Investing Reimagined In An AI World

okay who's excited after lunch to hear some uh panels about [Applause] investing um so I'm a Boaz faer I work with link Ventures uh as an investor my claim to fame is that I uh get to speak to John Werner every so often um who likes John Werner who doesn't who does it yeah um yeah and I'm really excited about this panel uh because a because we have this really amazing um High Caliber uh panelist but also because I both invest so any AI advancement can help me and I'm always looking for some advancements um and B because I'm investing in companies that are disrupting the investment industry um one of the latest is uh samir's company farsight so this is very exciting uh you know to hear all your uh different perspectives so why don't we start with uh some intros uh Lauren you can start hi everyone my name is Lauren Clemens I lead up Innovation and emerging technology work at credential uh nice to see you all today hi uh hear me okay perfect uh Samir daada co-founder and CEO of farset AI uh we automate and deliver proprietary AI agents that helped streamline workflows across financial services and insurance uh prior to this I was a private Equity investor at a firm called General Atlantic hi I'm Lisa um I lead a AI uh Technology Center at Fidelity uh for about the Last 5 Years um before that I I led the investing team at betterman uh on the I guess original robot advisors um and I'm really happy to be here today good afternoon my name is John Woo I'm president of a Labs aval laabs the team behind Avalanche a layer one blockchain and we actually help a lot of financial services firms JP Morgan KKR Apollo to name a few create workflow that's more efficient on a blockchain prior to being an operator I was an investor just like everyone else in this panel I worked at I was a tech investor at my own fund at uh Kingdon capital and at Tiger amazing um so let's start with the pessimism uh will go from risks of Investments uh in the AI world to Opportunities um so future of work are we afraid that AI is going to replace investors some portion of workers or is it somehow going to balance itself uh Lauren you want to address this well I I definitely wouldn't say that I'm afraid of it um but for sure it's going to change how we do a lot of things um and I think it's actually kind of an extension of the changes that have already happened a lot in the investment space right so for a long time we've been using algorithms we've been uh using technology to do you know the work with massive amounts of data with real-time computations with extremely highe speed trading all of those things have continued to get better and better and it is it's changed the profile of what you need to do for in the investment space right right um I think we'll continue to see that it'll it'll hit different positions differently um but at the end of the day if you're replacing you know someone who's making a decision on what to buy or sell um with a program that was created by a human who's decided what the goals of that bot should be you're you're kind of there's still that person who's directing it and at the end of the day there's no right answer to in investing right it's it there's an angle to it about what Will other people do right so if I'm right about what's going to happen uh it's meaningless unless I know how the stock is going to move or how the investment is going to change and that depends on other people so um I think it's going to be a little bit more subtle okay so you're not a strong believer in uh kind of like AGI um machines going to determine what the machines will decide to invest in well well they can but at the end of the day who are they buying and selling from if you know if it's all machines then who has the edge and so it's going to I think will progress the same way we've seen with other Technologies like extremely high spe speed algorithmic trading where you know you'll you'll see some odd behaviors at certain times there'll be more breakthroughs there'll be fewer breakthroughs but um I think it's not as simple as just computer versus human there uh Lisa do you want to address this question also yeah um I'm waiting for my little AI agent right here to help me with everything that I do um I think you know I'm embracing this you know this wave uh something definitely is different right uh today AI versus like even two years ago um I see I see AI now as just another kind of computer and and that should be really useful for for what you we do as investors right from like the entire value chain you know so I'm totally embracing this this wave of AI right now and with no fear um I don't think it will replace humans humans always have a place um you know we create the AI we destroy Ai and then we create it again with better AI so um I have more optimism oh yeah I thought it's going to be a little bit more pessimistic um Lisa I'll I'll continue with you um thinking about open Innovation versus closed uh we're seeing a lot of power concentrated with large organizations uh many of you are uh you know work for large organizations are we should should we be concerned what are what are the benefits of Open Source versus clothes if you take like Microsoft versus meta um yeah um um I'm really thankful for meta right I'm I'm All About open source the more open the better because you know that's that's how we progress right as Humanity you know and I think I think there's still going to be differentiation right because the models are going to they're going to be commoditized um they're going to be open sourc and you know when you put all the brain power of all humanity and open source it's going to win over any close close you know Source models I think but the differentiation comes from your own data right um and firms everybody has their own data firms have their own data you as individuals have your own data and how and the models suppli to your own data that's that's democratizing and that's powerful and then the second stage is really like how do you represent your own data there's IP there um and and that's not going to be you know commoditized away so easily um fair enough John as a know both investor uh have probably a very interesting view on AI and in the crypto space what is your take on open you're not going to ask a blockchain guy or a crypto person whether they believe in open inovation or not we I obviously am a big proponent of open Innovation and the benefits of open Innovation I'm going to start by going back to your first question I think AI is very important to the investment process and will change how you invest and will make it easier to do many many things you know that that's been the evolution of Technology improving how people work what I think open innovation in the hedge fund investing world or in the VC world may actually change business models in terms of how a fund is set up and how it works there are companies out there trying to do this and it's still very early stage I don't know how many people uh know the company numerai and what numerai is basically doing is it is trying to replicate Quant likee returns that generally speaking were reserved for places that has so much scale that they can hire the 200 new brightest smartest PhD kids coming out of MIT and they really had a monopoly over the talent but if you can create a mechanism where all the people in the system are line from users of the of the Quant models data scientists data contributors and people who can who are going to also fine-tune those models then I can see a world where you're crowdsourcing data science and data analytics to the point where it benefits everyone and as a user the disruption is you actually who cannot get into Renaissance or two Sigma because you're an individual or because you don't want to pay the 220 or two whatever they charge you're actually able to get uh equivalent type models that you can work yourself in by using you know some sort of payment and some sort of functionality like a token or something and get the benefits of that kind of Quant type investing so you have to believe that you can crowdsource thousands of people maybe not full-time but you're getting thousands of part-time data scientists and um data you know dat contributors will be as good or close to or maybe even better than that small closed nich up to a 300 phds being monopolized by some big player thank you um let's move uh on that note let's move to a little bit more uh opportunities in investment with AI uh as a venture capitalists both investing and trying to support the portfolio companies to uh to grow um one thing that we're struggling with a lot is how do how do we uh penetrate the Legacy players as vendors how do the portfolio companies sell to vendors um uh I think Samir that would be very interesting to hear your you know your experience and U yeah yeah so I I I can speak to this a little bit from the perspective of someone who's a smaller company on the outside and a little bit of how we found success uh working with Legacy players that you know credential Fidelity on the stage and and some uh other large players I think it really comes down to this idea of crawl walk run this is nothing new every time a new technology comes out that's how Enterprises think about adoption um and and the real challenge is both from the vendor perspective and then also from the perspective of the internal Champion within the organization it's your job to together figure out that road map that gets you from you know lowrisk low opportunity use cases but then have a very clear road map that iteratively adds layers of complexity layers of risk and ultimately unlocks that transformational value um and so one of the biggest things I'd say is it's sounds very easy right crawl walk run where it becomes difficult is at each phase of crawling and walking and running you need to align to three stakeholders right you have the business person who needs to see immediate business value and immediate Roi even though you're not allowed to touch proprietary data just yet then secondly you have the IT people who need everything to be auditable traceable high security compliance um and then lastly you need to appease uh the vendor as well right it needs to be a project that's repeatable enough and scalable enough uh for it to be worth their time and so uh what I found is two companies from the outside can look exactly the same and the road map of craww walk run that you come up with is totally different based on who the players are and who you're talking to internally it's very interesting uh would love to hear now the other side of the equation perspective uh of uh credential other Legacy players um how do you see this yeah so I guess my first message to this group is we want to use the newest latest greatest Technologies AI all of it um and so we want you to know that there's certain things that make that easier for us to do or or harder for us to do and Samir touched on a whole bunch of um the more that you can uh tell us about the quality of the output of what we're getting from the tools the easier it is for us to move ahead right so would I pick a tool that's right 90% of the time but H I have no way of judging the 10% or a tool that's right 60% of the time but it's really good at telling me hey this answer this is this is a really good answer um I would always take that second one because I can I can work uh operationally around those other instances right um same thing with explainability um oversight like building into the tool that walk what what was it crawl then walk then run I'm getting ahead of myself um if you can build that into the tool and give us a way to fine tune or say hey this is what's working then we can start to take the human out of it faster um so it and it doesn't necessarily have to be like you know a huge part of the products but just thinking that way from the start hey what's going to make someone like you know uh Lauren or Lisa like able to actually put this in the hands of people and start using it so what do you think is the role of proprietary data for instance in in that aspect yeah so proprietary data um you know as we think about what do we build ourselves what do we partner with people on what we buy off the shelf um data is a really important angle to that right so um I think there's an understanding amongst uh most large companies that proprietary data um is is like gold in this new AI community so um you're going to be very sensitive to um wanting to maintain that proprietary um angle uh to anything you use so uh I guess I would add that to to my list as well so it's kind of uh implied learnings from data uh all of that stuff you're going to need to be able to talk to that to you know people at large Enterprises especially highly regulated ones um so I would love to move a little bit to talk about the product themselves um Lisa maybe you can talk a little bit about your perspective on what is the winning value proposition is it just productivity you know uh what Clara just did with its uh call centers uh reducing cost 40% or are we more excited about valuable insights that create value um I'm excited about both but I think productivity is you know is there no brainer like that's the first thing that you're going to tackle right AI is a computer go use it right go use it for good go use it for your tasks for everything that you do in your entire workflow right in in finance so productivity yeah I think um it's so much easier to build with AI now like I actually think like you know we're underestimating how much productivity gain we can get you know sometimes people throw numbers like you know will be 40% more productive I actually think it's like multiples like it could be like 2x 3x more productive I don't know again maybe too optimistic hey we're going to talk about the investment field your product is simply your returns I don't really care if it's one guy with the great gut instinct or they have the most sophisticated generative AI model and have propri data sets if their output is not high returns I don't really care so in this field it is about returns and I don't think Genera AI is can be equally used by different functions different stages of investment and different industry for your job early stage investing you need to look that entrepreneur in the eye does he have that grit to do what is necessary to give your portfolio High returns for public Equity investing even there are Fields like you know bu bi biotech or or Pharmaceuticals where it's very binary whether that drug is a improved or not and and effective or not I don't think AI will help that what AI will definitely help is improve your process lower your cost probably somehow also um the inference in all of this gives you some insight so you can actually make better Investments and that's the whole for most of investing so that that's a great insight and I think you know it's a great segue also to my next question about black box versus explainability do you only like would you or your LPS or your investors accept blackbox that generates returns without explaining it or you you do need to find some explainity because it's very risky I mean I I live through it you know I started my career as a technology investor at one of the most well-known funds in the world we were long short fundamental Tech investors at Tiger and that was all good until the quants came and they were able to replicate to returns and there are actually very few fundamental discretionary long short managers today unless you're have been grandfathered in with large asset bases because asset money is still sticky so it it's going to change but again it's really just about returns if there's a new methodology a new Gizmo that gives people better Investments endowments Pension funds they'll migrate that there Samir what is your feed what is the feedback you're getting from uh from your customers yeah like blackbox and also productivity versus um no it's a it's a great question I've already learned in the last five minutes so great thoughts here um I think the number one takeaway is that productivity to me is more a byproduct of having built a good product so inherently if you are delivering AI to people investors in the ways that they're already working in PowerPoint Excel other uh systems that they might be using that just means the productivity is uh letting you know that people are using it and you're making their lives easier I think where the real differentiation of a of an AI product comes in is in the ability to consistently deliver those better returns um and that predominantly a little different in the hedge fund world but certainly in private Equity a lot of that knowhow just comes from the senior people who have done pattern recognition a lot of the information frankly is not written in a do somewhere that you can just feed into a rag system it lives up here and to my knowledge AI is not yet able to read people's minds and so the real challenge that that we've been faced with is how do we accurately get all the expertise that someone like John has into an AI system and the answer is it's the product ux whereby you allow partner or analyst doesn't matter you allow them to prod the output of an AI using uh natural language and Distilling their thoughts specific specifically to the AI can you share an example I don't know if something you can share from what you're doing is there like any example you can kind of give us for such a yeah for uh a great great example is actually a private Equity company we recently worked with they're evaluating a tech Market that has two main players um and they have a deal team staff of of five people on it and nobody thought to look at when they looked at Uber andyt and did a whole analysis on basically an identical Market that was also doop and the AI integrated with their systems was able to surface a five-page report with thorough analysis going through retention curves and all these things Boaz I'm sure loves going through um that was not a the AI was not asking the investors to say hey make a decision one way or another but it surfaced these very valuable insights that none of those five people had even been at the firm when they made the Uber and lift decision yeah it's like having unlimited number of First Year analysts but if you ask them to do the job of the more senior folks like there's a lot you need to teach it how to do right exactly yeah um okay I think now we can move to a little bit more of a you know generic question um like how do we what are the most significant changes John maybe you can address that um in a pre versus post gen investment world Samir and I were actually talking about this and I think it was written up in either New York Times or Wall Street Journal literally and I think Lauren just mentioned it the function of the first year analyst can be completely replicated now through Ai and I think that is one of the most startling changes because most of these kids who go to Wall Street either at a fund or in a banking you know it's about it's not just about learning and developing the tools and doing the grunt work it's also ingesting in the culture of the leader of the founder of the bank or whatever and and basically able to project that type of uh deal making or that type of investment strategy so I think the biggest change is the functionality is going to be um lowlevel functionality will be taken out of the equation but how do you actually train people who now are skipping that rung and teach them the culture and the style and the grit that you need to be a good investor I think that also partner level could be replaced very easily think about let's go straight to partner Roi on that um Lisa what what is your you know you have to think about strategy um how do you think about this the world before and after what are what are strategies that you're implementing already to to face that I I think about this a lot like pre and post J you know um well pre gen like I remember I spoke at this conference last year um I took a uber ride home and at the end of the ride my Uber driver said oh I wish the ride was longer because I want to talk more about Chachi BT right preach that couldn't have happened so that that was one big this is more excitement I think um you know more imagination of what can can be done uh in terms of like concretely pre I think AI was really used as a prediction ELO right I want to predict something with AI and I think you know adoption in finance tend to be low because of that reason because it was just used for prediction sometimes the prediction is back box so we can't use it sometimes it's wrong so we can't use it because the cost of error is very high right we're not you know we're not in a creative field we're not writing poetry for fun you know we're investing to make returns and you know we need we need Precision so that's pre gen post gen like I said I think gen post gen AI is just another computer right people throw like terms like semantic kernel well that sounds like a computer right a konel of a computer semantic I can a computer I can talk to so poaching I see AI is just a computer that you can talk to and and always can talk to you can talk to your data you can ask your data many questions you'll come back and you can ask it again you know you can look at different types of data and through that iterative process of talking to your data and talking to your new AI computer you're going to invest better and you're going to generate outcome and mitigate risk all in one um so I heard upstairs as one of the panels uh gave like a rapper question uh of give me and I really liked it so I'm going to adopt it so one to three words from each of you about Investments 10 years from now Lauren you can start oh you're going to stump me um you want to come back okay uh I know there's a better word for this but I'm thinking like all knowing all knowing can can I qualify it or I just have to leave it at that you can qualify I can qualify I I think the real differentiator in hedge funds private equities any sort of Institutional Investor is going to be the proprietary knowledge that they've built up over Decades of investing that's always been the differentiator and that's not going to change with Gen what will change is the number of people you need to execute on those Visions uh and the efficiency at which you're able to draw Knowledge from from the past yeah okay three words okay one to one to three one to three okay um I think open like open data you know like a way to aggregate all your data uh personalize you know personaliz is investing for everybody and the last one is democratizing democratizing returns for everybody I think in 10 years there's going to be two types of investing firms one is that closed source and it's some compan that has just got a bunch of proprietary data and proprietary models that give you extreme outsize returns and it's very hard to replicate and then the other world would be more of an open world where data is easily transparent the models and where they came from the efficacy of the models are transparent and then it will be more do it yourself just like everything turns into so the average individual will be able to contribute to this ecosystem somehow and also get access to these models and replicate hetron Returns on their own I'll say uh information arms race you want to elaborate or just uh I think I think we'll to your point before about like is it going to be computers versus computers computers versus people I think it's going to be all of the above just and then how the Dynamics play out hopefully we'll hit another uh balance okay uh thank you so much on that note we can uh we can move to the next demo thank you so much

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Id: nC7d98dp4OM

Length: 28min 28sec (1708 seconds)

Published: Wed May 22 2024