Beyond the AI Hype: Building Real Financial Intelligence with Justin Whitehead, CEO Pebble Finance
Builders Series
I'm a firm believer that AI is just a two-letter word. It certainly opens a lot of doors, but nobody has an AI problem. Advancements in technology, including machine learning and AI, have given people like myself more tools to break boundaries between what was previously accessible to certain demographics and make it available to the masses. – Justin Whitehead, CEO Pebble Finance
Justin Whitehead is the co-founder and CEO of Pebble Finance, a pioneering fintech-AI platform that helps investors make sense of portfolio performance with intuitive, data-driven insights. With over two decades at the nexus of finance and technology, Justin previously led development of FactSet’s industry-leading portfolio analysis tool, steered engineering at AI pioneer Kensho, and held technology leadership roles at BotKeeper before founding Pebble in 2021. Based in Cambridge, Massachusetts, Justin combines deep software development and product expertise with a clear vision: to simplify complex investment analytics for professionals and their clients alike.
Justin and I first met while we were both at Kensho, the AI-centric fintech I’ll refer to all too often in these interviews. It was an intense twenty-four months that I measure in dog years. We agreed early on to never hold back and never be offended. It was a formula that, fortunately, worked—as we often had different ideas about what was most important at the moment. Don’t read too much into the conflict, though, as the north stars for product and technology are rarely perfectly aligned.
Anyway, let’s get to it. Please enjoy my conversation with Justin.
Click here for FAQs and a Mind Map summaries of the key concepts from our interview with Justin.
In this interview, you’ll learn:
Why most retail investors are flying blind—and how AI can act as a portfolio co-pilot
The secret to scaling personalized investment insights across 10,000+ securities daily
Why explanations, not predictions, are the killer feature in AI-powered investing
How Pebble helps retail brokers monetize advice in a post-commission world
What wealth advisers really need to shine—and how Pebble arms them instantly
Why “build vs. buy” in financial AI isn’t a binary—it’s a dance
How small, focused LLMs outperform general models when speed, accuracy, and auditability matter
What happens when you embed AI in every layer of the investment stack
Why Justin believes the wealth management model of 2025 won’t survive until 2035
How Pebble is designed to be invisible, embedded, and indispensable
Some takeaways:
Retail investing is broken—and AI can help. Most retail investors lack a process. They chase brands like Apple or Tesla without understanding how to diversify or analyze exposure. Pebble uses AI to turn vague themes like “I want to invest in EVs” into full-spectrum portfolios mapped to supply chains, regulatory catalysts, and competitors.
The explanation engine is Pebble’s core differentiator. Forget predictions—Pebble focuses on helping investors understand why something is moving. Its engine identifies catalysts by scanning news, filings, correlations, and graph-based relationships. A biotech stock in San Diego jumps 30%? Pebble can trace it to an FDA approval for a peer in Boston.
Pebble produces 10k–15k explanations a day—with compliance baked in. Unlike static articles, explanations update dynamically as new information comes in. Human-in-the-loop QA ensures every piece meets regulatory standards—critical in partnerships with brokerages and institutions.
Every firm is building something—but Pebble moves the goalposts. Justin knows the instinct in finance is to “build first, buy later.” That’s fine—until compliance, speed, and scale catch up. Pebble lets teams prototype in-house, then plug into battle-tested infrastructure when it’s time to ship at production scale.
Retail brokers need new revenue streams. With commissions gone, the race is on to build Netflix-style subscriptions for investing. Pebble helps platforms deliver differentiated advice and context, creating retention and monetization opportunities at scale.
Wealth advisers need fast, clear answers. Whether it’s a tariff announcement or earnings surprise, advisers can instantly explain how a portfolio is affected. Pebble translates market complexity into client-ready insights—no spreadsheets required.
Institutional firms are stretched thinner than ever. With fee compression and geographic mandates expanding, asset managers need AI as “boots on the ground.” Pebble enables them to monitor portfolios, scan news, and detect movement—even across continents—without adding headcount.
The real AI challenge isn’t language—it’s infrastructure. Pebble combines small LLMs with traditional ML and human QA to achieve speed, precision, and auditability. Sub-second responses? Compliance-approved commentary? Scalable across clients? That’s not easy with an off-the-shelf model.
The future of wealth is automated, augmented, and embedded. Justin sees wealth management bifurcating—high-touch for the ultra-wealthy, and AI-augmented advice for everyone else. Pebble aims to power that bottom 70% of the market—at scale, invisibly, and affordably.
Don’t build another dashboard—build the insight layer. Pebble isn’t trying to own the front end. It wants to be the intelligence embedded inside your stack—whether you’re a broker, RIA, or asset manager. Pebble doesn’t care who gets the credit—it just wants to be the engine behind the best advice in finance.
Introduction
Rob Marsh: Welcome to AI Investor Dialogues. I’m Rob Marsh. Today we’re diving deep into the practical application of AI in investment processes with Justin Whitehead, co-founder and CEO of Pebble Finance. Justin brings over two decades of experience building technology for Wall Street, from his early days at FactSet to his role as CTO at Kensho Technologies.
We’ll explore how Pebble is democratizing sophisticated investment analysis, the reality of implementing AI in regulated financial services, and Justin’s bold predictions for the future of wealth management. Whether you’re a retail investor, wealth advisor, or institutional asset manager, this conversation offers actionable insights on leveraging AI to enhance investment outcomes.
Justin and I had the good fortune to work together at Kensho, where I oversaw product and Justin was, as noted, CTO. We had many vibrant conversations where he managed to keep me in line most of the time
Justin Whitehead: “Vibrant”—that’s an operative word right there.
Rob Marsh: So Justin, let’s start by defining your investment process at Pebble, then walk through what makes a good process and how AI plays a role. We’ll touch on what you’re doing at Pebble and discuss AI as an asset class. To kick it off, give us a brief background on your experience with AI and why you’re such a good person to be talking about this.
Justin’s Background: Building at the Intersection of Finance and Technology
Justin Whitehead: I’m a builder and a nerd through and through. I’ve been operating at the intersection of finance and technology before the term “fintech” was ever coined. I got my start as an early intern at Factset during the dot-com heyday, then joined full-time just as the bubble burst. I was one of the fortunate few computer scientists graduating who still had a job.
My early experiences shaped both my leadership style and understanding of markets while building a product called Portfolio Analysis. If you’re an institutional asset manager anywhere in the world today, there’s a high likelihood you’re either using FactSet PA or have used it at some point in your career.
After a long career at Factset, I joined Kensho, focusing on the sell side of Wall Street instead of the buy side. The name of the game flipped from teaching machines to understand portfolios and explain performance to asset managers, to teaching machines to distill events from news and quantitatively study them.
Today I’m co-founder and CEO of Pebble. Don’t let that fool you—I’m still very much a hands-on engineer. What my co-founder James Esdaile and I started was taking those two decades of experience and turning them into something understandable for retail investors. We wanted to make powerful analyses accessible to individual retail investors, eventually expanding to wealth advisors and institutional asset managers.
This is where AI comes in. I’m a firm believer that AI is just a two-letter word. It opens doors, but nobody has an AI problem. Advancements in technology, including machine learning and AI, have given teams like Pebble more tools to break boundaries between what was previously accessible to certain demographics and make it available to the masses.
Understanding Investment Processes: From Institutional to Retail
Rob Marsh: What does “investment process” mean to you, and how does it play into the key challenges Pebble Finance is addressing?
Justin Whitehead: I’ll take the investment process from two angles. First, as an engineer who built technologies for institutional asset managers. When you say investment process, I think of FactSet Portfolio Analysis and factor attribution—basically getting the math to mirror the asset manager’s investment process.
These managers were all benchmark-relative. You start by roughly copying the benchmark, then make allocation decisions. “I think tech is going to be great, healthcare is going to take it on the chin,” and you adjust your weights. Then you dive into each sector making active decisions that differentiate your portfolio from the benchmark.
The fascinating thing is when I went to the other end of the spectrum and started interacting with retail investors—guess what? There’s no formal process. You stumble into a portfolio. There are lots of brand-name companies where people think, “I know what Apple is, so I’m going to buy Apple stock,” without thinking about semiconductors, consumer technology, or Apple’s supply chain.
Early on, people thought Tesla and electric vehicles were synonymous. But there’s much more than just Tesla in the EV market. If you wanted to invest in electric vehicles, what might be advantageous is taking a more diversified approach.
A lot of Pebble’s early work was taking retail insights—when someone said they wanted to invest in EVs—and teaching a machine to do research on that topic. We collected our own datasets to understand what types of public companies produce what products, how they’re categorized, how they’re related, and who’s in whose supply chain. We did this by teaching machines to read public filings and investor presentations.
Generative AI gave us more techniques to turn a natural language statement into a query we could run on our graph. While it’s still not the institutional investment process, we focused on understanding the retail investment process, realizing it’s more haphazard, then creating technological ways to bake in diversification by default.
The Problem with Lack of Process
Rob Marsh: I hear what you’re saying about retail. The lack of good process is a barrier to returns. In relationships, ignorance may be bliss, but it’s not when it comes to investing. I think of all the calls I’ve taken from friends and family during major market dislocations when people are concerned and feel blind. That’s when people tend to make poor decisions.
Justin Whitehead: We are all genetically wired to be horrible investors.
Rob Marsh: We are. Being able to be informed is crucial. If you’re going to buy Amazon, you could tell someone it’s been a great investment over 25 years, but you had to sit through three to five 80-plus percent drawdowns along the way. It’s fine to say, “I get it, I understand, here’s the next Amazon, I’ll be able to do it.” No, you won’t—at least not if you don’t understand that these things happen, why they happen, and why something happening today connects to historical patterns.
Having a process or tool that helps you make those decisions is critical. The right decision may be not doing anything—in fact, most likely will be not doing anything.
Pebble’s Explanation Engine: Connecting the Dots
Justin Whitehead: One of our most successful technologies has been our explanation engine. Give me a portfolio, ETF, mutual fund, model portfolio, or stock. How do you look at that asset? Read the news, read available research, look at market data, understand its correlations and connections to other market aspects.
We call it identifying the catalysts of movement. Sometimes the catalyst isn’t in the public eye yet. When we were working on Market Journal—the grandfather of Pebble Explain—we took your classic real-time portfolio dashboard where you’re managing 100 stocks.
There’s no way any individual can stay on top of all 100 stocks equally. If you’re using a Bloomberg or FactSet terminal, it’s blinking green and red with news flying by. You know if your screen is all red, it’s a bad day; if it’s all green, it’s a good day. But most days, it’s a mix.
What we did brilliantly with Market Journal was help a person look at 100 assets and instantly understand where they need to look—not what is moving, but where they need to look now.
We had real-time quotes coming in and were statistically analyzing securities every 15 seconds, comparing them to cohorts. Was this a statistically significant day for the company itself—a “me day”? Was this just moving with the market—if the S&P 500 is down three standard deviations, everyone’s going out with the tide—a “they day”?
The most interesting was the “we day”—something in between. We understand to a quantitative degree the connections between companies, both structured quantitative and unstructured context. You might have some random company in San Diego researching CRISPR therapeutics for cancer, and dozens of these micro-cap companies scattered around the US all researching the same thing.
When one company gets FDA approval, there’ll be news about that company but not about the other 12. Did they react? If you have this random San Diego biotech company up 30% on no news, you’re automatically asking why. In our Market Journal dashboard, we’d say this looks like a “we day” because we see this cohort of six degrees of Kevin Bacon all rising. If you look across that cohort, you’re talking about a San Diego stock, but in Boston, there’s another stock that just got FDA approval, and the math tells us that’s probably what’s causing it.
Rob Marsh: I like to call that “catching strays.”
Justin Whitehead: Exactly. When we showed that to asset managers, it knocked things out of the park. We could sort that ticker list by who’s got the most interesting “we days.” There was always something fascinating every day in an arbitrarily large universe.
I remember during the 737 Max debacle, Spirit AeroSystems was down on no news. I initially thought it was Spirit Airlines, but when I clicked on it, I realized it was Spirit AeroSystems. Why would they be down? Because they make the fuselages for the 737 Max, and it all clicked together.
It was wildly fascinating to see those connections emerge in the real world. How that’s used today in Pebble—we call it the catalyst engine. Consumers using the Pebble engine see news being connected from other securities outside the cohort. This is how we use math to connect dots that aren’t otherwise readily apparent.
How Clients Access Pebble’s Capabilities
Rob Marsh: How are clients and investors accessing that capability? How much is browser-based versus APIs?
Justin Whitehead: Everything is API underneath the hood. The team at Pebble consists of people with my type of background—10 to 20-plus year veterans at fintech startups and AI. We’re heavily focused on the backend, so our surface area is our API.
Clients use us in two ways: either what we have in the cloud where we’ve connected data and market data and you can use our APIs there, or you can run a copy of Pebble on your premises—your own bare metal or in your cloud. We connect our engine to your data, and everything stays internal.
How users interact with it depends on what category of user you are. We sell to retail brokers for retail investors’ benefit, wealth management platforms for wealth advisors’ benefit, and institutional asset management platforms for asset managers’ benefit.
For example, FactSet’s institutional asset managers have access to generate portfolio AI commentary, and that’s partly Pebble underneath the hood. FactSet also sells APIs for this. Many financial institutions are exploring chatbots—starting with consuming the intranet for HR policies, then evolving to incorporate financial data.
My goal is to go where users already are. I don’t want Pebble to build yet another platform for people to log into. I’m more interested in focusing on answering certain classes of questions at incredible depth, scale, and speed.
Business Models and Market Opportunities
Rob Marsh: Walk us through the different client types you are serving. How big is the opportunity?
Justin Whitehead: There’s a pretty big addressable market across different industries. You’ve got the same engine being leveraged in completely different contexts. Serving retail brokers with millions or tens of millions of clients is one kind of scale with our revenue model around that. Then there are asset managers and hedge funds—there aren’t 180 million hedge funds, but these are professionals using the same explainer engine in a different context with completely different economics.
I love that it’s the same technology serving complementary categories.
Rob Marsh: Would it be fair to say this is ultimately B2B2C? What you’re really providing is trust through engagement and knowledge—retail investors engaging with their clients through relevant and credible information?
Justin Whitehead: In retail brokerage, brokers are trying to create a premium investing experience. Right now they’re trying to create differentiated and better retail investing experiences because if you do that, you can attract more clients who will stick around, ultimately fueling where revenue is made later in wealth.
Everyone got “Robin Hooded” five to ten years ago—commissions are down, big loss of revenue stream, everyone has to have a slick app. Everyone’s looking to figure out: “I can’t get commissions anymore, but can I get a Netflix-sized subscription for creating a better investing experience?” I think that’s actually an interesting model, and you’re seeing some innovators already generating significant revenues from it.
In wealth, this is all about helping that advisor shine. If you have a wealth advisor, to you they’re your hero, your sherpa into capital markets. But on the flip side, you’re probably one of 30, 100, or 150 client relationships going the other way. That wealth advisor isn’t sitting there actively managing and monitoring your portfolio unless you have your own private family office.
There’s this disconnect between what you pay an AUM fee for and what you get. What we do is help that wealth advisor. Click of a button: “This is what’s going on in the portfolio. This is exactly what happened over the past week. Trump just announced tariffs—this is how much money it cost you.”
By enabling that advisor to have conversations grounded in the same news their clients are seeing, it brings this notion of “this person working for me is on top of my portfolio, connecting dots with things I’m reading and concerned about.”
In asset management, it’s about portfolio monitoring. There was an asset manager in Canada expanding their mandate into Europe without boots on the ground, asking “How do we stay on top of that geography?” There’s fee compression on the institutional asset manager side and wealth side—people have to do more with less. There absolutely is a need to stay on top of markets where you don’t have boots on the ground and allow machines to be those boots on the ground, synthesizing and triaging what’s going on in the world that you need to be aware of.
AI Implementation at Pebble
Rob Marsh: We really haven’t talked about AI, which I think is ideal because it’s about understanding problems, challenges, and opportunities first. How is Pebble using AI to deliver that optimal experience and outcome for your clients?
Justin Whitehead: AI—to blur it a little bit—includes everything from generative AI like ChatGPT and LLMs, but also natural language generation, natural language processing, and linear models. Comically, at the end of the day, everything is just math underneath the hood. I hate saying AI because to me it’s just math, but AI gets someone to pick up the phone call.
Let’s talk about “explain” specifically. I mentioned we do portfolio analysis—it’s just math. All that quantitative six degrees of Kevin Bacon is all math. How did we produce our own knowledge graph of data? How did we procure this dataset and make these connections? Actually, a lot of AI techniques and generative AI techniques.
We’re a small company—I can’t spend $200,000 on a revenue database. I’m a startup, and I don’t know if there’s actually a business model here. So I’m going to build this database first before I try to buy it.
We ended up building unique proprietary datasets by teaching machines to extract structured data from unstructured data—SEC filings, investor presentations. It’s web scrapers, custom data feeds, then training extremely small, focused LLMs to do data extraction and populate a graph.
So AI techniques are used in building datasets. Then we do the portfolio math, the quantitative math, and we’ve got a mathematical understanding of where we think the catalysts are. Now we’re reading news using a mixture of traditional NLP techniques.
In the early days, we were pulling RSS news feeds. Most enterprises don’t like that, so we’ve matured and now have firm data agreements with actual underlying news sources. But you’re using traditional natural language processing techniques to cluster and pre-summarize data.
Natural language generation tech prior to LLMs still sounded like it was coming from a robot. But that ultimately gets fed into LLMs to clean up and rewrite. We can take the same engine outputs, the same math, the same pre-summarizations done very quickly and efficiently, but if we’re writing an explanation to a retail investor, we’ll use one class of language. If we’re talking to a sophisticated institutional asset manager, we’ll use completely different vernacular and provide a different level of depth.
That’s where the LLM gets involved. Also, because we sell to retail brokerage—a regulated entity—we have to answer to FINRA, the SEC, and firms’ internal legal risk and compliance teams. We do a lot of human-in-the-loop plus machine on the quality of output we write.
Our explanation engine produces about 10,000 to 15,000 explanations per day. We’ve got a human-in-the-loop process plus machine that can fact-check and eliminate hallucinations. Each explanation is actually vetted, potentially rewritten several times to iron out factual inaccuracies. If you send a factual inaccuracy through a retail broker, someone’s getting in trouble, and that’s going to be us.
Having all that auditability and statistical accuracy in what we’re writing gives us a huge edge in that space.
The Dynamic Nature of Explanations
Rob Marsh: You’re producing about 10,000 explanations a day—how many overlap?
Justin Whitehead: Hardly any. Each explanation lives only until fresher news or a bigger price move arrives—sometimes just 30 minutes. Heavy news days push the total to 15,000; quiet days, much less.
Rob: From a trader’s angle, how quickly does a narrative settle so I can act on it with confidence?
Justin: Depends on the company. For example, Apple once flipped overnight from “China sales” to “iPhone flop.” Clients today mostly want context—year-to-date drivers, three-day movers, real-time portfolio dashboards. But if someone wants deeper regime analysis, we’re ready to explore it.
Build vs. Buy: Pebble’s Competitive Advantage
Rob Marsh: You mentioned you’re small so you had to build everything, but you’re talking with big organizations. What’s the build-versus-buy decision, and what should be the main considerations?
Justin Whitehead: For anyone who knows financial market data providers, there’s a strong tribal pull to build everything here, don’t buy anything. I blame Bloomberg for being notorious for not wanting to partner with anyone—they build it here first. A lot of other companies followed that rhythm. It’s changing now, but not for Bloomberg.
I can safely say I can’t think of a client or prospect that isn’t already trying to do what we do—build portfolio commentary, build security commentary. A lot of this accelerated when ChatGPT was released two years ago and everybody became an AI expert. Boardrooms across the country had to come up with their AI strategy, everyone’s building chatbots, they spend tons of money on AI.
It’s really fast and easy to get a prototype of anything up and running. Everyone gets there like, “We can get a security explanation, just throw some news into an LLM.” Yeah, this is easy sauce.
Rob Marsh: I do it every night myself with Big Price Moves Explained.
Justin Whitehead: Then they find what’s not easy sauce about it. Now try to scale it. Now you want speed, you want to serve these explanations at cloud scale within certain latency times. Subsecond request and generative AI are two incompatible terms—you don’t get speed for that. So you have to start thinking very differently.
When you start dealing with legal risk and compliance—“How do we audit and make sure what was just said is truthful and not a hallucination?”—you get into the ugly, unsexy parts of AI anything, especially LLM anything in financial markets. That’s where most companies get stuck.
When it comes to Pebble, our advantage is—I say this upfront with every client and it gives us credibility—“Listen, I’ve been in fintech forever, so I get the whole ‘build it here first.’ I expect you guys, even after coming on as a client for Pebble, to continue your build-it-here efforts. I don’t mind—you should be trying to do this.”
But I’ll help get you to market faster and cheaper than before. Honestly, it’s shame on me if I can’t defensibly keep a moat between what you’re able to do and what I’m able to do. I don’t have a defensible business.
I’m in the business of getting into these clients and then just moving the goalposts forward faster and faster, doing more things with more data, solving the compliance headaches, solving the speed headaches, the scale headaches. Eventually people are like, “All right, we’ll allow Pebble.”
I’m not going to give you technology and then leave it. I like competition, and this is what infects my mind on a daily basis. I am living and breathing and loving life right now doing all of this.
Technology Evolution and Competitive Risks
Rob Marsh: What’s the risk that these great things you’ve done can have the technology move out from under you—like OpenAI comes out and basically a lot of the engineering you do becomes readily doable by something else?
Justin Whitehead: You get this in other firms—“Why couldn’t Google just come in and do all this stuff in finance? They’ve got all the data.” Google does not want to deal with the SEC or FINRA or any more regulatory bodies than they already have. There’s enough hairy, not-so-fun pieces to the financial services industry that OpenAI, if you want to come and deal with it, I give up. You’re going to hate yourself after a couple years.
The other thing is we have different endgame goals. OpenAI, Anthropic, Google through Gemini, even Facebook through Llama—everyone’s looking for large general AGI. That’s the goal: generalized intelligence so these machines can answer everything for everyone.
Where we do what we do, we actually focus on going the opposite direction. We start with large LLMs, but I’m in the explanation business. I need to do a few things super, super well. We can get tons of mileage going the opposite direction of the OpenAIs.
I go for speed, scale, operational efficiency, and security. OpenAI’s costs are going one direction. All my clients aren’t looking to spend more dollars on Azure—they’re looking for ROI, and if you can decrease the denominator, that’s also good.
Will I be able to have a drop-in replacement engine for the LLM portion that I can charge a client half as much—cut your LLM bill in half—but be quietly smiling because I’ve got an additional 80% margin on the other side? That’s where we’re going.
Being smaller and more focused is a direction we’re going, not necessarily where the big players are going. Nobody wants to be regulated. Google could do a lot more in finance if they wanted to—they’ve got the money—but there’s a reason why they don’t. You get a little bit of that protection as well.
Investment Thesis: The Future of Financial Services
Rob Marsh: What are the key things investors should know about Pebble as an investment?
Justin Whitehead: I’ve got some theories about how the financial services market is going to change over the next decade. An investor in Pebble—I want you to be on board with challenging me, but ultimately thinking this is a reasonable way the world is going.
I have very distinct opinions about what’s going to be the future for SaaS platforms at research shops and asset managers. I do not want to be a company building dashboards and tools for Wall Street individuals because I think the nature of how people are going to conduct research is going to change materially.
I can imagine a Goldman Sachs buying private licenses to their LLM du jour, and then it will be companies like Pebble, Bloomberg, FactSet, S&P, and MSCI all competing to add capabilities that these internal chat/research systems can do at the big banks.
While a lot of talk right now is about doing more with the same, the reality is it will be more with less at some point. It’s a hard thing to sell, but let’s be adults—this will have an effect on the jobs you actually need to run existing operations.
Even if you’ve gotten a boost in growth, you can still move more. I think we’re going to see AIs doing a lot more of the grunt work and monitoring. “Hey, ChatGPT, build me a DCF cash flow model, watch this company, give me a Teams message when the company posts earnings and blows past X.” That thing just quietly works behind the scenes. It’s not a junior analyst doing that—it’s a machine you’ve expressed your will to, and it will sit there and do that 24/7 without forming a union or requiring bathroom breaks.
Rob Marsh: And leaving an audit trail. A lot of intermediate information that presently gets lost becomes feedstock for compounding knowledge.
Justin Whitehead: I don’t think that’s a five-year scenario. I think you’re getting into the 10-year scenario.
The other aspect is how this is going to change wealth management, sandwiched between institutional asset management and retail brokerage. There are firms with their heads in the sand thinking the business model that worked in 2025 will work in 2035. I do not think that’s correct.
I think we’ll have a new form of wealth management brought to market by retail brokerage. Seventy percent of investors are not being served by wealth managers, but they still have similar needs. They have unsexy AUMs, but if they’re willing to spend 15 to 20 bucks a month to binge-watch Stranger Things on Netflix, you better believe they’re willing to spend 15 to 20 bucks to get comfort and automated understanding that they’re not headed toward financial ruin.
That business model will start to take shape over the next five years and have interesting ripple effects in wealth. The wealth industry will absolutely always be there, but the lower tier of existing wealth is going to get eaten from the bottom.
If you’re one of these vertically integrated firms—you’ve got brokerage, wealth management, asset management—you better understand there’s a war. The war has already started. The first battleground is on the unsexy retail brokerage side of the business. You better start investing fast because Robin Hood is coming to eat your lunch.
From the company standpoint, I am excited as hell to be part of that transformation. It’s not going to be Pebble alone, but you better understand that I see fire and I’m the guy with the gasoline cans. I’m going to make this thing burn faster than anyone can imagine.
If that excites you, great. You should come talk to us.
Key Lessons for AI Adoption
Rob Marsh: What are the key lessons you’ve learned about increasing the probability of successfully adopting AI in the investment process?
Justin Whitehead: Interestingly, I hear from the little birds on the street—and I’ve experienced this myself as a professional programmer—there’s all this talk about AI coming to replace software engineers, which I don’t think is true. However, man, is it useful.
But I still see in the financial profession people who aren’t experimenting and adopting. Get out there and just start experimenting with it, even in private. Get used to AI. Don’t worry about changing your investment practice or style.
I’ve used coding assistants to give me quick throwaway code. I know enough because I’m a professional to know when this thing is giving me garbage, but it does save time. What I’ve found most accretive has actually been on the non-engineering aspects—having a sounding board.
When I’m about to pitch a client, “Help me understand what this client is all about. What have they been talking about in the news?” What would take hours of dedicated reading and studying up on who I’m talking to can all be done now in seconds.
Find little micro problems and give it a try with ChatGPT. Learn when to trust it and what to look for when not to trust it. Make sure it’s citing sources so you can trust but verify. The more people experiment with it, the more it’ll end up being good for adopters in the industry and start sparking ideas of what can be possible.
I’m weirdly bumping into a lot of professional investors who are just like, “This is not for me.” Have you tried it? Don’t try it in a professional context—try it in a personal context. I think you’ll be amazed.
I’d encourage your listeners to hack around and experiment. I think you’ll be pleasantly surprised.
Rob Marsh: I would reduce it almost to the equivalent of 1985 to 1987: hit return on the keyboard. Just do it. The world’s not going to melt in front of you.
Justin Whitehead: Exactly.
Conclusion
Rob Marsh: That’s a wrap on today’s AI Investor Dialogues. Justin’s insights remind us that successful AI adoption isn’t about replacing human judgment—it’s about augmenting our capabilities to make better, more informed decisions faster than ever before.
Key takeaways: Start experimenting with AI tools in low-risk environments, focus on solving specific problems rather than chasing the latest technology, and remember that in financial services, trust and auditability aren’t optional—they’re essential.
If you found this conversation valuable, please share it with fellow investors and practitioners. Until next time, keep building, keep learning, and keep pushing the boundaries of what’s possible in investment management.
Again, Justin, thank you for sharing.
Justin Whitehead: Excited. Thank you for bringing me on.