Speaker 1: Stuff. This is the sound of a busy trading floor
Speaker 1: in Wells Fargo skyscraper in New York City. It's filled
Speaker 1: with stockbrokers who are researching companies, writing reports, chatting with investors,
Speaker 1: and processing their trades. These are classic and highly paid
Speaker 1: jobs that have been around for more than a century.
Speaker 1: Imagine if one day those jokes went away. Yeah, this
Speaker 1: is a scenario that Ken Center, a veteran Internet analyst,
Speaker 1: one of those classic brokers, is forcing himself to think about.
Speaker 1: Canada developer sidekick named Brian Healy have spent months building
Speaker 1: an artificially intelligent robo analysts that performs a lot of
Speaker 1: what Ken currently does for a living. Ken calls the
Speaker 1: system ERA, which stands for Artificially Intelligent Equity Research Analyst.
Speaker 1: It's an elaborate warning to investments, firms and banks that
Speaker 1: they should learn and embrace AI to catch up with
Speaker 1: Internet giants like Google, even if it means automation destroys
Speaker 1: some lucrative all street jobs. And it's a broader wake
Speaker 1: up call for all of us. Artificial intelligence is here
Speaker 1: and it's changing complex, high paying jobs for good. We
Speaker 1: better be ready. M Hi, I'm brad Stone, I'm Julie
Speaker 1: Verge and I'm Alice de Ba, and this week Undercrypted,
Speaker 1: we're taking you inside Ken's quest to build an ever
Speaker 1: evolving and always learning software program. This program could eventually
Speaker 1: put Ken out of a job, or at least do
Speaker 1: several parts of his job a lot better than him.
Speaker 1: Stay with us, So, Alian, Julie, you guys recently went
Speaker 1: to go meet the brains behind this project. We did.
Speaker 1: It was a cool October morning. We picked up an
Speaker 1: audio recorder and headed down a few blocks to Wells
Speaker 1: Fargo's office on Park Avenue. And Ali, You've known Ken
Speaker 1: the stock analysts for a while, Yeah, all the way
Speaker 1: back to the days when I was a reporter voters
Speaker 1: in the Wall Street Journal. I'd often call him for
Speaker 1: advice on tech companies and news. He's tall, with wavy,
Speaker 1: sweep back hair, and when we met him, he's wearing
Speaker 1: a crisp suit and tied like he was born in
Speaker 1: the outfit. Brian, on the other hand, comes from a
Speaker 1: tech background. He helped build Alexa, the digital at home
Speaker 1: assistant whose voice you hear on Amazon's Echo speaker. Brian
Speaker 1: has a goatee and no nonsense, close cropped hairstyle. And
Speaker 1: I have to admit he didn't look so comfortable in
Speaker 1: that suit that he was wearing. It sounds like an
Speaker 1: odd couple. How did they meet? It actually kind of
Speaker 1: started out by a chance. Ken was organizing an AI
Speaker 1: conference in two and had reached out to Brian's US
Speaker 1: to be a speaker. He was busy, so he recommended
Speaker 1: Brian go in his place. They hit it off, and
Speaker 1: soon after the conference they started working together on other projects.
Speaker 1: You know, I would come up on the weekends and
Speaker 1: stuff and um talk about the technology and just sort
Speaker 1: of be a resources available when he's talking to clients. UM.
Speaker 1: And then it just kind of kept evolving from you know,
Speaker 1: we kept working together. While spending all this time researching
Speaker 1: how Internet giants were using AI to create better products,
Speaker 1: they came upon a scary but important question. My goal
Speaker 1: was to try and get Bryan to help maybe just
Speaker 1: sort of deepen my understanding so that I could help
Speaker 1: our clients to understand this. And I guess we sort
Speaker 1: of put it out there is almost a question kind
Speaker 1: of like what what can't be automated? At this point,
Speaker 1: that's when they agreed to create a software version of
Speaker 1: Ken but better era. This is to Brian built reads
Speaker 1: news stories on companies and distills that into a sentiment
Speaker 1: score from one to zero, with one being wonderful and
Speaker 1: zero for absolutely terrible. Then she'd monitor the stock market
Speaker 1: to see if these positive or negative articles would move
Speaker 1: share prices. If she spotted a correlation, she'd remember that
Speaker 1: and use it to make predictions in the future, including buy, sell,
Speaker 1: and hold ratings. Finally, or would sure not short rand
Speaker 1: summaries explaining the predictions she was making. The project started
Speaker 1: out as an experiment not for public consumption. Could Brian
Speaker 1: use machine learning, a hot type of AI to make
Speaker 1: a robot analyst that was actually useful? And Brian said, well,
Speaker 1: you have to explain to me what you do. So
Speaker 1: I said, okay, well let's let's see if we can
Speaker 1: we can do this. Um, I'll take you through what
Speaker 1: I do as an equity research journealist, and you know
Speaker 1: to the best you know the best I can, and
Speaker 1: you take me through what would be sort of the
Speaker 1: tools that would could be used to replace or enhance
Speaker 1: what I do. He used techniques like natural language processing
Speaker 1: to build a system this summer in his spare time.
Speaker 1: Because we have to remember, Brian has a day job
Speaker 1: as head of AI at a company called Lola. You know,
Speaker 1: this would probably be a good time to define our
Speaker 1: terms for the uninitiated. What is artificial intelligence? He's Brian
Speaker 1: with an explanation. The most basic answer, Machine learning is
Speaker 1: any engineering technique that means the software is not discriminately programmed.
Speaker 1: So I didn't write code that had specific discrete branders
Speaker 1: and comportent things. It was a system of statistically learning.
Speaker 1: In general, the industry term for machine learning means learning
Speaker 1: specifically from large volumes of data. It gets very complicated.
Speaker 1: Beyond this, we won't take you down the rabbit hole,
Speaker 1: but if you only take away one thing from this
Speaker 1: podcast is this these computer programs update themselves without humans
Speaker 1: having to do very much work. You know a model
Speaker 1: that's it takes data and passes it to a data
Speaker 1: analytics team, and then they pass it to a product
Speaker 1: development team and their product development teams who works with
Speaker 1: the engineers to bring it back into the model. Um,
Speaker 1: you're you're allowing your essentially seeing where data can drive
Speaker 1: the changes within a model's performance. What you're talking about
Speaker 1: is removing certain bottlenecks in terms of how these companies
Speaker 1: innovative ERA makes stock calls, and it also tracks how
Speaker 1: these recommendations end up panning out. If their prediction turns
Speaker 1: out to be wrong, the system remembers it and it's
Speaker 1: less likely to make that decision again in the future. Okay,
Speaker 1: so let's get back to the story. So here Ken
Speaker 1: and Brian trying to build this robo analyst. Initially, eras
Speaker 1: summaries were tough to eat, they were disjointed, and the
Speaker 1: grammar was off. You could really tell it was written
Speaker 1: by a computer. But after a couple of months her
Speaker 1: reports started making sense. I think I sort of realized
Speaker 1: it could be a thing when it started actually producing language.
Speaker 1: So when that got generated, it was kind of like, Okay,
Speaker 1: this is actually kind of meat like in saying what
Speaker 1: it's doing. It sort of explaining itself, and it just
Speaker 1: sort of highlighted that as we keep consuming more data
Speaker 1: over time, these are only going to get better, and
Speaker 1: so we should just keep doing it. So this is
Speaker 1: when the project starts feeling real. Yeah, and Ken decided
Speaker 1: it was time to share ERA with the outside world,
Speaker 1: but first he needed to get permission from his company,
Speaker 1: which also happens to be one of the biggest banks
Speaker 1: in America Wells Fargo. This must have been a pretty
Speaker 1: big risk for Ken. Wells Fargo is a pretty traditional
Speaker 1: bank and it's come under a lot of regulatory scrutiny
Speaker 1: over the past few years for creating accounts that weren't
Speaker 1: opened by actual customers, and the bureaucracy that comes with
Speaker 1: doing anything new and a large company is significant, and
Speaker 1: bread on top of that, Ken had just joined the company.
Speaker 1: When he told the compliance department about what he was
Speaker 1: up to, they weren't very pleased. There was sort of
Speaker 1: a disbelief at first that we we actually wanted to
Speaker 1: put an analyst out there that we weekly was artificially intelligent,
Speaker 1: and that we wanted to provide predictions around the stocks,
Speaker 1: and that actually, you know, could write its own research,
Speaker 1: and not only that, but it could bold the sections
Speaker 1: of text that it felt really it wanted to underscores
Speaker 1: being important to its specific stock thesis. Right, So that
Speaker 1: was sort of I think people just sort of kind
Speaker 1: of through their head back and we're in a bit
Speaker 1: of disbelief. Why were they so shocked? So equity research
Speaker 1: analysts spend all day thinking about the future of the
Speaker 1: companies they cover and how that will impact stock prices.
Speaker 1: They send these reports to clients, making different recommendations on
Speaker 1: whether to buy, sell, or hold, along with a price target,
Speaker 1: and that's usually an estimate of where they think the
Speaker 1: stock is going to be in about twelve months. And
Speaker 1: if they're wrong. If they're wrong, investors can get mad
Speaker 1: and go somewhere else for advice and trading services, and
Speaker 1: these are things clients pay a lot lot of money
Speaker 1: for the analyst whole reputation, and by extension, the bank's
Speaker 1: reputation rests on the accuracy of their stock ratings, so
Speaker 1: there was a lot that could go wrong. Okay, so
Speaker 1: so far, the analyst Ken has formed an unlikely friendship
Speaker 1: with the developer Brian. They built this bot to recommend
Speaker 1: when to buy and sell certain companies. Their ambition shocks
Speaker 1: Wells Fargo's compliance department. What happens next well Can eventually
Speaker 1: succeeds in convincing the compliance department to let him proceed,
Speaker 1: and it comes time to unveil Era to the public.
Speaker 1: This happens on September two thousand seventeen. She started making
Speaker 1: stock recommendations using this complex new software that only a
Speaker 1: few people on Wall Street even understood. What was the
Speaker 1: response like, is your AI technological work with Era? Is
Speaker 1: it gonna put securities research a business? No, and we
Speaker 1: did it more as a study. I think for clients
Speaker 1: who tend to be a little longer term in duration,
Speaker 1: they're interested in how do you build it right? What
Speaker 1: is you know? How do you think about the application
Speaker 1: of this technology? For clients were a little bit more
Speaker 1: short term and focus, you know what are Era's predictions
Speaker 1: this week? Right? Can EARRA help me manage news flow
Speaker 1: and help me synthesize? What can means here is that
Speaker 1: the summaries error rights can help clients pick through the
Speaker 1: daily avalanche of online news to find real developments that
Speaker 1: will actually move the stock. We recently asked Brian to
Speaker 1: hook up ERA to an Amazon Echo speaker. Alexa. Ask
Speaker 1: my AI analyst what she thinks of Google as of yesterday.
Speaker 1: I think Google looks like a hold and that forecast
Speaker 1: is good until October. Here's why from Nashable dot com.
Speaker 1: Alphabet just took an important step towards becoming a major
Speaker 1: force in the online payments world. As tech Crunch reports,
Speaker 1: Let's say they mark AI is an important topic to
Speaker 1: cover for a number of names. Well, they're you know inbox?
Speaker 1: Could you know swell with all these AI articles, but
Speaker 1: they may or may not actually be relevant to the
Speaker 1: stock's performance. So this program launches in September and it
Speaker 1: starts making all kinds of calls. The first week went fine.
Speaker 1: The second week the Era through a curve bool downgrading
Speaker 1: shares of Facebook, recommending the investor to sell the shares
Speaker 1: of the company. And now we should add here that
Speaker 1: as of this taping, Facebook stock is of about fifty
Speaker 1: this year alone, and forty six analysts that we have
Speaker 1: listed on the Bloomberg terminal, only two of them have
Speaker 1: cell ratings on the stock. So Era made a contrarian call.
Speaker 1: And the funny thing is Era's downgrade contradicted Ken's own
Speaker 1: opinion because he has a by rating on the stock.
Speaker 1: Turns out Era had read thousands of stories about Russian
Speaker 1: ads on Facebook that were designed to divide US voters
Speaker 1: out of last year's US presidential election. Russia probe is
Speaker 1: focused on people, including three former Trump aids, charged with crimes.
Speaker 1: Another phase focuses on corporations like Twitter, Google, and especially Facebook. Today,
Speaker 1: politicians who are up in arms. Congress had called for
Speaker 1: hearings all these negative articles drowned out other positive pieces.
Speaker 1: Why is it so important that we all see these
Speaker 1: ads right now? Well, when you look at them, they
Speaker 1: are I have seen them, and this means eras AI
Speaker 1: algorithms picked up on this in advice selling to avoid
Speaker 1: a drop in the share price, which she had predicted
Speaker 1: over the next week, and what ended up happening, Erra
Speaker 1: was wrong. Facebook start ended up rising a bit over
Speaker 1: the next week. Some clients weren't happy. A few saw
Speaker 1: it as proof that what Ken and Brian were doing
Speaker 1: didn't matter. For others, it confirmed their view that the
Speaker 1: way Ken was using AI was fundamentally flawed. That was
Speaker 1: a disappointment because I think that you know, people are
Speaker 1: are their zero in on things that they're kind of
Speaker 1: missing the bigger picture here. To Richard Johnson, a vice
Speaker 1: president at research firm Greenwich Associates, it was a sign
Speaker 1: of how far AI and finance still has to go.
Speaker 1: The robot downgrading Facebook, I think that's probably where this
Speaker 1: type of analysis is going to struggle right now. I
Speaker 1: think we're very much in the early days of it.
Speaker 1: But you know, in that example, perhaps you know the
Speaker 1: album Nettle bit of fine tuning and to kind of
Speaker 1: you know, give less weight to the Russian fake news
Speaker 1: type stories that are in the media because I think,
Speaker 1: you know, we all kind of thing that your Facebook
Speaker 1: will survive, will will survive this. When Ken first explained
Speaker 1: error to Wells Fargo's compliance team, he said the only
Speaker 1: relevant rule they could find was recent guidance on robo advisors. Now,
Speaker 1: these programs automatically decide how much of your investment portfolio
Speaker 1: should be in stocks, bonds, and other assets. One recommendation
Speaker 1: that they had was to tell clients about quote changes
Speaker 1: to algorithmic code that may materially affect their portfolios. But
Speaker 1: this doesn't apply to era's self learning approach. What I
Speaker 1: think it's very interesting about this and why there's such
Speaker 1: a great learning opportunity as that robo advisors tend to
Speaker 1: tend to be programmed. What can means to say here
Speaker 1: is this, robo advisors are mostly programmed in the old
Speaker 1: fashioned way humans right, software code that gives step by
Speaker 1: step explicit constructions on what to do in certain situations.
Speaker 1: With a product like ERA, you don't program her, right,
Speaker 1: the data programs her so her, so her algorithms are
Speaker 1: constantly changing as a result of that, and so we
Speaker 1: to some extent we moved out, I think a little
Speaker 1: further than what you know, finn Ra and kind of
Speaker 1: these agencies that regulate, you know, our industry are are
Speaker 1: used to. The solution to this was to include a
Speaker 1: bold faced disclaimer and every report stressing the era's stock
Speaker 1: ratings were not investment advice and should only be read
Speaker 1: to gain a greater understanding of artificial intelligence. And when
Speaker 1: we met Ken in New York, we mentioned to him
Speaker 1: that he'd been putting the disclaimer near the top of
Speaker 1: each note he publishes about ever, maybe I did just
Speaker 1: to kind of, you know, continue to get it as
Speaker 1: soon as I break from in terms of what I'm
Speaker 1: writing about to what when Aaron starts writing, I'm just
Speaker 1: hitting with the disclaimer. I don't want to control what
Speaker 1: people trade on one way or the other. If they
Speaker 1: take her advice and they feel that, you know, they
Speaker 1: agree with her sun reads and they like her points great,
Speaker 1: you know, I don't want to tell them, don't you know,
Speaker 1: don't take an information that she has. So where's Eric today?
Speaker 1: She's now covering more than five hundred stocks and reading
Speaker 1: about half a million news stories a day. That's half
Speaker 1: a million a day, a few weeks ago, the system
Speaker 1: also analyzed tech company earnings for the first time and
Speaker 1: issued nine recommendations on stocks like Amazon, Google, and Netflix.
Speaker 1: And this time did I get it right? This time?
Speaker 1: They all proved to be accurate after a week. Well,
Speaker 1: recently these stocks have performed so well my dog can
Speaker 1: probably recommend it. But seriously, it does beg the question.
Speaker 1: Has Ken succeeded in creating his own autonomous replacement? Here's
Speaker 1: what he had to say about that. But I think
Speaker 1: in the end it's sort of evolved over time. Is
Speaker 1: one more of an enhancement. It certainly took took out
Speaker 1: maybe or it showed how a certain amount of the
Speaker 1: work that we do in terms of handling news volume
Speaker 1: and and trying to sort of be specific in terms
Speaker 1: of waitings of that news volume in terms of stock prediction,
Speaker 1: how it could be I think improved. Other analysts agree
Speaker 1: on the point that cutting the workload is a great idea,
Speaker 1: but they're way less keen to say that an AI
Speaker 1: analyst can help them make stock recommendations alongside their own calls.
Speaker 1: As far as having an air bra our official intelligence
Speaker 1: Bank research analyst, I don't think that's happening in the
Speaker 1: the next decade um, but I'll often watch my back.
Speaker 1: That's Mike Mayo, a veteran bank analyst at Wells Fargo,
Speaker 1: who came in to chat with us about Kennon Bryan's creation. Now.
Speaker 1: I was super excited to see Mike because I remember
Speaker 1: my first day as a reporter on the financial market.
Speaker 1: Mike was one of my first calls. He actually picked
Speaker 1: up the phone, and I feel like I'm some super
Speaker 1: cool twenty two year old talking to this big guy analyst.
Speaker 1: He's a sort of a finance nerd, but you can
Speaker 1: tell instantly that he's so passionate about what he does.
Speaker 1: For him. ARA is all about enriching his role and
Speaker 1: making banks more efficient. I don't want to spend all
Speaker 1: this time looking at data feeds and checking articles and
Speaker 1: you know, checking barons over the weekend and did I
Speaker 1: catch the Wall Street Journal story? And a lot of
Speaker 1: time for looking at all this information just to see
Speaker 1: if we're missing anything. Now, Mike said that is associate
Speaker 1: analyst probably spends about seventy of his time checking news articles,
Speaker 1: gathering other relevant information, and manipulating data, which are all
Speaker 1: tasks that AI can automate. Mike himself reckons that probably
Speaker 1: about a third of his time is spent on those tasks.
Speaker 1: The search. Yeah, you can automate looking for the needle
Speaker 1: in the haystack, or in this case, a few needs.
Speaker 1: You can automate finding a few needles in the haystack.
Speaker 1: That would be fantastic. That give us more time to
Speaker 1: go kick the tires and talk to management and have
Speaker 1: much more creative research. Richard, the researcher from Greenwich Associates,
Speaker 1: estimates that of finance jobs at risk from AI automation
Speaker 1: and research jobs among the most exposed. I think there
Speaker 1: will be an impact on jobs. I think for sure,
Speaker 1: we know that there's significant productivity and efficiency gains that
Speaker 1: can come from it. You still need, uh, you know,
Speaker 1: some people, you know, some human analysts to kind of
Speaker 1: interpret a lot of the data and so forth. Of course,
Speaker 1: in this example about the world's fargo on the programmer,
Speaker 1: he obviously had to put a lot of inputs into that,
Speaker 1: telling the machine learning algorithm what to look for and
Speaker 1: so forth. So that type of skill is still going
Speaker 1: to be needed. But maybe you don't need a team
Speaker 1: of fifty junior analysts trying to so will that iconic
Speaker 1: trading floor Hubbub and chaos ever be silenced by machines.
Speaker 1: For now, I'd say that the human analysts are safe,
Speaker 1: but I'm I'm definitely not ready to put my money
Speaker 1: on them and their associates all being there in five
Speaker 1: to ten years. Ken himself is holding out hope for
Speaker 1: human stock research. He used his final moments of the
Speaker 1: interview to re emphasize that ERA is an enhancement rather
Speaker 1: than a replacement to everything that Mike said. As we
Speaker 1: look out over the next so many years, it is
Speaker 1: really an opportunity for us to see kind of an
Speaker 1: enhanced performance and an ability for us to move into
Speaker 1: sort of a creative research sphere that would be difficult
Speaker 1: to do otherwise. So, Julie, are other banks starting to
Speaker 1: do this too? Were showing signs of following the path
Speaker 1: forged by Brian and Ken. We've seen a little bit
Speaker 1: like this. Um nothing exactly what Brian and Kenn are doing,
Speaker 1: but Morgan Stanley has a sort of bought that will
Speaker 1: help research analysts dive through earning season, which is obviously
Speaker 1: one of the busiest times of years for these guys.
Speaker 1: Tons of news articles coming out each day, so we'll
Speaker 1: help automate what those earnings are and whatnot. But otherwise
Speaker 1: there's not anything exactly like ERA that's skimming through news articles,
Speaker 1: social media and whatnot on a daily basis. I heard
Speaker 1: you refer to Era as a she a couple of times.
Speaker 1: Ken did as well, why are we giving this AI
Speaker 1: analyst agender. It's a little bit of a canned response,
Speaker 1: and that it's just because there aren't enough female analysts
Speaker 1: on Wall Street. So I guess if AARA takes over,
Speaker 1: then there won't be enough male analysts on Wall Street. Okay, Alice,
Speaker 1: it's a little surprising that it took Wall Street this
Speaker 1: long to do this when algorithmic trading it already transformed
Speaker 1: finance years ago. What took the industry so long? Well,
Speaker 1: some of it is evident in the performance of error actually,
Speaker 1: So each time it makes a call on a stock,
Speaker 1: that's a data point that can be fed back into
Speaker 1: the system. Now you compare that to you know, automated
Speaker 1: trading systems that hedge funds they trade, you know, maybe
Speaker 1: you know, ten times a minute or something like that.
Speaker 1: So you just have a lot more data points to
Speaker 1: train the systems on. Oh, so that leads to the
Speaker 1: last question here of course, as journalist, we are not
Speaker 1: allowed to invest or buy individual shares in the companies
Speaker 1: we cover. But let's put aside those rules for a second.
Speaker 1: With what we learned today about Era, Julie, would you
Speaker 1: follow the bot's recommendation? Would you take advice from an Ai?
Speaker 1: Not at this point, but I with how quickly she's
Speaker 1: gotten smarter and learned from her recommendations, I would say
Speaker 1: in a year or two, I want to be surprised
Speaker 1: if I was al Stair. I'm a I'm a human
Speaker 1: all the way. Um, the only thing I probably would
Speaker 1: use it for is to scan the all the news
Speaker 1: articles to see what's most important that that I would
Speaker 1: make my hand decisions. Yeah, I think I probably agree
Speaker 1: with you. I'm gonna I'm gonna sound not as future
Speaker 1: leaning as as Julie. I guess I just think that
Speaker 1: the inputs that Air is looking at, primarily it seems
Speaker 1: like it is the news is you know, one important
Speaker 1: to mention, but it's not everything. And it just does
Speaker 1: seem like there's so much intuition and knowledge of a
Speaker 1: management team and it's past mistakes and future opportunities that
Speaker 1: goes into making these stock picks. I don't think air
Speaker 1: is there yet. But Julie, maybe I do agree with
Speaker 1: you that one day, probably within our lifetimes, perhaps we
Speaker 1: we will be taking training advice from an artificial intelligence. Yeah,
Speaker 1: and I guess maybe the most likely scenario is that
Speaker 1: it's just Ken and Mike and not the three or
Speaker 1: four people they have working for them, right, Like, they
Speaker 1: just have an era and that's all they need. The
Speaker 1: problem is if they if they give you bad advice,
Speaker 1: you know, can you really blame the robot? Tbd And
Speaker 1: that's it for this week's episode of Decrypted. Thanks for listening.
Speaker 1: Is artificial intelligence changing your workplace? Send us an email
Speaker 1: at decrypted at Bloomberg dot net, or you can reach
Speaker 1: out to us on Twitter. I'm at at Julie Verhe,
Speaker 1: I'm at Alista M. Barr, and I'm at Bradstone. If
Speaker 1: you haven't already, please subscribe to our show wherever you
Speaker 1: get your podcasts. And while you're there, I hope you
Speaker 1: take a minute to leave us a rating and a review.
Speaker 1: This does so much to get us in front of
Speaker 1: more listeners. This episode was produced by Pie Good, Cary Akita,
Speaker 1: Liz Smith, and Magnus and Mixon