Artificial intelligence is transforming industries at a breakneck pace, and the entrepreneurs driving this innovation are at the forefront of this revolution. As part of Authority Magazine's series on AI innovators, journalist Bharat "Doc" Sangani sat down with Justin Whitehead, CEO and Co-Founder of Pebble Finance.
Justin Whitehead has a background in programming and software development, with a particular interest in building tools and technologies for the financial industry. For over 20 years, he has built products and managed teams with the aim of making financial analysis and investing easier and more efficient. At FactSet, Justin led the development of its industry-leading portfolio analysis tool before subsequently leaving to join fintech Kensho. After Kensho's acquisition by S&P Global, Justin left to become the CTO at BotKeeper before founding Pebble in 2021.
Can you tell us a bit about your childhood backstory?
I grew up in a middle-class, blue-collar town. Dad was an electrical engineer who loved working on the automation and process control systems — early-day computers that control big machines in manufacturing plants. Mom was a chemist before having me and raising the family. I'm the oldest of three.
My parents instilled two distinct core values: the value of education and the value of being able to DIY, which ranged from constructing an addition onto our house to fixing our own cars. Fast forward to today — I still do all my own work on my cars. I just recently DIY finished an entire basement. I'm hands-on to a fault.
Thinking back, one specific memory comes to mind. Mom loves to tell the story about going on a merry-go-round as a kid. All the other kids were laughing as we went round and round, but I was fixated on the gearbox in the ceiling and watching how it mechanically synchronized the horses moving up and down. I've always been fascinated with knowing how things work.
What's the most interesting story from your career?
There are tons of experiences that have shaped how I work and approach problems and people. But I'd say a singular story was when I decided to leave an approximately 13-year career at FactSet and jump into my first company — which, spoiler alert, went to a big fat zero. But man, I learned so much along the way that I would never trade it for the world.
To set the stage — it's 2013. I've had a very successful career at FactSet and am now a Senior Director of Portfolio Analytics. We grew a dream product and a dream team — but, as I was moving up the corporate ladder, I started feeling like something was off. When I started, there were maybe 50 engineers, and by the time I left, I think that number was about 1,500. I got to experience what is natural across all enterprises: as a company gets bigger, it also gets slower. And I don't do slow.
My wife (whom I met at FactSet) had just had our first child. So I'm a new dad, I've got a great career, lots of success to my name — but I'm feeling stuck in traffic. After talking with my wife, I did "the stupid thing": left the company to start my own. I told people it's a little like veering off into the breakdown lane, but at least I get to go 60mph. If I hit a glass bottle and spin out, I'll just have to figure it out.
I started a company called StreetWise, using connections I had in the portfolio management space to build technology that taught machines to read sell-side research and highlight what was unique so that buy-siders could consume research faster. This was all before machine learning and AI were in vogue. Sounds pretty cool in 2025, but in 2013, it was a tough sell. I gave myself a year to get some proof points and failed. Ultimately, the idea went to zero — but what I learned along the way was infinitely valuable. I learned more about myself, formed opinions on what I needed versus what was vanity, and most importantly: what do I need to learn between now and next time?
Who helped you get to where you are?
Career-wise: Gavin.
I'm an engineer. I like to build things; I like to see people using them — that's my driver. Early on in my career at FactSet, I was tapped to lead an engineering team on a new company effort. Fun, exciting stuff. Gavin was my "grand-boss" at the time and the person who tapped me for the effort.
The newly formed team was doing great — but I wasn't (and Gavin noticed). During a 1:1, it was the first time a manager actually helped me look inward.
As a natural builder, I love being hands-on, figuring out the puzzle behind the problem and getting novel solutions out the door. I measured my success by my personal contributions — very "me" oriented. The insight I needed was this: going from IC to lead means you have to recalibrate your intrinsic motivators from "me-things" to "we-things." Maybe it sounds silly in hindsight, but it was a wildly impactful reframing. And I became very aware that there would be future reframings I'd need to reckon with as I grew.
Gavin gave me an ingredient that unlocked a great deal of growth in myself. Little does he know that I have actively planted very similar seeds in every team member I've ever worked with since.
What's your favorite life lesson quote?
"The only absolute truth is that there are no absolute truths." — Mr. Haddad, my high school physics teacher.
As for relevance — I have a tendency to challenge norms a little more than most. I'm not a 100%-rebel, challenging anything and everything presented to me. But I do love to go deep into understanding how people arrived at their "absolute truths." And the more arrogant and shallow you are at presenting that truth, the more it entices me to go deeper.
What three character traits were most instrumental to your success?
The quality of the team and the cohesion around how we execute honestly all stem from those two values I mentioned earlier: DIY and education.
Being a hands-on leader has helped me attract the right talent. Super smart people don't want to work for disconnected, Dilbert-cartoon-type leaders. As a CEO, it's expected that I can zoom out and talk as a visionary about the future of an industry and how we're going to disrupt it. But what is unexpected is that, in the same breath, I can dive into the details — go from "why" to "how" — at an incredible level of detail. That builds extreme credibility.
The second value — education — is probably my favorite. I love to learn. I love to teach. Ask anyone on the team about the qualities I look for in a hire and you'll hear the phrase "T-shaped." I hire people with extreme expertise in at least one critical domain and the ability to teach others left and right of that domain. When you do that, three things naturally happen: everyone experiences individual growth, the team becomes more connected to the problems we're solving, and the best qualities of the individual morph into the broad qualities of the team.
What inspired you to start working with AI?
I'll be a little contrarian here — AI is just a buzzword. There isn't anything specific about AI that made me want to start using it because, to me, it's just another technique in my technological toolbox to solve problems. What interests me is the problem.
Investing is hard. For most people, it feels like a high-stakes poker game you're forced to play except you have no idea how to play poker. The financial industry, in my opinion, does not serve this pain point well. The origin story of Pebble is rooted in figuring out how we can make investing feel more natural — make it safer and less effort to "play the game."
With respect to AI at Pebble — the "aha" moment was in storytelling. You can conduct the most sophisticated portfolio analysis or quantitatively study event impact, but if the end user has no idea how to interpret the math (most don't), it's all pointless. Pebble takes all that complexity and creates unique, personalized stories that explain precisely what's going on in the world and how it's affecting your portfolio. This would be impossible without the most recent innovations in AI.
Can you describe a moment when AI achieved something you once thought impossible?
Back in early 2022, we needed to purchase a financial dataset for use by Pebble — product segmentation data. We had no customers, no business model, just early feedback on ideas, and we needed to get some prototypes running. For this type of dataset, there are only a few vendors in the world. We talked to a few and got "startup friendly quotes" telling us it was going to cost $150,000 per year for a single dataset.
No way was that going to work.
So we rolled up our sleeves and looked to see if we could build this dataset ourselves — DIY. Around this time, ChatGPT hit the public zeitgeist. While we had worked with language models before, now we had access to a cheap SaaS API. After about a week, for a total of $350, we were able to create our own lightweight version of the dataset — and in using it, we were even able to find errors in the sample data from the vendor.
No expensive operations, no offshore data collection team — just tightly controlled AI quietly eating unstructured data for a few days. The speed and cost to do R&D has massively lowered, and that insight influences how we approach new markets and new solutions.
What challenge did you face working with AI, and how did you overcome it?
While AI is a tailwind for R&D, it also creates significant headwinds for productization and commercialization in the financial services industry. For those unfamiliar with financial services — think of the banks and brokerages you use. They are huge institutions, generally slow-moving, and all highly regulated by the government to protect consumers.
AI adoption in this industry is hard because you need to prove how these dynamic and hard-to-control systems can do no harm first.
So how did we approach this? We built Pebble a bit differently than your classic Silicon Valley startup where the mantra is "move fast and break things." Our goal was to deliver innovation to consumers through their existing banks and brokerages, not build a new one. To do that, we needed to design and develop innovation with regulators (SEC and FINRA) and large corporate legal, risk, and compliance departments front of mind. We went as far as getting ourselves registered as an SEC federally covered RIA to learn where the puck was going.
This time and energy investment is now paying off. Our clients — large brokerage and wealth management firms all figuring out the AI playbook — are finding it easier to work with Pebble because we've had a two-year head start.
Can you share an example of AI's meaningful impact?
The thesis behind Pebble is straightforward. Investing is hard — for everyone. People seek help from the wealth management industry to address this, but the quality of service you get is very closely connected to how much money you have. The reason is that the wealth industry is very manual, very human — and those salaries need to be paid.
If you are a deca-millionaire, the wealth management industry is going to serve you really well in exchange for a 1% fee. And for the rest of us — well, the outcome is a mixed bag. Anything from good service, to expensive and ineffective service, to outright being ignored because you're too small.
Pebble's technology is being used by wealth management firms looking to bring their ultra-high-net-worth services to a wider clientele. If you can manage, monitor, and articulate twice the number of portfolios without having to hire more people, you can now start serving millionaires just as easily as deca-millionaires. In the same breath, we are helping brokerages create "wealth lite" experiences for their customers. If you have $10,000, a human financial advisor can't serve you for 1% — but a fully automated brokerage-wealth experience could.
Five things you need to know to help shape the future of AI
1. AI has defined a new expectation for how we interact with software. Talk to anyone in high school or college and you'll quickly learn just how prevalent AI is in doing "school work." In five years, these kids are going to be entering the workforce and will expect AI to continue to be part of their toolset. Will your organization live up to that expectation?
2. The internet is going to need a new business model. You see this with Google Gemini search results and the impact it's having on individual website traffic. AI trained on all this "free internet data," but now the very sources of that data are losing traffic — and thus ad revenue — due to the very same AI. This is not long-term sustainable.
3. We as a society will experience a crisis in lack of critical thought. Right now, AI feels like a cheat code to life. School is easier; work is easier. If we're spending those free extra cycles learning new trades or deepening our expertise, fantastic. But if people are just taking the easy road — and biologically we are programmed to do so — we're going to lose the collective ability to debate and think for ourselves. Society can't function well in that scenario.
4. Just like robots augmented or replaced swaths of industrial production, AI will have a similar effect on some of the more mechanical "knowledge worker" trades. Think back to the example where we built a proprietary dataset with just machines, versus needing to hire a data collection team to do it manually.
5. The most game-changing AI applications have not been invented yet. There were only hundreds of mobile apps in the first few years after the iPhone launched. There are millions today. If you have an open mind and unique expertise in a particular domain, you must think creatively about how AI might be applicable.
What about the future of AI excites you most?
In a weird way, I think AI is going to unlock a lot of human intellectual capital. I look back on my professional career — from software engineer to hands-on CEO. The sheer amount of time spent searching, reading, and typing rather than actually thinking is significant. It's exciting to think what else I could learn with more time on my hands. Could one just need to be an expert in one field and then apply that mindset to others?
There are a lot of smart people — scientists, engineers, knowledge workers — who will have extra bandwidth from AI. Will more people become more like Leonardo da Vinci, an expert inventor, painter, and sculptor all at once? It's exciting to see what gets unlocked.
What advice would you give to entrepreneurs innovating in AI?
If you are selling to large enterprises, be ready for an extremely long sales and diligence process. On the positive side, while there are some signs of "AI exhaustion," almost every enterprise has some sort of AI mandate. They're all trying to figure it out.
Not only does Pebble sell to enterprises, but these enterprises are regulated by the government — SEC and FINRA. AI solutions in this space are so new that the enterprises, their risk teams, and the regulators are all still wrapping their heads around it and will be doing so for years to come. To overcome this, we've had to lead a lot of conversations — help firms really understand what's going on under the hood, what checks are in place, how it compares to real-world (non-AI) alternatives. It's a lot of work and much more than a traditional enterprise sales process.
Is there someone you'd love to have breakfast or lunch with?
Jamie Dimon — CEO of JP Morgan. He's both incredibly smart and incredibly blunt. I think AI will have a significant impact on the financial services industry. I'm not saying it will make more money or less money — more so, how it makes money will change rapidly. I'd love to get his perspective.
Read the full interview on Authority Magazine. Follow Justin on LinkedIn or visit pebble.finance.
