The Pete Quiñones Show - Pete Reads Peter Thiel's 'Zero to One' - Part 6

Episode Date: March 13, 2024

53 MinutesPG-13Pete continues reading and commenting on Peter Thiel's best-seller, Zero to One. In this sixth episode, Pete covers chapters 12 and 13: Man and Machine and Seeing Green.VIP Summit 3-Truth To Freedom - Autonomy w/ Richard GroveSupport Pete on His WebsitePete's PatreonPete's Substack Pete's SubscribestarPete's VenmoPete's Buy Me a CoffeePete on FacebookPete on TwitterBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-pete-quinones-show--6071361/support.

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You can send me something in the mail. P.O. Box 413, Lineville, Alabama, 36266. I look forward to trying to do a lot more this year with the show, and any support you can give me helps with that. Thank you. I want to welcome everyone back to Part. part six of this reading of Peter Thiel, zero to one. We are almost to the end here. We are on chapter 12 of 14, and then we have a conclusion. Twelve is man and machine, and I'm going to

just jump right in. So, Chapter 12, Peter Teal, zero to one, man and machine. As mature industry stagnate, information technology has advanced so rapidly that it has now become synonymous with technology itself. Today, more than 1.5 billion people enjoy instant access to the world's knowledge using pocket-sized devices. Every one of today's smartphones has thousands of time more processing power than the computers that guided astronauts to the moon. And if Moore's Law continues to pace, tomorrow's computers will be even more powerful. Computers already have enough power to perform people and activities we used to think of as distinctly human. In 1997, IBM's Deep Blue defeated world chess champion Gary Kasparov.

Jeopardy's best ever contestant, Ken Jennings, succumbed to IBM's Watson in 2011. And Google self-driving cars are already on California roads today. Huh. Dale Earnhardt Jr. needn't feel threatened by them, but the Guardian worries on behalf of the millions of chauffeurs and cabbies in the world that, Self-driving cars could drive the next wave of unemployment. Everyone expects computers to do more in the future. So much more than some wonder.

30 years from now, will there be anything left for people to do? Software is eating the world, venture capitalist Mark Andreessen has announced with a tone of inevitability. V.C. Andy Kessler sounds almost gleeful when he explains that the best way to create productivity is to get rid of people. Forbes captured a more anxious attitude when it asked readers, Will a machine replace you? Futurists can seem like they hope the answer is yes. Luddites are so worried about being replaced that they would rather we stop building a new technology altogether. Neither side questions the premise that better computers will necessarily replace human workers.

But that premise is wrong. Computers are compliments for humans, not substitutes. The most valuable businesses of coming decades will be built by entrepreneurs who seek to empower people, rather than to try to make them obsolete. This is interesting, especially since now all the talk is of AI. And pretty much, even people I know who won't admit it in public, use AI for all sorts of things. Email, writing emails. You'd be surprised.

Substitution versus complementary. 15 years ago, American workers were worried about competition from cheaper Mexican substitutes, and that made sense because humans really can. and substitute for each other. Today, people think they can hear Ross Perrault's giant sucking sound once more, but they trace it back to server farms somewhere in Texas instead of cut-rate factories in Tijuana. Americans fear technology in the near future

because they see it as a replay of the globalization of the near past. But the situations are very different. People compete for jobs and for resources. Computers compete for neither. Globalization... Excuse me. Globalization means substitution. When Perrault warned about foreign competition, both George H.W. Bush and Bill Clinton preached

a gospel of free trade. Since every person has a relative strength at some particular job, in theory, the economy maximizes wealth when people specialize according to their advantages and then trade with each other. What's funny about Bush and Clinton preaching free trade is they preach NAFTA, which was that free trade? How'd that work out? In practice, it's not unambiguously clear how well free trade has worked, for many workers at least. Gains from trade are greatest when there's a big discrepancy and comparative advantage, but the global supply of workers willing to do repetitive tasks for an extremely small wage is extremely large.

I think it's interesting that the term free trade here is used as anyone would use it. Assuming a government exists, assuming that regulations exist, It's just not central, it's not, manufacturing isn't central planned. And, you know, I hear libertarian screeching. That wasn't real free trade. Real free trade has never been tried. Okay. Yeah, sure. I agree. And it never will. People don't just compete to supply labor. They also demand the same resources. While American consumers have benefited from access to cheap toys and textiles from China,

they've had to pay higher prices for gasoline newly desired by millions of Chinese motorists. Whether people eat shark fins in Shanghai or fish tacos in San Diego, they all need food and they all need shelter. And desire doesn't stop its subsistence. People will demand ever more as globalization continues. Now that millions of Chinese peasants can finally enjoy a secure supply of basic calories, they want more of them to come from pork instead of just grain. The convergence of desire is even more obvious at the top. All oligarchs have the same taste in crystal from Petersburg to Pyongyang.

Now, think about the prospect of competition from computers instead of competition from human workers. On the supply side, computers are far more different from people than any two people are different from each other. Men and machines are good at fundamentally different things. People have intentionality. We form plans and make decisions in complicated situations. We're less good at making sense of enormous amounts of data. Computers are exactly the opposite. They excel at efficient data processing,

but they struggle to make basic judgments that would be simple for any human. To understand the scale of this variance, consider another of Google's computer-for-human substitution projects. In 2012, one of their supercomputers made headlines when, after scanning 10 million thumbnails of YouTube videos, it learned to identify a cat with 75% accuracy. That seems impressive, until you remember that an average four-year-old can do it flawlessly.

When a cheap laptop beats the smartest mathematicians at some tasks, but even a supercomputer with 16,000 CPUs can't beat a child at others, you can tell that humans and computers are not just more or less powerful than each other. They're categorically different. We have another little thing here of supply of labor, substitution, globalization, supply of labor, substitution, the world is flat, demand globalization, mimetic consumer competition, technology, supply of labor, mostly complementary, demand technology, machines don't demand all values to people. The stark differences between man and machine means that, it mean they, they, they, they, they, they, they, they, they, Gains from working with computers are much higher than gains from trade with other people. We don't trade with computers any more than we trade with livestock or lamps.

And that's the point. Computers are tools, not rivals. The differences are even deeper on the demand side. Unlike people in industrializing countries, computers don't yearn for more luxurious foods or beachfront villas in Capfara. All they require is a nominal amount of electricity, which they're not even smart enough to want. When we design new computer technology to help solve problems, we get all the efficiency gains of a hyper-specialized trading partner without having to compete with it for resources. Properly understood technology is the one way for us to escape competition in a globalizing world. As computers become more and more powerful, they won't be substitutes for humans. They'll be compliments.

Let's just look at it as any other tool. Now, taking into consideration AI, I think AI is probably one of the greatest tools that researchers are going to have that it's remarkable. Once you start really playing with a good AI program, like Claude AI, you start to understand why they're so important. And you just have to figure out how it's going to benefit you and your business. or what you do. Make it work for you. Complementary businesses. Complementarity.

Complementarity between computers and humans isn't just a microscale fact. It's also the path to building a great business. I came to understand this from my experience at PayPal. In mid-2000, we had survived the dot-com crash and we were growing fast, but we faced one huge problem. We were losing upwards of 10 million to credit card fraud every month. Since we were processing hundreds or even thousands of transactions per minute, we couldn't possibly review each one. No human quality control team could work that fast.

So we did what any group of engineers would do. We tried to automate a solution. First, Max Leveskin assembled an elite team of mathematicians to study the fraudulent transfers in detail. Then we took what we learned and wrote software to automatically identify. and cancel bogus transactions in real time. I think anyone who's tried to try, I think most people have had to deal with this where your bank, your bank gets in touch with you and they're like, did you?

Is this a transaction that you tried? Yes. Okay. Go ahead and try it again. Then you have to figure out to try it again and do it manually because it's supposed to come out automatically and yada yada, yada down the line. But it quickly became clear that this approach wouldn't work either. After an hour or two, the thieves would catch on and change her tactics.

We were dealing with an adaptive enemy and our software couldn't adapt in response. The fraudster's adaptive evasions fooled our automatic detection algorithms, but we found that they didn't fool our human and analysts so easily. So Max and his engineers rewrote the software to take a hybrid approach. The computer would flag the most suspicious transactions on a well-designed user interface, and human operators would make the final judgment, as to their legitimacy. Thanks to this hybrid system, we named it Igor,

after the Russian fraudster who bragged that we'd never be able to stop him, we turned our first quarterly profit in the first quarter of 2002, as opposed to a quarterly loss of $29.3 million one year before. The FBI asked us if we'd let them use Igor to help detect financial crime, and Max was able to boast, grandiosely but truthfully, that he was the Sherlock Holmes of the Internet Underground. This kind of man-machine symbiosis enabled PayPal to stay in business,

which in turn enabled hundreds of thousands of small businesses to accept the payments they needed to thrive on the Internet. None of it would have been possible without the man-machine solution, even though most people would never see it or even hear about it. I think at this point, if you are doing business with a company and you know that everything they're doing is automated and it puts you out enough times, kicks something back to you, you're just going to go somewhere else. So I think that's another part of the competency crisis is people don't want to,

either don't want to hire humans, they want to do everything like this, or they hire humans that are incompetent or unmotivated, anyone who's been a manager or owned a business. That's what I'm talking about. So, yeah, I think most people, when you call somewhere because you have a problem, you want to speak to a human. You would love for a human to answer the phone. Unfortunately, you have to deal with some kind of hybrid, and that's probably the best way to do it because the hybrid makes it a lot easier to deal with. I continue to think about this after we sold PayPal in 2002. If humans and computers together could achieve dramatically better results than either could attain a loan, what other valuable businesses could be built on this core principle.

The next year, I pitched Alex Karp, an old Stanford classmate, and Stephen Cohn, a software engineer, on a new startup idea. We would use a computer hybrid approach from PayPal security system to identify terrorist networks and financial fraud. We already knew the FBI was interested, and in 2004, we founded Palantir, a software company that has, helps people extract insight from divergent sources of information. The company is on track to book sales of $1 billion in 2014, and Forbes is called Palantir's Software, the Killer App, for its rumored role in helping the government locate Osama bin Laden. Just a rumor.

We have no details to share from that operation, but we can say that neither human intelligence, but by itself nor computers alone will be able to make us safe. America's two biggest spy agencies take opposite of The Central Intelligence Agency is run by spies who privilege humans. The National Security Agency is run by generals who prioritize computers. CIA analysts have to wade through so much noise that is very difficult to identify the most serious threats. NSA computers can process huge quantities of data, but machines alone cannot authoritatively determine whether someone is plotting a terrorist attack.

Palantir aims to transcend these opposing biases. Its software analyzes the data the government feeds it. Phone records of radical clerics in Yemen or bank accounts linked to terror cell activity, for instance, and flag suspicious activities for a trained analyst to review. In addition to helping find terrorists, analysts using Palantier software have been able to predict where insurgents plant IEDs in Afghanistan, prosecute high-profile insider trading cases, take down the largest child pornography ring in the world, support, the Centers for Disease Control and Prevention and fighting food-borne disease outbreaks,

and save both commercial banks and the government hundreds of millions of dollars annually through advanced fraud detection. Advanced software made this possible, but even more important, where the human analysts, prosecutors, scientists, and financial professionals without whose active engagement the software would have been useless. Think of what professionals do in their jobs today. Lawyers must be able to articulate solutions to thorny, problems in several different ways. The pitch changes depending on whether you're talking to a client,

opposing counsel, or a judge. Doctors need to marry clinical understanding with an ability to communicate to it non-executive to non-expert patients. And good teachers aren't just experts in their disciplines. They must also understand how to tailor their instruction to different individual interests and learning styles. Computers might be able to do some of these tasks. but they can't combine them effectively. Better technology and law, medicine, and education won't replace professionals. It will allow them to do even more.

LinkedIn has done exactly this for recruiters. When LinkedIn was founded in 2003, they didn't pull recruiters to find discrete pain points in need of relief, and they didn't try to write software that would replace recruiters overnight. Recruiting is part-detective work and part-sales. You have to scrutinize applicants' history, assess their motives and compatibility and persuade the most promising ones to join you. Effectively replacing all those functions with a computer would be impossible.

Instead, LinkedIn set out to transform how recruiters did their jobs. Today, more than 97% of recruiters use LinkedIn and its powerful search and filtering functionality to source job applicants, and the network also creates value for hundreds of millions of professionals who use it to manage their personal brands. If LinkedIn had tried to simply replace recruiters with technology, they wouldn't have a business today. Still know a lot of people who use LinkedIn. Never. I have an account.

Never really used it because it kind of never was my business. The one business that I was recruited into was actually a head hunter found me. Otherwise, it was me making the approach, the old-fashioned way. The ideology of computer science. Why do so many people miss the power of complement, complement. It starts in school. Software engineers tend to work on projects that replace human efforts because that's what they're trained to do. Academics make their reputations through specialized research. Their primary goal is to publish papers and publication means respecting the limits of a particular discipline. For computer scientists, that means reducing human

capabilities into specialized tasks that computers can be trained to conquer one by one. Just look at the trendiest fields in computer science today. The very turn to the very turn to, term computer, the very term machine learning evokes imagery of replacement and its boosters seem to believe that computers can be taught to perform almost any task as long as we feed through enough training data. Any user of Netflix or Amazon has experienced a result of machine learning firsthand. Both companies use algorithms to recommend products based on your viewing and purchase history. Feed them more data and the recommendations get even better. Google Translate works the same way, providing rough

but serviceable translations into any of 80 languages it supports, not because the software understands human language, but because it has extracted patterns through statistical analysis of a huge corpus of text. The other buzzword that epitomizes a bias towards substitution is big data. Today's companies have an insatiable appetite for data, mistakenly believing the more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, don't know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst or the kind of generalized artificial intelligence that exists only in science fiction. Well, really would love to see them update this book.

Ten years later, today's technology, everything we're having, especially the aforementioned AI. We have let ourselves become enchanted by big data only because we as... exoticize technology. We're impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity between the human contribution, because the human contribution makes them less uncanny. Watson, Deep Blue, and Ever Better Machine Learning algorithms are cool, but the most valuable companies in the future won't ask what problems can be solved with computers alone. Instead, they'll ask, how can computers help humans, help humans solve hard problems.

Ever-smarter computers. Friend or foe. The future of computing is necessarily full of unknowns. It becomes conventional to see ever-smarter anthropomorphized robot intelligences like Siri and Watson as harbingers of things to come. Once computers can answer all, all our questions, perhaps they'll ask why they should remain subservient to us at all. The logical endpoint of this substitutionist thinking is called strong AI. Computers that eclipse humans on every important dimension. Of course, the Luddites are terrified about the possibility.

It even makes the futurist a little uneasy. It's not clear whether strong AI would save humanity or doom it. Technology is supposed to increase our mastery over nature and reduce the role of chance in our lives. Building smarter than human computers could actually bring chance back with a vengeance. Strong AI is like a cosmic lottery ticket. If we win, we get Utopia. If we lose, SkyNet substitutes us out of existence.

I've actually played with some AI where you tell us to write an essay on this. And the essay reads perfectly. You have to double check it. Sometimes AI has been known to make stuff up, like references. You have to check the reference. Be like, wait a minute, that book doesn't even exist. but it seems to me like every, I would think every student right now. And no matter what level they're at would be using AI, especially to write things.

You know, like Claude, you can just feed a PDF into it and say, summarize this PDF for me. I don't know why they wouldn't be using it. But even if strong AI is a real possibility rather than an imponderable mystery, it won't happen anytime soon. Replacement by computers is a worry for the 22nd century. Indefinite fears about the far future shouldn't stop us from making definite plans today. Lettides claim that we shouldn't build the computers that might replace people someday. Cray's futurists argue that we should. These two positions are mutually exclusive, but they are not exhaustive.

There is room in between for sane people to build a vastly better world than the decades ahead. As we find new ways to use computers, they won't just get better at the kind of things people already do. They'll help us to do what was previously unimaginable. The future of strong AI, human control over the universe, AI becomes superhuman time. Just a chart here that could, uh, of possibilities. Chapter 13. Seeing green. At the start of the 21st century, everyone agreed that the next big thing was clean technology. It had to be.

In Beijing, the smog had gotten so bad that people couldn't see from building to building. Even breathing was a health risk. Bangladesh, with its arsenic-laden water wells, was suffering what the New York Times called the biggest mass poisoning in history. In the U.S., hurricanes Ivan and Katrina were said to be harbingers of the coming devastation from global warming. Al Gore implored us to attack these problems with, quote, with the urgency and resolve that has previously been seen only when nations mobilized for war. People got busy. Entrepreneurs started thousands of clean tech companies and investors poured more than 50 billion into them. So began the quest to cleanse the world.

It didn't work. Instead of a healthier planet, we got a massive clean tech bubble. Cylindra is the most famous green ghost, but most clean tech companies met similarly disastrous ends. More than 40 solar manufacturers went out of business or filed for bankruptcy in 2012 alone. The leading index of alternative energy companies shows the bubble's dramatic deflation. You can see this chart, 2003, all the way down on the bottom. You get the peak right around 2007 to 2008. This is the renewable energy industrial index, and you come all the way down starting around 2008,

and by 2012, you're down below where it was in 2003. Why did clean tech fail? Conservatives think they already know the answer. As soon as green energy becomes a priority for the government, it was poisoned. Became a priority for the government, it was poison. But there really were, and there still are, good reasons for making energy a priority. And the truth about clean tech is more complex and more important than government failure. Most clean tech companies crash because they neglected one or more of the seven questions that every business must answer. The engineering question. 1. The engineering question. Can you create breakthrough technology instead of incremental improvements? 2. The timing question. Is now the right time to start your particular business? 3. The monopoly question. Are you starting with a big share of a small market? 4. The people question. Do you have the right team? 5. The distribution question. Do you have a way to not just create but deliver your product? 6. The durability question. Will your market position be defensible? 10.

in 20 years into the future. Seven, the secret question. Have you identified a unique opportunity that others don't see? We've discussed these elements before. Whatever your industry, any great business plan must address every one of them. If you don't have good answers to these questions, you'll run into lots of bad luck and your business will fail. If you nail all seven, you'll master fortune and succeed.

Even getting five or six correct might work. But the striking thing about the clean tech bubble was that people were starting companies with zero good answers, and that meant hoping for America. It's hard to know exactly why any particular clean tech company failed, since almost all of them made several serious mistakes. But since any one of these mistakes is enough to doom your company, it's worth reviewing clean tech's losing scorecard in more detail. The engineering question. A great technology company should have proprietary. technology in order of magnitude better than its nearest substitute. But clean tech companies rarely produced two times, let alone 10 times improvements. Sometimes their offerings were actually

worse than the products they sought to replace. Cylinder developed novel cylindrical solar cells, but to a first approximation, cylindrical cells are only one to the pie as efficient as flat one. They simply don't receive as much direct sunlight. The company tried to correct for this deficiency by using mirrors to reflect more sunlight to hit the bottoms of the panels, but it's hard to recover from a radically inferior starting point. Companies must strive for ten times better because merely incremental improvement often end up meaning no improvement at all for the end user. Suppose you develop a new wind turbine that's 20% more. efficient than any existing technology. When you tested in a laboratory, that sounds good at first, but the lab result won't begin

to compensate for the expenses and risks faced by any new product in the real world. And even if your system really is 20% better on net for the customer who buys it, people are so used to exaggerated claims that you'll be met with skepticism when you try to sell it. Only when your product is 10 times better can you offer the customer transparent superiority. Sorry. The timing question. Clean tech entrepreneurs worked hard to convince themselves that their appointed hour had arrived. When he announced his new company in 2008, SpectraWatt CEO Andrew Wilson stated that the solar industry is akin to where the microprocessor industry was in the late 1970s.

There is a lot to be figured out and improved. The second part was right, but the microprocessor analogy was way off. Ever since the first microprocessor was built in 1970, computing advanced not just rapidly, but exponentially. Look at Intel's early product release history. 4-bit generation was 1971, 8-bit 72, 16-bit 78, 32-1, 32-1981. The first silicon solar cell, by contrast, was created by Bell Labs in 1954, more than a half century before. Wilson's press release. Photovoltaic efficiency improved in the intervening decades, but slowly and linearly.

Bell's first solar cell had about 6% efficiency. Neither today's crystalline silicon cells nor modern thin film cells had exceeded 25% efficiency in the field. There were few engineering developments in the mid-2000s to suggest impending liftoff. Entering a slow-moving market can be a good strategy. but only if you have a definite and realistic plan to take over. The failed clean tech companies had none. The monopoly question.

In 2006, billionaire technology investor John Doer announced that green is the new red, white, and blue. He could have stopped at red. As Dorr himself said, internet-sized markets are in the billions of dollars, the energy markets are in the trillions. What he didn't say is that huge trillion-dollar markets mean ruthless, bloody competition. Others echoed doer over and over in the 2000s. I listened to dozens of Clean Tech entrepreneurs begin fantastically rosy PowerPoint presentations with all two true tales of trillion-dollar markets, as if that were a good thing. Clean Tech executives emphasized the bounty of an energy market big enough for all comers,

but each one typically believed that his own company had an edge. in 2000, it's already competitive talk using competitive language. In 2006, Dave Pierce, CEO of Solar Manufacturer Miasola, admitted to a congressional panel that his company was just one of several very strong startups working on one particularly kind of thin film solar cell development. Minutes later, Pierce predicted that Mia Sole would become the largest producer of thin film solar cells in the world within a year's time. That didn't happen. but it might not have helped them anyway.

Thin film is just one of more than a dozen kinds of solar cells. Customers won't care about any particular technology unless it solves a particular problem in a superior way. And if you can't monopolize a unique situation for a small market, you'll be stuck with vicious competition. That's what happened to Mia Soles, which was acquired in 2013 for hundreds of millions of dollars less than its investors had put into the company.

Exaggerating your own uniqueness is an easy way a botched a monopoly question. Suppose you're running a solar company that successfully installed hundreds of solar panel systems with a combined power generation capacity of 100 megawatts. Since total U.S. solar energy capacity is 950 megawatts, you own 10.53% of the market. Congratulations, you tell yourself. You're a player. But what if the U.S. solar energy market isn't the relevant market?

What if the relevant market is the global solar market? with a production capacity of 18 gigawatts. Your 100 megawatts now makes you a very small fish indeed. Suddenly you own less than 1% of the market. And what if the appropriate measure is in global solar, but rather renewable energy in general? Annual production capacity from renewable is 420 gigawatts globally. You just shrank to 0.02% of the market

and compared to the total global power generation capacity of 15,000 gigawatts, your 100 megawatts is just a drop in the ocean. Clean tech entrepreneurs thinking about markets was hopefully confused. They would rhetorically shrink their market in order to seem differentiated, only to turn around and ask to be valued based on huge, supposedly lucrative markets. But you can't dominate a sub-market if it's fictional, and huge markets are highly competitive, not highly attainable. Most clean tech founders would have been better off opening a new British restaurant in downtown Palo Alto.

The people question. Energy problems are engineering problems. So you would expect to find nerds running clean tech companies. You'd be wrong. The ones that failed were run by shockingly non-technical teams. These salesman executive were good at raising capital and securing government subsidies, but they were less good at building products that customers wanted to buy. At Founders Fund, we saw this coming.

The most obvious clue was Sartorial. Clean Tech executives were running around wearing suits and ties. This was a huge red flag because real technologists wear T-shirts and jeans. So we instituted a blanket rule, passed on any company whose founders dressed up for pitch meetings. Maybe we still would have avoided these bad investments if we had taken the time to evaluate each company's technology in detail. But the team insight never invest in a tech. CEO that wears a suit, got us to the truth a lot faster. The best sales is hidden. The best sales is hidden. There's nothing wrong with the CEO who can sell. But if he actually looks like a salesman,

he's probably bad at sales and worse than tech. That couple, that, um, Cylindra CEO picked, uh, it's like a drawing, a very good one of Cylindra CEO, Brian Harrison wearing a suit and tie. and Elon Musk wearing his Occupy Mars T-shirt. The distribution question. Clean tech companies effectively courted government and investors, but they often forgot about customers. They learn the hard way that the world is not a laboratory. Selling and delivering a product is at least as important as the product itself.

Just imagine, becoming so, especially if you're getting money from the government, especially if you're going to be subsidized by the government, that is going to become so much more important than the customer. It just makes sense, especially to guys wearing suits. Just Ask is Rarely Vehicle Start a Better Place, which from 2007 to 2012 raised and spent more than $800 million to build swappable battery packs and charging stations for electric cars. The company sought to create a green alternative

that would lessen our dependence on highly polluting transportation, technologies, and it did just that, at least by a thousand cars, the number it sold before filing for bankruptcy. Even selling that many was an achievement, because each of those cars was very hard for customers to buy. For starters, it was never clear what you were actually buying. Better place bought sedans from Renault, terrible cars, and refitted them with electric batteries and electric motors. When I lived in Romania, they would,

Renault, Dachio, made Renault, and Dachia was a, Dach cars, Renaos were called Dachas in Romania, and Dachia was a, slang word for shit. So were you buying an electric Renault or were you buying a better place?

In any case, if you decided to buy one, you had to jump through a series of hoops. First, you needed to seek approval from Better Place. To get that, you had to prove that you live close enough to a Better Place battery swapping station and promise to follow predictable routes. If you passed that test, you had to sign up for a fueling subscription in order to recharge your car. Only then could you start learning the new behavior of stop stopping to swap out battery packs on the road.

Better Place thought its technology spoke for itself, so they didn't bother to market it clearly. Reflecting on the company's failure, one frustrated customer asked, why wasn't there a billboard in Tel Aviv showing a picture of a Toyota Prius for 100,000 shekels and a picture of this car for 160,000 plus fuel for four years? He still bought one of the cars, but unlike most people, he was a hobbyist who would do anything to keep driving it. Unfortunately, he can't as the Better Place Board of Director started upon selling the company's assets for a meager 12 million in 2013. Quote, the technical challenges we ever came successfully, but the other obstacles we were not able to overcome. The durability question.

Every entrepreneurship plan to be the last mover in her particular market. That starts by asking yourself, what will the world look like 10 to 20 years from now and how will my business fit in? Few clean tech companies had a good answer. As a result, all their obituaries resemble each other. A few months before it filed for bankruptcy in 2011, Evergreen Solar explains this decision to close one of its U.S. factories. Here's the explanation. Solar manufacturers in China have received considerable government and financial support.

Although our production costs are now below originally planned levels and lower than most Western manufacturers, they are still much higher than those of our low-cost competitors in China. But it wasn't until 2012 that the blamed China chorus really exploded. Discussing its bankruptcy filing, U.S. Department of Energy, energy backed abound solar, blamed aggressive pricing actions from Chinese solar panel companies that made it very difficult for an early stage startup company to scale in current market conditions. When solar, that's just competition.

That's all this is. They're falling into the competition track. When solar panel maker energy conversion devices failed in February 2012, it went beyond blaming China in a press release and filed the 900. $150 million lawsuit against three prominent Chinese solar manufacturers. The same companies as cylindrous trustees in bankruptcy sued later that year on the grounds of attempted monopolization, conspiracy, and predatory pricing. But was competition from Chinese manufacturers really impossible to predict?

Clean tech entrepreneurs would have done well to rephrase the durability question and ask, what will stop China from wiping out my business? Without an answer, the result shouldn't have come as a surprise. Just whining. Just throwing tantrums. Using lawyers to do it. Beyond the failure to anticipate competition in manufacturing the same green products, clean tech embraced misguided assumptions about the energy market as a whole.

An industry premise on the supposed twilight of fossil fuels was blindsided by the rise of fracking. In 2000, just 1.7% of America's natural gas came from fracked shale. Five years later, the figure climbed to 4.1%. Nevertheless, nobody in Clean Tech took this trend seriously. Renewables were the only way forward. Fossil fuels couldn't possibly get cheaper and cleaner in the future, but they did. By 2013, Shell Gas accounted for 34% of America's natural gas, and gas prices had fallen more than 70% since 2008.

Devastating most renewable energy business models. fracking that may not be a durable energy solution either, but it was enough to doom clean tech companies that didn't see it coming. The secret question. Every clean tech company justified itself with conventional truths about the need for a cleaner world. They diluted themselves into believing that an overwhelming social need for alternative energy solutions implied an overwhelming business opportunity for clean tech companies of all kinds. consider how conventional it had become by 2006 to be bullish on solar. That year, President George W. Bush heralded a future of solar roofs that will enable the American family to be able to generate their own electricity. Investor and Clean Tech executive Bill Gross declared that the potential for solar is enormous.

Suvi Sharma, then CEO of Solar Manufacturer Solaria, admitted that while there is a gold rush feeling to solar, there's also real gold here, or, in our case, sunshine. But rushing to embrace the convention sent scores of solar panel companies, Q cells, Evergreen, solar, spectro watt, and even Gross's own energy innovations to name just a few

from promising beginnings to bankruptcy court very quickly. Each of the casualties had described their bright futures using broad conventions on which everybody agreed. Great companies have secrets, specific reasons for success, that other people don't see.

The myth of social entrepreneurship. Clean tech entrepreneurs aimed for more than just success as most businesses define it. The clean tech bubble was the biggest phenomenon and the biggest flop in the history of social entrepreneurship. This philanthropic approach to business starts with the idea that corporations and nonprofits have until now been polar opposites. Corporations have great power, but they're shackled to the profit motive. nonprofits pursue the public interest, but they're weak players in the wider economy.

Social entrepreneurs aim to combine the best of both worlds and do well by doing good. Usually they end up doing neither. The ambiguity between social and financial goals doesn't help, but the ambiguity in the word social is even more of a problem. If something is socially good, is it good for society or merely seen as good for society? Whatever is good enough to receive applause from all audiences can only be conventional by the general idea of green energy. Progress isn't held back by some difference between corporate greed and nonprofit goodness. Instead, we're held back by the sameness of both.

Justice corporations tend to copy each other. Nonprofits all tend to push the same priorities. Clean tech shows the result. Hundreds of undifferentiated products all in the name of one overbroad. goal. This almost seems like the like the birth of woke capital, right? Doing something different is what's truly good for society, and it's also what allows a business to profit by monopolizing a new market.

The best projects are likely to be overlooked, not trumpeted by a crowd. The best problems to work on are often the ones nobody else even tries to solve. Tesla, 7 for 7. Tesla is one of the few clean tech companies started last decade to be thriving today. They rode the social buzz of clean tech better than anyone, but they got the seven questions right. So their success is instructive. Technology Tesla's technology is so good that other car companies rely on it.

Dambler uses Tesla battery packs, Mercedes-Benz uses a Tesla power train, Toyota uses a Tesla motor. General Motors has even created a tech. task force to track Tesla's next move, next moves. But Tesla's greatest technological achievement isn't any single part or component, but rather its ability to integrate many components into one superior product. The Tesla Model S sedan, elegantly designed from end to end, is more the sum of its parts. Consumer reports rated it higher than any other car ever reviewed, and both Motor Trend and Automobile Magazines named it their 2013 car of the year. They could have been paid off to do that, but it doesn't matter at this point, does it?

Timing In 2009, it was easy to think that the government would continue to support clean tech. Green jobs were a political priority, federal funds were already earmarked, and Congress even seemed likely to pass cap-and-trade legislation. But where others saw generous subsidies that could flow indefinitely, Tesla CEO Elon Musk, rightly saw a one-time opportunity. In January 2010, about a year and a half before Cilindra imploded under the Obama administration and politicized the subsidy question, Tesla secured a $465 million loan from the U.S. Department of Energy. A half-billion dollar subsidy was unthinkable in the mid-2000s. It's unthinkable today. There was only one moment where that was possible, and Tesla played it perfectly.

Monopoly. Tesla started with a tiny sub-market that it could dominate. the market for high-end electric sports cars. Since the first roaster rolled off the production line in 2008, Tesla sold only about 3,000 of them, but at 109,000 apiece, that's not trivial. Starting small allowed Tesla to undertake the necessary R&D to build the slightly less expensive Model S,

and now Tesla owns the luxury electric sedan market too. They sold more than 20,000 sedans in 2013, and now Tesla is in prime position to expand the broader markets in the future. And they have. Team. Tesla CEO is a consummate engineer and salesman, so it's not surprising that he's assembled the team that's very good at both. Elon describes his staff this way.

If you're at Tesla, you're choosing to be at the equivalent of special forces. There's the regular army, and that's fine. But if you're working at Tesla, you're choosing to step up your game. Distribution. Most companies underestimate distribution. but Tesla took it so seriously that it decided to own the entire distribution chain. Other car companies are beholden to independent dealerships. Ford and Hyundai make cars, but they rely on other people to sell them.

Tesla sells and services its vehicles in its own store. The upfront cost of Tesla's approach are much higher than traditional dealership distribution, but it affords control over the customer experience, strengthens Tesla's brand, and saves the company money in the long run. Durability. Tesla had a head start, and it's moving faster than anyone else. And that combination means its lead is set to wide in the years ahead. A coveted brand is to clear a sign of Tesla's breakthrough.

A car is one of the biggest purchasing decisions that people ever make, and consumers trust in that category is hard to win. And unlike every other car company, at Tesla, the founder is still in charge. so it's not going to ease off anytime soon. Secrets. Tesla knew that fashion drove interest in clean tech. Rich people especially wanted to appear green, even if it meant driving a boxy Prius or clunky Honda Insight.

Those cars only made drivers look cool by association with the famous eco-conscious movie stars who own them as well. So Tesla decided to build cars that made drivers look cool, period. Leonardo DiCaprio even ditched his Prius for an expensive and expensive-looking Tesla roadster. While generic clean-tech companies struggled to differentiate themselves, Tesla built a unique brand around the secret that clean tech was even more of a social phenomenon than an environmental imperative. Energy 2.0.

Tesla's success proves that there was nothing inherently wrong with clean tech. The biggest idea behind it is right. The world really will need new sources of energy. energy. Energy is the master resource. It's how we feed ourselves, build shelter, and make everything we need to live comfortably. Most of the world dreams of living as comfortably as Americans do today, and globalization will cause increasingly severe energy challenges unless we build new technology. There simply aren't enough resources in the world to replicate old approaches or redistribute our way to prosperity. Clean Tech gave people a way to be optimistic about the

future of energy. But when indefinitely optimistic investors betting on the general idea of green energy funded clean tech companies that lack specific business plans, the result was a bubble. Plot the valuation of alternative energy firms in the 2000s alongside the NASDAX rise and fall a decade before, and you see the same shape. Basically, it's showing that the alternative, the clean tech energy bubble is basically rode the same exact wave as the as a dot-com bubble. The 1990s had one big idea. The internet is going to be big. But too many internet companies had exactly the same idea and no others.

An entrepreneur can't benefit from microscale insight unless his own plans begin at the microscale. Clean tech companies face the same problem. No matter how much the world needs energy, only a firm that offers a superior solution for a specific energy problem can make money. No sector will ever be so important that merely participating in it will be enough to build a great company. The tech bubble was far bigger than clean tech and the crash even more painful.

But the dream of the 90s turned out to be right. Skeptics who doubted that the internet would fundamentally change publishing or retail sales or everyday social life looked prescient in 2001, but they seemed comically foolish today. Could successful energy startups be founded after the clean tech crash, just as Web 2.0 startups successfully launched amid the debris of the dot-coms? The macro need for energy solutions is still real, but a valuable business must start by finding a niche and dominating a small market.

Facebook started as a service for just one university campus before it spread to other schools, and then the entire world. Finding small markets for energy solutions will be tricky. You could aim to replace diesel as a power source for remote islands or maybe build modular reactors for quick deployment at military installations and hostile territories. Paradoxically, the challenge for entrepreneurs who will create energy 2.0 is to think small.

And I'll stop right there. Come up to the last. The next episode will be Chapter 14. which will be the last, the founder's paradox, and then we'll do the conclusion, stagnation, or singularity. I hope you're enjoying this. I just instinctively know there are people out there

who they're so autistic that they're listening to this and they're just tearing it apart going, that's not how it worked. It was all crony capitalism. It was all this. It was all that. He's not hiding the government contracts and things like that. some things may be hidden, but

most people who we know who those people are. We know exactly who they are and what they're about. They're just the typical ankle biters, the typical people who just want to throw stones and can't listen to something or read something like this and take from it and learn from it

and apply it. own life. It's just, no, they did that because they had some kind of head start or something like that. Maybe. It's not always the case, though. So, all right. We'll be back in a couple days with the finale. And I know that there, I've mentioned this on the on all the episodes. There are commercial breaks in these. And if you go to freeman beyond the wall.com forward slash support and you subscribe through my website. There's a link there. Patreon or Subscribe Star.

You can get these early and ad free. All right. So you're back in a couple days. We'll finish this up. Hope you're getting something out of it. Take care. Bye.