>> welcome to bloomberg businessweek. jason: we are here at bloomberg headquarters in new york. carol: this is the sooner than you think issue. >> a deep dive into the future of technology. >> it's in cars, and your computers, and boats across the ocean. >> and we take it to our editor

with a preview. >> we want to go deeper into the world ai is making, what the big grail quests are our reporter went to switzerland. they spoke to the godfather of ai who has been written out of a lot of the history's. >> often at bloomberg, we are talking about ai and machine learning, but you guys dig into some unusual suspects. >> yeah, we tried to look at parts of the ai, folks in china who are making the default chips for bit coin miner rigs. jason: why is it that there are so many interesting characters here?

what draws people to this field, ai seems to attract a motley crew. >> for sure. what we cap seeing is that a lot of these folks have been working in the background for as long as 30-40 years. the baseline theory behind ai, known as neural networks, designed to mimic human thought with big schemas of computers, it's an idea that's only been practical in the past 5-6 years or so. a lot of the guys who have done the baseline research. carol: what are the conversations you have had getting it to this point? >> we started with a handful of

stories that seemed ai focused. in a larger package that would have been broader looking. at a certain point, we realized we had so many deep stories that it made just as much sense to just spackle in the gaps and more ai centered issue. jason: when we want to understand coded things, we turn to paul. carol: i love this guy. remember, he did that wonderful story about coding, explaining programming to the masses. here's what he had to say. >> you take a whole lot of data, like a billion pictures and captions for those pictures, and you teach the computer patterns. and the computer will say this has a shape that is like a cat, the word underneath is cat, and it does it a million times. now you give it a picture but no caption.

and you say what do you think the caption is? it'll go back to the database that it builds, and it will say it looks like an airplane. sometimes it is, sometimes it isn't, but the models and statistics have gotten better. they can say with some confidence that is kind of an airplane. that is when you hit reply in gmail and it says here are some replies you might want to use, that's because they have looked at civilians of emails and ions of emails and replies. and email before got a reply that was like this one. the first time you see it, you think what are you doing, then you think it is useful. jason: you have seen this of all over a longer -- evolve over a long period of time. where are we right now? >> we are in a moment where it is viable for the first time. it has always been an expensive process, and there is the sort of amazing confluence where's the cards that you put into your

computer for graphics processing like if you're into 3d gaming, turn out to do that sort of recognition task. they are really good at that because they can split it into thousands of discrete processes. the processor in your laptop, too slow. on a typical web server, too slow. but the processor in a 3-d card is really fast, so you have all of these cards sitting around, jason: you have been sitting -- jason: you have them sitting around, we don't. [laughter] >> you can do this all of the sudden. without spending millions of on dollars specialized hardware. now what is happening is that places like google are building their own processing unit. and you can rent them by the hour, they're more expensive places like google are building than regular cloud equipment, but they let you do that kind of

task. carol: is it becoming more productive because you have the technology that can do this processing, and you've also got companies like google which have so much information. he put them together and it is powerful. >> they've got the expertise and the channel. they say, wait a minute, we have billions of emails so we can learn from it. and then we can make a product out of that were people click and reply more quickly. they like that, they will keep using gmail. give us more data, and we can learn more. where it gets more serious is google maps and self driving cars, google maps is at some level a way of understanding and perceiving the world, they can train themselves and look for patterns. jason: and so you essentially applied machine learning to your life? tell us about this mini hack you did. >> i was on a tight deadline and people said we need a machine learning in the issue, and i said great. i have wanted to do this for a while. i'm going to give it all the on the i've done

paragraphs of my sentences. i'm getting paid by the word, so i thought this would be amazing. it turns out that that is nights and nights and nights of computers running. it was clear that it would be 3-4 days, and again i'm on deadline. before i could get any results at all. so instead, i just sat at my google calendar and try to see if it could generate meetings for me. carol: did it work? >> kind of. you got there a little bit. the first few passes, it was nonsense, but by the end, it was like drinks with gina, and i said, sounds about right. it's more about my life being a little boring, and less about the amazing programming. i would just running two scripts. carol: so what changed about your thoughts about machine

learning, going through this process? >> i knew it was a big deal and clearly it is guiding the strategies of the big whatever's. not just apple, microsoft, and google, and amazon, but also like uber. and land o'lakes. agribusiness is very interested. jason: like the butter. >> yeah, agribusiness is very interested. crop productions. for me, what changed is that it is more accessible. google released a huge open package which is become the standard. there is a way to learn it now. carol: they are sharing that? >> oh yeah. they want people coming into the tent. they need engineers and a community and ecosystem around this stuff. jason: ai is obviously a lot about science, but art as well.

carol: writes, this meant use artificial intelligence paintings. >> we really wanted something that could show you what these deep learning networks are capable of, and there are lots of technologies around for teaching them how to make images. we reached out to this guy who taught his ai how to do landscape paintings. he had us do the cover. jason: so this is painted by a machine? >> yes. he shows the machine a bunch of paintings, which trains it. carol: that's what it is all about, you feed in machine a lot of information and get a result. you can apply it to art. >> what you usually think about our data sets and analyzing numbers, but beauty is a whole another picture. >> and the ai will change how sort of styles the painting. then it changes over time to be more psychedelic.

you can see the sort of brushstrokes come out in a weird digitize way, it's interesting. carol: up next, big ambitions for the world leader in crypto currency mining. jason: and a company that says they can solve uber's car challenges. ♪

carol: welcome back. i'm carol massar. jason: i'm jason kelly. you can also find us online. carol: and on our mobile up. -- app. now we have a pair of stories by max chafkin. we really turned him loose on a lot of different things. carol: what i love is that he uncovers people and companies that nobody is talking about, but in a few months everybody will. jason: and everybody will be talking about bitmain. carol: they want to be a leader in artificial intelligence. >> they are a beijing-based semi conductor company. not super well known unless you are in the world of cryptocurrency, where they are

basically the only game in town. we are talking about a market share around 80%. they are selling the most popular chips for bitcoin mining and operating a great percentage of these mines. what is interesting is that they are now moving into ai. carol: they deftly are looking down the road and at the united states. >> they are in a great position because the prices for these chips move with the price of the currency. if you can make more money mining the currency, they could have more revenue, so they are in a perfect position and are trying to diversify. one aspect of that is open mining operations outside of china. at this point, most of bitcoin mining in terms of large-scale stuff is happening in china because of low energy. in rural china, you had

historically coal-fired electricity for little money, and bitcoin is all about cheap power. that's where the action was. they have got projects in canada, switzerland, and a couple in the united states. we have relatively inexpensive electricity here. another part is the ai push, which is interesting on two levels. one is psychological and the -- technological and the other is regulatory. the chinese government has been pushing ai really hard. china wants to be a powerhouse in this world. and people are starting to say maybe we had a new cold war over ai. so they are going into this industry that is kind of in a gray area. you never know what the regulatory landscape is going to to one where the air

is full throated support from beijing. jason: and when they are getting into the ai business, they're getting into a business populated by some big tech names. >> yeah, the chip they unveiled is named after a well-known chinese science fiction novel, i think all the three body problem. it is very similar to what google has done. google, last year, introduced a thing called a tpu, basically a chip that makes machine learning more efficient. you can only access it if you're a subscriber to google's cloud services. it means you can't just go out and buy one, but it also means you can't buy one in china, because google cloud is not available in mainland china. so bitmain has a niche products. jason: so you can't by the competing product.

>> absolutely. to be clear, they think there is a market for this worldwide, this is not some kind of copycat, at least as far as they are saying. they were developing it long before google made their announcement. but does have the sort of built-in advantage in the home market. jason: this week, max takes on the topic of autonomous driving. carol: he takes us to this company which is very involved in the space, and how they could make self driving cars safer. >> mobile i is a jerusalem-based startup that does driver assistance systems. if you've been in a car over the past couple of years, these are systems where if you are about to hit something, it will slam on the brakes for you or handle adaptive cruse control, like

cruise control, except it slows the car down if the car in front of you slows down they will also warn you if you're about to swerve out of your lane or falling asleep. it is a very large company that very few people have known about until now. there's something like 27 million of the systems on the road, all of the big automakers are using them. and last year, intel purchased the company for around $15 billion. jason: they are a part of a much broader trend that you get into in this story around autonomous driving, driverless vehicles. and yet there is a twist in terms of how they are approaching it. >> this is the driverless car company you have never heard of, but also the one in the lead. google, gm, and uber, have all attracted tons of press doing these very sophisticated systems that are driving around. and you know, we are talking that dozens of cars. whereas waymo has dozens of cars

-- hundreds of cars, and their idea is to leapfrog these companies and release a design that can be used by other automakers. we are talking about bmw, fiat chrysler, a chinese company called neo-, that would be able to offer what waymo is doing. carol: different can be better , different can be less expensive, but is it better? you talk about that unfortunate accident that happened with the uber driverless car. >> this is one of the things that make driverless car scary, is that it is hard for anyone to

know whether then one thing is better than the other. the uber incident, there was a woman in tempe, arizona walk across the street. know whether then one thing is better than the other. she crossed a very busy divided road outside of an intersection and an uber driverless car which had a safety driver, in theory to stop the car, plowed right into her. it didn't even try to stop. in doing this story, one of the reasons i wanted to the story, i want to understand how a country figures out how to stop a car. carol: the founder said we could have caught that, because the way they are doing it is different. >> yeah, he took the video the police released, set it into the company system, which is similar to what a honda is doing with autonomous breaking. to what a honda is doing with he told me that that fatality was avoidable, that this was a sign that the autonomous vehicle industry was getting it wrong.

jason: up next, tim cook on investing in the. -- in the united states. carol: and a long road to repairing u.s. china relations. jason: this is "bloomberg businessweek." ♪

jason: welcome back. carol: you can also listen to us on radio. jason: in an interview for the david rubenstein show, apple ceo tim cook talked about injecting $350 billion into the u.s. economy. >> we are investing a time in this country. -- a ton in this country.

and then yes, we are also going to buy some of our stock, because our stock is of good value. from a shareholder point of view, if we can buy stock from people who think it's worth less than we do, then that is good for the company, and it's good for the economy as well. as people sell stock, they sell taxes on their gains. carol: watch the new interview in the new season of the david rubenstein show. coming in june only on bloomberg television. jason: this week, we turned the entire economic section to take on a massive issue. carol: the relationship between the united states and china, we know it has been deteriorating and there is much at risk for both.

>> chimerica is a combo of china and america and was coined by british historian neil ferguson to express the idea of this symbolizes between the two giant economies. we have china, being high savings and poor, and the u.s. being rich and low savings, and finding that they work together. china would send their cheap goods to america, pretty jobs -- creating jobs for millions of chinese, and while america got cheap goods, and was able to sell high-tech products into china. this is a very optimistic vision. jason: the were coining this is something that was good for the global economy. >> it was sort of necessary. but there were problems, even back then, there were problems. what it did was it ended up helping, among other things, the cause the housing crisis, because it was a cheap debt that enables people to buy houses they couldn't afford which has global spillover effects.

at the time, it was preceded as being a necessary binding, and there were hopes that things would get better. jason: here we are, and how are you feeling about chimerica? >> it is unraveling. the biggest thing was a cte, -- zte, where the company had illegally violated u.s. sanctions by transacting with iran and north korea, so a $1.2 billion fine. but the zte officials did not comply with the terms of the agreement. they secretly paid full bonuses to the people who had been involved in this sanctions busting. when they found out about this, they almost put a death sentence

on zte, and cut it off from their u.s. suppliers for many years. it became apparent to everyone, including the chinese, that a national champion of china, that the country had been counting on, was extremely vulnerable to the winds, you might say, of the united states. this caused the chinese to think we cannot rely so much on the united states. we need to develop our own advanced technology. carol: but aren't they doing that? to some extent? we see more and more trade with europe and the rest of the world. >> they have been doing it. they have a program called made in china 2025, which involves massive subsidies for a whole range of high-tech, from

robotics to electric vehicles. the united states has been saying, that seems like an unfair trade practice. you're subsidizing your industries, you did the same with solar panels, you basically stole an american invention, now you've got most of the world market. and so, a trade mission to china led by secretary steve mnuchin came with a message, you guys can't be subsidizing. and then this comes along with zte, and the chinese are saying that if they had any doubts before, they are going ahead. behind the tension -- it heightened attention. it has created more of this unraveling chimerica. jason: up next, the ai entrepreneur. carol: and a new era of the ocean research. jason: this is bloomberg

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jason: welcome back. carol: still ahead, we've got the little drone that could. and a hacking threat. jason: but first, we are going to ashlee vance, the author of the definitive book on elon musk . we sent him to find the key players in artificial intelligence. he ended up in switzerland. carol: it's crazy. >> he is considered one of these

ai godfathers, one of the pioneers in the artificial field who has really pushed forward a lot of innovations. the trick about him is that unlike most of the other people, he's not really a beloved figure. he's come up with all these brilliant ideas, but he spends a lot of time accusing people of stealing his ideas or using them without credit, and so he's built up this kind of fearsome reputation in the ai world. jason: he's ubiquitous at a lot of conferences, and not just as an attendee. >> there are these big ai conferences where like everybody shows up, and he is famous for this thing, a term of art. it is when you are giving a presentation and showing off in front of all of your peers, and in the middle of this, as german

voice will appear and as tall striking guy stands up and he says, i have a question for you, and basically spends a few minutes telling the presenter how they have stolen his ideas or bastardized his work and haven't given him credit. usually is a bit of back-and-forth as he won't let go, and keeps trying to set the record straight at these conferences. this is why he has this kind of bad reputation. although in my story, there's a guy who says it's a sort of a rite of passage. jason: it seems like he is partially if not wholly right. >> he has been at this like 30 years and has come up with some of the biggest ideas in ai. the reasons ai systems have a memory, this temp oral ability

-- temporal ability to store information, comes from his research, and countless other things. often, he's totally right, and that's one of the things that's most frustrating about him. not only is he digging in, but he's correct. jason: where does he go from here? he has been at this for some time, sitting there in switzerland. he has created quite a cottage industry around himself. what to look for for him next? >> he is trying to create something known as an agi, artificial general intelligence. ai's today are still very dependent on humans, whereas this would be an ai which you just point at a direction and it figures out how to do it on its own. in a relatively short amount of time. and ultimately, if this ever happens, it would be smarter than human beings by a dramatic margin.

so he and his team, about 25 researchers that he's brought together, that is their goal. to make this agi, make a bunch of money, and then take humanity to the next stage where we basically merged with machine. carol: we get more from him. this times he's taking us to richard jenkins drone boat. jason: writes, in the middle of the ocean. i don't even know we had that much to learn. >> richard jenkins, i would describe him as an adventure of sorts and a sailor. he is from england, he spent his whole life building all kinds of boats for a period of 10 years from 1999 to 2009. he set out to break the world's land sailing record, which is where you have a sailboat on wheels and go across the desert.

you try to make it go as fast as you can. in 2009, he did break the record. he took this crazy qwest and turned it into a company. today, he's the ceo of a company saildrone, a robotic sailboat based on his designs, and it goes out into the ocean and obtains all kind of data. jason: what is he finding? >> i was kind of surprised. we have very few researched ships available to study the ocean. the united states has 16 research vessels, these boats cost about $150 million to build, about a hundred thousand dollars a day to run when they go out. jason: i want to stop you there because that is an extraordinary amount of money. how does it cost that much? >> well, these are massive ships

that are designed to hold dozens of researchers and are filled with all kinds of equipment. that costs a lot, they have full data centers. i was surprised to, but it's just the logistics of running these boats. the drone, you can rent it for $2500 a day, and the idea is to eventually have 1000 of these drones going all over the world, just constantly pulling back all caps of it. -- all kinds of information. jason: one of the things people are interested in is the habits of groups of sharks. why? >> there are about a dozen researchers out of this place called the white shark café, about halfway between hawaii and san diego. for decades, marine biologists have seen that pretty much all of the great white sharks off the coast of california, they all flock to this one spot every

spring. nobody has any idea why, and they died down 1500 feet to 3000 feet and nobody knows what they were doing. so this year, i went with richard to put a couple saildrones in the water from alameda california are several skill, it took them about three weeks. they found these transponders that about 37 of the sharks had on them, and were able to see the sharks are doing these crazy dives and chasing some kind of food supply. jason: are you optimistic about what he will find and uncover? is this goes deeper and deeper? >> i am. they just raised $60 million in the last couple of months which

brings them up to $90 million. that's enough to build hundreds of these drones. the biggest thing will be their insight around weather. today, our weather models come from a handful of buoys scattered around and from satellite. with these drones, you can sail one directly into a hurricane, find out what the temperature of the water is, you can find out how fast it's going, when it will land, and he would have this global map of weather like we have never had before. the idea is you could sell this to shipping companies, energy companies, but that remains to be seen if these companies would actually pay for that kind of information. jason: up next, a new reason to be worried about hackers. carol: a social network being used by the smartest kids, and now recruiters. jason: this is "bloomberg businessweek." ♪

jason: welcome back. carol: you can find us online. at businessweek.com. jason: and on our mobile app. a scarier side of the tech world. carol: a new threat, if you will. possibly thousands of computers at risk. here's reporter jordan robinson. >> we are writing this week about a researcher, a cybersecurity researcher from portland. he is the have a top job at intel security, hacking their microprocessors. what he has found his this, his research shows the meltdown from january which affected all of the world's processors. he is able to go a level deeper.

what that means is that those vulnerabilities were really bad, and it gets worse. carol: will that's it. really going into the core of how a computer works, this is deep stuff. >> when we think of computers, we think of stuff we interact with. that's not what he is concerned about. he is concerned about the firmware. most people don't know what that is, but it's the code that exists inside the chips on your computer. and what he has discovered is a way to use these hardware exploits to get inside the firmware of your computer, and that's where all the secrets are stored. jason: and you mentioned that he works at intel, and i want everybody to understand, the company that has all of these chips inside so many computers.

those are in personal computers, chips inside corporations, and chips inside the government. so how is the industry responded responding to this next level threat. >> in january, when meltdown and specter announced, the industry had a botched rollout. this was a new type of threat they weren't used to responding to. there were a lot of glitches, a lot of problems with the response. what is happening is you have a lot of different players. it's not like a typical vulnerability where microsoft can put out a patch. you update your system and you are in the clear. when it comes to firmware vulnerabilities, you have got to have dozens and dozens of companies that have firmware on motherboards, they need to update their firmware before attacks like these go away. you can just imagine the scale of the problem.

this is not going away. carol: what is interesting that you talk about intelligence systems which have known that hackers can get into firmware for decades. >> it's an open secret. they have been hacking firmware for years. for decades even. it is amazing to me that the cybersecurity industry has focused on other stuff, easier to detect attacks, because that's what companies have asked for. we don't want to get phished. we don't want to have more spam emails. so it has gone there. but when you talk about hardware, those are hard to detect. carol: here is a company you don't know about, but you would if you were in college. jason: if you are a super smart college kid you would know about piazza. >> it's a website kids used to help study for classes.

computer science, but also other technical fields. and it's a way for students to ask and answer questions for their problem sets. it's usually done under the supervision of the professor. the professor realizes that somebody's answering a question wrong, they'll put him straight. they might endorse a question saying that's a good question, or a good answer. the company claims their research shows that students are spending more than three hours every night on this website. so think about it, this is a gold mine. because what companies need now, they are desperate for technical talent, and here is a website where it is like fishing in a well stocked lake. they are using it now, in 2016, a company got the idea of putting students who opt in, the companies can contact you.

and interestingly, the companies that are most contacting students are the ones that are sort of like not top of mind for the students, and yet they need technical talent. jason: i want to go back to how this started. it seems like a no-brainer idea, and yet, it didn't exist. >> yeah, a woman born in a poor part of northeast india and lived there until age two, her father was a physicist and and brought her family to canada and the u.s. at age 11, went back to india, the same poor part of northeast india, also dealing with the the sexual discrimination and so on and india. it's hard to get ahead, and yet she aced the exams to get into the indian institute of

technology, superbright, of course. when she got there, she had a problem and the boys were all study with each other. she had nobody to study with, and so she felt very isolated. it was harder for her because of that. it helps the study with other people. so she came to the u.s. and worked for a couple of big companies including oracle, facebook, went to stanford business school. while she was there, she said i really want to help girls like me, what can i do? she came up with the idea of creating this website. carol: and it's not just a small little website. it's free, but you're talking about two and a half million users who stay there for several hours a day. >> that's right. it started with just a few professors, but in 2011, she opened it up to all comers. and it just took off.

like 98% of the computer science students of the top 50 american universities are on this website. jason: that's unbelievable. to go back to the companies, how do they get in? >> they pay and access the, and -- >> they pay an access fee, and again, for students who opt in, they can find out what courses they've taken, what specialties for example. machine learning, i need somebody in machine learning, graduating 2018, yes. check those boxes. carol: up next, a data scientist helping create ethical robots. jason: this is "bloomberg businessweek." ♪

carol: welcome back. jason: you can also listen to us on the radio. 90 91. 11 30 in new york, fm in washington, d.c. nam 960 in the bay area. carol: and in asia on the bloomberg radio plus app. time to check in with our editor joel webber to see what stood out for him. jason: a lot going on with him, ai of course, but we also talked about turkey, president erdogan. and a little bit about tom wolfe. >> when we put all of this together, we noticed one theme that we really wanted to capture and own in the issue, which was artificial intelligence. it is sort of changing everything, it's like making money, driving cars, just upending business as a whole and freaking people out.

>> and you really tapped some amazing writers to bring this to us. >> we really just outsourced it to ashlee. [laughter] and we were able to go to places that most people don't know about. like the oral history of artificial intelligence, and lo and behold, canada has been this driving force. justin trudeau is in the issue, talking about did you know that canada is this mecca for ai? jason: going beyond, what else jumped out at you? >> we had an ongoing theme of trying to talk to world leaders. we had another one from turkey, which is in everyone's eyes because erdogan, who has been there for more than a decade, has strengthened his grip on power. we had some exclusive interviews with him in london this week, and it really framed everything because capital markets are taking a look at what is happening in turkey and not liking the influence he is

exerting on his government. carol: people should go check out the interview. we also can't forget mr. wolf. >> rip tom wolfe, he is one of the reasons i got into journalism, he was a shame -- game changer for journalism going back into the 60's and 70's he was really a northstar for me personally. but the reason wall street remembers him is the seminal work he did in the 80's, and it bonfire of the vanities. it was really a zeitgeist of the 80's and in our finance section, we put a little white hat. rip tom wolfe. jason: next, we check with alan hewitt, who has been working with a lot of issues with harassment. carol: taking a look at ai, bias, and the criminal justice system. >> she has been pretty well

known amongst people who are championing fairness in machine learning and ai, but she became more well-known last december when she wrote a medium post about some of the harassment she has experienced in her career. jason: at first blush, people might wonder how a machine can be biased. >> it goes back to the data sets that people use to build these predictive models that they then applied to real-life setting. for example, they read a paper published by a predictive policing company and this company makes a product that helps police out on the beat figure out which areas in a city are most likely to have crime. it's not people, not quite like

minority report, but it is similar. it says, maybe you should check out this intersection at this time of day. so, a lot of that output is based off of police records that the police department has been gathering. so her and her coworkers looked at two things, they looked at arrest records related to drug crimes in oakland, and compared them to public health data about who is most likely to be using drugs in a similar area. just doing a comparison, they found police enforcement is disproportionately focused on communities of color. saying there is more criminal activity there rather than usage. when they ran their model, doing this sort of predictive outputs, they found that it was telling cops to go places where they would only be increasing and amplifying the bias that was already shown in these records.

so, even though people tend to have this idea of computers as fair, the reality is when you use data coming from human activities, such as policing it , is really hard to get past the things that made that data askew in the first place, especially if you don't have a complete set. like working off of data made by human choice. jason: so what are her recommendations to fix it? >> she said, and i had thought about this, what is heart is not convincing people out rhythms can be biased, but figuring out exactly what people agree on is fair. this is sort of a question of philosophy, what is fairness? it becomes even more complicated when you have to be as specific as saying we are making this algorithm and the output is going to be fair.

people want the same thing, but they disagree on how to get there. carol: bloomberg businessweek is available on newsstands now. jason: and at businessweek.com and on the mobile app. what was your must read? carol: you kind of have to read the whole issue. outside of that, i enjoyed talking to peter coy about his story about chimerica, the relationship between the united states and china. it's a flawed relationship and there's a lot at risk. yours? jason: i can't get enough of ashlee vance. we send this guy around the held to find characters and wrote the book, literally, on elon musk. he found this guy in switzerland and introduce a new verb. more bloomberg television starts, right now. ♪

♪ david: did you always know you that you wanted to run fidelity? abigail: i never felt any pressure to. david: did your father say if you work hard, in 20 or 30 years, you will be the ceo? abigail: he was not the guy who made promises to anybody. david: what do you think investors mostly want? abigail: everything. david: was it complicated growing up with your father and family being so famous? abigail: we were not famous at all. i mean, this this was the equity market in the 1970's, david. david: as you look at what your future will be? abigail: i think this is the moment that i have been waiting for. >> would you fix your tie, please? david: well, people would not recognize me if my tie was fixed, but ok. just leave it this way. all right. ♪