If your product is successful, you hire a bunch of lawyers to deal with the follow-up. But if no one uses your product, then it doesn't matter if you stole all the content. Of course, don't quote me on that.

Silicon Valley typically does these tests and handles the follow-up. That's common practice. I think you're going to see more and more performance systems and even better tests, and eventually adversarial testing, which will keep it within a framework. The technical term for this is called chain of thought reasoning. People believe that in the next few years, you will be able to generate a thousand-step chain of thought reasoning, like making a recipe. You can run it and actually test whether it produces the right result, and that's how the system works.

Student: In general, you're very optimistic about the potential for advancement in AI. I'm curious, what's driving that advancement? Is it more computing power? Is it more data? Is it a fundamental or practical shift?

Schmidt: The answer is all of the above. The amount of money being invested is incredible. I invest in basically everything because I don’t know who’s going to win and the amount of money I follow is so huge. Part of it is that the early money has been made and the ones that don’t know as well must have an AI component. Everything is AI investing now and they can’t tell the difference.

I define AI as learning systems, systems that really learn. I think that's one of them. The second point is that there are some very sophisticated new algorithms coming out now that are a little bit post-Transformer. I have a friend who is also my long-term partner who invented a new non-Transformer architecture. A group I funded in Paris claims to have done the same thing. There is a lot of invention there, and there is a lot of research at Stanford. The last point is that the market believes that there is an infinite return on the invention of intelligence. Let's say you put $50 billion of capital into a company, you have to make a lot of money from intelligence to pay it back. We may go through some huge investment bubbles and then it will resolve itself. It has been true in the past and it may be true now.

Professor: You mentioned earlier that leaders are distancing themselves from others.

Schmidt: Now, there's a company in France called Mistral, and they're doing very well. I'm obviously an investor. They've already made a second version, and their third model is probably closed because it's too expensive. They need the revenue and can't give their model away for free. There's a very heated debate in our industry about open source versus closed source. My entire career has been built on people being willing to share software in an open source way. Everything I've done is based on open source. Much of Google's foundation is built on open source. The work I've done has been mostly in the technology space. However, the huge capital costs could fundamentally change the way software is built.

My view of software programmers is that they will at least double in productivity. There are three or four software companies that are trying to do this right now, and I've invested in all of them over the time. They're all trying to make software programmers more productive. I recently came across a very interesting company called Augment. I often think of a programmer and they say that's not our goal. Our goal is those 100-person software programming teams that have millions of lines of code and nobody knows what's going on. This is a really great application of AI. Will they make money? I hope so, but there are a lot of questions here.

Student: At the beginning, you mentioned that the combination of context window extensions, proxies, and text-to-action will have incredible impact. First of all, why is this combination important? Secondly, I know you are not a prophet and cannot predict the future, but why do you think it is beyond our imagination?

Schmidt: I think mainly because context windows allow you to address the recency problem. Current models take about 18 months to train, six months of preparation, six months of training, and six months of fine-tuning, so they're always out of date. With context windows, you can input what's happened recently and ask questions about the Hamas-Israel war in context, which is very powerful and makes it as up-to-date as Google.

In the case of an agent, I can give you an example. I started a foundation that funds a nonprofit. I don't know much about chemistry, but there's a tool called ChatCrow, which is a system based on a large language model that learns chemistry. They run this system to generate chemical hypotheses about proteins, and then the lab tests them overnight, and the system learns. This is a huge accelerator for fields like chemistry, materials science, and so on. This is an agent model.

I think the concept of text-to-action was understood as long as there were lots of cheap programmers. I don't think we understand what happens when everyone has their own programmer. This is also your area of ​​expertise. I'm not talking about simple tasks, like turning a light on and off. I imagine another example, let's say you don't like Google, you could say, build me a competitor to Google. Yes, you personally could do that. Build me a competitor to Google that searches the web, build a UI, make a good copy, and add generative AI in interesting ways. Do it in 30 seconds and see if it works. A lot of people think that incumbents, including Google, are vulnerable to this attack.

Moderator: Now, let's take a look. Slido sent a number of questions, some of which have been uploaded. Last year we talked about how to stop AI from influencing public opinion and spreading misinformation, especially during the upcoming election.

Schmidt: We need to think about both short-term and long-term solutions. In the upcoming global elections, most of the misinformation will appear on social media, and social media companies are not currently organized enough to effectively regulate this information. For example, TikTok has been accused of favoring certain kinds of false information, although I have no evidence. I think we are facing a mess.

The nation needs to learn critical thinking, and that can be a difficult challenge for the U.S. Just because someone tells you something doesn't mean it's true.

Host: Are we going so far that some things that are true are no longer believed? Some people call this an epistemological crisis. Now, Elon Musk says he never did something, but how do you prove it?

Schmidt: We can use the example of Donald Trump to illustrate. I think we have a trust problem in our society, and democracy may fail because of it. The biggest threat to democracy is misinformation, because we've become very good at it.

When I ran YouTube, the biggest problem we had was people uploading fake videos where people died. We had a no-death policy to try to fix that, which was shocking and scary. This was before generative AI.

Student: I'm curious if you've explored distributed settings. I ask this because, of course, it's hard to make a large cluster, but MacBooks are powerful. There are a lot of small machines around the world. So do you think that Fold at Home or similar ideas would be applicable to training these systems?

Schmidt: Yeah, we've looked at this very carefully. So the way the algorithm works is you have a very large matrix and you have essentially a multiplication function. So think of it as going back and forth. And these systems are completely limited by the speed of memory to the CPU or GPU. In fact, the next generation of NVIDIA chips have all of these functions packed into a single chip. The chips are so big now that they're all glued together. In fact, the packaging is so sensitive that both the packaging and the chip itself are assembled in a clean room. So the answer looks like supercomputers and the speed of light, especially the memory interconnect, really prevail. I think it's not possible to segment large language models (LLMs) at this time.

Moderator: In the field of AI, it seems that there are some large companies that dominate the market, which is related to antitrust.

Schmidt: I worked on Microsoft's breakup in my career, but it didn't work. I also worked on Google's not being broken up, but it didn't work. So I think the trend is not to break up. As long as these companies don't become monopolies like John Rockefeller, the government is unlikely to take action.

These big companies dominate because they are the only ones who have the capital to build data centers. My friends Reed and Mustafa made the decision to spin off their business to Microsoft because they couldn't raise tens of billions of dollars. As for the specific numbers, you may need to let Reed tell you.

Student: Finally, I wonder what impact these developments will have on countries that are not involved in cutting-edge model development and calculations.

Schmidt: Rich countries will get richer, while poor countries can only do their best. This is actually a game for rich countries, which requires huge capital, technical talents and strong government support. Globally, many countries are facing various problems, especially when resources are scarce. They need to find partners and work with others to solve these problems.

Moderator: I remember that the last time we met, you were participating in a hackathon at AGI House. I know you spend a lot of time helping young people create wealth and are very passionate about it. Do you have any advice for people who are writing business plans for courses or writing policy proposals or research proposals in their careers?

Schmidt: I teach a course on this at the business school, and you should come and listen. I'm amazed at the speed at which you present new ideas.

At one hackathon I attended, the winning team was tasked with flying a drone between two towers. They generated code in Python in a virtual drone space and successfully completed the task in a simulator. A good professional programmer might take a week or two to do this. I think the ability to prototype quickly is very important because part of the problem entrepreneurs face is speed. If you can't prototype in a day with these tools, you need to rethink it because that's what your competitors are doing.

So my biggest advice is that it's OK to write a business plan when you start thinking about starting a company. In fact, you can have a computer write it for you, as long as it's legal. It's very important to prototype your idea as soon as possible using these tools, because there may be people doing the same thing in other companies, universities, or places you haven't been.

Moderator: Thank you very much, Schmidt. (The shorthand part comes from the Web3 Sky City account)

(This article was first published on Titanium Media App, author: Lin Zhijia, editor: Hu Runfeng)